Temporal Extension Module for Skeleton-Based Action Recognition

Yuya Obinata, Takuma Yamamoto

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Auto-TLDR; Extended Temporal Graph for Action Recognition with Kinetics-Skeleton

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We present a module that extends the temporal graph of a graph convolutional network (GCN) for action recognition with a sequence of skeletons. Existing methods attempt to represent a more appropriate spatial graph on an intra-frame, but disregard optimization of the temporal graph on the inter-frame. Concretely, these methods connect between vertices corresponding only to the same joint on the inter-frame. In this work, we focus on adding connections to neighboring multiple vertices on the inter-frame and extracting additional features based on the extended temporal graph. Our module is a simple yet effective method to extract correlated features of multiple joints in human movement. Moreover, our module aids in further performance improvements, along with other GCN methods that optimize only the spatial graph. We conduct extensive experiments on two large datasets, NTU RGB+D and Kinetics-Skeleton, and demonstrate that our module is effective for several existing models and our final model achieves state-of-the-art performance.

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Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition

Negar Heidari, Alexandros Iosifidis

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Auto-TLDR; Temporal Attention Module for Efficient Graph Convolutional Network-based Action Recognition

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Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatio-temporal graphs. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. This leads to a high number of floating point operations (ranging from 16G to 100G FLOPs) to process a single sample, making their adoption in restricted computation application scenarios infeasible. In this paper, we propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition by selecting the most informative skeletons of an action at the early layers of the network. We incorporate the TAM in a light-weight GCN topology to further reduce the overall number of computations. Experimental results on two benchmark datasets show that the proposed method outperforms with a large margin the baseline GCN-based method while having 2.9 times less number of computations. Moreover, it performs on par with the state-of-the-art with up to 9.6 times less number of computations.

Recurrent Graph Convolutional Networks for Skeleton-Based Action Recognition

Guangming Zhu, Lu Yang, Liang Zhang, Peiyi Shen, Juan Song

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Auto-TLDR; Recurrent Graph Convolutional Network for Human Action Recognition

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Human action recognition is one of the challenging and active research fields due to its wide applications. Recently, graph convolutions for skeleton-based action recognition have attracted much attention. Generally, the adjacency matrices of the graph are fixed to the hand-crafted physical connectivity of the human joints, or learned adaptively via deep learining. The hand-crafted or learned adjacency matrices are fixed when processing each frame of an action sequence. However, the interactions of different subsets of joints may play a core role at different phases of an action. Therefore, it is reasonable to evolve the graph topology with time. In this paper, a recurrent graph convolution is proposed, in which the graph topology is evolved via a long short-term memory (LSTM) network. The proposed recurrent graph convolutional network (R-GCN) can recurrently learn the data-dependent graph topologies for different layers, different time steps and different kinds of actions. Experimental results on the NTU RGB+D and Kinetics-Skeleton datasets demonstrate the advantages of the proposed R-GCN.

Channel-Wise Dense Connection Graph Convolutional Network for Skeleton-Based Action Recognition

Michael Lao Banteng, Zhiyong Wu

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Auto-TLDR; Two-stream channel-wise dense connection GCN for human action recognition

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Skeleton-based action recognition task has drawn much attention for many years. Graph Convolutional Network (GCN) has proved its effectiveness in this task. However, how to improve the model's robustness to different human actions and how to make effective use of features produced by the network are main topics needed to be further explored. Human actions are time series sequence, meaning that temporal information is a key factor to model the representation of data. The ranges of body parts involved in small actions (e.g. raise a glass or shake head) and big actions (e.g. walking or jumping) are diverse. It's crucial for the model to generate and utilize more features that can be adaptive to a wider range of actions. Furthermore, feature channels are specific with the action class, the model needs to weigh their importance and pay attention to more related ones. To address these problems, in this work, we propose a two-stream channel-wise dense connection GCN (2s-CDGCN). Specifically, the skeleton data was extracted and processed into spatial and temporal information for better feature representation. A channel-wise attention module was used to select and emphasize the more useful features generated by the network. Moreover, to ensure maximum information flow, dense connection was introduced to the network structure, which enables the network to reuse the skeleton features and generate more information adaptive and related to different human actions. Our model has shown its ability to improve the accuracy of human action recognition task on two large datasets, NTU-RGB+D and Kinetics. Extensive evaluations were conducted to prove the effectiveness of our model.

Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatio-Temporal Graph Convolutional Network for Action Recognition

Konstantinos Papadopoulos, Enjie Ghorbel, Djamila Aouada, Bjorn Ottersten

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Auto-TLDR; Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition

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Spatio-temporal Graph Convolutional Networks (ST-GCNs) have shown great performance in the context of skeleton-based action recognition. Nevertheless, ST-GCNs use raw skeleton data as vertex features. Such features have low dimensionality and might not be optimal for action discrimination. Moreover, a single layer of temporal convolution is used to model short-term temporal dependencies but can be insufficient for capturing both long-term. In this paper, we extend the Spatio-Temporal Graph Convolutional Network for skeleton-based action recognition by introducing two novel modules, namely, the Graph Vertex Feature Encoder (GVFE) and the Dilated Hierarchical Temporal Convolutional Network (DH-TCN). On the one hand, the GVFE module learns appropriate vertex features for action recognition by encoding raw skeleton data into a new feature space. On the other hand, the DH-TCN module is capable of capturing both short-term and long-term temporal dependencies using a hierarchical dilated convolutional network. Experiments have been conducted on the challenging NTU RGB-D 60, NTU RGB-D 120 and Kinetics datasets. The obtained results show that our method competes with state-of-the-art approaches while using a smaller number of layers and parameters; thus reducing the required training time and memory.

JT-MGCN: Joint-Temporal Motion Graph Convolutional Network for Skeleton-Based Action Recognition

Suekyeong Nam, Seungkyu Lee

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Auto-TLDR; Joint-temporal Motion Graph Convolutional Networks for Action Recognition

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Recently, action recognition methods using graph convolutional networks (GCN) have shown remarkable performance thanks to its concise but effective representation of human body motion. Prior methods construct human body motion graph building edges between neighbor or distant body joints. On the other hand, human action contains lots of temporal variations showing strong temporal correlations between joint motions. Thus the characterization of an action requires a comprehensive analysis of joint motion correlations on spatial and temporal domains. In this paper, we propose Joint-temporal Motion Graph Convolutional Networks (JT-MGCN) in which joint-temporal edges learn the correlations between different joints at different time. Experimental evaluation on large public data sets such as NTU rgb+d data set and kinetics-skeleton data set show outstanding action recognition performance.

DeepPear: Deep Pose Estimation and Action Recognition

Wen-Jiin Tsai, You-Ying Jhuang

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Auto-TLDR; Human Action Recognition Using RGB Video Using 3D Human Pose and Appearance Features

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Human action recognition has been a popular issue recently because it can be applied in many applications such as intelligent surveillance systems, human-robot interaction, and autonomous vehicle control. Human action recognition using RGB video is a challenging task because the learning of actions is easily affected by the cluttered background. To cope with this problem, the proposed method estimates 3D human poses first which can help remove the cluttered background and focus on the human body. In addition to the human poses, the proposed method also utilizes appearance features nearby the predicted joints to make our action prediction context-aware. Instead of using 3D convolutional neural networks as many action recognition approaches did, the proposed method uses a two-stream architecture that aggregates the results from skeleton-based and appearance-based approaches to do action recognition. Experimental results show that the proposed method achieved state-of-the-art performance on NTU RGB+D which is a largescale dataset for human action recognition.

Attention-Driven Body Pose Encoding for Human Activity Recognition

Bappaditya Debnath, Swagat Kumar, Marry O'Brien, Ardhendu Behera

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Auto-TLDR; Attention-based Body Pose Encoding for Human Activity Recognition

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This article proposes a novel attention-based body pose encoding for human activity recognition. Most of the existing human activity recognition approaches based on 3D pose data often enrich the input data using additional handcrafted representations such as velocity, super normal vectors, pairwise relations, and so on. The enriched data complements the 3D body joint position data and improves the model performance. In this paper, we propose a novel approach that learns enhanced feature representations from a given sequence of 3D body joints. To achieve this, the approach exploits two body pose streams: 1) a spatial stream which encodes the spatial relationship between various body joints at each time point to learn spatial structure involving the spatial distribution of different body joints 2) a temporal stream that learns the temporal variation of individual body joints over the entire sequence duration to present a temporally enhanced representation. Afterwards, these two pose streams are fused with a multi-head attention mechanism. We also capture the contextual information from the RGB video stream using a deep Convolutional Neural Network (CNN) model combined with a multi-head attention and a bidirectional Long Short-Term Memory (LSTM) network. Finally, the RGB video stream is combined with the fused body pose stream to give a novel end-to-end deep model for effective human activity recognition. The proposed model is evaluated on three datasets including the challenging NTU-RGBD dataset and achieves state-of-the-art results.

A Two-Stream Recurrent Network for Skeleton-Based Human Interaction Recognition

Qianhui Men, Edmond S. L. Ho, Shum Hubert P. H., Howard Leung

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Auto-TLDR; Two-Stream Recurrent Neural Network for Human-Human Interaction Recognition

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This paper addresses the problem of recognizing human-human interaction from skeletal sequences. Existing methods are mainly designed to classify single human action. Many of them simply stack the movement features of two characters to deal with human interaction, while neglecting the abundant relationships between characters. In this paper, we propose a novel two-stream recurrent neural network by adopting the geometric features from both single actions and interactions to describe the spatial correlations with different discriminative abilities. The first stream is constructed under pairwise joint distance (PJD) in a fully-connected mesh to categorize the interactions with explicit distance patterns. To better distinguish similar interactions, in the second stream, we combine PJD with the spatial features from individual joint positions using graph convolutions to detect the implicit correlations among joints, where the joint connections in the graph are adaptive for flexible correlations. After spatial modeling, each stream is fed to a bi-directional LSTM to encode two-way temporal properties. To take advantage of the diverse discriminative power of the two streams, we come up with a late fusion algorithm to combine their output predictions concerning information entropy. Experimental results show that the proposed framework achieves state-of-the-art performance on 3D and comparable performance on 2D interaction datasets. Moreover, the late fusion results demonstrate the effectiveness of improving the recognition accuracy compared with single streams.

Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction

Ziyang Song, Ziyi Yin, Zejian Yuan, Chong Zhang, Wanchao Chi, Yonggen Ling, Shenghao Zhang

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Auto-TLDR; Attention-Oriented Multi-Level Network for Action Recognition in Interaction Scenes

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Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction. In this paper, we deeply explore the characteristics of the action recognition task in interaction scenes and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. Specifically, a Pre-Attention network is employed to roughly focus on the interactor in the scene at low resolution firstly and then perform fine-grained pose estimation at high resolution. The other compact CNN receives the extracted skeleton sequence as input for action recognition, utilizing attention-like mechanisms to capture local spatial-temporal patterns and global semantic information effectively. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenes. Experimental results on our dataset and high efficiency (112 fps at 640 x 480 RGBD) on the mobile computing platform (Nvidia Jetson AGX Xavier) demonstrate excellent applicability of our method on action recognition in real-time human-robot interaction.

A Multi-Task Neural Network for Action Recognition with 3D Key-Points

Rongxiao Tang, Wang Luyang, Zhenhua Guo

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Auto-TLDR; Multi-task Neural Network for Action Recognition and 3D Human Pose Estimation

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Action recognition and 3D human pose estimation are the fundamental problems in computer vision and closely related. In this work, we propose a multi-task neural network for action recognition and 3D human pose estimation. The results of the previous methods are still error-prone especially when tested against the images taken in-the-wild, leading error results in action recognition. To solve this problem, we propose a principled approach to generate high quality 3D pose ground truth given any in-the-wild image with a person inside. We achieve this by first devising a novel stereo inspired neural network to directly map any 2D pose to high quality 3D counterpart. Based on the high-quality 3D labels, we carefully design the multi-task framework for action recognition and 3D human pose estimation. The proposed architecture can utilize the shallow, deep features of the images, and the in-the-wild 3D human key-points to guide a more precise result. High quality 3D key-points can fully reflect the morphological features of motions, thus boosting the performance on action recognition. Experiments demonstrate that 3D pose estimation leads to significantly higher performance on action recognition than separated learning. We also evaluate the generalization ability of our method both quantitatively and qualitatively. The proposed architecture performs favorably against the baseline 3D pose estimation methods. In addition, the reported results on Penn Action and NTU datasets demonstrate the effectiveness of our method on the action recognition task.

Vision-Based Multi-Modal Framework for Action Recognition

Djamila Romaissa Beddiar, Mourad Oussalah, Brahim Nini

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Auto-TLDR; Multi-modal Framework for Human Activity Recognition Using RGB, Depth and Skeleton Data

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Human activity recognition plays a central role in the development of intelligent systems for video surveillance, public security, health care and home monitoring, where detection and recognition of activities can improve the quality of life and security of humans. Typically, automated, intuitive and real-time systems are required to recognize human activities and identify accurately unusual behaviors in order to prevent dangerous situations. In this work, we explore the combination of three modalities (RGB, depth and skeleton data) to design a robust multi-modal framework for vision-based human activity recognition. Especially, spatial information, body shape/posture and temporal evolution of actions are highlighted using illustrative representations obtained from a combination of dynamic RGB images, dynamic depth images and skeleton data representations. Therefore, each video is represented with three images that summarize the ongoing action. Our framework takes advantage of transfer learning from pre trained models to extract significant features from these newly created images. Next, we fuse extracted features using Canonical Correlation Analysis and train a Long Short-Term Memory network to classify actions from visual descriptive images. Experimental results demonstrated the reliability of our feature-fusion framework that allows us to capture highly significant features and enables us to achieve the state-of-the-art performance on the public UTD-MHAD and NTU RGB+D datasets.

Pose-Based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation

Zhengyuan Yang, Amanda Kay, Yuncheng Li, Wendi Cross, Jiebo Luo

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Auto-TLDR; Body Language Based Emotion Recognition for Psychiatric Symptoms Prediction

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Inspired by the human ability to infer emotions from body language, we propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we further extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set and possess a good transferability. The proposed system in the first stage generates sequences of body language predictions based on human poses estimated from input videos. In the second stage, the predicted sequences are fed into a temporal network for emotion interpretation and psychiatric symptom prediction. We first validate the accuracy and transferability of the proposed body language recognition method on several public action recognition datasets. We then evaluate the framework on a proposed URMC dataset, which consists of conversations between a standardized patient and a behavioral health professional, along with expert annotations of body language, emotions, and potential psychiatric symptoms. The proposed framework outperforms other methods on the URMC dataset.

Feature-Supervised Action Modality Transfer

Fida Mohammad Thoker, Cees Snoek

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Auto-TLDR; Cross-Modal Action Recognition and Detection in Non-RGB Video Modalities by Learning from Large-Scale Labeled RGB Data

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This paper strives for action recognition and detection in video modalities like RGB, depth maps or 3D-skeleton sequences when only limited modality-specific labeled examples are available. For the RGB, and derived optical-flow, modality many large-scale labeled datasets have been made available. They have become the de facto pre-training choice when recognizing or detecting new actions from RGB datasets that have limited amounts of labeled examples available. Unfortunately, large-scale labeled action datasets for other modalities are unavailable for pre-training. In this paper, our goal is to recognize actions from limited examples in non-RGB video modalities, by learning from large-scale labeled RGB data. To this end, we propose a two-step training process: (i) we extract action representation knowledge from an RGB-trained teacher network and adapt it to a non-RGB student network. (ii) we then fine-tune the transfer model with available labeled examples of the target modality. For the knowledge transfer we introduce feature-supervision strategies, which rely on unlabeled pairs of two modalities (the RGB and the target modality) to transfer feature level representations from the teacher to the the student network. Ablations and generalizations with two RGB source datasets and two non-RGB target datasets demonstrate that an optical-flow teacher provides better action transfer features than RGB for both depth maps and 3D-skeletons, even when evaluated on a different target domain, or for a different task. Compared to alternative cross-modal action transfer methods we show a good improvement in performance especially when labeled non-RGB examples to learn from are scarce.

A Grid-Based Representation for Human Action Recognition

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

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Auto-TLDR; GRAR: Grid-based Representation for Action Recognition in Videos

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Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for the task, and are limited in the way they fuse temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets that demonstrate that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.

Learning Connectivity with Graph Convolutional Networks

Hichem Sahbi

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Auto-TLDR; Learning Graph Convolutional Networks Using Topological Properties of Graphs

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Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined. In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work.

Learning Recurrent High-Order Statistics for Skeleton-Based Hand Gesture Recognition

Xuan Son Nguyen, Luc Brun, Olivier Lezoray, Sébastien Bougleux

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Auto-TLDR; Exploiting High-Order Statistics in Recurrent Neural Networks for Hand Gesture Recog-nition

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High-order statistics have been proven useful inthe framework of Convolutional Neural Networks (CNN) fora variety of computer vision tasks. In this paper, we proposeto exploit high-order statistics in the framework of RecurrentNeural Networks (RNN) for skeleton-based hand gesture recog-nition. Our method is based on the Statistical Recurrent Units(SRU), an un-gated architecture that has been introduced as analternative model for Long-Short Term Memory (LSTM) andGate Recurrent Unit (GRU). The SRU captures sequential infor-mation by generating recurrent statistics that depend on a contextof previously seen data and by computing moving averages atdifferent scales. The integration of high-order statistics in theSRU significantly improves the performance of the original one,resulting in a model that is competitive to state-of-the-art methodson the Dynamic Hand Gesture (DHG) dataset, and outperformsthem on the First-Person Hand Action (FPHA) dataset.

Learning Group Activities from Skeletons without Individual Action Labels

Fabio Zappardino, Tiberio Uricchio, Lorenzo Seidenari, Alberto Del Bimbo

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Auto-TLDR; Lean Pose Only for Group Activity Recognition

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To understand human behavior we must not just recognize individual actions but model possibly complex group activity and interactions. Hierarchical models obtain the best results in group activity recognition but require fine grained individual action annotations at the actor level. In this paper we show that using only skeletal data we can train a state-of-the art end-to-end system using only group activity labels at the sequence level. Our experiments show that models trained without individual action supervision perform poorly. On the other hand we show that pseudo-labels can be computed from any pre-trained feature extractor with comparable final performance. Finally our carefully designed lean pose only architecture shows highly competitive results versus more complex multimodal approaches even in the self-supervised variant.

Kernel-based Graph Convolutional Networks

Hichem Sahbi

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Auto-TLDR; Spatial Graph Convolutional Networks in Recurrent Kernel Hilbert Space

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Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neighborhood aggregation scheme, where the representation of a node is recursively obtained by aggregating its neighboring node representations using averaging or sorting operations. However, these operations are either ill-posed or weak to be discriminant or increase the number of training parameters and thereby the computational complexity and the risk of overfitting. In this paper, we introduce a novel GCN framework that achieves spatial graph convolution in a reproducing kernel Hilbert space. The latter makes it possible to design, via implicit kernel representations, convolutional graph filters in a high dimensional and more discriminating space without increasing the number of training parameters. The particularity of our GCN model also resides in its ability to achieve convolutions without explicitly realigning nodes in the receptive fields of the learned graph filters with those of the input graphs, thereby making convolutions permutation agnostic and well defined. Experiments conducted on the challenging task of skeleton-based action recognition show the superiority of the proposed method against different baselines as well as the related work.

SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition

Raphael Memmesheimer, Nick Theisen, Dietrich Paulus

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Auto-TLDR; One-Shot Action Recognition using Metric Learning

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Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition or face-identification. We propose a metric learning approach to reduce the action recognition problem to a nearest neighbor search in embedding space. We encode signals into images and extract features using a deep residual CNN. Using triplet loss, we learn a feature embedding. The resulting encoder transforms features into an embedding space in which closer distances encode similar actions while higher distances encode different actions. Our approach is based on a signal level formulation and remains flexible across a variety of modalities. It further outperforms the baseline on the large scale NTU RGB+D 120 dataset for the One-Shot action recognition protocol by \ntuoneshotimpro%. With just 60% of the training data, our approach still outperforms the baseline approach by \ntuoneshotimproreduced%. With 40% of the training data, our approach performs comparably well as the second follow up. Further, we show that our approach generalizes well in experiments on the UTD-MHAD dataset for inertial, skeleton and fused data and the Simitate dataset for motion capturing data. Furthermore, our inter-joint and inter-sensor experiments suggest good capabilities on previously unseen setups.

What and How? Jointly Forecasting Human Action and Pose

Yanjun Zhu, Yanxia Zhang, Qiong Liu, Andreas Girgensohn

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Auto-TLDR; Forecasting Human Actions and Motion Trajectories with Joint Action Classification and Pose Regression

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Forecasting human actions and motion trajectories addresses the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics, while existing works perform them independently. In this paper, we present a method to jointly forecast categories of human action and the pose of skeletal joints in the hope that the two tasks can help each other. As a result, our system can predict not only the future actions but also the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method.

MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recognition

Kaiyu Shan, Yongtao Wang, Zhi Tang, Ying Chen, Yangyan Li

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Auto-TLDR; Mixed Temporal Convolution for Action Recognition

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To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone. However, they all exploit 1D temporal convolution of fixed kernel size (i.e., 3) in the network building block, thus have suboptimal temporal modeling capability to handle both long term and short-term actions. To address this problem, we first investigate the impacts of different kernel sizes for the 1D temporal convolutional filters. Then, we propose a simple yet efficient operation called Mixed Temporal Convolution (MixTConv) in methodology, which consists of multiple depthwise 1D convolutional filters with different kernel sizes. By plugging MixTConv into the conventional 2D CNN backbone ResNet-50, we further propose an efficient and effective network architecture named MSTNet for action recognition, and achieve state-of-the-art results on multiple large-scale benchmarks.

You Ought to Look Around: Precise, Large Span Action Detection

Ge Pan, Zhang Han, Fan Yu, Yonghong Song, Yuanlin Zhang, Han Yuan

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Auto-TLDR; YOLA: Local Feature Extraction for Action Localization with Variable receptive field

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For the action localization task, pre-defined action anchors are the cornerstone of mainstream techniques. State-of-the-art models mostly rely on a dense segmenting scheme, where anchors are sampled uniformly over the temporal domain with a predefined set of scales. However, it is not sufficient because action duration varies greatly. Therefore, it is necessary for the anchors or proposals to have a variable receptive field. In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features. In this part, we employ a stronger backbone I3D with SPN (Segment Pyramid Network) instead of C3D to obtain multi-scale features; 2) a simple but useful feature fusion module named LFE (Local Feature Extraction). Compared with the fully connected layer and global average pooling, our LFE model is more advantageous for network to fit and fuse features. 3) a new feature segment aligning method called TPGC (Two Pathway Graph Convolution), which allows one proposal to leverage semantic features of adjacent proposals to update its content and make sure the proposals have a variable receptive field. YOLA add only a small overhead to the baseline network, and is easy to train in an end-to-end manner, running at a speed of 1097 fps. YOLA achieves a mAP of 58.3%, outperforming all existing models including both RGB-based and two stream on THUMOS'14, and achieves competitive results on ActivityNet 1.3.

MFI: Multi-Range Feature Interchange for Video Action Recognition

Sikai Bai, Qi Wang, Xuelong Li

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Auto-TLDR; Multi-range Feature Interchange Network for Action Recognition in Videos

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Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

Context Aware Group Activity Recognition

Avijit Dasgupta, C. V. Jawahar, Karteek Alahari

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Auto-TLDR; A Two-Stream Architecture for Group Activity Recognition in Multi-Person Videos

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This paper addresses the task of group activity recognition in multi-person videos. Existing approaches decompose this task into feature learning and relational reasoning. Despite showing progress, these methods only rely on appearance features for people and overlook the available contextual information, which can play an important role in group activity understanding. In this work, we focus on the feature learning aspect and propose a two-stream architecture that not only considers person-level appearance features, but also makes use of contextual information present in videos for group activity recognition. In particular, we propose to use two types of contextual information beneficial for two different scenarios: \textit{pose context} and \textit{scene context} that provide crucial cues for group activity understanding. We combine appearance and contextual features to encode each person with an enriched representation. Finally, these combined features are used in relational reasoning for predicting group activities. We evaluate our method on two benchmarks, Volleyball and Collective Activity and show that joint modeling of contextual information with appearance features benefits in group activity understanding.

Single View Learning in Action Recognition

Gaurvi Goyal, Nicoletta Noceti, Francesca Odone

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Auto-TLDR; Cross-View Action Recognition Using Domain Adaptation for Knowledge Transfer

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Viewpoint is an essential aspect of how an action is visually perceived, with the motion appearing substantially different for some viewpoint pairs. Data driven action recognition algorithms compensate for this by including a variety of viewpoints in their training data, adding to the cost of data acquisition as well as training. We propose a novel methodology that leverages deeply pretrained features to learn actions from a single viewpoint using domain adaptation for knowledge transfer. We demonstrate the effectiveness of this pipeline on 3 different datasets: IXMAS, MoCA and NTU RGBD+, and compare with both classical and deep learning methods. Our method requires low training data and demonstrates unparalleled cross-view action recognition accuracies for single view learning.

Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning

Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan, Alessio Del Bue

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Auto-TLDR; Unsupervised Learning for Human Action Recognition from Skeletal Data

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This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data. Albeit state-of-the-art approaches designed for this application are all supervised, in this paper we pursue a more challenging direction: Solving the problem with unsupervised learning. To this end, we propose a novel subspace clustering method, which exploits covariance matrix to enhance the action’s discriminability and a timestamp pruning approach that allow us to better handle the temporal dimension of the data. Through a broad experimental validation, we show that our computational pipeline surpasses existing unsupervised approaches but also can result in favorable performances as compared to supervised methods.

G-FAN: Graph-Based Feature Aggregation Network for Video Face Recognition

He Zhao, Yongjie Shi, Xin Tong, Jingsi Wen, Xianghua Ying, Jinshi Hongbin Zha

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Auto-TLDR; Graph-based Feature Aggregation Network for Video Face Recognition

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In this paper, we propose a graph-based feature aggregation network (G-FAN) for video face recognition. Compared with the still image, video face recognition exhibits great challenges due to huge intra-class variability and high inter-class ambiguity. To address this problem, our G-FAN first uses a Convolutional Neural Network to extract deep features for every input face of a subject. Then, we build an affinity graph based on the relation between facial features and apply Graph Convolutional Network to generate fine-grained quality vectors for each frame. Finally, the features among multiple frames are adaptively aggregated into a discriminative vector to represent a video face. Different from previous works that take a single image as input, our G-FAN could utilize the correlation information between image pairs and aggregate a template of faces simultaneously. The experiments on video face recognition benchmarks, including YTF, IJB-A, and IJB-C show that: (i) G-FAN automatically learns to advocate high-quality frames while repelling low-quality ones. (ii) G-FAN significantly boosts recognition accuracy and outperforms other state-of-the-art aggregation methods.

AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network

Yuhang Zhang, Hongshuai Ren, Jiexia Ye, Xitong Gao, Yang Wang, Kejiang Ye, Cheng-Zhong Xu

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Auto-TLDR; Adjacency Matrix for Graph Convolutional Network in Non-Euclidean Space

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Graph Convolutional Network (GCN) is adopted to tackle the problem of the convolution operation in non-Euclidean space. Although previous works on GCN have made some progress, one of their limitations is that their input Adjacency Matrix (AM) is designed manually and requires domain knowledge, which is cumbersome, tedious and error-prone. In addition, entries of this fixed Adjacency Matrix are generally designed as binary values (i.e., ones and zeros) which can not reflect more complex relationship between nodes. However, many applications require a weighted and dynamic Adjacency Matrix instead of an unweighted and fixed Adjacency Matrix. To this end, there are few works focusing on designing a more flexible Adjacency Matrix. In this paper, we propose an end-to-end algorithm to improve the GCN performance by focusing on the Adjacency Matrix. We first provide a calculation method that called node information entropy to update the matrix. Then, we analyze the search strategy in a continuous space and introduce the Deep Deterministic Policy Gradient (DDPG) method to overcome the demerit of the discrete space search. Finally, we integrate the GCN and reinforcement learning into an end-to-end framework. Our method can automatically define the adjacency matrix without artificial knowledge. At the same time, the proposed approach can deal with any size of the matrix and provide a better value for the network. Four popular datasets are selected to evaluate the capability of our algorithm. The method in this paper achieves the state-of-the-art performance on Cora and Pubmed datasets, respectively, with the accuracy of 84.6% and 81.6%.

Exploring Severe Occlusion: Multi-Person 3D Pose Estimation with Gated Convolution

Renshu Gu, Gaoang Wang, Jenq-Neng Hwang

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Auto-TLDR; 3D Human Pose Estimation for Multi-Human Videos with Occlusion

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3D human pose estimation (HPE) is crucial in human behavior analysis, augmented reality/virtual reality (AR/VR) applications, and self-driving industry. Videos that contain multiple potentially occluded people captured from freely moving monocular cameras are very common in real-world scenarios, while 3D HPE for such scenarios is quite challenging, partially because there is a lack of such data with accurate 3D ground truth labels in existing datasets. In this paper, we propose a temporal regression network with a gated convolution module to transform 2D joints to 3D and recover the missing occluded joints in the meantime. A simple yet effective localization approach is further conducted to transform the normalized pose to the global trajectory. To verify the effectiveness of our approach, we also collect a new moving camera multi-human (MMHuman) dataset that includes multiple people with heavy occlusion captured by moving cameras. The 3D ground truth joints are provided by accurate motion capture (MoCap) system. From the experiments on static-camera based Human3.6M data and our own collected moving-camera based data, we show that our proposed method outperforms most state-of-the-art 2D-to-3D pose estimation methods, especially for the scenarios with heavy occlusions.

LFIR2Pose: Pose Estimation from an Extremely Low-Resolution FIR Image Sequence

Saki Iwata, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa

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Auto-TLDR; LFIR2Pose: Human Pose Estimation from a Low-Resolution Far-InfraRed Image Sequence

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In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy using an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

Learnable Higher-Order Representation for Action Recognition

Jie Shao, Xiangyang Xue

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Auto-TLDR; Learningable Higher-Order Operations for Spatiotemporal Dynamics in Video Recognition

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Capturing spatiotemporal dynamics is an essential topic in video recognition. In this paper, we present learnable higher-order operations as a generic family of building blocks for capturing spatiotemporal dynamics from RGB input video space. Similar to higher-order functions, the weights of higher-order operations are themselves derived from the data with learnable parameters. Classical architectures such as residual learning and network-in-network are first-order operations where weights are directly learned from the data. Higher-order operations make it easier to capture context-sensitive patterns, such as motion. Self-attention models are also higher-order operations, but the attention weights are mostly computed from an affine operation or dot product. The learnable higher-order operations can be more generic and flexible. Experimentally, we show that on the task of video recognition, our higher-order models can achieve results on par with or better than the existing state-of-the-art methods on Something-Something (V1 and V2), Kinetics and Charades datasets.

Modeling Long-Term Interactions to Enhance Action Recognition

Alejandro Cartas, Petia Radeva, Mariella Dimiccoli

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Auto-TLDR; A Hierarchical Long Short-Term Memory Network for Action Recognition in Egocentric Videos

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In this paper, we propose a new approach to understand actions in egocentric videos that exploit the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical Long Short-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks, without relying on motion information.

RWF-2000: An Open Large Scale Video Database for Violence Detection

Ming Cheng, Kunjing Cai, Ming Li

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Auto-TLDR; Flow Gated Network for Violence Detection in Surveillance Cameras

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In recent years, surveillance cameras are widely deployed in public places, and the general crime rate has been reduced significantly due to these ubiquitous devices. Usually, these cameras provide cues and evidence after crimes were conducted, while they are rarely used to prevent or stop criminal activities in time. It is both time and labor consuming to manually monitor a large amount of video data from surveillance cameras. Therefore, automatically recognizing violent behaviors from video signals becomes essential. In this paper, we summarize several existing video datasets for violence detection and propose a new video dataset with 2,000 videos all captured by surveillance cameras in real-world scenes. Also, we present a new method that utilizes both the merits of 3D-CNNs and optical flow, namely Flow Gated Network. The proposed approach obtains an accuracy of 87.25% on the test set of our proposed RWF-2000 database. The proposed database and source codes of this paper are currently open to access.

Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Nikolaos Bakalos

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Auto-TLDR; Tensor-Based Neural Network for Spatiotemporal Pose Classifiaction using Three-Dimensional Skeleton Data

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Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classifiaction using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.

RefiNet: 3D Human Pose Refinement with Depth Maps

Andrea D'Eusanio, Stefano Pini, Guido Borghi, Roberto Vezzani, Rita Cucchiara

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Auto-TLDR; RefiNet: A Multi-stage Framework for 3D Human Pose Estimation

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Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images. Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manually-annotated RGB datasets, achieving stunning results. In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and a depth map. The framework consists of three different modules, each one specialized in a particular refinement and data representation, i.e. depth patches, 3D skeleton and point clouds. Moreover, we collect a new dataset, namely Baracca, acquired with RGB, depth and thermal cameras and specifically created for the automotive context. Experimental results confirm the quality of the refinement procedure that largely improves the human pose estimations of off-the-shelf 2D methods.

On the Global Self-attention Mechanism for Graph Convolutional Networks

Chen Wang, Deng Chengyuan

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Auto-TLDR; Global Self-Attention Mechanism for Graph Convolutional Networks

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Applying Global Self-Attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutional Networks (GCNs) can similarly benefit from such a technique. In this paper, inspired by the similarity between CNNs and GCNs, we study the impact of the Global Self-Attention mechanism on GCNs. We find that consistent with the intuition, the GSA mechanism allows GCNs to capture feature-based vertex relations regardless of edge connections; As a result, the GSA mechanism can introduce extra expressive power to the GCNs. Furthermore, we analyze the impacts of the GSA mechanism on the issues of overfitting and over-smoothing. We prove that the GSA mechanism can alleviate both the overfitting and the over-smoothing issues based on some recent technical developments. Experiments on multiple benchmark datasets illustrate both superior expressive power and less significant overfitting and over-smoothing problems for the GSA-augmented GCNs, which corroborate the intuitions and the theoretical results.

2D Deep Video Capsule Network with Temporal Shift for Action Recognition

Théo Voillemin, Hazem Wannous, Jean-Philippe Vandeborre

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Auto-TLDR; Temporal Shift Module over Capsule Network for Action Recognition in Continuous Videos

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Action recognition in continuous video streams is a growing field since the past few years. Deep learning techniques and in particular Convolutional Neural Networks (CNNs) achieved good results in this topic. However, intrinsic CNNs limitations begin to cap the results since 2D CNN cannot capture temporal information and 3D CNN are to much resource demanding for real-time applications. Capsule Network, evolution of CNN, already proves its interesting benefits on small and low informational datasets like MNIST but yet its true potential has not emerged. In this paper we tackle the action recognition problem by proposing a new architecture combining Temporal Shift module over deep Capsule Network. Temporal Shift module permits us to insert temporal information over 2D Capsule Network with a zero computational cost to conserve the lightness of 2D capsules and their ability to connect spatial features. Our proposed approach outperforms or brings near state-of-the-art results on color and depth information on public datasets like First Person Hand Action and DHG 14/28 with a number of parameters 10 to 40 times less than existing approaches.

Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting

Yiwen Sun, Yulu Wang, Kun Fu, Zheng Wang, Changshui Zhang, Jieping Ye

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Auto-TLDR; GLT-GCRNN: Geographic and Long-term Temporal Graph Convolutional Recurrent Neural Network for Traffic Forecasting

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Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is challenging due to the fact that the road network contains complex and time-varying spatial-temporal dependencies. Recently, deep learning based methods have achieved promising results by adopting graph convolutional network (GCN) to extract the spatial correlations and recurrent neural network (RNN) to capture the temporal dependencies. However, the existing methods often construct the graph only based on road network connectivity, which limits the interaction between roads. In this work, we propose Geographic and Long-term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN), a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or long-term temporal patterns. Extensive experiments on a real-world traffic state dataset validate the effectiveness of our method by showing that GLT-GCRNN outperforms the state-of-the-art methods in terms of different metrics.

Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition

Mirco Planamente, Andrea Bottino, Barbara Caputo

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Auto-TLDR; A Single Stream Architecture for Egocentric Action Recognition from the First-Person Point of View

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Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view. An open challenge in egocentric action recognition is that videos lack detailed information about the main actor's pose and thus tend to record only parts of the movement when focusing on manipulation tasks. Thus, the amount of information about the action itself is limited, making crucial the understanding of the manipulated objects and their context. Many previous works addressed this issue with two-stream architectures, where one stream is dedicated to modeling the appearance of objects involved in the action, and another to extracting motion features from optical flow. In this paper, we argue that learning features jointly from these two information channels is beneficial to capture the spatio-temporal correlations between the two better. To this end, we propose a single stream architecture able to do so, thanks to the addition of a self-supervised block that uses a pretext motion prediction task to intertwine motion and appearance knowledge. Experiments on several publicly available databases show the power of our approach.

Late Fusion of Bayesian and Convolutional Models for Action Recognition

Camille Maurice, Francisco Madrigal, Frederic Lerasle

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Auto-TLDR; Fusion of Deep Neural Network and Bayesian-based Approach for Temporal Action Recognition

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The activities we do in our daily-life are generally carried out as a succession of atomic actions, following a logical order. During a video sequence, actions usually follow a logical order. In this paper, we propose a hybrid approach resulting from the fusion of a deep learning neural network with a Bayesian-based approach. The latter models human-object interactions and transition between actions. The key idea is to combine both approaches in the final prediction. We validate our strategy in two public datasets: CAD-120 and Watch-n-Patch. We show that our fusion approach yields performance gains in accuracy of respectively +4\% and +6\% over a baseline approach. Temporal action recognition performances are clearly improved by the fusion, especially when classes are imbalanced.

Video-Based Facial Expression Recognition Using Graph Convolutional Networks

Daizong Liu, Hongting Zhang, Pan Zhou

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Auto-TLDR; Graph Convolutional Network for Video-based Facial Expression Recognition

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Facial expression recognition (FER), aiming to classify the expression present in the facial image or video, has attracted a lot of research interests in the field of artificial intelligence and multimedia. In terms of video based FER task, it is sensible to capture the dynamic expression variation among the frames to recognize facial expression. However, existing methods directly utilize CNN-RNN or 3D CNN to extract the spatial-temporal features from different facial units, instead of concentrating on a certain region during expression variation capturing, which leads to limited performance in FER. In our paper, we introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based FER. First, the GCN layer is utilized to learn more contributing facial expression features which concentrate on certain regions after sharing information between nodes those represent CNN extracted features. Then, a LSTM layer is applied to learn long-term dependencies among the GCN learned features to model the variation. In addition, a weight assignment mechanism is also designed to weight the output of different nodes for final classification by characterizing the expression intensities in each frame. To the best of our knowledge, it is the first time to use GCN in FER task. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0, and the experimental results demonstrate that our method has superior performance to existing methods.

Boundary-Aware Graph Convolution for Semantic Segmentation

Hanzhe Hu, Jinshi Cui, Jinshi Hongbin Zha

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Auto-TLDR; Boundary-Aware Graph Convolution for Semantic Segmentation

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Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.

Motion Complementary Network for Efficient Action Recognition

Ke Cheng, Yifan Zhang, Chenghua Li, Jian Cheng, Hanqing Lu

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Auto-TLDR; Efficient Motion Complementary Network for Action Recognition

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Both two-stream ConvNet and 3D ConvNet are widely used in action recognition. However, both methods are not efficient for deployment: calculating optical flow is very slow, while 3D convolution is computationally expensive. Our key insight is that the motion information from optical flow maps is complementary to the motion information from 3D ConvNet. Instead of simply combining these two methods, we propose two novel techniques to enhance the performance with less computational cost: \textit{fixed-motion-accumulation} and \textit{balanced-motion-policy}. With these two techniques, we propose a novel framework called Efficient Motion Complementary Network(EMC-Net) that enjoys both high efficiency and high performance. We conduct extensive experiments on Kinetics, UCF101, and Jester datasets. We achieve notably higher performance while consuming 4.7$\times$ less computation than I3D, 11.6$\times$ less computation than ECO, 17.8$\times$ less computation than R(2+1)D. On Kinetics dataset, we achieve 2.6\% better performance than the recent proposed TSM with 1.4$\times$ fewer FLOPs and 10ms faster on K80 GPU.

Towards Practical Compressed Video Action Recognition: A Temporal Enhanced Multi-Stream Network

Bing Li, Longteng Kong, Dongming Zhang, Xiuguo Bao, Di Huang, Yunhong Wang

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Auto-TLDR; TEMSN: Temporal Enhanced Multi-Stream Network for Compressed Video Action Recognition

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Current compressed video action recognition methods are mainly based on completely received compressed videos. However, in real transmission, the compressed video packets are usually disorderly received and lost due to network jitters or congestion. It is of great significance to recognize actions in early phases with limited packets, e.g. forecasting the potential risks from videos quickly. In this paper, we proposed a Temporal Enhanced Multi-Stream Network (TEMSN) for practical compressed video action recognition. First, we use three compressed modalities as complementary cues and build a multi-stream network to capture the rich information from compressed video packets. Second, we design a temporal enhanced module based on Encoder-Decoder structure applied on each stream to infer the missing packets, and generate more complete action dynamics. Thanks to the rich modalities and temporal enhancement, our approach is able to better modeling the action with limited compressed packets. Experiments on HMDB-51 and UCF-101 dataset validate its effectiveness and efficiency.

Classification of Intestinal Gland Cell-Graphs Using Graph Neural Networks

Linda Studer, Jannis Wallau, Heather Dawson, Inti Zlobec, Andreas Fischer

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Auto-TLDR; Graph Neural Networks for Classification of Dysplastic Gland Glands using Graph Neural Networks

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We propose to classify intestinal glands as normal or dysplastic using cell-graphs and graph-based deep learning methods. Dysplastic intestinal glands can lead to colorectal cancer, which is one of the three most common cancer types in the world. In order to assess the cancer stage and thus the treatment of a patient, pathologists analyse tissue samples of affected patients. Among other factors, they look at the changes in morphology of different tissues, such as the intestinal glands. Cell-graphs have a high representational power and can describe topological and geometrical properties of intestinal glands. However, classical graph-based methods have a high computational complexity and there is only a limited range of machine learning methods available. In this paper, we propose Graph Neural Networks (GNNs) as an efficient learning-based approach to classify cell-graphs. We investigate different variants of so-called Message Passing Neural Networks and compare them with a classical graph-based approach based on approximated Graph Edit Distance and k-nearest neighbours classifier. A promising classification accuracy of 94.1% is achieved by the proposed method on the pT1 Gland Graph dataset, which is an increase of 11.5% over the baseline result.

Flow-Guided Spatial Attention Tracking for Egocentric Activity Recognition

Tianshan Liu, Kin-Man Lam

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Auto-TLDR; flow-guided spatial attention tracking for egocentric activity recognition

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The popularity of wearable cameras has opened up a new dimension for egocentric activity recognition. While some methods introduce attention mechanisms into deep learning networks to capture fine-grained hand-object interactions, they often neglect exploring the spatio-temporal relationships. Generating spatial attention, without adequately exploiting temporal consistency, will result in potentially sub-optimal performance in the video-based task. In this paper, we propose a flow-guided spatial attention tracking (F-SAT) module, which is based on enhancing motion patterns and inter-frame information, to highlight the discriminative features from regions of interest across a video sequence. A new form of input, namely the optical-flow volume, is presented to provide informative cues from moving parts for spatial attention tracking. The proposed F-SAT module is deployed to a two-branch-based deep architecture, which fuses complementary information for egocentric activity recognition. Experimental results on three egocentric activity benchmarks show that the proposed method achieves state-of-the-art performance.

GCNs-Based Context-Aware Short Text Similarity Model

Xiaoqi Sun

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Auto-TLDR; Context-Aware Graph Convolutional Network for Text Similarity

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Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which cannot capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extend the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

Dual-Mode Iterative Denoiser: Tackling the Weak Label for Anomaly Detection

Shuheng Lin, Hua Yang

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Auto-TLDR; A Dual-Mode Iterative Denoiser for Crowd Anomaly Detection

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Crowd anomaly detection suffers from limited training data under weak supervision. In this paper, we propose a dual-mode iterative denoiser to tackle the weak label challenge for anomaly detection. First, we use a convolution autoencoder (CAE) in image space to act as a cluster for grouping similar video clips, where the spatial-temporal similarity helps the cluster metric to represent the reconstruction error. Then we use the graph convolution neural network (GCN) to explore the temporal correlation and the feature similarity between video clips within different rough labels, where the classifier can be constantly updated in the label denoising process. Without specific image-level labels, our model can predict the clip-level anomaly probabilities for videos. Extensive experiment results on two public datasets show that our approach performs favorably against the state-of-the-art methods.