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Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video

Pu Li, Xiaobai Liu

Auto-TLDR; Sequential Estimation of Planar Homographic Transformations over Aerial Videos

Abstract Slides Poster

This paper presents an unsupervised approach that leverages raw aerial videos to learn to estimate planar homographic transformation between consecutive video frames. Previous learning-based estimators work on pairs of images to estimate their planar homographic transformations but suffer from severe over-fitting issues, especially when applying over aerial videos. To address this concern, we develop a sequential estimator that directly processes a sequence of video frames and estimates their pairwise planar homographic transformations in batches. We also incorporate a set of spatial-temporal knowledge to regularize the learning of such a sequence-to-sequence model. We collect a set of challenging aerial videos and compare the proposed method to the alternative algorithms. Empirical studies suggest that our sequential model achieves significant improvement over alternative image-based methods and the knowledge-rich regularization further boosts our system performance. Our codes and dataset could be found at https://github.com/Paul-LiPu/DeepVideoHomography

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Adaptive Estimation of Optimal Color Transformations for Deep Convolutional Network Based Homography Estimation

Miguel A. Molina-Cabello, Jorge García-González, Rafael Marcos Luque-Baena, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio

Auto-TLDR; Improving Homography Estimation from a Pair of Natural Images Using Deep Convolutional Neural Networks

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Homography estimation from a pair of natural images is a problem of paramount importance for computer vision. Specialized deep convolutional neural networks have been proposed to accomplish this task. In this work, a method to enhance the result of this kind of homography estimators is proposed. Our approach generates a set of tentative color transformations for the image pair. Then the color transformed image pairs are evaluated by a regressor that estimates the quality of the homography that would be obtained by supplying the transformed image pairs to the homography estimator. Then the image pair that is predicted to yield the best result is provided to the homography estimator. Experimental results are shown, which demonstrate that our approach performs better than the direct application of the homography estimator to the original image pair, both in qualitative and quantitative terms.

Mobile Augmented Reality: Fast, Precise, and Smooth Planar Object Tracking

Dmitrii Matveichev, Daw-Tung Lin

Auto-TLDR; Planar Object Tracking with Sparse Optical Flow Tracking and Descriptor Matching

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We propose an innovative method for combining sparse optical flow tracking and descriptor matching algorithms. The proposed approach solves the following problems that are inherent to keypoint-based and optical flow based tracking algorithms: spatial jitter, extreme scale transformation, extreme perspective transformation, degradation in the number of tracking points, and drifting of tracking points. Our algorithm provides smooth object-position tracking under six degrees of freedom transformations with a small computational cost for providing a high-quality real-time AR experience on mobile platforms. We experimentally demonstrate that our approach outperforms the state-of-the-art tracking algorithms while offering faster computational time. A mobile augmented reality (AR) application, which is developed using our approach, delivers planar object tracking with 30 FPS on modern mobile phones for a camera resolution of 1280$\times$720. Finally, we compare the performance of our AR application with that of the Vuforia-based AR application on the same planar objects database. The test results show that our AR application delivers better AR experience than Vuforia in terms of smooth transition of object-pose between video frames.

Extending Single Beam Lidar to Full Resolution by Fusing with Single Image Depth Estimation

Yawen Lu, Yuxing Wang, Devarth Parikh, Guoyu Lu

Auto-TLDR; Self-supervised LIDAR for Low-Cost Depth Estimation

Abstract Slides Similar

Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution Lidars are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a learning-based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 50-100 times cheaper price (e.g., \$80000 Velodyne HDL-64E LIDAR v.s. \$1000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.

Visual Saliency Oriented Vehicle Scale Estimation

Qixin Chen, Tie Liu, Jiali Ding, Zejian Yuan, Yuanyuan Shang

Auto-TLDR; Regularized Intensity Matching for Vehicle Scale Estimation with salient object detection

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Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.

Future Urban Scenes Generation through Vehicles Synthesis

Alessandro Simoni, Luca Bergamini, Andrea Palazzi, Simone Calderara, Rita Cucchiara

Auto-TLDR; Predicting the Future of an Urban Scene with a Novel View Synthesis Paradigm

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In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset.

What and How? Jointly Forecasting Human Action and Pose

Yanjun Zhu, Yanxia Zhang, Qiong Liu, Andreas Girgensohn

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.

NetCalib: A Novel Approach for LiDAR-Camera Auto-Calibration Based on Deep Learning

Shan Wu, Amnir Hadachi, Damien Vivet, Yadu Prabhakar

Auto-TLDR; Automatic Calibration of LiDAR and Cameras using Deep Neural Network

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A fusion of LiDAR and cameras have been widely used in many robotics applications such as classification, segmentation, object detection, and autonomous driving. It is essential that the LiDAR sensor can measure distances accurately, which is a good complement to the cameras. Hence, calibrating sensors before deployment is a mandatory step. The conventional methods include checkerboards, specific patterns, or human labeling, which is trivial and human-labor extensive if we do the same calibration process every time. The main propose of this research work is to build a deep neural network that is capable of automatically finding the geometric transformation between LiDAR and cameras. The results show that our model manages to find the transformations from randomly sampled artificial errors. Besides, our work is open-sourced for the community to fully utilize the advances of the methodology for developing more the approach, initiating collaboration, and innovation in the topic.

Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model

Congcong Li, Haoyu Ma, Qingmin Liao

Auto-TLDR; Adaptive object scene flow estimation using a hybrid CNN-CRF model and adaptive iteration

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Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.

Learning Non-Rigid Surface Reconstruction from Spatio-Temporal Image Patches

Matteo Pedone, Abdelrahman Mostafa, Janne Heikkilä

Auto-TLDR; Dense Spatio-Temporal Depth Maps of Deformable Objects from Video Sequences

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We present a method to reconstruct a dense spatio-temporal depth map of a non-rigidly deformable object directly from a video sequence. The estimation of depth is performed locally on spatio-temporal patches of the video, and then the full depth video of the entire shape is recovered by combining them together. Since the geometric complexity of a local spatio-temporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using other approaches like conventional non-rigid structure from motion.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

Partially Supervised Multi-Task Network for Single-View Dietary Assessment

Ya Lu, Thomai Stathopoulou, Stavroula Mougiakakou

Auto-TLDR; Food Volume Estimation from a Single Food Image via Geometric Understanding and Semantic Prediction

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Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods based on computer vision require either multi-image input or additional depth maps, reducing convenience of implementation and practical significance. Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved. In this paper, we propose a network architecture that jointly performs geometric understanding (i.e., depth prediction and 3D plane estimation) and semantic prediction on a single food image, enabling a robust and accurate food volume estimation regardless of the texture characteristics of the target plane. For the training of the network, only monocular videos with semantic ground truth are required, while the depth map and 3D plane ground truth are no longer needed. Experimental results on two separate food image databases demonstrate that our method performs robustly on texture-less scenarios and is superior to unsupervised networks and structure from motion based approaches, while it achieves comparable performance to fully-supervised methods.

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

Ming Cheng, Kunjing Cai, Ming Li

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.

Video Reconstruction by Spatio-Temporal Fusion of Blurred-Coded Image Pair

Anupama S, Prasan Shedligeri, Abhishek Pal, Kaushik Mitr

Auto-TLDR; Recovering Video from Motion-Blurred and Coded Exposure Images Using Deep Learning

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Learning-based methods have enabled the recovery of a video sequence from a single motion-blurred image or a single coded exposure image. Recovering video from a single motion-blurred image is a very ill-posed problem and the recovered video usually has many artifacts. In addition to this, the direction of motion is lost and it results in motion ambiguity. However, it has the advantage of fully preserving the information in the static parts of the scene. The traditional coded exposure framework is better-posed but it only samples a fraction of the space-time volume, which is at best $50\%$ of the space-time volume. Here, we propose to use the complementary information present in the fully-exposed (blurred) image along with the coded exposure image to recover a high fidelity video without any motion ambiguity. Our framework consists of a shared encoder followed by an attention module to selectively combine the spatial information from the fully-exposed image with the temporal information from the coded image, which is then super-resolved to recover a non-ambiguous high-quality video. The input to our algorithm is a fully-exposed and coded image pair. Such an acquisition system already exists in the form of a Coded-two-bucket (C2B) camera. We demonstrate that our proposed deep learning approach using blurred-coded image pair produces much better results than those from just a blurred image or just a coded image.

Context Matters: Self-Attention for Sign Language Recognition

Fares Ben Slimane, Mohamed Bouguessa

Auto-TLDR; Attentional Network for Continuous Sign Language Recognition

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This paper proposes an attentional network for the task of Continuous Sign Language Recognition. The proposed approach exploits co-independent streams of data to model the sign language modalities. These different channels of information can share a complex temporal structure between each other. For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components. Even though Sign Language is multi-channel, handshapes represent the central entities in sign interpretation. Seeing handshapes in their correct context defines the meaning of a sign. Taking that into account, we utilize the attention mechanism to efficiently aggregate the hand features with their appropriate Spatio-temporal context for better sign recognition. We found that by doing so the model is able to identify the essential Sign Language components that revolve around the dominant hand and the face areas. We test our model on the benchmark dataset RWTH-PHOENIX-Weather 2014, yielding competitive results.

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

Rongxiao Tang, Wang Luyang, Zhenhua Guo

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.

Learning to Find Good Correspondences of Multiple Objects

Youye Xie, Yingheng Tang, Gongguo Tang, William Hoff

Auto-TLDR; Multi-Object Inliers and Outliers for Perspective-n-Point and Object Recognition

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Given a set of 3D to 2D putative matches, labeling the correspondences as inliers or outliers plays a critical role in a wide range of computer vision applications including the Perspective-n-Point (PnP) and object recognition. In this paper, we study a more generalized problem which allows the matches to belong to multiple objects with distinct poses. We propose a deep architecture to simultaneously label the correspondences as inliers or outliers and classify the inliers into multiple objects. Specifically, we discretize the 3D rotation space into twenty convex cones based on the facets of a regular icosahedron. For each facet, a facet classifier is trained to predict the probability of a correspondence being an inlier for a pose whose rotation normal vector points towards this facet. An efficient RANSAC-based post-processing algorithm is also proposed to further process the prediction results and detect the objects. Experiments demonstrate that our method is very efficient compared to existing methods and is capable of simultaneously labeling and classifying the inliers of multiple objects with high precision.

A Two-Step Approach to Lidar-Camera Calibration

Yingna Su, Yaqing Ding, Jian Yang, Hui Kong

Auto-TLDR; Closed-Form Calibration of Lidar-camera System for Ego-motion Estimation and Scene Understanding

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Autonomous vehicles and robots are typically equipped with Lidar and camera. Hence, calibrating the Lidar-camera system is of extreme importance for ego-motion estimation and scene understanding. In this paper, we propose a two-step approach (coarse + fine) for the external calibration between a camera and a multiple-line Lidar. First, a new closed-form solution is proposed to obtain the initial calibration parameters. We compare our solution with the state-of-the-art SVD-based algorithm, and show the benefits of both the efficiency and stability. With the initial calibration parameters, the ICP-based calibration framework is used to register the point clouds which extracted from the camera and Lidar coordinate frames, respectively. Our method has been applied to two Lidar-camera systems: an HDL-64E Lidar-camera system, and a VLP-16 Lidar-camera system. Experimental results demonstrate that our method achieves promising performance and higher accuracy than two open-source methods.

Visual Oriented Encoder: Integrating Multimodal and Multi-Scale Contexts for Video Captioning

Bang Yang, Yuexian Zou

Auto-TLDR; Visual Oriented Encoder for Video Captioning

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Video captioning is a challenging task which aims at automatically generating a natural language description of a given video. Recent researches have shown that exploiting the intrinsic multi-modalities of videos significantly promotes captioning performance. However, how to integrate multi-modalities to generate effective semantic representations for video captioning is still an open issue. Some researchers proposed to learn multimodal features in parallel during the encoding stage. The downside of these methods lies in the neglect of the interaction among multi-modalities and their rich contextual information. In this study, inspired by the fact that visual contents are generally more important for comprehending videos, we propose a novel Visual Oriented Encoder (VOE) to integrate multimodal features in an interactive manner. Specifically, VOE is designed as a hierarchical structure, where bottom layers are utilized to extract multi-scale contexts from auxiliary modalities while the top layer is exploited to generate joint representations by considering both visual and contextual information. Following the encoder-decoder framework, we systematically develop a VOE-LSTM model and evaluate it on two mainstream benchmarks: MSVD and MSR-VTT. Experimental results show that the proposed VOE surpasses conventional encoders and our VOE-LSTM model achieves competitive results compared with state-of-the-art approaches.

Video Semantic Segmentation Using Deep Multi-View Representation Learning

Akrem Sellami, Salvatore Tabbone

Auto-TLDR; Deep Multi-view Representation Learning for Video Object Segmentation

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In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., $F_{a}$ and $F_{b}$. During the segmentation phase, the deep canonically correlated autoencoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

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

Mirco Planamente, Andrea Bottino, Barbara Caputo

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.

Vision-Based Multi-Modal Framework for Action Recognition

Djamila Romaissa Beddiar, Mourad Oussalah, Brahim Nini

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.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features

Maximilian Kraus, Seyed Majid Azimi, Emec Ercelik, Reza Bahmanyar, Peter Reinartz, Alois Knoll

Auto-TLDR; AerialMPTNet: A novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features

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Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial multi-pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. To the best of our knowledge, AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency.

Continuous Sign Language Recognition with Iterative Spatiotemporal Fine-Tuning

Kenessary Koishybay, Medet Mukushev, Anara Sandygulova

Auto-TLDR; A Deep Neural Network for Continuous Sign Language Recognition with Iterative Gloss Recognition

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This paper aims to develop a deep neural network for Continuous Sign Language Recognition (CSLR) with iterative Gloss Recognition (GR) fine-tuning. CSLR has been a popular research field in the last years and iterative optimization methods are well established. This paper introduces our proposed architecture involving Spatiotemporal feature-extraction model to segment useful ``gloss-unit" features and BiLSTM with CTC as a sequence model. Spatiotemporal Feature Extractor is used for both image features extraction and sequence length reduction. To this end, we compare different architectures for feature extraction and sequence model. In addition, we iteratively fine-tune feature extractor on gloss-unit video segments with alignments from the end2end model. During the iterative training, we use novel alignment correction technique, which is based on minimum transformations of Levenshtein distance. All the experiments were conducted on the RWTH-PHOENIX-Weather-2014 dataset.

User-Independent Gaze Estimation by Extracting Pupil Parameter and Its Mapping to the Gaze Angle

Sang Yoon Han, Nam Ik Cho

Auto-TLDR; Gaze Point Estimation using Pupil Shape for Generalization

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Since gaze estimation plays a crucial role in recognizing human intentions, it has been researched for a long time, and its accuracy is ever increasing. However, due to the wide variation in eye shapes and focusing abilities between the individuals, accuracies of most algorithms vary depending on each person in the test group, especially when the initial calibration is not well performed. To alleviate the user-dependency, we attempt to derive features that are general for most people and use them as the input to a deep network instead of using the images as the input. Specifically, we use the pupil shape as the core feature because it is directly related to the 3D eyeball rotation, and thus the gaze direction. While existing deep learning methods learn the gaze point by extracting various features from the image, we focus on the mapping function from the eyeball rotation to the gaze point by using the pupil shape as the input. It is shown that the accuracy of gaze point estimation also becomes robust for the uncalibrated points by following the characteristics of the mapping function. Also, our gaze network learns the gaze difference to facilitate the re-calibration process to fix the calibration-drift problem that typically occurs with glass-type or head-mount devices.

Residual Learning of Video Frame Interpolation Using Convolutional LSTM

Keito Suzuki, Masaaki Ikehara

Auto-TLDR; Video Frame Interpolation Using Residual Learning and Convolutional LSTMs

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Video frame interpolation aims to generate interme- diate frames between the original frames. This produces videos with a higher frame r ate and creates smoother motion. Many video frame interpolation methods first estimate the motion vector between the input frames and then synthesizes the intermediate frame based on the motion. However, these methods rely on the accuracy of the motion estimation step and fail to accurately generate the interpolated frame when the estimated motion vectors are inaccurate. Therefore, to avoid the uncertainties caused by motion estimation, this paper proposes a method that directly generates the intermediate frame. Since two consecutive frames are relatively similar, our method takes the average of these two frames and utilizes residual learning to learn the difference between the average of these frames and the ground truth middle frame. In addition, our method uses Convolutional LSTMs and four input frames to better incorporate spatiotemporal information. This neural network can be easily trained end to end without difficult to obtain data such as optical flow. Our experimental results show that the proposed method can perform favorably against other state-of-the-art frame interpolation methods.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

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Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

A Grid-Based Representation for Human Action Recognition

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

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.

Deep Realistic Novel View Generation for City-Scale Aerial Images

Koundinya Nouduri, Ke Gao, Joshua Fraser, Shizeng Yao, Hadi Aliakbarpour, Filiz Bunyak, Kannappan Palaniappan

Auto-TLDR; End-to-End 3D Voxel Renderer for Multi-View Stereo Data Generation and Evaluation

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In this paper we introduce a novel end-to-end frameworkfor generation of large, aerial, city-scale, realistic syntheticimage sequences with associated accurate and precise camerametadata. The two main purposes for this data are (i) to en-able objective, quantitative evaluation of computer vision al-gorithms and methods such as feature detection, description,and matching or full computer vision pipelines such as 3D re-construction; and (ii) to supply large amounts of high qualitytraining data for deep learning guided computer vision meth-ods. The proposed framework consists of three main mod-ules, a 3D voxel renderer for data generation, a deep neu-ral network for artifact removal, and a quantitative evaluationmodule for Multi-View Stereo (MVS) as an example. The3D voxel renderer enables generation of seen or unseen viewsof a scene from arbitary camera poses with accurate camerametadata parameters. The artifact removal module proposes anovel edge-augmented deep learning network with an explicitedgemap processing stream to remove image artifacts whilepreserving and recovering scene structures for more realis-tic results. Our experiments on two urban, city-scale, aerialdatasets for Albuquerque (ABQ), NM and Los Angeles (LA),CA show promising results in terms structural similarity toreal data and accuracy of reconstructed 3D point clouds

Deep Homography-Based Video Stabilization

Maria Silvia Ito, Ebroul Izquierdo

Auto-TLDR; Video Stabilization using Deep Learning and Spatial Transformer Networks

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Video stabilization is fundamental for providing good Quality of Experience for viewers and generating suitable content for video applications. In this scenario, Digital Video Stabilization (DVS) is convenient and economical for casual or amateur recording because it neither requires specific equipment nor demands knowledge of the device used for recording. Although DVS has been a research topic for decades, with a number of proposals from industry and academia, traditional methods tend to fail in a number of scenarios, e.g. with occlusion, textureless areas, parallax, dark areas, amongst others. On the other hand, defining a smooth camera path is a hard task in Deep Learning scenarios. This paper proposes a video stabilization system based on traditional and Deep Learning methods. First, we leverage Spatial Transformer Networks (STNs) to learn transformation parameters between image pairs, then utilize this knowledge to stabilize videos: we obtain the motion parameters between frame pairs and then smooth the camera path using moving averages. Our approach aims at combining the strengths of both Deep Learning and traditional methods: the ability of STNs to estimate motion parameters between two frames and the effectiveness of moving averages to smooth camera paths. Experimental results show that our system outperforms state-of-the-art proposals and a commercial solution.

Learning Object Deformation and Motion Adaption for Semi-Supervised Video Object Segmentation

Xiaoyang Zheng, Xin Tan, Jianming Guo, Lizhuang Ma

Auto-TLDR; Semi-supervised Video Object Segmentation with Mask-propagation-based Model

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We propose a novel method to solve the task of semi-supervised video object segmentation in this paper, where the mask annotation is only given at the first frame of the video sequence. A mask-propagation-based model is applied to learn the past and current information for segmentation. Besides, due to the scarcity of training data, image/mask pairs that model object deformation and shape variance are generated for the training phase. In addition, we generate the key flips between two adjacent frames for motion adaptation. The method works in an end-to-end way, without any online fine-tuning on test videos. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art algorithms on benchmark datasets, covering cases with single object or multiple objects. We also conduct extensive ablation experiments to analyze the effectiveness of our proposed method.

Cross-Lingual Text Image Recognition Via Multi-Task Sequence to Sequence Learning

Zhuo Chen, Fei Yin, Xu-Yao Zhang, Qing Yang, Cheng-Lin Liu

Auto-TLDR; Cross-Lingual Text Image Recognition with Multi-task Learning

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This paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image Recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image Recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a BLSTM model with CTC, are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.

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

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

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.

STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation

Pierre Godet, Alexandre Boulch, Aurélien Plyer, Guy Le Besnerais

Auto-TLDR; STaRFlow: A lightweight CNN-based algorithm for optical flow estimation

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We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. Our solution introduces a double recurrence over spatial scale and time through repeated use of a generic "STaR" (SpatioTemporal Recurrent) cell. It includes (i) a temporal recurrence based on conveying learned features rather than optical flow estimates; (ii) an occlusion detection process which is coupled with optical flow estimation and therefore uses a very limited number of extra parameters. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015 and involves significantly less parameters than all other methods with comparable results.

Video Face Manipulation Detection through Ensemble of CNNs

Nicolo Bonettini, Edoardo Daniele Cannas, Sara Mandelli, Luca Bondi, Paolo Bestagini, Stefano Tubaro

Auto-TLDR; Face Manipulation Detection in Video Sequences Using Convolutional Neural Networks

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In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.

Unconstrained Vision Guided UAV Based Safe Helicopter Landing

Arindam Sikdar, Abhimanyu Sahu, Debajit Sen, Rohit Mahajan, Ananda Chowdhury

Auto-TLDR; Autonomous Helicopter Landing in Hazardous Environments from Unmanned Aerial Images Using Constrained Graph Clustering

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In this paper, we have addressed the problem of automated detection of safe zone(s) for helicopter landing in hazardous environments from images captured by an Unmanned Aerial Vehicle (UAV). The unconstrained motion of the image capturing drone (the UAV in our case) makes the problem further difficult. The solution pipeline consists of natural landmark detection and tracking, stereo-pair generation using constrained graph clustering, digital terrain map construction and safe landing zone detection. The main methodological contribution lies in mathematically formulating epipolar constraint and then using it in a Minimum Spanning Tree (MST) based graph clustering approach. We have also made publicly available AHL (Autonomous Helicopter Landing) dataset, a new aerial video dataset captured by a drone, with annotated ground-truths. Experimental comparisons with other competing clustering methods i) in terms of Dunn Index and Davies Bouldin Index as well as ii) for frame-level safe zone detection in terms of F-measure and confusion matrix clearly demonstrate the effectiveness of the proposed formulation.

Learning to Take Directions One Step at a Time

Qiyang Hu, Adrian Wälchli, Tiziano Portenier, Matthias Zwicker, Paolo Favaro

Auto-TLDR; Generating a Sequence of Motion Strokes from a Single Image

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We present a method to generate a video sequence given a single image. Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes. Such control signal can be automatically transferred from other videos, e.g., via bounding box tracking. Each motion stroke provides the direction to the moving object in the input image and we aim to train a network to generate an animation following a sequence of such directions. To address this task we design a novel recurrent architecture, which can be trained easily and effectively thanks to an explicit separation of past, future and current states. As we demonstrate in the experiments, our proposed architecture is capable of generating an arbitrary number of frames from a single image and a sequence of motion strokes. Key components of our architecture are an autoencoding constraint to ensure consistency with the past and a generative adversarial scheme to ensure that images look realistic and are temporally smooth. We demonstrate the effectiveness of our approach on the MNIST, KTH, Human3.6M, Push and Weizmann datasets.

Context Visual Information-Based Deliberation Network for Video Captioning

Min Lu, Xueyong Li, Caihua Liu

Auto-TLDR; Context visual information-based deliberation network for video captioning

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Video captioning is to automatically and accurately generate a textual description for a video. The typical methods following the encoder-decoder architecture directly utilized hidden states to predict words. Nevertheless, these methods did not amend the inaccurate hidden states before feeding those states into word prediction. This led to a cascade of errors on generating word by word. In this paper, the context visual information-based deliberation network is proposed, abbreviated as CVI-DelNet. Its key idea is to introduce the deliberator into the encoder-decoder framework. The encoder-decoder firstly generates a raw hidden state sequence. Unlike the existing methods, the raw hidden state is no more directly used for word prediction but is fed into the deliberator to generate the refined hidden state. The words are then predicted according to the refined hidden states and the contextual visual features. Results on two datasets shows that the proposed method significantly outperforms the baselines.

Edge-Aware Monocular Dense Depth Estimation with Morphology

Zhi Li, Xiaoyang Zhu, Haitao Yu, Qi Zhang, Yongshi Jiang

Auto-TLDR; Spatio-Temporally Smooth Dense Depth Maps Using Only a CPU

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Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, a Warm-Start initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

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

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

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.

Minimal Solvers for Indoor UAV Positioning

Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck, Kalle Åström, Anders Heyden

Auto-TLDR; Relative Pose Solvers for Visual Indoor UAV Navigation

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In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art solvers. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other methods, and yields superior motion estimation.

Machine-Learned Regularization and Polygonization of Building Segmentation Masks

Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer

Auto-TLDR; Automatic Regularization and Polygonization of Building Segmentation masks using Generative Adversarial Network

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We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully convolutional network (FCN). A generative adversarial network (GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator’s response to create more realistic images. Finally, we train the backbone convolutional neural network (CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons.

Domain Siamese CNNs for Sparse Multispectral Disparity Estimation

David-Alexandre Beaupre, Guillaume-Alexandre Bilodeau

Auto-TLDR; Multispectral Disparity Estimation between Thermal and Visible Images using Deep Neural Networks

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Multispectral disparity estimation is a difficult task for many reasons: it as all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in addition of having very few common visual information between images (e.g. color information vs. thermal information). In this paper, we propose a new CNN architecture able to do disparity estimation between images from different spectrum, namely thermal and visible in our case. Our proposed model takes two patches as input and proceeds to do domain feature extraction for each of them. Features from both domains are then merged with two fusion operations, namely correlation and concatenation. These merged vectors are then forwarded to their respective classification heads, which are responsible for classifying the inputs as being same or not. Using two merging operations gives more robustness to our feature extraction process, which leads to more precise disparity estimation. Our method was tested using the publicly available LITIV 2014 and LITIV 2018 datasets, and showed best results when compared to other state of the art methods.

The Role of Cycle Consistency for Generating Better Human Action Videos from a Single Frame

Runze Li, Bir Bhanu

Auto-TLDR; Generating Videos with Human Action Semantics using Cycle Constraints

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This paper addresses the challenging problem of generating videos with human action semantics. Unlike previous work which predict future frames in a single forward pass, this paper introduces the cycle constraints in both forward and backward passes in the generation of human actions. This is achieved by enforcing the appearance and motion consistency across a sequence of frames generated in the future. The approach consists of two stages. In the first stage, the pose of a human body is generated. In the second stage, an image generator is used to generate future frames by using (a) generated human poses in the future from the first stage, (b) the single observed human pose, and (c) the single corresponding future frame. The experiments are performed on three datasets: Weizmann dataset involving simple human actions, Penn Action dataset and UCF-101 dataset containing complicated human actions, especially in sports. The results from these experiments demonstrate the effectiveness of the proposed approach.

Orthographic Projection Linear Regression for Single Image 3D Human Pose Estimation

Yahui Zhang, Shaodi You, Theo Gevers

Auto-TLDR; A Deep Neural Network for 3D Human Pose Estimation from a Single 2D Image in the Wild

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3D human pose estimation from a single 2D image in the wild is an important computer vision task but yet extremely challenging. Unlike images taken from indoor and well constrained environments, 2D outdoor images in the wild are extremely complex because of varying imaging conditions. Furthermore, 2D images usually do not have corresponding 3D pose ground truth making a supervised approach ill constrained. Therefore, in this paper, we propose to associate the 3D human pose, the 2D human pose projection and the 2D image appearance through a new orthographic projection based linear regression module. Unlike existing reprojection based approaches, our orthographic projection and regression do not suffer from small angle problems, which usually lead to overfitting in the depth dimension. Hence, we propose a deep neural network which adopts the 2D pose, 3D pose regression and orthographic projection linear regression module. The proposed method shows state-of-the art performance on the Human3.6M dataset and generalizes well to in-the-wild images.

Detecting Manipulated Facial Videos: A Time Series Solution

Zhang Zhewei, Ma Can, Gao Meilin, Ding Bowen

Auto-TLDR; Face-Alignment Based Bi-LSTM for Fake Video Detection

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We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: {Face-Alignment} and {Dense-Face-Alignment}, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of {Face-Alignment} and Pose Adaptive Feature (PAF) for {Dense-Face-Alignment}. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.

Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings

Daniele De Gregorio, Riccardo Zanella, Gianluca Palli, Luigi Di Stefano

Auto-TLDR; Automated Deep Learning for Robotic Grasping Applications

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In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training data. In the industrial field, where automation is the main goal, not bridging this gap is one of the main reasons why deep learning is not as widespread as it is in the academic world. For this reason, in this work we developed a system composed by a 3-DoF Pose Estimator based on Convolutional Neural Networks (CNNs) and an effective procedure to gather massive amounts of training images in the field with minimal human intervention. By automating the labeling stage, we also obtain very robust systems suitable for production-level usage. An open source implementation of our solution is provided, alongside with the dataset used for the experimental evaluation.

Mutual Information Based Method for Unsupervised Disentanglement of Video Representation

Aditya Sreekar P, Ujjwal Tiwari, Anoop Namboodiri

Auto-TLDR; MIPAE: Mutual Information Predictive Auto-Encoder for Video Prediction

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Video Prediction is an interesting and challenging task of predicting future frames from a given set context frames that belong to a video sequence. Video prediction models have found prospective applications in Maneuver Planning, Health care, Autonomous Navigation and Simulation. One of the major challenges in future frame generation is due to the high dimensional nature of visual data. In this work, we propose Mutual Information Predictive Auto-Encoder (MIPAE) framework, that reduces the task of predicting high dimensional video frames by factorising video representations into content and low dimensional pose latent variables that are easy to predict. A standard LSTM network is used to predict these low dimensional pose representations. Content and the predicted pose representations are decoded to generate future frames. Our approach leverages the temporal structure of the latent generative factors of a video and a novel mutual information loss to learn disentangled video representations. We also propose a metric based on mutual information gap (MIG) to quantitatively access the effectiveness of disentanglement on DSprites and MPI3D-real datasets. MIG scores corroborate with the visual superiority of frames predicted by MIPAE. We also compare our method quantitatively on evaluation metrics LPIPS, SSIM and PSNR.