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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

Abstract Slides Poster

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.

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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.

Attentive Visual Semantic Specialized Network for Video Captioning

Jesus Perez-Martin, Benjamin Bustos, Jorge Pérez

Auto-TLDR; Adaptive Visual Semantic Specialized Network for Video Captioning

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As an essential high-level task of video understanding topic, automatically describing a video with natural language has recently gained attention as a fundamental challenge in computer vision. Previous models for video captioning have several limitations, such as the existence of gaps in current semantic representations and the inexpressibility of the generated captions. To deal with these limitations, in this paper, we present a new architecture that we callAttentive Visual Semantic Specialized Network(AVSSN), which is an encoder-decoder model based on our Adaptive Attention Gate and Specialized LSTM layers. This architecture can selectively decide when to use visual or semantic information into the text generation process. The adaptive gate makes the decoder to automatically select the relevant information for providing a better temporal state representation than the existing decoders. Besides, the model is capable of learning to improve the expressiveness of generated captions attending to their length, using a sentence-length-related loss function. We evaluate the effectiveness of the proposed approach on the Microsoft Video Description(MSVD) and the Microsoft Research Video-to-Text (MSR-VTT) datasets, achieving state-of-the-art performance with several popular evaluation metrics: BLEU-4, METEOR, CIDEr, and ROUGE_L.

Enriching Video Captions with Contextual Text

Philipp Rimle, Pelin Dogan, Markus Gross

Auto-TLDR; Contextualized Video Captioning Using Contextual Text

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Understanding video content and generating caption with context is an important and challenging task. Unlike prior methods that typically attempt to generate generic video captions without context, our architecture contextualizes captioning by infusing extracted information from relevant text data. We propose an end-to-end sequence-to-sequence model which generates video captions based on visual input, and mines relevant knowledge such as names and locations from contextual text. In contrast to previous approaches, we do not preprocess the text further, and let the model directly learn to attend over it. Guided by the visual input, the model is able to copy words from the contextual text via a pointer-generator network, allowing to produce more specific video captions. We show competitive performance on the News Video Dataset and, through ablation studies, validate the efficacy of contextual video captioning as well as individual design choices in our model architecture.

Text Synopsis Generation for Egocentric Videos

Aidean Sharghi, Niels Lobo, Mubarak Shah

Auto-TLDR; Egocentric Video Summarization Using Multi-task Learning for End-to-End Learning

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Mass utilization of body-worn cameras has led to a huge corpus of available egocentric video. Existing video summarization algorithms can accelerate browsing such videos by selecting (visually) interesting shots from them. Nonetheless, since the system user still has to watch the summary videos, browsing large video databases remain a challenge. Hence, in this work, we propose to generate a textual synopsis, consisting of a few sentences describing the most important events in a long egocentric videos. Users can read the short text to gain insight about the video, and more importantly, efficiently search through the content of a large video database using text queries. Since egocentric videos are long and contain many activities and events, using video-to-text algorithms results in thousands of descriptions, many of which are incorrect. Therefore, we propose a multi-task learning scheme to simultaneously generate descriptions for video segments and summarize the resulting descriptions in an end-to-end fashion. We Input a set of video shots and the network generates a text description for each shot. Next, visual-language content matching unit that is trained with a weakly supervised objective, identifies the correct descriptions. Finally, the last component of our network, called purport network, evaluates the descriptions all together to select the ones containing crucial information. Out of thousands of descriptions generated for the video, a few informative sentences are returned to the user. We validate our framework on the challenging UT Egocentric video dataset, where each video is between 3 to 5 hours long, associated with over 3000 textual descriptions on average. The generated textual summaries, including only 5 percent (or less) of the generated descriptions, are compared to groundtruth summaries in text domain using well-established metrics in natural language processing.

A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning

Ruchika Chavhan, Biplab Banerjee, Xiao Xiang Zhu, Subhasis Chaudhuri

Auto-TLDR; Actor Dual-Critic Training for Remote Sensing Image Captioning Using Deep Reinforcement Learning

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We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning. Due to the high inter-class similarity in reference sentences describing remote sensing data, jointly encoding the sentences and images encourages prediction of captions that are semantically more precise than the ground truth in many cases. To this end, we introduce an Actor Dual-Critic training strategy where a second critic model is deployed in the form of an encoder-decoder RNN to encode the latent information corresponding to the original and generated captions. While all actor-critic methods use an actor to predict sentences for an image and a critic to provide rewards, our proposed encoder-decoder RNN guarantees high-level comprehension of images by sentence-to-image translation. We observe that the proposed model generates sentences on the test data highly similar to the ground truth and is successful in generating even better captions in many critical cases. Extensive experiments on the benchmark Remote Sensing Image Captioning Dataset (RSICD) and the UCM-captions dataset confirm the superiority of the proposed approach in comparison to the previous state-of-the-art where we obtain a gain of sharp increments in both the ROUGE-L and CIDEr measures.

Multi-Scale 2D Representation Learning for Weakly-Supervised Moment Retrieval

Ding Li, Rui Wu, Zhizhong Zhang, Yongqiang Tang, Wensheng Zhang

Auto-TLDR; Multi-scale 2D Representation Learning for Weakly Supervised Video Moment Retrieval

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Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotations which are expensive and time-consuming to label. Hence weakly supervised methods have been put forward recently by only using coarse video-level label. Despite effectiveness, these methods usually process moment candidates independently, while ignoring a critical issue that the natural temporal dependencies between candidates in different temporal scales. To cope with this issue, we propose a Multi-scale 2D Representation Learning method for weakly supervised video moment retrieval. Specifically, we first construct a two-dimensional map for each temporal scale to capture the temporal dependencies between candidates. Two dimensions in this map indicate the start and end time points of these candidates. Then, we select top-K candidates from each scale-varied map with a learnable convolutional neural network. With a newly designed Moments Evaluation Module, we obtain the alignment scores of the selected candidates. At last, the similarity between captions and language query is served as supervision for further training the candidates' selector. Experiments on two benchmark datasets Charades-STA and ActivityNet Captions demonstrate that our approach achieves superior performance to state-of-the-art results.

MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level

Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang, Qinru Qiu

Auto-TLDR; MAGNet: A Multi-Region Attention-Aware Grounding Network for Free-form Textual Queries

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Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spatial attention networks are known to learn this relationship and focus its gaze on salient objects in the image. Thus, we propose to utilize spatial attention networks for image-level visual-textual fusion preserving local (word) and global (phrase) information to refine region proposals with an in-network Region Proposal Network (RPN) and detect single or multiple regions for a phrase query. We focus only on the phrase query - ground truth pair (referring expression) for a model independent of the constraints of the datasets i.e. additional attributes, context etc. For such referring expression dataset ReferIt game, our Multi- region Attention-assisted Grounding network (MAGNet) achieves over 12% improvement over the state-of-the-art. Without the con- text from image captions and attribute information in Flickr30k Entities, we still achieve competitive results compared to the state- of-the-art.

Integrating Historical States and Co-Attention Mechanism for Visual Dialog

Tianling Jiang, Yi Ji, Chunping Liu

Auto-TLDR; Integrating Historical States and Co-attention for Visual Dialog

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Visual dialog is a typical multi-modal task which involves both vision and language. Nowadays, it faces two major difficulties. In this paper, we propose Integrating Historical States and Co-attention (HSCA) for visual dialog to solve them. It includes two main modules, Co-ATT and MATCH. Specifically, the main purpose of the Co-ATT module is to guide the image with questions and answers in the early stage to get more specific objects. It tackles the temporal sequence issue in historical information which may influence the precise answer for multi-round questions. The MATCH module is, based on a question with pronouns, to retrieve the best matching historical information block. It overcomes the visual reference problem which requires to solve pronouns referring to unknowns in the text message and then to locate the objects in the given image. We quantitatively and qualitatively evaluate our model on VisDial v1.0, at the same time, ablation studies are carried out. The experimental results demonstrate that HSCA outperforms the state-of-the-art methods in many aspects.

Dual Path Multi-Modal High-Order Features for Textual Content Based Visual Question Answering

Yanan Li, Yuetan Lin, Hongrui Zhao, Donghui Wang

Auto-TLDR; TextVQA: An End-to-End Visual Question Answering Model for Text-Based VQA

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As a typical cross-modal problem, visual question answering (VQA) has received increasing attention from the communities of computer vision and natural language processing. Reading and reasoning about texts and visual contents in the images is a burgeoning and important research topic in VQA, especially for the visually impaired assistance applications. Given an image, it aims to predict an answer to a provided natural language question closely related to its textual contents. In this paper, we propose a novel end-to-end textual content based VQA model, which grounds question answering both on the visual and textual information. After encoding the image, question and recognized text words, it uses multi-modal factorized high-order modules and the attention mechanism to fuse question-image and question-text features respectively. The complex correlations among different features can be captured efficiently. To ensure the model's extendibility, it embeds candidate answers and recognized texts in a semantic embedding space and adopts semantic embedding based classifier to perform answer prediction. Extensive experiments on the newly proposed benchmark TextVQA demonstrate that the proposed model can achieve promising results.

PIN: A Novel Parallel Interactive Network for Spoken Language Understanding

Peilin Zhou, Zhiqi Huang, Fenglin Liu, Yuexian Zou

Auto-TLDR; Parallel Interactive Network for Spoken Language Understanding

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Spoken Language Understanding (SLU) is an essential part of the spoken dialogue system, which typically consists of intent detection (ID) and slot filling (SF) tasks. Recently, recurrent neural networks (RNNs) based methods achieved the state-of-the-art for SLU. It is noted that, in the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied. In addition, few studies attempt to capture the local context information to enhance the performance of SF. Motivated by these findings, in this paper, Parallel Interactive Network (PIN) is proposed to model the mutual guidance between ID and SF. Specifically, given an utterance, a Gaussian self-attentive encoder is introduced to generate the context-aware feature embedding of the utterance which is able to capture local context information. Taking the feature embedding of the utterance, Slot2Intent module and Intent2Slot module are developed to capture the bidirectional information flow for ID and SF tasks. Finally, a cooperation mechanism is constructed to fuse the information obtained from Slot2Intent and Intent2Slot modules to further reduce the prediction bias. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach, which achieves a competitive result with state-of-the-art models. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.

Tackling Contradiction Detection in German Using Machine Translation and End-To-End Recurrent Neural Networks

Maren Pielka, Rafet Sifa, Lars Patrick Hillebrand, David Biesner, Rajkumar Ramamurthy, Anna Ladi, Christian Bauckhage

Auto-TLDR; Contradiction Detection in Natural Language Inference using Recurrent Neural Networks

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Natural Language Inference, and specifically Contradiction Detection, is still an unexplored topic with respect to German text. In this paper, we apply Recurrent Neural Network (RNN) methods to learn contradiction-specific sentence embeddings. Our data set for evaluation is a machine-translated version of the Stanford Natural Language Inference (SNLI) corpus. The results are compared to a baseline using unsupervised vectorization techniques, namely tf-idf and Flair, as well as state-of-the art transformer-based (MBERT) methods. We find that the end-to-end models outperform the models trained on unsupervised embeddings, which makes them the better choice in an empirical use case. The RNN methods also perform superior to MBERT on the translated data set.

Transformer Reasoning Network for Image-Text Matching and Retrieval

Nicola Messina, Fabrizio Falchi, Andrea Esuli, Giuseppe Amato

Auto-TLDR; A Transformer Encoder Reasoning Network for Image-Text Matching in Large-Scale Information Retrieval

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Image-text matching is an interesting and fascinating task in modern AI research. Despite the evolution of deep-learning-based image and text processing systems, multi-modal matching remains a challenging problem. In this work, we consider the problem of accurate image-text matching for the task of multi-modal large-scale information retrieval. State-of-the-art results in image-text matching are achieved by inter-playing image and text features from the two different processing pipelines, usually using mutual attention mechanisms. However, this invalidates any chance to extract separate visual and textual features needed for later indexing steps in large-scale retrieval systems. In this regard, we introduce the Transformer Encoder Reasoning Network (TERN), an architecture built upon one of the modern relationship-aware self-attentive architectures, the Transformer Encoder (TE). This architecture is able to separately reason on the two different modalities and to enforce a final common abstract concept space by sharing the weights of the deeper transformer layers. Thanks to this design, the implemented network is able to produce compact and very rich visual and textual features available for the successive indexing step. Experiments are conducted on the MS-COCO dataset, and we evaluate the results using a discounted cumulative gain metric with relevance computed exploiting caption similarities, in order to assess possibly non-exact but relevant search results. We demonstrate that on this metric we are able to achieve state-of-the-art results in the image retrieval task. Our code is freely available at https://github.com/mesnico/TERN.

Global Feature Aggregation for Accident Anticipation

Mishal Fatima, Umar Karim Khan, Chong Min Kyung

Auto-TLDR; Feature Aggregation for Predicting Accidents in Video Sequences

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Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance. In order to recognize abnormal events such as traffic accidents in a video sequence, it is important that the network takes into account interactions of objects in a given frame. We propose a novel Feature Aggregation (FA) block that refines each object's features by computing a weighted sum of the features of all objects in a frame. We use FA block along with Long Short Term Memory (LSTM) network to anticipate accidents in the video sequences. We report mean Average Precision (mAP) and Average Time-to-Accident (ATTA) on Street Accident (SA) dataset. Our proposed method achieves the highest score for risk anticipation by predicting accidents 0.32 sec and 0.75 sec earlier compared to the best results with Adaptive Loss and dynamic parameter prediction based methods respectively.

A Novel Attention-Based Aggregation Function to Combine Vision and Language

Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

Auto-TLDR; Fully-Attentive Reduction for Vision and Language

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The joint understanding of vision and language has been recently gaining a lot of attention in both the Computer Vision and Natural Language Processing communities, with the emergence of tasks such as image captioning, image-text matching, and visual question answering. As both images and text can be encoded as sets or sequences of elements - like regions and words - proper reduction functions are needed to transform a set of encoded elements into a single response, like a classification or similarity score. In this paper, we propose a novel fully-attentive reduction method for vision and language. Specifically, our approach computes a set of scores for each element of each modality employing a novel variant of cross-attention, and performs a learnable and cross-modal reduction, which can be used for both classification and ranking. We test our approach on image-text matching and visual question answering, building fair comparisons with other reduction choices, on both COCO and VQA 2.0 datasets. Experimentally, we demonstrate that our approach leads to a performance increase on both tasks. Further, we conduct ablation studies to validate the role of each component of the approach.

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.

Gaussian Constrained Attention Network for Scene Text Recognition

Zhi Qiao, Xugong Qin, Yu Zhou, Fei Yang, Weiping Wang

Auto-TLDR; Gaussian Constrained Attention Network for Scene Text Recognition

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Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduce an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://github.com/Pay20Y/GCAN.

Flow-Guided Spatial Attention Tracking for Egocentric Activity Recognition

Tianshan Liu, Kin-Man Lam

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.

Global Context-Based Network with Transformer for Image2latex

Nuo Pang, Chun Yang, Xiaobin Zhu, Jixuan Li, Xu-Cheng Yin

Auto-TLDR; Image2latex with Global Context block and Transformer

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Image2latex usually means converts mathematical formulas in images into latex markup. It is a very challenging job due to the complex two-dimensional structure, variant scales of input, and very long representation sequence. Many researchers use encoder-decoder based model to solve this task and achieved good results. However, these methods don't make full use of the structure and position information of the formula. %In this paper, we improve the encoder by employing Global Context block and Transformer. To solve this problem, we propose a global context-based network with transformer that can (1) learn a more powerful and robust intermediate representation via aggregating global features and (2) encode position information explicitly and (3) learn latent dependencies between symbols by using self-attention mechanism. The experimental results on the dataset IM2LATEX-100K demonstrate the effectiveness of our method.

Trajectory-User Link with Attention Recurrent Networks

Tao Sun, Yongjun Xu, Fei Wang, Lin Wu, 塘文钱, Zezhi Shao

Auto-TLDR; TULAR: Trajectory-User Link with Attention Recurrent Neural Networks

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The prevalent adoptions of GPS-enabled devices have witnessed an explosion of various location-based services which produces a huge amount of trajectories monitoring the individuals' movements. In this paper, we tackle Trajectory-User Link (TUL) problem, which identifies humans' movement patterns and links trajectories to the users who generated them. Existing solutions on TUL problem employ recurrent neural networks and variational autoencoder methods, which face the bottlenecks in the case of excessively long trajectories and fragmentary users' movements. However, these are common characteristics of trajectory data in reality, leading to performance degradation of the existing models. In this paper, we propose an end-to-end attention recurrent neural learning framework, called TULAR (Trajectory-User Link with Attention Recurrent Networks), which focus on selected parts of the source trajectories when linking. TULAR introduce the Trajectory Semantic Vector (TSV) via unsupervised location representation learning and recurrent neural networks, by which to reckon the weight of parts of source trajectory. Further, we employ three attention scores for the weight measurements. Experiments are conducted on two real world datasets and compared with several existing methods, and the results show that TULAR yields a new state-of-the-art performance. Source code is public available at GitHub: https://github.com/taos123/TULAR.

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.

AG-GAN: An Attentive Group-Aware GAN for Pedestrian Trajectory Prediction

Yue Song, Niccolò Bisagno, Syed Zohaib Hassan, Nicola Conci

Auto-TLDR; An attentive group-aware GAN for motion prediction in crowded scenarios

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Understanding human behaviors in crowded scenarios requires analyzing not only the position of the subjects in space, but also the scene context. Existing approaches mostly rely on the motion history of each pedestrian and model the interactions among people by considering the entire surrounding neighborhood. In our approach, we address the problem of motion prediction by applying coherent group clustering and a global attention mechanism on the LSTM-based Generative Adversarial Networks (GANs). The proposed model consists of an attentive group-aware GAN that observes the agents' past motion and predicts future paths, using (i) a group pooling module to model neighborhood interaction, and (ii) an attention module to specifically focus on hidden states. The experimental results demonstrate that our proposal outperforms state-of-the-art models on common benchmark datasets, and is able to generate socially-acceptable trajectories.

Zero-Shot Text Classification with Semantically Extended Graph Convolutional Network

Tengfei Liu, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin

Auto-TLDR; Semantically Extended Graph Convolutional Network for Zero-shot Text Classification

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As a challenging task of Natural Language Processing(NLP), zero-shot text classification has attracted more and more attention recently. It aims to detect classes that the model has never seen in the training set. For this purpose, a feasible way is to construct connection between the seen and unseen classes by semantic extension and classify the unseen classes by information propagation over the connection. Although many related zero-shot text classification methods have been exploited, how to realize semantic extension properly and propagate information effectively is far from solved. In this paper, we propose a novel zero-shot text classification method called Semantically Extended Graph Convolutional Network (SEGCN). In the proposed method, the semantic category knowledge from ConceptNet is utilized to semantic extension for linking seen classes to unseen classes and constructing a graph of all classes. Then, we build upon Graph Convolutional Network (GCN) for predicting the textual classifier for each category, which transfers the category knowledge by the convolution operators on the constructed graph and is trained in a semi-supervised manner using the samples of the seen classes. The experimental results on Dbpedia and 20newsgroup datasets show that our method outperforms the state of the art zero-shot text classification methods.

PICK: Processing Key Information Extraction from Documents Using Improved Graph Learning-Convolutional Networks

Wenwen Yu, Ning Lu, Xianbiao Qi, Ping Gong, Rong Xiao

Auto-TLDR; PICK: A Graph Learning Framework for Key Information Extraction from Documents

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Computer vision with state-of-the-art deep learning models have achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently. However, Key Information Extraction (KIE) from documents as the downstream task of OCR, having a large number of use scenarios in real-world, remains a challenge because documents not only have textual features extracting from OCR systems but also have semantic visual features that are not fully exploited and play a critical role in KIE. Too little work has been devoted to efficiently make full use of both textual and visual features of the documents. In this paper, we introduce PICK, a framework that is effective and robust in handling complex documents layout for KIE by combining graph learning with graph convolution operation, yielding a richer semantic representation containing the textual and visual features and global layout without ambiguity. Extensive experiments on real-world datasets have been conducted to show that our method outperforms baselines methods by significant margins.

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.

MFI: Multi-Range Feature Interchange for Video Action Recognition

Sikai Bai, Qi Wang, Xuelong Li

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.

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).

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.

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.

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.

Multi-Modal Contextual Graph Neural Network for Text Visual Question Answering

Yaoyuan Liang, Xin Wang, Xuguang Duan, Wenwu Zhu

Auto-TLDR; Multi-modal Contextual Graph Neural Network for Text Visual Question Answering

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Text visual question answering (TextVQA) targets at answering the question related to texts appearing in the given images, posing more challenges than VQA by requiring a deeper recognition and understanding of various shapes of human-readable scene texts as well as their meanings in different contexts. Existing works on TextVQA suffer from two weaknesses: i) scene texts and non-textual objects are processed separately and independently without considering their mutual interactions during the question understanding and answering process, ii) scene texts are encoded only through word embeddings without taking the corresponding visual appearance features as well as their potential relationships with other non-textual objects in the images into account. To overcome the weakness of exiting works, we propose a novel multi-modal contextual graph neural network (MCG) model for TextVQA. The proposed MCG model can capture the relationships between visual features of scene texts and non-textual objects in the given images as well as utilize richer sources of multi-modal features to improve the model performance. In particular, we encode the scene texts into richer features containing textual, visual and positional features, then model the visual relations between scene texts and non-textual objects through a contextual graph neural network. Our extensive experiments on real-world dataset demonstrate the advantages of the proposed MCG model over baseline approaches.

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.

Multi-Stage Attention Based Visual Question Answering

Aakansha Mishra, Ashish Anand, Prithwijit Guha

Auto-TLDR; Alternative Bi-directional Attention for Visual Question Answering

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Recent developments in the field of Visual Question Answering (VQA) have witnessed promising improvements in performance through contributions in attention based networks. Most such approaches have focused on unidirectional attention that leverage over attention from textual domain (question) on visual space. These approaches mostly focused on learning high-quality attention in the visual space. In contrast, this work proposes an alternating bi-directional attention framework. First, a question to image attention helps to learn the robust visual space embedding, and second, an image to question attention helps to improve the question embedding. This attention mechanism is realized in an alternating fashion i.e. question-to-image followed by image-to-question and is repeated for maximizing performance. We believe that this process of alternating attention generation helps both the modalities and leads to better representations for the VQA task. This proposal is benchmark on TDIUC dataset and against state-of-art approaches. Our ablation analysis shows that alternate attention is the key to achieve high performance in VQA.

Developing Motion Code Embedding for Action Recognition in Videos

Maxat Alibayev, David Andrea Paulius, Yu Sun

Auto-TLDR; Motion Embedding via Motion Codes for Action Recognition

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We propose a motion embedding strategy via the motion codes that is a vectorized representation of motions based on their salient mechanical attributes. We show that our motion codes can provide robust motion representation. We train a deep neural network model that learns to embed demonstration videos into motion codes. We integrate the extracted features from the motion embedding model into the current state-of-the-art action recognition model. The obtained model achieved higher accuracy than the baseline on a verb classification task from egocentric videos in EPIC-KITCHENS dataset.

MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Extraction

Jingjie Guo, Kelang Tian, Kejiang Ye, Cheng-Zhong Xu

Auto-TLDR; MA-LSTM: Multiple Attention based recurrent neural network for forget gate

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With the improvement of data, computing powerand algorithms, deep learning has achieved rapid developmentand showing excellent performance. Recently, many deep learn-ing models are proposed to solve the problems in different areas.A recurrent neural network (RNN) is a class of artificial neuralnetworks where connections between nodes form a directedgraph along a temporal sequence. This allows it to exhibittemporal dynamic behavior, which makes it applicable to taskssuch as handwriting recognition or speech recognition. How-ever, the RNN relies heavily on the automatic learning abilityto update parameters which concentrate on the data flow butseldom considers the feature extraction capability of the gatemechanism. In this paper, we propose a novel architecture tobuild the forget gate which is generated by multiple bases.Instead of using the traditional single-layer fully-connectednetwork, we use a Multiple Attention (MA) based network togenerate the forget gate which refines the optimization spaceof gate function and improve the granularity of the recurrentneural network to approximate the map in the ground truth.Credit to the MA structure on the gate mechanism. Our modelhas a better feature extraction capability than other knownmodels. MA-LSTM is an alternative module which can directly replace the recurrent neural network and has achieved good performance in many areas that people are concerned about.

Video Summarization with a Dual Attention Capsule Network

Hao Fu, Hongxing Wang, Jianyu Yang

Auto-TLDR; Dual Self-Attention Capsule Network for Video Summarization

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In this paper, we address the problem of video summarization, which aims at selecting a subset of video frames as a summary to represent the original video contents compactly and completely. We propose a simple but effective supervised approach with a dual attention capsule network towards this end. Unlike existing LSTM based methods, it pays attention to short- and long-term dependencies among video frames through an elaborate dual self-attention architecture, which can handle longer-term dependencies and admit parallel computing. To reconcile the outputs of dual self-attention, we rely on a two-stream capsule network to learn the underlying frame selection criteria. Experiments on real-world datasets show the advantages of the proposed approach compared with state-of-the-art methods.

Unsupervised Co-Segmentation for Athlete Movements and Live Commentaries Using Crossmodal Temporal Proximity

Yasunori Ohishi, Yuki Tanaka, Kunio Kashino

Auto-TLDR; A guided attention scheme for audio-visual co-segmentation

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Audio-visual co-segmentation is a task to extract segments and regions corresponding to specific events on unlabelled audio and video signals. It is particularly important to accomplish it in an unsupervised way, since it is generally very difficult to manually label all the objects and events appearing in audio-visual signals for supervised learning. Here, we propose to take advantage of temporal proximity of corresponding audio and video entities included in the signals. For this purpose, we newly employ a guided attention scheme to this task to efficiently detect and utilize temporal cooccurrences of audio and video information. The experiments using a real TV broadcasting of Sumo wrestling, a sport event, with live commentaries show that our model can automatically extract specific athlete movements and its spoken descriptions in an unsupervised manner.

VSR++: Improving Visual Semantic Reasoning for Fine-Grained Image-Text Matching

Hui Yuan, Yan Huang, Dongbo Zhang, Zerui Chen, Wenlong Cheng, Liang Wang

Auto-TLDR; Improving Visual Semantic Reasoning for Fine-Grained Image-Text Matching

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Image-text matching has made great progresses recently, but there still remains challenges in fine-grained matching. To deal with this problem, we propose an Improved Visual Semantic Reasoning model (VSR++), which jointly models 1) global alignment between images and texts and 2) local correspondence between regions and words in a unified framework. To exploit their complementary advantages, we also develop a suitable learning strategy to balance their relative importance. As a result, our model can distinguish image regions and text words in a fine-grained level, and thus achieves the current stateof-the-art performance on two benchmark datasets.

Efficient Sentence Embedding Via Semantic Subspace Analysis

Bin Wang, Fenxiao Chen, Yun Cheng Wang, C.-C. Jay Kuo

Auto-TLDR; S3E: Semantic Subspace Sentence Embedding

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A novel sentence embedding method built upon semantic subspace analysis, called semantic subspace sentence embedding (S3E), is proposed in this work. Given the fact that word embeddings can capture semantic relationship while semantically similar words tend to form semantic groups in a high-dimensional embedding space, we develop a sentence representation scheme by analyzing semantic subspaces of its constituent words. Specifically, we construct a sentence model from two aspects. First, we represent words that lie in the same semantic group using the intra-group descriptor. Second, we characterize the interaction between multiple semantic groups with the inter-group descriptor. The proposed S3E method is evaluated on both textual similarity tasks and supervised tasks. Experimental results show that it offers comparable or better performance than the state-of-the-art. The complexity of our S3E method is also much lower than other parameterized models.

Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks

Hyunjin Choi, Judong Kim, Seongho Joe, Youngjune Gwon

Auto-TLDR; Sentence Embedding Models for BERT and ALBERT: A Comparison and Evaluation

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Contextualized representations from a pre-trained language model are central to achieve a high performance on downstream NLP task. The pre-trained BERT and A Lite BERT (ALBERT) models can be fine-tuned to give state-of-the-art results in sentence-pair regressions such as semantic textual similarity (STS) and natural language inference (NLI). Although BERT-based models yield the [CLS] token vector as a reasonable sentence embedding, the search for an optimal sentence embedding scheme remains an active research area in computational linguistics. This paper explores on sentence embedding models for BERT and ALBERT. In particular, we take a modified BERT network with siamese and triplet network structures called Sentence-BERT (SBERT) and replace BERT with ALBERT to create Sentence-ALBERT (SALBERT). We also experiment with an outer CNN sentence-embedding network for SBERT and SALBERT. We evaluate performances of all sentence-embedding models considered using the STS and NLI datasets. The empirical results indicate that our CNN architecture improves ALBERT models substantially more than BERT models for STS benchmark. Despite significantly fewer model parameters, ALBERT sentence embedding is highly competitive to BERT in downstream NLP evaluations.

GCNs-Based Context-Aware Short Text Similarity Model

Xiaoqi Sun

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.

ACCLVOS: Atrous Convolution with Spatial-Temporal ConvLSTM for Video Object Segmentation

Muzhou Xu, Shan Zong, Chunping Liu, Shengrong Gong, Zhaohui Wang, Yu Xia

Auto-TLDR; Semi-supervised Video Object Segmentation using U-shape Convolution and ConvLSTM

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Semi-supervised video object segmentation aims at segmenting the target of interest throughout a video sequence when only the annotated mask of the first frame is given. A feasible method for segmentation is to capture the spatial-temporal coherence between frames. However, it may suffer from mask drift when the spatial-temporal coherence is unreliable. To relieve this problem, we propose an encoder-decoder-recurrent model for semi-supervised video object segmentation. The model adopts a U-shape architecture that combines atrous convolution and ConvLSTM to establish the coherence in both the spatial and temporal domains. Furthermore, the weight ratio for each block is also reconstructed to make the model more suitable for the VOS task. We evaluate our method on two benchmarks, DAVIS-2017 and Youtube-VOS, where state-of-the-art segmentation accuracy with a real-time inference speed of 21.3 frames per second on a Tesla P100 is obtained.

ConvMath : A Convolutional Sequence Network for Mathematical Expression Recognition

Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, Zhi Tang

Auto-TLDR; Convolutional Sequence Modeling for Mathematical Expressions Recognition

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Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout. In this paper, we propose a convolutional sequence modeling network, ConvMath, which converts the mathematical expression description in an image into a LaTeX sequence in an end-to-end way. The network combines an image encoder for feature extraction and a convolutional decoder for sequence generation. Compared with other Long Short Term Memory(LSTM) based encoder-decoder models, ConvMath is entirely based on convolution, thus it is easy to perform parallel computation. Besides, the network adopts multi-layer attention mechanism in the decoder, which allows the model to align output symbols with source feature vectors automatically, and alleviates the problem of lacking coverage while training the model. The performance of ConvMath is evaluated on an open dataset named IM2LATEX-100K, including 103556 samples. The experimental results demonstrate that the proposed network achieves state-of-the-art accuracy and much better efficiency than previous methods.

Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Xunzhu Tang, Rujie Zhu, Tiezhu Sun

Auto-TLDR; Moto: Enhancing Embedding with Multiple J\textbf{o}int Fac\textBF{to}rs

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Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, \textit{etc}. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with \textbf{M}ultiple J\textbf{o}int Fac\textbf{to}rs. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 ($F_1$-score, 2.11\% improvement) on Chinese news titles, 96.38 (1.24\% improvement) on Fudan Corpus and 0.9633 (3.26\% improvement) on THUCNews.

Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

Yue Wang, Zhuo Xu, Yao Wan, Lu Bai, Lixin Cui, Qian Zhao, Edwin Hancock, Philip Yu

Auto-TLDR; Joint-Event-extraction from Unstructured corpora using Structural Information Network

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Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. \revised{Most existing works do not fully address the sparse co-occurred relationships between entities and triggers. This exacerbates the error-propagation problem} which may degrade the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence-to-sequence labeling task with a tag set which is composed of tags of triggers and entities. Then, to incorporate the missing information in the aforementioned co-occurred relationships, we propose a \underline{C}ross-\underline{S}upervised \underline{M}echanism (CSM) to alternately supervise the extraction of either triggers or entities based on the type distribution of each other. Moreover, since the connected entities and triggers naturally form a heterogeneous information network (HIN), we leverage the latent pattern along meta-paths for a given corpus to further improve the performance of our proposed method. To verify the effectiveness of our proposed method, we conduct extensive experiments on real-world datasets as well as compare our method with state-of-the-art methods. Empirical results and analysis show that our approach outperforms the state-of-the-art methods in both entity and trigger extraction.

Multi-Scale Relational Reasoning with Regional Attention for Visual Question Answering

Yuntao Ma, Yirui Wu, Tong Lu

Auto-TLDR; Question-Guided Relational Reasoning for Visual Question Answering

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The main challenges of visual question answering (VQA) lie in modeling an alignment between image and question to find out informative regions in images that related to the question and reasoning relations among visual objects according to the question. In this paper, we propose question-guided relational reasoning in multi-scales for visual question answering, in which each region is enhanced by regional attention. Specifically, we present regional attention, which consists of a soft attention and a hard attention, to pick up informative regions of the image according to informative evaluations implemented by question-guided soft attention. And combinations of different informative regions are then concatenated with question embedding in different scales to capture relational information. Relational reasoning can extract question-based relational information between regions, and the multi-scale mechanism gives it the ability to analyze relationships in diversity and sensitivity to numbers by modeling scales of relationships. We conduct experiments to show that our proposed architecture is effective and achieves a new state-of-the-art on VQA v2.

Hierarchical Multimodal Attention for Deep Video Summarization

Melissa Sanabria, Frederic Precioso, Thomas Menguy

Auto-TLDR; Automatic Summarization of Professional Soccer Matches Using Event-Stream Data and Multi- Instance Learning

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The way people consume sports on TV has drastically evolved in the last years, particularly under the combined effects of the legalization of sport betting and the huge increase of sport analytics. Several companies are nowadays sending observers in the stadiums to collect live data of all the events happening on the field during the match. Those data contain meaningful information providing a very detailed description of all the actions occurring during the match to feed the coaches and staff, the fans, the viewers, and the gamblers. Exploiting all these data, sport broadcasters want to generate extra content such as match highlights, match summaries, players and teams analytics, etc., to appeal subscribers. This paper explores the problem of summarizing professional soccer matches as automatically as possible using both the aforementioned event-stream data collected from the field and the content broadcasted on TV. We have designed an architecture, introducing first (1) a Multiple Instance Learning method that takes into account the sequential dependency among events and then (2) a hierarchical multimodal attention layer that grasps the importance of each event in an action. We evaluate our approach on matches from two professional European soccer leagues, showing its capability to identify the best actions for automatic summarization by comparing with real summaries made by human operators.

Attention-Driven Body Pose Encoding for Human Activity Recognition

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

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.

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

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

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.