Vision and language understanding has emerged as a subject undergoing intense study in Artificial Intelligence. Among many tasks in this line of research, visual question answering (VQA) has been one of the most successful ones, where the goal is to learn a model that understands visual content at region-level details and finds their associations with pairs of questions and answers in the natural language form. Despite the rapid progress in the past few years, most existing work in VQA have focused primarily on images. In this paper, we focus on extending VQA to the video domain and contribute to the literature in three important ways. First, we propose three new tasks designed specifically for video VQA, which require spatio-temporal reasoning from videos to answer questions correctly. Next, we introduce a new large-scale dataset for video VQA named TGIF-QA that extends existing VQA work with our new tasks. Finally, we propose a dual-LSTM based approach with both spatial and temporal attention, and show its effectiveness over conventional VQA techniques through empirical evaluations.
Video Question Answering (QA) is an important task in understanding video temporal structure. We observe that there are three unique attributes of video QA compared with image QA: (1) it deals with long sequences of images containing richer information not only in quantity but also in variety; (2) motion and appearance information are usually correlated with each other and able to provide useful attention cues to the other; (3) different questions require different number of frames to infer the answer. Based these observations, we propose a motion-appearance comemory network for video QA. Our …
Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Nankai University（南开大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University of Science and Technology（南京理工大学）
A number of visual question answering approaches have been proposed recently, aiming at understanding the visual scenes by answering the natural language questions. While the image question answering has drawn significant attention, video question answering is largely unexplored. Video-QA is different from Image-QA since the information and the events are scattered among multiple frames. In order to better utilize the temporal structure of the videos and the phrasal structures of the answers, we propose two mechanisms: the re-watching and the re-reading mechanisms and combine them into the for…
Renmin University of China（中国人民大学）；University of Rochester（罗切斯特大学）
In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos. The problem requires comprehensive multimodal understanding and spatio-temporal reasoning over audio-visual scenes. To benchmark this task and facilitate our study, we introduce a large-scale MUSIC-AVQA dataset, which contains more than 45K question-answer pairs covering 33 different question templates spanning over different modalities and question types. We develop several baselines and introduce a spatio-temp…
Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University（航空航天智能科学与技术系，航天学院，北航）；Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, China（航天器设计优化与动态仿真技术重点实验室，教育部，中国）；College of Computer Science, Inner Mongolia University（计算机科学学院，内蒙古大学）；Shen Yuan Honors College of Beihang University（盛元荣誉学院，北航）
VOST-SGG: VLM-Aided One-Stage Spatio-Temporal Scene Graph Generation
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore（科学、技术与研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore（科学、技术与研究局前沿人工智能研究中心）
Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）信息通信研究院（I2R））；School of Computing, National University of Singapore（新加坡国立大学计算学院）；SCSE, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
R4: Retrieval-Augmented Reasoning for Vision-Language Models in 4D Spatio-Temporal Space
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Esslingen University of Applied Sciences（埃斯林根应用科学大学）；Dr. Ing. h.c. F. Porsche AG（德意志联邦汽车工业协会）；University of Michigan（密歇根大学）；Voxel51 Inc.（Voxel51公司）
University of Electronic Science Technology of China（电子科技大学）；Monash University（莫纳什大学）；JD AI Research（京东人工智能研究院）
To date, visual question answering (VQA) (i.e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA. Compared with image QA that focuses primarily on understanding the associations between image region-level details and corresponding questions, video QA requires a model to jointly reason across both spatial and long-range temporal structures of a video as well as text to provide an accurate answer. In this paper, we specifically tackle the problem of video QA by proposing a Structured Two-stream Attention network, namely STA, to answer a f…
Seoul National University（首尔国立大学）；AI Institute for Seoul National University (AIIS)（首尔国立大学人工智能研究院）；Hanyang University（汉阳大学）
Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between language and vision information. We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question's intentions. MASN consists of a motion module, an appea…
IBM Cloud（IBM云）；IBM Research（IBM研究院）；University of Amsterdam（阿姆斯特丹大学）
Transformer is a popularly used neural network architecture, especially for language understanding. We introduce an extended and unified architecture that can be used for tasks involving a variety of modalities like image, text, videos, etc. We propose a spatio-temporal cache mechanism that enables learning spatial dimension of the input in addition to the hidden states corresponding to the temporal input sequence. The proposed architecture further enables a single model to support tasks with multiple input modalities as well as asynchronous multi-task learning, thus we refer to it as OmniNet.…
Applied Artificial Intelligence Institute, Deakin University（迪肯大学应用人工智能研究所）
Video Question Answering (Video QA) is a powerful testbed to develop new AI capabilities. This task necessitates learning to reason about objects, relations, and events across visual and linguistic domains in space-time. High-level reasoning demands lifting from associative visual pattern recognition to symbol-like manipulation over objects, their behavior and interactions. Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects. At each stage of the video event flow, these objects interact with each othe…
South China University of Technology（华南理工大学）；Peng Cheng Laboratory, Shenzhen（鹏城实验室）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）
We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art methods attempt to apply spatio-temporal attention mechanism on video frame features without explicitly modeling the location and relations among object interaction occurred in videos. However, the relations between object interaction and their location information are very critical for both action recognition and question reasoning. In this work, we propose to represent the contents in the video as a location-aware graph …
Singapore Management University（新加坡管理大学）；Salesforce Research Asia（Salesforce亚洲研究院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances which query different segments and/or different objects in videos over multiple dialogue turns. However, existing approaches to video-grounded dialogues often focus on superficial temporal-level visual cues, but neglect more fine-grained spatial signals from videos. To address this drawback, we propose Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in video…
Applied Artificial Intelligence Institute, Deakin University（迪肯大学应用人工智能研究所）
What does it take to design a machine that learns to answer natural questions about a video? A Video QA system must simultaneously understand language, represent visual content over space-time, and iteratively transform these representations in response to lingual content in the query, and finally arriving at a sensible answer. While recent advances in lingual and visual question answering have enabled sophisticated representations and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process. Inspi…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural language questions about videos. We first augment the TVQA dataset with 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers. We name this augmented version as TVQA+. We then propose Spatio-Temporal Answerer with Grounded Evidence (STAGE), a unified framework that grounds evidence in both spatial and temporal domains to answer questions a…
Inria Paris（巴黎法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；DeepMind（深度思维）；CIIRC CTU Prague（布拉格捷克理工大学捷克信息学、机器人与控制研究所）
Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and c…
University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）
Generating natural language descriptions for videos, i.e., video captioning, essentially requires step-by-step reasoning along the generation process. For example, to generate the sentence "a man is shooting a basketball", we need to first locate and describe the subject "man", next reason out the man is "shooting", then describe the object "basketball" of shooting. However, existing visual reasoning methods designed for visual question answering are not appropriate to video captioning, for it requires more complex visual reasoning on videos over both space and time, and dynamic module composi…
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models
Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（上海交通大学图像通信与信息处理研究所）；MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院（教育部人工智能重点实验室））；Huawei Technologies（华为技术有限公司）
Video-Language Alignment via Spatio-Temporal Graph Transformer
School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB)（北京科技大学计算机与通信工程学院）；Institute of Artificial Intelligence, University of Science and Technology Beijing (USTB)（北京科技大学人工智能研究院）；USTB-EEasyTech Joint Lab of Artificial Intelligence（北京科技大学-易智科技人工智能联合实验室）；Tencent Hunyuan（腾讯混元）；Tencent（腾讯）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
University of Washington（华盛顿大学）；Stanford University（斯坦福大学）
Prior benchmarks have analyzed models' answers to questions about videos in order to measure visual compositional reasoning. Action Genome Question Answering (AGQA) is one such benchmark. AGQA provides a training/test split with balanced answer distributions to reduce the effect of linguistic biases. However, some biases remain in several AGQA categories. We introduce AGQA 2.0, a version of this benchmark with several improvements, most namely a stricter balancing procedure. We then report results on the updated benchmark for all experiments.
Mitsubishi Electric Research Labs (MERL)（三菱电机研究实验室）
Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequences of 2D "views" of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. Specifically, we first create a 2.5D (pseudo-3D) scene graph by transformin…
UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Geographic Sciences, Hunan Normal University（湖南师范大学）；City University of Hong Kong（香港城市大学）
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Zhejiang University（浙江大学）；University of Science and Technology Beijing（北京科技大学）；Bytedance Inc.（字节跳动公司）
C^2ROPE: Causal Continuous Rotary Positional Encoding for 3D Large Multimodal-Models Reasoning
State Key Laboratory of Internet of Things for Smart City, University of Macau（物联网智能城市国家重点实验室，澳门大学）；Department of Automation, Shanghai Jiaotong University（上海交通大学自动化系）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）；School of Computer Science and Technology, USTC（中国科学技术大学计算机科学与技术学院）；School of Artificial Intelligence and Data Science, USTC（中国科学技术大学人工智能与数据科学学院）
Applied AI Institute（应用人工智能研究院）；Deakin University（迪肯大学）
Video QA challenges modelers in multiple fronts. Modeling video necessitates building not only spatio-temporal models for the dynamic visual channel but also multimodal structures for associated information channels such as subtitles or audio. Video QA adds at least two more layers of complexity - selecting relevant content for each channel in the context of the linguistic query, and composing spatio-temporal concepts and relations in response to the query. To address these requirements, we start with two insights: (a) content selection and relation construction can be jointly encapsulated int…
Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model
SenSIP Center, School of ECEE, Arizona State University（SenSIP中心，电子与计算机工程学院，亚利桑那州立大学）；Prime Solutions Group Inc（Prime Solutions Group公司）；Intelligence Advanced Research Projects Activity（智能高级研究计划局）
