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Video Question Answering

9 papers with code · Computer Vision

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TVQA: Localized, Compositional Video Question Answering

EMNLP 2018 jayleicn/TVQA

Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks.

VIDEO QUESTION ANSWERING

TVQA+: Spatio-Temporal Grounding for Video Question Answering

25 Apr 2019jayleicn/TVQA-PLUS

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.

QUESTION ANSWERING VIDEO QUESTION ANSWERING

Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

CVPR 2019 fanchenyou/HME-VideoQA

In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention.

QUESTION ANSWERING VIDEO QUESTION ANSWERING VISUAL QUESTION ANSWERING

Hierarchical Conditional Relation Networks for Video Question Answering

25 Feb 2020thaolmk54/hcrn-videoqa

Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts.

QUESTION ANSWERING VIDEO QUESTION ANSWERING VISUAL QUESTION ANSWERING

ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

6 Jun 2019MILVLG/activitynet-qa

It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA).

QUESTION ANSWERING VIDEO QUESTION ANSWERING VISUAL QUESTION ANSWERING

Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA

13 May 2020hyounghk/VideoQADenseCapFrameGate-ACL2020

Moreover, our model is also comprised of dual-level attention (word/object and frame level), multi-head self/cross-integration for different sources (video and dense captions), and gates which pass more relevant information to the classifier.

IMAGE CAPTIONING MULTI-LABEL CLASSIFICATION QUESTION ANSWERING TEMPORAL LOCALIZATION VIDEO QUESTION ANSWERING

TutorialVQA: Question Answering Dataset for Tutorial Videos

LREC 2020 acolas1/TutorialVQAData

The results indicate that the task is challenging and call for the investigation of new algorithms.

QUESTION ANSWERING VIDEO QUESTION ANSWERING