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Found 12 papers, 11 papers with code
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories.
Can I Trust Your Answer? Visually Grounded Video Question Answering
We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video-language understanding.
Discovering Spatio-Temporal Rationales for Video Question Answering
We then conduct extensive studies to verify the importance of STR as well as the proposed answer interaction mechanism.
Contrastive Video Question Answering via Video Graph Transformer
CoVGT's uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning.
Ranked #11 on
Video Question Answering
on NExT-QA
(using extra training data)
Equivariant and Invariant Grounding for Video Question Answering
Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene.
Video Graph Transformer for Video Question Answering
VGT's uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it exploits disentangled video and text Transformers for relevance comparison between the video and text to perform QA, instead of entangled cross-modal Transformer for answer classification.
Ranked #18 on
Video Question Answering
on NExT-QA
(using extra training data)
Invariant Grounding for Video Question Answering
At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer.
Video Question Answering: Datasets, Algorithms and Challenges
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos.
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering
To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues.
Ranked #23 on
Video Question Answering
on NExT-QA
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions.
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions.
Visual Relation Grounding in Videos
In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV).
