Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios

Yuanyuan Jiang, Jianqin Yin
5/21/2023
cs.CV

No official code found. Best implementation: Bravo5542/TJSTG (6 stars, MIT, last commit 2025-04-16).

Abstract

Audio-visual question answering (AVQA) is a challenging task that requires multistep spatio-temporal reasoning over multimodal contexts. Recent works rely on elaborate target-agnostic parsing of audio-visual scenes for spatial grounding while mistreating audio and video as separate entities for temporal grounding. This paper proposes a new target-aware joint spatio-temporal grounding network for AVQA. It consists of two key components: the target-aware spatial grounding module (TSG) and the single-stream joint audio-visual temporal grounding module (JTG). The TSG can focus on audio-visual cues relevant to the query subject by utilizing explicit semantics from the question. Unlike previous two-stream temporal grounding modules that required an additional audio-visual fusion module, JTG incorporates audio-visual fusion and question-aware temporal grounding into one module with a simpler single-stream architecture. The temporal synchronization between audio and video in the JTG is facilitated by our proposed cross-modal synchrony loss (CSL). Extensive experiments verified the effectiveness of our proposed method over existing state-of-the-art methods.

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Code Implementations(1)

Bravo5542/TJSTGCommunity100%
MIT

PyTorch implementation of our Fingdings of EMNLP2023 paper: Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamic Audio-Visual Scenarios

60PythonOct 14, 20231 years agoMIT

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Cite this paper

@article{jiang2023targetaware,
  title  = {Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual Scenarios},
  author = {Yuanyuan Jiang and Jianqin Yin},
  year   = {2023},
  eprint = {2305.12397},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.12397v2}
}

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