Grounding is All You Need? Dual Temporal Grounding for Video Dialog

• URL: http://arxiv.org/abs/2410.05767v1
• Date: Tue, 8 Oct 2024 07:48:34 GMT
• Title: Grounding is All You Need? Dual Temporal Grounding for Video Dialog
• Authors: You Qin, Wei Ji, Xinze Lan, Hao Fei, Xun Yang, Dan Guo, Roger Zimmermann, Lizi Liao,
• Abstract summary: This paper introduces the Dual Temporal Grounding-enhanced Video Dialog model (DTGVD) It emphasizes dual temporal relationships by predicting dialog turn-specific temporal regions. It also filters video content accordingly, and grounding responses in both video and dialog contexts.
• Score: 48.3411605700214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of video dialog response generation, the understanding of video content and the temporal nuances of conversation history are paramount. While a segment of current research leans heavily on large-scale pretrained visual-language models and often overlooks temporal dynamics, another delves deep into spatial-temporal relationships within videos but demands intricate object trajectory pre-extractions and sidelines dialog temporal dynamics. This paper introduces the Dual Temporal Grounding-enhanced Video Dialog model (DTGVD), strategically designed to merge the strengths of both dominant approaches. It emphasizes dual temporal relationships by predicting dialog turn-specific temporal regions, filtering video content accordingly, and grounding responses in both video and dialog contexts. One standout feature of DTGVD is its heightened attention to chronological interplay. By recognizing and acting upon the dependencies between different dialog turns, it captures more nuanced conversational dynamics. To further bolster the alignment between video and dialog temporal dynamics, we've implemented a list-wise contrastive learning strategy. Within this framework, accurately grounded turn-clip pairings are designated as positive samples, while less precise pairings are categorized as negative. This refined classification is then funneled into our holistic end-to-end response generation mechanism. Evaluations using AVSD@DSTC-7 and AVSD@DSTC-8 datasets underscore the superiority of our methodology.

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