Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog

Unlike conventional visual question answering, video-grounded dialog requires a deep understanding of both the dialog history and the video content to generate accurate responses. Although existing methods have achieved promising results, they still struggle with progressively comprehending complex dialog history and effectively integrating video information. To address these challenges, we propose an iterative search and reasoning framework composed of a textual encoder, a visual encoder, and a generator. Specifically, the textual encoder adopts a path search and aggregation strategy to identify key cues in the dialog history that are essential for understanding the current question. Meanwhile, the visual encoder employs an iterative reasoning network to extract and highlight critical visual evidence from the video, thereby enabling more comprehensive visual understanding. Finally, we use a pretrained GPT-2 model as the answer generator to transform the discovered latent cues into coherent and contextually appropriate responses. Extensive experiments on three public datasets demonstrate the effectiveness and generalizability of the proposed framework.