Visual Answer Localization

Visual Answer Localization (VAL) aims to identify the specific visual segment within a video that best answers a given natural language question. Current research focuses on improving the accuracy of this localization by leveraging both visual and textual information, often employing multimodal models that integrate video frames and subtitles or transcripts, and incorporating techniques like cross-modal knowledge transfer and contrastive learning to enhance the alignment between visual and textual representations. This work is driven by the need for more effective methods to access and understand information presented in video format, with applications ranging from medical education and emergency response to broader instructional video comprehension.