AI-Based Video Question Answering for Healthcare Applications Using Relevant Segment Localization

Abstract

One important finding in real-world Video QA assignments is that, rather than needing full-sequence analysis, the textual question typically relates to a specific, brief segment of the entire movie. This encourages a more focused and effective learning strategy. In order to overcome this difficulty, we pre-sent Locate Before Answering (LocAns), a novel end-to-end architecture that performs answer prediction using only the localized section after first identifying the most pertinent temporal segment matching to the question. The two main parts of the suggested LocAns model are a response prediction module and a question localization module, both of which are integrated into a single pipeline. One significant improvement in our approach is the generation of training supervision. LocAns cleverly uses the ground-truth response labels to produce pseudo temporal supervision rather than depending just on manually marked temporal boundaries, which are sometimes unavailable or costly to collect. Three benchmark datasets such as NExT-QA, ActivityNet-QA, and AGQA are designed for long-term VideoQA were used for extensive research. In all three datasets, LocAns regularly beats current state-of-the-art techniques. In addition to producing excellent quantitative results, the model per-forms well qualitatively, as demonstrated by case studies in which it correctly identifies the most pertinent video segments prior to producing the right response. The locate-before-answer paradigm's efficacy is further supported by the localization module's enhanced interpretability. Overall, this study emphasizes how crucial temporally focused reasoning is while responding to lengthy video questions. LocAns creates a potential path for future Video QA research, especially for large-scale, complicated, and real-world video scenarios, by eliminating redundancy and improving the alignment between the question and pertinent visual evidence.