APSam: An Aggregating-then-Pruning Sampler for Question-Conditional Denoising

Video question answering (VideoQA) necessitates simultaneous understanding of visual and linguistic information, requiring both in-depth analysis of individual modality features and the establishment of cross-modal correlations to achieve precise reasoning. However, VideoQA models often struggle with irrelevant temporal and spatial noise due to the dense events and concepts in real-world complex video contents. Previous works reduce noise by only sampling a fixed number of visual tokens at the patch level, overlooking the variation in the required granularities of features and quantities of visual cues across different question conditions. To address these, we propose an Aggregating-then-Pruning Sampler (APSam), which diversifies feature granularities and adaptively denoises on a per-question basis. Specifically, we propose a conditional token aggregator to obtain multi-granularity visual semantics by merging similar question-relevant tokens. Then, we propose a conditional token pruner, which restricts noise tokens through a variable-capacity receptive field determined by the inputs. Experimental results show that APSam achieves significant performance on three challenging complex VideoQA datasets, i.e., AGQAv2, NExT-QA, and STAR. Further analyses reveal that the APSam also exhibits high reasoning capability and interpretability.