HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model

Video-Language Models (VLMs), pre-trained on large-scale video-caption datasets, are now standard for robust visual-language representation and downstream tasks. However, their reliance on global contrastive alignment limits their ability to capture fine-grained interactions between visual and textual elements. To address these challenges, we introduce HENASY (Hierarchical ENtities ASsemblY), a novel framework designed for egocentric video analysis that enhances the granularity of video content representations. HENASY employs a compositional approach using an enhanced slot-attention and grouping mechanisms for videos, assembling dynamic entities from video patches. It integrates a local entity encoder for dynamic modeling, a global encoder for broader contextual understanding, and an entity-aware decoder for late-stage fusion, enabling effective video scene dynamics modeling and granular-level alignment between visual entities and text. By incorporating innovative contrastive losses, HENASY significantly improves entity and activity recognition, delivering superior performance on benchmarks such as Ego4D and EpicKitchen, and setting new standards in both zero-shot and extensive video understanding tasks. Our results confirm groundbreaking capabilities of HENASY and establish it as a significant advancement in video-language multimodal research.