AdaFocus: Efficient Long Video Understanding via Adaptive Sampling
AdaFocus introduces a novel framework for understanding long videos, conceptualizing the task as a progressive acquisition of evidence instead of relying on one-pass encoding. This approach tackles the limitations of traditional one-shot methods, which either require high memory and latency for dense video encoding or compromise on detail by compressing videos into sparse frame sets. The framework consists of two main elements: a Query-Aware Adaptive Relevance-Diversity sampler (AdaRD) that generates a concise video preview and shifts to global clustering when local grounding is insufficient; and an uncertainty-triggered refinement mechanism that prevents the caching of extensive frame sequences. Its objective is to achieve a balance between temporal coverage, visual fidelity, and computational efficiency. The research is available on arXiv under ID 2605.12954.
Key facts
- AdaFocus is a framework for long video understanding.
- It uses progressive evidence acquisition instead of one-pass encoding.
- The framework includes a Query-Aware Adaptive Relevance-Diversity sampler (AdaRD).
- AdaRD switches to global clustering when query lacks reliable local grounding.
- An uncertainty-triggered refinement mechanism avoids caching exhaustive frame sequences.
- The paper is available on arXiv with ID 2605.12954.
- The approach aims to balance temporal coverage, visual details, and computational efficiency.
- Existing methods either densely encode videos or compress them into sparse frame sets.
Entities
Institutions
- arXiv