AI RESEARCH
Progressive Video Condensation with MLLM Agent for Long-form Video Understanding
arXiv CS.CV
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ArXi:2604.02891v1 Announce Type: new Understanding long videos requires extracting query-relevant information from long sequences under tight compute budgets. Existing text-then-LLM pipelines lose fine-grained visual cues, while video-based multimodal large language models (MLLMs) can keep visual details but are too frame-hungry and computationally expensive. In this work, we aim to harness MLLMs for efficient video understanding. We propose ProVCA, a progressive video condensation agent that iteratively locates key video frames at multiple granularities.