A proposer-solver agent pair achieves supervised-level video temporal grounding and fine-grained captioning from 2.5K unlabeled videos via self-reinforcing evolution.
Lita: Language instructed temporal-localization assistant
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Fully end-to-end training with a sentence-conditioned adapter outperforms frozen-backbone baselines for localizing video segments that match sentence queries.
MarkIt converts videos into query-conditioned marked versions via a linguistic-parsing and open-vocabulary segmentation bridge that embeds instance masks, semantic markers, and frame indices to improve Vid-LLM temporal grounding.
LLaVA-Video-178K is a new synthetic video instruction dataset that, when combined with existing data to train LLaVA-Video, produces strong results on video understanding benchmarks.
A temporal pooling layer added to LLaVA smooths video feature distributions and lifts performance on dense video captioning and QA to new SOTA levels without extra parameters.
citing papers explorer
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EvoGround: Self-Evolving Video Agents for Video Temporal Grounding
A proposer-solver agent pair achieves supervised-level video temporal grounding and fine-grained captioning from 2.5K unlabeled videos via self-reinforcing evolution.
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A Paradigm Shift: Fully End-to-End Training for Temporal Sentence Grounding in Videos
Fully end-to-end training with a sentence-conditioned adapter outperforms frozen-backbone baselines for localizing video segments that match sentence queries.
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MarkIt: Training-Free Visual Markers for Precise Video Temporal Grounding
MarkIt converts videos into query-conditioned marked versions via a linguistic-parsing and open-vocabulary segmentation bridge that embeds instance masks, semantic markers, and frame indices to improve Vid-LLM temporal grounding.
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LLaVA-Video: Video Instruction Tuning With Synthetic Data
LLaVA-Video-178K is a new synthetic video instruction dataset that, when combined with existing data to train LLaVA-Video, produces strong results on video understanding benchmarks.
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PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
A temporal pooling layer added to LLaVA smooths video feature distributions and lifts performance on dense video captioning and QA to new SOTA levels without extra parameters.