Systematic empirical comparison of temporal context placement across backbone, PEFT modules, and probes for low-resource video task adaptation on appearance, motion, and dense tasks.
Videoeval: Comprehensive benchmark suite for low-cost evaluation of video foundation model
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Reinforcement fine-tuning with temporal rewards produces VideoChat-R1, a video MLLM showing large gains on spatio-temporal perception benchmarks such as +31.8 temporal grounding and +31.2 object tracking.
citing papers explorer
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Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
Systematic empirical comparison of temporal context placement across backbone, PEFT modules, and probes for low-resource video task adaptation on appearance, motion, and dense tasks.
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VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
Reinforcement fine-tuning with temporal rewards produces VideoChat-R1, a video MLLM showing large gains on spatio-temporal perception benchmarks such as +31.8 temporal grounding and +31.2 object tracking.