VideoKR supplies 315K knowledge-intensive video reasoning examples and a dedicated benchmark, with experiments indicating post-training gains on reasoning tasks that require both video content and external knowledge.
Video-star: Self-training enables video instruction tuning with any supervision
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
SmolVLM-256M outperforms a 300-times larger model using under 1 GB GPU memory, while the 2.2B version matches state-of-the-art VLMs at half the memory cost.
VPiT enables pretrained LLMs to perform both visual understanding and generation by predicting discrete text tokens and continuous visual tokens, with understanding data proving more effective than generation-specific data.
Perception-verified self-training with PerceptEval and two-stage curriculum learning improves VLM reasoning by up to 16% over standard self-training baselines.
CurEvo integrates curriculum guidance into self-evolution to structure autonomous improvement of video understanding models, yielding gains on VideoQA benchmarks.
citing papers explorer
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VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
VideoKR supplies 315K knowledge-intensive video reasoning examples and a dedicated benchmark, with experiments indicating post-training gains on reasoning tasks that require both video content and external knowledge.
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SmolVLM: Redefining small and efficient multimodal models
SmolVLM-256M outperforms a 300-times larger model using under 1 GB GPU memory, while the 2.2B version matches state-of-the-art VLMs at half the memory cost.
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MetaMorph: Multimodal Understanding and Generation via Instruction Tuning
VPiT enables pretrained LLMs to perform both visual understanding and generation by predicting discrete text tokens and continuous visual tokens, with understanding data proving more effective than generation-specific data.
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Improving Reasoning in Vision-Language Models via Perception Verified Self-Training
Perception-verified self-training with PerceptEval and two-stage curriculum learning improves VLM reasoning by up to 16% over standard self-training baselines.
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CurEvo: Curriculum-Guided Self-Evolution for Video Understanding
CurEvo integrates curriculum guidance into self-evolution to structure autonomous improvement of video understanding models, yielding gains on VideoQA benchmarks.