Defines OZ-TAL task and presents a training-free VLM-based method that outperforms prior approaches for online and offline zero-shot temporal action localization on THUMOS14 and ActivityNet-1.3.
Improved baselines with visual instruction tuning
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
PStar adaptively selects pseudocode-based reasoning strategies via a Difficulty Feature Vector to reduce hallucinations in vision-language models, reporting SOTA results on POPE and MMStar benchmarks.
MedLVR interleaves latent visual reasoning segments in autoregressive decoding and uses two-stage training to raise average medical VQA accuracy from 48.3% to 53.4% over a Qwen2.5-VL-7B backbone on OmniMedVQA and five other benchmarks.
citing papers explorer
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OZ-TAL: Online Zero-Shot Temporal Action Localization
Defines OZ-TAL task and presents a training-free VLM-based method that outperforms prior approaches for online and offline zero-shot temporal action localization on THUMOS14 and ActivityNet-1.3.
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Pseudocode-Guided Structured Reasoning for Automating Reliable Inference in Vision-Language Models
PStar adaptively selects pseudocode-based reasoning strategies via a Difficulty Feature Vector to reduce hallucinations in vision-language models, reporting SOTA results on POPE and MMStar benchmarks.
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MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering
MedLVR interleaves latent visual reasoning segments in autoregressive decoding and uses two-stage training to raise average medical VQA accuracy from 48.3% to 53.4% over a Qwen2.5-VL-7B backbone on OmniMedVQA and five other benchmarks.