LLaVA-CoT adds autonomous multistage reasoning to vision-language models, delivering 9.4% gains over its base model and outperforming larger models like Gemini-1.5-pro on reasoning benchmarks via a 100k annotated dataset and SWIRES test-time scaling.
Visual cot: Advancing multi-modal language models with a comprehen- sive dataset and benchmark for chain-of-thought reasoning,
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OVOD-Agent models visual reasoning as a weakly Markovian decision process with bandit-driven exploration to create a self-evolving open-vocabulary detector that improves on rare categories in COCO and LVIS.
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LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
LLaVA-CoT adds autonomous multistage reasoning to vision-language models, delivering 9.4% gains over its base model and outperforming larger models like Gemini-1.5-pro on reasoning benchmarks via a 100k annotated dataset and SWIRES test-time scaling.
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OVOD-Agent: A Markov-Bandit Framework for Proactive Visual Reasoning and Self-Evolving Detection
OVOD-Agent models visual reasoning as a weakly Markovian decision process with bandit-driven exploration to create a self-evolving open-vocabulary detector that improves on rare categories in COCO and LVIS.