DART routes zero-shot video temporal grounding queries by difficulty using DPP entropy, achieving up to 3.5 mIoU gains with 7x fewer frames on Charades-STA and ActivityNet Captions.
Groundinggpt: Language enhanced multi-modal grounding model
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A pipeline of chain-of-thought data synthesis, LoRA-based supervised fine-tuning, rejection sampling, and rule-based reinforcement learning raises multi-image grounding accuracy by 9.04% on MIG-Bench and 4.41% on average across seven other benchmarks.
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DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding
DART routes zero-shot video temporal grounding queries by difficulty using DPP entropy, achieving up to 3.5 mIoU gains with 7x fewer frames on Charades-STA and ActivityNet Captions.
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Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning
A pipeline of chain-of-thought data synthesis, LoRA-based supervised fine-tuning, rejection sampling, and rule-based reinforcement learning raises multi-image grounding accuracy by 9.04% on MIG-Bench and 4.41% on average across seven other benchmarks.