GME achieves state-of-the-art results in universal multimodal retrieval by training on a balanced synthetic multimodal dataset.
How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites
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TwigVLM adds a twig module to VLMs for twig-guided token pruning and self-speculative decoding, retaining 96% performance after pruning 88.9% visual tokens and delivering 154% speedup on long responses for LLaVA-1.5-7B.
citing papers explorer
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GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
GME achieves state-of-the-art results in universal multimodal retrieval by training on a balanced synthetic multimodal dataset.
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Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models
TwigVLM adds a twig module to VLMs for twig-guided token pruning and self-speculative decoding, retaining 96% performance after pruning 88.9% visual tokens and delivering 154% speedup on long responses for LLaVA-1.5-7B.