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Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts

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arxiv 2501.04322 v2 pith:BROPQPHU submitted 2025-01-08 cs.CV

Miao Rang , Zhenni Bi , Chuanjian Liu , Yehui Tang , Kai Han , Yunhe Wang This is my paper · ORCID
classification cs.CV
keywords multimodallanguageefficientlinguisticmodelmodelsvisionvisual
verification ladder T0 review T1 audit T2 compute T3 formal
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Multimodal vision language models (VLMs) have made significant progress with the support of continuously increasing model sizes and data volumes. Running VLMs on edge devices has become a challenge for their widespread application. There are several efficient VLM efforts, but they often sacrifice linguistic capabilities to enhance multimodal abilities, or require extensive training. To address this quandary,we introduce the innovative framework of Efficient Vision Language Models with Elastic Visual Experts (Eve). By strategically incorporating adaptable visual expertise at multiple stages of training, Eve strikes a balance between preserving linguistic abilities and augmenting multimodal capabilities. This balanced approach results in a versatile model with only 1.8B parameters that delivers significant improvements in both multimodal and linguistic tasks. Notably, in configurations below 3B parameters, Eve distinctly outperforms in language benchmarks and achieves state-of-the-art results 68.87% in VLM Benchmarks. Additionally, its multimodal accuracy outstrips that of the larger 7B LLaVA-1.5 model. Our code is available at https://github.com/rangmiao/Eve.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Kwai Keye-VL Technical Report

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Kwai Keye-VL shows that a five-mode chain-of-thought cold-start plus mix-mode reinforcement learning can push an 8B multimodal model to strong short-video and general vision-language performance.

  2. DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World

    cs.CV 2025-06 conditional novelty 6.0 of 10

    DenseWorld-1M provides one million images with detailed object captions, pixel masks, and spatial relations by chaining SAM, APE, RAM++, and VLMs through a three-stage labeling pipeline.

  3. Dense360: Dense Understanding from Omnidirectional Panoramas

    cs.CV 2025-06 reject novelty 6.0 of 10

    Introduces a 160K-panorama auto-annotated dataset, a dense captioning and grounding benchmark, and ERP-RoPE; fine-tuning Qwen2.5VL on the data lifts benchmark scores.

  4. Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Mixed-R1 uses four reward types under GRPO, including a new bidirectional max-average token similarity (BMAS) reward, and lifts MLLM reasoning benchmarks by 2-5%.

  5. CyberV: Cybernetics for Test-time Scaling in Video Understanding

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A training-free test-time feedback loop, using attention drift to select key frames, improves video MLLM accuracy, with the largest gains on knowledge-heavy VideoMMMU.

  6. EvoMoE: Expert Evolution in Mixture of Experts for Multimodal Large Language Models

    cs.CL 2025-05 conditional novelty 4.0 of 10

    EvoMoE creates MoE experts as decaying averages of a single trained FFN and routes tokens with hypernetwork-generated weights, yielding small benchmark gains over MoE-LLaVA.

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