mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

Jiabo Ye, xuhaiyang, Haowei Liu, AnwenHu, Ming, Qi Qian, Ji Zhang, Fei Huang, Zhou

mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models: 33 upvotes on Hugging Face Daily Papers, #3 of 10 papers on 2024-08-12. Day-by-day upvote history.

Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in executing instructions for a variety of single-image tasks. Despite this progress, significant challenges remain in modeling long image sequences. In this work, we introduce the versatile multi-modal large language model, mPLUG-Owl3, which enhances the capability for long image-sequence understanding in scenarios that incorporate retrieved image-text knowledge, interleaved image-text, and lengthy videos. Specifically, we propose novel hyper attention blocks to efficiently integrate vision and language into a common language-guided semantic space, thereby facilitating the processing of extended multi-image scenarios. Extensive experimental results suggest that mPLUG-Owl3 achieves state-of-the-art performance among models with a similar size on single-image, multi-image, and video benchmarks. Moreover, we propose a challenging long visual sequence evaluation named Distractor Resistance to assess the ability of models to maintain focus amidst distractions. Finally, with the proposed architecture, mPLUG-Owl3 demonstrates outstanding performance on ultra-long visual sequence inputs. We hope that mPLUG-Owl3 can contribute to the development of more efficient and powerful multimodal large language models.

Paper page on Hugging Face · arXiv

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