mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal
  Large Language Models
Authors: name: Jiabo Ye, name: Haiyang Xu, name: Haowei Liu, name: Anwen Hu, name: Ming Yan, name: Qi Qian, name: Ji Zhang, name: Fei Huang, name: Jingren Zhou

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.