Haodong Duan

TL;DR: This work proposes InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition that achieves competitive text-image composition scores compared to public solutions, including GPT4-V and GPT3.5.

TL;DR: PYSKL implements six different algorithms under a unified framework with both the latest and original good practices to ease the comparison of efficacy and efficiency and provides an original GCN-based skeleton action recognition model named ST-GCN++, which achieves competitive recognition performance without any complicated attention schemes.

TL;DR: Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments.

TL;DR: InternLM2 is an open-source LLM that outperforms its predecessors in comprehensive evaluations across various tasks, including long-context modeling and open-ended subjective evaluations. It utilizes innovative pre-training and optimization techniques to capture long-term dependencies and achieve remarkable performance on the ``Needle-in-a-Haystack" test.