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+ + - - -Abstract
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+ --> + + + +Abstract
++ Multi-modal Large Language Models (MLLMs) are at the forefront of artificial intelligence research, aiming + to create models capable of understanding, learning, and generating multiple data types, including text, + images, and sound. Despite the potential, significant challenges persist, including the integration of + suitable vision encoders and LLMs, the scarcity of comprehensive multi-modal datasets, and the need for + efficient performance improvement. + + A performance gap currently exists between closed-source models, often developed by resource-rich tech + companies, and open-source models. However, the open-source community is making substantial strides, + driven by collaboration and resource availability. + + Our work with the Bumblebee model, an open-source MLLM, exemplifies this progress. By implementing token + shrinkage and developing an efficient projector called STSR (Scalable Token Shrinkage Resampler), + Bumblebee has surpassed the closed-source QwenVL Max on the MMBench-Test-CN with a score of 75.9, using + only open-source data and 14 billion LLM parameters. This surpasses the current open-source + state-of-the-art Yi-34B-VL by 5.9 points on MMBench-Test-CN, despite having fewer parameters. This + achievement underscores the potential of open-source models to compete with, and potentially surpass, + their closed-source counterparts, signaling a promising future for open-source multi-modal learning. +
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+ + We surpassed QwenVL Max on MMbench-Test CN set. +
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+ + Qualitative results by Bumblebee +
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Video Presentation
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-Poster
+ + + --> + - +BibTeX
-BibTex Code Here
+
+@article{Fagang,
+ title={Bumblebee: Advancing Beyond Closed-Source Multi-Modal Models through Token Shrinkage},
+ author={Fagang Jin, Chen Tong, Lin You},
+ year={2024},
+ primaryClass={cs.CV}
+}
+