From 82c116f1b0a194fd9d0cd5f6cb1677d9e7b78e4a Mon Sep 17 00:00:00 2001 From: lucasj Date: Mon, 6 May 2024 14:17:15 +0800 Subject: [PATCH] add --- .DS_Store | Bin 0 -> 6148 bytes index.html | 438 ++++++++++++++++++++++------------------ static/.DS_Store | Bin 0 -> 6148 bytes static/images/board.jpg | Bin 0 -> 362516 bytes static/images/res.webp | Bin 0 -> 219980 bytes 5 files changed, 241 insertions(+), 197 deletions(-) create mode 100644 .DS_Store create mode 100644 static/.DS_Store create mode 100644 static/images/board.jpg create mode 100644 static/images/res.webp diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..21ffbf0dd72d18a0cbef22dba4d06184c73f58dd GIT binary patch literal 6148 zcmeHK%}T>S5Z-NTn^J@v6nb3nS}@ish?h|73mDOZN=;1BV9b^#HHT8jSzpK}@p+ut z-5i1eZz6UEX203_+0A~C{b7vp@iIJM%w~*Ppokoe8bNn$s9};3xf&y44?&*JgIESN z1N}u8etVmxEMq=s_4_}9c^svg<9zZ)v$fUkSRJcp-FZ(k_cA|Qrel9`gS|^B%!WpF8UPnnN^RZkeVXA699k9AqcWU=ntC2Grh$CmX9*EV+ z&>4u);qiLiwRZRRPcNp=@k=7#G?5%QSF&xegm*A1YkKt-Ni35`@Rv1ZE+H{M3=jjv zz~(Vv&H}5qc}u4i69dG+4-DY`AV3jagM~)5bwGpHXNmunFpinIe7tZqJNSi4XWZ3DEiph0R2gXMu8rsaIs7uU zkNj#0^@sst;GZ$TTN7_GhDDjP_1p6BtQDa5peUGEpaBAQ + - - - + + + - - + + @@ -24,17 +25,15 @@ - Academic Project Page + Bumblebee: Advancing Beyond Closed-Source Multi-Modal Models through Token Shrinkage - + - + @@ -44,6 +43,7 @@ + @@ -52,50 +52,50 @@
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Academic Project Page

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Bumblebee: Advancing Beyond Closed-Source Multi-Modal Models + through Token Shrinkage

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Abstract

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Abstract

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+ 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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+ Qualitative results by Bumblebee +

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BibTeX

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+@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}
+}
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