Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917856865681408 |
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| author | Tong, Shengbang Brown, Ellis Wu, Penghao Woo, Sanghyun Middepogu, Manoj Akula, Sai Charitha Yang, Jihan Yang, Shusheng Iyer, Adithya Pan, Xichen Wang, Ziteng Fergus, Rob LeCun, Yann Xie, Saining |
| author_facet | Tong, Shengbang Brown, Ellis Wu, Penghao Woo, Sanghyun Middepogu, Manoj Akula, Sai Charitha Yang, Jihan Yang, Shusheng Iyer, Adithya Pan, Xichen Wang, Ziteng Fergus, Rob LeCun, Yann Xie, Saining |
| contents | We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. While stronger language models can enhance multimodal capabilities, the design choices for vision components are often insufficiently explored and disconnected from visual representation learning research. This gap hinders accurate sensory grounding in real-world scenarios. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations, offering new insights into different models and architectures -- self-supervised, strongly supervised, or combinations thereof -- based on experiments with over 20 vision encoders. We critically examine existing MLLM benchmarks, address the difficulties involved in consolidating and interpreting results from various tasks, and introduce a new vision-centric benchmark, CV-Bench. To further improve visual grounding, we propose the Spatial Vision Aggregator (SVA), a dynamic and spatially-aware connector that integrates high-resolution vision features with LLMs while reducing the number of tokens. Additionally, we discuss the curation of high-quality visual instruction-tuning data from publicly available sources, emphasizing the importance of data source balancing and distribution ratio. Collectively, Cambrian-1 not only achieves state-of-the-art performance but also serves as a comprehensive, open cookbook for instruction-tuned MLLMs. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes. We hope our release will inspire and accelerate advancements in multimodal systems and visual representation learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16860 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs Tong, Shengbang Brown, Ellis Wu, Penghao Woo, Sanghyun Middepogu, Manoj Akula, Sai Charitha Yang, Jihan Yang, Shusheng Iyer, Adithya Pan, Xichen Wang, Ziteng Fergus, Rob LeCun, Yann Xie, Saining Computer Vision and Pattern Recognition We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. While stronger language models can enhance multimodal capabilities, the design choices for vision components are often insufficiently explored and disconnected from visual representation learning research. This gap hinders accurate sensory grounding in real-world scenarios. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations, offering new insights into different models and architectures -- self-supervised, strongly supervised, or combinations thereof -- based on experiments with over 20 vision encoders. We critically examine existing MLLM benchmarks, address the difficulties involved in consolidating and interpreting results from various tasks, and introduce a new vision-centric benchmark, CV-Bench. To further improve visual grounding, we propose the Spatial Vision Aggregator (SVA), a dynamic and spatially-aware connector that integrates high-resolution vision features with LLMs while reducing the number of tokens. Additionally, we discuss the curation of high-quality visual instruction-tuning data from publicly available sources, emphasizing the importance of data source balancing and distribution ratio. Collectively, Cambrian-1 not only achieves state-of-the-art performance but also serves as a comprehensive, open cookbook for instruction-tuned MLLMs. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes. We hope our release will inspire and accelerate advancements in multimodal systems and visual representation learning. |
| title | Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16860 |