Data-efficient Large Vision Models through Sequential Autoregression
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910473768665088 |
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| author | Guo, Jianyuan Hao, Zhiwei Wang, Chengcheng Tang, Yehui Wu, Han Hu, Han Han, Kai Xu, Chang |
| author_facet | Guo, Jianyuan Hao, Zhiwei Wang, Chengcheng Tang, Yehui Wu, Han Hu, Han Han, Kai Xu, Chang |
| contents | Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens. In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://github.com/ggjy/DeLVM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04841 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-efficient Large Vision Models through Sequential Autoregression Guo, Jianyuan Hao, Zhiwei Wang, Chengcheng Tang, Yehui Wu, Han Hu, Han Han, Kai Xu, Chang Computer Vision and Pattern Recognition Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens. In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://github.com/ggjy/DeLVM. |
| title | Data-efficient Large Vision Models through Sequential Autoregression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.04841 |