CLIPS: An Enhanced CLIP Framework for Learning with Synthetic Captions

• URL: http://arxiv.org/abs/2411.16828v1
• Date: Mon, 25 Nov 2024 18:49:02 GMT
• Title: CLIPS: An Enhanced CLIP Framework for Learning with Synthetic Captions
• Authors: Yanqing Liu, Xianhang Li, Zeyu Wang, Bingchen Zhao, Cihang Xie,
• Abstract summary: We introduce two simple yet effective designs to better leverage richly described synthetic captions. First, we observe a strong inverse effect in learning with synthetic captions. Second, we incorporate an autoregressive captioner to mimic the recaptioning process.
• Score: 31.624782806591682
• License:
• Abstract: Previous works show that noisy, web-crawled image-text pairs may limit vision-language pretraining like CLIP and propose learning with synthetic captions as a promising alternative. Our work continues this effort, introducing two simple yet effective designs to better leverage richly described synthetic captions. Firstly, by observing a strong inverse effect in learning with synthetic captions -- the short synthetic captions can generally lead to MUCH higher performance than full-length ones -- we therefore fed only partial synthetic captions to the text encoder. Secondly, we incorporate an autoregressive captioner to mimic the recaptioning process -- by conditioning on the paired image input and web-crawled text description, the captioner learns to predict the full-length synthetic caption generated by advanced MLLMs. Experiments show that our framework significantly improves zero-shot performance in cross-modal retrieval tasks, setting new SOTA results on MSCOCO and Flickr30K. Moreover, such trained vision encoders can enhance the visual capability of LLaVA, showing strong improvements on a range of MLLM benchmarks. Our project page is https://ucsc-vlaa.github.io/CLIPS/.

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