FineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions

• URL: http://arxiv.org/abs/2603.17326v1
• Date: Wed, 18 Mar 2026 03:39:04 GMT
• Title: FineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions
• Abstract summary: FineViT is a novel vision encoder specifically designed to unlock fine-grained perception.<n>We introduce FineViT, a novel vision encoder specifically designed to unlock fine-grained perception.
• Score: 52.366937743884314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Multimodal Large Language Models (MLLMs) have experienced rapid advancements, their visual encoders frequently remain a performance bottleneck. Conventional CLIP-based encoders struggle with dense spatial tasks due to the loss of visual details caused by low-resolution pretraining and the reliance on noisy, coarse web-crawled image-text pairs. To overcome these limitations, we introduce FineViT, a novel vision encoder specifically designed to unlock fine-grained perception. By replacing coarse web data with dense recaptions, we systematically mitigate information loss through a progressive training paradigm.: first, the encoder is trained from scratch at a high native resolution on billions of global recaptioned image-text pairs, establishing a robust, detail rich semantic foundation. Subsequently, we further enhance its local perception through LLM alignment, utilizing our curated FineCap-450M dataset that comprises over $450$ million high quality local captions. Extensive experiments validate the effectiveness of the progressive strategy. FineViT achieves state-of-the-art zero-shot recognition and retrieval performance, especially in long-context retrieval, and consistently outperforms multimodal visual encoders such as SigLIP2 and Qwen-ViT when integrated into MLLMs. We hope FineViT could serve as a powerful new baseline for fine-grained visual perception.

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