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--- |
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license: mit |
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datasets: |
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- BleachNick/UltraEdit |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- openai/clip-vit-large-patch14-336 |
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- openai/clip-vit-large-patch14 |
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- timm/ViT-SO400M-14-SigLIP |
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- timm/ViT-SO400M-14-SigLIP2 |
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- timm/ViT-SO400M-16-SigLIP2-384 |
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- timm/ViT-SO400M-14-SigLIP-384 |
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pipeline_tag: zero-shot-image-classification |
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--- |
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# CLIP-IN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions |
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<!-- Provide a quick summary of what the model is/does. --> |
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### Model Description |
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Despite the success of Vision-Language Models (VLMs) like CLIP in aligning vision and language, their proficiency in detailed, fine-grained visual comprehension remains a key challenge. We present CLIP-IN, a novel framework that bolsters CLIP’s fine-grained perception through two core innovations. Firstly, we lever- |
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age instruction-editing datasets, originally designed for image manipulation, as a |
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unique source of hard negative image-text pairs. Coupled with a symmetric hard |
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negative contrastive loss, this enables the model to effectively distinguish subtle |
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visual-semantic differences. Secondly, CLIP-IN incorporates long descriptive cap- |
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tions, utilizing rotary positional encodings to capture rich semantic context often |
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missed by standard CLIP. Our experiments demonstrate that CLIP-IN achieves sub- |
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stantial gains on the MMVP benchmark and various fine-grained visual recognition |
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tasks, without compromising robust zero-shot performance on broader classifica- |
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tion and retrieval tasks. Critically, integrating CLIP-IN’s visual representations into |
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Multimodal Large Language Models significantly reduces visual hallucinations |
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and enhances reasoning abilities. This work underscores the considerable potential |
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of synergizing targeted, instruction-based contrastive learning with comprehensive |
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descriptive information to elevate the fine-grained understanding of VLMs |
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## Evaluation |
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**BibTeX:** |
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@article{Wang2025CLIPINEF, |
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title={CLIP-IN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions}, |
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author={Ziteng Wang and Siqi Yang and Limeng Qiao and Lin Ma}, |
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journal={NeurIPS}, |
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year={2025} |
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} |
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