Update README.md

README.md

@@ -1,3 +1,61 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- BleachNick/UltraEdit
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- openai/clip-vit-large-patch14-336
+- openai/clip-vit-large-patch14
+- timm/ViT-SO400M-14-SigLIP
+- timm/ViT-SO400M-14-SigLIP2
+- timm/ViT-SO400M-16-SigLIP2-384
+- timm/ViT-SO400M-14-SigLIP-384
+pipeline_tag: zero-shot-image-classification
+---
+
+# CLIP-IN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+### Model Description
+
+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-
+age instruction-editing datasets, originally designed for image manipulation, as a
+unique source of hard negative image-text pairs. Coupled with a symmetric hard
+negative contrastive loss, this enables the model to effectively distinguish subtle
+visual-semantic differences. Secondly, CLIP-IN incorporates long descriptive cap-
+tions, utilizing rotary positional encodings to capture rich semantic context often
+missed by standard CLIP. Our experiments demonstrate that CLIP-IN achieves sub-
+stantial gains on the MMVP benchmark and various fine-grained visual recognition
+tasks, without compromising robust zero-shot performance on broader classifica-
+tion and retrieval tasks. Critically, integrating CLIP-IN’s visual representations into
+Multimodal Large Language Models significantly reduces visual hallucinations
+and enhances reasoning abilities. This work underscores the considerable potential
+of synergizing targeted, instruction-based contrastive learning with comprehensive
+descriptive information to elevate the fine-grained understanding of VLMs
+
+
+## Evaluation
+
+![result1](https://cdn-uploads.huggingface.co/production/uploads/678db465b6eed17d5640d492/bpm9k7S7TOBwE5bO4QASM.jpeg)
+
+![result2](https://cdn-uploads.huggingface.co/production/uploads/678db465b6eed17d5640d492/PweGh-UQSH3iKN7lEBc3t.jpeg)
+
+![result3](https://cdn-uploads.huggingface.co/production/uploads/678db465b6eed17d5640d492/N89bao5CAvgLh_ptaBEo7.jpeg)
+
+![result4](https://cdn-uploads.huggingface.co/production/uploads/678db465b6eed17d5640d492/qV6V3155gNkDvU5NkWzJB.png)
+
+![result5](https://cdn-uploads.huggingface.co/production/uploads/678db465b6eed17d5640d492/feciTXg-I0BILZ_HVIxOr.png)
+
+**BibTeX:**
+
+@article{Wang2025CLIPINEF,
+  title={CLIP-IN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions},
+  author={Ziteng Wang and Siqi Yang and Limeng Qiao and Lin Ma},
+  journal={NeurIPS},
+  year={2025}
+}