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@@ -33,10 +33,12 @@ This repo releases data introduced in our paper accepted:
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  > **PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval**
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  > **Authors**: **[Yue Duan](https://njuyued.github.io/)**, Zhangxuan Gu, Zhenzhe Ying, Lei Qi, Changhua Meng and Yinghuan Shi
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- - **Quick links:** [[Code](https://github.com/alipay/PC2-NoiseofWeb) | [[PDF](https://arxiv.org/pdf/2408.01349)/[Abs](https://arxiv.org/abs/2408.01349)-arXiv | [PDF/Abs-Published (coming soon)]() | [Slides/Video (coming soon)]() | [Zhihu](https://zhuanlan.zhihu.com/p/711149124) | [Dataset download](https://huggingface.co/datasets/NJUyued/NoW/resolve/main/NoW.zip?download=true)]
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  - 📰 **Latest news:**
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- - **We write a detailed explanation (in chinese) of this work on [Zhihu](https://zhuanlan.zhihu.com/p/711149124).**
 
 
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  - Our paper is accepted by **ACM International Conference on Multimedia (ACM MM) 2024** 🎉🎉. Thanks to users.
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  ## Data Collection
 
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  > **PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval**
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  > **Authors**: **[Yue Duan](https://njuyued.github.io/)**, Zhangxuan Gu, Zhenzhe Ying, Lei Qi, Changhua Meng and Yinghuan Shi
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+ - **Quick links:** [[Code](https://github.com/alipay/PC2-NoiseofWeb) | [[PDF](https://arxiv.org/pdf/2408.01349)/[Abs](https://arxiv.org/abs/2408.01349)-arXiv | [PDF/Abs-Published (coming soon)]() | [Slides/Video (coming soon)]() | [文章解读-(Zhihu)](https://zhuanlan.zhihu.com/p/711149124) | [视频解读-bilibili](https://www.bilibili.com/video/BV1zppMezEQe/) | [Dataset download](https://huggingface.co/datasets/NJUyued/NoW/resolve/main/NoW.zip?download=true)]
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  - 📰 **Latest news:**
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+
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+ - We provide a **video presentation (in chinese)** of this work on [bilibili](https://www.bilibili.com/video/BV1zppMezEQe/).
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+ - We write a **detailed explanation (in chinese)** of this work on [Zhihu](https://zhuanlan.zhihu.com/p/711149124).
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  - Our paper is accepted by **ACM International Conference on Multimedia (ACM MM) 2024** 🎉🎉. Thanks to users.
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  ## Data Collection