title: Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
summary: Contrastive Language-Image Pre-training (CLIP) plays an essential role in
  extracting valuable content information from images across diverse tasks. It
  aligns textual and visual modalities to comprehend the entire image, including
  all the details, even those irrelevant to specific tasks. However, for a finer
  understanding and controlled editing of images, it becomes crucial to focus on
  specific regions of interest, which can be indicated as points, masks, or boxes
  by humans or perception models. To fulfill the requirements, we introduce
  Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to
  suggest attentive regions and fine-tuned with constructed millions of RGBA
  region-text pairs. Alpha-CLIP not only preserves the visual recognition ability
  of CLIP but also enables precise control over the emphasis of image contents.
  It demonstrates effectiveness in various tasks, including but not limited to
  open-world recognition, multimodal large language models, and conditional 2D /
  3D generation. It has a strong potential to serve as a versatile tool for
  image-related tasks.
paper_authors._id: 65727be57b5f2b6f3c3f32d1
paper_authors.name: Zeyi Sun
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paper_authors._id: 65727be57b5f2b6f3c3f32d2
paper_authors.name: Ye Fang
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paper_authors.fullname: Ye Fang
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paper_authors.user: aleafy
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paper_authors.name: Tong Wu
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paper_authors.fullname: Tong Wu
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paper_authors.user: wutong16
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paper_authors.name: Pan Zhang
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paper_authors.fullname: Pan Zhang
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paper_authors.user: Gatorburger
paper_authors._id: 65727be57b5f2b6f3c3f32d5
paper_authors.name: Yuhang Zang
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paper_authors.fullname: Yuhang Zang
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paper_authors.user: yuhangzang
paper_authors._id: 65727be57b5f2b6f3c3f32d6
paper_authors.name: Shu Kong
paper_authors.status: admin_assigned
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paper_authors.fullname: KongShu
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paper_authors.user: KongShu
paper_authors._id: 65727be57b5f2b6f3c3f32d7
paper_authors.name: Yuanjun Xiong
paper_authors._id: 65727be57b5f2b6f3c3f32d8
paper_authors.name: Dahua Lin
paper_authors.status: admin_assigned
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paper_authors.fullname: Dahua Lin
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paper_authors.user: lindahua
paper_authors._id: 65727be57b5f2b6f3c3f32d9
paper_authors.name: Jiaqi Wang
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paper_authors.fullname: Jiaqi Wang
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paper_authors.user: myownskyW7
paper_title: Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
paper_summary: Contrastive Language-Image Pre-training (CLIP) plays an essential role in
  extracting valuable content information from images across diverse tasks. It
  aligns textual and visual modalities to comprehend the entire image, including
  all the details, even those irrelevant to specific tasks. However, for a finer
  understanding and controlled editing of images, it becomes crucial to focus on
  specific regions of interest, which can be indicated as points, masks, or boxes
  by humans or perception models. To fulfill the requirements, we introduce
  Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to
  suggest attentive regions and fine-tuned with constructed millions of RGBA
  region-text pairs. Alpha-CLIP not only preserves the visual recognition ability
  of CLIP but also enables precise control over the emphasis of image contents.
  It demonstrates effectiveness in various tasks, including but not limited to
  open-world recognition, multimodal large language models, and conditional 2D /
  3D generation. It has a strong potential to serve as a versatile tool for
  image-related tasks.
paper_ai_summary: Alpha-CLIP enhances CLIP by adding an auxiliary alpha channel for attentive region suggestion, enabling precise control over image content across various tasks like open-world recognition and multimodal generation.
paper_ai_keywords: CLIP Contrastive Language-Image Pre-training alpha channel attentive regions rgba fine-tuned multimodal large language models conditional 2D/3D generation