Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
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.
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Zeyi Sun
claimed_verified
2023-12-08T08:00:24.451Z
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Zeyi Sun
user
Zery
65727be57b5f2b6f3c3f32d2
Ye Fang
claimed_verified
2023-12-08T12:08:27.723Z
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Ye Fang
user
aleafy
65727be57b5f2b6f3c3f32d3
Tong Wu
claimed_verified
2024-12-11T09:25:04.397Z
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Tong Wu
user
wutong16
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Pan Zhang
admin_assigned
2023-12-08T10:05:07.497Z
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Pan Zhang
user
Gatorburger
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Yuhang Zang
admin_assigned
2023-12-08T10:05:14.653Z
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Yuhang Zang
user
yuhangzang
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Shu Kong
admin_assigned
2023-12-08T10:05:29.699Z
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KongShu
user
KongShu
65727be57b5f2b6f3c3f32d7
Yuanjun Xiong
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Dahua Lin
admin_assigned
2023-12-08T10:05:50.146Z
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Dahua Lin
user
lindahua
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Jiaqi Wang
claimed_verified
2024-06-09T20:07:24.944Z
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Jiaqi Wang
user
myownskyW7
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
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.
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.
CLIP Contrastive Language-Image Pre-training alpha channel attentive regions rgba fine-tuned multimodal large language models conditional 2D/3D generation