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impact: "Beyond theoretical guarantees, we demonstrate the improvements achieved by LResNet in building hyperbolic deep learning models, where we conduct extensive experiments to show its superior performance in graph and image modalities across CNNs, GNNs, and graph Transformers.",
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tags: [Tag.MultiModalFoundationModel],
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},
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{
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title: "D-Edit: An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control",
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authors:
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"Aosong Feng, Weikang Qiu, Jinbin Bai,Xiao Zhang, Zhen Dong, Kaicheng Zhou, Rex Ying, and Leandros Tassiulas",
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venue: "AAAI 2025",
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page: "dedit",
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paper: "https://arxiv.org/abs/2403.04880",
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code: "https://github.com/collovlabs/d-edit",
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tags: [Tag.Applications],
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abstract:
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"D-Edit is a novel framework for diffusion-based image editing framework that disentangles image-prompt into item-prompt associations, enabling precise and harmonious edits across image, achieving state-of-the-art results in a unified, versatile approach.",
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impact:
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"The proposed method is a unified editing framework that supports image-based, text-based, mask-based editing, and item removal within a single cohesive system.",
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},
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{
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title: "Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer",
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authors: "Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin",
impact: "The proposed algorithm is theoretically guaranteed and can consistently improve nine contrastive learning methods across graph, image, and language modalities.",
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tags: []
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},
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{
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title: "D-Edit: An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control",
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authors:
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"Aosong Feng, Weikang Qiu, Jinbin Bai,Xiao Zhang, Zhen Dong, Kaicheng Zhou, Rex Ying, and Leandros Tassiulas",
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venue: "AAAI 2025",
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page: "dedit",
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paper: "https://arxiv.org/abs/2403.04880",
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code: "https://github.com/collovlabs/d-edit",
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tags: [Tag.Applications],
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abstract:
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"D-Edit is a novel framework for diffusion-based image editing framework that disentangles image-prompt into item-prompt associations, enabling precise and harmonious edits across image, achieving state-of-the-art results in a unified, versatile approach.",
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impact:
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"The proposed method is a unified editing framework that supports image-based, text-based, mask-based editing, and item removal within a single cohesive system.",
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