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Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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樓主: 珍愛
51#
發(fā)表于 2025-3-30 09:36:20 | 只看該作者
,Learning Modality-Agnostic Representation for?Semantic Segmentation from?Any Modalities,ity of existing multi-modal (., Image+X) semantic segmentation methods when confronting modality absence or failure, as often occurred in real-world applications. Inspired by the open-world learning capability of multi-modal vision-language models (MVLMs), we explore a new direction in learning the
52#
發(fā)表于 2025-3-30 13:30:56 | 只看該作者
Kinetic Typography Diffusion Model, guided video diffusion models to achieve visually-pleasing text appearances. To do this, we first construct a kinetic typography dataset, comprising about 600K videos. Our dataset is made from a variety of combinations in 584 templates designed by professional motion graphics designers and involves
53#
發(fā)表于 2025-3-30 20:17:01 | 只看該作者
,Refine, Discriminate and?Align: Stealing Encoders via?Sample-Wise Prototypes and?Multi-relational Eined encoders: (1) suboptimal performances attributed to biased optimization objectives, and (2) elevated query costs stemming from the end-to-end paradigm that necessitates querying the target encoder every epoch. Specifically, we initially .efine the representations of the target encoder for each
54#
發(fā)表于 2025-3-30 23:50:59 | 只看該作者
55#
發(fā)表于 2025-3-31 00:57:27 | 只看該作者
GroupDiff: Diffusion-Based Group Portrait Editing,allenging due to the intricate dynamics of human interactions and the diverse gestures. In this work, we present ., a pioneering effort to tackle group photo editing with three dedicated contributions: . Since there are no labeled data for group photo editing, we create a data engine to generate pai
56#
發(fā)表于 2025-3-31 05:02:47 | 只看該作者
57#
發(fā)表于 2025-3-31 12:21:55 | 只看該作者
,Inter-Class Topology Alignment for?Efficient Black-Box Substitute Attacks,ver, existing schemes merely train the substitute model to mimic the outputs of the target model without fully simulating the decision space, resulting in the adversarial samples generated by the substitute model being classified into the non-target class by the target model. To alleviate this issue
58#
發(fā)表于 2025-3-31 14:55:00 | 只看該作者
59#
發(fā)表于 2025-3-31 19:42:56 | 只看該作者
60#
發(fā)表于 2025-4-1 00:33:05 | 只看該作者
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