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

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https://doi.org/10.1007/978-3-642-49689-9ask. Starting with images that facilitate depth prediction due to the absence of unfavorable factors, we systematically generate new, user-defined scenes with a comprehensive set of challenges and associated depth information. This is achieved by leveraging cutting-edge text-to-image diffusion model

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https://doi.org/10.1007/978-3-642-49689-9 through various query styles. However, current retrieval tasks predominantly focus on text-query retrieval exploration, leading to limited retrieval query options and potential ambiguity or bias in user intention. In this paper, we propose the Style-Diversified Query-Based Image Retrieval task, whi

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Die Stellungnahme des Kranken zur Krankheitone dominate the other? Our analysis of a pretrained image diffusion model that integrates gated self-attention into the U-Net reveals that spatial grounding often outweighs textual grounding due to the . flow from gated self-attention to cross-attention. We demonstrate that such bias can be signifi

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Computer Vision – ECCV 2024978-3-031-73337-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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978-3-031-73336-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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