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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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,FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance,e utilized CLIP by incorporating manually designed visual prompts like colored circles and blur masks into the images to guide the model’s attention, showing enhanced zero-shot performance in downstream tasks. Although these methods have achieved promising results, they inevitably alter the original
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,: Taking a Further Step to Universal 9D Category-Level Object Pose Estimation,r respective domains. However, a universal framework capable of estimating the pose of both rigid and articulated objects has yet to be reported. In this paper, we introduce a .niversal 9D .ategory-level .bject .ose .stimation (.) framework, designed to address this gap. Our approach offers a novel
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,Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization,e learning. Causal domain generalization methods aim to identify latent causal variables that generate input data and build invariant causal mechanisms for prediction tasks, thereby improving out-of-distribution (OOD) prediction performance. However, there is no consensus on the best approach for se
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,Rotary Position Embedding for Vision Transformer,RoPE on computer vision domains have been underexplored, even though RoPE appears capable of enhancing Vision Transformer (ViT) performance in a way similar to the language domain. This study provides a comprehensive analysis of RoPE when applied to ViTs, utilizing practical implementations of RoPE
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,MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection,l weather conditions, characterized by scenarios with clear and optimal visibility. However, the challenge of autonomous driving requires the ability to handle changes in weather conditions, such as foggy weather, not just clear weather. We introduce MonoWAD, a novel weather-robust monocular 3D obje
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