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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,Self-supervised Co-salient Object Detection via Feature Correspondences at Multiple Scales,egmentation annotations. Unlike existing unsupervised methods that rely solely on patch-level information (..clustering patch descriptors) or on computation heavy off-the-shelf components for CoSOD, our lightweight model leverages feature correspondences at both patch and region levels, significantl

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,: Slot-Guided Feature Lifting for Learning Object-Centric Radiance Fields, in object-centric learning methods, learning object-centric representations in the 3D physical world remains a crucial challenge. In this work, we propose ., a novel object-centric radiance model addressing scene reconstruction and decomposition jointly via slot-guided feature lifting. Such a desig

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,: Scaling 3D Vision-Language Learning for Grounded Scene Understanding,s. In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces two significant challenges: (i) the scarcity of paired 3D-VL data to support grounded learning of 3D scenes, especially considering complexities within diverse object configurations, rich attributes, and

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,ADMap: Anti-disturbance Framework for Vectorized HD Map Construction,high-performance HD map construction models to meet the demand. However, the point sequences generated by recent HD map construction models are jittery or jagged due to prediction bias and impact subsequent tasks. To mitigate this jitter issue, we propose the Anti-Disturbance Map construction framew

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