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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,InsMapper: Exploring Inner-Instance Information for?Vectorized HD Mapping,. The first two modules can better initialize queries for line detection, while the last one refines predicted line instances. InsMapper is highly adaptable and can be seamlessly modified to align with the most recent HD map detection frameworks. Extensive experimental evaluations are conducted on t

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,KDProR: A Knowledge-Decoupling Probabilistic Framework for?Video-Text Retrieval,h utilizes our proposed Expectation-Knowledge-Maximization (EKM) algorithm for optimization. Specifically, in E-step, KDProR obtains relevant contextual semantics from knowledge stores and achieves efficient knowledge injection through interpolation and alignment correction. During the K-step, KDPro

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Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforceme

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0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; r

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Moulay Alaoui-Jamali,Rongyao Zhougmentation models. The resulting model, Segment3D, generalizes significantly better than the models trained on costly manual 3D labels and enables easily adding new training data to further boost the segmentation performance.

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U. Kellhammer,B. Giesecke,K. überlan the public TOD dataset. Furthermore, trained on simulated data, CODERS generalize well to unseen category-level object instances in real-world robot manipulation experiments. Our dataset, code, and demos will be available at ..

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,Active Coarse-to-Fine Segmentation of?Moveable Parts from?Real Images,45% of the images. This translates to significant (60%) time saving over manual effort required by the best non-AL model to attain the same segmentation accuracy. At last, we contribute a dataset of 2,550 real images with annotated moveable?parts, demonstrating its superior quality and diversity over the best alternatives.