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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,Syn-to-Real Domain Adaptation for?Point Cloud Completion via?Part-Based Approach,s methods have been proposed to overcome this limitation by leveraging synthetic complete point clouds. While access to complete point clouds offers a notable advantage, they often struggle to bridge domain gaps, leading to sub-optimal performance. As a remedy, we propose a novel part-based framewor

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,Learn to?Preserve and?Diversify: Parameter-Efficient Group with?Orthogonal Regularization for?Domaiseen test data occurs. Recently, foundation models with enormous parameters have been pre-trained with huge datasets, demonstrating strong generalization ability and showing promising direction for solving the DG problem. However, fully Fine-Tuning (FT) the foundation models results in unsatisfactor

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,SCOMatch: Alleviating Overtrusting in?Open-Set Semi-supervised Learning,nd out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with labeled ID data, subsequently employing self-training to refine this boundary. These methods, however, suffer from the tend

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,Region-Aware Distribution Contrast: A Novel Approach to?Multi-task Partially Supervised Learning,and surface normal estimation, particularly when dealing with partially annotated data (MTPSL). The complexity arises from the absence of complete task labels for each training image. Given the inter-related nature of these pixel-wise dense tasks, our focus is on mining and capturing cross-task rela

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,MasterWeaver: Taming Editability and?Face Identity for?Personalized Text-to-Image Generation, human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they often suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, esp

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,PointRegGPT: Boosting 3D Point Cloud Registration Using Generative Point-Cloud Pairs for?Training,hile rendering-based synthetic data suffers from domain gaps. In this work, we present ., boosting 3D . cloud .istration using .enerative .oint-cloud pairs for .raining. Given a single depth map, we first apply a random camera motion to re-project it into a target depth map. Converting them to point

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,Long-CLIP: Unlocking the?Long-Text Capability of?CLIP,by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the text token is restricted to 77, and an empirical study shows the actual effective length is even less than 20. This prevents CLIP fr

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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. The papers deal with topics such as computer vision; machine learning; deep neural netwo