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

ATOPY

,ByteEdit: Boost, Comply and?Accelerate Generative Image Editing,npainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) inferior quality; ii) poor consistency; iii) insufficient instrcution adherence; iv) suboptimal generation efficiency. To address these obstacles, we present ., an innovative feedback learning framewor

虛弱的神經

,ProDepth: Boosting Self-supervised Multi-frame Monocular Depth with?Probabilistic Fusion,scene. However, the presence of moving objects in dynamic scenes introduces inevitable inconsistencies, causing misaligned multi-frame feature matching and misleading self-supervision during training. In this paper, we propose a novel framework called ProDepth, which effectively addresses the mismat

職業(yè)拳擊手

天文臺

,Accelerating Image Super-Resolution Networks with?Pixel-Level Classification,or DNN-based SISR, decomposing images into overlapping patches is typically necessary due to computational constraints. In such patch-decomposing scheme, one can allocate computational resources differently based on each patch’s difficulty to further improve efficiency while maintaining SR performan

賞心悅目

讓你明白

SOBER

,Click-Gaussian: Interactive Segmentation to?Any 3D Gaussians,y of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they struggle to provide detailed segmentation, which is important for fine-grained manipulation of 3D scenes. In this study, we propose Click-Gaussian, w

infinite

CLAMP

,DySeT: A?Dynamic Masked Self-distillation Approach for?Robust Trajectory Prediction,address this is via self-supervised pre-training through masked trajectory prediction. However, the existing models rely on uniform random sampling of tokens, which is sub-optimal because it implies that all components of driving scenes are equally informative. In this paper, to enable more robust r

comely