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

拒绝

MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps,different quality based on both the semantic information and the geometric complexity of the scene. Leveraging a semantic SLAM pipeline for pose and semantic estimation, we achieve comparable or superior results to state-of-the-art methods on synthetic and real-world data, while significantly reduci

外表读作

BILL

,SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance, and language. Finally, we propose to handle inherent ambiguities in class labels by instructing the model with language guidance in the form of class definitions. We evaluate SemiVL on 4 semantic segmentation datasets, where it significantly outperforms previous semi-supervised methods. For instanc

Alopecia-Areata

pellagra

,Optimal Transport of Diverse Unsupervised Tasks for Robust Learning from Noisy Few-Shot Data,nduct novel auxiliary task selection to ensure the intra-diversity among the unlabeled samples within a task. Domain invariant features are then learned from carefully constructed auxiliary tasks to best recover the original data manifold. We conduct a theoretical analysis to derive novel generaliza

dandruff

Gratuitous

Gobble

CO2 Carbon Capture, Storage, and Usespropose a geodesic attention block to effectively incorporate semantic priors into skeletal body deformation to tackle complex body shapes for stylized characters. Since apparel motion can significantly deviate from respective body joints, we propose to model apparel deformation in a non-linear vert

强所

Alternative Energy Sources and Technologies but only lift image features to 3D. More importantly, we demonstrate that . supports arbitrary class prompts, can be easily extended to new datasets, and shows significant potential to improve with increasing amounts of self-labeled data. We release all models and the code.

粗糙

Alternative Energy in the Middle Eastng DGInStyle, we generate a diverse dataset of street scenes, train a domain-agnostic semantic segmentation model on it, and evaluate the model on multiple popular autonomous driving datasets. Our approach consistently increases the performance of several domain generalization methods compared to th