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

Coronation

TP2O: Creative Text Pair-to-Object Generation Using Balance Swap-Sampling,ons. Last, we employ a segmentation method to compare CLIP distances among the segmented components, ultimately selecting the most promising object from the sampled subset. Extensive experiments demonstrate that our approach outperforms recent SOTA T2I methods. Surprisingly, our results even rival those of human artists, such as . in Fig.?..

舊石器

Efficient Bias Mitigation Without Privileged Information,er-free framework that leverages the . training history of a helper model to identify spurious samples, and . a group-balanced training set from which a robust model can be trained. We show that TAB improves worst-group performance without . group information or model selection, outperforming existing methods while maintaining overall accuracy.

jovial

narcissism

Amnesty

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

冷淡周邊

,Robust Nearest Neighbors for?Source-Free Domain Adaptation Under Class Distribution Shift,ng additional generic features free of the source model’s CDS bias. This provides a “second-opinion” regarding which nearest neighbors are more suitable for adaptation. We evaluate our method?using various types of features, datasets and tasks, outperforming previous methods in SFDA under CDS. Our c

種屬關系

,Time-Efficient and?Identity-Consistent Virtual Try-On Using A Variant of?Altered Diffusion Models,dividual features and a try-on module refining the attire?and generating missing parts integrated with a mask-aware post-processing technique ensuring the integrity of the individual’s identity. It demonstrates impressive results, surpassing?the state-of-the-art in speed by nearly 20 times during in

宮殿般

,Feature Diversification and?Adaptation for?Federated Domain Generalization,while preserving privacy. Our resultant global model shows robust performance on unseen test domain data. To enhance performance further, we develop an instance-adaptive inference approach tailored for test domain data. Our proposed instance feature adapter dynamically adjusts feature statistics to

修正案

Between

,RoDUS: Robust Decomposition of?Static and?Dynamic Elements in?Urban Scenes, the reconstructed background, all by using self-supervision. Notably, experimental evaluations on KITTI-360 and Pandaset datasets demonstrate the effectiveness of our method in decomposing challenging urban scenes into precise static and dynamic components.