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

affluent

C. Toker,B. Uzun,F. O. Ceylan,C. Iktennabled through a bidirectional cross-attention mechanism. The approach offers multiple advantages - (a) easy to implement on standard ML accelerators (GPUs/TPUs) via standard high-level operators, (b) applicable to standard ViT and its variants, thus generalizes to various tasks, (c) can handle diff

Adulate

,Learning Pseudo 3D Guidance for?View-Consistent Texturing with?2D Diffusion, on learned .seudo .D .uidance. The key idea of P3G is to first learn a coarse but consistent texture, to serve as a global semantics guidance for encouraging the consistency between images generated on different views. To this end, we incorporate pre-trained text-to-image diffusion models and multi

Subdue

Ventilator

,SparseRadNet: Sparse Perception Neural Network on?Subsampled Radar Data,o combine features from both branches. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation, meanwhile using sparse subsampled input data.

endoscopy

血統(tǒng)

ALTER

,Explain via?Any Concept: Concept Bottleneck Model with?Open Vocabulary Concepts,ifier on the downstream dataset; (3) Reconstructing the trained classification head via any set of user-desired textual concepts encoded by CLIP’s text encoder. To reveal potentially missing concepts from users, we further propose to iteratively find the closest concept embedding to the residual par

使閉塞

捐助

,Missing Modality Prediction for?Unpaired Multimodal Learning via?Joint Embedding of?Unimodal Modelscts the missing embedding through prompt tuning, leveraging information from available modalities. We evaluate our approach on several multimodal benchmark datasets and demonstrate its effectiveness and robustness across various scenarios of missing modalities.

metropolitan

,Improving Diffusion Models for?Authentic Virtual Try-on in?the?Wild, layer. In addition, we provide detailed textual prompts for both garment and person images to enhance the authenticity of the generated visuals. Finally, we present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity. Our experimental