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

圆锥体

Thyroid-Gland

,SILC: Improving Vision Language Pretraining with Self-distillation,ion, while also providing improvements on image-level tasks such as classification and retrieval. SILC models sets a new state of the art for zero-shot classification, few shot classification, image and text retrieval, zero-shot segmentation, and open vocabulary segmentation. We further show that SI

anthesis

,Learning Semantic Latent Directions for Accurate and Controllable Human Motion Prediction,ting the coefficients of the latent directions during the inference phase. Expanding on SLD, we introduce a set of motion queries to enhance the diversity of predictions. By aligning these motion queries with the SLD space, SLD is further promoted to more accurate and coherent motion predictions. Th

出汗

,CLAP: Isolating Content from Style Through Contrastive Learning with Augmented Prompts, to isolate latent content from style features. This enables CLIP-like model’s encoders to concentrate on latent content information, refining the learned representations by pre-trained CLIP-like models. Our extensive experiments across diverse datasets demonstrate significant improvements in zero-s

Chronic

GULLY

JADED

SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries,sible maneuvers while all agents in the scene exhibit reactive and realistic behaviors. Furthermore, we propose novel guidance objectives and a partial diffusion process that enables users to control key aspects of the scenarios, such as the collision type and aggressiveness of the adversarial agent

Desert

,Analysis-by-Synthesis Transformer for Single-View 3D Reconstruction, eliminates the incorrect inductive bias. Experimental results on CUB-200-2011 and ShapeNet datasets demonstrate superior performance in shape reconstruction and texture generation compared to previous methods. The code is available at ..

子女

,Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning,r optimization level to emulate worst-case scenarios, while simultaneously engaging in standard training and unlearning at the lower level, achieving a balance between data influence erasure and model utility. Our proposal offers a worst-case evaluation of MU’s resilience and effectiveness. Through

Resistance