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

Cytology

挑剔为人

,NeuroNCAP: Photorealistic Closed-Loop Safety Testing for Autonomous Driving,p evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this work, we use our simulator to test the responses of AD models to safety-critical scenarios i

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,OLAF: A Plug-and-Play Framework for Enhanced Multi-object Multi-part Scene Parsing,ts. To address the task, we propose a plug-and-play approach termed OLAF. First, we augment the input (RGB) with channels containing object-based structural cues (fg/bg mask, boundary edge mask). We propose a weight adaptation technique which enables regular (RGB) pre-trained models to process the a

老人病学

,Progressive Pretext Task Learning for Human Trajectory Prediction,ges from short-term to long-term within a trajectory. However, existing works attempt to address the entire trajectory prediction with a singular, uniform training paradigm, neglecting the distinction between short-term and long-term dynamics in human trajectories. To overcome this limitation, we in

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myocardium

平淡而无味

,Attention Prompting on Image for Large Vision-Language Models,rgent capabilities and demonstrating impressive performance on various vision-language tasks. Motivated by text prompting in LLMs, visual prompting has been explored to enhance LVLMs’ capabilities of perceiving visual information. However, previous visual prompting techniques solely process visual i

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,Learning Cross-Hand Policies of High-DOF Reaching and Grasping,eused on another gripper. In this paper, we propose a novel method that can learn a unified policy model that can be easily transferred to different dexterous grippers. Our method consists of two stages: a gripper-agnostic policy model that predicts the displacements of pre-defined key points on the

shrill

,Reprojection Errors as Prompts for Efficient Scene Coordinate Regression,r, many existing SCR approaches train on samples from all image regions, including dynamic objects and texture-less areas. Utilizing these areas for optimization during training can potentially hamper the overall performance and efficiency of the model. In this study, we first perform an in-depth an

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