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

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Ursus-Nikolaus Riede,Martin Wernere that allows us to handle various generation tasks with varying degrees of conditioning with a single model. Empirically, LayoutFlow performs on par with state-of-the-art models while being significantly faster. The project page, including our code, can be found at ..

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,Asynchronous Large Language Model Enhanced Planner for?Autonomous Driving, avenues for enhancing the interpretability and controllability of motion planning. Nevertheless, LLM-based planners continue to encounter significant challenges, including elevated resource consumption and extended inference times, which pose substantial obstacles to practical deployment. In light

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,Make a?Cheap Scaling: A Self-Cascade Diffusion Model for?Higher-Resolution Adaptation,jects when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models to higher resolution demands substantial computational and optimization resources, yet achieving generation capabilities comparable to low-resolution models remains challengin

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,Making Large Language Models Better Planners with?Reasoning-Decision Alignment,lity. Inspired by the knowledge-driven nature of human driving, recent approaches explore the potential of large language models (LLMs) to improve understanding and decision-making in traffic scenarios. They find that the pretrain-finetune paradigm of LLMs on downstream data with the Chain-of-Though

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,Representation Enhancement-Stabilization: Reducing Bias-Variance of?Domain Generalization,t domains. This paper explores DG through the lens of bias-variance decomposition, uncovering that test errors in DG predominantly arise from cross-domain bias and variance. Inspired by this insight, we introduce a Representation Enhancement-Stabilization (RES) framework, comprising a Representation

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