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

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,Event-Aided Time-to-Collision Estimation for Autonomous Driving,linear optimization problem. Experiments on both synthetic and real data demonstrate the effectiveness of the proposed method, outperforming other alternative methods in terms of efficiency and accuracy. Dataset used in this paper can be found at ..

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,The Devil Is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable S statistics-aggregated branch for domain-invariant feature learning. Furthermore, to simulate unseen domains with statistics difference, we approach this from two aspects, ., a perturbation with histogram matching at image level and a random batch normalization selection strategy at feature level, p

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VEON: Vocabulary-Enhanced Occupancy Prediction,ong-tail problem. To address these issues, we propose VEON for .ocabulary-.nhanced .ccupancy predictio. by not only assembling but also adapting these foundation models. We first equip MiDaS with a Zoedepth head and low-rank adaptation (LoRA) for relative-metric-bin depth transformation while reserv

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,Adapt Without Forgetting: Distill Proximity from Dual Teachers in Vision-Language Models, sample. Experimental results demonstrate a considerable improvement over existing methodologies, which illustrate the effectiveness of the proposed method in the field of continual learning. Code is available at ..

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,The Sky’s the Limit: Relightable Outdoor Scenes via a Sky-Pixel Constrained Illumination Prior and g gradients from appearance loss to flow from shadows to influence the estimation of illumination and geometry. Our method estimates high-quality albedo, geometry, illumination and sky visibility, achieving state-of-the-art results on the NeRF-OSR relighting benchmark. Our code and models can be fou

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,Hetecooper: Feature Collaboration Graph for Heterogeneous Collaborative Perception,sformer is designed to transfer feature messages in the feature collaboration graph. Firstly, the number of node channels and the semantic space are unified by the semantic mapper. Then, the feature information is aggregated by the edge weight guided attention, and finally the fusion of heterogeneou

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Learning-based Axial Video Motion Magnification,specific axes are critical, by providing simplified and easily readable motion information. To achieve this, we propose a novel Motion Separation Module that enables the disentangling and magnifying of motion representation along axes of interest. Furthermore, we build a new synthetic training datas