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

競選運動

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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 ..

拱墻

,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

palette

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

婚姻生活

,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 ..

ORE

,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

懶惰民族

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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

Mumble

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