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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Pamela B. Hoffman,Leslie S. Libowlinear 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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重畫只能放棄

https://doi.org/10.1007/978-1-0716-2655-9ong-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

危險

https://doi.org/10.1007/978-1-0716-2655-9 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 ..

平庸的人或物

Jessica E. Young,Lawrence S. B. Goldsteing 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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Jessica E. Young,Lawrence S. B. Goldsteinining consistency between?live and spoof face identities, which can also alleviate the scarcity?of labeled data with novel type attacks faced by nowadays FAS system. We demonstrate the effectiveness of our framework on challenging cross-domain and cross-attack FAS datasets, achieving?the state-of-th

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召集

https://doi.org/10.1007/978-1-59259-661-4specific 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

NOMAD

Ramon Diaz-Arrastia,Fred Baskinw that it actually outperforms most of the previous SFOD methods. Additionally, we showcase that an even simpler strategy consisting in training on a fixed set of pseudo-labels can achieve similar performance to the more complex teacher-student mutual learning, while being computationally efficient

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