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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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樓主: Interjection
31#
發(fā)表于 2025-3-26 22:28:49 | 只看該作者
NSGANetV2: Evolutionary Multi-objective Surrogate-Assisted Neural Architecture Search,NSGANetV2s improve the state-of-the-art (under mobile setting), suggesting that NAS can be a viable alternative to conventional transfer learning approaches in handling diverse scenarios such as small-scale or fine-grained datasets. Code is available at ..
32#
發(fā)表于 2025-3-27 01:40:57 | 只看該作者
Self6D: Self-supervised Monocular 6D Object Pose Estimation,ometrically optimal alignment. Extensive evaluations demonstrate that our proposed self-supervision is able to significantly enhance the model’s original performance, outperforming all other methods relying on synthetic data or employing elaborate techniques from the domain adaptation realm.
33#
發(fā)表于 2025-3-27 07:45:16 | 只看該作者
34#
發(fā)表于 2025-3-27 10:54:45 | 只看該作者
Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry,ructions from a single input image. We evaluate Ladybird on a large scale 3D dataset (ShapeNet) demonstrating highly competitive results in terms of Chamfer distance, Earth Mover’s distance and Intersection Over Union (IoU).
35#
發(fā)表于 2025-3-27 15:55:48 | 只看該作者
36#
發(fā)表于 2025-3-27 20:02:50 | 只看該作者
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset,r associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask R-CNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. Fashionpedia is available at: ..
37#
發(fā)表于 2025-3-27 23:44:57 | 只看該作者
38#
發(fā)表于 2025-3-28 04:11:48 | 只看該作者
39#
發(fā)表于 2025-3-28 08:15:32 | 只看該作者
https://doi.org/10.1007/978-1-349-13230-0odule, which computes the difference between the synthesized image and the input image. We validate our framework on three challenging datasets and improve the state-of-the-arts by large margins, ., 6% AUPR-Error on Cityscapes, 7% Pearson correlation on pancreatic tumor segmentation in MSD and 20% AUPR on StreetHazards anomaly segmentation.
40#
發(fā)表于 2025-3-28 12:42:15 | 只看該作者
A Model of Rights and Government,e realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.
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