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of 19
pro vyhledávání: '"Jiang, XiRuo"'
Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature predominantly focuses on pair-based and proxy-based methods to maximize i
Externí odkaz:
http://arxiv.org/abs/2407.03106
Though adversarial erasing has prevailed in weakly supervised semantic segmentation to help activate integral object regions, existing approaches still suffer from the dilemma of under-activation and over-expansion due to the difficulty in determinin
Externí odkaz:
http://arxiv.org/abs/2407.02768
Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream approaches typically utilize multi-stage inferences. Despite their rema
Externí odkaz:
http://arxiv.org/abs/2405.15465
Matching visible and near-infrared (NIR) images remains a significant challenge in remote sensing image fusion. The nonlinear radiometric differences between heterogeneous remote sensing images make the image matching task even more difficult. Deep l
Externí odkaz:
http://arxiv.org/abs/2404.19311
Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitate the incorporation of additional hyperparameters, notably the threshold,
Externí odkaz:
http://arxiv.org/abs/2404.19282
Recently, the advancement of self-supervised learning techniques, like masked autoencoders (MAE), has greatly influenced visual representation learning for images and videos. Nevertheless, it is worth noting that the predominant approaches in existin
Externí odkaz:
http://arxiv.org/abs/2402.19082
Few-shot video object segmentation (FSVOS) aims to segment dynamic objects of unseen classes by resorting to a small set of support images that contain pixel-level object annotations. Existing methods have demonstrated that the domain agent-based att
Externí odkaz:
http://arxiv.org/abs/2307.07933
Publikováno v:
In Information Fusion September 2024 109
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