很像弓]
发表于 2025-3-30 08:43:58
A Digression on the Four Cost Curvesile recent self-supervised learning methods have achieved good performances with evaluation set on the same domain as the training set, they will have an undesirable performance decrease when tested on a different domain. Therefore, the self-supervised learning from multiple domains task is proposed
储备
发表于 2025-3-30 14:57:53
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环形
发表于 2025-3-30 19:15:46
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假装是你
发表于 2025-3-30 21:28:38
Two Applications of Characteristics Theorysses over time without forgetting pre-trained classes. However, a given model will be challenged by test images with finer-grained classes, e.g., a basenji is at most recognized as a dog. Such images form a new training set (i.e., support set) so that the incremental model is hoped to recognize a ba
Thymus
发表于 2025-3-31 01:32:22
Is Imperfect Competition Empirically Empty?eally, the source and target distributions should be aligned to each other equally to achieve unbiased knowledge transfer. However, due to the significant imbalance between the amount of annotated data in the source and target domains, usually only the target distribution is aligned to the source do
Root494
发表于 2025-3-31 05:59:20
Imperfect Competition After Fifty Yearspace of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to learn what makes a “good” video for action recognition and select only high-quality samples for augmentation. In particular, we choose video compositin
庄严
发表于 2025-3-31 12:05:33
https://doi.org/10.1007/978-1-349-08630-6lex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. In this work, we propose a framework which tackles this problem via joint learning of representations and segmentation. W
整体
发表于 2025-3-31 16:15:11
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高谈阔论
发表于 2025-3-31 18:31:42
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过于平凡
发表于 2025-4-1 00:52:05
Enrique Martínez-García,Jens Søndergaardate-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common “argmax” labeling method often ignores many instances of objects. To alleviate this issue, we propose a novel multiple instance la