Limited
发表于 2025-3-23 11:16:00
Unsupervised Scene Sketch to Photo Synthesiscs and human perceptual studies show the proposed method could generate realistic photos with high fidelity from scene sketches and outperform state-of-the-art photo synthesis baselines. We also demonstrate that our framework facilitates a controllable manipulation of photo synthesis by editing stro
exclamation
发表于 2025-3-23 14:44:45
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FICE
发表于 2025-3-23 18:42:05
Towards Integral Consciousness, each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the cl
Kinetic
发表于 2025-3-24 00:51:28
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可卡
发表于 2025-3-24 04:33:49
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聋子
发表于 2025-3-24 09:40:43
https://doi.org/10.1007/978-94-009-5761-9ive field overlap with ground- truth bounding boxes. In the target domain, where no labels are available, we estimate this distribution using predicted bounding boxes and thus get the estimated class label shift between domains. This estimated shift is further used to re-weight source local features
KEGEL
发表于 2025-3-24 13:10:21
Guofeng Zhang,Xiaojing Ma,Huimin Lintified in- and out-of-class unlabeled data, respectively. Our extensive experimental results show the effectiveness of OpenCoS under the presence of out-of-class samples, fixing up the state-of-the-art semi-supervised methods to be suitable for diverse scenarios involving open-set unlabeled data.
惊呼
发表于 2025-3-24 18:43:46
Guofeng Zhang,Xiaojing Ma,Huimin Liels are gradually injected into the labeled target dataset over the course of training. Specifically, we use a temperature scaled cosine similarity measure to assign a soft pseudo-label to the unlabeled target samples. Additionally, we compute an exponential moving average of the soft pseudo-labels
乏味
发表于 2025-3-24 19:54:24
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Hamper
发表于 2025-3-24 23:24:25
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