负担
发表于 2025-3-26 20:57:17
,LTRL: Boosting Long-Tail Recognition via Reflective Learning, are lightweight enough to plug and play with existing long-tail learning methods, achieving state-of-the-art performance in popular long-tail visual benchmarks. The experimental results highlight the great potential of reflecting learning in dealing with long-tail recognition. The code will be available at ..
AMITY
发表于 2025-3-27 03:24:58
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EWER
发表于 2025-3-27 08:30:53
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Infraction
发表于 2025-3-27 11:02:58
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偏离
发表于 2025-3-27 14:19:21
Analyse und Interpretation der Ergebnisseons and high dynamic range which are well-suited for correspondence tasks such as optical flow and point tracking. However, so far there is still a lack of comprehensive benchmarks for correspondence tasks with both event data and images. To fill this gap, we propose ., a large-scale and diverse ben
GLOOM
发表于 2025-3-27 18:25:07
https://doi.org/10.1007/978-3-642-72495-4 controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to synthesize a broader coverage of anomalies under the manifold and hype
容易生皱纹
发表于 2025-3-28 01:52:32
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BROOK
发表于 2025-3-28 04:50:18
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有节制
发表于 2025-3-28 10:02:14
https://doi.org/10.1007/978-3-642-72495-4ey do not address the issues of sufficient target interaction and efficient parallel processing simultaneously, thereby constraining the learning of dynamic, target-aware features. To tackle these limitations, this paper proposes a spatial-temporal multi-level association framework, which jointly as
CRUE
发表于 2025-3-28 11:03:33
https://doi.org/10.1007/978-3-642-72495-4ate on high-resolution images (.., 8 megapixels) to capture the fine details. However, this comes at the cost of considerable computational complexity, hindering the deployment in latency-sensitive scenarios. In this paper, we introduce ., a novel approach that enhances . predictions with . refineme