Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

負(fù)擔(dān)

,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

EWER

Infraction

偏離

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

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

容易生皺紋

BROOK

有節(jié)制

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

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