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

Eisenhower

書目名稱Computer Vision – ECCV 2024影響因子(影響力)




書目名稱Computer Vision – ECCV 2024影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ECCV 2024被引頻次




書目名稱Computer Vision – ECCV 2024被引頻次學(xué)科排名




書目名稱Computer Vision – ECCV 2024年度引用




書目名稱Computer Vision – ECCV 2024年度引用學(xué)科排名




書目名稱Computer Vision – ECCV 2024讀者反饋




書目名稱Computer Vision – ECCV 2024讀者反饋學(xué)科排名

FRONT

,Flash Cache: Reducing Bias in?Radiance Cache Based Inverse Rendering,pute the color arriving along a ray. Using these representations for more general inverse rendering—reconstructing geometry, materials, and lighting from observed images—is challenging because recursively path-tracing such volumetric representations is expensive. Recent works alleviate this issue th

殺蟲劑

散布

BILK

,AddressCLIP: Empowering Vision-Language Models for?City-Wide Image Address Localization,s where an image was taken. Existing two-stage approaches involve predicting geographical coordinates and converting them?into human-readable addresses, which can lead to ambiguity and?be resource-intensive. In contrast, we propose an end-to-end framework named . to solve the problem with more seman

令人悲傷

RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classificion, and very limited efforts?have been devoted for rotation invariant property. Several recent studies achieve rotation invariance at the cost of lower accuracies. In?this work, we close this gap by proposing a novel yet effective rotation invariant architecture for 3D point cloud classification?an

令人悲傷

Saline

,Bidirectional Uncertainty-Based Active Learning for?Open-Set Annotation,es data from both known and unknown classes. Traditional methods prioritize selecting informative examples with low confidence, with the risk of mistakenly selecting unknown-class examples with similarly low confidence. Recent methods favor the most probable known-class examples, with the risk?of pi

飛行員

Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspectiion problem. Among various AT methods,?fast AT (FAT), which employs a single-step attack strategy to guide?the training process, can achieve good robustness against adversarial attacks at a low cost. However, FAT methods suffer from?the catastrophic overfitting problem, especially on complex tasks?o

grieve

,Projecting Points to?Axes: Oriented Object Detection via?Point-Axis Representation,?and geometrically intuitive nature with two key components: points?and axes. 1) . delineate the spatial extent and contours of objects, providing detailed shape descriptions. 2) . define the primary directionalities of objects, providing essential orientation cues crucial for precise detection. The