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

Eisenhower

书目名称Computer Vision – ECCV 2024影响因子(影响力)




书目名称Computer Vision – ECCV 2024影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2024网络公开度




书目名称Computer Vision – ECCV 2024网络公开度学科排名




书目名称Computer Vision – ECCV 2024被引频次




书目名称Computer Vision – ECCV 2024被引频次学科排名




书目名称Computer Vision – ECCV 2024年度引用




书目名称Computer Vision – ECCV 2024年度引用学科排名




书目名称Computer Vision – ECCV 2024读者反馈




书目名称Computer Vision – ECCV 2024读者反馈学科排名

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

杀虫剂

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

令人悲伤

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

令人悲伤

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

飞行员

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