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20172024
most citedCross-Modality Deep Feature Learning for Brain Tumor Segmentation

290 citations · 719 across the 19 of their papers we have counts for

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22 papers · 1 filter

cs.CV2024

GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time

Hao Li, Yuanyuan Gao, Chenming Wu +7

This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, an…

cs.CV2024

Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure

De Cheng, Yanling Ji, Dong Gong +4

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-worl…

cs.CV2023

VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning

Ziyang Luo, Nian Liu, Wangbo Zhao +5

Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities, sharing commonalities…

cs.CV2023

GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding

Hao Li, Dingwen Zhang, Yalun Dai +5

Applying NeRF to downstream perception tasks for scene understanding and representation is becoming increasingly popular. Most existing methods treat semantic prediction as an addi…

cs.CV20227 cited

Compound Batch Normalization for Long-tailed Image Classification

Lechao Cheng, Chaowei Fang, Dingwen Zhang +2

Significant progress has been made in learning image classification neural networks under long-tail data distribution using robust training algorithms such as data re-sampling, re-…

cs.CV20221 cited

Combating Noisy Labels in Long-Tailed Image Classification

Chaowei Fang, Lechao Cheng, Huiyan Qi +1

Most existing methods that cope with noisy labels usually assume that the class distributions are well balanced, which has insufficient capacity to deal with the practical scenario…