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20152025
most citedVisual Saliency Based on Multiscale Deep Features

259 citations · 567 across the 35 of their papers we have counts for

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50 papers · 1 filter

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.CV202229 cited

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

Ziyi Zhang, Weikai Chen, Hui Cheng +4

We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to…

cs.CV202216 cited

View-Disentangled Transformer for Brain Lesion Detection

Haofeng Li, Junjia Huang, Guanbin Li +5

Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation. However, locating small lesions in 2D MRI slices is challenging, and requires to ba…

cs.CV20228 cited

Open Set Domain Adaptation By Novel Class Discovery

Jingyu Zhuang, Ziliang Chen, Pengxu Wei +2

In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain. Due to the lack of their specif…

cs.CV20221 cited

Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning

Pengxiang Yan, Ziyi Wu, Mengmeng Liu +3

Recent advances in deep learning significantly boost the performance of salient object detection (SOD) at the expense of labeling larger-scale per-pixel annotations. To relieve the…

cs.CV20208 cited

Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting

Lingbo Liu, Jiaqi Chen, Hefeng Wu +3

Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited…