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20212023
most citedIterative Few-shot Semantic Segmentation from Image Label Text

18 citations · 26 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2023

Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection

Supeng Wang, Yuxi Li, Ming Xie +4

Change detection is a widely adopted technique in remote sense imagery (RSI) analysis in the discovery of long-term geomorphic evolution. To highlight the areas of semantic changes…

cs.CV2023

Dual Path Transformer with Partition Attention

Zhengkai Jiang, Liang Liu, Jiangning Zhang +3

This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective. The dual attention mechanism consists of two parallel components: l…

cs.CV20231 cited

Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution

Xuhai Chen, Jiangning Zhang, Chao Xu +3

Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-v…

cs.CV20231 cited

MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection

Liang Liu, Boshen Zhang, Jiangning Zhang +6

Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particu…

cs.CV2023

Calibrated Teacher for Sparsely Annotated Object Detection

Haohan Wang, Liang Liu, Boshen Zhang +6

Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidab…

cs.CV202318 cited

Iterative Few-shot Semantic Segmentation from Image Label Text

Haohan Wang, Liang Liu, Wuhao Zhang +5

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of…