26 citations · 32 across the 5 of their papers we have counts for
5 papers
AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing
Hanbo Bi, Yingchao Feng, Yongqiang Mao +4
Few-shot Segmentation (FSS) aims to segment the interested objects in the query image with just a handful of labeled samples (i.e., support images). Previous schemes would leverage…
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation
Hanbo Bi, Yingchao Feng, Wenhui Diao +5
For more efficient generalization to unseen domains (classes), most Few-shot Segmentation (FSS) would directly exploit pre-trained encoders and only fine-tune the decoder, especial…
SCLNet: A Scale-Robust Complementary Learning Network for Object Detection in UAV Images
Xuexue Li
Most recent UAV (Unmanned Aerial Vehicle) detectors focus primarily on general challenge such as uneven distribution and occlusion. However, the neglect of scale challenges, which…
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection
Xiaonan Lu, Wenhui Diao, Yongqiang Mao +4
Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learn…
Learning to Evaluate Performance of Multi-modal Semantic Localization
Zhiqiang Yuan, Wenkai Zhang, Chongyang Li +6
Semantic localization (SeLo) refers to the task of obtaining the most relevant locations in large-scale remote sensing (RS) images using semantic information such as text. As an em…