637 citations · 855 across the 12 of their papers we have counts for
13 papers
Square Superpixel Generation and Representation Learning via Granular Ball Computing
Shuyin Xia, Meng Yang, Dawei Dai +6
Superpixels provide a compact region-based representation that preserves object boundaries and local structures, and have therefore been widely used in a variety of vision tasks to…
Multi-State Tracker: Enhancing Efficient Object Tracking via Multi-State Specialization and Interaction
Shilei Wang, Gong Cheng, Pujian Lai +2
Efficient trackers achieve faster runtime by reducing computational complexity and model parameters. However, this efficiency often compromises the expense of weakened feature repr…
Auto-selected Knowledge Adapters for Lifelong Person Re-identification
Xuelin Qian, Ruiqi Wu, Gong Cheng +1
Lifelong Person Re-Identification (LReID) extends traditional ReID by requiring systems to continually learn from non-overlapping datasets across different times and locations, ada…
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning
Xiang Yuan, Gong Cheng, Kebing Yan +2
The past few years have witnessed the immense success of object detection, while current excellent detectors struggle on tackling size-limited instances. Concretely, the well-known…
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Long Li, Junwei Han, Ni Zhang +5
Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring explicit exploration of…
Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images
Xuxiang Sun, Gong Cheng, Lei Pei +2
Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attenti…
Fewer is More: Efficient Object Detection in Large Aerial Images
Xingxing Xie, Gong Cheng, Qingyang Li +3
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no…
Progressively Dual Prior Guided Few-shot Semantic Segmentation
Qinglong Cao, Yuntian Chen, Xiwen Yao +1
Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples. Currently, few-shot segmentation methods mainly focus on l…
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
Yuanwei Liu, Nian Liu, Xiwen Yao +1
Few-shot semantic segmentation aims to segment the target objects in query under the condition of a few annotated support images. Most previous works strive to mine more effective…
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Gong Cheng, Xiang Yuan, Xiwen Yao +4
With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactor…
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation
Chunbo Lang, Binfei Tu, Gong Cheng +1
Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community. Existing ap…
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Yuanwei Liu, Nian Liu, Qinglong Cao +3
Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target r…
Learning What Not to Segment: A New Perspective on Few-Shot Segmentation
Chunbo Lang, Gong Cheng, Binfei Tu +1
Recently few-shot segmentation (FSS) has been extensively developed. Most previous works strive to achieve generalization through the meta-learning framework derived from classific…