51 citations · 87 across the 11 of their papers we have counts for
12 papers
Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
Qirui Wu, Shizhou Zhang, De Cheng +4
Incremental Object Detection (IOD) aims to continuously learn new object classes without forgetting previously learned ones. A persistent challenge is catastrophic forgetting, prim…
Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL
Shibin Su, Guoqiang Liang, De Cheng +2
Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams can be seen only once, it is more suit…
Flow-CDNet: A Novel Network for Detecting Both Slow and Fast Changes in Bitemporal Images
Haoxuan Li, Chenxu Wei, Haodong Wang +9
Change detection typically involves identifying regions with changes between bitemporal images taken at the same location. Besides significant changes, slow changes in bitemporal i…
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation
Lingyan Ran, Yali Li, Tao Zhuo +2
In semi-supervised semantic segmentation (SSSS), data augmentation plays a crucial role in the weak-to-strong consistency regularization framework, as it enhances diversity and imp…
DiffV2IR: Visible-to-Infrared Diffusion Model via Vision-Language Understanding
Lingyan Ran, Lidong Wang, Guangcong Wang +2
The task of translating visible-to-infrared images (V2IR) is inherently challenging due to three main obstacles: 1) achieving semantic-aware translation, 2) managing the diverse wa…
AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluation
Ran Lingyan, Wen Dongcheng, Zhuo Tao +3
Change Detection (CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bi-temporal image pairs captured at varying intervals of the s…
Frequency-Guided Spatial Adaptation for Camouflaged Object Detection
Shizhou Zhang, Dexuan Kong, Yinghui Xing +5
Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that e…
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach
Shizhou Zhang, Wenlong Luo, De Cheng +4
In this paper, we construct a large-scale benchmark dataset for Ground-to-Aerial Video-based person Re-Identification, named G2A-VReID, which comprises 185,907 images and 5,576 tra…
DDF: A Novel Dual-Domain Image Fusion Strategy for Remote Sensing Image Semantic Segmentation with Unsupervised Domain Adaptation
Lingyan Ran, Lushuang Wang, Tao Zhuo +1
Semantic segmentation of remote sensing images is a challenging and hot issue due to the large amount of unlabeled data. Unsupervised domain adaptation (UDA) has proven to be advan…
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
Lingyan Ran, Yali Li, Guoqiang Liang +1
Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. However, supervised deep…
Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network
Xinting Li, Shiguang Zhang, Yue LU +2
This paper investigates the zero-shot object goal visual navigation problem. In the object goal visual navigation task, the agent needs to locate navigation targets from its egocen…
Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection
Yinghui Xing, Dexuan Kong, Shizhou Zhang +4
Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicat…