44 citations · 58 across the 12 of their papers we have counts for
12 papers
InterPruner: Interactive Structured Pruning via Taylor-Implicit Criterion and Language-Prior Modulator for Multimodal Object Detection
Qi Ming, Zihan Yang, Shaoguang Huang +6
Multimodal object detection proves effective in remote sensing, especially the RGB-Infrared paradigm. The parallel feature extractors provide rich multimodal information for robust…
Geometry Meets Semantics: Fractional Gradient Stabilization for Semantic-Driven Bounding Box Optimization in Visual Detection Tasks
Qi Ming, Zheng Zhou, Haitian Yang +4
Bounding boxes are fundamental for object localization in visual detection tasks. Among them, oriented bounding boxes are widely used in visual detection tasks, which provide a mor…
Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation
Yanghui Song, Nanqing Liu, Haonan Yin +3
Open-vocabulary semantic segmentation (OVSS) in remote sensing images aims to segment categories beyond a fixed label space. Recent SAM 3-based methods provide a promising training…
Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation
Yitao Zhao, Sen Lei, Nanqing Liu +3
As an essential procedure in earth observation system, change detection (CD) aims to reveal the spatial-temporal evolution of the observation regions. A key prerequisite for existi…
RCCFormer: A Robust Crowd Counting Network Based on Transformer
Peng Liu, Heng-Chao Li, Sen Lei +3
Crowd counting, which is a key computer vision task, has emerged as a fundamental technology in crowd analysis and public safety management. However, challenges such as scale varia…
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
Nanqing Liu, Xun Xu, Yongyi Su +2
Segment Anything Model (SAM) is an advanced foundational model for image segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the domain gap betwe…
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
Yingjie Gao, Yanan Zhang, Ziyue Huang +2
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power…
SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation
Yitao Zhao, Turgay Celik, Nanqing Liu +2
In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent ful…
Toward distortion-aware change detection in realistic scenarios
Yitao Zhao, Heng-Chao Li, Nanqing Liu +1
In the conventional change detection (CD) pipeline, two manually registered and labeled remote sensing datasets serve as the input of the model for training and prediction. However…
CLIP-Guided Source-Free Object Detection in Aerial Images
Nanqing Liu, Xun Xu, Yongyi Su +3
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather…
Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
Nanqing Liu, Xun Xu, Yingjie Gao +1
Annotating remote sensing images (RSIs) presents a notable challenge due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods tackle this issue by generat…
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Nanqing Liu, Xun Xu, Turgay Celik +2
Object detection in remote sensing images relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustiv…