5 citations · 7 across the 13 of their papers we have counts for
13 papers
T-STAR: A Large-Scale Benchmark for Spatio-Temporal Panoptic Scene Graph Generation in Satellite Video
Linlin Wang, Xue Yang, Zhihuang Zhou +3
Structured understanding of satellite video is essential for advancing dynamic geospatial scene analysis from low-level perception to high-level cognition. To move beyond object-ce…
AeroDeshadow: Physics-Guided Shadow Synthesis and Penumbra-Aware Deshadowing for Aerospace Imagery
Wei Lu, Zi-Yang Bo, Fei-Fei Sang +3
Shadows are prevalent in high-resolution aerospace imagery (ASI). They often cause spectral distortion and information loss, which degrade downstream interpretation tasks. While de…
PIEDet: Prototype-Driven Intrinsically Explainable Object Detection
Jianlin Xiang, Linhui Dai, Xue Yang +2
Existing object detectors typically make predictions in a black-box manner and struggle to simultaneously provide discriminative evidence for their predictions, which limits their…
SAPNet++: Evolving Point-Prompted Instance Segmentation with Semantic and Spatial Awareness
Zhaoyang Wei, Xumeng Han, Xuehui Yu +4
Single-point annotation is increasingly prominent in visual tasks for labeling cost reduction. However, it challenges tasks requiring high precision, such as the point-prompted ins…
Partial Weakly-Supervised Oriented Object Detection
Mingxin Liu, Peiyuan Zhang, Yuan Liu +8
The growing demand for oriented object detection (OOD) across various domains has driven significant research in this area. However, the high cost of dataset annotation remains a m…
When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning
Junwei Luo, Yingying Zhang, Xue Yang +5
Efficient vision-language understanding of large Remote Sensing Images (RSIs) is meaningful but challenging. Current Large Vision-Language Models (LVLMs) typically employ limited p…
Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection
Yi Yu, Xue Yang, Yansheng Li +3
Accurately estimating the orientation of visual objects with compact rotated bounding boxes (RBoxes) has become a prominent demand, which challenges existing object detection parad…
Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances
Yi Yu, Botao Ren, Peiyuan Zhang +6
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained gre…
PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection
Peiyuan Zhang, Junwei Luo, Xue Yang +9
With the growing demand for oriented object detection (OOD), recent studies on point-supervised OOD have attracted significant interest. In this paper, we propose PointOBB-v3, a st…
Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation
Yan Li, Weiwei Guo, Xue Yang +5
In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited to detecting a set of pre-…
PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection
Botao Ren, Xue Yang, Yi Yu +2
Single point supervised oriented object detection has gained attention and made initial progress within the community. Diverse from those approaches relying on one-shot samples or…
STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery
Yansheng Li, Linlin Wang, Tingzhu Wang +11
Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variati…
Context-Aware Aerial Object Detection: Leveraging Inter-Object and Background Relationships
Botao Ren, Botian Xu, Xue Yang +3
In most modern object detection pipelines, the detection proposals are processed independently given the feature map. Therefore, they overlook the underlying relationships between…