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20242026
most citedPoint2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances

1 citations · 2 across the 7 of their papers we have counts for

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cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV20261 cited

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…

cs.CV2025

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…

cs.CV2025

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…