119 citations · 123 across the 3 of their papers we have counts for
3 papers
cs.CV2026
Does YOLO Really Need to See Every Training Image in Every Epoch?
Xingxing Xie, Jiahua Dong, Junwei Han +1
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image…
cs.CV2022★ 119 cited
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…
cs.CV2021★ 4 cited
Oriented R-CNN for Object Detection
Xingxing Xie, Gong Cheng, Jiabao Wang +2
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes. This diminishes the detectors' speed, thereby becoming the computational bo…