24 citations · 63 across the 11 of their papers we have counts for
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cs.CV2022★ 1 cited
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning
Bo Li, Yongqiang Yao, Jingru Tan +4
Data in real-world object detection often exhibits the long-tailed distribution. Existing solutions tackle this problem by mitigating the competition between the head and tail cate…
cs.CV2022★ 2 cited
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition
Jingru Tan, Bo Li, Xin Lu +4
Long-tail distribution is widely spread in real-world applications. Due to the extremely small ratio of instances, tail categories often show inferior accuracy. In this paper, we f…
cs.CV2022★ 4 cited
Equalized Focal Loss for Dense Long-Tailed Object Detection
Bo Li, Yongqiang Yao, Jingru Tan +4
Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors…