108 citations · 143 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
Towards Frame Rate Agnostic Multi-Object Tracking
Weitao Feng, Lei Bai, Yongqiang Yao +2
Multi-Object Tracking (MOT) is one of the most fundamental computer vision tasks that contributes to various video analysis applications. Despite the recent promising progress, cur…
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…
Cross-dataset Training for Class Increasing Object Detection
Yongqiang Yao, Yan Wang, Yu Guo +3
We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for differen…
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
Shifeng Zhang, Cheng Chi, Yongqiang Yao +2
Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In t…