24 citations · 55 across the 7 of their papers we have counts for
7 papers
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…
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
Gang Zhang, Xin Lu, Jingru Tan +4
The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsa…
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection
Jingru Tan, Xin Lu, Gang Zhang +2
Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimiz…
1st Place Solution of LVIS Challenge 2020: A Good Box is not a Guarantee of a Good Mask
Jingru Tan, Gang Zhang, Hanming Deng +4
This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020. In this work, two characteristics of LVIS dataset are mainly considered: the long-tailed dis…
Equalization Loss for Long-Tailed Object Recognition
Jingru Tan, Changbao Wang, Buyu Li +4
Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on larg…