Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
By Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, Jian‐Yuan Sun. 2021
365 citations recorded by the source corpus.
Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition. Specifically, we develop an adaptive calibration function that enables us to adjust the classification scores for each data point. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior, which provides a flexible and unified solution to diverse scenarios in visual recognition tasks. We validate our method by extensive experiments on four tasks, including image classification, semantic segmentation, object detection, and instance segmentation. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ishan Misra
- Rohit Girdhar
- Armand Joulin
- Andrew D. Bagdanov
- Xialei Liu
- Ke Li
- Mingg-Ming Cheng
- Xingyi Zhou
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Dacheng Tao
- Cordelia Schmid
- Trevor Darrell
- Andrea Vedaldi
- Andrew Zisserman
- Xiaogang Wang
Citations in the corpus
- Long-Tailed Class Incremental Learning (2022, methodology): “Two-stage methods have shown state-of-the-art performance in long-tailed recognition [8,16,47,50].”
- An EM Framework for Online Incremental Learning of Semantic Segmentation (2021, neutral): “It is worth noting that we are the first to adopt cosine normalization in the online class incremental learning problem, even though it has been used in other problems [22, 26, 35].”
- Detecting Twenty-thousand Classes using Image-level Supervision (2022, background): “Many existing works focus on handling the long-tail problem [6, 13, 27, 34, 59, 68].”