Distribution Alignment: A Unified Framework for Long-tail Visual Recognition

Abstract

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.

Publication
In Conference on Computer Vision and Pattern Recognition 2021
Songyang Zhang
Songyang Zhang
PhD Student

My research interests include few/low-shot learning, graph neural networks and video understanding.

Shipeng Yan
Shipeng Yan
PhD Student

My research interests include few/low-shot learning, incremental learning and representation learning.

Xuming He
Xuming He
Associate Professor

My research interests include few/low-shot learning, graph neural networks and video understanding.

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