Decoupling Representation and Classifier for Long-Tailed Recognition 图像领域长尾分布分类问题方法

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Introduction

When learning with long-tailed data, a common challenge is that instance-rich (or head) classes dominate the training procedure. The learned classification model tends to perform better on these classes, while performance is significantly worse for instance-scarce (or tail) classes (under-fitting).

The general scheme for long-tailed recognition is: classifiers are either learned jointly with the representations end-to-end, or via a two-stage approach where the classifier and the representation are jointly fine-tuned with variants of class-balanced sampling as a second stage.

In our work, we argue for decoupling representation and classification. We demonstrate that in a long-tailed scenario, this separation allows straightforward approaches to achieve high recognition performance, without the need for designing sampling strategies, balance-aware losses or adding memory modules.

Recent Directions

Recent studies’ directions on solving long-tailed recognition problem:

• Data distribution re-balancing. Re-sample the dataset to achieve a more balanced data distribution. These methods include over-sampling, down-sampling and class-balanced sampling.
• Class-balanced Losses. Assign different losses to different training samples for each class.
• Transfer learning from head- to tail classes. Transfer features learned from head classes with abundant training instances to under-represented tail classes. However it is usually a non-trivial task to design specific modules for feature transfer.

Sampling Strategies

For most sampling strategies presented below, the probability $p_j$ of sampling a data point from class $j$ is given by: $$p_j=\frac{n_j^q}{{\sum }_{i=1}^C{n}_i^q}$$ where $q\in [0,1]$, $n_j$ denote the number of training sample for class j j