Real-world imagery is often characterized by a significant imbalance of the
number of images per class, leading to long-tailed distributions. An effective
and simple approach to long-tailed visual recognition is to learn feature
representations and a classifier separately, with instance and class-balanced
sampling, respectively. In this work, we introduce a new framework, by making
the key observation that a feature representation learned with instance
sampling is far from optimal in a long-tailed setting. Our main contribution is
a new training method, referred to as Class-Balanced Distillation (CBD), that
leverages knowledge distillation to enhance feature representations. CBD allows
the feature representation to evolve in the second training stage, guided by
the teacher learned in the first stage. The second stage uses class-balanced
sampling, in order to focus on under-represented classes. This framework can
naturally accommodate the usage of multiple teachers, unlocking the information
from an ensemble of models to enhance recognition capabilities. Our experiments
show that the proposed technique consistently outperforms the state of the art
on long-tailed recognition benchmarks such as ImageNet-LT, iNaturalist17 and
iNaturalist18.

提出了一种新的框架 CBD，利用知识蒸馏增强特征表示，通过使用类平衡采样，第二阶段的训练使得特征表示能够在少数类上进化，可以自然的适用多个 Teachers，并在 ImageNet-LT，iNaturalist17 和 iNaturalist18 数据集上取得比现有技术更好的性能。

类平衡蒸馏在长尾视觉识别中的应用

Class-Balanced Distillation for Long-Tailed Visual Recognition

The current evaluation protocol of long-tailed visual recognition trains the
classification model on the long-tailed source label distribution and evaluates
its performance on the uniform target label distribution. Such protocol has
questionable practicality since the target may also be long-tailed. Therefore,
we formulate long-tailed visual recognition as a label shift problem where the
target and source label distributions are different. One of the significant
hurdles in dealing with the label shift problem is the entanglement between the
source label distribution and the model prediction. In this paper, we focus on
disentangling the source label distribution from the model prediction. We first
introduce a simple but overlooked baseline method that matches the target label
distribution by post-processing the model prediction trained by the
cross-entropy loss and the Softmax function. Although this method surpasses
state-of-the-art methods on benchmark datasets, it can be further improved by
directly disentangling the source label distribution from the model prediction
in the training phase. Thus, we propose a novel method, LAbel distribution
DisEntangling (LADE) loss based on the optimal bound of Donsker-Varadhan
representation. LADE achieves state-of-the-art performance on benchmark
datasets such as CIFAR-100-LT, Places-LT, ImageNet-LT, and iNaturalist 2018.
Moreover, LADE outperforms existing methods on various shifted target label
distributions, showing the general adaptability of our proposed method.

该研究论文提出了一种基于标签分布解缠模型预测的方法 (LADE) 用于解决长尾视觉识别的问题，其在多个基准数据集上表现优于现有方法，具有很好的实用价值。

长尾视觉识别中的标签分布解耦

Disentangling Label Distribution for Long-tailed Visual Recognition

Human-object interaction (HOI) detection is a core task in computer vision.
The goal is to localize all human-object pairs and recognize their
interactions. An interaction defined by a <verb, noun> tuple leads to a
long-tailed visual recognition challenge since many combinations are rarely
represented. The performance of the proposed models is limited especially for
the tail categories, but little has been done to understand the reason. To that
end, in this paper, we propose to diagnose rarity in HOI detection. We propose
a three-step strategy, namely Detection, Identification and Recognition where
we carefully analyse the limiting factors by studying state-of-the-art models.
Our findings indicate that detection and identification steps are altered by
the interaction signals like occlusion and relative location, as a result
limiting the recognition accuracy.

本文主要探讨计算机视觉中的人 - 物体交互检测任务（HOI detection）, 针对该任务的长尾可视化识别困境，对现有模型进行检测、识别和识别三步分析，研究发现交互信号（如遮挡和相对位置）会干扰检测和识别步骤，从而降低了识别准确性