Equalization Loss for Long-Tailed Object Recognition (2003.05176v2)

Abstract: Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS. In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https: //github.com/tztztztztz/eql.detectron2

• The paper introduces Equalization Loss to balance gradients in long-tailed object detection, effectively enhancing rare category accuracy.
• It modifies traditional cross-entropy loss by selectively ignoring negative gradients from frequent categories with a frequency threshold mechanism.
• Empirical results on LVIS demonstrate notable AP gains—4.1% for rare and 4.8% for common categories—outperforming existing sampling and loss methods.

Overview of "Equalization Loss for Long-Tailed Object Recognition"

The paper "Equalization Loss for Long-Tailed Object Recognition" addresses a critical issue in the domain of object detection, particularly with datasets that have a long-tailed distribution of categories. The authors propose a novel loss function called the "Equalization Loss" (EQL) aimed at enhancing the performance of object detection models on rare categories in such datasets. The problem they tackle is that traditional training paradigms, using standard classification loss functions, tend to overwhelm the learning of rare categories due to the abundance of negative samples from frequent categories.

Problem Context and Existing Approaches

The problem of long-tailed distributions is prevalent in large vocabulary datasets such as LVIS, where a small subset of categories have many annotations while the majority have very few. Existing solutions typically revolve around re-sampling techniques or specialized re-weighting methods to address sample imbalance. However, these methods often do not adequately differentiate between the imbalances within positive samples of infrequent categories and negative samples of other categories.

Proposed Solution: Equalization Loss

The fundamental insight behind EQL is that each positive sample for a category acts as a negative sample for all other categories, leading to discouraging gradients for these other categories. This results in a bias against less represented categories in favor of the abundant ones. The proposed EQL mitigates this issue by introducing a mechanism that ignores the gradients from negative samples of frequent categories when updating the parameters for rare categories.

The EQL modifies the traditional cross-entropy loss by incorporating a weight term that selectively ignores discouraging gradients based on category frequency. The balance is controlled by defining a frequency threshold, 𝜆, and a threshold function $T_𝜆(f)$, which helps adjust the impact on the rare categories.

Experimental Results

The paper provides extensive empirical evidence to support the efficacy of EQL. It demonstrates remarkable improvements in Average Precision (AP) for rare and common categories, with AP gains of 4.1% and 4.8% on the LVIS dataset compared to the baseline Mask R-CNN. The superiority of EQL is further established across various architectures and frameworks, consistently boosting the performance on under-represented categories.

Comparisons and Analysis

The comparison with other techniques like class-aware sampling and focal loss shows EQL's advantage in preserving frequent category performance while significantly enhancing rare category recognition. Through elaborative ablation studies, the paper also sheds light on the impact of different components and hyperparameters within their proposed methodology, ensuring a robust understanding of EQL's functioning.

Implications and Future Prospects

The implications of this research are significant in both practical applications and theoretical exploration. From an application standpoint, using EQL can improve detection systems' accuracy in scenarios where encountering a wide variety of categories, including several rare ones, is necessary, such as wildlife monitoring or medical imaging. Theoretically, the approach highlights the importance of handling inter-class sample competition in deep learning systems effectively. Future work could further explore adaptive mechanisms for balancing gradients dynamically, refining frequency threshold selections, or extending these insights to other tasks such as image segmentation or LLMs.

In conclusion, the introduction of Equalization Loss marks a substantial methodological advancement in tackling the challenges associated with long-tailed object recognition. It provides a nuanced understanding and approach towards handling skewed category distributions within large-scale, diverse datasets.