Adaptive Class Suppression Loss for Long-Tail Object Detection (2104.00885v1)

Abstract: To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies. These methods bring the following two problems. One is the training inconsistency between adjacent categories of similar sizes, and the other is that the learned model is lack of discrimination for tail categories which are semantically similar to some of the head categories. In this paper, we devise a novel Adaptive Class Suppression Loss (ACSL) to effectively tackle the above problems and improve the detection performance of tail categories. Specifically, we introduce a statistic-free perspective to analyze the long-tail distribution, breaking the limitation of manual grouping. According to this perspective, our ACSL adjusts the suppression gradients for each sample of each class adaptively, ensuring the training consistency and boosting the discrimination for rare categories. Extensive experiments on long-tail datasets LVIS and Open Images show that the our ACSL achieves 5.18% and 5.2% improvements with ResNet50-FPN, and sets a new state of the art. Code and models are available at https://github.com/CASIA-IVA-Lab/ACSL.

• The paper introduces ACSL, which dynamically adjusts suppression gradients to improve training consistency and enhance rare category discrimination.
• It eliminates manual class grouping by using a statistic-free approach, addressing inconsistencies between adjacent categories.
• Experiments on LVIS and Open Images show ACSL improves mAP by over 5%, demonstrating significant performance gains in long-tail detection.

An Analysis of Adaptive Class Suppression Loss for Long-Tail Object Detection

The paper "Adaptive Class Suppression Loss for Long-Tail Object Detection" introduces a novel approach to address the challenges associated with long-tail object detection tasks. Traditional methods have often relied on manual grouping of classes to manage the imbalance between frequent, common, and rare categories. This method, however, leads to issues such as training inconsistency between adjacent categories and poor discrimination for tail categories that share semantic similarities with head categories.

This paper proposes an Adaptive Class Suppression Loss (ACSL) strategy that moves away from static categorization and instead adapts the suppression gradients for each class sample dynamically. This approach aims to ensure consistent training while enhancing discrimination for less frequent categories.

Key Contributions

1. Statistic-Free Analysis: The ACSL method introduces a statistic-free way of understanding long-tail distributions. By avoiding the necessity of manual category grouping, ACSL addresses the common issue of training inconsistency between similarly sized adjacent categories.
2. Adaptive Gradient Adjustment: The introduction of ACSL allows for adaptive adjustment of suppression gradients for each class based on their learning status throughout the training process, which helps in maintaining a balance between categories and improving the discrimination of rare categories.
3. Experimental Validation: Through extensive experimentation on long-tail datasets such as LVIS and Open Images, the ACSL implementation demonstrated significant improvements. With a ResNet-50-FPN backbone, an improvement of 5.18% and 5.2% in mAP on LVIS and Open Images benchmarks, respectively, was reported, confirming the efficacy of ACSL in setting new performance standards.

Theoretical and Practical Implications

The introduction of a statistic-free perspective to analyze long-tail distributions presents novel implications, both practically and theoretically. Practically, it alleviates the need for manual labor in dividing categories into groups when training on different datasets, thereby reducing the workload and potential for human error in determining optimal divisions.

Theoretically, this method pushes the boundaries of how class imbalances can be tackled by focusing on in-training analytics—namely, network output confidences. By allowing a dynamic, response-driven adjustment to class categorization and suppression during training, ACSL opens new avenues for the application of AI and machine learning approaches to classification problems with skewed data distributions.

Future Developments

The scope for future research utilizing ACSL is vast, particularly in its potential applications to other machine learning domains afflicted by similar class imbalance issues. Further investigation could involve integrating ACSL into varied neural network architectures or exploring its potential alongside different optimization algorithms. The method’s adaptability could indeed catalyze advancements in hierarchical classification systems and contribute significantly to improving detection accuracy in real-world scenarios. Moreover, exploring the potential for ACSL in online learning environments, where data distributions can evolve continuously, represents an exciting frontier.

In conclusion, the ACSL method represents a substantial advancement in addressing long-tail object detection challenges, offering a flexible yet effective solution to enhance classification performance across imbalanced datasets. The results promise enhanced precision and usability in practical AI deployments, mollifying some of the core issues prevalent in traditional class balance maintenance strategies.