Friday 21 March 2025
Recent advancements in computer vision have led to significant improvements in object detection, a crucial task in many applications such as self-driving cars and medical imaging. However, despite these gains, deep neural networks still struggle with long-tailed datasets, where some classes have significantly fewer instances than others. This imbalance can lead to biased models that favor frequent classes over rare ones.
To address this issue, researchers have proposed various methods aimed at reducing the impact of class imbalance on object detection performance. One such approach is the use of category information amount (CIA), a measure of how difficult it is for a model to learn each category. The idea is to dynamically adjust the decision space of each category based on its CIA, thereby reducing bias towards frequent classes.
The authors of this paper introduce a new loss function called Information Amount-Guided Angular Margin (IGAM) Loss, which incorporates CIA into the training process. IGAM Loss is designed to reduce the gap between the performance of models on frequent and rare classes by adjusting the margin between positive and negative samples based on their CIAs.
The researchers evaluated IGAM Loss on several long-tailed object detection benchmarks, including LVIS v1.0 and COCO- LT. The results show that IGAM Loss significantly improves the performance of baseline models on rare classes, with improvements ranging from 2.5% to 3.3% in terms of average precision.
Furthermore, the authors demonstrated the effectiveness of IGAM Loss on a non-long-tailed dataset, Pascal VOC, where it also achieved significant improvements over the baseline model. This suggests that IGAM Loss is not limited to long-tailed datasets and can be applied more broadly.
The paper’s findings have important implications for the development of object detection models in various applications. By reducing bias towards frequent classes, IGAM Loss can help improve the accuracy and fairness of these models. Additionally, the use of CIA as a measure of learning difficulty has potential applications beyond object detection, such as in natural language processing and reinforcement learning.
Overall, the authors’ work represents an important step forward in addressing class imbalance issues in object detection. By incorporating CIA into the training process, IGAM Loss shows promise for improving model performance on rare classes and reducing bias towards frequent ones.
Cite this article: “Addressing Class Imbalance in Object Detection with Information Amount-Guided Angular Margin Loss”, The Science Archive, 2025.
Computer Vision, Object Detection, Class Imbalance, Deep Neural Networks, Long-Tailed Datasets, Category Information Amount, Igam Loss, Information Amount-Guided Angular Margin Loss, Average Precision, Pascal Voc.
