Adaptive effective class suppression loss for long-tailed object detection

Ying Tao; Tianran Hao; Peng Dong; Jialu Xing

doi:10.1117/12.3008205

14 November 2023 Adaptive effective class suppression loss for long-tailed object detection

Ying Tao, Tianran Hao, Peng Dong, Jialu Xing

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341Z (2023) https://doi.org/10.1117/12.3008205
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Object detection is an important research in the computer vision area, the mainstream object detection tasks are generally performed on class-balanced datasets, and have made great progress. However, the data in real scenarios are usually presented as long-tailed distributions, there is an imbalance between the number of classes samples, which causes a significant decrease in the performance of object detection. Most of the current long-tailed object detection algorithms enhance the performance of the tail classes at the expense of the accuracy of the head and common classes. In this paper, we adopt Adaptive Effective Class Suppression Loss (AECSL) to adjust the attention of the model to the tail classes by allocating different weight costs to different classes during the training process. We conduct comprehensive experiments on the challenging LVIS benchmark, AECSL achieved the competitive results, with 28.6% segmentation AP and 28.5% box AP on LVIS v0.5 and 27.5% segmentation AP and 27.6% box AP on LVIS v1.0 based on ResNet-101.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ying Tao, Tianran Hao, Peng Dong, and Jialu Xing "Adaptive effective class suppression loss for long-tailed object detection", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341Z (14 November 2023); https://doi.org/10.1117/12.3008205

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KEYWORDS

Object detection

Head

Target detection

Performance modeling

Ablation

Detection and tracking algorithms

Education and training

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