Indexed by:
Abstract:
In object detection, the imbalance problem often occurs when the number of training samples of different categories varies greatly, or multiple loss functions need to be minimized which is harmful to the performance of the detector. In this paper, we consider that the imbalance problem can be implied by the imbalance of gradient distribution. To address these imbalance issues, we analyze the gradient of cross-entropy loss and propose balanced cross-entropy (BLCE) loss and balanced binary cross-entropy (BBCE) loss for solving objective imbalance and class imbalance issues respectively. The BLCE loss significantly reduces the overall classification loss and keeps the classification loss and regression loss balanced. Furthermore, the BBCE loss automatically down-weight the contribution of inliers during training and rapidly focus the model on outliers. Ablation studies on object detection and image classification demonstrate the effectiveness of our loss function. We replace the corresponding losses in Libra R-CNN and evaluate our detector on the COCO test-dev. Our results show that Libra R-CNN can surpass the accuracy of many existing state-of-the-art detectors when training with our balanced loss. © 2020, Springer Nature Switzerland AG.
Keyword:
Reprint 's Address:
Email:
Version:
Source :
ISSN: 0302-9743
Year: 2020
Volume: 12307 LNCS
Page: 342-354
Language: English
0 . 4 0 2
JCR@2005
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
Affiliated Colleges: