.png){kind=icon}
返回
Dynamic multi-scale loss optimization for object detection
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-022-13164-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the continuous improvement of deep object detectors via advanced model architectures, imbalance problems in the training process have received more attention. It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale level is neither equally important nor independent. Different from the existing solutions of setting multi-task weights, we dynamically optimize the loss weight of each scale level in the training process. Specifically, we propose an Adaptive Variance Weighting (AVW) to balance multi-scale loss according to the statistical variance. Then we develop a novel Reinforcement Learning Optimization (RLO) to decide the weighting scheme probabilistically during training. It makes better utilization of multi-scale training loss without extra computational complexity and learnable parameters for backpropagation. Without bells and whistles, the proposed method improves ATSS by 0.9 AP on the MS COCO benchmark. And it achieves 82.1 mAP on Pascal VOC 2007 test set, which outperforms other reinforcement-learning-based methods.
Keyword:
Object detection
Multi-scale imbalance
Reinforcement learning
Multi-task
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
暂无机构信息
引用论文
分享收藏Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor Air
OAAI
Indoor AirIF0
OAAI分享收藏分享收藏
查看更多内容