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Association Loss for Visual Object Detection
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DOI：10.1109/LSP.2020.3013160.png)
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Abstract
En 中文
Convolutional neural network (CNN) is a popular choice for visual object detection where two sub-nets are often used to achieve object classification and localization separately. However, the intrinsic relation between the localization and classification sub-nets was not exploited explicitly for object detection. In this letter, we propose a novel association loss, namely, the proxy squared error (PSE) loss, to entangle the two sub-nets, thus use the dependency between the classification and localization scores obtained from these two sub-nets to improve the detection performance. We evaluate our proposed loss on the MS-COCO dataset and compare it with the loss in a recent baseline, i.e. the fully convolutional one-stage (FCOS) detector. The results show that our method can improve the AP from 33.8 to 35.4 and AP(75) from 35.4 to 37.8, as compared with the FCOS baseline.
Keywords:
Object detection
Detectors
Training
Heating systems
Feature extraction
Visualization
Convolutional neural networks
Association loss
object detection
object localization
object classification
convolutional neural networks
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