2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-CIF-1.4
Paper Title MARGIN LOSS BASED ON ADAPTIVE METRIC FOR IMAGE RECOGNITION
Authors Zhihong Liu, Lei Song, Xiaowan Hu, Haoqian Wang, Tsinghua Shenzhen International Graduate School, China
SessionIMT-CIF-1: Computational Imaging 1
LocationArea J
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Cross-entropy (CE) loss is one of the most commonly used supervision in image recognition. However, the features trained by CE loss are not discriminative enough. There are many methods using the angular-softmax CE loss to learn angularly discriminative features. But these methods still use manually designed metric, which cannot deal with complicated distribution of high-dimensional features, and there are no appropriate inter-class restraint. Therefore, we propose a novel method to restrain the distribution of features in high-dimensional. We construct trainable centers, and design adaptive metric to express the distance between features. Specifically, we design AdaMetricLoss which can manipulate inter-class distance and intra-class distance simultaneously. We evaluate the effectiveness of AdaMetricLoss on Cifar10 and Cifar100 datasets, and our method shows preferable classification performance. We also visualize the discriminative distribution of features on MNIST, proves the proposed method is more suitable for image recognition.