Prof. Hui Yan Profile

Prof. Hui Yan

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Proceedings Article | 30 June 2021 Paper

Task-relevant graph to propagate labels for few-shot classification

Minjie Zhang, Hui Yan

Proceedings Volume 11878, 118780W (2021) https://doi.org/10.1117/12.2600836

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Few-shot learning aims to learn a classifier with limited training instances to recognize unseen classes in test. Recently, some effective few-shot learning approaches have achieved promising classification performance. However, these approaches do not pay attention to the importance of task-relevant features in image classification. To address this issue, we propose Task-Relevant Graph Metric Learning method that adopts a novel meta-learning framework for transductive inference. Specifically, we first extract task-relevant features by a Squeeze-and-Excitation module. Then learning a graph construction module in order to obtain the manifold structure in the data. Afterward, self-training is utilized to propagate labels from labeled instances to unlabeled test instances. Experiments on benchmark datasets demonstrate that TRGML improves classification performance (4%-5%) over baseline systems on miniImageNet and tieredImageNet.

Proceedings Article | 30 June 2021 Paper

Jinkai Zhang, Hui Yan, Tao Chen

Proceedings Volume 11878, 118780O (2021) https://doi.org/10.1117/12.2601023

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Semantic segmentation with deep learning has achieved remarkable progress in classifying the pixels in the image. Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. Image-level label-based weakly supervised semantic segmentation (WSSS) aims to adopt image-level labels to train semantic segmentation models, saving vast human labors for costly pixel-level annotations. A typical pipeline for this problem is to adopt Class Activation Maps (CAMs) with image-level labels to generate pseudo-masks (a.k.a. seeds) and then use them for training segmentation models. The main difficulty is that seeds are usually sparse and incomplete. In recent years, GCNs have made great strides in various fields. GCN can perform global modeling and reasoning on the relationship between regions, which is beneficial for many computer vision tasks. This is our motivation to combine these two aspects. Therefore we propose the module of class-related graph convolution. Because there are differences between classes, our GCN is parallel. Each GCN can learn the classrelated region extension strategy. To enable GCN to learn more authentic relationships, we also introduce the attention mechanisms. We conduct lots of experiments on the public PASCAL VOC dataset, and our model yields state-of-the-art performance.

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