Dr. Zhenmin Tang Profile

Dr. Zhenmin Tang

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Proceedings Article | 25 March 2024 Paper

Towards robust visual explanations for deep convolutional networks with weight-wise perturbations

Kaifan Lang, Chen Chen, Junshuai Zheng, Xiyuan Hu, Zhenmin Tang

Proceedings Volume 13089, 130891B (2024) https://doi.org/10.1117/12.3021090

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Explaining the complicated mechanisms underlying the image classification capabilities of deep Convolutional Neural Network (CNN) models, continue to pose huge challenges within the field of computer vision. To address this concern, numerous interpretability methods have been devised, aimed at clarifying the image classification process. These methods include approaches such as sensitivity maps, which involve computing the gradients of class outputs with respect to input images, and techniques like class activation mapping (CAM). Furthermore, the incorporation of noise into input images has emerged as an effective strategy for augmenting visualization quality and removing noise. In this paper, we propose two key contributions: the introduction of a novel approach that injects noise into network weights to enhance visualization, involving image gradient updates and average gradient computations; and a new indicator for evaluating interpretability - the center of gravity, and comprehensive experiments were conducted on multiple datasets and different deep neural network models. In the subsequent experimental sections, we demonstrate that our method achieves superior visualization quality and can be combined with other interpretability methods to enhance their performance.

Proceedings Article | 27 June 2023 Paper

SCGRNet: shape completion-guided registration network for face point cloud

Zeyu Jia, Yichao Zhou, Xiyuan Hu, Zhenmin Tang

Proceedings Volume 12705, 127052F (2023) https://doi.org/10.1117/12.2680183

KEYWORDS: Point clouds, Transformers, Feature extraction, Deep learning, 3D modeling

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Point cloud registration is a fundamental problem in computer vision and robotics, and has been widely used in various applications, including 3D reconstruction, simultaneous localization and mapping, and autonomous driving. Over the last decades, numerous researchers have devoted themselves to tackling this challenging problem. With the success of deep learning in geometric vision tasks, various types of deep learning-based point cloud registration methods have been proposed, and some of them exhibit good performance. However, their performance degrades rapidly when the overlap between point clouds is reduced. In this paper, we propose a shape completion-guided registration network for face point cloud registration, which takes face shape as a prior knowledge to guide the network to learn a complete face features at the low overlap. Another noteworthy point is that we use the Regularized Projective Manifold Gradient (RPMG) layer to bridge the gap between the neural network output measured in Euclidean space and the rotation parameters described in the SO(3) manifold. In addition, our proposed method infers the translation parameters from the two complementary point clouds and the predicted rotation rather than estimates them directly. For the problem of low overlap point cloud registration, our method can effectively improve the registration accuracy compared to the current state-of-the-art methods. The rotation error and translation error are, respectively, less than 2 degrees and 2mm on the FaceScape-based low overlap face registration dataset.

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