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36. Visible and Infrared Object Tracking via Convolution-Transformer Network With Joint Multimodal Feature Learning.

Author

Qiu, Jiazhu, Yao, Rui, Zhou, Yong, Wang, Peng, Zhang, Yanning, and Zhu, Hancheng

Abstract

The existing Transformer-based redgreenblue-thermal (RGBT) tracker mainly focuses on the enhancement of features extracted by convolutional neural network (CNN). The potential of the Transformer in representation learning remains underexplored. In this letter, we propose a Convolution-Transformer network with joint multimodal feature learning (JMFL), in which both representation learning and feature fusion leverage Transformer. Specifically, we use the multibranch Convolution-Transformer feature extraction network to process the extraction task of local modality-independent features and global modality-shared features, respectively. Several simplified Transformer encoder layers form the Transformer backbone network, which is more suitable for real-time object tracking. Besides, we found that intermodality correlation is an important factor for modality interactions and mutual exploitation. Therefore, we propose a JMFL module, which uses cross-attention to capture the dependencies of cross-modal and enhance multimodal fusion by bidirectional guidance of multimodal information. The proposed method is fully experimented on two large benchmark datasets and compared with some current well-performing methods. The experimental results show that the proposed method performs well in terms of tracking accuracy and speed. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Gan-Based Joint Activity Detection and Channel Estimation for Grant-Free Random Access

Author

Liang, Shuang, Zou, Yinan, and Zhou, Yong

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)
Abstract

Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only achieve either activity detection or channel estimation, but not both. In this paper, we propose a novel model-free learning method based on generative adversarial network (GAN) to tackle the JADCE problem. We adopt the U-net architecture to build the generator rather than the standard GAN architecture, where a pre-estimated value that contains the activity information is adopted as input to the generator. By leveraging the properties of the pseudoinverse, the generator is refined by using an affine projection and a skip connection to ensure the output of the generator is consistent with the measurement. Moreover, we build a two-layer fully-connected neural network to design pilot matrix for reducing the impact of receiver noise. Simulation results show that the proposed method outperforms the existing methods in high SNR regimes, as both data consistency projection and pilot matrix optimization improve the learning ability., Comment: 5 pages, 5 figures IEEE ICASSP2022

Published
2022

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