Search

Your search keyword '"Zhang, Tong"' showing total 128 results
128 results on '"Zhang, Tong"'

7. Fine-Grained Interpretability for EEG Emotion Recognition: Concat-Aided Grad-CAM and Systematic Brain Functional Network.

Author

Liu, Bingxiu, Guo, Jifeng, Chen, C. L. Philip, Wu, Xia, and Zhang, Tong

Abstract

EEG emotion recognition plays a significant role in various mental health services. Deep learning-based methods perform excellently, but still suffer from interpretability. Although methods such as Gradient-weighted Class Activation Mapping(Grad-CAM) can cope with the above problem, their coarse granularity cannot accurately reveal the mechanism to promote emotional intelligence. In this paper, fine-grained interpretability is proposed, called Concat-aided Grad-CAM. Specifically, the multi-level feature mapping before the fully connected layer is concatenated to obtain the gradients of the target concept so that the discriminant information can be directly located in the high-precision area. Unlike coarse-grained interpretability methods applied in EEG emotion recognition, it can accurately highlight the EEG channels related to emotion rather than an obscure area. In addition, a systematic brain functional network is proposed to reveal the relationship between those channels and to further improve emotion recognition performance. The channels with greater contributions are connected, and those connections are learned by dynamic graph convolutional networks, while the others are independent to eliminate interference. Experiments on four EEG emotion recognition datasets manifest that Concat-aided Grad-CAM can be interpreted by the fine-grained. In addition, it has been shown that the learned brain functional network can improve the performance of the baselines. Significantly, the experiment results achieve state-of-the-art performance in multiple experiments. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

31. ADAPT: Action-aware Driving Caption Transformer

Author

Jin, Bu, primary, Liu, Xinyu, additional, Zheng, Yupeng, additional, Li, Pengfei, additional, Zhao, Hao, additional, Zhang, Tong, additional, Zheng, Yuhang, additional, Zhou, Guyue, additional, and Liu, Jingjing, additional

Published
2023
Full Text
View/download PDF

35. MIA-Net: Multi-Modal Interactive Attention Network for Multi-Modal Affective Analysis.

Author

Li, Shuzhen, Zhang, Tong, Chen, Bianna, and Chen, C. L. Philip

Abstract

When a multi-modal affective analysis model generalizes from a bimodal task to a trimodal or multi-modal task, it is usually transformed into a hierarchical fusion model based on every two pairwise modalities, similar to a binary tree structure. This easily leads to large growth in model parameters and computation as the number of modalities increases, which limits the model's generalization. Moreover, many multi-modal fusion methods ignore that different modalities contribute differently to affective analysis. To tackle these challenges, this article proposes a general multi-modal fusion model that supports trimodal or multi-modal affective analysis tasks, called Multi-modal Interactive Attention Network (MIA-Net). Instead of treating different modalities equally, MIA-Net takes the modality that contributes the most to emotion as the main modality and the others as auxiliary modalities. MIA-Net introduces multi-modal interactive attention modules to adaptively select the important information of each auxiliary modality one by one to improve the main-modal representation. Moreover, MIA-Net enables quick generalization to trimodal or multi-modal tasks through stacking multiple MIA modules, which maintains efficient training and only requires linear computation and stable parameter counts. Experimental results of the transfer, generalization, and efficiency experiments on the widely-used datasets demonstrate the effectiveness and generalization of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

40. Contour Modeling Arbitrary-Oriented Ship Detection From Very High-Resolution Optical Remote Sensing Imagery.

Author

Zhuang, Yin, Liu, Yuqun, Zhang, Tong, and Chen, He

Abstract

Under the multiscale distribution, due to dramatic aspect ratio variance leading to prominent arbitrary-oriented character of ships in very high-resolution (VHR) optical remote sensing imagery, how to generate accurate oriented bounding box (OBB) becomes a hot research topic for arbitrary-oriented ship detection. Consequently, in this letter, a concise and effective one-stage anchor-free contour modeling detector called CMDet is proposed for accurate arbitrary-oriented ship detection. Different from currently existed methods via carefully decoupling several independently characteristic parameters for OBB modeling and regression, we resolve the OBB modeling by jointly regressing the contour information. Specifically, the contour information is expressed as a series of Fourier transform coefficients, which are generated by setting up the mapping relation of 1-D Fourier contour coefficients and spatial OBB contour. In addition, a new inherent geometry loss is designed to make detector better learn the geometry information in training phase. After that, the proposed CMDet only needs to predict the correct center point of ships and regress the corresponding entire 1-D Fourier contour coefficients to generate accurate OBB for ship detection. Finally, extensive experiments are carried out on two public OBB ship detection datasets (e.g., HRSC2016 and DIOR-ship), and comparison results demonstrate that the proposed CMDet can obtain the competitive result than the state-of-the-art (SOTA) detectors. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

49. Secure Rate-Splitting for MIMO Broadcast Channel with Imperfect CSIT and a Jammer

Author

Zhang, Tong, Chen, Dongsheng, Li, Na, Zhuang, Yufan, Lv, Bojie, and Wang, Rui

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, FOS: Electrical engineering, electronic engineering, information engineering, Data_CODINGANDINFORMATIONTHEORY, Electrical Engineering and Systems Science - Signal Processing, Computer Science::Cryptography and Security, Computer Science::Information Theory
Abstract

In this paper, we investigate the secure rate-splitting for the two-user multiple-input multiple-output (MIMO) broadcast channel with imperfect channel state information at the transmitter (CSIT) and a multiple-antenna jammer, where each receiver has an equal number of antennas and the jammer has perfect channel state information (CSI). Specifically, we design a secure rate-splitting multiple-access strategy, where the security of split private and common messages is ensured by precoder design with joint nulling and aligning the leakage information, regarding different antenna configurations. Moreover, we show that the sum-secure degrees-of-freedom (SDoF) achieved by secure rate-splitting is optimal and outperforms that by conventional zero-forcing. Therefore, we reveal the sum-SDoF of the two-user MIMO broadcast channel with imperfect CSIT and a jammer, and validate the superiority of rate-splitting for the security purpose in this scenario with emphasis of MIMO., Comment: 6 pages, 3 figures

Published
2022

50. The Generalized Degrees-of-Freedom of the Asymmetric Interference Channel with Delayed CSIT

Author

Zhang, Tong, Zhuang, Yufan, and Xu, Yinfei

Subjects
FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), Computer Science::Information Theory
Abstract

In this paper, we investigate the generalized degrees-of-freedom (GDoF) of the asymmetric interference channel with delayed channel state information at the transmitter (CSIT), where each transmitter has two antennas, each receiver has one antenna, and the strength for each interfering link can vary. The optimal sum-GDoF is characterized by matched converse and achievability proof. Through our results, we also reveal that in our antenna setting, the symmetric GDoF lower bound in [Mohanty et. al, TIT 2019] can be elevated, and the symmetric GDoF upper bound in [Mohanty et. al, TIT 2019] is tight in fact., Comment: Accepted by IEEE ISIT 2022

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results