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Ting Luo

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4 papers

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JBHI Journal 2025 Journal Article

M3D: Manifold-Based Domain Adaptation With Dynamic Distribution for Non-Deep Transfer Learning in Cross-Subject and Cross-Session EEG-Based Emotion Recognition

Ting Luo
Jing Zhang
Yingwei Qiu
Li Zhang
Yaohua Hu
Zhuliang Yu
Zhen Liang

Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG’s non-stationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on three benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 6. 67%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of M3D to enhance the practicality and applicability of aBCIs in real-world scenarios.

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NeurIPS Conference 2022 Conference Paper

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine

Jiayi Weng
Min Lin
Shengyi Huang
Bo Liu
Denys Makoviichuk
Viktor Makoviychuk
Zichen Liu
Yufan Song

There has been significant progress in developing reinforcement learning (RL) training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others, aim to improve the system's overall throughput. In this paper, we aim to address a common bottleneck in the RL training system, i. e. , parallel environment execution, which is often the slowest part of the whole system but receives little attention. With a curated design for paralleling RL environments, we have improved the RL environment simulation speed across different hardware setups, ranging from a laptop and a modest workstation, to a high-end machine such as NVIDIA DGX-A100. On a high-end machine, EnvPool achieves one million frames per second for the environment execution on Atari environments and three million frames per second on MuJoCo environments. When running EnvPool on a laptop, the speed is 2. 8x that of the Python subprocess. Moreover, great compatibility with existing RL training libraries has been demonstrated in the open-sourced community, including CleanRL, rl_games, DeepMind Acme, etc. Finally, EnvPool allows researchers to iterate their ideas at a much faster pace and has great potential to become the de facto RL environment execution engine. Example runs show that it only takes five minutes to train agents to play Atari Pong and MuJoCo Ant on a laptop. EnvPool is open-sourced at https: //github. com/sail-sg/envpool.

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EAAI Journal 2022 Journal Article

Light-field image watermarking based on geranion polar harmonic Fourier moments

Chunpeng Wang
Qinghua Zhang
Bin Ma
Zhiqiu Xia
Jian Li
Ting Luo
Qi Li

Light-field images provide a spatial and angular description of the light, and they can capture rich visual information in the natural world. In recent years, the research on light-field imaging has achieved many fruitful results. As more and more people are now focusing their attention to light-field imaging, the copyright protection of light-field images has become an urgent problem that needs to be addressed. Digital watermarking can effectively protect the copyright ownership of light-field images. Currently, there exists no light-field image watermarking scheme that can resist various geometric attacks. In this work, relying on geranion theory and polar harmonic Fourier moments (PHFMs), geranion polar harmonic Fourier moments (GPHFMs) are constructed and utilized for light-field image watermarking. The geranion represents a hypercomplex number containing one real part and thirty-one imaginary parts, and the imaginary parts of geranion can be used to encode multiple image color components while maintaining the correlation between each component. GPHFMs are stable image features, with good image reconstruction ability and stability. Essentially, the light-field image watermarking based on GPHFMs offers good imperceptibility and robustness, can resist various attacks, and effectively solves the problem of current watermarking schemes related to their inability of resisting geometric attacks. Furthermore, it is verified through experiments that the proposed scheme is more robust than the previously reported schemes.

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IS Journal 2021 Journal Article

TSNet: Three-Stream Self-Attention Network for RGB-D Indoor Semantic Segmentation

Wujie Zhou
Jianzhong Yuan
Jingsheng Lei
Ting Luo

This article proposes a three-stream self-attention network (TSNet) for indoor semantic segmentation comprising two asymmetric input streams (asymmetric encoder structure) and a cross-modal distillation stream with a self-attention module. The two asymmetric input streams are ResNet34 for the red-green-blue (RGB) stream and VGGNet16 for the depth stream. Accompanying the RGB and depth streams, a cross-modal distillation stream with a self-attention module extracts new RGB plus depth features in each level in the bottom-up path. In addition, while using bilinear upsampling to recover the spatial resolution of the feature map, we incorporated the feature information of both the RGB flow and the depth flow through the self-attention module. We constructed the NYU Depth V2 dataset to evaluate the TSNet and achieved results comparable to those of current state-of-the-art methods.

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