Author name cluster

Hao Jia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

IROS Conference 2025 Conference Paper

ACP-MVS: Efficient Multi-View Stereo with Attention-based Context Perception

Hao Jia
Gangwei Xu
Miaojie Feng
Xianqi Wang 0001
Junda Cheng
Min Lin 0006
Xin Yang 0008

The core of Multi-View Stereo (MVS) is to find corresponding pixels in neighboring images. However, due to challenging regions in input images such as untextured areas, repetitive patterns, or reflective surfaces, existing methods struggle to find precise pixel correspondence therein, resulting in inferior reconstruction quality. In this paper, we present an efficient context-perception MVS network, termed ACP-MVS. The ACP-MVS constructs a context-aware cost volume that can enhance pixels containing essential context information while suppressing irrelevant or noisy information via our proposed Context-stimulated Weighting Fusion module. Furthermore, we introduce a new Context-Guided Global Aggregation module, based on the insight that similar-looking pixels tend to have similar depths, which exploits global contextual cues to implicitly guide depth detail propagation from high-confidence regions to low-confidence ones. These two modules work in synergy to substantially improve reconstruction quality of ACP-MVS without incurring significant additional computational and time cost. Extensive experiments demonstrate that our approach not only achieves state-of-the-art performance but also offers the fastest inference speed and minimal GPU memory usage, providing practical value for practitioners working with high-resolution MVS image sets. Notably, our method ranks 2nd on the challenging Tanks and Temples advanced benchmark among all published methods. Code is available at https://github.com/HaoJia-mongh/ACP-MVS.

Details

EAAI Journal 2024 Journal Article

Enabling temporal–spectral decoding in multi-class single-side upper limb classification

Hao Jia
Shuning Han
Cesar F. Caiafa
Feng Duan
Yu Zhang
Zhe Sun
Jordi Solé-Casals

This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal–spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain–computer interfaces (BCIs). In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal–spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer. To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0. 7707 ± 0. 1168, surpassing the performance of EEGNet (accuracy: 0. 7340 ± 0. 1246) and FBTRCA (accuracy: 0. 7487 ± 0. 1250). In multi-class tasks, TTSNet achieved an accuracy of 0. 4588 ± 0. 0724, exhibiting a 4. 27% and 3. 95% accuracy increase over EEGNet and FBTRCA, respectively. The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.

Details DOI

IROS Conference 2024 Conference Paper

Online Hand Movement Recognition System with EEG-EMG Fusion Using One-Dimensional Convolutional Neural Network

Haozheng Wang
Hao Jia
Zhe Sun 0009
Feng Duan 0006

Upper limb amputees face significant challenges in their daily lives due to the loss of hand or arm functionality. Researchers have developed upper limb prostheses to restore normal hand movements for them. Most hand movement recognition systems of prostheses use electromyography (EMG) as the input signal source, but ignore the interrelationship with electroencephalography (EEG), which may contain valuable movement-related information as well. In order to enhance the accuracy of hand movement classification, we proposed a hand movement recognition system based on a one-dimensional convolutional neural network (1D-CNN) that combines EEG and EMG as the input signal sources to increase the quantity of accessible information. In this work, we collected the EEG and EMG of five subjects during the hand movements and used a 1D-CNN based model to classify the preprocessed signals. The average accuracy of using EEG-EMG fusion is 96. 59±2. 63%, significantly higher than 74. 99±8. 24% of using single EEG and 90. 31±7. 16% of using single EMG. Then, we applied the model trained by offline experiment for online recognition, and controlled the Pepper robot to complete the corresponding hand movements. The average accuracy of online recognition can reach 93. 00±4. 85% by using majority voting method. The results indicate that the method of EEG-EMG fusion can effectively enhance the performance of hand movement recognition system, which promote the development of upper limb prostheses and contribute to the rehabilitation of upper limb amputees.

Details

JBHI Journal 2023 Journal Article

Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis

Hao Jia
Fan Feng
Cesar F. Caiafa
Feng Duan
Yu Zhang
Zhe Sun
Jordi Solé-Casals

The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0. 4193 $\pm$ 0. 0780 (7 classes) and 0. 4032 $\pm$ 0. 0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0. 3590 $\pm$ 0. 0645 and 0. 3159 $\pm$ 0. 0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.

Details DOI

IJCAI Conference 2022 Conference Paper

AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation

Chang Jin
Shigui Qiu
Nini Xiao
Hao Jia

In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. In this paper, we propose a novel data augmentation approach for NMT, which is independent of any additional training data. Our approach, AdMix, consists of two parts: 1) introduce faint discrete noise (word replacement, word dropping, word swapping) into the original sentence pairs to form augmented samples; 2) generate new synthetic training data by softly mixing the augmented samples with their original samples in training corpus. Experiments on three translation datasets of different scales show that AdMix achieves significant improvements (1. 0 to 2. 7 BLEU points) over strong Transformer baseline. When combined with other data augmentation techniques (e. g. , back-translation), our approach can obtain further improvements.

PDF Details DOI