About Me
I am currently a Senior Research Scientist at Visual Intelligence Department of Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore.
I obtained my Bachelor Degree from Beijing Institute of Technology at 2013 and PhD Degree from Nanyang Technological University (NTU), Singapore at 2019. After graduation, I have been working in I2R, A*STAR as a research scientist.
My current reserach interests are in thoery and application in machine learning, deep learning and computer vision.
For my detailed working and research experience, please refer to CV for my complete CV.
Recent News
One paper entitled “PartCLIP: How does CLIP assist mechanical part image retrieval?” has been accepted in ICMEW2024.
My Research Work on Multi-View 3D Object Recognition and Retrieval is shown in A*STAR Research Highlight Page Link
My Research Work on Defect Detection and Segmentation is shown in A*STAR Research Highlight Page Link
One paper entitled “SCA-PVNet: Self-and-cross attention based aggregation of point cloud and multi-view for 3D object retrieval” has been published in Knowledge-Based Systems.
One paper entitled “Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering” has been published in IEEE Transactions on Multimedia.
One US Patent entitled “Method and system for image classification” has been granted with Patent Number: 11836632.
Our solution has won the 1st Place in EPIC-Kitchens Dataset Challenges: Unsupervised Domain Adaptation for Action Recognition Track, CVPR2023 Leaderboard Link
One paper entitled “DDR-ID: Dual deep reconstruction networks based image decomposition for anomaly detection” has been published in Journal of Ambient Intelligence and Humanized Computing.
One paper entitled “Multi-range view aggregation network with vision transformer feature fusion for 3D object retrieval” has been published in IEEE Transactions on Multimedia.
One paper entitled “An effective industrial defect classification method under the few-shot setting via two-stream training” has been published in Optics and Lasers in Engineering.
One paper entitled “Biomedical image classification based on a feature concatenation and ensemble of deep CNNs” has been published in Journal of Ambient Intelligence and Humanized Computing.
Three papers entitled “MLSA-UNet: End-to-End multi-level spatial attention guided UNet for industrial defect segmentation”, “Masked face recognition via self-attention based local consistency regularization” and “Implicit shape biased few-shot learning for 3d object generalization” have been published in ICIP2022.
One paper entitled “Multi-view 3D object retrieval leveraging the aggregation of view and instance attentive features” has been published in Knowledge-Based Systems.
One paper entitled “On the use of component structural characteristics for voxel segmentation in semicon 3D images” has been published in ICASSP2022.
Our solution has won the 1st Place in EPIC-Kitchens Dataset Challenges: Unsupervised Domain Adaptation for Action Recognition Track, CVPR2022 Leaderboard Link
One paper entitled “A progressive multi-view learning approach for multi-loss optimization in 3d object recognition” has been published in IEEE Signal Processing Letters.
One paper entitled “CAM-guided Multi-Path Decoding U-Net with Triplet Feature Regularization for defect detection and segmentation” has been published in Knowledge-Based Systems.
Two papers entitled “CAM-Guided u-net with adversarial regularization for defect segmentation” and “Action Relational Graph for Weakly-Supervised Temporal Action Localization” have been published in ICIP2021.
One paper entitled “Few-shot defect segmentation leveraging abundant defect-free training samples through normal background regularization and crop-and-paste operation” has been published in ICME2021(Oral Presentation).
One paper entitled “Rethinking of deep models parameters with respect to data distribution” has been published in ICPR2021.
One paper entitled “maskedfacenet: A progressive semi-supervised masked face detector” has been published in WACV2021.
Two papers entitled “CAM-UNET: class activation MAP guided UNET with feedback refinement for defect segmentation” and “Improving 3d brain tumor segmentation with predict-refine mechanism using saliency and feature maps” have been published in ICIP2020.
One paper entitled “RefineU-Net: Improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation” has been published in Pattern Recognition Letters.
One paper entitled “Discriminative features for incremental learning classifier” has been published in ICIP2019.
One paper entitle “A two-stage method for automated detection of ring-like endosomes in fluorescent microscopy images” has been published in Plos One.
One paper entitled “Vibration source classification and propagation distance estimation system based on spectrogram and KELM” has been published in Cognitive Computation and Systems.
One paper entitled “Meta module generation for fast few-shot incremental learning” has been published in ICCVW2019.
One paper entitled “Multicomponent signal decomposition using morphological operations” has been published in DSP2018.
One paper entitled “Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation” has been published in ISCAS2018.
One paper entitled “Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis” has been published in Computers in biology and medicine.
One paper entitled “Twin SVM with a reject option through ROC curve” has been published in Journal of the Franklin Institute.
Two papers entitled “Automatic endosomal structure detection and localization in fluorescence microscopic images” and “LLC encoded BoW features and softmax regression for microscopic image classification” have been published in ISCAS2017.
One paper entitled “Reaction-diffusion based level set method with local entropy thresholding for melasma image segmentation” has been published in ISCAS2017.
One paper entiteld “An SVM based scoring evaluation system for fluorescence microscopic image classification” has been published in DSP2015.