Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks184 from public data
- A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification314
This work designed Transformer-based models for classifications of motor imagery EEG based on the PhysioNet dataset and revealed a pattern of event-related desynchronization (ERD) which was consistent with the results from the spectral analysis of Mu and beta rhythm over the sensorimotor areas.
- Mask-Guided Attention Network for Occluded Pedestrian Detection223
A novel mask-guided attention network is introduced that emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features and it is empirically demonstrated that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts.
- 209
- From Handcrafted to Deep Features for Pedestrian Detection: A Survey160
An extensive review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches is presented and it is found that hand crafted features with large freedom degrees in shape and space have better performance.
- 157
- MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation146
A novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to alleviate the trade-off between accuracy, inference speed, and model size.
- 139
- 138
- From handcrafted to learned representations for human action recognition: A survey136
This work provides a detailed overview of recent advancements in human action representations and provides comprehensive analysis and comparisons between learning-based and handcrafted action representations respectively, so as to inspire action recognition researchers towards the study of both kinds of representation techniques.
- SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network132
A semi-supervised semantic point cloud segmentation network, named SSPC-Net, where the labels of unlabeled points are inferred from the few annotated 3D points and the pseudo labels of the unsupervised superpoints are generated.
- 122
- Spectral Clustering by Joint Spectral Embedding and Spectral Rotation111
This paper proposes a joint model to simultaneously compute the optimal real matrix and binary matrix which is an orthonormal matrix for spectral clustering and demonstrates the effectiveness of the proposed method on benchmark datasets.
- 106
- 89
- PSTR: End-to-End One-Step Person Search With Transformers88
The proposed PSTR is the first to propose an end-to-end one-step transformer-based person search framework that jointly performs person detection and re-identification (re-id) in a single architecture and sets a new state-of-the-art on both benchmarks.
- 88
- 86
- Progressive Point Cloud Deconvolution Generation Network84
An effective point cloud generation method that can generate multi-resolution point clouds of the same shape from a latent vector is proposed and a shape-preserving adversarial loss is proposed to train the point cloud deconvolution generation network.
- 76
- Learning Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval75
Experimental results suggest that both CDNN and PCDNN can outperform state-of-the-art performance, where PCdNN can further improve CDNN when employing a hierarchical structure.
- 74
- 74
- Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval73
A novel deep correlated metric learning (DCML) method to mitigate the discrepancy between sketch and 3D shape domains is proposed and the experimental results demonstrate the superiority of the proposed method over the state-of-the-art methods.
- 71
- 70
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.