Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Ashraf Ali KassimSuggested from co-authorship
- Shuicheng YanSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works267 from public data
- MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification1,572
Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label).
- HCP: A Flexible CNN Framework for Multi-Label Image Classification784
Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts, where an arbitrary number of object segment hypotheses are taken as the inputs.
- Adversarial Domain Adaptation with Domain Mixup520
This paper presents adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
- Crowded Scene Analysis: A Survey488
The background knowledge and the available features related to crowded scenes are provided and existing models, popular algorithms, evaluation protocols, and system performance are provided corresponding to different aspects of the crowded scene analysis.
- MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis475
MedMNIST Classification Decathlon is designed to benchmark AutoML algorithms on all 10 datasets, and has compared several baseline methods, including open-source or commercial AutoML tools.
- Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling455
This work develops Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention, and proposes an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points.
- 397
- 373
- 364
- 361
- Unsupervised Deep Learning for Optical Flow Estimation327
This work devise a simple yet effective unsupervised method for learning optical flow, by directly minimizing photometric consistency by using image warping by the estimated flow.
- 292
- Cross-Domain Detection via Graph-Induced Prototype Alignment284
A Graph-induced Prototype Alignment framework to seek for category-level domain alignment via elaborate prototype representations through graph-based information propagation among region proposals, and in order to alleviate the negative effect of class-imbalance on domain adaptation, a Class-reweighted Contrastive Loss is designed to harmonize the adaptation training process.
- Person Re-identification via Recurrent Feature Aggregation253
This work shows that a progressive/sequential fusion framework based on long short term memory (LSTM) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation.
- 3D Human Action Representation Learning via Cross-View Consistency Pursuit248
CrosSCLR consists of both single-view contrastive learning (Skeleton-CLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner, and achieves remarkable action recognition results on NTU-60 andNTU-120 datasets under unsupervised settings.
- 246
- Deep Regression Tracking with Shrinkage Loss243
This work proposes a novel shrinkage loss to penalize the importance of easy training data and applies residual connections to fuse multiple convolutional layers as well as their output response maps to balance training data.
- 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas223
A deep learning system based on 3D convolutional neural networks and multitask learning, which automatically predicts tumor invasiveness, together with 3D nodule segmentation masks is developed, which could help doctors work efficiently and facilitate the application of precision medicine.
- Omni Aggregation Networks for Lightweight Image Super-Resolution211
Two enhanced components under a new Omni-SR architecture are proposed based on dense interaction principle, which can simultaneously model pixel-interaction from both spatial and channel dimensions, and a multi-scale interaction scheme is proposed to mitigate sub-optimal ERF in shallow models.
- Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet211
The proposed FracNet provided increasing detection sensitivity of rib fractures with significantly decreased clinical time consumed, which established a clinically applicable method to assist the radiologist in clinical practice.
- Learning Context Graph for Person Search209
This work proposes a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene, and builds a graph learning framework to effectively employ context pairs to update target similarity.
- 205
- 194
- 170
- 169
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 2026-10-14. 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.