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Works89 from public data
- Robust Face Recognition via Multimodal Deep Face Representation460
A comprehensive deep learning framework to jointly learn face representation using multimodal information is proposed and a small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.
- Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition447
A Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components, achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces.
- Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition387
Experimental results indicate that DCP outperforms the state-of-the-art local descriptors for both face identification and face verification tasks and the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
- GPS-Net: Graph Property Sensing Network for Scene Graph Generation348
A novel message passing module that augments the node feature with node-specific contextual information and encodes the edge direction information via a tri-linear model is proposed that achieves state-of-the-art performance on three popular databases: VG, OI, and VRD by significant gains under various settings and metrics.
- Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task340
A novel two-stage cascaded U-Net to segment the substructures of brain tumors from coarse to fine is devised and won the 1st place in the BraTS 2019 challenge segmentation task.
- A Comprehensive Survey on Pose-Invariant Face Recognition330
The inherent difficulties in PIFR are discussed and a comprehensive review of established techniques are presented, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches.
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- Dual-force convolutional neural networks for accurate brain tumor segmentation197
Results on the two databases indicate that the proposed approaches consistently promote the segmentation performance of the two popular deep models, BRATS 2017 and BRATS 2015.
- Learning Granularity-Unified Representations for Text-to-Image Person Re-identification196
An end-to-end framework based on transformers to learn granularity-unified representations for both modalities, denoted as LGUR, which consistently outperforms state-of-the-arts by large margins on both CUHK-PEDES and ICFG-P EDES datasets.
- Pose-invariant face recognition with homography-based normalization128
An highly efficient PIFR algorithm that effectively handles the main challenges caused by pose variation and an effective approach for occlusion detection, which enables face recognition with visible patches only is proposed.
- Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection123
This paper proposes a novel one-stage method, namely Glance and Gaze Network (GGNet), which adaptively models a set of action-aware points (ActPoints) via glance and gaze steps, and outperforms state-of-the-art methods by significant margins on both V-COCO and HICO-DET benchmarks.
- Learning Contextual and Attentive Information for Brain Tumor Segmentation118
This work designs multiple deep architectures of varied structures to learning contextual and attentive information, then ensemble the predictions of these models to obtain more robust segmentation results, and shows that the risk of overfitting in segmentation is reduced.
- CPP-Net: Context-Aware Polygon Proposal Network for Nucleus Segmentation109
A Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation that substantially enhances contextual information and thereby improves the robustness of the prediction and is found to achieve state-of-the-art performance on three publicly available databases.
- Correcting the Triplet Selection Bias for Triplet Loss107
A new variant of triplet loss is proposed, which tries to reduce the bias in triplet selection by adaptively correcting the distribution shift on the selected triplets.
- Multi-Task Learning With Coarse Priors for Robust Part-Aware Person Re-Identification104
A robust, compact, and easy-to-use method called the Multi-task Part-aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images and consistently outperforms state-of-the-art approaches by significant margins.
- Quality-Aware Part Models for Occluded Person Re-Identification101
A novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID, which does not rely on any outside tools in either the training or inference stages, and handles occlusions caused by both objects and other pedestrians; it is highly computationally efficient.
- Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID91
A novel method is introduced that automatically identifies words in a description that do not correspond with the image, based on the similarity between one text and all patch token embeddings in the image, which alleviates the impact of noisy textual descriptions.
- MobileFaceSwap: A Lightweight Framework for Video Face Swapping87
A lightweight Identity-aware Dynamic Network (IDN) is proposed for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information by introducing two dynamic neural network techniques, including the weights prediction and weights modulation.
- One-Pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation86
This paper decompose brain tumor segmentation into three different but related tasks and proposes a multi-task deep model that trains them together to exploit their underlying correlation and introduces a simple yet effective post-processing method that can further improve the segmentation performance significantly.
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- Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification80
A new unified pre-training pipeline (UniPT) designed specifically for the T2I-ReID task is presented, in which pseudo-textual descriptions of images are automatically generated by the CLIP paradigm using a divide-conquer-combine strategy.
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- Polysemy Deciphering Network for Human-Object Interaction Detection73
A novel Polysemy Deciphering Network (PD-Net), which decodes the visual polysemy of verbs for HOI detection in three ways, and achieves the best performance on both HICO-DET and V-COCO datasets.
- Polysemy Deciphering Network for Robust Human–Object Interaction Detection66
A novel Polysemy Deciphering Network (PD-Net) is proposed that decodes the visual polysemy of verbs for HOI detection in three distinct ways and is demonstrated to outperform state-of-the-art methods by significant margins on the HICO-DET, V-COCO, and HOI-VP databases.
- Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On64
An Texture-Preserving Diffusion (TPD) model for virtual try-on is proposed, which enhances the fidelity of the results and introduces no additional image encoders, and a novel diffusion-based method is proposed that predicts a precise inpainting mask based on the person and reference garment images, further enhancing the reliability of the try-on results.
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 2026-10-12. 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.
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