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- Stan Ziqing LiSuggested from co-authorship
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Works76 from public data
- 1,147
- 1,137
- S^3FD: Single Shot Scale-Invariant Face Detector693
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.
- Embedding Deep Metric for Person Re-identification: A Study Against Large Variations313
A novel moderate positive sample mining method to train robust CNN for person re-identification, dealing with the problem of large variation is proposed and the learning by a metric weight constraint is improved, so that the learned metric has a better generalization ability.
- FaceBoxes: A CPU real-time face detector with high accuracy298
This work proposes a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy, and proposes a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces.
- Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition281
A solution, named DMUE, is proposed to address the problem of annotation ambiguity from two perspectives: the latent Distribution Mining and the pairwise Uncertainty Estimation.
- A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing208
A large-scale multi-modal dataset, namely CASIA-SURF, is introduced, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities and a new multi- modal fusion method is presented, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal.
- Mis-Classified Vector Guided Softmax Loss for Face Recognition189
This paper develops a novel loss function, which adaptively emphasizes the mis-classified feature vectors to guide the discriminative feature learning and is the first attempt to inherit the advantages of feature margin and feature mining into a unified loss function.
- Multi-view subspace clustering with intactness-aware similarity182
A novel multi-view subspace clustering model that attempts to form an informative intactness-aware similarity based on the intact space learning technique is proposed and its superior performance over other state-of-the-art alternatives is revealed.
- CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification174
This paper systematically study the manually designed architectures, and identifies that appropriately separating Batch Normalization layers is the key to bring a great boost towards cross-modality matching, and proposes a novel method, named Cross-Modality Neural Architecture Search (CM-NAS).
- The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances165
This survey article presents a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved their capability of them.
- ScratchDet: Training Single-Shot Object Detectors From Scratch141
The ScratchDet achieves the state-of-the-art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the train-from-scratch detectors and even performs better than several one-stage pretrained methods.
- 140
- FaceX-Zoo124
A novel open-source project, named FaceX-Zoo, which is constructed with modular and scalable design, and oriented to the academic and industrial community of face-related analysis, to help the community to easily build stateof-the-art solutions for deep face recognition.
- 119
- A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing95
A simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information.
- 76
- Edge-Aware Graph Representation Learning and Reasoning for Face Parsing72
This work proposes to model and reason the region-wise relations by learning graph representations, and leverage the edge information between regions for optimized abstraction, and encodes a facial image onto a global graph representation where a collection of pixels with similar features are projected to each vertex.
- 66
- Large-Scale Bisample Learning on ID Versus Spot Face Recognition57
To tackle the bisample problem with only two samples for each class, a classification–verification–classification training strategy is proposed to progressively enhance the IvS performance, and a dominant prototype softmax is incorporated to make the deep learning scalable on large-scale classes.
- Single-Shot Scale-Aware Network for Real-Time Face Detection51
This work proposes a scale-aware detection network using a wide scale range of layers associated with appropriate scales of anchors to handle faces with various scales, and describes a new equal density principle to ensure anchors with different scales to be evenly distributed on the image.
- Dual Spoof Disentanglement Generation for Face Anti-Spoofing With Depth Uncertainty Learning49
A lightweight Depth Uncertainty Module (DUM), which alleviates the adverse effects of noisy samples by depth uncertainty learning, developed without extra-dependency, thus can be flexibly integrated with any depth supervised network for face anti-spoofing.
- 47
- C2RL: Content and Context Representation Learning for Gloss-Free Sign Language Translation and Retrieval39
This paper introduces an innovative pretraining paradigm for gloss-free SLRL, called C2RL, and confirms that the joint optimization of ICL and ECL results in robust sign language representation and significant performance gains in gloss-free SLT and SLRet tasks.
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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 2026-10-15. 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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