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Works146 from public data
- A Discriminative Feature Learning Approach for Deep Face Recognition4,103
This paper proposes a new supervision signal, called center loss, for face recognition task, which simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers.
- Backdoor Learning: A Survey898
This article summarizes and categorizes existing backdoor attacks and defenses based on their characteristics, and provides a unified framework for analyzing poisoning-based backdoor attacks, and summarizes widely adopted benchmark datasets.
- LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment490
This work proposes LanguageBind, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics, and freezes the language encoder acquired by VL pretraining, then train encoders for other modalities with contrastive learning.
- Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition478
This paper evaluates the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.
- 417
- Squeeze-and-Attention Networks for Semantic Segmentation334
A novel squeeze-and-attention network (SANet) architecture is proposed that leverages an effective squeeze- and-att attention (SA) module to account for two distinctive characteristics of segmentation: i) pixel-group attention, and ii) pixels-wise prediction.
- 277
- Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network267
Comprehensive experiments on both synthesized and realistic occluded face datasets show that the proposed algorithm significantly outperforms the state-of-the-art systems.
- 239
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- Nonparametric Discriminant Analysis for Face Recognition201
A dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance and shows the improvements of the developed new algorithms over the traditional subspace methods.
- 197
- Boundary-Aware Cascade Networks for Temporal Action Segmentation178
A new boundary-aware cascade network is presented by introducing a new cascading paradigm, called Stage Cascade, and a general and principled smoothing operation is designed, termed as local barrier pooling, to aggregate local predictions by leveraging semantic boundary information.
- SynFace: Face Recognition with Synthetic Data175
This paper first explores the performance gap between recent state-of-the-art face recognition models trained with synthetic and real face images, and devise the SynFace with identity mixup (IM) and domain mix up (DM) to mitigate the above performance gap.
- End2End Occluded Face Recognition by Masking Corrupted Features123
A novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
- A maximum entropy feature descriptor for age invariant face recognition110
A new maximum entropy feature descriptor (MEFD) is developed that encodes the microstructure of facial images into a set of discrete codes in terms of maximum entropy, to overcome the representation and matching problems in age invariant face recognition.
- A Comprehensive Study on Center Loss for Deep Face Recognition102
This paper addresses the open-set property of face recognition by developing the center loss, which simultaneously learns a center for each class, and penalizes the distances between the deep features of the face images and their corresponding class centers.
- Heterogeneous Face Recognition: A Common Encoding Feature Discriminant Approach102
This paper proposes a new feature descriptor called common encoding model for heterogeneous face recognition, which is able to capture common discriminant information, such that the large modality gap can be significantly reduced at the feature extraction stage.
- 101
- Sparse Adversarial Attack via Perturbation Factorization101
An e-cient algorithm is developed by equivalently reformulating the MIP problem as a continuous optimization problem to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.
- 100
- Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack93
This work finds that a benign sample, the current and the next adversarial examples can naturally construct a triangle in a subspace for any iterative attacks, and proposes a novel Triangle Attack (TA) to optimize the perturbation by utilizing the geometric information that the longer side is always opposite the larger angle in any triangle.
- 90
- Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection88
This paper proposes a hierarchical model based on two-level learning for face aging recognition that greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized.
- Target Adaptive Context Aggregation for Video Scene Graph Generation86
This paper designs an efficient method for frame-level VidSGG, termed as Target Adaptive Context Aggregation Network (TRACE), with a focus on capturing spatio-temporal context information for relation recognition.
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 2026-10-13. 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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