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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.
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- 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.
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- 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.
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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.
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- 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.
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- 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.
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- 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.
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- 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.
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- Joint Face Image Restoration and Frontalization for Recognition75
Qualitative and quantitative experiments demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration and can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity.
- Follow-Your-Click: Open-domain Regional Image Animation via Motion Prompts73
This paper proposes a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click and a motion prompt, and proposes flow-based motion magnitude control to control the speed of target movement more precisely.
- Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts68
This paper proposes a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click and a short motion prompt and has simpler yet precise user control and better generation performance than previous methods.
- Image-to-Video Generation via 3D Facial Dynamics62
This paper proposes to “imagine” a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression.
- Backdoor Attack With Sparse and Invisible Trigger58
This paper revisits the trigger patterns of existing backdoor attacks and formulate the trigger generation as a bi-level optimization problem with sparsity and invisibility constraints and proposes an effective method to solve it, dubbed sparse and invisible backdoor attack (SIBA).
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- AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks53
This work proposes a fully differentiable search framework, dubbed alphaGAN, where the searching process is formalized as solving a bi-level minimax optimization problem, and provides a comprehensive analysis of the behavior of the search process and the properties of searched architectures, which would benefit further research on architectures for generative models.
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- Mutual Component Analysis for Heterogeneous Face Recognition48
A novel approach called Mutual Component Analysis (MCA) is proposed to infer the mutual components for robust heterogeneous face recognition that are associated with the person’s identity, thus enabling fast and effective matching for cross-modality face recognition.
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- Nearest is Not Dearest: Towards Practical Defense Against Quantization-Conditioned Backdoor Attacks40
This paper conducts the first in-depth analysis of QCBs and proposes Error-guided Flipped Rounding with Activation Preservation (EFRAP), an effective and practical defense against QCBs that flips the rounding strategies of neurons crucial for back-door effects but with minimal impact on clean accuracy.
- Multi-feature canonical correlation analysis for face photo-sketch image retrieval40
The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space to improve the performance of automatic face photo-sketch image retrieval.
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- Versatile Weight Attack via Flipping Limited Bits30
A novel attack paradigm, the bit-flip based weight attack, which directly modifies weight bits of the attacked model in the deployment stage and can be effectively and efficiently obtained by the alternating direction method of multipliers method.
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- UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing21
A novel 3D temporal loss constraint is introduced based on the barycentric coordinate interpolation and a region-aware conditional normalization layer is proposed to replace the traditional AdaIN or SPADE to synthesize more context-harmonious results.
- Pixel‐wise Dense Detector for Image Inpainting21
A novel detection‐based generative framework for image inpainting, which adopts the min‐max strategy in an adversarial process and explicitly inserts the output of the detector into the reconstruction loss with a weighting criterion, which balances the weight of the adversarial loss and reconstruction loss automatically rather than manual operation.
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- Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions With Multi-Level Representations19
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- Regional Adversarial Training for Better Robust Generalization14
A new adversarial training framework that considers the diversity as well as characteristics of the perturbed points in the vicinity of benign samples, and proposes a Regional Adversarial Training (RAT) defense method that first utilizes the attack path generated by the typical iterative attack method of projected gradient descent (PGD).
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- Feature-level Incongruence Reduction for Multimodal Translation6
This work proposes to extend MNMT architecture with a harmonization network, which harmonizes multimodal features(linguistic and visual features) by unidirectional modal space conversion by leading to the competitive performance to the-state-of-the-art.
- Efficient Black-Box Adversarial Attack Guided by the Distribution of Adversarial Perturbations.6
This work proposed to transform the Gaussian-distributed variable to another space through a conditional flow-based model, to enhance the capability and flexibility of capturing the intrinsic distribution of adversarial perturbations conditioned on the benign example, and pre-train the conditional flow model based on some white-box surrogate models.
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- DGL-GAN: discriminator-guided GAN compression5
This work proposes a novel yet simple Discriminator-Guided Learning approach for compressing vanilla GAN, dubbed DGL-GAN, and applies it to compress the two most representative large-scale vanilla GANs, i.e., StyleGAN2 and BigGAN.
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- Dynamic PDGAN: discriminator-boosted knowledge distillation for StyleGANs–
This work proposes to exploit the teacher discriminator to additionally supervise the student generator of StyleGANs so as to leverage the knowledge in the teacher discriminator, and can outperform original StyleGANs on several large-scale datasets.
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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 2026-10-13. 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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