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- Jun Hai GuoSuggested from co-authorship
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Works274 from public data
- Deep visual domain adaptation: A survey2,407
A taxonomy of different deep domain adaption scenarios according to the properties of data that define how two domains are diverged is presented, and some potential deficiencies of current methods and several future directions are highlighted.
- Deep Facial Expression Recognition: A Survey1,796
This survey provides a comprehensive review of deep FER, including datasets and algorithms that provide insights into these intrinsic problems, and introduces existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences.
- Deep face recognition: A survey1,485
A comprehensive review of the recent developments on deep FR, covering broad topics on algorithm designs, databases, protocols, and application scenes, and the technical challenges and several promising directions are highlighted.
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- Mixed High-Order Attention Network for Person Re-Identification415
This paper proposes the High-Order Attention (HOA) module to model and utilize the complex and high-order statistics information in attention mechanism, so as to capture the subtle differences among pedestrians and to produce the discriminative attention proposals.
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- Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"387
This communication shows that the basic ideas of UDP and LPP are identical, and UDP is just a simplified version of LPP on the assumption that the local density is uniform.
- Representative Forgery Mining for Fake Face Detection294
This work proposes an attention-based data augmentation framework to guide detector refine and enlarge its attention, and tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery.
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- Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing213
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space and a new large-scale benchmark for FAS is built up to further evaluate the performance of algorithms in reality.
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- Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation146
This paper empirically verifies the superiority of the early softmax desaturation, and proposes Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration and improves the generalization ability of CNN model by regularization.
- A Deeper Look at Facial Expression Dataset Bias144
A novel deep Emotion-Conditional Adaption Network (ECAN) is proposed to learn domain-invariant and discriminative feature representations, which can match not only the marginal distribution but also the class-conditional distribution across domains by exploring the underlying label information of the target dataset.
- Blended Emotion in-the-Wild: Multi-label Facial Expression Recognition Using Crowdsourced Annotations and Deep Locality Feature Learning139
A new deep manifold learning network is proposed, called Deep Bi-Manifold CNN, to learn the discriminative feature for multi-label expressions by jointly preserving the local affinity of deep features and the manifold structures of emotion labels.
- SwinFace: A Multi-Task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation134
A multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation based on a single Swin Transformer based on a single Swin Transformer is presented.
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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