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Works303 from public data
- Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification321
A novel augmentation network in the embedding space, called diverse embedding expansion network (DEEN), which can effectively generate diverse embeddings to learn the informative feature representations and reduce the modality discrepancy between the VIS and IR images.
- Towards a Unified Middle Modality Learning for Visible-Infrared Person Re-Identification220
A non-linear middle modality generator (MMG) is proposed, which helps to reduce the modality discrepancy in visible-infrared person re-identification (VI-ReID), and amiddle modality network (MMN) is presented to further enhance the discrimination and richness of features in an explicit manner.
- Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition213
Experimental results show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods, which clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.
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- A consensus-based method for tracking: Modelling background scenario and foreground appearance179
The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model of thebackground, per pixel, which is employed to segment and track people through occlusions.
- Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes147
This paper proposes a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes, which achieves a good trade-off between accuracy and speed.
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- Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking95
This paper constructs an appearance model using the 3D discrete cosine transform (3D-DCT) and designs a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object that is embedded into a particle filtering framework for object state inference over time.
- MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation95
A novel and highly robust estimator, called MDPE1 (Maximum Density Power Estimator), which applies nonparametric density estimation and density gradient estimation techniques in parametric estimation (“model fitting”).
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- Linear discriminant analysis using rotational invariant L1 norm78
A novel rotational invariant L"1 norm (i.e., R"1norm) based discriminant criterion (referred to as DCL"1) is proposed, which better characterizes the intra-class compactness and the inter-class separability by using the rotations of the Frobenius norm.
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- Multi-label learning based deep transfer neural network for facial attribute classification75
A novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-labelLearning Network (MNet) and the Transfer learning Network (TNet).
- Deep Disturbance-Disentangled Learning for Facial Expression Recognition74
This paper proposes a novel Deep Disturbance-disentangled Learning (DDL) method for FER that is capable of simultaneously and explicitly disentangling multiple disturbing factors by taking advantage of multi-task learning and adversarial transfer learning.
- A fast face detection method via convolutional neural network73
Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed fast face detection method based on discriminative complete features extracted by an elaborately designed convolutional neural network.
- Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression73
A novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers is proposed, which improves the convergence speed of the cascaded method, which directly uses random-ferns regressors.
- Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes71
A novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes is presented and an auxiliary training strategy is developed to explicitly alleviate the multi-scale object problem without bringing additional computational costs during the inference phase.
- A Novel Robust Statistical Method for Background Initialization and Visual Surveillance70
A novel robust method for the background initialization that can tolerate more than 50% of foreground pixels and noise and achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.
- Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation65
This work proposes a novel MoE-based method called Self-Heterogeneous Integration with Knowledge Excavation (SHIKE), and proposes Depth-wise Knowledge Fusion (DKF) to fuse features between different shallow parts and the deep part in one network for each expert, which makes experts more diverse in terms of representation.
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 2026-10-14. 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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