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- Facial expression recognition based on Local Binary Patterns: A comprehensive study2,383
This paper empirically evaluates facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition, and observes that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in compressed low-resolution video sequences captured in real-world environments.
- Harmonious Attention Network for Person Re-identification1,463
A novel Harmonious Attention CNN (HA-CNN) model is formulated for joint learning of soft pixel attention and hard regional attention along with simultaneous optimisation of feature representations, dedicated to optimise person re-id in uncontrolled (misaligned) images.
- Semantic Autoencoder for Zero-Shot Learning921
This work presents a novel solution to ZSL based on learning a Semantic AutoEncoder (SAE), which outperforms significantly the existing ZSL models with the additional benefit of lower computational cost and beats the state-of-the-art when the SAE is applied to supervised clustering problem.
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- Person Re-Identification by Support Vector Ranking772
This work converts the person re-identification problem from an absolute scoring p roblem to a relative ranking problem and develops an novel Ensemble RankSVM to overcome the scalability limitation problem suffered by existing SVM-based ranking methods.
- Learning a Deep Embedding Model for Zero-Shot Learning769
This paper proposes to use the visual space as the embedding space instead of embedding into a semantic space or an intermediate space, and argues that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective.
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- Feature Mining for Localised Crowd Counting677
This paper presents a single regression model based approach that is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions.
- Learning a Discriminative Null Space for Person Re-identification669
This work proposes to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data, which has a fixed dimension, a closed-form solution and is very efficient to compute.
- Person Re-identification by Video Ranking642
A novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id is presented.
- Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification637
This work introduces an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identity-discriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain.
- Transductive Multi-View Zero-Shot Learning513
A novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space that rectifies the projection shift between the auxiliary and target domains, exploits the complementarity of multiple semantic representations, and significantly outperforms existing methods for both zero- shot and N-shot recognition.
- Associating Groups of People510
A novel people group representation and a group matching algorithm are proposed that addresses changes in the relative positions of people in a group and the latter deals with variations in illumination and viewpoint across camera views.
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- Person Re-Identification by Deep Joint Learning of Multi-Loss Classification426
This work designs a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information.
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- Unsupervised Person Re-Identification by Soft Multilabel Learning421
The reference agent learning to represent each reference person by a reference agent in a joint embedding is introduced and the unified deep model outperforms the state-of-the-art unsupervised RE-ID methods by clear margins.
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- Tracking colour objects using adaptive mixture models382
The use of adaptive Gaussian mixtures to model the colour distributions of objects is described to perform robust, real-time tracking under varying illumination, viewing geometry and camera parameters.
- Imbalanced Deep Learning by Minority Class Incremental Rectification373
These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.
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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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