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Facial expression recognition based on Local Binary Patterns: A comprehensive study
TLDR
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. Expand
Person Re-identification by Video Ranking
TLDR
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. Expand
Harmonious Attention Network for Person Re-identification
  • Wei Li, Xiatian Zhu, S. Gong
  • Computer Science
  • IEEE/CVF Conference on Computer Vision and…
  • 22 February 2018
TLDR
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. Expand
Person re-identification by probabilistic relative distance comparison
TLDR
A novel Probabilistic Relative Distance Comparison (PRDC) model is introduced, which differs from most existing distance learning methods in that it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair, which makes the model more tolerant to appearance changes and less susceptible to model over-fitting. Expand
Semantic Autoencoder for Zero-Shot Learning
TLDR
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. Expand
Reidentification by Relative Distance Comparison
  • W. Zheng, S. Gong, T. Xiang
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 March 2013
TLDR
This paper formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images and develops an ensemble RDC model. Expand
Learning a Deep Embedding Model for Zero-Shot Learning
  • Li Zhang, T. Xiang, S. Gong
  • Computer Science, Mathematics
  • IEEE Conference on Computer Vision and Pattern…
  • 15 November 2016
TLDR
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. Expand
Learning a Discriminative Null Space for Person Re-identification
  • Li Zhang, T. Xiang, S. Gong
  • Computer Science, Mathematics
  • IEEE Conference on Computer Vision and Pattern…
  • 7 March 2016
TLDR
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. Expand
Person Re-Identification by Support Vector Ranking
TLDR
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. Expand
Feature Mining for Localised Crowd Counting
TLDR
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. Expand
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