no code implementations • 20 Oct 2020 • Ali Akbari, Muhammad Awais, Zhen-Hua Feng, Ammarah Farooq, Josef Kittler
Compared with existing loss functions, the lower gradient of the proposed loss function leads to the convergence of SGD to a better optimum point, and consequently a better generalisation.
no code implementations • 25 Jul 2020 • Jingqiao Zhao, Zhen-Hua Feng, Qiuqiang Kong, Xiaoning Song, Xiao-Jun Wu
This paper presents a Depthwise Disout Convolutional Neural Network (DD-CNN) for the detection and classification of urban acoustic scenes.
1 code implementation • 10 Jul 2020 • He-Feng Yin, Xiao-Jun Wu, Zhen-Hua Feng, Josef Kittler
Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces.
no code implementations • 27 May 2020 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
To this end, we propose a failure-aware system, realised by a Quality Prediction Network (QPN), based on convolutional and LSTM modules in the decision stage, enabling online reporting of potential tracking failures.
no code implementations • 5 Dec 2019 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features.
no code implementations • 23 Nov 2019 • He-Feng Yin, Xiao-Jun Wu, Josef Kittler, Zhen-Hua Feng
To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition.
no code implementations • 19 Sep 2019 • Cong Hu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose.
1 code implementation • ICCV 2019 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking.
Ranked #1 on
Visual Object Tracking
on VOT2017
1 code implementation • 30 Jul 2018 • Tianyang Xu, Zhen-Hua Feng, Xiao-Jun Wu, Josef Kittler
The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold.
no code implementations • 14 Mar 2018 • Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter JB Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, Matthias Rätsch
To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans.
6 code implementations • CVPR 2018 • Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu
We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs).
Ranked #1 on
Face Alignment
on 300W
(NME_inter-pupil (%, Common) metric)
no code implementations • 5 May 2017 • Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu
The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation.
no code implementations • 30 Dec 2016 • Zhen-Hua Feng, Josef Kittler, William Christmas, Xiao-Jun Wu
To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces.
no code implementations • CVPR 2017 • Zhen-Hua Feng, Josef Kittler, William Christmas, Patrik Huber, Xiao-Jun Wu
We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on unconstrained faces.
Ranked #18 on
Face Alignment
on AFLW-19
no code implementations • 1 Nov 2016 • Xiaoning Song, Zhen-Hua Feng, Guosheng Hu, Josef Kittler, William Christmas, Xiao-Jun Wu
The paper presents a dictionary integration algorithm using 3D morphable face models (3DMM) for pose-invariant collaborative-representation-based face classification.
1 code implementation • 8 Mar 2015 • Patrik Huber, Zhen-Hua Feng, William Christmas, Josef Kittler, Matthias Rätsch
Our approach is unique in that we are the first to use local features to fit a Morphable Model.