no code implementations • NeurIPS 2020 • Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Qinfeng Shi, Anton Van Den Hengel
The task of vision-and-language navigation (VLN) requires an agent to follow text instructions to find its way through simulated household environments.
no code implementations • 2 Aug 2020 • Alireza Abedin, Farbod Motlagh, Qinfeng Shi, Seyed Hamid Rezatofighi, Damith Chinthana Ranasinghe
Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data.
no code implementations • 14 Jul 2020 • Alireza Abedin, Mahsa Ehsanpour, Qinfeng Shi, Hamid Rezatofighi, Damith C. Ranasinghe
Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis.
no code implementations • 23 Apr 2020 • Qingsen Yan, Bo wang, Dong Gong, Chuan Luo, Wei Zhao, Jianhu Shen, Qinfeng Shi, Shuo Jin, Liang Zhang, Zheng You
Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.
no code implementations • 30 Jan 2020 • Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Anton Milan, Daniel Cremers, Laura Leal-Taixé, Ian Reid
In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.
no code implementations • 8 Jan 2020 • Dong Gong, Wei Sun, Qinfeng Shi, Anton Van Den Hengel, Yanning Zhang
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs.
no code implementations • 12 Nov 2019 • Liangyi Kang, Jie Liu, Lingqiao Liu, Qinfeng Shi, Dan Ye
Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation.
no code implementations • 9 Oct 2019 • Yinglong Wang, Haokui Zhang, Yu Liu, Qinfeng Shi, Bing Zeng
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
no code implementations • 6 Jul 2019 • Mehdi Neshat, Ehsan Abbasnejad, Qinfeng Shi, Bradley Alexander, Markus Wagner
The installed amount of renewable energy has expanded massively in recent years.
no code implementations • 22 Jun 2019 • Yinglong Wang, Qinfeng Shi, Ehsan Abbasnejad, Chao Ma, Xiaoping Ma, Bing Zeng
Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network.
no code implementations • 6 Jun 2019 • Alireza Abedin, S. Hamid Rezatofighi, Qinfeng Shi, Damith C. Ranasinghe
Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people.
no code implementations • 14 May 2019 • Yinglong Wang, Dong Gong, Jie Yang, Qinfeng Shi, Anton Van Den Hengel, Dehua Xie, Bing Zeng
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.
5 code implementations • CVPR 2019 • Qingsen Yan, Dong Gong, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes.
1 code implementation • 27 Mar 2019 • Yong Guo, Qi Chen, Jian Chen, Qingyao Wu, Qinfeng Shi, Mingkui Tan
To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details.
no code implementations • CVPR 2019 • Jie Li, Yu Liu, Dong Gong, Qinfeng Shi, Xia Yuan, Chunxia Zhao, Ian Reid
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).
Ranked #19 on 3D Semantic Scene Completion on NYUv2
no code implementations • 12 Jan 2019 • Yu Liu, Lingqiao Liu, Hamid Rezatofighi, Thanh-Toan Do, Qinfeng Shi, Ian Reid
As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years.
no code implementations • 20 Nov 2018 • Alireza Abedin Varamin, Ehsan Abbasnejad, Qinfeng Shi, Damith Ranasinghe, Hamid Rezatofighi
Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing.
no code implementations • CVPR 2019 • Yuhang Liu, Wenyong Dong, Lei Zhang, Dong Gong, Qinfeng Shi
Then, we incorporate such a prior into inferring the joint posterior over network weights and the variance in the hierarchical prior, with which both the network training and the dropout rate estimation can be cast into a joint optimization problem.
no code implementations • 12 Oct 2018 • Dong Gong, Mingkui Tan, Qinfeng Shi, Anton Van Den Hengel, Yanning Zhang
Compared to existing methods, MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization.
no code implementations • ECCV 2018 • Yuhang Liu, Wenyong Dong, Dong Gong, Lei Zhang, Qinfeng Shi
Existing sparsity-based priors are usually rooted in modeling the response of images to some specific filters (e. g., image gradients), which are insufficient to capture the complicated image structures.
1 code implementation • ECCV 2018 • Jie Yang, Dong Gong, Lingqiao Liu, Qinfeng Shi
Reflections often obstruct the desired scene when taking photos through glass panels.
no code implementations • ICLR 2019 • S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Daniel Cremers, Laura Leal-Taixé, Ian Reid
We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.
no code implementations • 10 Apr 2018 • Yingqi Qu, Jie Liu, Liangyi Kang, Qinfeng Shi, Dan Ye
To preserve more original information, we propose an attentive recurrent neural network with similarity matrix based convolutional neural network (AR-SMCNN) model, which is able to capture comprehensive hierarchical information utilizing the advantages of both RNN and CNN.
1 code implementation • 10 Apr 2018 • Dong Gong, Zhen Zhang, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Yanning Zhang
Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.
no code implementations • ICCV 2017 • Dong Gong, Mingkui Tan, Yanning Zhang, Anton Van Den Hengel, Qinfeng Shi
Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the estimation process.
no code implementations • 13 Sep 2017 • S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi, Anthony Dick, Ian Reid
We present a novel approach for learning to predict sets using deep learning.
no code implementations • 3 Aug 2017 • Lei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton Van Den Hengel, Yanning Zhang
The prior for the non-low-rank structure is established based on a mixture of Gaussians which is shown to be flexible enough, and powerful enough, to inform the completion process for a variety of real tensor data.
no code implementations • 17 Jun 2017 • M. Ehsan Abbasnejad, Qinfeng Shi, Iman Abbasnejad, Anton Van Den Hengel, Anthony Dick
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish.
no code implementations • 20 Feb 2017 • Rui Yao, Guosheng Lin, Qinfeng Shi, Damith Ranasinghe
We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of classification and label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
no code implementations • CVPR 2017 • Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, Qinfeng Shi
The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content.
1 code implementation • 6 Nov 2016 • Yong Guo, Jian Chen, Qing Du, Anton Van Den Hengel, Qinfeng Shi, Mingkui Tan
As a result, the representation power of intermediate layers can be very weak and the model becomes very redundant with limited performance.
no code implementations • CVPR 2016 • Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi
Trace-norm regularization plays an important role in many areas such as machine learning and computer vision.
no code implementations • CVPR 2016 • Zhen Zhang, Qinfeng Shi, Julian McAuley, Wei Wei, Yanning Zhang, Anton Van Den Hengel
Feature matching is a key problem in computer vision and pattern recognition.
no code implementations • CVPR 2016 • Dong Gong, Mingkui Tan, Yanning Zhang, Anton Van Den Hengel, Qinfeng Shi
We show here that a subset of the image gradients are adequate to estimate the blur kernel robustly, no matter the gradient image is sparse or not.
no code implementations • CVPR 2016 • Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid
Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.
no code implementations • 11 Mar 2016 • Roberto L. Shinmoto Torres, Damith C. Ranasinghe, Qinfeng Shi, Anton Van Den Hengel
The present study introduces a method for improving the classification performance of imbalanced multiclass data streams from wireless body worn sensors.
no code implementations • ICCV 2015 • Lei Zhang, Wei Wei, Yanning Zhang, Fei Li, Chunhua Shen, Qinfeng Shi
To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study.
1 code implementation • ICCV 2015 • Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.
no code implementations • 15 Jun 2015 • Julian McAuley, Christopher Targett, Qinfeng Shi, Anton Van Den Hengel
Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance.
no code implementations • CVPR 2015 • Mingkui Tan, Qinfeng Shi, Anton Van Den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, Zhen Zhang
Exploiting label dependency for multi-label image classification can significantly improve classification performance.
no code implementations • 10 Mar 2015 • Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, Qinfeng Shi
Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR).
no code implementations • 2 Dec 2014 • Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, Zhenmin Tang, Heng Tao Shen
In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
no code implementations • 27 Nov 2014 • Hui Li, Chunhua Shen, Anton Van Den Hengel, Qinfeng Shi
Our method is also much faster and more scalable than standard interior-point SDP solvers based WLDA.
1 code implementation • CVPR 2014 • Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, David Suter
Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data.
no code implementations • 9 Feb 2014 • Qinfeng Shi, Mark Reid, Tiberio Caetano, Anton Van Den Hengel, Zhenhua Wang
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs).
no code implementations • 17 Dec 2013 • Zhen Zhang, Qinfeng Shi, Yanning Zhang, Chunhua Shen, Anton Van Den Hengel
We show that using Marginal Polytope Diagrams allows the number of constraints to be reduced without loosening the LP relaxations.
no code implementations • CVPR 2013 • Rui Yao, Qinfeng Shi, Chunhua Shen, Yanning Zhang, Anton Van Den Hengel
Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking.
no code implementations • CVPR 2013 • Zhenhua Wang, Qinfeng Shi, Chunhua Shen, Anton Van Den Hengel
Markov Random Fields (MRFs) have been successfully applied to human activity modelling, largely due to their ability to model complex dependencies and deal with local uncertainty.
no code implementations • CVPR 2013 • Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, Zhenmin Tang
We particularly show that hashing on the basis of t-SNE .