Search Results for author: Qinfeng Shi

Found 49 papers, 6 papers with code

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen

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.

Embodied Question Answering Question Answering +1

Towards Deep Clustering of Human Activities from Wearables

no code implementations2 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.

Deep Clustering Human Activity Recognition +1

Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors

no code implementations14 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.

Human Activity Recognition

COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

no code implementations23 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.

Computed Tomography (CT) Medical Image Segmentation +1

Learn to Predict Sets Using Feed-Forward Neural Networks

no code implementations30 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.

Multi-Label Image Classification object-detection +1

Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation

no code implementations8 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.

Super-Resolution

Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction

no code implementations12 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.

Gradient Information Guided Deraining with A Novel Network and Adversarial Training

no code implementations9 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.

Rain Removal

Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes

no code implementations22 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.

Computer Vision Single Image Deraining

SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

no code implementations6 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.

Human Activity Recognition

An Effective Two-Branch Model-Based Deep Network for Single Image Deraining

no code implementations14 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.

Autonomous Driving Single Image Deraining

Attention-guided Network for Ghost-free High Dynamic Range Imaging

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.

Optical Flow Estimation

Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

1 code implementation27 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.

Image Generation Scene Understanding

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

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).

3D Semantic Scene Completion Scene Labeling

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

no code implementations12 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.

object-detection Object Detection

Variational Bayesian Dropout with a Hierarchical Prior

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.

MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution

no code implementations12 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.

Computer Vision Image Deconvolution

Deblurring Natural Image Using Super-Gaussian Fields

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.

Blind Image Deblurring Image Deblurring

Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks

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.

object-detection Object Detection

Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN

no code implementations10 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.

Question Answering

Learning Deep Gradient Descent Optimization for Image Deconvolution

1 code implementation10 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.

Blind Image Deblurring Image Deblurring +1

Self-Paced Kernel Estimation for Robust Blind Image Deblurring

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.

Blind Image Deblurring Image Deblurring

Beyond Low Rank: A Data-Adaptive Tensor Completion Method

no code implementations3 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.

Bayesian Conditional Generative Adverserial Networks

no code implementations17 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.

Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks

no code implementations20 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.

From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur

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.

The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs

1 code implementation6 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.

Model Compression Model Selection

Blind Image Deconvolution by Automatic Gradient Activation

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.

Image Deconvolution

Joint Probabilistic Matching Using m-Best Solutions

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.

Person Re-Identification

Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior

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.

Compressive Sensing

Joint Probabilistic Data Association Revisited

no code implementations 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.

Image-based Recommendations on Styles and Substitutes

no code implementations15 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.

Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient

no code implementations10 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).

Matrix Completion

Hashing on Nonlinear Manifolds

no code implementations2 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.

Image Classification Quantization +1

Fast Supervised Hashing with Decision Trees for High-Dimensional Data

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.

A Hybrid Loss for Multiclass and Structured Prediction

no code implementations9 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).

Action Recognition Structured Prediction

Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations

no code implementations17 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.

Part-Based Visual Tracking with Online Latent Structural Learning

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.

Structured Prediction Visual Tracking

Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs

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.

Human Activity Recognition

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