3.5k citations · 10.5k across the 119 of their papers we have counts for
24 papers · 1 filter
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
Bohan Zhuang, Chunhua Shen, Mingkui Tan +2
In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices wi…
Scalable Deep -Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi +2
Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a metho…
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette
Kejie Li, Ravi Garg, Ming Cai +1
3D shape reconstruction from a single image is a highly ill-posed problem. Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image…
Visual Localization Under Appearance Change: Filtering Approaches
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin +4
A major focus of current research on place recognition is visual localization for autonomous driving. In this scenario, as cameras will be operating continuously, it is realistic t…
Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks
Zhibin Liao, Tom Drummond, Ian Reid +1
In this paper, we introduce a novel methodology for characterising the performance of deep learning networks (ResNets and DenseNet) with respect to training convergence and general…
Light-Weight RefineNet for Real-Time Semantic Segmentation
Vladimir Nekrasov, Chunhua Shen, Ian Reid
We consider an important task of effective and efficient semantic image segmentation. In particular, we adapt a powerful semantic segmentation architecture, called RefineNet, into…
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
Vladimir Nekrasov, Hao Chen, Chunhua Shen +1
Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this…
Diagnostics in Semantic Segmentation
Vladimir Nekrasov, Chunhua Shen, Ian Reid
Over the past years, computer vision community has contributed to enormous progress in semantic image segmentation, a per-pixel classification task, crucial for dense scene underst…
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento +2
We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the im…
Real-Time Monocular Object-Model Aware Sparse SLAM
Mehdi Hosseinzadeh, Kejie Li, Yasir Latif +1
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While sparse point-based SLAM methods provide accurate camera localization, the generated…
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek +3
Deployment of deep learning models in robotics as sensory information extractors can be a daunting task to handle, even using generic GPU cards. Here, we address three of its most…
Training Compact Neural Networks with Binary Weights and Low Precision Activations
Bohan Zhuang, Chunhua Shen, Ian Reid
In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption. Most previous…
MatchBench: An Evaluation of Feature Matchers
JiaWang Bian, Ruihan Yang, Yun Liu +4
Feature matching is one of the most fundamental and active research areas in computer vision. A comprehensive evaluation of feature matchers is necessary, since it would advance bo…
Multi-modal Cycle-consistent Generalized Zero-Shot Learning
Rafael Felix, B. G. Vijay Kumar, Ian Reid +1
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes a…
Bayesian Semantic Instance Segmentation in Open Set World
Trung Pham, Vijay Kumar B G, Thanh-Toan Do +2
This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of ex…
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley +2
There is a heated debate on how to interpret the decisions provided by deep learning models (DLM), where the main approaches rely on the visualization of salient regions to interpr…
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Chamara Saroj Weerasekera, Thanuja Dharmasiri, Ravi Garg +2
We present ``just-in-time reconstruction" as real-time image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In part…
Training Medical Image Analysis Systems like Radiologists
Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento +2
The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training…
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
S. Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh +4
Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which…
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera +3
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner. Recen…
Structure Aware SLAM using Quadrics and Planes
Mehdi Hosseinzadeh, Yasir Latif, Trung Pham +2
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps la…
Object Captioning and Retrieval with Natural Language
Anh Nguyen, Thanh-Toan Do, Ian Reid +2
We address the problem of jointly learning vision and language to understand the object in a fine-grained manner. The key idea of our approach is the use of object descriptions to…
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
Thanh-Toan Do, Ming Cai, Trung Pham +1
Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual o…
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +4
Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task…