Publications (24)
Infinite Variational Autoencoder for Semi-Supervised Learning
Ehsan Abbasnejad, Anthony Dick, Anton van den Hengel
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are…
Deconstruction of compound objects from image sets
Anton van den Hengel, John Bastian, Anthony Dick +1
We propose a method to recover the structure of a compound object from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined libra…
Non-sparse Linear Representations for Visual Tracking with Online Reservoir Metric Learning
Xi Li, Chunhua Shen, Qinfeng Shi +2
Most sparse linear representation-based trackers need to solve a computationally expensive L1-regularized optimization problem. To address this problem, we propose a visual tracker…
Level-Set Parameters: Novel Representation for 3D Shape Analysis
Huan Lei, Hongdong Li, Andreas Geiger +1
3D shape analysis has been largely focused on traditional 3D representations of point clouds and meshes, but the discrete nature of these data makes the analysis susceptible to var…
Bayesian Conditional Generative Adverserial Networks
M. Ehsan Abbasnejad, Qinfeng Shi, Iman Abbasnejad +2
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input to a sample that the discriminator seeks to…
Learning Hash Functions Using Column Generation
Xi Li, Guosheng Lin, Chunhua Shen +2
Fast nearest neighbor searching is becoming an increasingly important tool in solving many large-scale problems. Recently a number of approaches to learning data-dependent hash fun…
Visual Question Answering with Prior Class Semantics
Violetta Shevchenko, Damien Teney, Anthony Dick +1
We present a novel mechanism to embed prior knowledge in a model for visual question answering. The open-set nature of the task is at odds with the ubiquitous approach of training…
DeepSetNet: Predicting Sets with Deep Neural Networks
S. Hamid Rezatofighi, Vijay Kumar B G, Anton Milan +3
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detectio…
Contextual Hypergraph Modelling for Salient Object Detection
Xi Li, Yao Li, Chunhua Shen +2
Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this wor…
Explicit Knowledge-based Reasoning for Visual Question Answering
Peng Wang, Qi Wu, Chunhua Shen +2
We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base.…
Context-Aware Hypergraph Construction for Robust Spectral Clustering
Xi Li, Weiming Hu, Chunhua Shen +2
Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we propose a context-aware hypergraph similarity measure (CAHSM), which leads to robust spectr…
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering
Violetta Shevchenko, Ehsan Abbasnejad, Anthony Dick +2
The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vi…
Online Multi-Target Tracking Using Recurrent Neural Networks
Anton Milan, Seyed Hamid Rezatofighi, Anthony Dick +2
We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, incl…
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Xi Li, Chunhua Shen, Anthony Dick +2
In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions,…
Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking
Xi Li, Anthony Dick, Chunhua Shen +2
Visual tracking usually requires an object appearance model that is robust to changing illumination, pose and other factors encountered in video. In this paper, we construct an app…
Visual Question Answering with Memory-Augmented Networks
Chao Ma, Chunhua Shen, Anthony Dick +4
In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory ne…
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
Violetta Shevchenko, Damien Teney, Anthony Dick +1
The limits of applicability of vision-and-language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense a…
Joint Learning of Set Cardinality and State Distribution
S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi +2
We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural langua…
Visual Question Answering: A Survey of Methods and Datasets
Qi Wu, Damien Teney, Peng Wang +3
Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an…
What value do explicit high level concepts have in vision to language problems?
Qi Wu, Chunhua Shen, Lingqiao Liu +2
Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs…
FVQA: Fact-based Visual Question Answering
Peng Wang, Qi Wu, Chunhua Shen +2
Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the r…
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Qi Wu, Chunhua Shen, Anton van den Hengel +2
Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This appro…
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Qi Wu, Peng Wang, Chunhua Shen +2
We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to a…
A Survey of Appearance Models in Visual Object Tracking
Xi Li, Weiming Hu, Chunhua Shen +3
Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. In…