2.7k citations · 7.3k across the 172 of their papers we have counts for
18 papers · 2 filters
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions
Peng Wang, Qi Wu, Chunhua Shen +1
One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them de…
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Dong Gong, Jie Yang, Lingqiao Liu +5
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior,…
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Zifeng Wu, Chunhua Shen, Anton van den Hengel
The trend towards increasingly deep neural networks has been driven by a general observation that increasing depth increases the performance of a network. Recently, however, eviden…
Sequential Person Recognition in Photo Albums with a Recurrent Network
Yao Li, Guosheng Lin, Bohan Zhuang +3
Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting an…
Zero-Shot Visual Question Answering
Damien Teney, Anton van den Hengel
Part of the appeal of Visual Question Answering (VQA) is its promise to answer new questions about previously unseen images. Most current methods demand training questions that ill…
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
Yong Guo, Jian Chen, Qing Du +3
Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet, we are able to train very deep CNNs as the gradient vanishing issue has be…
Graph-Structured Representations for Visual Question Answering
Damien Teney, Lingqiao Liu, Anton van den Hengel
This paper proposes to improve visual question answering (VQA) with structured representations of both scene contents and questions. A key challenge in VQA is to require joint reas…
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…
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach
Lin Wu, Chunhua Shen, Anton van den Hengel
In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification. Give…
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification
Lin Wu, Chunhua Shen, Anton van den Hengel
Person re-identification is to seek a correct match for a person of interest across views among a large number of imposters. It typically involves two procedures of non-linear feat…
Bridging Category-level and Instance-level Semantic Image Segmentation
Zifeng Wu, Chunhua Shen, Anton van den Hengel
We propose an approach to instance-level image segmentation that is built on top of category-level segmentation. Specifically, for each pixel in a semantic category mask, its corre…
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
Zifeng Wu, Chunhua Shen, Anton van den Hengel
We propose a method for high-performance semantic image segmentation (or semantic pixel labelling) based on very deep residual networks, which achieves the state-of-the-art perform…
Less is more: zero-shot learning from online textual documents with noise suppression
Ruizhi Qiao, Lingqiao Liu, Chunhua Shen +1
Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it allevia…
Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution
Yao Li, Linqiao Liu, Chunhua Shen +1
Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can…
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…
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution
Peng Wang, Lingqiao Liu, Chunhua Shen +2
In this work, we study the challenging problem of identifying the irregular status of objects from images in an "open world" setting, that is, distinguishing the irregular status o…
PersonNet: Person Re-identification with Deep Convolutional Neural Networks
Lin Wu, Chunhua Shen, Anton van den Hengel
In this paper, we propose a deep end-to-end neu- ral network to simultaneously learn high-level features and a corresponding similarity metric for person re-identification. The net…
Compositional Model based Fisher Vector Coding for Image Classification
Lingqiao Liu, Peng Wang, Chunhua Shen +4
Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Mo…