44 citations · 279 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
Multi-Label Image Classification with Regional Latent Semantic Dependencies
Junjie Zhang, Qi Wu, Chunhua Shen +2
Deep convolution neural networks (CNN) have demonstrated advanced performance on single-label image classification, and various progress also have been made to apply CNN methods on…
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…
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…