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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…
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…
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…