96 citations · 451 across the 20 of their papers we have counts for
22 papers
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…
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…
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…
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 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…
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…