Search Results for author:
Found 9 papers, 3 papers with code
An experimental study of the vision-bottleneck in VQA
We also study the impact of two methods to incorporate the information about objects necessary for answering a question, in the reasoning module directly, and earlier in the object selection stage.
Are E2E ASR models ready for an industrial usage?
The Automated Speech Recognition (ASR) community experiences a major turning point with the rise of the fully-neural (End-to-End, E2E) approaches.
Supervising the Transfer of Reasoning Patterns in VQA
Methods for Visual Question Anwering (VQA) are notorious for leveraging dataset biases rather than performing reasoning, hindering generalization.
How Transferable are Reasoning Patterns in VQA?
Since its inception, Visual Question Answering (VQA) is notoriously known as a task, where models are prone to exploit biases in datasets to find shortcuts instead of performing high-level reasoning.
VisQA: X-raying Vision and Language Reasoning in Transformers
First, as a result of a collaboration of three fields, machine learning, vision and language reasoning, and data analytics, the work lead to a better understanding of bias exploitation of neural models for VQA, which eventually resulted in an impact on its design and training through the proposition of a method for the transfer of reasoning patterns from an oracle model.
Estimating semantic structure for the VQA answer space
Since its appearance, Visual Question Answering (VQA, i. e. answering a question posed over an image), has always been treated as a classification problem over a set of predefined answers.
Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?
Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to reason, leading them to perform educated guesses instead.
Weak Supervision helps Emergence of Word-Object Alignment and improves Vision-Language Tasks
The large adoption of the self-attention (i. e. transformer model) and BERT-like training principles has recently resulted in a number of high performing models on a large panoply of vision-and-language problems (such as Visual Question Answering (VQA), image retrieval, etc.).
Face Aging With Conditional Generative Adversarial Networks
It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity.
