Visual Question Answering is a semantic task that aims to answer questions based on an image.
Image Source: visualqa.org
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Referring Expression Comprehension (REC) has become one of the most important tasks in visual reasoning, since it is an essential step for many vision-and-language tasks such as visual question answering.
Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions.
Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers.
The key feature of our model is its ability to aggregate three different-level features (local context, scene, and dataset-level) to compositionally predict the visual relationship.
In this work, we perform the first empirical study to assess whether such trainable subnetworks also exist in pre-trained V+L models.
Images are more than a collection of objects or attributes -- they represent a web of relationships among interconnected objects.
Ranked #1 on Graph Question Answering on GQA