Deep Neural Networks for Visual Reasoning

TL;DR

This paper explores how deep neural networks can be used for multimodal reasoning involving visual scenes and language, advancing methods for content selection, temporal relation understanding, and reasoning frameworks.

Contribution

It introduces new neural network frameworks for multimodal reasoning and mechanisms for content selection and temporal relation modeling in visual-linguistic tasks.

Findings

Effective content selection mechanisms developed

New frameworks for visual-linguistic reasoning proposed

Improved understanding of multimodal associations

Abstract

Visual perception and language understanding are - fundamental components of human intelligence, enabling them to understand and reason about objects and their interactions. It is crucial for machines to have this capacity to reason using these two modalities to invent new robot-human collaborative systems. Recent advances in deep learning have built separate sophisticated representations of both visual scenes and languages. However, understanding the associations between the two modalities in a shared context for multimodal reasoning remains a challenge. Focusing on language and vision modalities, this thesis advances the understanding of how to exploit and use pivotal aspects of vision-and-language tasks with neural networks to support reasoning. We derive these understandings from a series of works, making a two-fold contribution: (i) effective mechanisms for content selection and construction of temporal relations from dynamic visual scenes in response to a linguistic query and preparing adequate knowledge for the reasoning process (ii) new frameworks to perform reasoning with neural networks by exploiting visual-linguistic associations, deduced either directly from data or guided by external priors.