Visual Question Answering systems target answering open-ended textual questions given
input images. They are a testbed for learning high-level reasoning with a primary use in HCI …

This paper comprehensively reviews medical VQA models, structures, and datasets,
focusing on combining vision and language. Over 75 models and their statistical and SWOT …

Visual dialog requires models to give reasonable answers according to a series of coherent
questions and related visual concepts in images. However, most current work either focuses …

Abstract Vision-and-language (V&L) reasoning necessitates perception of visual concepts
such as objects and actions, understanding semantics and language grounding, and …

Abstract 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 …

Visual reasoning between visual images and natural language is a long-standing challenge
in computer vision. Most of the methods aim to look for answers to questions only on the …

Image-text matching plays an important role in bridging vision and language. Most existing
research works embed both images and sentences into a joint latent space to measure their …

3D Referring Expression Segmentation (3D-RES) aims to segment 3D objects by correlating
referring expressions with point clouds. However, traditional approaches frequently …

Visual reasoning between visual images and natural language remains a long-standing
challenge in computer vision. Conventional deep supervision methods target at finding …

Abstract Methods for Visual Question Anwering (VQA) are notorious for leveraging dataset
biases rather than performing reasoning, hindering generalization. It has been recently …