VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning
This addresses the limitation of existing VQA benchmarks that lack attention to text and open-ended answers, targeting researchers in AI and computer vision for improved scene understanding.
The paper tackles the problem of Visual Question Answering (VQA) by introducing a new challenge that requires aligning multimedia representations of entities and performing cross-media reasoning, resulting in a dataset with 23,781 questions based on 10,124 image-text pairs.
The ideal form of Visual Question Answering requires understanding, grounding and reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most existing VQA benchmarks are limited to just picking the answer from a pre-defined set of options and lack attention to text. We present a new challenge with a dataset that contains 23,781 questions based on 10124 image-text pairs. Specifically, the task requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this challenge is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation.