RESEARCH

NLP

Spatially Aware Multimodal Transformers for TextVQA

July 14, 2020

Abstract

Textual cues are essential for everyday tasks like buying groceries and using public transport. To develop this assistive technology, we study the TextVQA task, i.e., reasoning about text in images to answer a question. Existing approaches are limited in their use of spatial relations and rely on fully-connected transformer-based architectures to implicitly learn the spatial structure of a scene. In contrast, we propose a novel spatially aware self-attention layer such that each visual entity only looks at neighboring entities defined by a spatial graph. Further, each head in our multi-head self-attention layer focuses on a different subset of relations. Our approach has two advantages: (1) each head considers local context instead of dispersing the attention amongst all visual entities; (2) we avoid learning redundant features. We show that our model improves the absolute accuracy of current state-of-the-art methods on TextVQA by 2.2% overall over an improved baseline, and 4.62% on questions that involve spatial reasoning and can be answered correctly using OCR tokens. Similarly on ST-VQA, we improve the absolute accuracy by 4.2%. We further show that spatially aware self-attention improves visual grounding.

Download the Paper

AUTHORS

Written by

Devi Parikh

Dhruv Batra

Alex Shwing

Harsh Agrawal

Peter Anderson

Yash Kant

Publisher

ECCV

Related Publications

October 04, 2024

HUMAN & MACHINE INTELLIGENCE

CONVERSATIONAL AI

Beyond Turn-Based Interfaces: Synchronous LLMs as Full-Duplex Dialogue Agents

Bandhav Veluri, Benjamin Peloquin, Bokai Yu, Hongyu Gong, Shyam Gollakota

October 04, 2024

October 03, 2024

NLP

BLASER 2.0: a metric for evaluation and quality estimation of massively multilingual speech and text translation

David Dale, Marta R. Costa-jussa

October 03, 2024

September 26, 2024

SPEECH & AUDIO

NLP

Unveiling the Role of Pretraining in Direct Speech Translation

Belen Alastruey, Gerard I. Gállego, Marta R. Costa-jussa

September 26, 2024

September 05, 2024

CONVERSATIONAL AI

NLP

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Luke Zettlemoyer, Omer Levy, Xuezhe Ma

September 05, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.