As an important task in multimodal context understanding, Text-VQA aims at question
answering through reading text information in images. It differentiates from the original VQA …

Transformer-based models have achieved some success in time series forecasting. Existing
methods mainly model time series from limited or fixed scales, making it challenging to …

Most of the image-text retrieval methods carry out accurate results using fine-grained
features for feature alignment. However, extracting the robustness features while …

Text-based visual question answering (TextVQA), which answers a visual question by
considering both visual contents and scene texts, has attracted increasing attention recently …

All graphical user interfaces are comprised of one or more screens that may be shown to the
user depending on their interactions. Identifying different screens of an app and …

We generalize the notion of social biases from language embeddings to grounded vision
and language embeddings. Biases are present in grounded embeddings, and indeed seem …

One of the ways blind people understand their surroundings is by clicking images and
relying on descriptions generated by image captioning systems. Current work on captioning …

Abstract Both Visual Question Answering (VQA) and image captioning are the problems
which involve Computer Vision (CV) and Natural Language Processing (NLP) domains. In …

Text-based Visual Question Answering (TextVQA) aims at answering questions about the
text in images. Most works in this field focus on designing network structures or pre-training …

The ability to recognize and reason about text embedded in visual inputs is often lacking in
vision-and-language (V&L) models, perhaps because V&L pre-training methods have often …