Multimodel Large Language Models (MLLMs) have achieved promising OCR-free
Document Understanding performance by increasing the supported resolution of document …

While existing video benchmarks largely consider specialized downstream tasks like
retrieval or question-answering (QA) contemporary multimodal AI systems must be capable …

Everyday news coverage has shifted from traditional broadcasts towards a wide range of
presentation formats such as first-hand, unedited video footage. Datasets that reflect the …

Text and signs around roads provide crucial information for drivers, vital for safe navigation
and situational awareness. Scene text recognition in motion is a challenging problem, while …

Text-based VQA aims at answering questions by reading the text present in the images. It
requires a large amount of scene-text relationship understanding compared to the VQA task …

The rapid explosion of video distribution is accompanied by a massive amount of video text,
which encompasses rich information about the video content. While previous research has …

Researchers have extensively studied the field of vision and language, discovering that both
visual and textual content is crucial for understanding scenes effectively. Particularly …

Existing efforts in text-based video question answering (TextVideoQA) are criticized for their
opaque decisionmaking and heavy reliance on scene-text recognition. In this paper, we …

This paper addresses the daily challenges encountered by visually impaired individuals,
such as limited access to information, navigation difficulties, and barriers to social …

While VideoQA Transformer models demonstrate competitive performance on standard
benchmarks, the reasons behind their success are not fully understood. Do these models …