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Found 7 papers, 2 papers with code
DocVLM: Make Your VLM an Efficient Reader
Vision-Language Models (VLMs) excel in diverse visual tasks but face challenges in document understanding, which requires fine-grained text processing.
TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models
In this paper, we focus on enhancing the first strategy by introducing a novel method, named TAP-VL, which treats OCR information as a distinct modality and seamlessly integrates it into any VL model.
Optical Character Recognition
Optical Character Recognition (OCR)
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Document translation poses a challenge for Neural Machine Translation (NMT) systems.
Question Aware Vision Transformer for Multimodal Reasoning
This integration results in dynamic visual features focusing on relevant image aspects to the posed question.
Towards Models that Can See and Read
Visual Question Answering (VQA) and Image Captioning (CAP), which are among the most popular vision-language tasks, have analogous scene-text versions that require reasoning from the text in the image.
Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer
Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components.
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision.
