Search Results for author:
Found 15 papers, 7 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)
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models.
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.
GRAM: Global Reasoning for Multi-Page VQA
The increasing use of transformer-based large language models brings forward the challenge of processing long sequences.
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.
CLIPTER: Looking at the Bigger Picture in Scene Text Recognition
Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text.
Out-of-Vocabulary Challenge Report
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge.
Optical Character Recognition
Optical Character Recognition (OCR)
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Multimodal Semi-Supervised Learning for Text Recognition
In a novel setup, consistency is enforced on each modality separately.
LaTr: Layout-Aware Transformer for Scene-Text VQA
Accounting for this, we propose a single objective pre-training scheme that requires only text and spatial cues.
TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers
However in some cases, their decisions are based on unintended information leading to high performance on standard benchmarks but also to a lack of generalization to challenging testing conditions and unintuitive failures.
On Calibration of Scene-Text Recognition Models
Although the topic of confidence calibration has been an active research area for the last several decades, the case of structured and sequence prediction calibration has been scarcely explored.
Sequence-to-Sequence Contrastive Learning for Text Recognition
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition.
SCATTER: Selective Context Attentional Scene Text Recognizer
The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer.
