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Found 20 papers, 10 papers with code
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromises the inherent safety capabilities embedded in the original LLMs.
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning
Recent advancements in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility.
CREPE: Coordinate-Aware End-to-End Document Parser
CREPE's abilities including OCR and semantic parsing not only mitigate error propagation issues in existing OCR-dependent methods, it also significantly enhance the functionality of sequence generation models, ushering in a new era for document understanding studies.
document understanding
Optical Character Recognition (OCR)
+3
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging.
SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain Gap
In this paper, we investigate effective pre-training tasks in the broader domains and also propose a novel pre-training method called SCOB that leverages character-wise supervised contrastive learning with online text rendering to effectively pre-train document and scene text domains by bridging the domain gap.
Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models
Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain.
On Web-based Visual Corpus Construction for Visual Document Understanding
In recent years, research on visual document understanding (VDU) has grown significantly, with a particular emphasis on the development of self-supervised learning methods.
document understanding
Optical Character Recognition (OCR)
+1
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching
Semi-structured query systems for document-oriented databases have many real applications.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
OCR-free Document Understanding Transformer
Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
Ranked #7 on
Key-value Pair Extraction
on SIBR
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings
(Q2) Ordinary additive compositionality can be seen as an AND operation of word meanings, but it is not well understood how other operations, such as OR and NOT, can be computed by the embeddings.
Cost-effective End-to-end Information Extraction for Semi-structured Document Images
A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost.
Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model
This paper introduces a method that efficiently reduces the computational cost and parameter size of Transformer.
Stochastic Neighbor Embedding of Multimodal Relational Data for Image-Text Simultaneous Visualization
Multimodal relational data analysis has become of increasing importance in recent years, for exploring across different domains of data, such as images and their text tags obtained from social networking services (e. g., Flickr).
Group-Transformer: Towards A Lightweight Character-level Language Model
When compared to Transformers with a comparable number of parameters and time complexity, the proposed model shows better performance.
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
Many new proposals for scene text recognition (STR) models have been introduced in recent years.
Ranked #7 on
Scene Text Recognition
on ICDAR 2003
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities
In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values.
Word-like character n-gram embedding
We propose a new word embedding method called \textit{word-like character} n\textit{-gram embedding}, which learns distributed representations of words by embedding word-like character n-grams.
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embedding (GE).
Segmentation-free Compositional $n$-gram Embedding
We propose a new type of representation learning method that models words, phrases and sentences seamlessly.
