Search Results for author:
Found 15 papers, 7 papers with code
TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models
Classical Chinese is a gateway to the rich heritage and wisdom of ancient China, yet its complexities pose formidable comprehension barriers for most modern people without specialized knowledge.
Scaling Laws for Fact Memorization of Large Language Models
We find that LLMs' fact knowledge capacity has a linear and negative exponential law relationship with model size and training epochs, respectively.
Datasets for Large Language Models: A Comprehensive Survey
Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from 444 datasets, covering 8 language categories and spanning 32 domains.
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy
Lung cancer is a devastating disease with the highest mortality rate among cancer types.
UPOCR: Towards Unified Pixel-Level OCR Interface
Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models.
DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures
Furthermore, considering the importance of fine-grained elements in document images, we present a details recurrent refinement module to enhance the output in a high-resolution space.
M$^{6}$Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis
Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion.
M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis
Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion.
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild
To address this issue, we propose a novel approach called Marior (Margin Removal and \Iterative Content Rectification).
Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context
To address this issue, we propose a Contextual-guided Text Removal Network, termed as CTRNet.
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition.
Ranked #3 on
Text Spotting
on Inverse-Text
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models.
Ranked #5 on
Key-value Pair Extraction
on SIBR
Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences
Visual information extraction (VIE) has attracted increasing attention in recent years.
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs
The results demonstrate how random bit flips in the output of particular mathematical operations and layers of NNs affect the classification accuracy.
Agent Prioritization for Autonomous Navigation
We propose a system to rank agents around an autonomous vehicle (AV) in real time.
