Search Results for author:
Found 16 papers, 10 papers with code
A Tree-Structured Decoder for Image-to-Markup Generation
Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup.
CORE: Mitigating Catastrophic Forgetting in Continual Learning through Cognitive Replay
The former adaptively modulates the replay buffer allocation for each task based on its forgetting rate, while the latter guarantees the inclusion of representative data that best encapsulates the characteristics of each task within the buffer.
Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction
This poses a challenge when dealing with an unseen misspelled character, as the decoder may generate an IDS sequence that matches a seen character instead.
HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures
Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.
SEMv2: Table Separation Line Detection Based on Instance Segmentation
Table structure recognition is an indispensable element for enabling machines to comprehend tables.
Multimodal Tree Decoder for Table of Contents Extraction in Document Images
Next, to parse the hierarchical relationship between the heading entities, a tree-structured decoder is designed.
Multimodal Pre-training Based on Graph Attention Network for Document Understanding
Its main task is to automatically read, understand, and analyze documents.
Split, embed and merge: An accurate table structure recognizer
However, due to the complexity and diversity in their structure and style, it is very difficult to parse the tabular data into the structured format which machines can understand easily, especially for complex tables.
Ranked #8 on
Table Recognition
on PubTabNet
Stroke Constrained Attention Network for Online Handwritten Mathematical Expression Recognition
For single-modal HMER, SCAN first employs a CNN-GRU encoder to extract point-level features from input traces in online mode and employs a CNN encoder to extract pixel-level features from input images in offline mode, then use stroke constrained information to convert them into online and offline stroke-level features.
Episodic Training for Domain Generalization
In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime.
Ranked #76 on
Domain Generalization
on PACS
DenseRAN for Offline Handwritten Chinese Character Recognition
Recently, great success has been achieved in offline handwritten Chinese character recognition by using deep learning methods.
Attention Based Fully Convolutional Network for Speech Emotion Recognition
In this paper, we present a novel attention based fully convolutional network for speech emotion recognition.
Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition
The RNN decoder aims at generating the caption by detecting radicals and spatial structures through an attention model.
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols.
A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition
In this study, we present a novel end-to-end approach based on the encoder-decoder framework with the attention mechanism for online handwritten mathematical expression recognition (OHMER).
Radical analysis network for zero-shot learning in printed Chinese character recognition
Chinese characters have a huge set of character categories, more than 20, 000 and the number is still increasing as more and more novel characters continue being created.
