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Found 16 papers, 5 papers with code
An End-to-End OCR Text Re-organization Sequence Learning for Rich-text Detail Image Comprehension
Nowadays rich description on detail images help users know more about the commodities.
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
The core of LayoutLLM is a layout instruction tuning strategy, which is specially designed to enhance the comprehension and utilization of document layouts.
Less is More: A Closer Look at Semantic-based Few-Shot Learning
To address this, we propose a simple but effective framework for few-shot learning tasks, specifically designed to exploit the textual information and language model.
LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training
We model TSR as a logical location regression problem and propose a new TSR framework called LORE, standing for LOgical location REgression network, which for the first time regresses logical location as well as spatial location of table cells in a unified network.
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process.
LORE: Logical Location Regression Network for Table Structure Recognition
Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning Strategy
Besides, CuDFKD adapts the generation target dynamically according to the status of student model.
A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks
Tokamaks allow to confine fusion plasma with magnetic fields and one of the main challenges in the control of the magnetic configuration is the prediction/reconstruction of the Last Closed-Flux Surface (LCFS).
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Cross-modal Image Retrieval with Deep Mutual Information Maximization
Prior work usually uses a three-stage strategy to tackle this task: 1) extract the features of the inputs; 2) fuse the feature of the source image and its modified text to obtain fusion feature; 3) learn a similarity metric between the desired image and the source image + modified text by using deep metric learning.
Experiment data-driven modeling of tokamak discharge in EAST
By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$.
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
Matching Text with Deep Mutual Information Estimation
Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.
Hierarchical Graph Pooling with Structure Learning
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on
Graph Classification
on PROTEINS
Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking
Recent works on convolutional neural networks (CNNs) for facial alignment have demonstrated unprecedented accuracy on a variety of large, publicly available datasets.
