Search Results for author:
Found 14 papers, 4 papers with code
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval
Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the student model is gradually compressed during the training.
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting
By investigating iterative methods for a constrained linear model, we propose a new class of fully connected V-cycle MgNet for long-term time series forecasting, which is one of the most difficult tasks in forecasting.
An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations
This study used a multigrid-based convolutional neural network architecture known as MgNet in operator learning to solve numerical partial differential equations (PDEs).
Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-Resolution
Instead of assuming degradation are spatially invariant across the whole image, we learn correction filters to adjust degradations to known degradations in a spatially variant way by a novel linearly-assembled pixel degradation-adaptive regression module (DARM).
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
A Challenging Benchmark of Anime Style Recognition
Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem.
An Interpretive Constrained Linear Model for ResNet and MgNet
We propose a constrained linear data-feature-mapping model as an interpretable mathematical model for image classification using a convolutional neural network (CNN).
GiT: Graph Interactive Transformer for Vehicle Re-identification
In the macro view, a list of GiT blocks are stacked to build a vehicle re-identification model, in where graphs are to extract discriminative local features within patches and transformers are to extract robust global features among patches.
Ranked #2 on
Vehicle Re-Identification
on VehicleID Small
(mAP metric)
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Secondly, a novel pyramidal graph network (PGN) is designed to comprehensively explore the spatial significance of feature maps at multiple scales.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Small
(mAP metric)
Vehicle Re-identification Using Quadruple Directional Deep Learning Features
Specifically, the same basic deep learning architecture is a shortly and densely connected convolutional neural network to extract basic feature maps of an input square vehicle image in the first stage.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Large
(mAP metric)
Automatic Recognition of Coal and Gangue based on Convolution Neural Network
We designed a gangue sorting system, and built a convolutional neural network model based on AlexNet.
Deep Hybrid Similarity Learning for Person Re-identification
In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed.
Open-set Person Re-identification
Person re-identification is becoming a hot research for developing both machine learning algorithms and video surveillance applications.
