most citedVisual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects

3 citations · 7 across the 5 of their papers we have counts for

collaborators

7 papers

cs.AI20213 cited

Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects

Bing Wei, Yudi Zhao, Kuangrong Hao +1

Visual sensation and perception refers to the process of sensing, organizing, identifying, and interpreting visual information in environmental awareness and understanding. Computa…

cs.CV2021

Enhanced Gradient for Differentiable Architecture Search

Haichao Zhang, Kuangrong Hao, Lei Gao +2

In recent years, neural architecture search (NAS) methods have been proposed for the automatic generation of task-oriented network architecture in image classification. However, th…

cs.CV20211 cited

MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images

Bing Wei, Kuangrong Hao, Lei Gao

For the sake of recognizing and classifying textile defects, deep learning-based methods have been proposed and achieved remarkable success in single-label textile images. However,…

cs.CL20212 cited

Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification

Yan Xiao, Yaochu Jin, Kuangrong Hao

Relation classification (RC) task is one of fundamental tasks of information extraction, aiming to detect the relation information between entity pairs in unstructured natural lang…

cs.CV20201 cited

Optimizing Deep Neural Networks through Neuroevolution with Stochastic Gradient Descent

Haichao Zhang, Kuangrong Hao, Lei Gao +2

Deep neural networks (DNNs) have achieved remarkable success in computer vision; however, training DNNs for satisfactory performance remains challenging and suffers from sensitivit…

cs.CL2020

Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction

Yan Xiao, Yaochu Jin, Ran Cheng +1

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As…