Search Results for author:
Found 9 papers, 2 papers with code
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models
Neural Architecture Search (NAS) has become the de fecto tools in the industry in automating the design of deep neural networks for various applications, especially those driven by mobile and edge devices with limited computing resources.
Named Entity Recognition for Chinese biomedical patents
There is a large body of work on Biomedical Entity Recognition (Bio-NER) for English but there have only been a few attempts addressing NER for Chinese biomedical texts.
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset
We formulate the problem as a very fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task.
Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD).
Texture retrieval using periodically extended and adaptive curvelets
Image retrieval is an important problem in the area of multimedia processing.
Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks
In this paper, we propose a multi-level texture encoding and representation network (MuLTER) for texture-related applications.
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows.
A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i. e., seismic volume labeling.
Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval
Second, texts in these datasets are written in well-organized language, leading to inconsistency with realistic applications.
