Cross-modal retrieval with CNN visual features: A new baseline
Recently, convolutional neural network (CNN) visual features have demonstrated their
powerful ability as a universal representation for various recognition tasks. In this paper …
powerful ability as a universal representation for various recognition tasks. In this paper …
Deformable MR prostate segmentation via deep feature learning and sparse patch matching
Automatic and reliable segmentation of the prostate is an important but difficult task for
various clinical applications such as prostate cancer radiotherapy. The main challenges for …
various clinical applications such as prostate cancer radiotherapy. The main challenges for …
FUIQA: fetal ultrasound image quality assessment with deep convolutional networks
The quality of ultrasound (US) images for the obstetric examination is crucial for accurate
biometric measurement. However, manual quality control is a labor intensive process and …
biometric measurement. However, manual quality control is a labor intensive process and …
6G connected vehicle framework to support intelligent road maintenance using deep learning data fusion
The growth of IoT, edge and mobile Artificial Intelligence (AI) is supporting urban authorities
exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV) …
exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV) …
BATCH: A scalable asymmetric discrete cross-modal hashing
Supervised cross-modal hashing has attracted much attention. However, there are still some
challenges, eg, how to effectively embed the label information into binary codes, how to …
challenges, eg, how to effectively embed the label information into binary codes, how to …
SCRATCH: A scalable discrete matrix factorization hashing framework for cross-modal retrieval
In this paper, we present a novel supervised cross-modal hashing framework, namely
Scalable disCRete mATrix faCtorization Hashing (SCRATCH). First, it utilizes collective …
Scalable disCRete mATrix faCtorization Hashing (SCRATCH). First, it utilizes collective …
Learning cross-modal common representations by private–shared subspaces separation
Due to the inconsistent distributions and representations of different modalities (eg, images
and texts), it is very challenging to correlate such heterogeneous data. A standard solution is …
and texts), it is very challenging to correlate such heterogeneous data. A standard solution is …
An efficient Wikipedia semantic matching approach to text document classification
Z Wu, H Zhu, G Li, Z Cui, H Huang, J Li, E Chen… - Information Sciences, 2017 - Elsevier
A traditional classification approach based on keyword matching represents each text
document as a set of keywords, without considering the semantic information, thereby …
document as a set of keywords, without considering the semantic information, thereby …
Discrete hashing with multiple supervision
Supervised hashing methods have achieved more promising results than unsupervised
ones by leveraging label information to generate compact and accurate hash codes. Most of …
ones by leveraging label information to generate compact and accurate hash codes. Most of …
Discrete nonnegative spectral clustering
Spectral clustering has been playing a vital role in various research areas. Most traditional
spectral clustering algorithms comprise two independent stages (eg, first learning …
spectral clustering algorithms comprise two independent stages (eg, first learning …