Fine-grained visual classification via progressive multi-granularity training of jigsaw patches
Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …
classification tasks due to the inherently subtle intra-class object variations. Recent works …
The devil is in the channels: Mutual-channel loss for fine-grained image classification
The key to solving fine-grained image categorization is finding discriminate and local
regions that correspond to subtle visual traits. Great strides have been made, with complex …
regions that correspond to subtle visual traits. Great strides have been made, with complex …
Intelligent machinery fault diagnosis with event-based camera
Event-based cameras are the emerging bioinspired technology in vision sensing. Different
from the traditional standard cameras, the event-based cameras asynchronously record the …
from the traditional standard cameras, the event-based cameras asynchronously record the …
A deep learning based traffic crash severity prediction framework
Highway work zones are most vulnerable roadway segments for congestion and traffic
collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions …
collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions …
Deep learning serves traffic safety analysis: A forward‐looking review
This paper explores deep learning (DL) methods that are used or have the potential to be
used for traffic video analysis, emphasising driving safety for both autonomous vehicles and …
used for traffic video analysis, emphasising driving safety for both autonomous vehicles and …
BSNet: Bi-similarity network for few-shot fine-grained image classification
Few-shot learning for fine-grained image classification has gained recent attention in
computer vision. Among the approaches for few-shot learning, due to the simplicity and …
computer vision. Among the approaches for few-shot learning, due to the simplicity and …
An improved deep network-based scene classification method for self-driving cars
J Ni, K Shen, Y Chen, W Cao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A self-driving car is a hot research topic in the field of the intelligent transportation system,
which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is …
which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is …
Vision-based autonomous vehicle recognition: A new challenge for deep learning-based systems
A Boukerche, X Ma - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention
recently. Particularly given the reliance on emerging deep learning methods, which have …
recently. Particularly given the reliance on emerging deep learning methods, which have …
Convolutional neural network with margin loss for fake news detection
The advent of online news platforms such as social media, news blogs, and online
newspapers in recent years and their facilitated features such as swift information flow, easy …
newspapers in recent years and their facilitated features such as swift information flow, easy …
AP-CNN: Weakly supervised attention pyramid convolutional neural network for fine-grained visual classification
Classifying the sub-categories of an object from the same super-category (eg, bird species
and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature …
and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature …