|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks.
From the experimental analysis, it is observed that the YOLO v3 with Deepsort tracking scheme displayed best results with balanced mAP and FPS score to monitor the social distancing in real-time.
In comparison with ResNet-based, DenseNet-based and attention network (AttnNet) based methods within the same encoder-decoder network structure, our method achieves significantly higher segmentation accuracy with fewer number of model parameters than DenseNet and AttnNet.
The specific focus of this paper is the development of a high-fidelity simulation environment for generating realistic light curves.
A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments.
The technology of vehicle and driver detection in Intelligent Transportation System(ITS) is a hot topic in recent years.
Based on this criterion, we introduce a novel image transformation that we call limited context inpainting (LCI).
Then, we aggregate RoI pooled deep features through the tubelet using a temporal pooling operator that summarizes the information with a fixed size output independent of the number of input frames.
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area.
Classification is a pivotal function for many computer vision tasks such as object classification, detection, scene segmentation.