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Step length measurement in the wild using FMCW radar
To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using radar point cloud, followed by Doppler speed profiling of the torso to obtain step lengths in the home.
Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis
As such, there has been a recent focus on unsupervised learning approaches to mitigate the data annotation issue; however, current approaches in literature have limited performance compared to supervised learning approaches as well as limited applicability for adoption in new environments.
Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
There has been recent interest in tackling this challenge using cross-domain approaches, which leverages data from source domains that are different than the target domain.
Exploiting Prunability for Person Re-Identification
Then, these techniques are analysed according to their pruningcriteria and strategy, and according to different scenarios for exploiting pruningmethods to fine-tuning networks to target domains.
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID).
Transfer Learning by Ranking for Weakly Supervised Object Annotation
Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set.
Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)
The problem of automated crowd segmentation and counting has garnered significant interest in the field of video surveillance.
Domain Adaptation and Transfer Learning in StochasticNets
Transfer learning is a recent field of machine learning research that aims to resolve the challenge of dealing with insufficient training data in the domain of interest.
Efficient Deep Feature Learning and Extraction via StochasticNets
Experimental results show that features learned using deep convolutional StochasticNets, with fewer neural connections than conventional deep convolutional neural networks, can allow for better or comparable classification accuracy than conventional deep neural networks: relative test error decrease of ~4. 5% for classification on the STL-10 dataset and ~1% for classification on the SVHN dataset.
StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
A pivotal study on the brain tissue of rats found that synaptic formation for specific functional connectivity in neocortical neural microcircuits can be surprisingly well modeled and predicted as a random formation.
A deep-structured fully-connected random field model for structured inference
In this study, we investigate the feasibility of unifying fully-connected and deep-structured models in a computationally tractable manner for the purpose of structured inference.
Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection
We propose a principled probabilistic formulation of object saliency as a sampling problem.
