372 papers with code • 27 benchmarks • 50 datasets
Person re-identification is the task of associating images of the same person taken from different cameras or from the same camera in different occasions.
( Image credit: PRID2011 dataset )
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.