Correction to: Neural Architecture Search

Elsken, Thomas; Metzen, Jan Hendrik; Hutter, Frank

doi:10.1007/978-3-030-05318-5_11

Cited by 108 publications

(16 citation statements)

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“…In the future, we will consider conducting verification experiments on our proposed algorithm in more real interactive scenarios, including some interactive scenarios in microsurgery. In addition, automatic deep learning is widely used in many fields [33][34][35], and in the future, it can be considered to be introduced into our model optimization.…”

Section: Discussionmentioning

confidence: 99%

Visual Interaction Force Estimation Based on Time-Sensitive Dual-Resolution Learning Network

et al. 2022

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Haptic force feedback is an important perception method for humans to understand the surrounding environment. It can estimate tactile force in real time and provide appropriate feedback. It has important research value for robot-assisted minimally invasive surgery, interactive tactile robots, and other application fields. However, most of the existing noncontact visual power estimation methods are implemented using traditional machine learning or 2D/3D CNN combined with LSTM. Such methods are difficult to fully extract the contextual spatiotemporal interaction semantic information of consecutive multiple frames of images, and their performance is limited. To this end, this paper proposes a time-sensitive dual-resolution learning network-based force estimation model to achieve accurate noncontact visual force prediction. First, we perform continuous frame normalization processing on the robot running the video captured by the camera and use the hybrid data augmentation to improve the data diversity; secondly, a deep semantic interaction model is constructed based on the time-sensitive dual-resolution learning network, which is used to automatically extract the deep spatiotemporal semantic interaction information of continuous multiframe images; finally, we construct a simplified prediction model to realize the efficient estimation of interaction force. The results based on the large-scale robot hand interaction dataset show that our method can estimate the interaction force of the robot hand more accurately and faster. The average prediction MSE reaches 0.0009 N, R 2 reaches 0.9833, and the average inference time for a single image is 6.5532 ms; in addition, our method has good prediction generalization performance under different environments and parameter settings.

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Section: Discussionmentioning

confidence: 99%

Visual Interaction Force Estimation Based on Time-Sensitive Dual-Resolution Learning Network

et al. 2022

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“…An advanced learning model's architectures must be manually created, which takes time, effort, and is prone to mistakes. The approach of automating architectural engineering, known as neural architecture search (NAS) [90], has recently been applied to address this issue. The study of NAS is currently receiving greater attention.…”

Section: Architecture For Automated Re-idmentioning

confidence: 99%

Review on deep learning based techniques for person re-identification

Khobragade¹

2022

3C TIC

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In-depth study has recently been concentrated on human re-identification, which is a crucial component of automated video surveillance. Re-identification is the act of identifying someone in photos or videos acquired from other cameras after they have already been recognized in an image or video from one camera. Re-identification, which involves generating consistent labelling between several cameras, or even just one camera, is required to reconnect missing or interrupted tracks. In addition to surveillance, it may be used in forensics, multimedia, and robotics.Re-identification of the person is a difficult problem since their look fluctuates across many cameras with visual ambiguity and spatiotemporal uncertainty. These issues can be largely caused by inadequate video feeds or lowresolution photos that are full of unnecessary facts and prevent re-identification. The geographical or temporal restrictions of the challenge are difficult to capture. The computer vision research community has given the problem a lot of attention because of how widely used and valuable it is. In this article, we look at the issue of human re-identification and discuss some viable approaches.

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“…Inspired by recent researches on Neural Architecture Search (NAS) [32], authors in [33] introduced a part-aware module for the person Re-ID called Auto-ReID and used it as a baseline for building a number of optimal Re-ID architectures in NAS search space. This was the first work for automated NAS for Re-ID task that considered the body structure.…”

Section: ) Rgb Image-based Person Re-idmentioning

confidence: 99%

“…Building the architectures of deep learning model manually by a human is consuming time and effort and exposed to mistakes. Recently, the neural architecture search (NAS) which is the process of automating architecture engineering [32] is used to tackle this challenge. Currently, the research on NAS is getting more attention.…”

Section: G Automated Person Re-id Architecturementioning

confidence: 99%

A Survey on Deep Learning-Based Person Re-Identification Systems

2019

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Person re-identification systems (person Re-ID) have recently gained more attention between computer vision researchers. They are playing a key role in intelligent visual surveillance systems and have widespread applications like applications for public security. The person Re-ID systems can identify if a person has been seen by a non-overlapping camera over large camera network in an unconstrained environment. It is a challenging issue since a person appears differently under different camera views and faces many challenges such as pose variation, occlusion and illumination changes. Many methods had been introduced for generating handcrafted features aimed to handle the person Re-ID problem. In recent years, many studies have started to apply deep learning methods to enhance the person Re-ID performance due the deep learning yielded significant results in computer vision issues. Therefore, this paper is a survey of the recent studies that proposed to improve the person Re-ID systems using deep learning. The public datasets that are used for evaluating these systems are discussed. Finally, the paper addresses future directions and current issues that must be considered toward improving the person Re-ID systems.INDEX TERMS Deep learning, person re-identification, video surveillance.

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Correction to: Neural Architecture Search

Cited by 108 publications

References 0 publications

Visual Interaction Force Estimation Based on Time-Sensitive Dual-Resolution Learning Network

Visual Interaction Force Estimation Based on Time-Sensitive Dual-Resolution Learning Network

Review on deep learning based techniques for person re-identification

A Survey on Deep Learning-Based Person Re-Identification Systems

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