Attention-Aware Age-Agnostic Visual Place Recognition

Wang, Ziqi; Li, Jiahui; Khademi, Seyran; Gemert, Jan C. van

doi:10.1109/iccvw.2019.00181

Cited by 19 publications

(25 citation statements)

References 37 publications

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“…Attention module is widely used in natural language processing [7] and computer vision [43], [56] fields by allowing the network to focus on key words or pixels. Self-attention mechanism is proposed to capture the relative relationship between words or pixels.…”

Section: Self-attentionmentioning

confidence: 99%

Assessment of Parkinson’s Disease Severity From Videos Using Deep Architectures

Yin

Geraedts

Wang

et al. 2022

IEEE J. Biomed. Health Inform.

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Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism.Seven tasks from the Movement Disorders Society -Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

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Section: Self-attentionmentioning

confidence: 99%

Assessment of Parkinson’s Disease Severity From Videos Using Deep Architectures

Yin

Geraedts

Wang

et al. 2022

IEEE J. Biomed. Health Inform.

Self Cite

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“…One specific case of cross-domain place recognition is considered in [205]: the database is composed of present day RGB images and the queries are historic images from the same area. In this case the domain shift is caused not only by possible changes in the scene but also by the different technology used to take the photos.…”

Section: E Adapting To Different Environmental Conditionsmentioning

confidence: 99%

A Survey on Deep Visual Place Recognition

2021

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In recent years visual place recognition (VPR), i.e., the problem of recognizing the location of images, has received considerable attention from multiple research communities, spanning from computer vision to robotics and even machine learning. This interest is fueled on one hand by the relevance that visual place recognition holds for many applications and on the other hand by the unsolved challenge of making these methods perform reliably in different conditions and environments. This paper presents a survey of the state-of-the-art of research on visual place recognition, focusing on how it has been shaped by the recent advances in deep learning. We start discussing the image representations used in this task and how they have evolved from using hand-crafted to deep-learned features. We further review how metric learning techniques are used to get more discriminative representations, as well as techniques for dealing with occlusions, distractors, and shifts in the visual domain of the images. The survey also provides an overview of the specific solutions that have been proposed for applications in robotics and with aerial imagery. Finally the survey provides a summary of datasets that are used in visual place recognition, highlighting their different characteristics. INDEX TERMS Visual place recognition, image representation learning, deep learning.

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“…To learn domain-invariant features for cross-domain visual place recognition, Wang et al [160] propose an approach that combines weakly supervised learning with unsupervised learning. The proposed architecture has three primary modules: an attention module, an attention-aware VLAD module, and a domain adaptation module.…”

Section: Semi-supervised Place Recognitionmentioning

confidence: 99%

Place recognition survey: An update on deep learning approaches

Barros¹,

Pereira²,

Garrote³

et al. 2021

Preprint

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Autonomous Vehicles (AV) are becoming more capable of navigating in complex environments with dynamic and changing conditions. A key component that enables these intelligent vehicles to overcome such conditions and become more autonomous is the sophistication of the perception and localization systems. As part of the localization system, place recognition has benefited from recent developments in other perception tasks such as place categorization or object recognition, namely with the emergence of deep learning (DL) frameworks. This paper surveys recent approaches and methods used in place recognition, particularly those based on deep learning. The contributions of this work are twofold: surveying recent sensors such as 3D LiDARs and RADARs, applied in place recognition; and categorizing the various DL-based place recognition works into supervised, unsupervised, semi-supervised, parallel, and hierarchical categories. First, this survey introduces key place recognition concepts to contextualize the reader. Then, sensor characteristics are addressed. This survey proceeds by elaborating on the various DL-based works, presenting summaries for each framework. Some lessons learned from this survey include: the importance of NetVLAD for supervised end-to-end learning; the advantages of unsupervised approaches in place recognition, namely for cross-domain applications; or the increasing tendency of recent works to seek, not only for higher performance but also for higher efficiency.

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Attention-Aware Age-Agnostic Visual Place Recognition

Cited by 19 publications

References 37 publications

Assessment of Parkinson’s Disease Severity From Videos Using Deep Architectures

Assessment of Parkinson’s Disease Severity From Videos Using Deep Architectures

A Survey on Deep Visual Place Recognition

Place recognition survey: An update on deep learning approaches

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