Spatial Attentional Bilinear 3D Convolutional Network for Video-Based Autism Spectrum Disorder Detection

Sun, Kangbo; Li, Lin; Li, Lianqiang; He, Ningyu; Zhu, Jie

doi:10.1109/icassp40776.2020.9054641

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…In our work, attentional bilinear pooling method [13], [18] is adopt to generate attention maps. It is assumed that F is the feature maps obtained from the CNN network, and A is the attention maps produced by 1 × 1 convolutional layer from the feature maps.…”

Section: Searching Discriminative Regionsmentioning

confidence: 99%

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Sun

Zhu

2022

IEICE Trans. Inf. & Syst.

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Local discriminative regions play important roles in finegrained image analysis tasks. How to locate local discriminative regions with only category label and learn discriminative representation from these regions have been hot spots. In our work, we propose Searching Discriminative Regions (SDR) and Learning Discriminative Regions (LDR) method to search and learn local discriminative regions in images. The SDR method adopts attention mechanism to iteratively search for highresponse regions in images, and uses this as a clue to locate local discriminative regions. Moreover, the LDR method is proposed to learn compact within category and sparse between categories representation from the raw image and local images. Experimental results show that our proposed approach achieves excellent performance in both fine-grained image retrieval and classification tasks, which demonstrates its effectiveness.

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Section: Searching Discriminative Regionsmentioning

confidence: 99%

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Sun

Zhu

2022

IEICE Trans. Inf. & Syst.

Self Cite

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“…In our work, we adopt the attentional bilinear pooling [7,12] to constrain the attention maps to ensure that the attention maps can have a high response value in the most discriminative region. It is assumed that A ∈ R S×C 2 is the attention maps, and C 2 is the channel size of the attention maps, the attentional bilinear pooling is defined as:…”

Section: Attentional Bilinear Poolingmentioning

confidence: 99%

Learning Representation of Multi-Scale Object for Fine-Grained Image Retrieval

Sun

Zhu

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Extracting discriminative local features has attracted many research focus in fine-grained image retrieval task. With attention mechanism and softmax-like loss functions, deep neural networks could locate and learn the representation of the most discriminative region of objects, however, which also makes other non-most discriminative regions be ignored to some extent. In our work, to extract more local features, we propose a method that could proposes multiple discriminative regions on different scales, which could provide more refined local and multi-sacle representation for fine-grained image retrieval. Experimental results show that our proposed method achieves excellent performance on two benchmark fine-grained datasets, which demonstrates its effectiveness.

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“…Their study reported 82% classification accuracy using an LSTM network followed by a VGG-16 architecture. Later on, Tian et al [109] used SA-B3D with the LSTM network, and Sun et al [99] used a temporal pyramid network (TPN) on the same dataset [126]. These latter studies reported 87.17% and 95.2% classification accuracy, respectively.…”

Section: Study Of Repetitive Behaviormentioning

confidence: 99%

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

Shahid¹,

Rahman²,

Ahmed³

et al. 2020

Preprint

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Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social interactions, cognitive skills, and abilities. Since ASD can last during an affected person's entire life cycle, the diagnosis at the early onset can yield a significant positive impact. The current medical diagnostic systems (e.g., DSM-5/ICD-10) are somewhat subjective; rely purely on the behavioral observation of symptoms, and hence, some individuals often go misdiagnosed or late-diagnosed. Therefore, researchers have focused on developing data-driven automated diagnosis systems with less screening time, low cost, and improved accuracy while significantly reducing professional intervention. Human Activity Analysis (HAA) is considered one of the most promising niches in computer vision research. This paper aims to analyze its potentialities in the automated detection of autism by tracking the exclusive characteristics of autistic individuals such as repetitive behavior, atypical walking style, and unusual visual saliency. This review provides a detailed inspection of HAA-based autism detection literature published in 2011 on-wards depicting core approaches, challenges, probable solutions, available resources, and scopes of future exploration in this arena. According to our study, deep learning outperforms machine learning in ASD detection with a classification accuracy of 76\% to 95\% on different datasets comprise of video, image, or skeleton data that recorded participants performing a large number of actions. However, machine learning provides satisfactory results on datasets with a small number of action classes and has a range of 60\% to 93\% accuracy among numerous studies. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

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Spatial Attentional Bilinear 3D Convolutional Network for Video-Based Autism Spectrum Disorder Detection

Cited by 9 publications

References 15 publications

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Learning Representation of Multi-Scale Object for Fine-Grained Image Retrieval

Data-Driven Automated Detection of Autism Spectrum Disorder Using Activity Analysis: A Review

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