Attention-guided and fine-grained feature extraction from face images for gaze estimation

Article Computer Science, Artificial Intelligence

CTNet: Context-Based Tandem Network for Semantic Segmentation

Zechao Li et al.

Summary: This study proposes a novel Context-based Tandem Network (CTNet) that explores spatial and channel contextual information for semantic segmentation. The CTNet demonstrates superior performance by adaptively integrating the results of two context modules, leading to improved learning representations.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

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Article Computer Science, Artificial Intelligence

Eye gaze estimation: A survey on deep learning-based approaches

Primesh Pathirana et al.

Summary: This paper presents a comprehensive survey of single-user and multi-user gaze estimation approaches with deep learning. State-of-the-art methods are analyzed, and the limitations, challenges, and future directions of multi-user gaze estimation are discussed.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Computer Science, Artificial Intelligence

Learning attention-guided pyramidal features for few-shot fine-grained recognition

Hao Tang et al.

Summary: This article proposes an effective bidirectional pyramid architecture for enhancing internal representations of features in few-shot fine-grained recognition. Experimental results show that the proposed method performs favorably in the one-shot scenarios.

PATTERN RECOGNITION (2022)

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Article Computer Science, Information Systems

Customer Gaze Estimation in Retail Using Deep Learning

Shashimal Senarath et al.

Summary: This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. Extensive experiments demonstrate that our approach significantly improves remote gaze target estimation performance on different datasets.

IEEE ACCESS (2022)

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Article Automation & Control Systems

Computational Decision Support System for ADHD Identification

Senuri De Silva et al.

Summary: This study focuses on building an ADHD identification system using fMRI and eye movement data, achieving good performance through neural networks and ensemble models.

INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING (2021)

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Article Computer Science, Software Engineering