Occluded Face In-painting Using Generative Adversarial Networks—A Review

Ivamoto, Victor; Simões, Rodolfo; Kemmer, Bruno; Lima, Clodoaldo

doi:10.1007/978-3-031-45389-2_17

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Despite the notorious stability issues and training challenges of GANs, they prove instrumental in extending incomplete representations to complete ones [47]. However, their applicability varies across tasks; for instance, GANs perform exceptionally well in amodal appearance reconstruction but are less commonly employed in amodal segmentation and order recovery tasks.…”

Section: Generative Adversarial Network Approachmentioning

confidence: 99%

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Ouardirhi,

Mahmoudi,

Zbakh

2024

Electronics

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Smart video surveillance systems (SVSs) have garnered significant attention for their autonomous monitoring capabilities, encompassing automated detection, tracking, analysis, and decision making within complex environments, with minimal human intervention. In this context, object detection is a fundamental task in SVS. However, many current approaches often overlook occlusion by nearby objects, posing challenges to real-world SVS applications. To address this crucial issue, this paper presents a comprehensive comparative analysis of occlusion-handling techniques tailored for object detection. The review outlines the pretext tasks common to both domains and explores various architectural solutions to combat occlusion. Unlike prior studies that primarily focus on a single dataset, our analysis spans multiple benchmark datasets, providing a thorough assessment of various object detection methods. By extending the evaluation to datasets beyond the KITTI benchmark, this study offers a more holistic understanding of each approach’s strengths and limitations. Additionally, we delve into persistent challenges in existing occlusion-handling approaches and emphasize the need for innovative strategies and future research directions to drive substantial progress in this field.

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Section: Generative Adversarial Network Approachmentioning

confidence: 99%

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Ouardirhi,

Mahmoudi,

Zbakh

2024

Electronics

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Image In-Painting for Video Processing: Techniques and Performance Evaluation

Goswami,

Gupta,

2024

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)

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Challenges and Solutions in Emotion Detection Using Deep Learning Approaches

Mallik,

Rana

2024

Advances in Psychology, Mental Health, and Behavioral Studies

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Emotion detection using deep learning techniques has gained significant attention due to its wide-ranging applications in fields such as healthcare, marketing, human-computer interaction, and more. However, several challenges hinder the accurate detection and interpretation of emotions from various modalities such as text, speech, facial expressions, and physiological signals. This chapter systematically reviews the challenges faced in emotion detection and proposes innovative solutions leveraging deep learning methodologies. Through a combination of literature review, empirical analysis, and case studies, this chapter offers insights into overcoming these challenges and improving the performance and reliability of emotion detection systems across diverse applications.

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Occluded Face In-painting Using Generative Adversarial Networks—A Review

Cited by 3 publications

References 43 publications

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Image In-Painting for Video Processing: Techniques and Performance Evaluation

Challenges and Solutions in Emotion Detection Using Deep Learning Approaches

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