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Article
Computer Science, Theory & Methods
Wu Liu et al.
Summary: This article provides a comprehensive overview of monocular human pose estimation from a 2D to 3D perspective. It summarizes the 2D and 3D representations of the human body, as well as the mainstream approaches since 2014. The article also analyzes the challenges and future research directions in the field.
ACM COMPUTING SURVEYS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Kaiming He et al.
Summary: This paper presents a self-supervised learning method for computer vision based on masked autoencoders. By masking a portion of the input image and reconstructing the missing pixels, large models can be trained efficiently and effectively. The approach achieves high generalization performance and outperforms supervised pretraining in transfer learning tasks.
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
(2022)
Article
Computer Science, Artificial Intelligence
Yuval Nirkin et al.
Summary: The proposed method utilizes two networks to detect face swapping and other identity manipulations in single images, achieving state of the art results in detection accuracy.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Yisroel Mirsky et al.
Summary: Generative deep learning algorithms have advanced to a stage where distinguishing between real and fake has become increasingly challenging. The unethical and malicious applications of this technology, such as the creation of deepfakes for spreading misinformation and impersonating individuals, have raised concerns. This article delves into the creation, detection, current trends, shortcomings in defense solutions, and areas requiring further research and attention in the realm of deepfakes.
ACM COMPUTING SURVEYS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yinglin Zheng et al.
Summary: This research introduces a novel end-to-end framework for video face forgery detection that utilizes temporal coherence. By combining a fully temporal convolution network and a Temporal Transformer network, the framework is able to extract temporal features and explore long-term temporal coherence effectively.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Sowmen Das et al.
Summary: This paper examines the limitations and issues of existing deepfake detection frameworks, identifying the problem of oversampling in deepfake datasets leading to model overfitting. It introduces a data augmentation method called Face-Cutout to enhance data variation and alleviate overfitting. The method shows a significant reduction in LogLoss on various datasets compared to other occlusion-based techniques, and a general-purpose data preprocessing guideline is proposed to enhance the generalizability of models in deepfake detection.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zekun Sun et al.
Summary: Deepfakes is a malicious technique involving transplanting faces in videos, LRNet proposes an efficient framework for detecting such videos by utilizing temporal modeling and precise geometric features, showing robustness even with highly compressed or noise corrupted videos, with an AUC of 0.999 on the FaceForensics+ + dataset.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiangyu Zhu et al.
Summary: This paper proposes a method based on decomposition to detect digital face manipulation, utilizing facial details to uncover hidden forgery details. By decomposing face images into different elements such as 3D shape, textures, etc., subtle forgery patterns can be better detected. Experimental results show that our method achieves state-of-the-art performance in detecting subtle forgery patterns.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Article
Computer Science, Information Systems
Qian Bao et al.
Summary: This paper presents a method that integrates pose information into the tracking task, effectively addressing the challenges of multi-person pose tracking in challenging scenes through a pose-guided detection and tracking framework.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
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Ruben Tolosana et al.
INFORMATION FUSION
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Yaohui Wang et al.
2020 15TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2020)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Yeh Chin-Yuan et al.
2020 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WORKSHOPS (WACVW)
(2020)
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Computer Science, Software Engineering
Justus Thies et al.
ACM TRANSACTIONS ON GRAPHICS
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Joao Carreira et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Computer Science, Software Engineering
Justus Thies et al.
ACM TRANSACTIONS ON GRAPHICS
(2015)
Article
Computer Science, Software Engineering
Kevin Dale et al.
ACM TRANSACTIONS ON GRAPHICS
(2011)
