Comprehensive Survey
Comprehensive surveys in various scientific fields systematically review existing research, aiming to synthesize key findings, identify gaps, and guide future directions. Current research focuses on evaluating and improving the trustworthiness, efficiency, and bias mitigation of models across diverse domains, including large language models, image generation, and autonomous systems. These surveys are crucial for advancing understanding within specific subfields and facilitating the development of more robust and reliable technologies with broader practical applications.
Papers
Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges
Efe Bozkir, Süleyman Özdel, Mengdi Wang, Brendan David-John, Hong Gao, Kevin Butler, Eakta Jain, Enkelejda Kasneci
Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models
Luke K. Topham, Wasiq Khan, Dhiya Al-Jumeily, Abir Hussain
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
Abdelwahed Khamis, Russell Tsuchida, Mohamed Tarek, Vivien Rolland, Lars Petersson
A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions
Sitara Afzal, Haseeb Ali Khan, Imran Ullah Khan, Md. Jalil Piran, Jong Weon Lee
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
Yanna Jiang, Baihe Ma, Xu Wang, Ping Yu, Guangsheng Yu, Zhe Wang, Wei Ni, Ren Ping Liu