Timid semi–supervised learning for face expression analysis

Badea, Mihai; Florea, Corneliu; Racoviteanu, Andrei; Florea, Laura; Vertan, Constantin

doi:10.1016/j.patcog.2023.109417

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of image recognition, semi-supervised learning is often used for facial expression analysis. For example, in the application of a semi-supervised learning algo-rithm for facial expression recognition proposed by Badea et al [296] in 2023, the authors proposed a timid semi-supervised learning algorithm to improve the performance of supervised methods by introducing additional unique unlabeled data into the database. Their experimental results indicated that the semi-supervised algorithm possesses good performance in facial expression labeling.…”

Section: (B) Unsupervised Learning Algorithmsmentioning

confidence: 99%

Overview of Multi-Robot Collaborative SLAM from the Perspective of Data Fusion

et al. 2023

View full text Add to dashboard Cite

In the face of large-scale environmental mapping requirements, through the use of lightweight and inexpensive robot groups to perceive the environment, the multi-robot cooperative (V)SLAM scheme can resolve the individual cost, global error accumulation, computational load, and risk concentration problems faced by single-robot SLAM schemes. Such schemes are robust and stable, form a current research hotspot, and relevant algorithms are being updated rapidly. In order to enable the reader to understand the development of this field rapidly and fully, this paper provides a comprehensive review. First, the development history of multi-robot collaborative SLAM is reviewed. Second, the fusion algorithms and architectures are detailed. Third, from the perspective of machine learning classification, the existing algorithms in this field are discussed, including the latest updates. All of this will make it easier for readers to discover problems that need to be studied further. Finally, future research prospects are listed.

show abstract

Section: (B) Unsupervised Learning Algorithmsmentioning

confidence: 99%