Summary of Learning with Alignments: Tackling the Inter- and Intra-domain Shifts For Cross-multidomain Facial Expression Recognition, by Yuxiang Yang et al.
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
by Yuxiang Yang, Lu Wen, Xinyi Zeng, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Yan Wang
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel Learning with Alignments Cross-Multidomain Facial Expression Recognition (LA-CMFER) framework to handle both inter-domain and intra-domain shifts in facial expression recognition. Existing methods solely transfer knowledge from a single labeled source domain to an unlabeled target domain, neglecting comprehensive information across multiple sources. LA-CMFER consists of a global branch and local branch extracting features from full images and subtle expressions, respectively. The framework presents dual-level inter-domain alignment and multi-view intra-domain alignment methods to address shifts. Extensive experiments on six benchmark datasets validate the superiority of LA-CMFER. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving facial expression recognition in computers. Right now, most methods only use information from one source to recognize expressions in another. This can be tricky because different sources might have different rules for what an expression looks like. The researchers propose a new way to do this called LA-CMFER. It uses two parts: one that looks at the whole face and one that focuses on small details. They also use special tricks to make sure the computer is learning from both good and hard-to-learn examples. This helps the computer get better at recognizing expressions. |
Keywords
» Artificial intelligence » Alignment
