CMNet: Contrastive Magnification Network for Micro-Expression Recognition Article Swipe
Mengting Wei
,
Xingxun Jiang
,
Wenming Zheng
,
Yuan Zong
,
Cheng Lu
,
Jiateng Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i1.25083
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v37i1.25083
Micro-Expression Recognition (MER) is challenging because the Micro-Expressions' (ME) motion is too weak to distinguish. This hurdle can be tackled by enhancing intensity for a more accurate acquisition of movements. However, existing magnification strategies tend to use the features of facial images that include not only intensity clues as intensity features, leading to the intensity representation deficient of credibility. In addition, the intensity variation over time, which is crucial for encoding movements, is also neglected. To this end, we provide a reliable scheme to extract intensity clues while considering their variation on the time scale. First, we devise an Intensity Distillation (ID) loss to acquire the intensity clues by contrasting the difference between frames, given that the difference in the same video lies only in the intensity. Then, the intensity clues are calibrated to follow the trend of the original video. Specifically, due to the lack of truth intensity annotation of the original video, we build the intensity tendency by setting each intensity vacancy an uncertain value, which guides the extracted intensity clues to converge towards this trend rather some fixed values. A Wilcoxon rank sum test (Wrst) method is enforced to implement the calibration. Experimental results on three public ME databases i.e. CASME II, SAMM, and SMIC-HS validate the superiority against state-of-the-art methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v37i1.25083
- https://ojs.aaai.org/index.php/AAAI/article/download/25083/24855
- OA Status
- diamond
- Cited By
- 8
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382240667
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382240667Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v37i1.25083Digital Object Identifier
- Title
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CMNet: Contrastive Magnification Network for Micro-Expression RecognitionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-26Full publication date if available
- Authors
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Mengting Wei, Xingxun Jiang, Wenming Zheng, Yuan Zong, Cheng Lu, Jiateng LiuList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v37i1.25083Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/25083/24855Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/25083/24855Direct OA link when available
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Intensity (physics), Computer science, Intensity mapping, Artificial intelligence, Magnification, Pattern recognition (psychology), Optics, Redshift, Physics, Galaxy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 7, 2023: 1Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3194352463, https://openalex.org/W6761091710, https://openalex.org/W2949662773, https://openalex.org/W2426188534, https://openalex.org/W6776700526, https://openalex.org/W2527254703, https://openalex.org/W6634373223, https://openalex.org/W2805056734, https://openalex.org/W2406432402, https://openalex.org/W3182710365, https://openalex.org/W3092753574, https://openalex.org/W2044106642, https://openalex.org/W4281477025, https://openalex.org/W1972978214, https://openalex.org/W3100951556, https://openalex.org/W2996310233, https://openalex.org/W2808064875, https://openalex.org/W4224942959, https://openalex.org/W4295884636, https://openalex.org/W6639213037, https://openalex.org/W2910652840, https://openalex.org/W2909115907, https://openalex.org/W2006426145, https://openalex.org/W2072038603, https://openalex.org/W3016260756, https://openalex.org/W2795270851, https://openalex.org/W2949258649, https://openalex.org/W1991467301, https://openalex.org/W3026092005, https://openalex.org/W2964606879, https://openalex.org/W2891138649, https://openalex.org/W3093000000, https://openalex.org/W4362223627, https://openalex.org/W4287812705 |
| referenced_works_count | 34 |
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