iMiGUE-3K is the largest in-the-wild micro-gesture video dataset with 3.4K clips and 37M frames from real interviews, supporting self-supervised foundation models and benchmarks that show micro-gestures improve emotion understanding.
Samm: A spontaneous micro-facial movement dataset,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MEDN decouples explicit motion and implicit emotion features with a dual-branch design, AU restriction, orthogonal loss, SEVit, and adaptive fusion to improve micro-expression recognition on benchmarks.
citing papers explorer
-
iMiGUE-3K: A Large-Scale Benchmark for Micro-Gesture Analysis with Self-Supervised Learning
iMiGUE-3K is the largest in-the-wild micro-gesture video dataset with 3.4K clips and 37M frames from real interviews, supporting self-supervised foundation models and benchmarks that show micro-gestures improve emotion understanding.
-
MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition
MEDN decouples explicit motion and implicit emotion features with a dual-branch design, AU restriction, orthogonal loss, SEVit, and adaptive fusion to improve micro-expression recognition on benchmarks.