MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918161107910656 |
|---|---|
| author | Fan, Xinqi Li, Jingting See, John Yap, Moi Hoon Cheng, Wen-Huang Li, Xiaobai Hong, Xiaopeng Wang, Su-Jing Davision, Adrian K. |
| author_facet | Fan, Xinqi Li, Jingting See, John Yap, Moi Hoon Cheng, Wen-Huang Li, Xiaobai Hong, Xiaopeng Wang, Su-Jing Davision, Adrian K. |
| contents | Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. In recent years, substantial advancements have been made in the areas of ME recognition, spotting, and generation. However, conventional approaches that treat spotting and recognition as separate tasks are suboptimal, particularly for analyzing long-duration videos in realistic settings. Concurrently, the emergence of multimodal large language models (MLLMs) and large vision-language models (LVLMs) offers promising new avenues for enhancing ME analysis through their powerful multimodal reasoning capabilities. The ME grand challenge (MEGC) 2025 introduces two tasks that reflect these evolving research directions: (1) ME spot-then-recognize (ME-STR), which integrates ME spotting and subsequent recognition in a unified sequential pipeline; and (2) ME visual question answering (ME-VQA), which explores ME understanding through visual question answering, leveraging MLLMs or LVLMs to address diverse question types related to MEs. All participating algorithms are required to run on this test set and submit their results on a leaderboard. More details are available at https://megc2025.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering Fan, Xinqi Li, Jingting See, John Yap, Moi Hoon Cheng, Wen-Huang Li, Xiaobai Hong, Xiaopeng Wang, Su-Jing Davision, Adrian K. Computer Vision and Pattern Recognition Multimedia Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. In recent years, substantial advancements have been made in the areas of ME recognition, spotting, and generation. However, conventional approaches that treat spotting and recognition as separate tasks are suboptimal, particularly for analyzing long-duration videos in realistic settings. Concurrently, the emergence of multimodal large language models (MLLMs) and large vision-language models (LVLMs) offers promising new avenues for enhancing ME analysis through their powerful multimodal reasoning capabilities. The ME grand challenge (MEGC) 2025 introduces two tasks that reflect these evolving research directions: (1) ME spot-then-recognize (ME-STR), which integrates ME spotting and subsequent recognition in a unified sequential pipeline; and (2) ME visual question answering (ME-VQA), which explores ME understanding through visual question answering, leveraging MLLMs or LVLMs to address diverse question types related to MEs. All participating algorithms are required to run on this test set and submit their results on a leaderboard. More details are available at https://megc2025.github.io. |
| title | MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2506.15298 |