mlsanityData to 14 Oct 2026

MEGC2026: Micro-Expression Grand Challenge on Visual Question Answering

arXiv 2603.08927 ↗First posted 9 Mar 2026cs.CV cs.MMcited by 0

On the map: Multimodal LLMs / vision-language models → Multimodal emotion understanding

Abstract (arXiv)

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. 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) 2026 introduces two tasks that reflect these evolving research directions: (1) ME video question answering (ME-VQA), which explores ME understanding through visual question answering on relatively short video sequences, leveraging MLLMs or LVLMs to address diverse question types related to MEs; and (2) ME long-video question answering (ME-LVQA), which extends VQA to long-duration video sequences in realistic settings, requiring models to handle temporal reasoning and subtle micro-expression detection across extended time periods. All participating algorithms are required to submit their results on a public leaderboard. More details are available at https://megc2026.github.io.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Multimodal emotion understanding, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 6 of its 22 references that are in the map. First posted on arXiv 9 Mar 2026.

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6 of 22 references.

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  1. 2025MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering18 Jun

Other references · 5

  1. 2025Qwen2.5-VL Technical Reportcited by 5,587
  2. 2025Qwen3-VL Technical Reportcited by 2,793
  3. 2022Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithmscited by 51
  4. 2019SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Datasetcited by 25
  5. 20213D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Framecited by 16

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  1. 2025MELLM: A Flow-Guided Large Language Model for Micro-Expression Understanding
  2. 2025Rethinking Facial Expression Recognition in the Era of Multimodal Large Language Models: Benchmark, Datasets, and Beyond
  3. 2026MA-Bench: Towards Fine-grained Micro-Action Understanding
  4. 2024MicroEmo: Time-Sensitive Multimodal Emotion Recognition with Micro-Expression Dynamics in Video Dialogues
  5. 2024EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
  6. 2025Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
  7. 2024PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
  8. 2025MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs