MEGC2026: Micro-Expression Grand Challenge on Visual Question Answering

TL;DR

The MEGC2026 challenge advances micro-expression analysis by introducing VQA tasks on short and long videos, leveraging multimodal models to improve understanding and reasoning about involuntary facial movements.

Contribution

This paper presents the MEGC2026, a new benchmark with two VQA tasks on micro-expressions, integrating multimodal large language and vision-language models for improved analysis.

Findings

Introduction of ME-VQA and ME-LVQA tasks.

Benchmark results on multimodal models for micro-expression understanding.

Public leaderboard for ongoing evaluation and comparison.

Abstract

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.