Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling

TL;DR

This paper introduces a point-supervised approach for facial expression spotting that uses Gaussian-based intensity modeling to improve pseudo-labeling and distinguishes macro- and micro-expressions with a dual-branch framework, reducing annotation costs.

Contribution

The paper proposes a novel two-branch framework with Gaussian-based intensity modeling and apex classification for efficient, point-supervised facial expression spotting, reducing reliance on detailed annotations.

Findings

Effective in identifying expressions with minimal supervision

Outperforms existing methods on multiple datasets

Enhances feature discrimination with intensity-aware contrastive loss

Abstract

Automatic facial expression spotting, which aims to identify facial expression instances in untrimmed videos, is crucial for facial expression analysis. Existing methods primarily focus on fully-supervised learning and rely on costly, time-consuming temporal boundary annotations. In this paper, we investigate point-supervised facial expression spotting (P-FES), where only a single timestamp annotation per instance is required for training. We propose a unique two-branch framework for P-FES. First, to mitigate the limitation of hard pseudo-labeling, which often confuses neutral and expression frames with various intensities, we propose a Gaussian-based instance-adaptive intensity modeling (GIM) module to model instance-level expression intensity distribution for soft pseudo-labeling. By detecting the pseudo-apex frame around each point label, estimating the duration, and constructing an instance-level Gaussian distribution, GIM assigns soft pseudo-labels to expression frames for more reliable intensity supervision. The GIM module is incorporated into our framework to optimize the class-agnostic expression intensity branch. Second, we design a class-aware apex classification branch that distinguishes macro- and micro-expressions solely based on their pseudo-apex frames. During inference, the two branches work independently: the class-agnostic expression intensity branch generates expression proposals, while the class-aware apex-classification branch is responsible for macro- and micro-expression classification. Furthermore, we introduce an intensity-aware contrastive loss to enhance discriminative feature learning and suppress neutral noise by contrasting neutral frames with expression frames with various intensities. Extensive experiments on the SAMM-LV, CAS(ME)$^2$, and CAS(ME)$^3$ datasets demonstrate the effectiveness of our proposed framework.