Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding (2304.00058v2)

Abstract: Contrastive learning has shown promising potential for learning robust representations by utilizing unlabeled data. However, constructing effective positive-negative pairs for contrastive learning on facial behavior datasets remains challenging. This is because such pairs inevitably encode the subject-ID information, and the randomly constructed pairs may push similar facial images away due to the limited number of subjects in facial behavior datasets. To address this issue, we propose to utilize activity descriptions, coarse-grained information provided in some datasets, which can provide high-level semantic information about the image sequences but is often neglected in previous studies. More specifically, we introduce a two-stage Contrastive Learning with Text-Embeded framework for Facial behavior understanding (CLEF). The first stage is a weakly-supervised contrastive learning method that learns representations from positive-negative pairs constructed using coarse-grained activity information. The second stage aims to train the recognition of facial expressions or facial action units by maximizing the similarity between image and the corresponding text label names. The proposed CLEF achieves state-of-the-art performance on three in-the-lab datasets for AU recognition and three in-the-wild datasets for facial expression recognition.

• The paper presents a novel two-stage CLEF framework that leverages weakly-supervised learning with textual activity descriptions to improve facial behavior analysis.
• It employs vision-text contrastive learning to align image features with textual labels, enhancing recognition of facial expressions and action units.
• Results show state-of-the-art performance on both lab-controlled and in-the-wild datasets, demonstrating significant practical implications for AI-driven facial analysis.

Insights on "Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding"

The paper "Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding" introduces a novel approach intended to improve facial behavior recognition through a method that leverages weakly-supervised text-driven contrastive learning. This approach targets the complications inherent in creating effective positive-negative pairs in contrastive learning settings within facial behavior datasets.

Methodology Overview

The authors propose a two-stage framework termed "CLEF" (Contrastive Learning with Text-Embedded Framework) to address the inherent challenges. The framework utilizes activity descriptions as a resource to overcome the limitation of subject-ID information encoding in contrastive learning. The two-stage method begins with weakly-supervised contrastive learning that takes advantage of activity information to formulate positive-negative pairs. This first stage minimizes intra-activity differences among learned representations.

The second stage involves employing vision-text contrastive learning to maximize the similarity between images and their corresponding textual label names, focusing on facial expression and action units recognition. By doing so, CLEF aligns image features closer to textual features, promoting more effective learning and representation of facial behavior features.

Performance and Results

CLEF delivers promising results, achieving state-of-the-art performance on a total of six datasets split between lab-controlled (for AU recognition) and in-the-wild environments (for facial expression recognition). These findings suggest that the proposed text-driven methodology enables a more enriched understanding of both facial expressions and action units.

Implications and Future Directions

This research contributes notably to both the theoretical understanding and practical application of facial behavior analysis through AI. In particular, it underscores how text-embedded methodologies can enrich facial behavior datasets' representations, ultimately enhancing recognition systems' accuracy. The paper also suggests the potential for similar methodologies to streamline and simplify the data processing requirement in future models, making use of readily available coarse-grained dataset annotations.

Given its demonstrated efficacy, future exploration could expand CLEF beyond its current operational scope by incorporating more sophisticated textual information or generating synthetic coarse-grained data descriptions using natural language processing tools. Additionally, exploring text-driven learning within unseen or novel dataset domains could help establish CLEF's broader applicability, especially in non-laboratory uncontrolled environments.

This paper effectively fuses vision and text-based learning, proving the approach's utility in facial behavior understanding. The findings establish a base for future research to build upon, especially in areas exploring the rich symbiosis between multimodal data coalitions in AI.