Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer

• URL: http://arxiv.org/abs/2405.19100v2
• Date: Tue, 18 Jun 2024 10:30:13 GMT
• Title: Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer
• Authors: Zengqun Zhao, Yu Cao, Shaogang Gong, Ioannis Patras,
• Abstract summary: We propose a novel method, Exp-CLIP, to enhance zero-shot FER by transferring the task knowledge from large language models (LLMs) Specifically, based on the pre-trained vision-language encoders, we incorporate a projection head designed to map the initial joint vision-language space into a space that captures representations of facial actions. Given unlabelled facial data, Exp-CLIP achieves superior zero-shot results to the CLIP models and several other large vision-language models (LVLMs) on seven in-the-wild FER datasets.
• Score: 40.47880613758304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current facial expression recognition (FER) models are often designed in a supervised learning manner and thus are constrained by the lack of large-scale facial expression images with high-quality annotations. Consequently, these models often fail to generalize well, performing poorly on unseen images in inference. Vision-language-based zero-shot models demonstrate a promising potential for addressing such challenges. However, these models lack task-specific knowledge and therefore are not optimized for the nuances of recognizing facial expressions. To bridge this gap, this work proposes a novel method, Exp-CLIP, to enhance zero-shot FER by transferring the task knowledge from large language models (LLMs). Specifically, based on the pre-trained vision-language encoders, we incorporate a projection head designed to map the initial joint vision-language space into a space that captures representations of facial actions. To train this projection head for subsequent zero-shot predictions, we propose to align the projected visual representations with task-specific semantic meanings derived from the LLM encoder, and the text instruction-based strategy is employed to customize the LLM knowledge. Given unlabelled facial data and efficient training of the projection head, Exp-CLIP achieves superior zero-shot results to the CLIP models and several other large vision-language models (LVLMs) on seven in-the-wild FER datasets.

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