PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression Recognition Article Swipe
Ibtissam Saadi
,
Abdenour Hadid
,
Douglas W. Cunningham
,
Abdelmalik Taleb‐Ahmed
,
Yassin El Hillali
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2503.16945
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2503.16945
Vision-Language Models (VLMs) like CLIP offer promising solutions for Dynamic Facial Expression Recognition (DFER) but face challenges such as inefficient full fine-tuning, high complexity, and poor alignment between textual and visual representations. Additionally, existing methods struggle with ineffective temporal modeling. To address these issues, we propose PE-CLIP, a parameter-efficient fine-tuning (PEFT) framework that adapts CLIP for DFER while significantly reducing trainable parameters while maintaining high accuracy. PE-CLIP introduces two specialized adapters: a Temporal Dynamic Adapter (TDA) and a Shared Adapter (ShA). The TDA is a GRU-based module with dynamic scaling that captures sequential dependencies while emphasizing informative temporal features and suppressing irrelevant variations. The ShA is a lightweight adapter that refines representations within both textual and visual encoders, ensuring consistency and efficiency. Additionally, we integrate Multi-modal Prompt Learning (MaPLe), introducing learnable prompts for visual and action unit-based textual inputs, enhancing semantic alignment between modalities and enabling efficient CLIP adaptation for dynamic tasks. We evaluate PE-CLIP on two benchmark datasets, DFEW and FERV39K, achieving competitive performance compared to state-of-the-art methods while requiring fewer trainable parameters. By balancing efficiency and accuracy, PE-CLIP sets a new benchmark in resource-efficient DFER. The source code of the proposed PE-CLIP will be publicly available at https://github.com/Ibtissam-SAADI/PE-CLIP .
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- Landing Page
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https://doi.org/10.48550/arxiv.2503.16945Digital Object Identifier
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PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression RecognitionWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-21Full publication date if available
- Authors
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Ibtissam Saadi, Abdenour Hadid, Douglas W. Cunningham, Abdelmalik Taleb‐Ahmed, Yassin El HillaliList of authors in order
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https://hal.science/hal-05033150Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://hal.science/hal-05033150Direct OA link when available
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| primary_location.landing_page_url | https://hal.science/hal-05033150 |
| publication_date | 2025-03-21 |
| publication_year | 2025 |
| referenced_works_count | 0 |
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