SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages
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Computer Science > Computation and Language
Title:SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages
Abstract:Multilingual text-vision embedding models are essential for cross-lingual image-text retrieval, but Southeast Asian languages remain poorly supported due to the region's linguistic diversity and limited data and computing resources. In this paper, we introduce SEA-CLIP-Tiny, a compact multilingual text-vision embedding model for Southeast Asia with fewer than 50M parameters. Our model adapts a CLIP-KD-style framework to Southeast Asian multilingual settings through regional data curation and multilingual teacher guidance. Experiments across seven Southeast Asian languages show that SEA-CLIP-Tiny achieves the strongest average retrieval performance among the evaluated student models, reaching 12.9%, 31.5%, and 42.2% at R@1, R@5, and R@10, respectively. Compared with MobileCLIP2, it improves average R@10 by 12.1 points while using 38.4% fewer parameters and lower measured CPU latency. These results highlight the importance of region-aware training for efficient multilingual text-vision models in Southeast Asia.
| Comments: | Accepted to ACCV 2026. Model weights and datasets are available at this https URL and code for training, evaluation, and preprocessing at this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.30739 [cs.CL] |
| (or arXiv:2609.30739v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30739
arXiv-issued DOI via DataCite (pending registration)
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