SEA-CLIP-Tiny: Efficient Multilingual Text-Vision Embedding for Southeast Asian Languages

arXiv:2609.30739 · cs.CL · Submitted 2026-09-25 · Read on arXiv

cs.CL

Submitted: 2026-09-25

Updated: 2026-09-29

Code: https://github.com/fassabilf/sea-clip-tiny

Terminology

• Benchmark — 25× in this paper · explained in 2 episodes
• Distillation — 18× in this paper · explained in 2 episodes
• Ablation — 16× in this paper · explained in 1 episode
• Embeddings — 12× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Vision-language models — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Knowledge Distillation — 3× in this paper · explained in 11 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Cross-lingual Transfer — 2× in this paper · explained in 2 episodes
• Interactive Contrastive Learning — 2× in this paper
• Provenance — 2× in this paper · explained in 4 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Contrastive Language-Image Pre-training for the Italian Language
• Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
• Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
• VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
• SEA-LION-Embedding: Open and Reproducible Text Embeddings for Southeast Asia
• WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
• SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• NLLB-CLIP -- train performant multilingual image retrieval model on a budget

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