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

Puja Ahmad Habibi; Faiz Assabil Firdaus; Ashvanth S; Ekapol Chuangsuwanich; Pume Tuchinda; Peerat Limkonchotiwat

cs.CLSep 25, 2026

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

Authors: Puja Ahmad Habibi, Faiz Assabil Firdaus, Ashvanth S, Ekapol Chuangsuwanich, Pume Tuchinda, Peerat Limkonchotiwat

Organizations: SEACrowd · University of Indonesia · Cohere Labs Community · Technical University of Denmark · Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University · Vidyasirimedhi Institute of Science and Technology · AI Singapore

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.

Figures & tables

Panel of Figure 1 — Figure 1: Performance-size comparison of compact CLIP models on Southeast Asian image-text retrieval. (a) Language-average R@10 is computed from mean(I2T, T2I) retrieval scores over the available retrieval benchmarks for each of the seven SEA languages. (b) Task-average R@10 is the mean retrieval R@10 over XM3600, Flickr30k-200, and XTD-200.

Figure 2: SEA languages and countries covered in this work, amounting to 569M speakers in the SEA region.

Figure 3: The overview of SEA-CLIP-Tiny . We split the module into two major components: data selection and training framework.

Dataset	Indonesian	Javanese	Malay	Sundanese	Myanmar	Thai	Vietnamese	Total
CG Open-Ended (filtered) [ 19 ]	223,098	39,747	161,397	21,238	—	58,433	199,562	703,475
WIT-HF [ 21 ]	146,744	11,926	59,504	—	9,941	55,594	203,979	487,688
BLOOM [ 14 ] †	2,169	—	102	—	419	3,009	22	21,534
SEA-Mammoth [ 3 ] ‡	54,040	—	54,040	—	54,040	54,040	54,040	540,400
Total	426,051	51,673	275,043	21,238	64,400	171,076	457,603	1,753,097

Table 1: Multilingual training data sources used for SEA-CLIP-Tiny , reported as the number of training examples per target language. The Total column includes all examples used from each dataset, including additional languages not shown in the table. † BLOOM covers 76 languages in total. ‡ SEA-Mammoth covers 10 languages in total; zh, ta, lo, km, and fil are included only in the Total column.

Model	#Params (M)	CPU ms	Thai			Myanmar			Malay			Indonesian			Javanese			Sundanese			Vietnamese			Avg
Model	#Params (M)	CPU ms	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10
Teacher	$86.19+499.77$	90.5	48.7	75.5	84.7	25.3	49.1	60.6	56.1	81.9	89.5	59.9	84.2	91.2	27.4	51.8	63.6	24.5	47.6	58.8	57.1	83.5	90.7	42.7	67.6	77.0
CLIP-KD	$5.62+40.49$	24.1	0.2	0.6	1.0	0.4	1.3	1.9	1.9	5.2	7.5	2.6	6.9	9.7	2.9	8.6	12.6	4.0	9.9	15.2	0.6	1.6	2.4	1.8	4.9	7.2
TinyCLIP	$8.28+15.17$	14.9	0.1	0.5	1.0	0.7	1.9	2.5	2.5	6.1	8.9	3.0	8.2	11.4	3.7	9.3	13.4	4.4	11.2	16.5	1.3	3.6	5.2	2.2	5.8	8.4
MobileCLIP2	$11.41+63.43$	61.6	0.9	1.9	2.7	1.7	3.8	4.8	20.9	40.6	50.8	30.1	53.4	64.1	13.6	30.4	39.8	14.0	29.7	39.5	3.3	7.2	9.3	12.1	23.9	30.1
SEA-CLIP-Tiny	$5.62+40.49$	24.1	8.6	24.1	33.9	3.6	12.1	18.7	22.6	49.3	63.2	22.5	49.0	62.5	9.8	25.4	35.8	7.1	20.8	29.2	16.4	39.6	52.4	12.9	31.5	42.2

Table 2: Per-language image-text retrieval performance, reported as %. For each language, we report mean(I2T, T2I) R@

k

, averaged over the retrieval benchmarks covering that language. CPU ms denotes median single-sample CPU latency for encoding one image and one caption; full computational measurements are reported in Tables 10 and 11 . Bold denotes the best student model in each column.

Model	#Params (M)	CPU ms	Retrieval (R@ $k$ )												Classification (acc@ $k$ )			VQA
			XM3600			Flickr30k-200			XTD-200			Avg			Babel-IN			CVQA
			@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	acc@1
Teacher	$86.19+499.77$	90.5	55.7	81.4	88.8	46.0	69.3	77.0	39.2	65.8	77.0	47.0	72.2	81.0	42.3	61.3	67.8	48.6
CLIP-KD	$5.62+40.49$	24.1	1.1	2.6	3.5	1.5	3.7	5.4	2.2	6.2	9.4	1.6	4.2	6.1	4.2	7.7	10.5	25.9
TinyCLIP	$8.28+15.17$	14.9	1.6	3.7	4.9	2.1	4.7	6.8	2.4	7.1	10.5	2.0	5.2	7.4	4.4	8.2	10.8	26.8
MobileCLIP2	$11.41+63.43$	61.6	11.7	20.8	24.6	11.2	22.0	27.5	12.9	25.8	33.1	11.9	22.8	28.4	15.6	23.5	27.5	33.5

Table 3: Per-task SEA performance, reported as %. Retrieval reports mean(I2T, T2I) R@

k

on XM3600, Flickr30k-200, and XTD-200, with Avg computed only across these three retrieval benchmarks. Classification reports Babel-IN zero-shot acc@

k

, and VQA reports CVQA acc@1 on the LOCAL split. Bold denotes the best student model in each column.

Figure 4: Human preference evaluation comparing SEA-CLIP-Tiny against MobileCLIP2 across four Southeast Asian languages (Indonesian, Javanese, Thai, Sundanese). Each bar shows the number of questions (out of 100) where SEA-CLIP wins (blue), ties (yellow), or MobileCLIP wins (red), per annotator and on average.

Setup	Train data (#sample)	CG R@1	WIT R@1	Bloom R@1	ImageNet	R@1-Avg
SEA-CLIP-Tiny	CC12M + CG-OE-filt + WIT-HF + Bloom + Mammoth (12.72M)	29.3	21.8	23.3	36.4	13.9
Dataset Ablation
Only SEA datasets	CG-OE-filt + WIT-HF + Bloom (1.21M)	63.7	54.6	24.9	5.6	5.9
Only CC12M	CC12M (10.97M)	1.0	1.9	1.2	40.0	6.5
Only WIT	WIT-hf (487K)	2.0	27.7	2.2	1.3	6.2
Only Bloom	Bloom-only (21K)	0.0	0.0	1.0	0.1	5.2

Table 4: Controlled ablations of training data and distillation objectives for SEA-CLIP-Tiny , reported as %. All variants use the same student architecture and hyperparameters. Retrieval Avg R@1 is the mean retrieval R@1 over XM3600, Flickr30k-200, and XTD-200, consistent with Table 3 . Note that we use the same multilingual teacher model, ViT-B-16-worldwide@WorldWideCLIP .

Model	#Params (M)	CPU ms	Thai			Myanmar †			Malay †			Indonesian			Javanese †			Sundanese †			Vietnamese			Avg
Model	#Params (M)	CPU ms	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10
Teacher	$86.19+499.77$	90.5	50.7	77.2	86.4	27.2	51.1	62.3	57.4	82.8	90.5	61.3	85.7	91.9	28.1	52.7	63.9	24.6	48.2	59.1	59.2	85.5	91.9	44.1	69.0	78.0
CLIP-KD ∗	$5.62+40.49$	24.1	0.2	0.7	1.1	0.8	1.7	2.2	2.4	6.2	8.8	3.0	8.2	11.4	3.6	10.0	14.4	4.9	11.4	17.1	1.0	2.6	3.7	2.3	5.8	8.4
TinyCLIP	$8.28+15.17$	14.9	0.2	0.6	0.8	1.2	2.4	3.3	3.4	7.8	10.8	3.9	10.1	13.6	4.7	11.7	15.9	5.5	13.3	18.6	1.8	4.9	6.7	3.0	7.2	10.0
MobileCLIP2	$11.41+63.43$	61.6	1.2	2.5	3.6	2.7	5.2	6.1	24.1	46.1	56.8	34.4	60.2	71.2	16.7	35.5	45.7	15.8	33.6	43.3	4.3	9.2	11.9	14.2	27.5	34.1
SEA-CLIP-Tiny	$5.62+40.49$	24.1	9.1	25.5	35.4	3.8	13.2	19.7	23.7	51.9	66.2	23.6	50.1	63.7	10.7	26.2	36.6	7.5	21.6	30.2	17.4	41.6	54.6	13.7	32.9	43.7

Table 5: Per-language performance (mean across XM3600, Flickr30k-200, XTD-200 I2T R@k), reported as %. Bold = best among student models per column.

\dagger

Averaged over Flickr30k-200 and XTD-200 only; XM3600 does not cover these languages.

*

Retrained by on CC12M following CLIPKD’s scripts.

Model	#Params (M)	CPU ms	Retrieval (R@ $k$ )												Classification (acc@ $k$ )			VQA
			XM3600			Flickr30k-200			XTD-200			Avg			Babel-IN			CVQA
			@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	acc@1
Teacher	$86.19+499.77$	90.5	58.9	84.4	90.9	47.1	70.6	78.0	40.3	66.8	77.6	48.7	73.9	82.2	42.3	61.3	67.8	48.6
CLIP-KD ∗	$5.62+40.49$	24.1	1.5	3.4	4.4	1.8	4.4	6.3	2.7	7.4	10.9	2.0	5.1	7.2	4.2	7.7	10.5	25.9
TinyCLIP	$8.28+15.17$	14.9	2.1	4.7	5.9	2.7	5.8	8.0	3.1	8.9	12.4	2.6	6.5	8.8	4.4	8.2	10.8	26.8
MobileCLIP2	$11.41+63.43$	61.6	14.2	24.2	28.5	13.2	25.9	31.8	14.8	28.9	36.6	14.0	26.3	32.3	15.6	23.5	27.5	33.5

Table 6: Per-task SEA performance, reported as %. Babel-IN = acc@

k

(SEA-lang avg); XM3600, Flickr30k-200, XTD-200 = I2T (image

\to

text) R@

k

; CVQA = acc@1 (LOCAL, SEA-7 subset). Retrieval Avg = mean(Babel-IN, XM3600, Flickr30k-200, XTD-200). Total Avg = equal-weight mean over all 5 tasks. Bold = best student per column.

Model	#Params (M)	CPU ms	Thai			Myanmar †			Malay †			Indonesian			Javanese †			Sundanese †			Vietnamese			Avg
Model	#Params (M)	CPU ms	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10
Teacher	$86.19+499.77$	90.5	46.7	73.8	82.9	23.4	47.0	58.9	54.9	81.0	88.5	58.5	82.6	90.4	26.7	50.9	63.4	24.4	47.1	58.6	55.0	81.4	89.5	41.4	66.3	76.0
CLIP-KD ∗	$5.62+40.49$	24.1	0.1	0.6	1.0	0.1	0.8	1.7	1.5	4.2	6.2	2.1	5.5	7.9	2.2	7.3	10.9	3.2	8.4	13.4	0.2	0.6	1.1	1.3	3.9	6.0
TinyCLIP	$8.28+15.17$	14.9	0.0	0.4	1.1	0.3	1.4	1.8	1.5	4.5	7.1	2.2	6.3	9.1	2.6	6.8	10.8	3.3	9.1	14.3	0.9	2.3	3.8	1.5	4.4	6.9
MobileCLIP2	$11.41+63.43$	61.6	0.6	1.3	1.8	0.8	2.4	3.5	17.7	35.1	44.8	25.8	46.6	56.9	10.4	25.3	33.9	12.2	25.9	35.6	2.3	5.3	6.8	10.0	20.3	26.2
SEA-CLIP-Tiny	$5.62+40.49$	24.1	8.0	22.6	32.4	3.3	11.0	17.7	21.6	46.7	60.2	21.3	48.0	61.3	8.9	24.6	35.1	6.6	19.9	28.2	15.4	37.6	50.2	12.2	30.0	40.7

Table 7: Per-language performance (mean across Babel-IN = acc@k, XM3600, Flickr30k-200, XTD-200 T2I (text

\to

image) R@

k

), reported as %. Bold = best among student models per column.

\dagger

Averaged over Flickr30k-200 and XTD-200 only; XM3600 does not cover these languages.

*

Retrained by on CC12M following CLIPKD’s scripts.

Model	#Params (M)	CPU ms	Retrieval (R@ $k$ )												Classification (acc@ $k$ )			VQA
			XM3600			Flickr30k-200			XTD-200			Avg			Babel-IN			CVQA
			@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	@1	@5	@10	acc@1
Teacher	$86.19+499.77$	90.5	52.6	78.5	86.8	45.0	68.0	76.1	38.1	64.8	76.4	45.2	70.5	79.7	42.3	61.3	67.8	48.6
CLIP-KD ∗	$5.62+40.49$	24.1	0.7	1.8	2.5	1.1	2.9	4.4	1.7	5.1	8.0	1.1	3.3	5.0	4.2	7.7	10.5	25.9
TinyCLIP	$8.28+15.17$	14.9	1.0	2.8	3.8	1.5	3.6	5.6	1.6	5.2	8.6	1.4	3.9	6.0	4.4	8.2	10.8	26.8
MobileCLIP2	$11.41+63.43$	61.6	9.2	17.3	20.8	9.2	18.0	23.2	10.9	22.7	29.6	9.8	19.3	24.5	15.6	23.5	27.5	33.5

Table 8: Per-task SEA performance, reported as %. Babel-IN = acc@

k

(SEA-lang avg); XM3600, Flickr30k-200, XTD-200 = T2I (text

\to

image) R@

k

; CVQA = acc@1 (LOCAL, SEA-7 subset). Retrieval Avg = mean(Babel-IN, XM3600, Flickr30k-200, XTD-200). Total Avg = equal-weight mean over all 5 tasks. Bold = best student per column.

Figure 5: Human preference evaluation on XTD-200 only. Each language contains 50 samples. Bars show the number of questions where SEA-CLIP-Tiny wins, ties with MobileCLIP2, or MobileCLIP2 wins, reported per annotator and on average.

Figure 6: Human preference evaluation on Flickr30k-200 only. Each language contains 50 samples. Bars show the number of questions where SEA-CLIP-Tiny wins, ties with MobileCLIP2, or MobileCLIP2 wins, reported per annotator and on average.

Figure 7: Example interface for the human preference evaluation. Annotators compare two anonymized captions for the same image and select the better caption, or mark a tie when both captions are equally acceptable or equally incorrect.

Benchmark	Task	Metric	Provenance	SEA language coverage							$n$ /lang
Benchmark	Task	Metric	Provenance	Tha	Mya	Mal	Ind	Jav	Sun	Vie	$n$ /lang
Babel-ImageNet	Zero-shot classification	acc@ $k$	BabelNet lexical mapping	✓	✓	✓	✓	✓	✓	✓	98–896
XM3600	Image–text retrieval	R@ $k$	Native-speaker annotation	✓	–	–	✓	–	–	✓	3,600
Flickr30k-200	Image–text retrieval	R@ $k$	Machine translation (NLLB)	✓	✓	✓	✓	✓	✓	✓	1,000
XTD-200	Image–text retrieval	R@ $k$	Machine translation (NLLB)	✓	✓	✓	✓	✓	✓	✓	1,000
CVQA	Visual question answering	acc@1	Native-speaker authored	no per-language split released							–

Table 9: Coverage and provenance of the evaluation benchmarks. XM3600 covers Thai, Indonesian, and Vietnamese; therefore, the per-language retrieval averages for these languages in Table 2 are computed over three benchmarks, while Malay, Javanese, Sundanese, and Myanmar are averaged over Flickr30k-200 and XTD-200. Flickr30k-200 and XTD-200 rely on machine-translated text rather than native annotation, which is an important limitation for evaluating lower-resource languages. Babel-ImageNet also has uneven test-set sizes across languages. Filipino, Khmer, and Lao occur in our training mixture but are not covered by these evaluation sets and are therefore excluded from the main language-level evaluation.

n

/lang denotes the number of evaluation examples per language.

Model	Image	Text	Image	Text	Image	Text	Image	Text	(MB)
	Params (M)		GFLOPs/sample		Latency (ms, bs=1)		Throughput (samples/s, bs=1024)		Peak mem.
TinyCLIP	8.28	15.17	3.57	0.38	5.27	2.60	14,479	112,589	2,278
MobileCLIP2-S0	11.41	63.43	4.79	3.88	14.44	5.14	1,930	14,364	9,258
SEA-CLIP-Tiny	5.62	40.49	2.51	3.38	6.57	6.65	13,213	18,544	1,739
MetaCLIP-2 (ViT-B-16-worldwide@WorldWideCLIP) (teacher)	86.19	499.77	35.13	5.96	6.58	6.81	3,429	15,112	7,745

Table 10: Efficiency comparison on a single NVIDIA A100-SXM4-40GB. GFLOPs are measured per sample for one forward pass using torch.utils.flop_counter (1 MAC = 2 FLOPs) at each model’s native input resolution and context length. Latency is measured at batch size 1 after 20 warm-up iterations and 100 timed runs, while throughput and peak memory are measured at batch size 1,024. Parameters include the final projection heads and token-embedding tables.

Model	Image (ms)	Text (ms)	Image + text (ms)
TinyCLIP	12.52	2.40	14.92
MobileCLIP2-S0	41.57	20.03	61.60
SEA-CLIP-Tiny	10.40	13.73	24.13
MetaCLIP-2 (ViT-B-16-worldwide@WorldWideCLIP) (teacher)	70.24	20.25	90.49

Table 11: Single-sample CPU inference latency measured in fp32 at batch size 1 on an AMD EPYC 7713 with OMP_NUM_THREADS=8 . We report the median of 30 runs after 5 warm-up iterations. FLOPs are reported using CUDA profiling in Table 10 , since the CPU fused-attention implementation is not fully captured by our FLOP counter.

	Text tower (M)			Image tower (M)
Model	Embedding table	Transformer + head	Total	Total
TinyCLIP	12.65	2.52	15.17	8.28
MobileCLIP2-S0	25.30	38.13	63.43	11.41
SEA-CLIP-Tiny	18.97	21.52	40.49	5.62
MetaCLIP-2 (ViT-B-16-worldwide@WorldWideCLIP) (teacher)	461.63	38.13	499.77	86.19

Table 12: Parameter breakdown of the text and image towers. We report the token-embedding table separately because published compact CLIP models use different parameter-counting conventions. TinyCLIP excludes the text token-embedding table from its reported text-encoder size; under its published convention, our implementation reproduces the reported parameter counts. For consistency, our efficiency comparisons include the embedding table and final projection heads for all models.

Tokenizer	Vocab.	Thai	Myanmar	Khmer	Lao	VI Latin	SEA-script total	Byte frag.
Student CLIP-BPE	49,408	51	0	0	0	59	51	532
Teacher XLM-V-Base	901,629	14,243	4,214	3,029	2,112	58,773	23,598	–

Table 13: Tokenizer vocabulary coverage of Southeast Asian scripts. For each tokenizer, we count vocabulary entries containing at least one character from the corresponding Unicode script. VI Latin counts entries containing Vietnamese-specific Latin characters with diacritics. SEA-script total is the union of Thai, Myanmar, Khmer, and Lao script entries and therefore excludes Latin-script entries. Byte frag. denotes student vocabulary entries that do not decode to a valid UTF-8 character independently.

Benchmark	Language	$n$	Chars/text	Mean	Median	P95	Max	Trunc. (%)
XM3600	English	3,600	51.9	13.0	13	20	31	0.0
	Indonesian	3,600	93.3	31.4	29	55	95	0.1
	Vietnamese	3,600	74.9	68.4	66	116	193	33.3
	Thai	3,600	54.0	59.2	59	100	163	26.1
Flickr30k-200	English	1,000	94.6	22.3	20	35	86	0.1
	Indonesian	1,000	106.0	37.1	34	61	141	1.5

Table 14: Student CLIP-BPE tokenization statistics by benchmark and language. Token lengths are measured before truncation and include start- and end-of-text tokens.

n

denotes the number of evaluated text inputs. P95 and Max summarize the pre-truncation token-length distribution, and Trunc. is the percentage of inputs exceeding the context length of 77 tokens.

Language	Student CLIP-BPE				Teacher XLM-V-Base
	Tok/cap	Tok/char	Trunc	Frag	Tok/cap	Tok/char	Trunc	unk
English	17.0	0.21	0.0	0.0	17.3	0.25	0.0	0.00
Indonesian	30.9	0.32	0.6	0.0	17.2	0.19	0.0	0.00
Javanese	28.4	0.33	0.6	0.0	18.9	0.24	0.1	0.00
Malay	31.6	0.33	0.9	0.0	17.4	0.20	0.0	0.00
Sundanese	30.1	0.34	0.8	0.0	20.9	0.25	0.1	0.00

Table 15: Caption statistics per language, macro-averaged over the three retrieval benchmarks (XM3600, Flickr30k-200, XTD-200) at a context length of 77 tokens. All Trunc , Frag and unk values are percentages. Trunc is the share of captions exceeding the context length; Frag is the share of emitted student tokens that are UTF-8 byte fragments; unk is the share of emitted teacher tokens that are the unknown token.

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Text Embeddings Text Embedding Evaluation Text Embedding Models

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Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-art embedding models are not reproducible because they rely on closed or undisclosed training data, and they remain insufficiently robust for Southeast Asian languages. We present SEA-LION-Embedding, a fully open and reproducible text-embedding pipeline for Southeast Asian languages trained only on publicly available data, and use it to study three core factors of robust embedding design: data composition, training objective, and base encoder initialization. SEA-LION-Embedding achieves state-of-the-art results on SEA-BED while enabling systematic and reproducible analysis of robust text embeddings for the region.

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