Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural
  Vision-Language Dataset for Southeast Asia
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural
diversity, yet it remains significantly underrepresented in vision-language
(VL) research. This often results in artificial intelligence (AI) models that
fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an
open-source initiative dedicated to developing high-quality, culturally
relevant data for SEA languages. By involving contributors from SEA countries,
SEA-VL aims to ensure better cultural relevance and diversity, fostering
greater inclusivity of underrepresented languages in VL research. Beyond
crowdsourcing, our initiative goes one step further in the exploration of the
automatic collection of culturally relevant images through crawling and image
generation. First, we find that image crawling achieves approximately ~85%
cultural relevance while being more cost- and time-efficient than
crowdsourcing. Second, despite the substantial progress in generative vision
models, synthetic images remain unreliable in accurately reflecting SEA
cultures. The generated images often fail to reflect the nuanced traditions and
cultural contexts of the region. Collectively, we gather 1.28M SEA
culturally-relevant images, more than 50 times larger than other existing
datasets. Through SEA-VL, we aim to bridge the representation gap in SEA,
fostering the development of more inclusive AI systems that authentically
represent diverse cultures across SEA.
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Holy Lovenia
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Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural
  Vision-Language Dataset for Southeast Asia
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural
diversity, yet it remains significantly underrepresented in vision-language
(VL) research. This often results in artificial intelligence (AI) models that
fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an
open-source initiative dedicated to developing high-quality, culturally
relevant data for SEA languages. By involving contributors from SEA countries,
SEA-VL aims to ensure better cultural relevance and diversity, fostering
greater inclusivity of underrepresented languages in VL research. Beyond
crowdsourcing, our initiative goes one step further in the exploration of the
automatic collection of culturally relevant images through crawling and image
generation. First, we find that image crawling achieves approximately ~85%
cultural relevance while being more cost- and time-efficient than
crowdsourcing. Second, despite the substantial progress in generative vision
models, synthetic images remain unreliable in accurately reflecting SEA
cultures. The generated images often fail to reflect the nuanced traditions and
cultural contexts of the region. Collectively, we gather 1.28M SEA
culturally-relevant images, more than 50 times larger than other existing
datasets. Through SEA-VL, we aim to bridge the representation gap in SEA,
fostering the development of more inclusive AI systems that authentically
represent diverse cultures across SEA.
SEA-VL is an open-source project aimed at creating a large, culturally relevant dataset for Southeast Asian languages to improve AI inclusivity.
vision-language research image crawling generative vision models image generation cultural relevance