title: Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural
    Vision-Language Dataset for Southeast Asia
summary: 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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paper_authors.name: Peerat Limkonchotiwat
paper_title: Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural
    Vision-Language Dataset for Southeast Asia
paper_summary: 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.
paper_ai_keywords: vision-language (VL) research cultural relevance crowdsourcing image crawling image generation generative vision models synthesized images datasets