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cs.CL2024
JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
Shota Onohara, Atsuyuki Miyai, Yuki Imajuku +5
Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations. In this paper, we introduce J…
cs.CV2024★ 3 cited
FoodMLLM-JP: Leveraging Multimodal Large Language Models for Japanese Recipe Generation
Yuki Imajuku, Yoko Yamakata, Kiyoharu Aizawa
Research on food image understanding using recipe data has been a long-standing focus due to the diversity and complexity of the data. Moreover, food is inextricably linked to peop…