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AgroBench: Vision-Language Model Benchmark in Agriculture
Risa Shinoda, Nakamasa Inoue, Hirokatsu Kataoka +2
Precise automated understanding of agricultural tasks such as disease identification is essential for sustainable crop production. Recent advances in vision-language models (VLMs)…
AnimalClue: Recognizing Animals by their Traces
Risa Shinoda, Nakamasa Inoue, Iro Laina +2
Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Rec…
Industrial Synthetic Segment Pre-training
Shinichi Mae, Hirokatsu Kataoka, Ryousuke Yamada +3
Vision Foundation Models (VFMs) have made remarkable progress and are increasingly being applied to segmentation tasks in real-world industrial settings. However, VFMs pre-trained…
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
Risa Shinoda, Kuniaki Saito, Shohei Tanaka +2
Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and g…
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning
Risa Shinoda, Ryo Hayamizu, Kodai Nakashima +3
Pre-training is a strong strategy for enhancing visual models to efficiently train them with a limited number of labeled images. In semantic segmentation, creating annotation masks…