多様な海岸環境における視覚言語モデルの評価
Evaluation of Vision-Language Models Across Diverse Coastal Environments
ハワイの海岸で収集した1000枚以上の画像データセットを構築し、7つの視覚言語モデルを3つの実験で評価した。海岸特有の概念の認識が難しいが、その原因は環境よりもセグメンテーションと言語表現にあることを示した。
著者: Seth Knoop, Chad R. Samuelson, Gabriel R. Slade, Brady Moon, Joshua G. Mangelson
分類: cs.CV, cs.RO
原文アブストラクト
Vision-language models (VLMs) enable robotic per- ception by associating visual observations with natural-language concepts. Yet their performance in coastal environments remains largely unexplored. We introduce a densely labeled coastal dataset containing more than 1,000 images collected across seven missions in three regions of Oahu, Hawaii, with 18 semantic classes and over 7,400 annotated instances. We evaluate seven modern VLMs through three complementary experiments mea- suring text-to-mask, mask-to-mask, and mask-to-text alignment. Broad landscape classes are generally recognized more accurately than conventional object and coastal classes, with coastal con- cepts presenting the greatest challenge. However, comparisons of shared conventional classes across coastal and terrestrial datasets reveal no consistent performance difference attributable solely to environmental context. Mask-to-mask matching also remains similar across conventional and coastal classes, while alternative textual labels substantially improve recognition of several coastal concepts. These results suggest that lower performance on coastal classes (at least on the objects/query categories evaluated) is heavily influenced by segmentation and linguistic representation.