Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
Fishes are integral to both ecological systems and economic sectors, and studying fish traits is crucial for understanding biodiversity patterns and macro-evolution trends. To enable the analysis of visual traits from fish images, we introduce the Fish-Visual Trait Analysis (Fish-Vista) dataset - a large, annotated collection of about 60K fish images spanning 1900 different species, supporting several challenging and biologically relevant tasks including species classification, trait identification, and trait segmentation. These images have been curated through a sophisticated data processing pipeline applied to a cumulative set of images obtained from various museum collections. Fish-Vista provides fine-grained labels of various visual traits present in each image. It also offers pixel-level annotations of 9 different traits for 2427 fish images, facilitating additional trait segmentation and localization tasks. The ultimate goal of Fish-Vista is to provide a clean, carefully curated, high-resolution dataset that can serve as a foundation for accelerating biological discoveries using advances in AI. Finally, we provide a comprehensive analysis of state-of-the-art deep learning techniques on Fish-Vista.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model
The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems unifying various vision-language (VL) tasks by instruction tuning. However due to the enormous div…
AllAttributeLanguage ModelingLanguage Modelling+1Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model
The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tuning. However, due to the enormous diver…
AllAttributeLanguage ModelingLanguage Modelling+1RoboVista: Evaluating Vision Language Models for Diverse Robot Applications
Diverse applications for robotics, such as industry and agriculture, require robots to operate across various embodiments, changing visual conditions, and complex planning. Vision-Language Models (VLMs) offer a promising…
Visual Question AnsweringAutonomous DrivingVISTA: Video Interaction Spatio-Temporal Analysis Benchmark
Existing benchmarks for Vision-Language Models (VLMs) primarily evaluate spatio-temporal understanding on simple single-action videos, closed attribute sets and restricted entity types, failing to capture the freeform, m…
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
Large Language Models (LLMs) and Large Multimodal Models (LMMs) exhibit impressive problem-solving skills in many tasks and domains, but their ability in mathematical reasoning in visual contexts has not been systematica…
ChatbotImage CaptioningMathMathematical Reasoning+3