Image and text question-answer pairs representing 90 distinct animal species. It provides structured data for Visual Question Answering (VQA) tasks, focusing on the identification and description of fauna.
Use Cases
- Train vision-language models to generate text answers based on animal images and natural language questions.
- Benchmark the performance of VQA architectures on fine-grained biological classification tasks.
- Develop zero-shot or few-shot learning algorithms for identifying 90 specific animal species from visual cues.
Strengths
- Contains image and text QA pairs for 90 different animal species.
- Formatted specifically for Visual Question Answering (VQA) benchmarks.
- Includes multi-modal data combining visual features with natural language questions and answers.