Neural Naturalist: Generating Fine-Grained Image Comparisons

Maxwell Forbes, Christine Kaeser-Chen, Piyush Sharma, Serge Belongie
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., "heart-shaped face," "squat body"). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance-drawn from a novel stratified sampling approach-with the appropriate level of detail. We propose a new model called Neural Naturalist
more » ... d Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.
doi:10.18653/v1/d19-1065 dblp:conf/emnlp/ForbesKSB19 fatcat:cggpdza7avfz7eto6khheqtd6e