Papers with Birds-to-Words dataset
Neural Naturalist: Generating Fine-Grained Image Comparisons (D19-1)
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| Challenge: | a dataset of 41k sentences describes fine-grained differences between photographs of birds . human observers are adept at making fine-grain comparisons, but sometimes require aid in distinguishing visually similar classes. |
| Approach: | They propose a model that generates comparative language from a dataset of 41k sentences describing fine-grained differences between photographs of birds. |
| Outcome: | The proposed model can explain differences in visual embedding space using natural language . it evaluates the results with humans who must use the descriptions to distinguish real images . |
L2C: Describing Visual Differences Needs Semantic Understanding of Individuals (2021.eacl-main)
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| Challenge: | Existing methods for captioning images without understanding individual's semantics are not effective . a new task, visual comparison, has drawn increasing attention in the field of language and vision . |
| Approach: | They propose a learning-to-compare model which learns to understand semantic structures of two images and compares them while learning to describe each one. |
| Outcome: | The proposed model outperforms the baseline and human evaluation on the Birds-to-Words dataset. |