Describing Differences in Image Sets with Natural Language
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911856302489600 |
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| author | Dunlap, Lisa Zhang, Yuhui Wang, Xiaohan Zhong, Ruiqi Darrell, Trevor Steinhardt, Jacob Gonzalez, Joseph E. Yeung-Levy, Serena |
| author_facet | Dunlap, Lisa Zhang, Yuhui Wang, Xiaohan Zhong, Ruiqi Darrell, Trevor Steinhardt, Jacob Gonzalez, Joseph E. Yeung-Levy, Serena |
| contents | How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets, yet manually sifting through thousands of images is impractical. To aid in this discovery process, we explore the task of automatically describing the differences between two $\textbf{sets}$ of images, which we term Set Difference Captioning. This task takes in image sets $D_A$ and $D_B$, and outputs a description that is more often true on $D_A$ than $D_B$. We outline a two-stage approach that first proposes candidate difference descriptions from image sets and then re-ranks the candidates by checking how well they can differentiate the two sets. We introduce VisDiff, which first captions the images and prompts a language model to propose candidate descriptions, then re-ranks these descriptions using CLIP. To evaluate VisDiff, we collect VisDiffBench, a dataset with 187 paired image sets with ground truth difference descriptions. We apply VisDiff to various domains, such as comparing datasets (e.g., ImageNet vs. ImageNetV2), comparing classification models (e.g., zero-shot CLIP vs. supervised ResNet), summarizing model failure modes (supervised ResNet), characterizing differences between generative models (e.g., StableDiffusionV1 and V2), and discovering what makes images memorable. Using VisDiff, we are able to find interesting and previously unknown differences in datasets and models, demonstrating its utility in revealing nuanced insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_02974 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Describing Differences in Image Sets with Natural Language Dunlap, Lisa Zhang, Yuhui Wang, Xiaohan Zhong, Ruiqi Darrell, Trevor Steinhardt, Jacob Gonzalez, Joseph E. Yeung-Levy, Serena Computer Vision and Pattern Recognition Computation and Language Computers and Society Machine Learning How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets, yet manually sifting through thousands of images is impractical. To aid in this discovery process, we explore the task of automatically describing the differences between two $\textbf{sets}$ of images, which we term Set Difference Captioning. This task takes in image sets $D_A$ and $D_B$, and outputs a description that is more often true on $D_A$ than $D_B$. We outline a two-stage approach that first proposes candidate difference descriptions from image sets and then re-ranks the candidates by checking how well they can differentiate the two sets. We introduce VisDiff, which first captions the images and prompts a language model to propose candidate descriptions, then re-ranks these descriptions using CLIP. To evaluate VisDiff, we collect VisDiffBench, a dataset with 187 paired image sets with ground truth difference descriptions. We apply VisDiff to various domains, such as comparing datasets (e.g., ImageNet vs. ImageNetV2), comparing classification models (e.g., zero-shot CLIP vs. supervised ResNet), summarizing model failure modes (supervised ResNet), characterizing differences between generative models (e.g., StableDiffusionV1 and V2), and discovering what makes images memorable. Using VisDiff, we are able to find interesting and previously unknown differences in datasets and models, demonstrating its utility in revealing nuanced insights. |
| title | Describing Differences in Image Sets with Natural Language |
| topic | Computer Vision and Pattern Recognition Computation and Language Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2312.02974 |