Can Modern Vision Models Understand the Difference Between an Object and a Look-alike?
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| Format: | Preprint |
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2025
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| _version_ | 1866914169753698304 |
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| author | Cohen, Itay Fetaya, Ethan Rosenfeld, Amir |
| author_facet | Cohen, Itay Fetaya, Ethan Rosenfeld, Amir |
| contents | Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One subtle ability is to judge whether an image looks like a given object without being an instance of that object. We study whether vision-language models such as CLIP capture this distinction. We curated a dataset named RoLA (Real or Lookalike) of real and lookalike exemplars (e.g., toys, statues, drawings, pareidolia) across multiple categories, and first evaluate a prompt-based baseline with paired "real"/"lookalike" prompts. We then estimate a direction in CLIP's embedding space that moves representations between real and lookalike. Applying this direction to image and text embeddings improves discrimination in cross-modal retrieval on Conceptual12M, and also enhances captions produced by a CLIP prefix captioner. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can Modern Vision Models Understand the Difference Between an Object and a Look-alike? Cohen, Itay Fetaya, Ethan Rosenfeld, Amir Computer Vision and Pattern Recognition Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One subtle ability is to judge whether an image looks like a given object without being an instance of that object. We study whether vision-language models such as CLIP capture this distinction. We curated a dataset named RoLA (Real or Lookalike) of real and lookalike exemplars (e.g., toys, statues, drawings, pareidolia) across multiple categories, and first evaluate a prompt-based baseline with paired "real"/"lookalike" prompts. We then estimate a direction in CLIP's embedding space that moves representations between real and lookalike. Applying this direction to image and text embeddings improves discrimination in cross-modal retrieval on Conceptual12M, and also enhances captions produced by a CLIP prefix captioner. |
| title | Can Modern Vision Models Understand the Difference Between an Object and a Look-alike? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.19200 |