GeneCIS: A Benchmark for General Conditional Image Similarity
We argue that there are many notions of 'similarity' and that models, like
humans, should be able to adapt to these dynamically. This contrasts with most
representation learning methods, supervised or self-supervised, which learn a
fixed embedding function and hence implicitly assume a single notion of
similarity. For instance, models trained on ImageNet are biased towards object
categories, while a user might prefer the model to focus on colors, textures or
specific elements in the scene. In this paper, we propose the GeneCIS
('genesis') benchmark, which measures models' ability to adapt to a range of
similarity conditions. Extending prior work, our benchmark is designed for
zero-shot evaluation only, and hence considers an open-set of similarity
conditions. We find that baselines from powerful CLIP models struggle on
GeneCIS and that performance on the benchmark is only weakly correlated with
ImageNet accuracy, suggesting that simply scaling existing methods is not
fruitful. We further propose a simple, scalable solution based on automatically
mining information from existing image-caption datasets. We find our method
offers a substantial boost over the baselines on GeneCIS, and further improves
zero-shot performance on related image retrieval benchmarks. In fact, though
evaluated zero-shot, our model surpasses state-of-the-art supervised models on
MIT-States. Project page at https://sgvaze.github.io/genecis/.
648939664cf3474dd1041d51
Sagar Vaze
admin_assigned
2023-06-14T12:12:04.852Z
643838e5c5a91b84ece168dd
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Sagar Vaze
user
sgvaze
648939664cf3474dd1041d52
Nicolas Carion
admin_assigned
2023-06-14T12:12:21.063Z
6253c57927a8414d3bbe6d50
/avatars/9d7cb38db7831a05e35c157a71f5be4a.svg
Nicolas Carion
user
alcinos
648939664cf3474dd1041d53
Ishan Misra
GeneCIS: A Benchmark for General Conditional Image Similarity
We argue that there are many notions of 'similarity' and that models, like
humans, should be able to adapt to these dynamically. This contrasts with most
representation learning methods, supervised or self-supervised, which learn a
fixed embedding function and hence implicitly assume a single notion of
similarity. For instance, models trained on ImageNet are biased towards object
categories, while a user might prefer the model to focus on colors, textures or
specific elements in the scene. In this paper, we propose the GeneCIS
('genesis') benchmark, which measures models' ability to adapt to a range of
similarity conditions. Extending prior work, our benchmark is designed for
zero-shot evaluation only, and hence considers an open-set of similarity
conditions. We find that baselines from powerful CLIP models struggle on
GeneCIS and that performance on the benchmark is only weakly correlated with
ImageNet accuracy, suggesting that simply scaling existing methods is not
fruitful. We further propose a simple, scalable solution based on automatically
mining information from existing image-caption datasets. We find our method
offers a substantial boost over the baselines on GeneCIS, and further improves
zero-shot performance on related image retrieval benchmarks. In fact, though
evaluated zero-shot, our model surpasses state-of-the-art supervised models on
MIT-States. Project page at https://sgvaze.github.io/genecis/.
GeneCIS benchmark evaluates models' adaptability to various similarity conditions, revealing that existing methods struggle and proposing an automatic mining solution to enhance zero-shot performance.
GeneCIS similarity conditions representation learning embedding function CLIP models zero-shot evaluation image-caption datasets image retrieval benchmarks MIT-States