GeneCIS: A Benchmark for General Conditional Image Similarity
Sagar Vaze Nicolas Carion Ishan Misra
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/.