Text-To-Concept (and Back) via Cross-Model Alignment

• URL: http://arxiv.org/abs/2305.06386v1
• Date: Wed, 10 May 2023 18:01:06 GMT
• Title: Text-To-Concept (and Back) via Cross-Model Alignment
• Authors: Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi
• Abstract summary: We show that mapping between an image's representation in one model to its representation in another can be learned surprisingly well with just a linear layer. We convert fixed off-the-shelf vision encoders to surprisingly strong zero-shot classifiers for free. We show other immediate use-cases of text-to-concept, like building concept bottleneck models with no concept supervision.
• Score: 48.133333356834186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We observe that the mapping between an image's representation in one model to its representation in another can be learned surprisingly well with just a linear layer, even across diverse models. Building on this observation, we propose $\textit{text-to-concept}$, where features from a fixed pretrained model are aligned linearly to the CLIP space, so that text embeddings from CLIP's text encoder become directly comparable to the aligned features. With text-to-concept, we convert fixed off-the-shelf vision encoders to surprisingly strong zero-shot classifiers for free, with accuracy at times even surpassing that of CLIP, despite being much smaller models and trained on a small fraction of the data compared to CLIP. We show other immediate use-cases of text-to-concept, like building concept bottleneck models with no concept supervision, diagnosing distribution shifts in terms of human concepts, and retrieving images satisfying a set of text-based constraints. Lastly, we demonstrate the feasibility of $\textit{concept-to-text}$, where vectors in a model's feature space are decoded by first aligning to the CLIP before being fed to a GPT-based generative model. Our work suggests existing deep models, with presumably diverse architectures and training, represent input samples relatively similarly, and a two-way communication across model representation spaces and to humans (through language) is viable.

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