Delving into the Openness of CLIP
- URL: http://arxiv.org/abs/2206.01986v3
- Date: Sun, 7 May 2023 15:04:28 GMT
- Title: Delving into the Openness of CLIP
- Authors: Shuhuai Ren, Lei Li, Xuancheng Ren, Guangxiang Zhao, Xu Sun
- Abstract summary: We evaluate the openness of Contrastive Language-Image Pre-training models.
Our evaluation shows that CLIP-like models are not truly open, and their performance deteriorates as the vocabulary expands.
Our investigation reveals that the overestimation of openness is due to confusion among competing text features.
- Score: 35.371811948506796
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Contrastive Language-Image Pre-training (CLIP) formulates image
classification as an image-to-text matching task, i.e., matching images to the
corresponding natural language descriptions instead of discrete category IDs.
This allows for open-vocabulary visual recognition, where the model can
recognize images from an open class set (also known as an open vocabulary) in a
zero-shot manner. However, evaluating the openness of CLIP-like models is
challenging, as the models are open to arbitrary vocabulary in theory, but
their accuracy varies in practice. To address this, we resort to an incremental
perspective to assess the openness through vocabulary expansions, and define
extensibility to measure a model's ability to handle novel classes. Our
evaluation shows that CLIP-like models are not truly open, and their
performance deteriorates as the vocabulary expands. We further dissect the
feature space of CLIP from the perspectives of representation alignment and
uniformity. Our investigation reveals that the overestimation of openness is
due to confusion among competing text features, rather than a failure to
capture the similarity between image features and text features of novel
classes. We hope that our investigation and analysis will facilitate future
research on the CLIP openness issue.
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