Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models

• URL: http://arxiv.org/abs/2402.08473v1
• Date: Tue, 13 Feb 2024 14:07:49 GMT
• Title: Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models
• Authors: Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu, Lingjiong Zhu
• Abstract summary: Transformer-based models have dominated natural language processing and other areas in the last few years due to their superior (zero-shot) performance on benchmark datasets. In this paper, based on a new gradient descent optimization method, we are able to explore the embedding space of a commonly used vision-language model. Using the Imagenette dataset, we show that while the model achieves over 99% zero-shot classification performance, it fails systematic evaluations completely.
• Score: 7.360937524701675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based models have dominated natural language processing and other areas in the last few years due to their superior (zero-shot) performance on benchmark datasets. However, these models are poorly understood due to their complexity and size. While probing-based methods are widely used to understand specific properties, the structures of the representation space are not systematically characterized; consequently, it is unclear how such models generalize and overgeneralize to new inputs beyond datasets. In this paper, based on a new gradient descent optimization method, we are able to explore the embedding space of a commonly used vision-language model. Using the Imagenette dataset, we show that while the model achieves over 99\% zero-shot classification performance, it fails systematic evaluations completely. Using a linear approximation, we provide a framework to explain the striking differences. We have also obtained similar results using a different model to support that our results are applicable to other transformer models with continuous inputs. We also propose a robust way to detect the modified images.

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