Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models

• URL: http://arxiv.org/abs/2403.12952v2
• Date: Tue, 10 Dec 2024 22:53:16 GMT
• Title: Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
• Authors: Elaine Sui, Xiaohan Wang, Serena Yeung-Levy,
• Abstract summary: Test-Time Prototype Shifting (TPS) is a pioneering approach designed to adapt vision-language models to test datasets using unlabeled test inputs. TPS not only facilitates optimization-free prototype reuse for subsequent predictions but also enables seamless integration with current advancements in prompt engineering. A notable aspect of our framework is its significantly reduced memory and computational demands when compared to conventional text-prompt tuning methods.
• Score: 19.683461002518147
• License:
• Abstract: Advancements in vision-language models (VLMs) have propelled the field of computer vision, particularly in the zero-shot learning setting. Despite their promise, the effectiveness of these models often diminishes due to domain shifts in test environments. To address this, we introduce the Test-Time Prototype Shifting (TPS) framework, a pioneering approach designed to adapt VLMs to test datasets using unlabeled test inputs. Our method is based on the notion of modulating per-class prototypes in the shared embedding space. By pre-computing and caching prototypes generated with the pre-trained text encoder, TPS not only facilitates optimization-free prototype reuse for subsequent predictions but also enables seamless integration with current advancements in prompt engineering. At test-time, TPS dynamically learns shift vectors for each prototype based solely on the given test sample, effectively bridging the domain gap and enhancing classification accuracy. A notable aspect of our framework is its significantly reduced memory and computational demands when compared to conventional text-prompt tuning methods. Extensive evaluations across 15 image classification datasets involving natural distribution shifts and cross-dataset generalization, as well as in context-dependent visual reasoning, demonstrate TPS's superior performance, achieving state-of-the-art results while reducing resource requirements.

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