Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation

• URL: http://arxiv.org/abs/2406.00481v1
• Date: Sat, 1 Jun 2024 16:21:42 GMT
• Title: Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation
• Authors: Manogna Sreenivas, Soma Biswas,
• Abstract summary: We propose a novel framework to address the real-world challenging task of Single Image Test Time Adaptation. We leverage large scale Vision Language Models like CLIP to enable real time adaptation on a per-image basis. The proposed framework ROSITA combines these components, enabling continuous online adaptation of Vision Language Models.
• Score: 15.621092104244003
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a novel framework to address the real-world challenging task of Single Image Test Time Adaptation in an open and dynamic environment. We leverage large scale Vision Language Models like CLIP to enable real time adaptation on a per-image basis without access to source data or ground truth labels. Since the deployed model can also encounter unseen classes in an open world, we first employ a simple and effective Out of Distribution (OOD) detection module to distinguish between weak and strong OOD samples. We propose a novel contrastive learning based objective to enhance the discriminability between weak and strong OOD samples by utilizing small, dynamically updated feature banks. Finally, we also employ a classification objective for adapting the model using the reliable weak OOD samples. The proposed framework ROSITA combines these components, enabling continuous online adaptation of Vision Language Models on a single image basis. Extensive experimentation on diverse domain adaptation benchmarks validates the effectiveness of the proposed framework. Our code can be found at the project site https://manogna-s.github.io/rosita/

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