Efficient Test-Time Prompt Tuning for Vision-Language Models

• URL: http://arxiv.org/abs/2408.05775v1
• Date: Sun, 11 Aug 2024 13:55:58 GMT
• Title: Efficient Test-Time Prompt Tuning for Vision-Language Models
• Authors: Yuhan Zhu, Guozhen Zhang, Chen Xu, Haocheng Shen, Xiaoxin Chen, Gangshan Wu, Limin Wang,
• Abstract summary: Self-TPT is a framework leveraging Self-supervised learning for efficient Test-time Prompt Tuning. We show that Self-TPT not only significantly reduces inference costs but also achieves state-of-the-art performance.
• Score: 41.90997623029582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of test-time prompt tuning; however, these methods typically require per-image prompt adaptation during inference, which incurs high computational budgets and limits scalability and practical deployment. To overcome this issue, we introduce Self-TPT, a novel framework leveraging Self-supervised learning for efficient Test-time Prompt Tuning. The key aspect of Self-TPT is that it turns to efficient predefined class adaptation via self-supervised learning, thus avoiding computation-heavy per-image adaptation at inference. Self-TPT begins by co-training the self-supervised and the classification task using source data, then applies the self-supervised task exclusively for test-time new class adaptation. Specifically, we propose Contrastive Prompt Learning (CPT) as the key task for self-supervision. CPT is designed to minimize the intra-class distances while enhancing inter-class distinguishability via contrastive learning. Furthermore, empirical evidence suggests that CPT could closely mimic back-propagated gradients of the classification task, offering a plausible explanation for its effectiveness. Motivated by this finding, we further introduce a gradient matching loss to explicitly enhance the gradient similarity. We evaluated Self-TPT across three challenging zero-shot benchmarks. The results consistently demonstrate that Self-TPT not only significantly reduces inference costs but also achieves state-of-the-art performance, effectively balancing the efficiency-efficacy trade-off.

Related papers

• BaFTA: Backprop-Free Test-Time Adaptation For Zero-Shot Vision-Language Models [20.88680592729709]
  We propose a novel backpropagation-free algorithm BaFTA for test-time adaptation of vision-language models. BaFTA directly estimates class centroids using online clustering within a projected embedding space. We demonstrate that BaFTA consistently outperforms state-of-the-art test-time adaptation methods in both effectiveness and efficiency.
  arXiv  Detail & Related papers  (2024-06-17T08:16:24Z)
• DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection [52.100335904875614]
  We present a novel prompt tuning approach, namely, Decomposed Context Optimization (DeCoOp), which introduces new-class detectors and sub-classifiers to further enhance the base-class and new-class discriminability. Experimental results on 11 benchmark datasets validate the effectiveness of DePT and demonstrate that DeCoOp outperforms current state-of-the-art methods, providing a significant 2% average accuracy improvement.
  arXiv  Detail & Related papers  (2024-06-01T07:46:42Z)
• Revisiting the Power of Prompt for Visual Tuning [50.11465784194896]
  This study explores the correlation evolvement between prompts and patch tokens during proficient training. Inspired by the observation that the prompt tokens tend to share high mutual information with patch tokens, we propose initializing prompts with downstream token prototypes. Our method significantly advances the adaptation for self-supervised pretraining, achieving impressive task performance gains of at least 10% to 30%.
  arXiv  Detail & Related papers  (2024-02-04T07:49:02Z)
• Test-Time Training for Semantic Segmentation with Output Contrastive Loss [12.535720010867538]
  Deep learning-based segmentation models have achieved impressive performance on public benchmarks, but generalizing well to unseen environments remains a major challenge. This paper introduces Contrastive Loss (OCL), known for its capability to learn robust and generalized representations, to stabilize the adaptation process. Our method excels even when applied to models initially pre-trained using domain adaptation methods on test domain data, showcasing its resilience and adaptability.
  arXiv  Detail & Related papers  (2023-11-14T03:13:47Z)
• Approximated Prompt Tuning for Vision-Language Pre-trained Models [54.326232586461614]
  In vision-language pre-trained models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks. We propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning.
  arXiv  Detail & Related papers  (2023-06-27T05:43:47Z)
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [107.05966685291067]
  We propose test-time prompt tuning (TPT) to learn adaptive prompts on the fly with a single test sample. TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data.
  arXiv  Detail & Related papers  (2022-09-15T17:55:11Z)
• Learning Domain Adaptive Object Detection with Probabilistic Teacher [93.76128726257946]
  We present a simple yet effective framework, termed as Probabilistic Teacher (PT) PT aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner. We also present a novel Entropy Focal Loss (EFL) to further facilitate the uncertainty-guided self-training.
  arXiv  Detail & Related papers  (2022-06-13T16:24:22Z)
• Guiding Attention for Self-Supervised Learning with Transformers [24.785500242464646]
  We propose a technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities.
  arXiv  Detail & Related papers  (2020-10-06T00:04:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.