In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model

• URL: http://arxiv.org/abs/2403.06126v2
• Date: Mon, 19 Aug 2024 14:22:42 GMT
• Title: In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model
• Authors: Junhui Yin, Xinyu Zhang, Lin Wu, Xiaojie Wang,
• Abstract summary: We propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks. InCPL associates a new test sample with very few labeled examples as context information. We introduce a context-aware unsupervised loss to optimize visual prompts tailored to test samples.
• Score: 13.983810804606264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current pre-trained vision-language models, such as CLIP, have demonstrated remarkable zero-shot generalization capabilities across various downstream tasks. However, their performance significantly degrades when test inputs exhibit different distributions. In this paper, we explore the concept of test-time prompt tuning (TTPT), which facilitates the adaptation of the CLIP model to novel downstream tasks through a one-step unsupervised optimization that involves only test samples. Inspired by in-context learning in natural language processing (NLP), we propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks, which empowers a pre-trained vision-language model with labeled examples as context information on downstream task. Specifically, InCPL associates a new test sample with very few labeled examples (sometimes just one) as context information, enabling reliable label estimation for the test sample and facilitating model adaptation. To achieve this, InCPL employs an efficient language-to-vision translator to explore the textual prior information for visual prompt learning. Further, we introduce a context-aware unsupervised loss to optimize visual prompts tailored to test samples. Finally, we design a cyclic learning strategy for visual and textual prompts to ensure mutual synergy across different modalities. This enables a pre-trained, frozen CLIP model to adapt to any task using its learned adaptive prompt. Our method demonstrates superior performance and achieves state-of-the-art results across various downstream datasets.

Related papers

• Words Matter: Leveraging Individual Text Embeddings for Code Generation in CLIP Test-Time Adaptation [21.20806568508201]
  We show how to leverage class text information to mitigate distribution drifts encountered by vision-language models (VLMs) during test-time inference. We propose to generate pseudo-labels for the test-time samples by exploiting generic class text embeddings as fixed centroids of a label assignment problem. Experiments on multiple popular test-time adaptation benchmarks presenting diverse complexity empirically show the superiority of CLIP-OT.
  arXiv  Detail & Related papers  (2024-11-26T00:15:37Z)
• Active Prompt Learning with Vision-Language Model Priors [9.173468790066956]
  We introduce a class-guided clustering that leverages the pre-trained image and text encoders of vision-language models. We propose a budget-saving selective querying based on adaptive class-wise thresholds.
  arXiv  Detail & Related papers  (2024-11-23T02:34:33Z)
• Debiasing Multimodal Large Language Models [61.6896704217147]
  Large Vision-Language Models (LVLMs) have become indispensable tools in computer vision and natural language processing. Our investigation reveals a noteworthy bias in the generated content, where the output is primarily influenced by the underlying Large Language Models (LLMs) prior to the input image. To rectify these biases and redirect the model's focus toward vision information, we introduce two simple, training-free strategies.
  arXiv  Detail & Related papers  (2024-03-08T12:35:07Z)
• Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior [14.232144691524528]
  Recent Vision-Language Pretrained models have become the backbone for many downstream tasks. MLE training can lead the context vector to over-fit dominant image features in the training data. This paper presents a Bayesian-based framework of prompt learning, which could alleviate the overfitting issues on few-shot learning application.
  arXiv  Detail & Related papers  (2024-01-09T10:15:59Z)
• Fairness-guided Few-shot Prompting for Large Language Models [93.05624064699965]
  In-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. We introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes. We propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning.
  arXiv  Detail & Related papers  (2023-03-23T12:28:25Z)
• Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration [12.334422701057674]
  We propose a novel nearest-neighbor calibration framework for in-context learning. It is inspired by a phenomenon that the in-context learning paradigm produces incorrect labels when inferring training instances. Experiments on various few-shot text classification tasks demonstrate that our method significantly improves in-context learning.
  arXiv  Detail & Related papers  (2022-12-05T12:49:41Z)
• CPL: Counterfactual Prompt Learning for Vision and Language Models [76.18024920393245]
  This paper presents a novel underlinetextbfCounterfactual underlinetextbfPrompt underlinetextbfLearning (CPL) method for vision and language models. CPL simultaneously employs counterfactual generation and contrastive learning in a joint optimization framework. Experiments demonstrate that CPL can obtain superior few-shot performance on different vision and language tasks.
  arXiv  Detail & Related papers  (2022-10-19T08:06:39Z)
• 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)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)
• CLIP-Adapter: Better Vision-Language Models with Feature Adapters [79.52844563138493]
  We show that there is an alternative path to achieve better vision-language models other than prompt tuning. In this paper, we propose CLIP-Adapter to conduct fine-tuning with feature adapters on either visual or language branch. Experiments and extensive ablation studies on various visual classification tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-10-09T11:39:30Z)
• Prompt-Learning for Fine-Grained Entity Typing [40.983849729537795]
  We investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios. We propose a self-supervised strategy that carries out distribution-level optimization in prompt-learning to automatically summarize the information of entity types.
  arXiv  Detail & Related papers  (2021-08-24T09:39:35Z)

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.