NLPrompt: Noise-Label Prompt Learning for Vision-Language Models

• URL: http://arxiv.org/abs/2412.01256v2
• Date: Wed, 26 Mar 2025 09:08:24 GMT
• Title: NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
• Authors: Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi,
• Abstract summary: Real-world datasets often contain noisy labels that can degrade prompt learning performance.<n>In this paper, we demonstrate that using mean absolute error (MAE) loss in prompt learning, named PromptMAE, significantly enhances robustness against noisy labels.<n>We also introduce PromptOT, a prompt-based optimal transport data purification method to enhance the robustness further.
• Score: 45.35555264802591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can degrade prompt learning performance. In this paper, we demonstrate that using mean absolute error (MAE) loss in prompt learning, named PromptMAE, significantly enhances robustness against noisy labels while maintaining high accuracy. Though MAE is straightforward and recognized for its robustness, it is rarely used in noisy-label learning due to its slow convergence and poor performance outside prompt learning scenarios. To elucidate the robustness of PromptMAE, we leverage feature learning theory to show that MAE can suppress the influence of noisy samples, thereby improving the signal-to-noise ratio and enhancing overall robustness. Additionally, we introduce PromptOT, a prompt-based optimal transport data purification method to enhance the robustness further. PromptOT employs text features in vision-language models as prototypes to construct an optimal transportation matrix. This matrix effectively partitions datasets into clean and noisy subsets, allowing for the application of cross-entropy loss to the clean subset and MAE loss to the noisy subset. Our Noise-Label Prompt Learning method, named NLPrompt, offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of vision-language models for robust prompt learning. We validate NLPrompt through extensive experiments across various noise settings, demonstrating significant performance improvements.

Related papers

• Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning [23.129998055266245]
  Current pseudo-labeling strategies often struggle with mismatches between semantic and visual information. We introduce a simple yet effective approach called textbfAugmenting Dtextbfiscriminative textbfRichness via Diffusions (AiR)
  arXiv  Detail & Related papers  (2025-04-16T10:09:45Z)
• Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance [17.359530437698723]
  Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios where collected training data can contain incorrect or corrupted labels. Most existing solutions identify noisy labels and adopt active learning to query human experts on them for denoising. In this paper, we propose an innovative collaborative learning framework NoiseAL based on active learning to combine large language models with small models for learning from noisy labels.
  arXiv  Detail & Related papers  (2025-04-03T04:36:39Z)
• Vision-Language Models are Strong Noisy Label Detectors [76.07846780815794]
  This paper presents a Denoising Fine-Tuning framework, called DeFT, for adapting vision-language models. DeFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DeFT in both noisy label detection and image classification.
  arXiv  Detail & Related papers  (2024-09-29T12:55:17Z)
• MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training [9.023648972811458]
  RagVL is a novel framework with knowledge-enhanced reranking and noise-injected training. We instruction-tune the MLLM with a simple yet effective instruction template to induce its ranking ability. For generation, we inject visual noise during training at the data and token levels to enhance the generator's robustness.
  arXiv  Detail & Related papers  (2024-07-31T08:43:17Z)
• Can Better Text Semantics in Prompt Tuning Improve VLM Generalization? [28.041879000565874]
  We introduce a prompt-tuning method that leverages class descriptions obtained from Large Language Models. Our approach constructs part-level description-guided image and text features, which are subsequently aligned to learn more generalizable prompts. Our comprehensive experiments conducted across 11 benchmark datasets show that our method outperforms established methods.
  arXiv  Detail & Related papers  (2024-05-13T16:52:17Z)
• Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning [67.0609518552321]
  We propose to conduct Machine Vision Therapy which aims to rectify the noisy predictions from vision models. By fine-tuning with the denoised labels, the learning model performance can be boosted in an unsupervised manner.
  arXiv  Detail & Related papers  (2023-12-05T07:29:14Z)
• Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance [61.809732058101304]
  We introduce an innovative approach for fine-tuning PLMs using noisy labels. This approach incorporates the guidance of Large Language Models (LLMs) like ChatGPT. This guidance assists in accurately distinguishing between clean and noisy samples.
  arXiv  Detail & Related papers  (2023-11-02T09:20:38Z)
• Learning to Detect Noisy Labels Using Model-Based Features [16.681748918518075]
  We propose Selection-Enhanced Noisy label Training (SENT) SENT does not rely on meta learning while having the flexibility of being data-driven. It improves performance over strong baselines under the settings of self-training and label corruption.
  arXiv  Detail & Related papers  (2022-12-28T10:12:13Z)
• LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models [67.19124099815645]
  We propose a novel Language-Aware Soft Prompting (LASP) learning method to alleviate base class overfitting. LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available. LASP matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets.
  arXiv  Detail & Related papers  (2022-10-03T17:56:35Z)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)
• Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction [109.44933866397123]
  Noise robustness is essential for deploying automatic speech recognition systems in real-world environments. We employ a noise-robust representation learned by a refined self-supervised framework for noisy speech recognition. We achieve comparable performance to the best supervised approach reported with only 16% of labeled data.
  arXiv  Detail & Related papers  (2021-10-28T20:39:02Z)

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.