DETA: Denoised Task Adaptation for Few-Shot Learning

• URL: http://arxiv.org/abs/2303.06315v3
• Date: Mon, 18 Dec 2023 00:39:48 GMT
• Title: DETA: Denoised Task Adaptation for Few-Shot Learning
• Authors: Ji Zhang, Lianli Gao, Xu Luo, Hengtao Shen and Jingkuan Song
• Abstract summary: Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge. With only a handful of samples available, the adverse effect of either the image noise (a.k.a. X-noise) or the label noise (a.k.a. Y-noise) from support samples can be severely amplified. We propose DEnoised Task Adaptation (DETA), a first, unified image- and label-denoising framework to existing task adaptation approaches.
• Score: 135.96805271128645
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled support samples. Previous approaches generally focus on developing advanced algorithms to achieve the goal, while neglecting the inherent problems of the given support samples. In fact, with only a handful of samples available, the adverse effect of either the image noise (a.k.a. X-noise) or the label noise (a.k.a. Y-noise) from support samples can be severely amplified. To address this challenge, in this work we propose DEnoised Task Adaptation (DETA), a first, unified image- and label-denoising framework orthogonal to existing task adaptation approaches. Without extra supervision, DETA filters out task-irrelevant, noisy representations by taking advantage of both global visual information and local region details of support samples. On the challenging Meta-Dataset, DETA consistently improves the performance of a broad spectrum of baseline methods applied on various pre-trained models. Notably, by tackling the overlooked image noise in Meta-Dataset, DETA establishes new state-of-the-art results. Code is released at https://github.com/JimZAI/DETA.

Related papers

• One-step Noisy Label Mitigation [86.57572253460125]
  Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical. We propose One-step Anti-Noise (OSA), a model-agnostic noisy label mitigation paradigm. We empirically demonstrate the superiority of OSA, highlighting its enhanced training robustness, improved task transferability, ease of deployment, and reduced computational costs.
  arXiv  Detail & Related papers  (2024-10-02T18:42:56Z)
• 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)
• Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition [34.88916568947695]
  We propose a simple but effective task-specific adaptation method (Task-Adapter) for few-shot action recognition. By introducing the proposed Task-Adapter into the last several layers of the backbone, we mitigate the overfitting problem caused by full fine-tuning. Experimental results consistently demonstrate the effectiveness of our proposed Task-Adapter on four standard few-shot action recognition datasets.
  arXiv  Detail & Related papers  (2024-08-01T03:06:56Z)
• Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification [34.37262622415682]
  We propose a new adaptation framework called Data Adaptive Traceback. Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data. We adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-07-11T18:01:58Z)
• DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data [16.501973201535442]
  We reformulate the partially-labeled multi-task dense prediction as a pixel-level denoising problem. We propose a novel multi-task denoising framework coined as DiffusionMTL. It designs a joint diffusion and denoising paradigm to model a potential noisy distribution in the task prediction or feature maps.
  arXiv  Detail & Related papers  (2024-03-22T17:59:58Z)
• Leveraging Foundation models for Unsupervised Audio-Visual Segmentation [49.94366155560371]
  Audio-Visual (AVS) aims to precisely outline audible objects in a visual scene at the pixel level. Existing AVS methods require fine-grained annotations of audio-mask pairs in supervised learning fashion. We introduce unsupervised audio-visual segmentation with no need for task-specific data annotations and model training.
  arXiv  Detail & Related papers  (2023-09-13T05:05:47Z)
• Dynamic Relevance Learning for Few-Shot Object Detection [6.550840743803705]
  We propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN) The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features.
  arXiv  Detail & Related papers  (2021-08-04T18:29:42Z)
• Data-driven Meta-set Based Fine-Grained Visual Classification [61.083706396575295]
  We propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition. Specifically, guided by a small amount of clean meta-set, we train a selection net in a meta-learning manner to distinguish in- and out-of-distribution noisy images.
  arXiv  Detail & Related papers  (2020-08-06T03:04:16Z)
• Adaptive Task Sampling for Meta-Learning [79.61146834134459]
  Key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time. We propose an adaptive task sampling method to improve the generalization performance.
  arXiv  Detail & Related papers  (2020-07-17T03:15:53Z)

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.