Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting

• URL: http://arxiv.org/abs/2406.16422v1
• Date: Mon, 24 Jun 2024 08:14:09 GMT
• Title: Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
• Authors: Tiange Zhang, Qing Cai, Feng Gao, Lin Qi, Junyu Dong,
• Abstract summary: Cross-Domain Few-Shot Learning has witnessed great stride with the development of meta-learning. We propose a Frequency-Aware Prompting method with mutual attention for Cross-Domain Few-Shot classification.
• Score: 37.721042095518044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-Domain Few-Shot Learning has witnessed great stride with the development of meta-learning. However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomenon that deep networks tend to rely more on high-frequency cues to make the classification decision, which thus degenerates the robustness of learned inductive bias since high-frequency information is vulnerable and easy to be disturbed by noisy information. Hence in this paper, we make one of the first attempts to propose a Frequency-Aware Prompting method with mutual attention for Cross-Domain Few-Shot classification, which can let networks simulate the human visual perception of selecting different frequency cues when facing new recognition tasks. Specifically, a frequency-aware prompting mechanism is first proposed, in which high-frequency components of the decomposed source image are switched either with normal distribution sampling or zeroing to get frequency-aware augment samples. Then, a mutual attention module is designed to learn generalizable inductive bias under CD-FSL settings. More importantly, the proposed method is a plug-and-play module that can be directly applied to most off-the-shelf CD-FLS methods. Experimental results on CD-FSL benchmarks demonstrate the effectiveness of our proposed method as well as robustly improve the performance of existing CD-FLS methods. Resources at https://github.com/tinkez/FAP_CDFSC.

Related papers

• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• Towards Building More Robust Models with Frequency Bias [8.510441741759758]
  This paper presents a plug-and-play module that adaptively reconfigures the low- and high-frequency components of intermediate feature representations. Empirical studies show that our proposed module can be easily incorporated into any adversarial training framework.
  arXiv  Detail & Related papers  (2023-07-19T05:46:56Z)
• Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition [14.450381668547259]
  Open-set image recognition is a challenging topic in computer vision. We propose a Complementary Frequency-varying Awareness Network that could better capture both high-frequency and low-frequency information. Based on CFAN, we propose an open-set fine-grained image recognition method, called CFAN-OSFGR.
  arXiv  Detail & Related papers  (2023-07-14T08:15:36Z)
• Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics [77.13542705329328]
  We propose a framework of disentangled representation learning(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning. The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics. Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments.
  arXiv  Detail & Related papers  (2022-08-04T15:46:48Z)
• Adaptive Frequency Learning in Two-branch Face Forgery Detection [66.91715092251258]
  We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
  arXiv  Detail & Related papers  (2022-03-27T14:25:52Z)
• Deep Frequency Filtering for Domain Generalization [55.66498461438285]
  Deep Neural Networks (DNNs) have preferences for some frequency components in the learning process. We propose Deep Frequency Filtering (DFF) for learning domain-generalizable features. We show that applying our proposed DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks.
  arXiv  Detail & Related papers  (2022-03-23T05:19:06Z)
• Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation [0.7349727826230862]
  We propose an approach of spectrum sensing based on long short term memory (LSTM) which is a critical element of deep learning networks (DLN) The proposed sensing technique is validated with the help of an empirical testbed setup using Adalm Pluto.
  arXiv  Detail & Related papers  (2021-11-21T08:51:48Z)
• Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection [7.324459578044212]
  Face presentation attack detection (PAD) is attracting a lot of attention and playing a key role in securing face recognition systems. We propose a dual-stream convolution neural networks (CNNs) framework to deal with unseen scenarios. We successfully prove the design of our proposed PAD solution in a step-wise ablation study.
  arXiv  Detail & Related papers  (2021-09-16T13:06:43Z)
• Deep Learning Radio Frequency Signal Classification with Hybrid Images [0.0]
  We focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed Deep Learning architecture. We propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem.
  arXiv  Detail & Related papers  (2021-05-19T11:12:09Z)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)

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.