Background Prompt for Few-Shot Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2509.21055v1
• Date: Thu, 25 Sep 2025 12:03:24 GMT
• Title: Background Prompt for Few-Shot Out-of-Distribution Detection
• Abstract summary: We propose a new FG-BG decomposition framework, namely Mambo, for FS-OOD detection.<n>Specifically, we propose to first learn a background prompt to obtain the local background similarity containing both the background and image semantic information.<n>We use both the refined local background similarity and the local class similarity to conduct background extraction, reducing the dependence of the local class similarity in previous methods.
• Score: 39.66477903922584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing foreground-background (FG-BG) decomposition methods for the few-shot out-of-distribution (FS-OOD) detection often suffer from low robustness due to over-reliance on the local class similarity and a fixed background patch extraction strategy. To address these challenges, we propose a new FG-BG decomposition framework, namely Mambo, for FS-OOD detection. Specifically, we propose to first learn a background prompt to obtain the local background similarity containing both the background and image semantic information, and then refine the local background similarity using the local class similarity. As a result, we use both the refined local background similarity and the local class similarity to conduct background extraction, reducing the dependence of the local class similarity in previous methods. Furthermore, we propose the patch self-calibrated tuning to consider the sample diversity to flexibly select numbers of background patches for different samples, and thus exploring the issue of fixed background extraction strategies in previous methods. Extensive experiments on real-world datasets demonstrate that our proposed Mambo achieves the best performance, compared to SOTA methods in terms of OOD detection and near OOD detection setting. The source code will be released at https://github.com/YuzunoKawori/Mambo.

Related papers

• Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background [22.29204897783237]
  We propose a new plug-and-play framework for CLIP-based foreground-background (FG-BG) decomposition.<n>For background regions, existing methods adopt a uniform suppression strategy for all patches, overlooking the varying contributions of different patches to the prediction.<n>For foreground regions, existing methods fail to adequately consider that some local patches may exhibit appearance or semantic similarity to other classes, which may mislead the training process.
  arXiv  Detail & Related papers  (2026-01-21T15:12:11Z)
• BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors [50.09148454840245]
  Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications.<n>Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers.<n>We propose a new method, Background Mix (BackMix), that mixes the foreground of an image with different backgrounds to remove the underlying fore-background priors.
  arXiv  Detail & Related papers  (2025-03-22T10:23:11Z)
• Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning [39.85737063875394]
  This study develops a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions.
  arXiv  Detail & Related papers  (2024-04-26T10:38:17Z)
• Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models [72.07462371883501]
  We propose emphProjection Regret (PR), an efficient novelty detection method that mitigates the bias of non-semantic information. PR computes the perceptual distance between the test image and its diffusion-based projection to detect abnormality. Extensive experiments demonstrate that PR outperforms the prior art of generative-model-based novelty detection methods by a significant margin.
  arXiv  Detail & Related papers  (2023-12-05T09:44:47Z)
• Masking Strategies for Background Bias Removal in Computer Vision Models [8.168621685852521]
  This research investigates the impact of background-induced bias on fine-grained image classification. We evaluate standard backbone models such as Conal Neural Network (CNN) and Vision Transformers (ViT) Experiments assess the behavior of CNN and ViT models under different masking strategies.
  arXiv  Detail & Related papers  (2023-08-23T13:33:39Z)
• Score-based Source Separation with Applications to Digital Communication Signals [72.6570125649502]
  We propose a new method for separating superimposed sources using diffusion-based generative models. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature. Our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme.
  arXiv  Detail & Related papers  (2023-06-26T04:12:40Z)
• Improving Out-of-Distribution Detection with Disentangled Foreground and Background Features [23.266183020469065]
  We propose a novel framework that disentangles foreground and background features from ID training samples via a dense prediction approach. It is a generic framework that allows for a seamless combination with various existing OOD detection methods.
  arXiv  Detail & Related papers  (2023-03-15T16:12:14Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment [53.401889855278704]
  Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. We propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local to local (L2L) similarity metric. Experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2022-10-04T07:54:40Z)

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.