Title: FAST: Foreground‑aware Diffusion with Accelerated Sampling Trajectory for Segmentation‑oriented Anomaly Synthesis
Venue: NeurIPS 2025
Accepted: yes
Keywords: Anomaly synthesis

anomaly segmentation
Abstract: Industrial anomaly segmentation relies heavily on pixel-level annotations, yet real-world anomalies are often scarce, diverse, and costly to label. Segmentation-oriented industrial anomaly synthesis (SIAS) has emerged as a promising alternative; however, existing methods struggle to balance sampling efficiency and generation quality. Moreover, most approaches treat all spatial regions uniformly, overlooking the distinct statistical differences between anomaly and background areas. This uniform treatment hinders the synthesis of controllable, structure-specific anomalies tailored for segmentation tasks. In this paper, we propose FAST, a foreground-aware diffusion framework featuring two novel modules: the Anomaly-Informed Accelerated Sampling (AIAS) and the Foreground-Aware Reconstruction Module (FARM). AIAS is a training-free sampling algorithm specifically designed for segmentation-oriented industrial anomaly synthesis, which accelerates the reverse process through coarse-to-fine aggregation and enables the synthesis of state-of-the-art segmentation-oriented anomalies in as few as 10 steps. Meanwhile, FARM adaptively adjusts the anomaly-aware noise within the masked foreground regions at each sampling step, preserving localized anomaly signals throughout the denoising trajectory. Extensive experiments on multiple industrial benchmarks demonstrate that FAST consistently outperforms existing anomaly synthesis methods in downstream segmentation tasks. We release the code in https://github.com/Chhro123/fast-foreground-aware-anomaly-synthesis.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FAST, a diffusion-based framework for generating industrial anomaly synthesis data for segmentation tasks. The framework consists of two main components: first, AIAS, a training-free sampling strategy that reduces the denoising steps from the DDPM standard of 1000 to 10, and second, FARM, a module that reconstructs anomaly-specific content from noisy inputs and injects anomaly-aware noise at each sampling step to preserve anomaly signals throughout the overall denoising process. The authors evaluate their method by generating 500 synthetic samples per category on two datasets and training multiple segmentation models with the synthetic data, achieving performance improvements.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n1. AIAS provide well-formulated theorems that justify the multi-step backward approximation. The supplementary material clearly explains the proof and derivation process for this procedure.\n2. There is computational efficiency by reducing sampling steps to around 10 during the generation phase.\n3. Systematic comparison and justification through various ablation studies.\n4. FARM's foreground-aware processing tends to address the uniform spatial processing limitation of existing diffusion models.\n\nWeaknesses\n1. It is questionable whether the characteristic feature of this paper - reducing sampling steps to around 10 - is truly a significant advantage for synthetic data generation. In industrial anomaly detection, fast inference is important only during the anomaly segmentation stage, but for synthetic data generation, wouldn't quality be more important? Wouldn't it be better to keep only the foreground-aware processing and use multiple steps for sampling?\n2. While the fixed x̂_0 assumption is somewhat acknowledged, isn't it the case that x̂_0 actually changes quite significantly over 10 steps? This raises concerns since Lemma 2 relies on this assumption.\n3. In the experimental phase, it was mentioned that 500 synthetic samples were generated, with 1/3 used for training and the rest for evaluation, but a clearer explanation seems necessary regarding the methods used, including masks.\n4. In core multi-step aggregation formula x_te = Πx_ts + Σx̂_o + ε_te of AIAS, there is insufficient theoretical analysis of how approximation errors accumulate with respect to segment length (t_s - t_e). Information about how errors are amplified in the actual diffusion process seem necessary.\n\nQUESTIONS:\nread weakness please\n\nLIMITATIONS:\nThe core approximation of AIAS relies on assumptions that may not hold in practice, particularly with complex noise schedules or highly variable x^_0 predictions. The theoretical analysis of approximation error bounds and convergence properties is limited, and the error accumulation patterns according to segment length are not sufficiently characterized. And it is questionable whether reducing sampling steps to 10 is truly a priority in synthetic data generation. In industrial deployment, final segmentation performance may be more important than generation speed, and the potential quality degradation at extremely few steps has not been sufficiently analyzed.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n1. Existing methods uniformly process the entire region of defect images and are unable to generate specific defect structures for downstream segmentation. This paper proposes a defect-aware generation method based on diffusion models, namely FAST.\n2. The method consists of two modules. The first module, AIAS, is a training-free sampling algorithm for industrial defect generation. This module reduces the number of sampling steps and accelerates the generation speed through a coarse-to-fine aggregation approach. The second module, FARM, adaptively adjusts the noise in the masked region after each sampling step to preserve the defect features in the denoising process.\n3. Experiments demonstrate that the proposed method outperforms existing anomaly generation methods in terms of metrics on downstream segmentation tasks across multiple datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. This paper proposes an accelerated sampling algorithm AIAS. Experiments demonstrate that compared with the DDIM and PLMS sampling methods, AIAS can improve the metrics of anomaly segmentation.\n2. This paper proposes FARM, which reconstructs the foreground defect and adds noise during the sampling process, enabling the model to generate realistic local foreground defects. The effectiveness of the FARM module is verified through ablation experiments.\n\nWeaknesses\n1. The paper did not compare with the few-shot defect generation method AnoGen [1]. We worry that such experiments may not be sufficient.\n2. Only mIoU and Acc are used as evaluation metrics. We are afraid that such a setting may not be perfect. We think that it is necessary to test the Inception Score (IS) of the generated images and the Intra-cluster pairwise LPIPS distance (IC - LPIPS) [2].\n\n[1]\tGui, Guan, et al. \"Few-shot anomaly-driven generation for anomaly classification and segmentation.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.\n\n[2]\tOjha U, Li Y, Lu J, et al. Few-shot image generation via cross-domain correspondence[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 10743-10752.\n\nQUESTIONS:\n1. When conducting experiments, the strategy of generating 500 images for each category of objects seems to lack a basis. Is the number of generated images insufficient?\n2. The paper requires an input mask during inference. Therefore, we would like to know how the input mask is obtained during the experiments.\n3. This paper proposes an accelerated sampling algorithm. Thus, we are curious about how long it takes to train a defect generation model and how long the inference process takes.\n4. At what value of t does the experiment start to perform the fine-grained standard DDPM?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a new method to synthesize anomalies in industrial settings. The paper proposes an anomaly-informed accelerated sampling module and a foreground-aware reconstruction module. The experiments are performed on MVTec and BTAD datasets, obtaining state-of-the-art results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n-\tThe quality of the synthesized anomalies is high.\n-\tThe quantitative results on both datasets are state-of-the-art.\n\nWeaknesses:\n\n-\tThe paper is not self-contained. A brief introduction of Segformer, BiseNetV2 and STDC should be added, at least in the supplementary file.\n-\tThe evaluation metrics used mean Intersection over Union and pixel-wise accuracy are not very common in anomaly detection. I understand that in terms of image-level AUROC and pixel-level AUROC, the performance is saturated, but most methods report these metrics (including DRAEM, GLASS etc).\n-\tThe method section is full of formulas, however the underlying intuition behind the method is not present. Therefore, the method is hard to follow.\n-\tSome data sets are not used such as VisA and MPDD datasets.\n-\tHow are the masks generated in the first place?\n\n\nOverall: The paper presents a novel high quality anomaly synthesis model, however, major details are missing (see weaknesses) and the method section is not clear, missing the underlying intuition behind the proposed method.\n\nQUESTIONS:\n-\tThe evaluation metrics used mean Intersection over Union and pixel-wise accuracy are not very common in anomaly detection. I understand that in terms of image-level AUROC and pixel-level AUROC, the performance is saturated, but most methods report these metrics (including DRAEM, GLASS etc).\n-\tThe method section is full of formulas, however the underlying intuition behind the method is not present. Therefore, the method is hard to follow.\n-\tSome data sets are not used such as VisA and MPDD datasets.\n-\tHow are the masks generated in the first place?\n\nLIMITATIONS:\nI could not find a limitation section, where the authors describe the limitation of their methods. I would have liked to find one paragraph describing the limitation along with the conclusion section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FAST, a novel framework for unsupervised anomaly segmentation. Existing self-training methods often suffer from foreground-background imbalance and error accumulation in pseudo-labels. To address this, FAST introduces two key components:  \n1. Foreground Guidance Module: Uses CLIP-based saliency to focus learning on foreground objects, reducing background bias in normal-only training.  \n2. Two-Branch Self-Training: Combines a soft label branch (probabilistic supervision) and a hard label branch (high-confidence pseudo-labels filtered by saliency) to enhance training stability and segmentation accuracy.\n\nSTRENGTHS AND WEAKNESSES:\n**Strenghts**\n1. Clear and Well-Motivated Problem Setup.  \nThe paper identifies a key challenge in anomaly segmentation—foreground suppression due to background-dominated normal samples—and addresses it directly.\n2. Foreground-Aware Design.  \nLeveraging CLIP-based saliency maps for guidance is a novel and practical way to enhance semantic focus.\n3. Solid Theoretical Justification.  \nThe framework is conceptually well-grounded, and the proposed design choices are supported with theoretical proofs.\n\n**Weakness**\n1. Marginal Performance Gains.  \nThe improvements over strong baselines are relatively small on standard benchmarks, limiting the perceived practical impact.\n2. CLIP Dependency.  \nThe method relies heavily on CLIP saliency, which may not generalize well to domains with domain shift or non-natural images.\n3. Saliency Map Heuristic.  \nThe use of CLIP-derived saliency as ground-truth guidance is not learned, and may introduce biases or fail in complex backgrounds.\n\nQUESTIONS:\n1. CLIP Reliability.  \nHave the authors tested the robustness of CLIP-based saliency guidance across different domains (e.g., grayscale, medical, or low-resolution images)?  \n2. Inference Latency.  \nCan the authors provide runtime analysis or memory benchmarks compared to one-branch baselines? Is the method suitable for real-time applications?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}