Title: Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
Venue: NeurIPS 2025
Accepted: no
Keywords: Topological Data Analysis

Test Time Training

Multi Level Filterisation
Abstract: Test-time adaptation (TTA) has emerged as a powerful paradigm for handling distribution shifts in deep models, particularly for anomaly segmentation, where pixel-wise labels of anomalous regions are typically unavailable during training. We introduce TopoTTA (Topological Test-Time Adaptation), a novel framework that incorporates persistent homology, a tool from topological data analysis, into the TTA pipeline to enforce structural consistency in segmentation. By applying multi-level cubical complex filtrations to anomaly score maps, TopoTTA generates robust topological pseudo-labels that guide a lightweight test-time classifier, enhancing binary segmentation quality without retraining the backbone model. Our method eliminates the need for heuristic thresholding and generalises across both 2D and 3D modalities. Extensive experiments on MVTec AD and BraTS datasets demonstrate significant improvements over state-of-the-art unsupervised anomaly detection and segmentation methods in terms of F1 score, particularly on anomalies with complex geometries.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an anomaly detection method named TopoTTA for Test-time adaptation. This paper highlights the differences between the data at test time and at training time, and shows how the data can be used at re-test time to re-modify the representation model to suit more accurate anomaly detection needs. The proposed method is strongly related to anomaly detection, and the authors claim that it achieves good generalisation on 2D/3D data, which is expected to be a new paradigm. Furthermore, the qualitative research in this thesis is distinctive, as the multi-step visualisation in Figure 1 clearly expresses the thesis' claims.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The motivation for the thesis is sound. Several studies[1] have pointed out the importance of domain bias, which is attributed to the fact that the differences that exist between the training set and the test set lead to difficult problems in the characterisation of the model. Compared to previous studies, the test set adaptation proposed in this paper solves the problem of severe bias in characterisation during testing.\n2. The visualisations of the thesis are aesthetically pleasing, and I think that the thesis legend demonstrates the claim asserted, and the most critical visualisation comes from Figure 1.\n\nWeaknesses:\n1. I have some degree of doubt about the claimed generalisability of this paper, especially in 3D. This is because the MvTec3D-AD dataset used is not a pure 3D dataset, but rather 2.5D. Some papers point out the 3D > 2.5D problem[2], which leads to the fact that the 2.5D dataset you are validating may not be perfect. I would suggest validating on a pure 3D dataset such as Real3D-AD[2] or Anomaly-ShapeNet[3] (just pick one, only two certain classes are needed). This will lead to effective generalisability validation. Also, please try to compare with a pure 3D anomaly detection method[4].\n\n2. I have some questions about its claimed topology, especially its ability to work on 3D point clouds. Figure 6 in the paper claims its capabilities in different dimensions. However, a common form of anomaly detection in 3D is the point cloud, and this data is often discrete and not very topological. This leads to perhaps sub-optimal anomaly detection. I need to clarify this further.\n\n3. There may be some lack of discussion in the parts of the paper that describe related work. For example, there may be some shortcomings in exploring anomaly detection in 2.1. On the one hand, there is a lack of exploration of 2D and on the other hand, there is a complete lack of involvement in 3D anomaly detection. It is suggested that section 2.1 be divided into 2D and 3D anomaly detection and discussed separately in the revised version. Emphasise the relevance of the proposed method to the proposed method.\n\n4. Efficiency and memory usage are essential in anomaly detection. Could you please add further efficiency (FPS) and memory (GB) used in anomaly detection, and further compare it with existing methods.\n\n[1] Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift. Jingyi Liao, et al.\n[2] Real3D-AD: A Dataset of Point Cloud Anomaly Detection. Jiaqi Liu, et al.\n[3] Anomaly-ShapeNet: A Synthetic Dataset of Point Cloud Anomaly Detection. Wenqiao Li, et al.\n[4] Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection. Hanzhe Liang, et al.\n\nQUESTIONS:\nSee weaknesses. Please try to add as much explanation as I need during the rebuttal phase. I will review the feedback and other reviewer comments carefully.\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-09"}

comments: SUMMARY:
This paper proposes a topology-aware test-time adaptation framework (TopoTTA) for anomaly segmentation, which leverages persistent homology to generate topological pseudo-labels from anomaly score maps and guides pixel-level contrastive learning. Extensive experiments demonstrate that TopoTTA effectively enhances the structural consistency in anomaly segmentation and generalization ability across both 2D and 3D modalities.

STRENGTHS AND WEAKNESSES:
Strengths

1.	The paper is innovative in introducing persistent homology into the TTA framework to generate structure-aware pseudo-labels for anomaly segmentation—a strategy not previously explored in this field.

2.	Experimental results demonstrate the generalization capability of the method on both 2D and 3D anomaly segmentation tasks.

3.	The paper is well organized.

Weakness

The experimental evaluation is not sufficiently comprehensive. Specifically:
- It remains unclear whether TopoTTA consistently outperforms other state-of-the-art anomaly detection methods, such as Dinomaly and MambaAD.
- The effectiveness of TopoTTA on more complex and large-scale datasets (e.g., RealIAD, ViSA) has not been reported, raising concerns about the robustness and effectiveness of TopoTTA.

If the authors can address the above concerns, I am willing to raise my score.

QUESTIONS:
See Weakness

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo enhance the precision of anomaly segmentation, this paper introduces the TopoTTA (Topological Test-Time Adaptation) framework. At its core, the framework features an innovative multi-level cubical complex filtration module that extracts robust topological features from the initial anomaly score map, thereby refining it.\n\nDuring the mask generation process, the method adeptly integrates two complementary topological analysis strategies:\n\nSuperlevel Filtering: Focuses on analyzing the topological structure of high-score regions (anomalous areas).\n\nSublevel Filtering: Focuses on analyzing the topological structure of low-score regions (normal areas).\n\nBy analyzing the persistence diagrams generated from these filtration processes, the system can dynamically create an adaptive threshold mask. This mask can then be optimized or fused, potentially guided by the IoU (Intersection over Union) metric, to ensure the final output's accuracy.\n\nFurthermore, this high-quality mask serves as a pseudo-label to drive the PCES (Pixel-Level Contrastive Encoder for Binary Segmentation) module during the test phase. The PCES module employs a contrastive learning strategy to fine-tune the features from the encoder, thereby enhancing their discriminative power.\n\nOverall, this closed-loop design, which integrates topological analysis with test-time training, is proven to significantly enhance the model's performance on both 2D and 3D anomaly segmentation and detection (AS&D) tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nBalanced Precision & Recall: The authors effectively utilize sublevel and superlevel analysis to complement each other, achieving a strong balance between precision and recall.\n\nVersatile Applicability: This approach is adaptable for both 2D and 3D anomaly segmentation and detection (AS&D) tasks.\n\nSignificant Performance Improvement: It delivers a significant improvement in F1 score.\n\nReduced Manual Effort: The method eliminates the need for users to rely on handcrafted intensity thresholds or unsupervised peak suppression.\n\nWeaknesses\nPerformance Bottleneck: The filtration process is CPU-based, leading to time-consuming operations.\n\nRecall Degradation: Recall decreases when the method employs a lower number of top-K persistent features.\n\nQUESTIONS:\nRegarding the PCES Module Clarity:\n\"The explanation of the PCES (Persistent Coordinated Eigen-Segmentation) module in the paper is challenging to comprehend. Could Figure 2 be updated to more intuitively illustrate the relationship between the PCES module, TDA-refined pseudo labels, and the anomaly map? Currently, the figure's connection to the textual explanation requires significant effort to decipher.\"\n\nNotation Clarity in Section 3.3:\n\"The notational consistency in Section 3.3 could be improved. While notations are expected to be reflected in figures, it is currently difficult to identify the corresponding notations within Figure 2.\"\n\nReal-world Time Consumption Analysis:\n\"Given the importance of time consumption in real-world scenarios, do you have a detailed table analyzing the computational time of your method in comparison to state-of-the-art approaches?\"\n\nAnomaly in Table 3 Highlight and Recall:\n\"The highlighting in Table 3 appears incorrect. Furthermore, could you please elaborate on why the recall is significantly low specifically for the top 1 farthest persistence components?\"\n\nComparison with Memory Bank Methods:\n\"Could you explain the rationale behind not comparing your method with other state-of-the-art approaches that utilize a memory bank method?\"\n\nLIMITATIONS:\nYes, the author explain the limitation in the paper.", "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 tackles an important challenge in unsupervised anomaly segmentation: the need for query-dependent abnormality thresholds. By leveraging topological consistency within anomaly maps, the proposed method effectively filters out pseudo-anomalies and produces high-fidelity segmentation masks. Experimental results demonstrate that the approach is compatible with various existing anomaly detection methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.The proposed method introduces a plug-and-play post-processing module for anomaly segmentation, converting anomaly maps into segmentation masks using a query-dependent threshold.\n\n2. The paper is well-written and easy to follow. The motivation behind the proposed approach is clearly articulated and well-supported.\n\n3. Experimental results demonstrate that the method is compatible with various existing anomaly detection frameworks.\n\nWeaknesses:\n\n1. The method consists of two main components: topological filtration and PCES. While the former is derived from established topological analysis, the latter introduces a new self-supervised learning strategy built on top of it. From an engineering perspective, the integration is effective and well-executed. However, the level of methodological innovation appears to be relatively modest.\n\n2. The topological filtration seems to help stabilize anomaly representations, especially when dealing with noisy anomaly scores. It would be helpful to understand the standalone effectiveness of this module. Specifically, what would the performance look like if only the output of the filtration module (i.e., the refined anomaly mask) were used as the final segmentation result? This form of ablation study is currently missing.\n\n3. While the inclusion of two different anomaly detection methods for the 3D-AD dataset is appreciated, the evaluation on 2D-AD tasks relies solely on PatchCore. To better demonstrate the generality of the proposed method, it is strongly recommended to include results using additional 2D anomaly detection methods.\n\nQUESTIONS:\nI would appreciate the authors’ responses to the 2nd and 3rd weaknesses outlined above.\n\nLIMITATIONS:\nThe paper does not discuss the limitations of the proposed approach or its potential negative societal impacts.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}