Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation (2211.14512v3)

Abstract: Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on their ability not only to classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., country surroundings) outside the training set (e.g., city surroundings). In this paper, we mitigate these issues with: (i) a new residual pattern learning (RPL) module that assists the segmentation model to detect OoD pixels without affecting the inlier segmentation performance; and (ii) a novel context-robust contrastive learning (CoroCL) that enforces RPL to robustly detect OoD pixels among various contexts. Our approach improves by around 10\% FPR and 7\% AuPRC the previous state-of-the-art in Fishyscapes, Segment-Me-If-You-Can, and RoadAnomaly datasets. Our code is available at: https://github.com/yyliu01/RPL.

An Essay on "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation"

The paper "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation" introduces a novel methodology addressing the challenge of detecting out-of-distribution (OoD) pixels in semantic segmentation tasks. This task is pivotal for the reliability of computer vision systems deployed in open-world settings, such as autonomous driving, where encountering OoD objects is a common occurrence.

The authors present a key innovation in the form of a Residual Pattern Learning (RPL) module. This module serves as an add-on to existing segmentation networks and is trained to enhance the network's ability to discern between in-distribution (inlier) and out-of-distribution (anomaly) pixels. A significant highlight of the RPL module is its design, which allows the core segmentation network to remain frozen during training. This ensures the integrity of the original in-distribution segmentation performance is maintained, thus leading to minimal degradation, a common pitfall in conventional re-training methodologies employed for OoD detection.

A significant challenge in detecting OoD pixels is the imbalance between inlier and outlier samples. To address this, the authors propose a novel Positive Energy Loss function that focuses exclusively on optimizing the energy score for anomaly detection, thereby mitigating the limitations of previous hinge-loss based energy optimization methods. This approach is shown to excel, particularly in identifying small anomalies, which are typically challenging to detect.

The paper further introduces Context-robust Contrastive Learning (CoroCL), which aims to ensure the method's robustness across varying open-world contexts. By employing contrastive learning strategies and exploring the relationships between anomalies and their contexts, CoroCL facilitates effective generalization of the learning process. This focus on context-awareness addresses a critical gap left by prior methods that often fail under context shifts not witnessed during training.

Empirical results strongly support the proposed methodology. The RPL module demonstrates impressive improvements in pixel-wise anomaly detection metrics, outperforming state-of-the-art approaches on Fishyscapes, Segment-Me-If-You-Can, and RoadAnomaly datasets. For instance, the paper reports improvements of around 10% in False Positive Rate (FPR) and 7% in Area under the Precision Recall Curve (AuPRC) compared to leading methods such as PEBAL and Meta-OoD. Additionally, the RPL integration reflects minimal impact on the inlier segmentation accuracy, unlike its re-training counterparts.

Beyond the immediate practical implications, this research opens avenues for future exploration in anomaly detection in vision systems. The modular nature of RPL means it can potentially be adapted to other computer vision tasks beyond semantic segmentation, offering a robust framework for anomaly detection. Further research could expand the experimental evaluations across more diverse datasets and explore the interplay of RPL with other learning paradigms, particularly in areas requiring real-time inference and adaptability.

In summary, this paper contributes a significant advancement to the field of computer vision by refining semantic segmentation models with the ability to effectively detect OoD pixels, ensuring robustness across various contexts without compromising on accuracy for in-distribution classes. This balance of maintaining the integrity of original segmentation performance while enhancing anomaly detection sets a new benchmark for future developments and applications in the domain.