Title: T-norm Selection for Object Detection in Autonomous Driving with Logical Constraints
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neurosymbolic

Computer Vision

Fuzzy Logic
Abstract: Integrating logical constraints into object detection models for autonomous driving (AD) is a promising way to enhance their compliance with rules and thereby increase the safety of the system. T-norms have been utilized to calculate the constrained loss, i.e., the violations of logical constraints as losses. While prior works have statically selected a few t-norms, we conduct an extensive experimental study to identify the most effective choices, as suboptimal t-norms can lead to undesired model behavior. To this end, we present MOD-ECL, a neurosymbolic framework that implements a wide range of t-norms and applies them in an adaptive manner. It includes an algorithm that selects well-performing t-norms during training and a scheduler that regulates the impact of the constrained loss. We evaluate its effectiveness on the ROAD-R and ROAD-Waymo-R datasets for object detection in AD, using attached common-sense constraints. Our results show that careful selection of parameters is crucial for effective constrained loss behavior. Moreover, our framework not only reduces constraint violations but also, in some cases, improves detection performance. Additionally, our methods offer fine-grained control over the trade-off between accuracy and constraint violation.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe main contributions of the paper are as follows:\n1. T-norm selection: The authors device an algorithm which dynamically selects the best t-norm based on a score. This t-norm selection lets the model explore different t-norms during training and the score helps the user understand why a certain t-norm was selected.\n2. Dynamic scheduler: The model learns to satisfy the t-norms by adding a constrained loss term on top of the object detection loss. To help the model balance both losses, the authors add a scheduler which weighs the constrained loss. Based on the scheduler, the authors hope that the model will learn to do object detection in the early stages of training and then focus on satisfying the t-norms by increasing the weight of the constrained loss term in the later parts of training.\n3. MOD-ECL: A model based on MOD-YOLO, with the addition of adaptive t-norm selection and dynamically scheduled constrained loss to optimize model performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The adaptive t-norm selection provides a good balance of exploration vs exploitations, giving the model a good chance at choosing the right t-norm for the given scenario.\n2. The authors have conducted thorough experimentation and have provided good insights into their finding.\n3. The experiments show clear improvement compared to the baseline which is the same model without the constrained loss.\n4. Good mathematical support provided by the theoretical guarantees.\n\nWeakness:\n1. In terms of novelty, the adaptive t-norm selection is the main novel item, while the constrained loss or the scheduler concepts are not that novel in general in deep learning. MOD-ECL is a novel framework, but the core model part is MOD-YOLO which is not the novelty of this paper. The addition of the adaptive t-norm is the main change to MOD-YOLO.\n2. There is no comparison with SOTA on the given datasets, which might help support or deny the need for the motivation of adaptive t-norm selection. The only comparison is with vanilla MOD-YOLO.\n3. There are a lot of hyper-parameters, making the tuning part the main difficulty in using this method.\n4. The experimental results are not very conclusive on the t-norm selection itself. As pointed by the authors, different t-norms suite different timelines in the training, raising a question, whether a single t norm selection is the right way to go.\n\nQUESTIONS:\n1. In section 3.1, it would be good to give some examples for (o, l, a). \n2. Equation 2 would be easier to understand if it is supported by real examples of constrains and t-norms.\n3. What is the significance of the multi-label part of MOD-ECL? Does it need to be multi-label?\n4. Why not use multiple t-norms for inference?\n5. Merging section 6 into 5.3 might be more intuitive to the reader.\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenge of integrating logical constraints into object detection models for autonomous driving to enhance safety. It argues that prior work relies on a static selection of t-norms to formulate a \"constrained loss,\" which can be suboptimal. To solve this, the authors propose MOD-ECL, a neuro-symbolic framework featuring two main contributions: an adaptive algorithm that dynamically selects the most effective t-norm during training, and a scheduler that adjusts the weight of the constrained loss. Through extensive experiments on the ROAD-R and ROAD-Waymo-R datasets, the paper demonstrates that this dynamic approach significantly reduces constraint violations, and in some cases, also improves detection accuracy, enabling fine-grained control over the trade-off between performance and rule compliance.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\nThe paper addresses a significant and practical problem in autonomous driving: ensuring object detection models adhere to real-world logical rules to improve safety. The use of intuitive examples (e.g., a pedestrian on a moving vehicle) effectively highlights the importance of the work. The paper proposes a novel adaptive algorithm for dynamically selecting the best-performing t-norm during training, which is a clever application of multi-armed bandit principles. The paper introduces a λ-scheduler to control the influence of the constrained loss over time, based on the sensible intuition that the model should first learn detection and then focus on constraint satisfaction. The proposed MOD-ECL framework is presented as a clear and modular system for integrating logical constraints. The diagrams and descriptions make the architecture easy to understand.\n\n### Weaknesses\n\nThe paper's proposed t-norm method and logic-constrained loss are only compared within the YOLO framework, which demonstrates that different t-norms and λ values indeed influence performance. However, it might be more compelling to compare them against current state-of-the-art (SOTA) methods.\n\nAdditionally, multi-modal large language models have shown remarkable performance in scene understanding and reasoning tasks, with a growing number of works extending these models for object detection tasks. It may be beneficial to include comparisons against these approaches to more effectively highlight the advantages of the proposed methods.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nSince T-norm selection is a critical factor in detection systems with logical constraints to ensure safety, this paper explores the relationship between T-norm selection and model performance. To address the risk of suboptimal or unstable T-norm choices, the authors conduct a systematic search and analysis for T-norm selection. Building on these findings, they propose the MOD-ECL neuromyotonic framework. This framework dynamically and adaptively selects high-performing T-norms and regulate constrained loss function during training.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n1.This paper is well-written and presents a novel insight on guiding t-norm selection for object detection with logical constraints.\n\n2.Extensive experiments on the ROAD-R and ROAD-Waymo-R datasets support the effectiveness and robustness of the proposed t-norm selection.\n\n3.The release of well-documented open-source code and logs enhances the reproducibility and transparency of the work.\n\n**Weaknesses**:\n\n1.There are some little typos, like the mod-ec in Line 156, and the organization of appendix materials. \n\n2.The presentation of paper could be better. For instance, it is hard to follow the difference between the proposed framework MOD-ECL and based framework MOD-CL. Meanwhile, although 5.1 propose a methodology, it is not explained how a subset of t-norm selection chooser and scheduler choose.\n\n3.The contribution of MOD-ECL framework compared with MOD-CL should be clarified in both experiment results sections and method sections.\n\nQUESTIONS:\n1.What is the computational cost associated with the t-norm selection process? It would be beneficial to report experimental results comparing training time or epoch-level cost with and without t-norm selection.\n\n2.What are the optimal results achieved for subset-based t-norm selection discussed in section 5.1? A more detailed analysis of performance across different t-norm subsets would strengthen the validity of the proposed approach.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes MOD-ECL, a neurosymbolic framework that enhances multi-label object-detection networks for autonomous driving by incorporating logical-constraint supervision through three key innovations: a broadened library of t-norms (standard, non-parameterized, and parameterized) for translating first-order rules into differentiable losses, an adaptive t-norm selection algorithm that treats the choice as a multi-armed bandit problem with discounted reward updates, and a λ-scheduler that gradually increases the weight of constrained loss to prioritize object detection learning before rule compliance. Experiments on ROAD-R and ROAD-Waymo-R benchmarks using YOLOv8n/11n backbones demonstrate that careful t-norm and λ selection can reduce mean per-box violations by up to ~30% and occasionally improve frame-mAP by a few points compared to unconstrained baselines, with the adaptive algorithm favoring parameterized Yager t-norms while the scheduler provides controllable trade-offs between accuracy and compliance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The formulation of the constrained loss, adaptive t-norm selection, and λ-scheduler is well-defined, and an upper-bound regret theorem is provided.\n\n- The experiments comprehensively cover 14 different t-norms, two datasets, and two distinct model sizes.\n\n- The paper's structure follows a logical progression (background → method → experiments), enhancing readability and comprehension.\n\n- Figures effectively illustrate the trade-offs between constraint violation and accuracy (Fig. 1, Fig. 3), while the inclusion of pseudocode significantly aids reproducibility.\n\nWeaknesses\n\n-  The statistical rigor of the experiments is limited. All results presented are from single experimental runs, without reporting confidence intervals or statistical significance tests (as noted in Checklist Q7). Additionally, the memory and computational time overhead associated with the expanded set of t-norms has not been quantified.\n\n-  The reported improvements in mAP scores are modest.\n\nQUESTIONS:\n- Could the authors run at least 3–5 repetitions using different seeds and report mean ± standard deviation or 95% confidence intervals for mAP and MPBV?\n\n- The expanded t-norm set and adaptive algorithm may introduce additional computational costs. Please quantify the GPU memory usage, FLOPs, and wall-clock time overhead compared to baseline and previously published constrained-loss methods.\n\n- Currently, the exponential λ-scheduler is only compared against a static λ. Could alternative schedules (e.g., linear, cosine) or an automatically tuned final λ value be explored, and the sensitivity of the trade-off curve discussed?\n\n- The paper hypothesizes potential applicability to healthcare and other domains (§7). Could a preliminary experiment with multilabel medical images incorporating simple anatomical constraints be included to demonstrate transferability?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}