Title: Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy
Venue: NeurIPS 2025
Accepted: yes
Keywords: Object Detection under Adverse Conditions; Image Restoration; Lipschitz Continuity
Abstract: To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration---an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector’s feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the instability in cascaded fashion, image restoration, and object detection, under adverse conditions, and proposes a smooth Lipschitz continuity framework,  namely LR-YOLO, which integrates restoration into the detection network and adds regularization in both input and parameter spaces.\nLR-YOLO attributes the instability problem to a mismatch in Lipschitz continuity—restoration networks are smooth, while detection networks are sensitive to small changes. \n LR-YOLO improves detection robustness and stability with low computational cost, outperforming prior approaches on haze and low-light benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) This paper's motivation is quite clear and straightforward.\n2) The implementation looks simple and effective.\n\nWeaknesses\n- The low-light images are synthesized by the gamma correction. However, in real scenes, low-light images are typically accompanied by noise.\n\n-  In row 206. It seems that the detection loss can also suppress the Lipschitz constant.  Since the main task is detection, the gradient of the detection loss is generally aligned with the Jacobian norm of one. Thus, the detection loss also satisfies the assumption in row 204. \nOverall, suppressing the Lipschitz constant doesn't seem to be solely driven by the restoration loss.\n\nI am concerning that the penalty of $$\\| \\nabla_{\\theta} f_{\\theta}(x) \\|$$ is enough to harmonize the instability.  \n\n\n**Presentation**:\n\n- Figure 2 (a) does not clearly explain why the purple dot cannot be seen in 'ours'.\n\n- In Row 203, what does 'small' mean?  \n\n**Experiments**:\n\n- In the Experiment Section, it looks like the Image enhancement methods are just training on the synthesis image pairs. I think the author should also compare with the pretrained image enhancement methods. (For example, using the Image enhancement methods to yield clear images for training and testing).\n\nQUESTIONS:\nPlease see my review.\n\nLIMITATIONS:\nNo potential negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a Lipschitz-regularized object detection framework (LROD) to address the functional mismatch between image restoration and detection networks in adverse conditions. The key innovation lies in analyzing the Lipschitz continuity disparity between restoration (low-Lipschitz) and detection (high-Lipschitz) tasks, which causes instability in traditional cascade frameworks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：The Lipschitz continuity analysis in both input and parameter spaces provides theoretical grounding for the instability issues in cascade frameworks.\nThe dual regularization approach  is well-motivated and demonstrates superior performance over existing methods.\nExtensive experiments on multiple datasets with detailed per-class results  validate the method's robustness.\n\nWeaknesses:\n1. ​​Limited Generalization Tests​​: While haze and low-light conditions are covered, other adverse conditions (rain, snow, mixed weather) mentioned in Limitations are not experimentally validated. \n2. The claim of \"extending to diverse detection architectures\" lacks supporting evidence.\n\nQUESTIONS:\n1. ​​Limited Generalization Tests​​: While haze and low-light conditions are covered, other adverse conditions (rain, snow, mixed weather) mentioned in Limitations are not experimentally validated. \n\n2. The claim of \"extending to diverse detection architectures\" lacks supporting evidence.\n\n3. I am excited to see this theoretical support between restoration and detection. Actually, i  am wondering if some interesting findings are also in Camouflaged Object Detection. These relevance reference are encouraged to be discussed, [2] also mentioned the high-resolution is beneficial for object detection:\n\n[1] Camouflaged Object Detection;\n\n[2] High-resolution Iterative Feedback Network for Camouflaged Object Detection\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nGenerally in adverse visual conditions (like haze or low light), it is common to apply image restoration before object detection. But this cascade often suffers from instability, where small artifacts from restoration get amplified in detection. The paper investigates this issue through Lipschitz continuity, showing that the mismatch in smoothness between restoration and detection leads to poor performance. The authors had the following key observations: i) Restoration networks are low-Lipschitz (smooth). ii) Detection networks are high-Lipschitz (non-smooth). iii) Cascading the two causes gradient amplification and instability in both input and parameter space. Therefore, they proposed a unified framework called LROD (Lipschitz-Regularized Object Detection) that integrates image restoration within the detector’s feature extraction stage and adds parameter-space regularization to smooth training dynamics. This results in LR-YOLO, an enhanced YOLO-based detector that outperforms traditional cascades under adverse conditions. The Authors argued that LR-YOLO achieves higher mAP scores, better training stability and lower Lipschitz constants.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe discussion around restoration and detection instability is through Lipschitz continuity, which is both novel and mathematically sound.\nAccording to the authors, the LROD framework is a lightweight modification and low computational overhead. Moreover, the authors provided analytical derivations in both input and parameter spaces, including smoothness bounds and optimization stability. Personally I like its clear architectural design, and that it is designed to be lug-and-play for most YOLO variants.\n\nWeaknesses:\ni) It is nice that the authors have shown their extensive evaluation on synthetic and real-world datasets. However, the focus seems to be on only haze and low-light scenarios. Other degradation types (rain, motion blur, snow, etc.) are only mentioned as future work. It would be interesting to see how well this framework generalizes to other conditions and if the difference between these condition types are clearly stated.\nii) In Lipschitz constants estimation, empirical Jacobian norms are used to estimate Lipschitz continuity, which may be noisy or dataset-specific. A discussion on estimation variance or robustness would be preferred.\niii) Although the two-part regularization is evaluated, the backbone-sharing choice and stage-wise contribution of restoration to detection aren’t dissected in detail.\niv) While Figure 6 helps, more clarity on how gradients flow jointly through detection and restoration heads would help readers understand the training interaction.\n\nQUESTIONS:\ni) Can there be a discussion about how you see the framework extends to more adverse scenarios as you mentioned in the future direction, if not extendable what are the limitations there? \nii) Analysis about adversarial robustness is preferred, e.g. with the presence of Gaussian noise.\niii) For regularization coefficients, it is better to include a sensitivity analysis to see how that affects mAP.\niv) It would be good to also include non-YOLO detectors as baselines in the experiments although they are not fully studied.\n\nLIMITATIONS:\nThe authors have listed a few limitations of their work, however I don't see discussion about potential negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the instability in cascaded image restoration and object detection pipelines under adverse conditions (e.g., haze, low light), attributing the issue to a mismatch in Lipschitz continuity between the two tasks. Restoration networks exhibit smooth, continuous mappings, whereas detection networks are highly sensitive and discontinuous. To address this, the authors propose Lipschitz-Regularized Object Detection (LROD), a framework that integrates low-Lipschitz restoration directly into the detection backbone and applies parameter-space smoothing to stabilize the training process. Implemented as LR-YOLO on top of YOLO detectors, the method improves detection robustness, stability, and accuracy across challenging benchmarks, demonstrating its effectiveness and efficiency over traditional cascaded or jointly trained approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper presents a well-motivated and original perspective on the instability of cascaded image restoration and object detection systems by analyzing their functional mismatch through the lens of Lipschitz continuity. The theoretical analysis is thorough and clearly articulated, spanning both input and parameter spaces, which enhances the paper's clarity and depth. The proposed solution—Lipschitz-Regularized Object Detection (LROD)—is simple yet effective, demonstrating strong empirical results across multiple benchmarks and outperforming existing cascaded and joint training baselines. The integration into YOLO detectors also highlights the practical significance and deployability of the method in real-time applications.\n\nWeaknesses:\nWhile the paper provides a compelling analysis and effective solution, it focuses exclusively on YOLO-based detectors, thereby limiting its generalizability to other detection paradigms, such as Transformer-based models (e.g., DETR). Additionally, the paper lacks an ablation on alternative regularization strategies or deeper architectural variants. The experimental scope, while strong in haze and low-light conditions, could be broadened to include other forms of degradation (e.g., motion blur, rain, snow, or a combination of these), further validating the robustness and versatility of the approach.\n\nQUESTIONS:\n1. Generality Beyond YOLO-based Detectors\nQuestion: The proposed method is closely connected to the YOLO architecture. Have you tried or considered applying LROD to other object detection frameworks, such as Transformer-based models (e.g., DETR, RT-DETR) or two-stage detectors like Faster R-CNN? Are there any gradient flow or optimization issues with the models mentioned above?\n\n2. Clarification of Restoration Loss Contribution During Inference\nQuestion: Is the restoration module used during inference, or is it only for training regularization? If it’s discarded after training, how does its supervision affect feature stability during inference?\n\n3. Ablation on Alternative Regularization Strategies\nQuestion: What are the primary advantages of this method compared to other regularization techniques or some adversarial training methods?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}