Title: Searching Efficient Semantic Segmentation Architectures via Dynamic Path Selection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neural Architecture Search

Semantic Segmentation

Dynamic Path Selection
Abstract: Existing NAS methods for semantic segmentation typically apply uniform optimization to all candidate networks (paths) within a one-shot supernet. However, the concurrent existence of both promising and suboptimal paths often results in inefficient weight updates and gradient conflicts. This issue is particularly severe in semantic segmentation due to its complex multi-branch architectures and large search space, which further degrade the supernet's ability to accurately evaluate individual paths and identify high-quality candidates. To address this issue, we propose Dynamic Path Selection (DPS), a selective training strategy that leverages multiple performance proxies to guide path optimization. DPS follows a stage-wise paradigm, where each phase emphasizes a different objective: early stages prioritize convergence, the middle stage focuses on expressiveness, and the final stage emphasizes a balanced combination of expressiveness and generalization. At each stage, paths are selected based on these criteria, concentrating optimization efforts on promising paths, thus facilitating targeted and efficient model updates. Additionally, DPS integrates a dynamic stage scheduler and a diversity-driven exploration strategy, which jointly enable adaptive stage transitions and maintain structural diversity among selected paths. Extensive experiments demonstrate that, under the same search space, DPS can discover efficient models with strong generalization and superior performance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe work introduces Dynamic Path Selection (DPS), a novel strategy for one-shot neural architecture search (NAS) in semantic segmentation that addresses inefficiencies in training large supernets with mixed-quality paths. DPS adopts a stage-wise training paradigm that dynamically prioritizes different path quality proxies—convergence (via angle score), expressiveness (via a learned performance predictor), and generalization (via a multi-scale information bottleneck metric)—to focus optimization on promising architectures throughout training. The approach includes a Pareto-optimal path selection mechanism, diversity-driven exploration to maintain structural variety, and an adaptive stage scheduler for effective proxy utilization. Extensive experiments on benchmarks like Cityscapes and CamVid demonstrate that DPS discovers architectures with better performances.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n\nAddresses a critical limitation in one-shot NAS: the inefficient training and gradient conflict caused by simultaneous optimization of good and poor candidate paths within large search spaces.\n\n\nImplements a stage-wise optimization strategy that aligns training effort with proxy reliability: convergence (angle score), expressiveness (performance predictor), and generalization (MS-IB)\n\nCombines Pareto optimality-based path selection, diversity-driven exploration, and dynamic stage scheduling, enabling focused and adaptive path evaluation.\n\nThe experimental performances achieved a balance between accuracy and computational efficiency, outperforming prior NAS baselines within the same search space.\n\nDemonstrates consistent gains across datasets and robustness scenarios without external data or extra training tricks (e.g., knowledge distillation).\n\n\nWeaknesses:\n\nThe problem framing centers mainly on NAS inefficiencies, which is a well-recognized issue; the paper does not offer theoretical analysis of gradient conflicts or path interference, limiting deeper insight.\n\nThe performance predictor assumes path accuracy estimates from the supernet are reliable enough to train regressors, which may not hold early in training or with larger search spaces.\n\nNo error bars or statistical significance tests are reported, which limits confidence in the generalization of results.\n\nSome of descriptions are not fluent or precise.\n\nQUESTIONS:\nNA\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a dynamic path selection method for architecture search of semantic segmentation. In particular, the proposed DPS leverages multiple performance proxies to guide path optimization. In addition, a dynamic stage scheduler and a diversity-driven exploration strategy are used to enable adaptive stage transition and maintain path diversity. Experimental results proved the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n(1) The motivation is well presented of considering dynamic path selection for architecture search.\n\n(2) The explanations and illustrations are mostly clear and intuitive of the multiple performance proxies and the dynamic stage scheduler.\n\nWeaknesses:\n(1) As a sampling-based NAS method, this paper provides no novel ideas to the field but a simple combination of typical optimization techniques such as angle score, accuracy predictor and information bottleneck.\n\n(2) The motivation of dynamically choosing loss terms for different training stages is not theoretically proved or investigated. The paper only presents empirical results that are also problematic.\n\n(3) The overall design of the dynamic path selection seems to have nothing specific to the semantic segmentation.\n\n(4) On Page 2, Line 42-43, the authors use a specific path during supernet training to get the results in Figure 1. There are no details on the experimental setting. In addition, if the observed path is not representative and the experiment is not conducted multiple runs, the results show no statistical signification. Besides, the validation loss could fluctuate due to the randomness of gradient-based optimization during initial stage of training.\n\n(5) On Page 3, Line 74, the authors claim a diversity-driven exploration strategy but present no specific explanation on it.\n\nQUESTIONS:\nNo.\n\nLIMITATIONS:\nNo.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis submission introduces ​Dynamic Path Selection (DPS)​, an improved one-shot neural architecture search (NAS) method for semantic segmentation. DPS employs a ​stage-wise strategy​ to dynamically prioritize high-potential network paths: early stages optimize for convergence using an ​angle score​ metric, middle stages evaluate expressiveness via a ​random forest predictor, and final stages balance expressiveness and generalization using a novel ​Multi-Scale Information Bottleneck (MS-IB)​. By selectively training paths based on these proxies and incorporating ​diversity-driven exploration​ and ​adaptive stage scheduling, DPS reduces gradient conflicts and improves supernet reliability. This method improves over SOTAs with a not small margin.\n\nSTRENGTHS AND WEAKNESSES:\nThe innovations include\nDynamic Stage Scheduler: Auto-transitions stages based on improvement trends (e.g., switches when metric plateaus).\n​Diversity-Driven Exploration: Maintains structural diversity using weighted Hamming distance to avoid search collapse.\n​Pareto-Optimal Path Selection: Selects paths balancing FLOPs, convergence, expressiveness, and generalization.\n​Elite Population in Evolution Search: Preserves top paths to accelerate optimal architecture discovery.\n\nWeakness:\nThe three-stage proxy system (angle score, predictor, MS-IB) may introduce significant computational overhead. Training and maintaining these proxies—especially the mutual information estimators for MS-IB—demands extra resources beyond standard supernet training.\n\nQUESTIONS:\nI only have one question: in table 4, the performance of this method on CamVid drops when using multi-scale IB, comparing to only using single-scale IB. The authors claimed that it's because CamVid's resolution is low so that the additional features may only introduce redundancies. I doubt about this hypothesis here. Do you have any evidence to support this claim? For example, visualize such redundancies? In addition, why this learning will result in some kinds of redundancy?\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a Dynamic Path Selection (DPS) strategy for semantic segmentation tasks, aiming to improve the efficiency and generalization ability of one-shot NAS methods under large-scale, multi-branch architectures. The approach systematically integrates multiple performance proxies-convergence, expressiveness, and generalization-along with a multi-stage dynamic scheduling mechanism to address issues such as gradient conflicts and interference among subnetworks during supernet training.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.The method combines multiple performance proxies, including Angle Score, a performance predictor, and Multi-Scale Information Bottleneck (MS-IB), enabling a systematic, multi-perspective evaluation of path quality.\n\n2.A dynamic stage scheduling mechanism is introduced, which adaptively adjusts the training stages based on metric trends, effectively avoiding manual hyperparameter tuning.\n\n3.A diversity-driven exploration strategy is designed to mitigate search space collapse and promote structural diversity during the search process.\n\nWeaknesses:\n\n1.Although MS-IB effectively improves generalization, its computational complexity is relatively high, and the additional cost for practical deployment has not been thoroughly discussed.\n\n2.The performance of the dynamic stage scheduling mechanism is sensitive to hyperparameters such as ε and window size, yet systematic sensitivity analyses are lacking.\n\n3.The core innovation primarily integrates and optimizes existing NAS and Information Bottleneck (IB) theories, with limited theoretical novelty and a lack of formal mathematical reasoning or convergence guarantees.\n\nQUESTIONS:\nQ1:The proposed DSS strategy relies on hyperparameters such as window size (t_win) and sensitivity coefficients (ε), which appear to be set empirically. The robustness of the method to these parameters is unclear, raising concerns about generalizability.\n\nQ2:The performance predictor is trained on only 1,000 sampled paths, while the overall search space is extremely large. It remains unclear whether such a sample size is sufficiently representative. Please provide additional experiments showing how the predictor's performance varies under different sampling scales.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}