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 stagewise 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.

Adverse weather severely impairs real-world visual perception, while existing vision models trained on synthetic data with fixed parameters struggle to generalize to complex degradations. To address this, we first construct HFLS-Weather, a physics-driven, high-fidelity dataset that simulates diverse weather phenomena, and then design a dual-level reinforcement learning framework initialized with HFLS-Weather for cold-start training. Within this framework, at the local level, weather-specific restoration models are refined through perturbation-driven image quality optimization, enabling reward-based learning without paired supervision; at the global level, a meta-controller dynamically orchestrates model selection and execution order according to scene degradation. This framework enables continuous adaptation to real-world conditions and achieves state-of-the-art performance across a wide range of adverse weather scenarios.

AAppendix1 In the supplementary material, we provide more experimental results summarized as follows:2 In A.1, we use ResNet101 as the backbone network and compare our method with state-of-3 the-art methods, demonstrating that our method achieves consistent top results on different4 In A.2, we provide more t-SNE visualization results for a comprehensive analysis on the6 feature space learned from different models.7 In A.3, we study the effect of the image-to-image translation model on the performance of8 domain adaptive semantic segmentation.9 In A.4, we discuss the limitations of our method and provide the URL link of code to10 reproduce the main experimental results.11 "V" and "R" indicate the method using VGG16 and ResNet101 backbone networks, respectively. In the main paper, we report results using VGG1613 as the backbone for both settings: single-target14 and multi-target domain adaptation.

To avoid redundancy in these synthetic datasets, it is crucial that each element contains unique features and remains diverse from others during the synthesis stage. In this paper, we provide a thorough theoretical and empirical analysis of diversity within synthesized datasets. We argue that enhancing diversity can improve the parallelizable yet isolated synthesizing approach.

In this paper, we introduce a novel one-step drag-based image editing method, i.e., FastDrag, to accelerate the editing process.

Pre-training plays a vital role in various vision tasks, such as object recognition and detection.

Humans live in a three-dimensional (3D) environment comprising various 3D objects with rich information,includingshape,color,texture,andsize.