Title: ACDC: Adaptive Cloud-Device Collaboration for Efficient and Accurate Semantic Segmentation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Cloud-Device Collaboration, Semantic Segmentation, Uncertainty Quantification

Abstract: Semantic segmentation is a vital task in computer vision with wide-ranging practical applications. Advanced segmentation models achieve high accuracy but face computational challenges for device-based deployment, while lightweight models often produce coarse predictions, potentially losing pixel-level details. To address these limitations, this paper introduces the Adaptive Cloud-Device Collaboration (ACDC) framework, which combines the efficiency of device-side models with the robust capabilities of cloud-side models. ACDC employs an adaptive uncertainty detection mechanism to capture pixel-wise distributional shifts, filtering challenging samples for fine-grained processing on the cloud, and fuses dual-granularity predictions to achieve precise results. The framework comprises three key modules: Device-Aware Adaptive Segmentor (DAS) for coarse segmentation and uncertainty detection using a two-stage Uncertainty Decoupler; Dynamic Cloud Augmentation Module (DCAM) for processing challenging samples and adaptive update; and Collaborative Fusion Engine (CFE) for dual-granularity integration. Extensive experiments demonstrate that ACDC improves segmentation accuracy with minimal data transmission, adapts to dynamic environments, and effectively identifies uncertain samples. Code is provided in the supplementary materials.