[2603.28233] TwinMixing: A Shuffle-Aware Feature Interaction Model for Multi-Task Segmentation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28233 (cs) [Submitted on 30 Mar 2026] Title:TwinMixing: A Shuffle-Aware Feature Interaction Model for Multi-Task Segmentation Authors:Minh-Khoi Do, Huy Che, Dinh-Duy Phan, Duc-Khai Lam, Duc-Lung Vu View a PDF of the paper titled TwinMixing: A Shuffle-Aware Feature Interaction Model for Multi-Task Segmentation, by Minh-Khoi Do and 4 other authors View PDF HTML (experimental) Abstract:Accurate and efficient perception is essential for autonomous driving, where segmentation tasks such as drivable-area and lane segmentation provide critical cues for motion planning and control. However, achieving high segmentation accuracy while maintaining real-time performance on low-cost hardware remains a challenging problem. To address this issue, we introduce TwinMixing, a lightweight multi-task segmentation model designed explicitly for drivable-area and lane segmentation. The proposed network features a shared encoder and task-specific decoders, enabling both feature sharing and task specialization. Within the encoder, we propose an Efficient Pyramid Mixing (EPM) module that enhances multi-scale feature extraction through a combination of grouped convolutions, depthwise dilated convolutions and channel shuffle operations, effectively expanding the receptive field while minimizing computational cost. Each decoder adopts a Dual-Branch Upsampling (DBU) Block composed of a learnable transposed convolution-based Fine deta...