DPNeXt: Lightweight Multi-Task Decoder for Efficient ViT-Based Dense Prediction
A new preprint introduces DPNeXt, a lightweight multi-scale feature fusion decoder designed to replace the standard Dense Prediction Transformer (DPT) for multi-task dense prediction in robotics. DPNeXt employs dual depthwise separable inverted bottlenecks and a Multi-Task Boundary Guidance strategy to improve efficiency and mitigate negative transfer between tasks. The method reduces trainable parameters by 78.6% compared to DPT and achieves state-of-the-art results on Cityscapes and NYUv2 benchmarks, with faster inference on resource-constrained hardware.
Why it matters: This work advances efficient multi-task perception for robotics, enabling real-time scene understanding on devices with limited computational resources.
Full story at: arXiv Robotics ↗