RGPNet: A Real-Time General Purpose Semantic Segmentation
This addresses the need for efficient semantic segmentation in applications requiring real-time processing, though it appears incremental in improving speed-accuracy trade-offs.
The paper tackles real-time semantic segmentation by proposing RGPNet, which achieves comparable accuracy to state-of-the-art non-real-time models while operating in real-time, and reduces training time by up to 60% with optimized techniques.
We propose a real-time general purpose semantic segmentation architecture, RGPNet, which achieves significant performance gain in complex environments. RGPNet consists of a light-weight asymmetric encoder-decoder and an adaptor. The adaptor helps preserve and refine the abstract concepts from multiple levels of distributed representations between the encoder and decoder. It also facilitates the gradient flow from deeper layers to shallower layers. Our experiments demonstrate that RGPNet can generate segmentation results in real-time with comparable accuracy to the state-of-the-art non-real-time heavy models. Moreover, towards green AI, we show that using an optimized label-relaxation technique with progressive resizing can reduce the training time by up to 60% while preserving the performance. We conclude that RGPNet obtains a better speed-accuracy trade-off across multiple datasets.