HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
This work addresses the need for efficient high-resolution networks in computer vision tasks like segmentation and detection, offering a novel NAS approach that is incremental in combining existing ideas like HRNet and transformers.
The paper tackles the problem of designing efficient neural architectures for high-resolution dense prediction tasks, which previous NAS methods ignored, and achieves state-of-the-art trade-offs between performance and computational cost, such as surpassing SqueezeNAS in semantic segmentation with a 45.9% efficiency improvement.
High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored in previous Neural Architecture Search (NAS) methods that focus on image classification. This work proposes a novel NAS method, called HR-NAS, which is able to find efficient and accurate networks for different tasks, by effectively encoding multiscale contextual information while maintaining high-resolution representations. In HR-NAS, we renovate the NAS search space as well as its searching strategy. To better encode multiscale image contexts in the search space of HR-NAS, we first carefully design a lightweight transformer, whose computational complexity can be dynamically changed with respect to different objective functions and computation budgets. To maintain high-resolution representations of the learned networks, HR-NAS adopts a multi-branch architecture that provides convolutional encoding of multiple feature resolutions, inspired by HRNet. Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources. HR-NAS is capable of achieving state-of-the-art trade-offs between performance and FLOPs for three dense prediction tasks and an image classification task, given only small computational budgets. For example, HR-NAS surpasses SqueezeNAS that is specially designed for semantic segmentation while improving efficiency by 45.9%. Code is available at https://github.com/dingmyu/HR-NAS