ImageSig: A signature transform for ultra-lightweight image recognition
This enables ultra-lightweight, energy-efficient image recognition for embedded AI at the edge, such as on Raspberry Pi and Jetson-nano.
The paper tackles image recognition by introducing ImageSig, a signature transform-based method that achieves state-of-the-art accuracy on 64x64 RGB images while requiring orders of magnitude less FLOPS, power, and memory, with models as small as 44.2 KB.
This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure or an attention-based encoder. It is striking to the authors that it achieves: a) an accuracy for 64 X 64 RGB images that exceeds many of the state-of-the-art methods and simultaneously b) requires orders of magnitude less FLOPS, power and memory footprint. The pretrained model can be as small as 44.2 KB in size. ImageSig shows unprecedented performance on hardware such as Raspberry Pi and Jetson-nano. ImageSig treats images as streams with multiple channels. These streams are parameterized by spatial directions. We contribute to the functionality of signature and rough path theory to stream-like data and vision tasks on static images beyond temporal streams. With very few parameters and small size models, the key advantage is that one could have many of these "detectors" assembled on the same chip; moreover, the feature acquisition can be performed once and shared between different models of different tasks - further accelerating the process. This contributes to energy efficiency and the advancements of embedded AI at the edge.