Xiaopeng Zhang

TL;DR: An improved image dehazing scheme using a pair of color and NIR images, which effectively estimates the airlight color and transfers details from the NIR, and can achieve substantial improvements on the detail recovery and the color distribution over the existing imageDehazing algorithms.

TL;DR: Zhang et al. as mentioned in this paper proposed a quantization-friendly separable convolution architecture, which can effectively offload GPU and make it possible to deploy DL on fixed-point pipeline.

TL;DR: In this article, the authors proposed a method for generating high resolution iris templates and detecting spoofs, enabling more reliable and secure iris authentication using RGB and NIR images.

TL;DR: This work analyzed the root cause of quantization loss and proposed a quantization-friendly separable convolution architecture that can archive 8-bit inference top-1 accuracy and almost closed the gap to the float pipeline.

TL;DR: The state of the art for low-power solutions to detect objects in images is examined to suggest directions for research as well as opportunities forLow-power computer vision.