Secondly, we propose a new method of feature drift compensation that accommodates feature drift in the backbone when learning new tasks.
In this paper we present a path planning algorithm which provides a step-by-step explanation of the output produced by state of the art enhancement methods, overcoming black-box limitation.
In this paper we present a framework for the design and implementation of offset equivariant networks, that is, neural networks that preserve in their output uniform increments in the input.
Given as input a low-light image, TreEnhance produces as output its enhanced version together with the sequence of image editing operations used to obtain it.
Ranked #1 on Image Enhancement on MIT-Adobe FiveK