Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

Images captured under poor illumination conditions often exhibit characteristics such as low
brightness, low contrast, a narrow gray range, and color distortion, as well as considerable …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various
tasks with only a handful of prompts and examples. But in computer vision, the difficulties for …

Retinex model-based methods have shown to be effective in layer-wise manipulation with
well-designed priors for low-light image enhancement. However, the commonly used hand …

Existing low-light image enhancement techniques are mostly not only difficult to deal with
both visual quality and computational efficiency but also commonly invalid in unknown …

This paper presents a new solution for low-light image enhancement by collectively
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

When enhancing low-light images, many deep learning algorithms are based on the Retinex
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …