The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …

Subject-driven text-to-image generation models create novel renditions of an input subject
based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties …

The presence of haze in the atmosphere degrades the quality of images captured by visible
camera sensors. The removal of haze, called dehazing, is typically performed under the …

Single image haze removal is a challenging ill-posed problem. Existing methods use
various constraints/priors to get plausible dehazing solutions. The key to achieve haze …

Single image haze removal has been a challenging problem due to its ill-posed nature. In
this paper, we propose a simple but powerful color attenuation prior for haze removal from a …

In this paper, we propose a novel explicit image filter called guided filter. Derived from a
local linear model, the guided filter computes the filtering output by considering the content …

In this paper, we propose a simple but effective image prior-dark channel prior to remove
haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Large-scale data are of crucial importance for learning semantic segmentation models, but
annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic …

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation
network for depth completion. The proposed network takes RGB and sparse depth images …