Tasks across diverse application domains can be posed as large-scale optimization
problems, these include graphics, vision, machine learning, imaging, health, scheduling …

Depth completion is the task of generating a dense depth map given an image and a sparse
depth map as inputs. In this paper, we present OGNI-DC, a novel framework for depth …

In recent years, there has been a growing interest in combining learnable modules with
numerical optimization to solve low-level vision tasks. However, most existing approaches …

Emotion recognition based on electroencephalography (EEG) has attracted significant
attention and achieved considerable advances in the fields of affective computing and …

Reconstructing the 3D shape of objects observed in a single image is a challenging task.
Recent approaches rely on visual cues extracted from a given image learned from a deep …

The prominence of high-quality video services has become so substantial that by 2030, it is
estimated that approximately 80% of internet traffic will consist of videos. On the contrary …

Achieving clear images during X-ray angiography is sometimes a challenge for cardiologists
due to unwanted shadows of various fixed and moving organs inside the chest. To face this …

Multispectral images contain light information in different wavelengths of objects, which
convey spectral-spatial information and help improve the performance of various image …

Computer vision methods that explicitly detect object parts and reason on them are a step
towards inherently interpretable models. Existing approaches that perform part discovery …

While neural networks have made significant strides in many AI tasks, they remain
vulnerable to a range of noise types, including natural corruptions, adversarial noise, and …