7 accepted papers
Recent advances in deep learning have significantly improved underwater image super-resolution (UISR) performance. However, their large and complex architectures result in huge computational complexity, making them unsuitable for low-power devices such as autonomous underwater vehicles (AUVs) and re…
Knee Osteoarthritis (KOA) is a degenerative musculoskeletal joint disorder that significantly impacts middle-aged and elderly individuals. Although X-rays and MRIs are clinically used to identify such disorders, combining these imaging modalities is challenging due to the distinct nature of the data…
Underwater image enhancement is crucial for marine engineering and aquatic robotics. However, most recent methods have focused on ocean environments, where they trained and tested on oceanic images. As a result, these methods are less effective in river water, where relatively blurry images are prod…
Underwater single image super-resolution (UISR) is a challenging task as these images frequently suffer from poor visibility. The best-published UISR works continue to suffer from color degradation, poor texture representation, and loss of finer (high-frequency) details. We propose a novel deep lear…
Human-Object Interaction (HOI) detection is a crucial task that involves localizing interactive human-object pairs and identifying the actions being performed. Most existing HOI detectors are supervised in nature and lack the ability of zero-shot discovery of unseen interactions. Recently, transform…
Self-supervised learning with a contrastive batch approach has become a powerful tool for representation learning in computer vision. The performance of downstream tasks is proportional to the quality of visual features learned while self-supervised pre-training. The existing contrastive batch appro…
Underwater image enhancement is essential to mitigate the environment-centric noise in images, such as haziness, color degradation, etc. With most existing works focused on processing an RGB image as a whole, the explicit context that can be mined from each color channel separately goes unaccounted…