Visual semantic segmentation aims at separating a visual sample into diverse blocks with
specific semantic attributes and identifying the category for each block, and it plays a crucial …

Aerial imagery is widely utilized in visual data dependent applications such as military
surveillance, earthquake assessment, etc. For these applications, minute texture in the aerial …

One of the major challenges facing video object segmentation (VOS) is the gap between the
training and test datasets due to unseen category in test set, as well as object appearance …

In this paper, we propose a simple yet effective approach for self-supervised video object
segmentation (VOS). Previous self-supervised VOS techniques majorly resort to auxiliary …

Coral reefs formulate the most valuable and productive marine ecosystems, providing
habitat for many marine species. Coral reef surveying and analysis are currently confined to …

Current video enhancement approaches have achieved good performance in specific rainy,
hazy, foggy, and snowy weather conditions. However, they currently suffer from two …

Automated surveillance is widely opted for appli-cations such as traffic monitoring, vehicle
identification, etc. But, various weather degradation factors such as rain and snow streaks …

Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and
RGB-Event, has garnered attention due to its capability to address challenging scenarios …

We propose a universal background subtraction framework based on the Arithmetic
Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our …

Presence of rainy artifacts severely degrade the overall visual quality of a video and tend to
overlap with the useful information present in the video frames. This degraded video affects …