Understanding illumination and reducing the need for supervision pose a significant
challenge in low-light enhancement. Current approaches are highly sensitive to data usage …

Foundational vision models, such as the Segment Anything Model (SAM), have achieved
significant breakthroughs through extensive pre-training on large-scale visual datasets …

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification
systems. Noncontact methods such as fingerprint biometrics are becoming popular because …

Effective aggregation of temporal information of consecutive frames is the core of achieving
video super-resolution. Many scholars have utilized structures such as sliding windows and …

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution
ones, space-time video super-resolution techniques can enhance visual experiences and …

Joint space-time video super-resolution aims to increase both the spatial resolution and the
frame rate of a video sequence. As a result, details become more apparent, leading to a …

Existing deraining Transformers employ self-attention mechanisms with fixed-range
windows or along channel dimensions, limiting the exploitation of non-local receptive fields …

Uncertainty estimation is crucial in scientific data for machine learning. Current uncertainty
estimation methods mainly focus on the model's inherent uncertainty, while neglecting the …

Abstract Unsupervised Domain Adaptation (UDA) seeks to utilize the knowledge acquired
from a source domain, abundant in labeled data, and apply it to a target domain that …