5 accepted papers
Recent advancements in gait recognition have significantly enhanced performance by treating silhouettes as either an unordered set or an ordered sequence. However, both set-based and sequence-based approaches exhibit notable limitations. Specifically, set-based methods tend to overlook short-range t…
Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existing methods focus primarily on RGB-derived modalities, which fall short in real-world scenarios requiring multi-modal collaboration and cross-modal r
Gait attracts growing interest from researchers due to its advantages as a non-invasive and non-cooperative biometric feature. Current gait-based attribute recognition methods primarily focus on estimating attributes such as gender, age, and emotions. However, there is insufficient attention to dive…
While medical image segmentation has achieved impressive progress, it usually being constrained by labor-intensive and costly pixel-wise annotations. The existing semi-supervised learning methods ignore the inherent imbalance and high similarity of different categories in medical images. To address…
Siamese neural network has been well investigated by tracking frameworks due to its fast speed and high accuracy. However, very few efforts were spent on background-extraction by those approaches. In this paper, a Pixel to Global Matching Network (PG-Net) is proposed to suppress the influence of bac…