While CNN-based models have made remarkable progress on human pose estimation, what
spatial dependencies they capture to localize keypoints remains unclear. In this work, we …

Robustness and discrimination power are two fundamental requirements in visual object
tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-…

Multiview learning (MVL), which enhances the learners’ performance by coordinating
complementarity and consistency among different views, has attracted much attention. The …

Human pose estimation deeply relies on visual clues and anatomical constraints between
parts to locate keypoints. Most existing CNN-based methods do well in visual representation, …

Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both
academia and industry due to their remarkable performance in various applications such as …

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image
feature extraction and pattern recognition based on graph embedded learning and under …

Sparse representation has attracted great attention in the past few years. Sparse
representation based classification (SRC) algorithm was developed and successfully used for …

The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for
years due to high performance. However, the long-standing quantization error problem in the …

In graph embedding based methods, we usually need to manually choose the nearest neighbors
and then compute the edge weights using the nearest neighbors via L2 norm (eg LLE). …

Recently, there are many works on discriminant analysis, which promote the robustness of
models against outliers by using L 1 - or L 2,1 -norm as the distance metric. However, both of …