State-of-the-art parametric and non-parametric style transfer approaches are prone to either
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …

Weakly supervised semantic segmentation (WSSS) has gained significant popularity as it
relies only on weak labels such as image level annotations rather than the pixel level …

Recent advances in deep learning have provided procedures for learning one network to
amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural …

Among the existing deep learning-based person re-identification (ReID) methods, human
parsing based on semantic segmentation is the most promising solution for ReID because …

The human parsing task is dedicated to allocating pixel-level fine-grained semantic labels.
However, recent solutions are limited to fully utilize the prior information (poses and edges) …

Graph neural networks (GNNs) have emerged as the dominant paradigm for graph
representation learning, igniting widespread interest in utilizing sophisticated GNNs for …

Location is an important distinguishing information for instance segmentation. In this paper,
we propose a novel model, called Location Sensitive Network (LSNet), for human instance …

We consider the problem of improving the human instance segmentation mask quality for a
given test image using keypoints estimation. We compare two alternative approaches. The …

We propose a novel network to perform auxiliary-free video matting task. Unlike most
existing approaches that require trimaps or pre-captured backgrounds as auxiliary inputs …

The task of instance segmentation is widely acknowledged as being one of the most
formidable challenges in the field of computer vision. Current methods have low utilization of …