Learning graph neural networks for image style transfer
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 …
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …
[HTML][HTML] A multi-strategy contrastive learning framework for weakly supervised semantic segmentation
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 …
relies only on weak labels such as image level annotations rather than the pixel level …
Data-free knowledge amalgamation via group-stack dual-gan
Recent advances in deep learning have provided procedures for learning one network to
amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural …
amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural …
Face and body-shape integration model for cloth-changing person re-identification
O Agbodike, W Zhang, J Chen, L Wang - Image and Vision Computing, 2023 - Elsevier
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 …
parsing based on semantic segmentation is the most promising solution for ReID because …
MVSN: A multi-view stack network for human parsing
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) …
However, recent solutions are limited to fully utilize the prior information (poses and edges) …
[PDF][PDF] Efficient representation learning with graph neural networks
Y Jing - 2023 - core.ac.uk
Graph neural networks (GNNs) have emerged as the dominant paradigm for graph
representation learning, igniting widespread interest in utilizing sophisticated GNNs for …
representation learning, igniting widespread interest in utilizing sophisticated GNNs for …
Location sensitive network for human instance segmentation
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 propose a novel model, called Location Sensitive Network (LSNet), for human instance …
Test-time adaptation vs. training-time generalization: A case study in human instance segmentation using keypoints estimation
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 …
given test image using keypoints estimation. We compare two alternative approaches. The …
Portrait matting using an attention-based memory network
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 …
existing approaches that require trimaps or pre-captured backgrounds as auxiliary inputs …
[HTML][HTML] A Novel Boundary-Guided Global Feature Fusion Module for Instance Segmentation
L Gao, S Wang, S Chen - Neural Processing Letters, 2024 - Springer
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 …
formidable challenges in the field of computer vision. Current methods have low utilization of …