Search Results for author:
Found 6 papers, 2 papers with code
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations.
Ranked #2 on
Unsupervised Semantic Segmentation
on COCO-Stuff-171
(using extra training data)
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively.
A novel graph structure for salient object detection based on divergence background and compact foreground
Different from prior methods, we calculate the saliency value of each node based on the relationship between the corresponding node and the virtual node.
Saliency detection by aggregating complementary background template with optimization framework
This paper proposes an unsupervised bottom-up saliency detection approach by aggregating complementary background template with refinement.
Saliency Detection combining Multi-layer Integration algorithm with background prior and energy function
In this paper, we propose an improved mechanism for saliency detection.
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
These parts are referred to as Frequent Local Parts or FLPs.
