Search Results for author:
Found 7 papers, 1 papers with code
LSR: A Light-Weight Super-Resolution Method
A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work.
End-to-End Video Matting With Trimap Propagation
Although recent studies exploit video object segmentation methods to propagate the given trimaps, they suffer inconsistent results.
Natural World Distribution via Adaptive Confusion Energy Regularization
The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task.
ACE: Adaptive Confusion Energy for Natural World Data Distribution
With the development of deep learning, standard classification problems have achieved good results.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Fine-Grained Visual Classification with Batch Confusion Norm
We introduce a regularization concept based on the proposed Batch Confusion Norm (BCN) to address Fine-Grained Visual Classification (FGVC).
Ranked #17 on
Fine-Grained Image Classification
on FGVC Aircraft
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
