Search Results for author:
Found 7 papers, 5 papers with code
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
Demystifying Neural Style Transfer
Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry.
Factorized Bilinear Models for Image Recognition
Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations.
Revisiting Batch Normalization For Practical Domain Adaptation
However, it is still a common annoyance during the training phase, that one has to prepare at least thousands of labeled images to fine-tune a network to a specific domain.
The Secrets of Salient Object Segmentation
The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing.
Boundary Detection Benchmarking: Beyond F-Measures
For an ill-posed problem like boundary detection, human labeled datasets play a critical role.
Dynamic visual attention: searching for coding length increments
A visual attention system should respond placidly when common stimuli are presented, while at the same time keep alert to anomalous visual inputs.
