Unpaired image-to-image translation aims to translate images from the source class to target one by providing sufficient data for these classes.
In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting.
Ranked #1 on Overlapped 100-50 on ADE20K
We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range.
Several mainstream methods utilize extra cues (e. g., human pose information) to distinguish human parts from obstacles to alleviate the occlusion problem.
Further, since breathing can be monitored without body contact by analyzing the radio signal in the environment, we show that oxygen too can be monitored without any wearable devices.
Our framework introduces two sub-modules -- one maps testing samples to the valid input domain of the I2I model, and the other transforms the output of I2I model to expected results.
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain.
We also show that this framework can be applied to multi-domain and multi-modal translation.
Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks.
We propose a principled convolutional neural pyramid (CNP) framework for general low-level vision and image processing tasks.
The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion.
In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural Network (CNN) features.