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Found 13 papers, 2 papers with code
Adaptive Compact Attention For Few-shot Video-to-video Translation
This paper proposes an adaptive compact attention model for few-shot video-to-video translation.
Task-agnostic Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
Extracting effective deep features to represent content and style information is the key to universal style transfer.
CPOT: Channel Pruning via Optimal Transport
Recent advances in deep neural networks (DNNs) lead to tremendously growing network parameters, making the deployments of DNNs on platforms with limited resources extremely difficult.
Quantized Adam with Error Feedback
In this paper, we present a distributed variant of adaptive stochastic gradient method for training deep neural networks in the parameter-server model.
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL.
Multi-Frame Content Integration with a Spatio-Temporal Attention Mechanism for Person Video Motion Transfer
Specifically, we transfer the motion of one person in a target video to another person in a source video, while preserving the appearance of the source person.
FaceShapeGene: A Disentangled Shape Representation for Flexible Face Image Editing
On the basis of the FaceShapeGene, a novel part-wise face image editing system is developed, which contains a shape-remix network and a conditional label-to-face transformer.
Temporally Coherent Video Harmonization Using Adversarial Networks
Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network.
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
Recent studies on unsupervised image-to-image translation have made a remarkable progress by training a pair of generative adversarial networks with a cycle-consistent loss.
Pose2Seg: Detection Free Human Instance Segmentation
We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
Ranked #1 on
Human Instance Segmentation
on OCHuman
Real-Time Neural Style Transfer for Videos
More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.
