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Found 12 papers, 0 papers with code
Kernel Aware Resampler
This renders our model applicable for different types of data not seen during the training such as normals.
Frame Interpolation Transformer and Uncertainty Guidance
Through this error estimation, our method can produce even higher-quality intermediate frames using only a fraction of the time compared to a full rendering.
Microdosing: Knowledge Distillation for GAN based Compression
Recently, significant progress has been made in learned image and video compression.
Blind Image Restoration with Flow Based Priors
In contrast to this, we propose using normalizing flows to model the distribution of the target content and to use this as a prior in a maximum a posteriori (MAP) formulation.
Lossy Image Compression with Normalizing Flows
However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization.
Neural Inter-Frame Compression for Video Coding
We propose to compute residuals directly in latent space instead of in pixel space as this allows to reuse the same image compression network for both key frames and intermediate frames.
Content Adaptive Optimization for Neural Image Compression
The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches.
Disentangled Dynamic Representations from Unordered Data
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input.
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames.
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Our normalized cut loss approach to segmentation brings the quality of weakly-supervised training significantly closer to fully supervised methods.
PhaseNet for Video Frame Interpolation
We show that this is superior to the hand-crafted heuristics previously used in phase-based methods and also compares favorably to recent deep learning based approaches for video frame interpolation on challenging datasets.
On Regularized Losses for Weakly-supervised CNN Segmentation
This approach simplifies weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and efficiency of training.
