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Found 13 papers, 6 papers with code
Semantic Photo Manipulation with a Generative Image Prior
First, it is hard for GANs to precisely reproduce an input image.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Ranked #66 on
Monocular Depth Estimation
on KITTI Eigen split
Seeing What a GAN Cannot Generate
Differences in statistics reveal object classes that are omitted by a GAN.
Using latent space regression to analyze and leverage compositionality in GANs
In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs.
Learning to See by Looking at Noise
We investigate a suite of image generation models that produce images from simple random processes.
Procedural Image Programs for Representation Learning
Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias.
Optical Flow in Mostly Rigid Scenes
Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes.
Ranked #13 on
Optical Flow Estimation
on Sintel-clean
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation
The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video.
A Fully-Connected Layered Model of Foreground and Background Flow
Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another.
Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers
Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields.
Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data
Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth.
Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space
We show that, under a simple nonlinear operation, the data distribution can be modeled as Gaussian and therefore expressed using sufficient statistics.
Do We Still Need Clinical Language Models?
To investigate this question, we conduct an extensive empirical analysis of 12 language models, ranging from 220M to 175B parameters, measuring their performance on 3 different clinical tasks that test their ability to parse and reason over electronic health records.
