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Found 11 papers, 2 papers with code
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis
Our proposed multi-task Siamese network (MTSN) can employ any backbone CNN, and we demonstrate with four backbone CNNs that its accuracy with limited training data approaches the accuracy of backbone CNNs trained with a dataset that is 50 times larger.
Neural Reflectance Fields for Appearance Acquisition
We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.
Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images
We also show that our learned reflectance volumes are editable, allowing for modifying the materials of the captured scenes.
Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images
We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting.
Detecting the Starting Frame of Actions in Video
In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud.
Image to Image Translation for Domain Adaptation
This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network.
Depth and Image Restoration From Light Field in a Scattering Medium
First, we present a new single image restoration algorithm which removes backscatter and attenuation from images better than existing methods, and apply it to each view in the light field.
Dense Volume-to-Volume Vascular Boundary Detection
In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data.
Photometric Stereo in a Scattering Medium
Next, we model the blur due to scattering of light from the object.
Learning Concept Embeddings with Combined Human-Machine Expertise
This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels.
Locally Uniform Comparison Image Descriptor
Keypoint matching between pairs of images using popular descriptors like SIFT or a faster variant called SURF is at the heart of many computer vision algorithms including recognition, mosaicing, and structure from motion.
