Search Results for author:
Found 27 papers, 10 papers with code
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations.
Change-Aware Sampling and Contrastive Learning for Satellite Images
While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled.
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions.
AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning
To our knowledge, this is the first system that can automatically explore an environment to capture an aesthetic photo with respect to a learned aesthetic estimator.
Field-Guide-Inspired Zero-Shot Learning
Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment.
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images.
Ranked #5 on
Surface Normals Estimation
on Stanford-ORB
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
With our novel learning objective, our framework can learn high-level semantic concepts.
Ranked #3 on
Unsupervised Semantic Segmentation
on COCO-Stuff-171
Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering
Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing problem in computer vision.
Discovering Underground Maps from Fashion
The fashion sense -- meaning the clothing styles people wear -- in a geographical region can reveal information about that region.
What Can Style Transfer and Paintings Do For Model Robustness?
First, we investigate the role of paintings as style images for stylization-based data augmentation.
Insights From A Large-Scale Database of Material Depictions In Paintings
We also find that FasterRCNN, a model which has been designed for object recognition in natural scenes, can be quickly repurposed for detection of materials in paintings.
Block Annotation: Better Image Annotation for Semantic Segmentation with Sub-Image Decomposition
We propose block sub-image annotation as a replacement for full-image annotation.
GeoStyle: Discovering Fashion Trends and Events
Understanding fashion styles and trends is of great potential interest to retailers and consumers alike.
Learning Material-Aware Local Descriptors for 3D Shapes
Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods.
Inverse Transport Networks
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination.
Deep Painterly Harmonization
Copying an element from a photo and pasting it into a painting is a challenging task.
Graphics
StreetStyle: Exploring world-wide clothing styles from millions of photos
Each day billions of photographs are uploaded to photo-sharing services and social media platforms.
Shading Annotations in the Wild
We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms.
Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
Deep Feature Interpolation for Image Content Changes
We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation.
From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators
We propose a new neural network architecture for solving single-image analogies - the generation of an entire set of stylistically similar images from just a single input image.
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest.
Ranked #213 on
Object Detection
on COCO test-dev
Deep Manifold Traversal: Changing Labels with Convolutional Features
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership.
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
In this paper, we propose a novel learning framework to help answer these types of questions.
On the Appearance of Translucent Edges
We also discuss the existence of shape and material metamers, or combinations of distinct shape or material parameters that generate the same edge profile.
Material Recognition in the Wild with the Materials in Context Database
In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild.
Photometric Ambient Occlusion
We present a method for computing ambient occlusion (AO) for a stack of images of a scene from a fixed viewpoint.
