Search Results for author:
Found 9 papers, 5 papers with code
Disentangled 3D Scene Generation with Layout Learning
We introduce a method to generate 3D scenes that are disentangled into their component objects.
Diffusion Self-Guidance for Controllable Image Generation
However, many aspects of an image are difficult or impossible to convey through text.
BlobGAN: Spatially Disentangled Scene Representations
Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network.
Learning Temporal Dynamics from Cycles in Narrated Video
Learning to model how the world changes as time elapses has proven a challenging problem for the computer vision community.
Globetrotter: Connecting Languages by Connecting Images
Machine translation between many languages at once is highly challenging, since training with ground truth requires supervision between all language pairs, which is difficult to obtain.
Learning Goals from Failure
We introduce a framework that predicts the goals behind observable human action in video.
Learning to Learn Words from Visual Scenes
Language acquisition is the process of learning words from the surrounding scene.
Oops! Predicting Unintentional Action in Video
We train a supervised neural network as a baseline and analyze its performance compared to human consistency on the tasks.
NEUZZ: Efficient Fuzzing with Neural Program Smoothing
However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs.
