Search Results for author:
Found 10 papers, 4 papers with code
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize.
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video
Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object.
Self-supervised AutoFlow
Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric.
Omnimatte: Associating Objects and Their Effects in Video
We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semi-transparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.
Self-supervised Video Object Segmentation by Motion Grouping
We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.
Ranked #7 on
Unsupervised Object Segmentation
on DAVIS 2016
On the Origin of Species of Self-Supervised Learning
In the quiet backwaters of cs. CV, cs. LG and stat. ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for external supervision.
Layered Neural Rendering for Retiming People in Video
We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur.
MAST: A Memory-Augmented Self-supervised Tracker
Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.
Ranked #4 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+2
Class-Agnostic Counting
The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images.
Learning to See Physics via Visual De-animation
At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics engines.
