Search Results for author:
Found 15 papers, 7 papers with code
Unsupervised Keypoints from Pretrained Diffusion Models
Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised counterpart, making their practicability questionable.
Ranked #1 on
Unsupervised Human Pose Estimation
on Tai-Chi-HD
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization
This strategy enables the model to preserve fine details of the desired subjects, such as text and logos.
Few-shot Geometry-Aware Keypoint Localization
Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude.
Neural Partial Differential Equations with Functional Convolution
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs.
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
Our key ingredients are i) an encoder that predicts keypoint locations in an input image, ii) a shared graph as a latent variable that links the same pairs of keypoints in every image, iii) an intermediate edge map that combines the latent graph edge weights and keypoint locations in a soft, differentiable manner, and iv) an inpainting objective on randomly masked images.
Ranked #1 on
Unsupervised Landmark Detection
on MAFL Unaligned
LatentKeypointGAN: Controlling Images via Latent Keypoints -- Extended Abstract
Generative adversarial networks (GANs) can now generate photo-realistic images.
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing.
LatentKeypointGAN: Controlling GANs via Latent Keypoints
Generative adversarial networks (GANs) have attained photo-realistic quality in image generation.
Ranked #5 on
Unsupervised Keypoint Estimation
on CUB
VortexNet: Learning Complex Dynamic Systems with Physics-Embedded Networks
Since the number of such vortices are much smaller than that of the Eulerian, grid discretization, this Lagrangian discretization in essence encodes the system dynamics on a compact physics-based latent space.
Nonseparable Symplectic Neural Networks
The enabling mechanics of our approach is an augmented symplectic time integrator to decouple the position and momentum energy terms and facilitate their evolution.
Learning Physical Constraints with Neural Projections
A neural projection operator lies at the heart of our approach, composed of a lightweight network with an embedded recursive architecture that interactively enforces learned underpinning constraints and predicts the various governed behaviors of different physical systems.
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data
The ability of our model to predict long-term discontinuity from a short window of continuous training data is in general considered impossible using traditional machine learning approaches.
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian Dynamics
To tackle this challenge, we propose a novel learning-based framework, the Neural Vortex Method (NVM), which builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics to reconstruct the high-resolution Eulerian flow field in a physically-precise manner.
Symplectic Neural Networks in Taylor Series Form for Hamiltonian Systems
We propose an effective and lightweight learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian dynamic system based on sparse, short-term observations.
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics.
