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Found 34 papers, 16 papers with code
Fuzzy K-Means Clustering without Cluster Centroids
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis.
ByteEdit: Boost, Comply and Accelerate Generative Image Editing
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks.
SpatialTracker: Tracking Any 2D Pixels in 3D Space
Recovering dense and long-range pixel motion in videos is a challenging problem.
Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization
Nevertheless, existing multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix and often overlook the inter-view information.
Label Learning Method Based on Tensor Projection
Multi-view clustering method based on anchor graph has been widely concerned due to its high efficiency and effectiveness.
Doppelgangers: Learning to Disambiguate Images of Similar Structures
Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions.
Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis
The model pretrained on large-scale rs-fMRI data has been released to the public.
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification
In the personalized branch, we integrate vectorized demographic information (i. e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics.
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI
Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain and is widely used for brain disorder analysis. Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis.
Neural Scene Chronology
Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions.
Tracking Everything Everywhere All at Once
We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
Emergent Correspondence from Image Diffusion
We propose a simple strategy to extract this implicit knowledge out of diffusion networks as image features, namely DIffusion FeaTures (DIFT), and use them to establish correspondences between real images.
Rethinking k-means from manifold learning perspective
Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points.
Multi-View Clustering via Semi-non-negative Tensor Factorization
Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability.
DynIBaR: Neural Dynamic Image-Based Rendering
Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects, but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories.
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene.
Neural 3D Reconstruction in the Wild
We are witnessing an explosion of neural implicit representations in computer vision and graphics.
3D Moments from Near-Duplicate Photos
As output, we produce a video that smoothly interpolates the scene motion from the first photo to the second, while also producing camera motion with parallax that gives a heightened sense of 3D.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos
Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
Ultrasonic Backscatter Communication for Implantable Medical Devices
The tag backscatters the pulses based on the piezoelectric effect of a piezo transducer.
Neural Rays for Occlusion-aware Image-based Rendering
On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction.
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies
Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model.
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
IBRNet: Learning Multi-View Image-Based Rendering
Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.
Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated.
Designing Efficient Metal Contacts to Two-Dimensional Semiconductors MoSi$_2$N$_4$ and WSi$_2$N$_4$ Monolayers
Our findings reveal the potential of MoSi$_2$N$_4$ and WSi$_2$N$_4$ monolayers as a novel 2D material platform for designing high-performance and energy-efficient 2D nanodevices.
Mesoscale and Nanoscale Physics Materials Science Applied Physics Computational Physics
Hidden Footprints: Learning Contextual Walkability from 3D Human Trails
Predicting where people can walk in a scene is important for many tasks, including autonomous driving systems and human behavior analysis.
Learning Feature Descriptors using Camera Pose Supervision
Recent research on learned visual descriptors has shown promising improvements in correspondence estimation, a key component of many 3D vision tasks.
Generative Partial Multi-View Clustering
Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis.
Lifelong Spectral Clustering
As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks.
Visual Tactile Fusion Object Clustering
To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering.
Representative Task Self-selection for Flexible Clustered Lifelong Learning
Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e. g., knowledge library or deep network weights.
Multi-Image Semantic Matching by Mining Consistent Features
This work proposes a multi-image matching method to estimate semantic correspondences across multiple images.
