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Found 16 papers, 4 papers with code
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks
We introduce a large-scale annotated mechanical components benchmark for classification and retrieval tasks named MechanicalComponents Benchmark (MCB): a large-scale dataset of 3D objects of mechanical components.
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition.
AdamsFormer for Spatial Action Localization in the Future
SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately.
InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition
Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention.
Ranked #7 on
Skeleton Based Action Recognition
on N-UCLA
RitzNet: A Deep Neural Network Method for Linear Stress Problems
This paper presents RitzNet, an unsupervised learning method which takes any point in the computation domain as input, and learns a neural network model to output its corresponding function value satisfying the underlying governing PDEs.
Egocentric View Hand Action Recognition by Leveraging Hand Surface and Hand Grasp Type
We introduce a multi-stage framework that uses mean curvature on a hand surface and focuses on learning interaction between hand and object by analyzing hand grasp type for hand action recognition in egocentric videos.
DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation
Traditional grid/neighbor-based static pooling has become a constraint for point cloud geometry analysis.
Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution
The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks.
Latent Transformations for Object View Points Synthesis
We propose a fully-convolutional conditional generative model, the latent transformation neural network (LTNN), capable of view synthesis using a light-weight neural network suited for real-time applications.
3D Object Classification via Spherical Projections
Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection.
Robust Hand Pose Estimation During the Interaction With an Unknown Object
Our main insight is that the shape of an object causes a configuration of the hand in the form of a hand grasp.
Learning Hand Articulations by Hallucinating Heat Distribution
As an additional modality to depth data, we present a function of geometric properties on the surface of the hand described by heat diffusion.
SurfNet: Generating 3D shape surfaces using deep residual networks
3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface.
Deconvolving Feedback Loops in Recommender Systems
Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences.
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features
Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to to estimate the unknown pose parameters of the input feature vector.
A Collaborative Filtering Approach to Real-Time Hand Pose Estimation
Specifically, we recast the hand pose estimation problem as the cold-start problem for a new user with unknown item ratings in a recommender system.
