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Found 10 papers, 2 papers with code
PI-FL: Personalized and Incentivized Federated Learning
Personalized FL has been widely used to cater to heterogeneity challenges with non-IID data.
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source.
A New Amharic Speech Emotion Dataset and Classification Benchmark
This was done by training two VGGb SER models on ASED, one using Mel-spectrograms and the other using MFCC.
Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks
We consider the task of estimating the 3D orientation of an object of known category given an image of the object and a bounding box around it.
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images
Since 3D pose is a continuous quantity, a natural formulation for this task is to solve a pose regression problem.
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding
We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level action primitives.
Ranked #5 on
Action Segmentation
on JIGSAWS
Stretching Domain Adaptation: How far is too far?
This work pushes the limit of unsupervised domain adaptation through an in-depth evaluation of several state of the art methods on benchmark datasets and the new dataset suite.
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization.
3D Pose Regression using Convolutional Neural Networks
3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding.
Two-Stream RNN/CNN for Action Recognition in 3D Videos
The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes.
