Search Results for author:
Found 11 papers, 1 papers with code
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds
We investigate a variation of the 3D registration problem, named multi-model 3D registration.
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
POLAR3D: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation
POLAR3D is the set of digital assets comprising of rock/shadow labels and obj files associated with the digital twins of lunar terrain scenarios.
APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency
One underlying cause is that traditional diffusion models approximate Gaussian noise distribution by utilizing predictive noise, without fully accounting for the impact of inherent information within the input itself.
DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation.
FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects
We propose a vision-based system that learns to predict the potential motions of the parts of a variety of articulated objects to guide downstream motion planning of the system to articulate the objects.
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images.
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations.
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN
Recent consumer demand for home robots has accelerated performance of robotic grasping.
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
With speech input, if the user corrects only the names, the name recall rate improves to 64. 4%.
