Search Results for author:
Found 8 papers, 2 papers with code
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation
We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training scaled DiTs ranging from 0. 3B upto 8B parameters on datasets up to 600M images.
Robot Localization and Mapping Final Report -- Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
For VO, we can use these deep networks to extract depth and pose estimates using these high level features.
CoCoNets: Continuous Contrastive 3D Scene Representations
This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection.
HyperDynamics: Meta-Learning Object and Agent Dynamics with Hypernetworks
We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
HyperDynamics: Generating Expert Dynamics Models by Observation
We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators
Object motion predictions are computed by a graph neural network that operates over the object features extracted from the 3D neural scene representation.
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification.
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations
We can compare the 3D feature maps of two objects by searching alignment across scales and 3D rotations, and, as a result of the operation, we can estimate pose and scale changes without the need for 3D pose annotations.
