Search Results for author:
Found 9 papers, 3 papers with code
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering
This work explores the zero-shot capabilities of foundation models in Visual Question Answering (VQA) tasks.
Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge
This work presents an enhanced approach to generating scene graphs by incorporating a relationship hierarchy and commonsense knowledge.
Learning Generalizable Tool-use Skills through Trajectory Generation
We propose to learn a generative model of the tool-use trajectories as a sequence of tool point clouds, which generalizes to different tool shapes.
Instance-Agnostic Geometry and Contact Dynamics Learning
This work presents an instance-agnostic learning framework that fuses vision with dynamics to simultaneously learn shape, pose trajectories, and physical properties via the use of geometry as a shared representation.
HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation
In this work, we introduce Hybrid Actor-Critic Maps for Manipulation (HACMan), a reinforcement learning approach for 6D non-prehensile manipulation of objects using point cloud observations.
Hierarchical Relationships: A New Perspective to Enhance Scene Graph Generation
This paper presents a finding that leveraging the hierarchical structures among labels for relationships and objects can substantially improve the performance of scene graph generation systems.
Batch Active Learning from the Perspective of Sparse Approximation
Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators.
SABAL: Sparse Approximation-based Batch Active Learning
We propose a novel and general framework (i. e., SABAL) that formulates batch active learning as a sparse approximation problem.
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection
When performing classification tasks, raw high dimensional features often contain redundant information, and lead to increased computational complexity and overfitting.
