Search Results for author:
Found 7 papers, 4 papers with code
Continual Learning of Nonlinear Independent Representations
Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset.
Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information
Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild
Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing photorealistic views from multi-view images of static scenes, but face challenges in dynamic, real-world environments with distractors like moving objects, shadows, and lighting changes.
A Conditional Independence Test in the Presence of Discretization
Applying existing test methods to the observations of $X_1$, $\tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence of variables $X_1$, $X_2$ and $X_3$.
Subspace Identification for Multi-Source Domain Adaptation
To mitigate the need for these strict assumptions, we propose a subspace identification theory that guarantees the disentanglement of domain-invariant and domain-specific variables under less restrictive constraints regarding domain numbers and transformation properties, thereby facilitating domain adaptation by minimizing the impact of domain shifts on invariant variables.
Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach
However, localizing e. g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes.
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
The proposed framework infers task failures by evaluating the individual prediction, across multiple visual perception tasks for different regions in an image.
