Search Results for author:
Found 12 papers, 4 papers with code
Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations
Can conversational videos captured from multiple egocentric viewpoints reveal the map of a scene in a cost-efficient way?
Retrospectives on the Embodied AI Workshop
We present a retrospective on the state of Embodied AI research.
Few-Shot Audio-Visual Learning of Environment Acoustics
Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics.
Active Audio-Visual Separation of Dynamic Sound Sources
We explore active audio-visual separation for dynamic sound sources, where an embodied agent moves intelligently in a 3D environment to continuously isolate the time-varying audio stream being emitted by an object of interest.
Move2Hear: Active Audio-Visual Source Separation
We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment.
Model Agnostic Answer Reranking System for Adversarial Question Answering
While numerous methods have been proposed as defenses against adversarial examples in question answering (QA), these techniques are often model specific, require retraining of the model, and give only marginal improvements in performance over vanilla models.
Learning to Set Waypoints for Audio-Visual Navigation
In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e. g., a phone ringing in another room).
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition.
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge.
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset
Recognition of defects in concrete infrastructure, especially in bridges, is a costly and time consuming crucial first step in the assessment of the structural integrity.
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures
We characterize convolutional neural networks with respect to the relative amount of features per layer.
Handwritten Digit Recognition by Elastic Matching
A simple model of MNIST handwritten digit recognition is presented here.
