Search Results for author:
Found 8 papers, 3 papers with code
Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models
We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments, with the ultimate goal of facilitating the training of lightweight models for various downstream tasks.
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem.
Learning-Augmented Model-Based Planning for Visual Exploration
Our approach surpasses the greedy strategies by 2. 1% and the RL-based exploration methods by 8. 4% in terms of coverage.
Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene
Given the spatial and temporal consistency cues used for pixel level data association, we use a variant of contrastive learning to train a DCNN model for predicting semantic segmentation from RGB views in the target environment.
Uncertainty Aware Proposal Segmentation for Unknown Object Detection
Recent efforts in deploying Deep Neural Networks for object detection in real world applications, such as autonomous driving, assume that all relevant object classes have been observed during training.
Learning View and Target Invariant Visual Servoing for Navigation
The advances in deep reinforcement learning recently revived interest in data-driven learning based approaches to navigation.
Simultaneous Mapping and Target Driven Navigation
This work presents a modular architecture for simultaneous mapping and target driven navigation in indoors environments.
Video Question Answering via Attribute-Augmented Attention Network Learning
Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question.
