no code implementations • 17 Nov 2023 • Yimeng Li, Navid Rajabi, Sulabh Shrestha, Md Alimoor Reza, Jana Kosecka
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.
1 code implementation • 17 Dec 2022 • Yimeng Li, Arnab Debnath, Gregory J. Stein, Jana Kosecka
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.
no code implementations • 15 Nov 2022 • Yimeng Li, Arnab Debnath, Gregory Stein, Jana Kosecka
Our approach surpasses the greedy strategies by 2. 1% and the RL-based exploration methods by 8. 4% in terms of coverage.
no code implementations • 4 Oct 2022 • Sulabh Shrestha, Yimeng Li, Jana Kosecka
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.
no code implementations • 25 Nov 2021 • Yimeng Li, Jana Kosecka
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.
1 code implementation • 4 Mar 2020 • Yimeng Li, Jana Kosecka
The advances in deep reinforcement learning recently revived interest in data-driven learning based approaches to navigation.
2 code implementations • 18 Nov 2019 • Georgios Georgakis, Yimeng Li, Jana Kosecka
This work presents a modular architecture for simultaneous mapping and target driven navigation in indoors environments.
no code implementations • 20 Jul 2017 • Yunan Ye, Zhou Zhao, Yimeng Li, Long Chen, Jun Xiao, Yueting Zhuang
Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question.