MARVIS: Motion & Geometry Aware Real and Virtual Image Segmentation

1 code implementation • 14 Mar 2024 • Jiayi Wu, Xiaomin Lin, Shahriar Negahdaripour, Cornelia Fermüller, Yiannis Aloimonos

By creating realistic synthetic images that mimic the complexities of the water surface, we provide fine-grained training data for our network (MARVIS) to discern between real and virtual images effectively.

3D Reconstruction Autonomous Navigation +4

Detecting Olives with Synthetic or Real Data? Olive the Above

no code implementations • 16 Aug 2023 • Yianni Karabatis, Xiaomin Lin, Nitin J. Sanket, Michail G. Lagoudakis, Yiannis Aloimonos

When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.

Whale Detection Enhancement through Synthetic Satellite Images

1 code implementation • 15 Aug 2023 • Akshaj Gaur, Cheng Liu, Xiaomin Lin, Nare Karapetyan, Yiannis Aloimonos

With a number of marine populations in rapid decline, collecting and analyzing data about marine populations has become increasingly important to develop effective conservation policies for a wide range of marine animals, including whales.

SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water

1 code implementation • 26 Oct 2022 • Xiaomin Lin, Cheng Liu, Allen Pattillo, Miao Yu, Yiannis Aloimonous

To this end, we present a new benchmark suite, SeaDroneSim, that can be used to create photo-realistic aerial image datasets with the ground truth for segmentation masks of any given object.

OysterNet: Enhanced Oyster Detection Using Simulation

no code implementations • 16 Sep 2022 • Xiaomin Lin, Nitin J. Sanket, Nare Karapetyan, Yiannis Aloimonos

However, systems for accurate oyster detection require large datasets obtaining which is an expensive and labor-intensive task in underwater environments.

Following Instructions by Imagining and Reaching Visual Goals

no code implementations • 25 Jan 2020 • John Kanu, Eadom Dessalene, Xiaomin Lin, Cornelia Fermuller, Yiannis Aloimonos

While traditional methods for instruction-following typically assume prior linguistic and perceptual knowledge, many recent works in reinforcement learning (RL) have proposed learning policies end-to-end, typically by training neural networks to map joint representations of observations and instructions directly to actions.

Instruction Following Reinforcement Learning (RL)

Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games

no code implementations • 26 Jun 2018 • Xiaomin Lin, Stephen C. Adams, Peter A. Beling

Problem uNE-MIRL is similar to uCE-MIRL in total game value maximization, but it is assumed that the agents are playing a Nash equilibrium.

reinforcement-learning Reinforcement Learning (RL)

Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example

no code implementations • 26 Mar 2014 • Xiaomin Lin, Peter A. Beling, Randy Cogill

Comparison between IRL and MIRL is made in the context of an abstract soccer game, using both a game model in which the reward depends only on state and one in which it depends on both state and action.

reinforcement-learning Reinforcement Learning (RL)

Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games

no code implementations • 25 Mar 2014 • Xiaomin Lin, Peter A. Beling, Randy Cogill

The focus of this paper is a Bayesian framework for solving a class of problems termed multi-agent inverse reinforcement learning (MIRL).

reinforcement-learning Reinforcement Learning (RL) +1