1.2k citations · 2.7k across the 112 of their papers we have counts for
10 papers · 1 filter
Learning to Play Imperfect-Information Games by Imitating an Oracle Planner
Rinu Boney, Alexander Ilin, Juho Kannala +1
We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have…
FACEGAN: Facial Attribute Controllable rEenactment GAN
Soumya Tripathy, Juho Kannala, Esa Rahtu
The face reenactment is a popular facial animation method where the person's identity is taken from the source image and the facial motion from the driving image. Recent works have…
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning
Rinu Boney, Jussi Sainio, Mikko Kaivola +2
Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a mini…
Movement-induced Priors for Deep Stereo
Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala +1
We propose a method for fusing stereo disparity estimation with movement-induced prior information. Instead of independent inference frame-by-frame, we formulate the problem as a n…
Can You Trust Your Pose? Confidence Estimation in Visual Localization
Luca Ferranti, Xiaotian Li, Jani Boutellier +1
Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations. The research so far has fo…
Image Stylization for Robust Features
Iaroslav Melekhov, Gabriel J. Brostow, Juho Kannala +1
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization…
Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
Ari Viitala, Rinu Boney, Yi Zhao +2
We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has…
Data-Efficient Ranking Distillation for Image Retrieval
Zakaria Laskar, Juho Kannala
Recent advances in deep learning has lead to rapid developments in the field of image retrieval. However, the best performing architectures incur significant computational cost. Re…
Interpolation-based semi-supervised learning for object detection
Jisoo Jeong, Vikas Verma, Minsung Hyun +2
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection…
Sensor Networks TDOA Self-Calibration: 2D Complexity Analysis and Solutions
Luca Ferranti, Kalle Åström, Magnus Oskarsson +2
Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration. In this paper we consider…