Search Results for author:
Found 24 papers, 9 papers with code
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning.
Estimation of control area in badminton doubles with pose information from top and back view drone videos
In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance.
Learning to Predict Navigational Patterns from Partial Observations
We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field.
Ranked #1 on
Lane Detection
on nuScenes
Predictive World Models from Real-World Partial Observations
By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications.
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
We also revealed that the machine learning model detects faults according to the rules of race walking.
RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems.
Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution.
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates.
Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios
Evaluation of intervention in a multi-agent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.
Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision
A formal road lane network model is presented and proves that any structured road scene can be represented by a directed acyclic graph of at most depth three while retaining the notion of intersection regions, and that this is the most compressed representation.
Anomalous Sound Detection Using a Binary Classification Model and Class Centroids
Our results showed that multi-task learning using binary classification and metric learning to consider the distance from each class centroid in the feature space is effective, and performance can be significantly improved by using even a small amount of anomalous data during training.
Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
And this provides us a great opportunity to think about how shall these data be organized and exploited.
Policy learning with partial observation and mechanical constraints for multi-person modeling
Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields.
Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform
In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware.
LIBRE: The Multiple 3D LiDAR Dataset
In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations.
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection
The proposed method is publicly available.
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience.
Robotics Systems and Control Systems and Control
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer
The best result, with a 0. 937 AUC score, was obtained with the proposed network.
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation
This paper deals with a multichannel audio source separation problem under underdetermined conditions.
Back-Translation-Style Data Augmentation for End-to-End ASR
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-Head Decoder for End-to-End Speech Recognition
This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model.
Causal analysis of task completion errors in spoken music retrieval interactions
In this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs.
