Search Results for author:
Found 15 papers, 3 papers with code
Starting engagement detection towards a companion robot using multimodal features
Within the context of engagement, non-verbal signals are used to communicate the intention of starting the interaction with a partner.
Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving
In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems.
Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data
Autonomous Vehicles navigating in urban areas have a need to understand and predict future pedestrian behavior for safer navigation.
Deep learning investigation for chess player attention prediction using eye-tracking and game data
The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces.
Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach
This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020 1.
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
In this work, a deep reinforcement learning based decision-making approach for high-level driving behavior is proposed for urban environments in the presence of pedestrians.
SocialInteractionGAN: Multi-person Interaction Sequence Generation
In this paper, we focus on a unimodal representation of interactions and propose to tackle interaction generation in a data-driven fashion.
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem.
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
Ranked #4 on
Unsupervised Instance Segmentation
on COCO val2017
Autoregressive GAN for Semantic Unconditional Head Motion Generation
In this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose.
Preliminary Study on SSCF-derived Polar Coordinate for ASR
According to the experimental results evaluated on the BRAF100 dataset, the polar coordinates achieved significantly higher accuracy than the angles in the mixed and cross-gender speech recognitions, demonstrating that these representations are superior at defining the acoustic trajectory of the speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation
Animating still face images with deep generative models using a speech input signal is an active research topic and has seen important recent progress.
Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features
Noticeably, our findings highlight that it is possible to reach this accuracy level with privacy-compliant features using only 5 frames uniformly distributed on the video.
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home.
