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Found 26 papers, 11 papers with code
Entropy Minimisation Framework for Event-based Vision Model Estimation
We propose a novel EMin framework for event-based vision model estimation.
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation
This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle (AV) training and testing.
Self-Supervised RGB-T Tracking with Cross-Input Consistency
We propose a novel cross-input consistency-based self-supervised training strategy based on the idea that tracking can be performed using different inputs.
Hearables: Ear EEG Based Driver Fatigue Detection
Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which are often cumbersome and impractical.
On Specifying for Trustworthiness
As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial.
Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision
We show that training our network solely with synthetic data and the proposed DA yields results competitive with models trained on real data.
Continuous Non-Invasive Eye Tracking In Intensive Care
The system's performance was measured in a replicated laboratory environment with healthy volunteers revealing an accuracy and precision that outperforms what is required while simultaneously being non-invasive and calibration-free The system was then deployed as part CONfuSED, a clinical feasibility study where we report aggregated data from 5 patients as well as the acceptability of the system to bedside nursing staff.
Disentangled Sequence Clustering for Human Intention Inference
To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner.
D2D: Keypoint Extraction with Describe to Detect Approach
In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations.
Support-weighted Adversarial Imitation Learning
To address the challenges, we propose Support-weighted Adversarial Imitation Learning (SAIL), a general framework that extends a given AIL algorithm with information derived from support estimation of the expert policies.
Structured Prediction for Conditional Meta-Learning
We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks.
RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments
We further incorporate our proposed RT-BENE baselines in the recently presented RT-GENE gaze estimation framework where it provides a real-time inference of the openness of the eyes.
Ranked #1 on
Blink estimation
on RT-BENE
Support-guided Adversarial Imitation Learning
We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
(iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals.
Real-Time Workload Classification during Driving using HyperNetworks
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics.
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
We first record a novel dataset of varied gaze and head pose images in a natural environment, addressing the issue of ground truth annotation by measuring head pose using a motion capture system and eye gaze using mobile eyetracking glasses.
Ranked #1 on
Gaze Estimation
on RT-GENE
Hierarchical Behavioral Repertoires with Unsupervised Descriptors
This paper presents a step in this direction with hierarchical behavioral repertoires that stack several behavioral repertoires to generate sophisticated behaviors.
Context-aware Deep Feature Compression for High-speed Visual Tracking
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers.
Ranked #15 on
Visual Object Tracking
on VOT2017/18
Attentional Correlation Filter Network for Adaptive Visual Tracking
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency.
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot.
Quality and Diversity Optimization: A Unifying Modular Framework
Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper.
MAGAN: Margin Adaptation for Generative Adversarial Networks
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function.
Kinematic Structure Correspondences via Hypergraph Matching
In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching.
Visual Tracking Using Attention-Modulated Disintegration and Integration
In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types.
Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information
The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points.
