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Found 11 papers, 6 papers with code
Hybrid-Prediction Integrated Planning for Autonomous Driving
First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-wise perceptions.
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition
With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention.
PGDQN: Preference-Guided Deep Q-Network
Stochastic exploration is the key to the success of the Deep Q-network (DQN) algorithm.
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation
Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue.
Learning to Weight Samples for Dynamic Early-exiting Networks
Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers.
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning
Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL.
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
We theoretically prove that the policy improvement theorem holds for the preference-guided $\epsilon$-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values.
Glance and Focus Networks for Dynamic Visual Recognition
Spatial redundancy widely exists in visual recognition tasks, i. e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand.
Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving
A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm.
A Marker-free Head Tracker Using Vision-based Head Pose Estimation with Adaptive Kalman Filter
To handle the error of the head pose estimation model, this paper proposes an adaptive Kalman Filter.
Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning
Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions.
Robotics
