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Found 17 papers, 9 papers with code
SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting
In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter v2, which seeks to find a better synergy between text detection and recognition.
CAT: Closed-loop Adversarial Training for Safe End-to-End Driving
Driving safety is a top priority for autonomous vehicles.
Guarded Policy Optimization with Imperfect Online Demonstrations
Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, providing safety guarantee and exploration guidance.
Human-AI Shared Control via Policy Dissection
Inspired by the neuroscience approach to investigate the motor cortex in primates, we develop a simple yet effective frequency-based approach called \textit{Policy Dissection} to align the intermediate representation of the learned neural controller with the kinematic attributes of the agent behavior.
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
Specifically, we train an inverse dynamic model with a small amount of labeled data and use it to predict action labels for all the YouTube video frames.
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition.
Ranked #3 on
Text Spotting
on Inverse-Text
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
HACO can train agents to drive in unseen traffic scenarios with a handful of human intervention budget and achieve high safety and generalizability, outperforming both reinforcement learning and imitation learning baselines with a large margin.
Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization
Self-Driven Particles (SDP) describe a category of multi-agent systems common in everyday life, such as flocking birds and traffic flows.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Safe Driving via Expert Guided Policy Optimization
Offline RL technique is further used to learn from the partial demonstration generated by the expert.
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic.
Safe Exploration by Solving Early Terminated MDP
Safe exploration is crucial for the real-world application of reinforcement learning (RL).
Improving the Generalization of End-to-End Driving through Procedural Generation
We validate that training with the increasing number of procedurally generated scenes significantly improves the generalization of the agent across scenarios of different traffic densities and road networks.
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
Conventional Reinforcement Learning (RL) algorithms usually have one single agent learning to solve the task independently.
Novel Policy Seeking with Constrained Optimization
In problem-solving, we humans can come up with multiple novel solutions to the same problem.
Learning with Social Influence through Interior Policy Differentiation
In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers.
AXNet: ApproXimate computing using an end-to-end trainable neural network
To guarantee the approximation quality, existing works deploy two neural networks (NNs), e. g., an approximator and a predictor.
Approximate Random Dropout
In this paper, we propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and predefined patterns to eliminate the unnecessary computation and data access.
