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Found 13 papers, 4 papers with code
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
To address this issue, we propose an efficient law article recommendation approach utilizing a Knowledge Graph (KG) and a Large Language Model (LLM).
End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning
Dynamic and interactive traffic scenarios pose significant challenges for autonomous driving systems.
CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
Existing camouflaged object detection~(COD) methods depend heavily on large-scale pixel-level annotations. However, acquiring such annotations is laborious due to the inherent camouflage characteristics of the objects. Semi-supervised learning offers a promising solution to this challenge. Yet, its application in COD is hindered by significant pseudo-label noise, both pixel-level and instance-level. We introduce CamoTeacher, a novel semi-supervised COD framework, utilizing Dual-Rotation Consistency Learning~(DRCL) to effectively address these noise issues. Specifically, DRCL minimizes pseudo-label noise by leveraging rotation views' consistency in pixel-level and instance-level. First, it employs Pixel-wise Consistency Learning~(PCL) to deal with pixel-level noise by reweighting the different parts within the pseudo-label. Second, Instance-wise Consistency Learning~(ICL) is used to adjust weights for pseudo-labels, which handles instance-level noise. Extensive experiments on four COD benchmark datasets demonstrate that the proposed CamoTeacher not only achieves state-of-the-art compared with semi-supervised learning methods, but also rivals established fully-supervised learning methods. Our code will be available soon.
HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios
This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios.
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships.
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems.
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings.
Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops
We introduce a hybrid partitioning method that uses both target set partitioning (TSP) and backreachable set partitioning (BRSP) to overcome a lower bound on estimation error that is present when using BRSP.
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
The performance of PEARL$^+$ is validated by solving three safety-critical problems related to robots and AVs, including two MuJoCo benchmark problems.
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions.
Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera.
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function.
