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Found 11 papers, 8 papers with code
Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
By incorporating the guidance from the teacher agent, the student agent can distill the prior knowledge of the LLM into its own model.
SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM
In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism.
Enhancing Mapless Trajectory Prediction through Knowledge Distillation
Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents.
Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
In this paper, we explore how to enable intelligent cost-effective interactions between the agent and an LLM.
FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction.
Towards Commonsense Knowledge based Fuzzy Systems for Supporting Size-Related Fine-Grained Object Detection
To achieve accurate fine-grained detection, one needs to employ a large enough model and a vast amount of data annotations.
SwinLSTM: Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM
In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism.
Ranked #7 on
Video Prediction
on Moving MNIST
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints.
Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding
Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories.
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction.
SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism.
