C2INet: Continual Causal Intervention with memorizable priors for incremental trajectory forecasting
Authors:
- Xiaohe LiXLXiaohe Li
- Kaixin ZhangKZKaixin Zhang
- Zide FanZFZide Fan
- Fangli MouFMFangli Mou
- Leilei LinLLLeilei Lin
- Ying GengYGYing Geng
- Yingyan HouYHYingyan Hou
- 22 April 2026
- journal article
- Published by Elsevier in Pattern Recognition
- Vol. 179
Abstract
No abstract availableFunding Information
- Chinese Academy of Sciences (XDA0310502)
This publication has 23 references indexed in Scilit:
- Producing and Leveraging Online Map Uncertainty in Trajectory Prediction2024
- MFAN: Mixing Feature Attention Network for trajectory predictionPattern Recognition, 2023
- FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-Tail Trajectory Prediction2023
- Continual learning-based trajectory prediction with memory augmented networksKnowledge-Based Systems, 2022
- GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning2022
- Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective2022
- Multi-Agent Tensor Fusion for Contextual Trajectory Prediction2019
- Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks2018
- Overcoming catastrophic forgetting in neural networksProceedings of the National Academy of Sciences, 2017
- Social LSTM: Human Trajectory Prediction in Crowded Spaces2016