AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction
Accurate prediction of pedestrians' future motions is critical for intelligent driving systems. Developing models for this task requires rich datasets containing diverse sets of samples. However, the existing naturalistic trajectory prediction datasets are generally imbalanced in favor of simpler samples and lack challenging scenarios. Such a long-tail effect causes prediction models to underperform on the tail portion of the data distribution containing safety-critical scenarios. Previous methods tackle the long-tail problem using methods such as contrastive learning and class-conditioned hypernetworks. These approaches, however, are not modular and cannot be applied to many machine learning architectures. In this work, we propose a modular model-agnostic framework for trajectory prediction that leverages a specialized mixture of experts. In our approach, each expert is trained with a specialized skill with respect to a particular part of the data. To produce predictions, we utilise a router network that selects the best expert by generating relative confidence scores. We conduct experimentation on common pedestrian trajectory prediction datasets and show that our method improves performance on long-tail scenarios. We further conduct ablation studies to highlight the contribution of different proposed components.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMixture-of-ExpertsPedestrian Trajectory PredictionPredictionTrajectory PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LPT++: Efficient Training on Mixture of Long-tailed Experts
We introduce LPT++, a comprehensive framework for long-tailed classification that combines parameter-efficient fine-tuning (PEFT) with a learnable model ensemble. LPT++ enhances frozen Vision Transformers (ViTs) through …
Mixture-of-Expertsparameter-efficient fine-tuningPreserving Long-Tailed Expert Information in Mixture-of-Experts Tuning
Despite MoE models leading many benchmarks, supervised fine-tuning (SFT) for the MoE architectures remains difficult because its router layers are fragile. Methods such as DenseMixer and ESFT mitigate router collapse wit…
Mathematical ReasoningLong-Tailed Distribution-Aware Router For Mixture-of-Experts in Large Vision-Language Model
The mixture-of-experts (MoE) architecture, which replaces dense networks with sparse ones, has attracted significant attention in large vision-language models (LVLMs) for achieving comparable performance while activating…
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heavily biased toward the majority classes. …
Mixture-of-ExpertsTransfer LearningToward Inference-optimal Mixture-of-Expert Large Language Models
Mixture-of-Expert (MoE) based large language models (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown great promise in scaling model size without suffering from the quadratic growth of training cost of dens…