Discussing the article: "Neural Networks in Trading: Unified Trajectory Generation Model (UniTraj)"
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Check out the new article: Neural Networks in Trading: Unified Trajectory Generation Model (UniTraj).
Understanding agent behavior is important in many different areas, but most methods focus on just one of the tasks (understanding, noise removal, or prediction), which reduces their effectiveness in real-world scenarios. In this article, we will get acquainted with a model that can adapt to solving various problems.
The analysis of multi-agent behavior plays a crucial role in various domains, including finance, autonomous driving, and surveillance systems. Understanding agent actions requires solving several key tasks: object tracking, identification, trajectory modeling, and action recognition. Among these, trajectory modeling is particularly significant in the process of analyzing agent movements. Despite the complexities associated with environmental dynamics and subtle agent interactions, significant progress has recently been made in addressing this problem. The main achievements are concentrated in three key areas: trajectory prediction, missing data recovery, and spatiotemporal modeling.
However, most approaches remain specialized for specific tasks. This makes them difficult to generalize to other problems. Some tasks require both forward and backward spatiotemporal dependencies, which are often overlooked in prediction-oriented models. While some algorithms have successfully addressed the conditional calculation of multi-agent trajectories, they frequently fail to account for future trajectories. This limitation reduces their practical applicability in fully understanding movement, where predicting future trajectories is essential for planning subsequent actions rather than merely reconstructing past trajectories.
The paper "Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent" presents the Unified TrajectoryGeneration (UniTraj) model, a universal framework integrating various trajectory-related tasks into a unified scheme. Specifically, the authors consolidate different types of input data into a single unified format: an arbitrary incomplete trajectory with a mask indicating the visibility of each agent at each time step. The model processes all task inputs uniformly as masked trajectories, aiming to generate complete trajectories based on incomplete ones.
To model spatiotemporal dependencies in different trajectory representations, the authors introduce the Ghost Spatial Masking(GSM) module, embedded within a Transformer-based encoder. Using the capabilities of recent popular state-space models (SSM), particularly the Mamba model, the authors adapt and enhance it into a bidirectional temporal encoder Mamba for long-term multi-agent trajectory generation. Additionally, they propose a simple yet effective Bidirectional Temporal Scaled (BTS) module, which comprehensively scans trajectories while preserving temporal relationships within the sequence. The experimental results presented in the paper confirm the robust and exceptional performance of the proposed method.
Author: Dmitriy Gizlyk