Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories

Abstract: This paper presents Text2Traj2Text, a novel learning-by-synthesis framework for captioning possible contexts behind shopper's trajectory data in retail stores. Our work will impact various retail applications that need better customer understanding, such as targeted advertising and inventory management. The key idea is leveraging LLMs to synthesize a diverse and realistic collection of contextual captions as well as the corresponding movement trajectories on a store map. Despite learned from fully synthesized data, the captioning model can generalize well to trajectories/captions created by real human subjects. Our systematic evaluation confirmed the effectiveness of the proposed framework over competitive approaches in terms of ROUGE and BERT Score metrics.

• The paper presents a framework that synthesizes training data using LLMs to convert human trajectories into contextual captions for retail applications.
• It employs a two-phase process—Text2Traj for data synthesis and Traj2Text for model fine-tuning—to improve caption accuracy.
• Evaluations show enhanced performance over baselines and robust generalization to human-created trajectories and unseen store layouts.

Overview of the Text2Traj2Text Framework

The paper "Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories" introduces a novel framework designed for generating contextual captions from human movement data in retail environments. This enables automated understanding of customer behavior, which is crucial for enhancing applications such as targeted advertising and inventory management.

Central Idea and Methodology

The core of the Text2Traj2Text framework is the synthesis of training data and subsequent model fine-tuning. The framework utilizes LLMs to generate realistic, diverse contextual captions and corresponding synthetic movement trajectories. It then trains a LLM that takes these trajectories as inputs to produce relevant contextual captions.

Text2Traj: Data Synthesis

The Text2Traj phase involves:

1. Generating Contextual Captions: LLMs produce detailed descriptions encompassing various customer shopping intentions, such as preferences for product categories, quality over quantity decisions, and specific shopping habits.
2. Creating Action Plans: Contextual captions are converted into detailed action plans defining product categories and quantities.
3. Generating Item Lists: Action plans are expanded into specific lists of items the customer is likely to purchase or show interest in, taking into account predefined factors like purchase consideration.
4. Formulating Movement Trajectories: A trajectory planner generates realistic pathways on a store map that align with the detailed item lists, reflecting plausible customer trajectories.

Traj2Text: Model Fine-Tuning

The Traj2Text phase involves:

1. Input Translation: Realistic input sequences are generated, combining movement trajectories and items in contact to create textual representations.
2. Data Augmentation: Diverse training data are ensured through paraphrasing, enhancing the model's capability to generalize from synthesized data to real-world scenarios.

Experimental Results

The framework's effectiveness was validated through systematic evaluations under different settings. The evaluations aimed to answer:

1. The appropriateness of captions generated for synthesized trajectories.
2. The model's generalizability to human-created trajectories/captions.
3. The model's robustness to unseen store maps.

Evaluations on Synthesized Data

The trained model demonstrated high performance on synthetic data, outperforming baseline models and exhibiting a direct correlation between added paraphrases and improved metrics such as ROUGE and BERT Score.

Generalizability to Human Data

The model generalized effectively to real human-created trajectories/captions, maintaining robust performance with high semantic consistency. This highlighted its practical reliability in real-world applications.

Robustness to Unseen Maps

Minimal performance degradation on unseen store maps showcased the model’s potential applicability across different store layouts, further solidifying its practical utility.

Implications and Future Directions

The Text2Traj2Text framework presents significant advancements in customer behavior analysis through automated contextual captioning. Practical implications include:

• Enhanced Retail Applications: Improved targeted advertising and inventory management through better customer understanding.
• Scalability: Potential to scale across multiple stores with different layouts due to the model’s robust generalization capabilities.

Future research directions could explore:

• Long Trajectory Handling: Integrating techniques to handle exceedingly long shopping activities beyond current model capabilities.
• Hallucination Mitigation: Developing methods to prevent models from generating irrelevant or inappropriate captions.
• Opt-out Mechanisms: Ensuring user privacy by incorporating opt-out options for customers regarding the use of inferred contextual data.

Conclusion

The Text2Traj2Text framework demonstrates an innovative approach to automating human movement trajectory captioning in retail settings. It offers a scalable solution leveraging synthesized data and LLMs, potentially transforming customer behavior analysis and enhancing operational efficiencies in retail environments. The detailed experimental validations and promising results underline the framework's practical viability and pave the way for further research and development in automated human activity understanding.