arXiv subjects

Yuntian Wu

Publications and source records attributed to Yuntian Wu.

2 recordsLinked to original sources

arXiv2026

Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series

Generative models have changed how machine learning represents complex data distributions, especially in language and vision, yet many real-world systems are observed instead as continuous, high-dimensional, and noisy sensor time series. Existing generative modeling of sensor data, however, remains fragmented across modalities, datasets, and task formulations, limiting a systematic understanding of when, how, and why generative models succeed or fail in real-world settings. To address this gap, we introduce SensorGen, a large-scale study of sensor-signal generation spanning 14 settings across 4 domains, 7 datasets, and 12 signal modalities. Leveraging SensorGen, we systematically evaluate generative models from five major families and uncover three key findings: (1) flow-matching models provide strong overall performance across most settings; (2) signal properties matter, with demographic covariates improving longitudinal generation and time-frequency modeling improving high-frequency signal generation; and (3) generated signals have practical utility beyond visual realism, with scaling improving generation quality and synthetic data improving downstream performance. Together, SensorGen establishes a broader understanding of design choices, evaluation protocols, and failure modes in real-world sensor data generation.

cs.LG↗

arXiv2025

TATTOO: Training-free AesTheTic-aware Outfit recOmmendation

The global fashion e-commerce market relies significantly on intelligent and aesthetic-aware outfit-completion tools to promote sales. While previous studies have approached the problem of fashion outfit-completion and compatible-item retrieval, most of them require expensive, task-specific training on large-scale labeled data, and no effort is made to guide outfit recommendation with explicit human aesthetics. In the era of Multimodal Large Language Models (MLLMs), we show that the conventional training-based pipeline could be streamlined to a training-free paradigm, with better recommendation scores and enhanced aesthetic awareness. We achieve this with TATTOO, a Training-free AesTheTic-aware Outfit recommendation approach. It first generates a target-item description using MLLMs, followed by an aesthetic chain-of-thought used to distill the images into a structured aesthetic profile including color, style, occasion, season, material, and balance. By fusing the visual summary of the outfit with the textual description and aesthetics vectors using a dynamic entropy-gated mechanism, candidate items can be represented in a shared embedding space and be ranked accordingly. Experiments on a real-world evaluation set Aesthetic-100 show that TATTOO achieves state-of-the-art performance compared with existing training-based methods. Another standard Polyvore dataset is also used to measure the advanced zero-shot retrieval capability of our training-free method.

cs.CV↗