Authors: Y Ba, MV Mancenido, EK Chiou, R Pan
Year: 2025
Published in: Behavior Research Methods, 2025 - Springer
Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University
Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research
Discipline: Computer Science, Artificial Intelligence, LLM
The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.
Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.
Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.
Citations: 2
Authors: Y Wu, C Huang, F Yang, F Wang
Year: 2025
Published in: ArXiv
Institution: Nvidia, National Taiwan University
Research Area: Motion Customization of Text-to-Video Diffusion Models
Discipline: Computer Vision, Pattern Recognition
MotionMatcher is a novel framework for motion customization in text-to-video (T2V) diffusion models, using high-level spatio-temporal motion features rather than pixel-level objectives, achieving state-of-the-art performance.
Methods: Fine-tuning pre-trained text-to-video diffusion models at feature level by comparing spatio-temporal motion features instead of pixel-level objectives to address motion customization from reference videos.
Key Findings: Efficacy of motion customization in T2V models; ability to accurately capture complex motion and avoid content leakage from reference videos.