3 papers
cs.CV2026
On the Reliability of Cue Conflict and Beyond
Pum Jun Kim, Seung-Ah Lee, Seongho Park +2
Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict benchmark has been influential in pro…
cs.CV2024
STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models
Pum Jun Kim, Seojun Kim, Jaejun Yoo
Image generative models have made significant progress in generating realistic and diverse images, supported by comprehensive guidance from various evaluation metrics. However, cur…
cs.LG2023
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
Pum Jun Kim, Yoojin Jang, Jisu Kim +1
We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, su…