22 citations · 73 across the 29 of their papers we have counts for
8 papers · 1 filter
Looking Into The Past: The Impact of Context on Detecting Deceptive Statements
Alex Clay, Ernesto Jimenez-Ruiz, Pranava Madhyastha
Deception detection typically considers messages in isolation. Previous works in claim verification and role detection have displayed that additional information such as gameplay h…
In-Context Learning Can Help Vision-Language Models Overcome Training Priors
Kun Wang, Xindi Wu, Sanghyuk Chun +2
Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence when input images violate those…
Depth as Time in One-Step Generative Models
Arnold Caleb Asiimwe, William Yang, Sanghyuk Chun +2
The recent wave of one-step generative models, which compress the multi-step trajectory of diffusion via either distillation or learned flow maps, has reached an inflection point w…
Addressable Memory for Video World Models
Xindi Wu, Sven Elflein, James Lucas +5
We study visual persistence in interactive video world models. These models rely on a Key-Value (KV) cache as a growing visual memory to carry forward previously generated frames.…
Personalized Generative Models for Contextual Debiasing
Xinran Liang, Esin Tureci, Prachi Sinha +3
Different visual patterns appear with different frequencies in the world: e.g., beach balls appear on sand more often than they do on a road. These statistics are reflected in visi…
Reinforced Fast Weights with Next-Sequence Prediction
Hee Seung Hwang, Xindi Wu, Sanghyuk Chun +1
Fast weight architectures offer a promising alternative to attention-based transformers for long-context modeling by maintaining constant memory overhead regardless of context leng…
GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation
Ye Zhu, Kaleb S. Newman, Johannes F. Lutzeyer +3
Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. In this work, we enhance the T2I dive…
Motion Attribution for Video Generation
Xindi Wu, Despoina Paschalidou, Jun Gao +5
Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a m…