2 citations · 2 across the 8 of their papers we have counts for
8 papers
Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion
Mang Ning, Mingxiao Li, Le Zhang +4
In this paper, we study the diffusability (learnability) of variational autoencoders (VAEs) in latent diffusion. We first derive distribution-free bounds on the maximum achievable…
DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space
Mang Ning, Mingxiao Li, Jianlin Su +6
This paper explores image modeling from the frequency space and introduces DCTdiff, an end-to-end diffusion generative paradigm that efficiently models images in the discrete cosin…
Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
Mang Ning, Albert Ali Salah, Itir Onal Ertugrul
Facial Action Unit (AU) detection has gained significant attention as it enables the breakdown of complex facial expressions into individual muscle movements. In this paper, we rev…
Elucidating the Exposure Bias in Diffusion Models
Mang Ning, Mingxiao Li, Jianlin Su +2
Diffusion models have demonstrated impressive generative capabilities, but their \textit{exposure bias} problem, described as the input mismatch between training and sampling, lack…
Fully-attentive and interpretable: vision and video vision transformers for pain detection
Giacomo Fiorentini, Itir Onal Ertugrul, Albert Ali Salah
Pain is a serious and costly issue globally, but to be treated, it must first be detected. Vision transformers are a top-performing architecture in computer vision, with little res…
Synthetic Expressions are Better Than Real for Learning to Detect Facial Actions
Koichiro Niinuma, Itir Onal Ertugrul, Jeffrey F Cohn +1
Critical obstacles in training classifiers to detect facial actions are the limited sizes of annotated video databases and the relatively low frequencies of occurrence of many acti…
Modeling Brain Networks with Artificial Neural Networks
Baran Baris Kivilcim, Itir Onal Ertugrul, Fatos T. Yarman Vural
In this study, we propose a neural network approach to capture the functional connectivities among anatomic brain regions. The suggested approach estimates a set of brain networks,…
Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification
Itir Onal Ertugrul, Mete Ozay, Fatos T. Yarman Vural
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of…