3 citations · 5 across the 15 of their papers we have counts for
15 papers
SEDTalker: Emotion-Aware 3D Facial Animation Using Frame-Level Speech Emotion Diarization
Farzaneh Jafari, Stefano Berretti, Anup Basu
We introduce SEDTalker, an emotion-aware framework for speech-driven 3D facial animation that leverages frame-level speech emotion diarization to achieve fine-grained expressive co…
Exploring Image Generation via Mutually Exclusive Probability Spaces and Local Correlation Hypothesis
Chenqiu Zhao, Anup Basu
A common assumption in probabilistic generative models for image generation is that learning the global data distribution suffices to generate novel images via sampling. We investi…
Accelerating Inference of Networks in the Frequency Domain
Chenqiu Zhao, Guanfang Dong, Anup Basu
It has been demonstrated that networks' parameters can be significantly reduced in the frequency domain with a very small decrease in accuracy. However, given the cost of frequency…
Deep Clustering via Distribution Learning
Guanfang Dong, Zijie Tan, Chenqiu Zhao +1
Distribution learning finds probability density functions from a set of data samples, whereas clustering aims to group similar data points to form clusters. Although there are deep…
JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model
Farzaneh Jafari, Stefano Berretti, Anup Basu
In recent years, the talking head generation has become a focal point for researchers. Considerable effort is being made to refine lip-sync motion, capture expressive facial expres…
Medical Image Denosing via Explainable AI Feature Preserving Loss
Guanfang Dong, Anup Basu
Denoising algorithms play a crucial role in medical image processing and analysis. However, classical denoising algorithms often ignore explanatory and critical medical features pr…
Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module
Zijie Tan, Guanfang Dong, Chenqiu Zhao +1
Although Convolutional Neural Networks (CNNs) have achieved promising results in image classification, they still are vulnerable to affine transformations including rotation, trans…
Bridging Distribution Learning and Image Clustering in High-dimensional Space
Guanfang Dong, Chenqiu Zhao, Anup Basu
Distribution learning focuses on learning the probability density function from a set of data samples. In contrast, clustering aims to group similar objects together in an unsuperv…
Learning Distributions via Monte-Carlo Marginalization
Chenqiu Zhao, Guanfang Dong, Anup Basu
We propose a novel method to learn intractable distributions from their samples. The main idea is to use a parametric distribution model, such as a Gaussian Mixture Model (GMM), to…
Is Deep Learning Network Necessary for Image Generation?
Chenqiu Zhao, Guanfang Dong, Anup Basu
Recently, images are considered samples from a high-dimensional distribution, and deep learning has become almost synonymous with image generation. However, is a deep learning netw…
Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks
Chenqiu Zhao, Guanfang Dong, Shupei Zhang +2
Convolutional neural networks have demonstrated impressive results in many computer vision tasks. However, the increasing size of these networks raises concerns about the informati…
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
Guanfang Dong, Chenqiu Zhao, Xichen Pan +1
The goal of moving object segmentation is separating moving objects from stationary backgrounds in videos. One major challenge in this problem is how to develop a universal model f…
SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
Zihan Wang, Ruimin Chen, Mengxuan Liu +2
We propose a method SPGNet for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning. In this method, the 2D-to-3D-lifting network predicts t…
Real-time Street Human Motion Capture
Yanquan Chen, Fei Yang, Tianyu Lang +2
In recent years, motion capture technology using computers has developed rapidly. Because of its high efficiency and excellent performance, it replaces many traditional methods and…
Universal Background Subtraction based on Arithmetic Distribution Neural Network
Chenqiu Zhao, Kangkang Hu, Anup Basu
We propose a universal background subtraction framework based on the Arithmetic Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our ADNN mo…