71 citations · 416 across the 50 of their papers we have counts for
52 papers
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
Gaurav Patel, Jun Fang, Greg Ver Steeg +2
Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to generate harmful or undesire…
Binary Latent Diffusion
Ze Wang, Jiang Wang, Zicheng Liu +1
In this paper, we show that a binary latent space can be explored for compact yet expressive image representations. We model the bi-directional mappings between an image and the co…
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation
Gaurav Patel, Konda Reddy Mopuri, Qiang Qiu
Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neur…
Energy-Inspired Self-Supervised Pretraining for Vision Models
Ze Wang, Jiang Wang, Zicheng Liu +1
Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effec…
Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition
Gaurav Patel, Jan Allebach, Qiang Qiu
This paper looks at semi-supervised learning (SSL) for image-based text recognition. One of the most popular SSL approaches is pseudo-labeling (PL). PL approaches assign labels to…
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu +1
In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank chara…
Exploiting a Zoo of Checkpoints for Unseen Tasks
Jiaji Huang, Qiang Qiu, Kenneth Church
There are so many models in the literature that it is difficult for practitioners to decide which combinations are likely to be effective for a new task. This paper attempts to add…
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
Atul Sharma, Wei Chen, Joshua Zhao +3
Federated learning---multi-party, distributed learning in a decentralized environment---is vulnerable to model poisoning attacks, even more so than centralized learning approaches.…
Adaptive Convolutions with Per-pixel Dynamic Filter Atom
Ze Wang, Zichen Miao, Jun Hu +1
Applying feature dependent network weights have been proved to be effective in many fields. However, in practice, restricted by the enormous size of model parameters and memory foo…
Cirrus: A Long-range Bi-pattern LiDAR Dataset
Ze Wang, Sihao Ding, Ying Li +7
In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for autonomous driving tasks such as 3D object detection, critical to highway driving and timel…
Using Text to Teach Image Retrieval
Haoyu Dong, Ze Wang, Qiang Qiu +1
Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space. Building on the concept of image manifold, we first propose to…
Learning to Learn Variational Semantic Memory
Xiantong Zhen, Yingjun Du, Huan Xiong +3
In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores…
ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro +1
Convolutional Neural Networks (CNNs) are known to be significantly over-parametrized, and difficult to interpret, train and adapt. In this paper, we introduce a structural regulari…
Graph Convolution with Low-rank Learnable Local Filters
Xiuyuan Cheng, Zichen Miao, Qiang Qiu
Geometric variations like rotation, scaling, and viewpoint changes pose a significant challenge to visual understanding. One common solution is to directly model certain intrinsic…
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Yingjun Du, Jun Xu, Huan Xiong +4
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We…
Low to High Dimensional Modality Hallucination using Aggregated Fields of View
Kausic Gunasekar, Qiang Qiu, Yezhou Yang
Real-world robotics systems deal with data from a multitude of modalities, especially for tasks such as navigation and recognition. The performance of those systems can drastically…
Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method
J. Matias Di Martino, Fernando Suzacq, Mauricio Delbracio +2
Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we sho…
SalGaze: Personalizing Gaze Estimation Using Visual Saliency
Zhuoqing Chang, Matias Di Martino, Qiang Qiu +2
Traditional gaze estimation methods typically require explicit user calibration to achieve high accuracy. This process is cumbersome and recalibration is often required when there…
Range Adaptation for 3D Object Detection in LiDAR
Ze Wang, Sihao Ding, Ying Li +5
LiDAR-based 3D object detection plays a crucial role in modern autonomous driving systems. LiDAR data often exhibit severe changes in properties across different observation ranges…
Single-shot 3D shape reconstruction using deep convolutional neural networks
Hieu Nguyen, Hui Li, Qiang Qiu +2
A robust single-shot 3D shape reconstruction technique integrating the fringe projection profilometry (FPP) technique with the deep convolutional neural networks (CNNs) is proposed…
Stochastic Conditional Generative Networks with Basis Decomposition
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro +1
While generative adversarial networks (GANs) have revolutionized machine learning, a number of open questions remain to fully understand them and exploit their power. One of these…
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro +1
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Netw…
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Wei Zhu, Qiang Qiu, Robert Calderbank +2
Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when deali…
In Defense of Single-column Networks for Crowd Counting
Ze Wang, Zehao Xiao, Kai Xie +3
Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban pl…