b'Qiang Qiu'

research

∙ 04/10/2023

Binary Latent Diffusion

In this paper, we show that a binary latent space can be explored for co...

0 Ze Wang, et al. ∙

research

∙ 02/28/2023

Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation

Data-free Knowledge Distillation (DFKD) has gained popularity recently, ...

0 Gaurav patel, et al. ∙

research

∙ 02/02/2023

Energy-Inspired Self-Supervised Pretraining for Vision Models

Motivated by the fact that forward and backward passes of a deep network...

0 Ze Wang, et al. ∙

research

∙ 08/31/2022

Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition

This paper looks at semi-supervised learning (SSL) for image-based text ...

0 Gaurav patel, et al. ∙

research

∙ 02/01/2022

Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?

In this paper, we question the rationale behind propagating large number...

0 Sheikh Shams Azam, et al. ∙

research

∙ 11/05/2021

Exploiting a Zoo of Checkpoints for Unseen Tasks

There are so many models in the literature that it is difficult for prac...

0 Jiaji Huang, et al. ∙

research

∙ 10/19/2021

TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks

Federated learning—multi-party, distributed learning in a decentralized ...

9 Atul Sharma, et al. ∙

research

∙ 08/17/2021

Adaptive Convolutions with Per-pixel Dynamic Filter Atom

Applying feature dependent network weights have been proved to be effect...

2 Ze Wang, et al. ∙

research

∙ 12/05/2020

Cirrus: A Long-range Bi-pattern LiDAR Dataset

In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR pu...

0 Ze Wang, et al. ∙

research

∙ 11/19/2020

Using Text to Teach Image Retrieval

Image retrieval relies heavily on the quality of the data modeling and t...

17 Haoyu Dong, et al. ∙

research

∙ 10/20/2020

Learning to Learn Variational Semantic Memory

In this paper, we introduce variational semantic memory into meta-learni...

4 Xiantong Zhen, et al. ∙

research

∙ 09/04/2020

ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution

Convolutional Neural Networks (CNNs) are known to be significantly over-...

15 Ze Wang, et al. ∙

research

∙ 08/04/2020

Graph Neural Networks with Low-rank Learnable Local Filters

For the classification of graph data consisting of features sampled on a...

0 Xiuyuan Cheng, et al. ∙

research

∙ 07/15/2020

Learning to Learn with Variational Information Bottleneck for Domain Generalization

Domain generalization models learn to generalize to previously unseen do...

0 Yingjun Du, et al. ∙

research

∙ 07/13/2020

Low to High Dimensional Modality Hallucination using Aggregated Fields of View

Real-world robotics systems deal with data from a multitude of modalitie...

8 Kausic Gunasekar, et al. ∙

research

∙ 04/03/2020

Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method

Active illumination is a prominent complement to enhance 2D face recogni...

20 J. Matias Di Martino, et al. ∙

research

∙ 10/23/2019

SalGaze: Personalizing Gaze Estimation Using Visual Saliency

Traditional gaze estimation methods typically require explicit user cali...

20 Zhuoqing Chang, et al. ∙

research

∙ 09/26/2019

Range Adaptation for 3D Object Detection in LiDAR

LiDAR-based 3D object detection plays a crucial role in modern autonomou...

2 Ze Wang, et al. ∙

research

∙ 09/25/2019

Stochastic Conditional Generative Networks with Basis Decomposition

While generative adversarial networks (GANs) have revolutionized machine...

4 Ze Wang, et al. ∙

research

∙ 09/25/2019

Domain-invariant Learning using Adaptive Filter Decomposition

Domain shifts are frequently encountered in real-world scenarios. In thi...

17 Ze Wang, et al. ∙

research

∙ 09/24/2019

Scale-Equivariant Neural Networks with Decomposed Convolutional Filters

Encoding the input scale information explicitly into the representation ...

12 Wei Zhu, et al. ∙

research

∙ 09/17/2019

Single-shot 3D shape reconstruction using deep convolutional neural networks

A robust single-shot 3D shape reconstruction technique integrating the f...

5 Hieu Nguyen, et al. ∙

research

∙ 08/18/2018

In Defense of Single-column Networks for Crowd Counting

Crowd counting usually addressed by density estimation becomes an increa...

0 Ze Wang, et al. ∙

research

∙ 07/04/2018

LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

Lane detection is to detect lanes on the road and provide the accurate l...

14 Ze Wang, et al. ∙

research

∙ 05/18/2018

Learning to Collaborate for User-Controlled Privacy

It is becoming increasingly clear that users should own and control thei...

0 Martin Bertran, et al. ∙

research

∙ 05/18/2018

Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning

Deep neural networks (DNNs) typically have enough capacity to fit random...

1 Wei Zhu, et al. ∙

research

∙ 05/17/2018

RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

Explicit encoding of group actions in deep features makes it possible fo...

0 Xiuyuan Cheng, et al. ∙

research

∙ 04/03/2018

Weakly Supervised Instance Segmentation using Class Peak Response

Weakly supervised instance segmentation with image-level labels, instead...

0 Yanzhao Zhou, et al. ∙

research

∙ 03/15/2018

Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification

In this paper we introduce an ensemble method for convolutional neural n...

0 Albert Gong, et al. ∙

research

∙ 02/12/2018

DCFNet: Deep Neural Network with Decomposed Convolutional Filters

Filters in a Convolutional Neural Network (CNN) contain model parameters...

0 Qiang Qiu, et al. ∙

research

∙ 12/05/2017

OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning

Deep neural networks trained using a softmax layer at the top and the cr...

0 José Lezama, et al. ∙

research

∙ 11/22/2017

ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Hash codes are efficient data representations for coping with the ever g...

0 Qiang Qiu, et al. ∙

research

∙ 11/16/2017

LDMNet: Low Dimensional Manifold Regularized Neural Networks

Deep neural networks have proved very successful on archetypal tasks for...

0 Wei Zhu, et al. ∙

research

∙ 09/06/2017

Soft Proposal Networks for Weakly Supervised Object Localization

Weakly supervised object localization remains challenging, where only im...

0 Yi Zhu, et al. ∙

research

∙ 05/23/2017

Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems

Security, privacy, and fairness have become critical in the era of data ...

0 Jure Sokolic, et al. ∙

research

∙ 01/07/2017

Oriented Response Networks

Deep Convolution Neural Networks (DCNNs) are capable of learning unprece...

0 Yanzhao Zhou, et al. ∙

research

∙ 11/22/2016

Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model

In this paper, a self-learning approach is proposed towards solving scen...

0 Qixiang Ye, et al. ∙

research

∙ 11/21/2016

Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding

Surveillance cameras today often capture NIR (near infrared) images in l...

0 José Lezama, et al. ∙

research

∙ 12/21/2015

GraphConnect: A Regularization Framework for Neural Networks

Deep neural networks have proved very successful in domains where large ...

0 Jiaji Huang, et al. ∙

research

∙ 12/18/2014

Data Representation using the Weyl Transform

The Weyl transform is introduced as a rich framework for data representa...

0 Qiang Qiu, et al. ∙

research

∙ 12/16/2014

Random Forests Can Hash

Hash codes are a very efficient data representation needed to be able to...

0 Qiang Qiu, et al. ∙

research

∙ 12/19/2013

Learning Transformations for Classification Forests

This work introduces a transformation-based learner model for classifica...

0 Qiang Qiu, et al. ∙

research

∙ 09/09/2013

Learning Transformations for Clustering and Classification

A low-rank transformation learning framework for subspace clustering and...

0 Qiang Qiu, et al. ∙

research

∙ 08/01/2013

Sparse Dictionary-based Attributes for Action Recognition and Summarization

We present an approach for dictionary learning of action attributes via ...

0 Qiang Qiu, et al. ∙

research

∙ 08/01/2013

Domain-invariant Face Recognition using Learned Low-rank Transformation

We present a low-rank transformation approach to compensate for face var...

0 Qiang Qiu, et al. ∙

research

∙ 08/01/2013

Learning Robust Subspace Clustering

We propose a low-rank transformation-learning framework to robustify sub...

0 Qiang Qiu, et al. ∙

research

∙ 08/01/2013

Compositional Dictionaries for Domain Adaptive Face Recognition

We present a dictionary learning approach to compensate for the transfor...

0 Qiang Qiu, et al. ∙

research

∙ 08/21/2012

A Unified Approach for Modeling and Recognition of Individual Actions and Group Activities

Recognizing group activities is challenging due to the difficulties in i...

0 Qiang Qiu, et al. ∙

research

∙ 08/17/2012

Information-theoretic Dictionary Learning for Image Classification

We present a two-stage approach for learning dictionaries for object cla...

0 Qiang Qiu, et al. ∙

Qiang Qiu

Featured Co-authors