
ACDC: Weight Sharing in AtomCoefficient Decomposed Convolution
Convolutional Neural Networks (CNNs) are known to be significantly over...
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Graph Neural Networks with Lowrank Learnable Local Filters
For the classification of graph data consisting of features sampled on a...
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Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen do...
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Low to High Dimensional Modality Hallucination using Aggregated Fields of View
Realworld robotics systems deal with data from a multitude of modalitie...
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Differential 3D Facial Recognition: Adding 3D to Your StateoftheArt 2D Method
Active illumination is a prominent complement to enhance 2D face recogni...
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SalGaze: Personalizing Gaze Estimation Using Visual Saliency
Traditional gaze estimation methods typically require explicit user cali...
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Range Adaptation for 3D Object Detection in LiDAR
LiDARbased 3D object detection plays a crucial role in modern autonomou...
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Stochastic Conditional Generative Networks with Basis Decomposition
While generative adversarial networks (GANs) have revolutionized machine...
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Domaininvariant Learning using Adaptive Filter Decomposition
Domain shifts are frequently encountered in realworld scenarios. In thi...
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ScaleEquivariant Neural Networks with Decomposed Convolutional Filters
Encoding the input scale information explicitly into the representation ...
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Singleshot 3D shape reconstruction using deep convolutional neural networks
A robust singleshot 3D shape reconstruction technique integrating the f...
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In Defense of Singlecolumn Networks for Crowd Counting
Crowd counting usually addressed by density estimation becomes an increa...
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LaneNet: RealTime Lane Detection Networks for Autonomous Driving
Lane detection is to detect lanes on the road and provide the accurate l...
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Learning to Collaborate for UserControlled Privacy
It is becoming increasingly clear that users should own and control thei...
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Stop memorizing: A datadependent regularization framework for intrinsic pattern learning
Deep neural networks (DNNs) typically have enough capacity to fit random...
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RotDCF: Decomposition of Convolutional Filters for RotationEquivariant Deep Networks
Explicit encoding of group actions in deep features makes it possible fo...
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Weakly Supervised Instance Segmentation using Class Peak Response
Weakly supervised instance segmentation with imagelevel labels, instead...
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Virtual CNN Branching: Efficient Feature Ensemble for Person ReIdentification
In this paper we introduce an ensemble method for convolutional neural n...
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DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Filters in a Convolutional Neural Network (CNN) contain model parameters...
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OLÉ: Orthogonal Lowrank Embedding, A Plug and Play Geometric Loss for Deep Learning
Deep neural networks trained using a softmax layer at the top and the cr...
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ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Hash codes are efficient data representations for coping with the ever g...
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LDMNet: Low Dimensional Manifold Regularized Neural Networks
Deep neural networks have proved very successful on archetypal tasks for...
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Soft Proposal Networks for Weakly Supervised Object Localization
Weakly supervised object localization remains challenging, where only im...
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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 ...
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Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning unprece...
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Selflearning Scenespecific Pedestrian Detectors using a Progressive Latent Model
In this paper, a selflearning approach is proposed towards solving scen...
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Not Afraid of the Dark: NIRVIS Face Recognition via Crossspectral Hallucination and Lowrank Embedding
Surveillance cameras today often capture NIR (near infrared) images in l...
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GraphConnect: A Regularization Framework for Neural Networks
Deep neural networks have proved very successful in domains where large ...
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Data Representation using the Weyl Transform
The Weyl transform is introduced as a rich framework for data representa...
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Random Forests Can Hash
Hash codes are a very efficient data representation needed to be able to...
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Learning Transformations for Classification Forests
This work introduces a transformationbased learner model for classifica...
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Learning Transformations for Clustering and Classification
A lowrank transformation learning framework for subspace clustering and...
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Sparse Dictionarybased Attributes for Action Recognition and Summarization
We present an approach for dictionary learning of action attributes via ...
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Domaininvariant Face Recognition using Learned Lowrank Transformation
We present a lowrank transformation approach to compensate for face var...
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Learning Robust Subspace Clustering
We propose a lowrank transformationlearning framework to robustify sub...
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Compositional Dictionaries for Domain Adaptive Face Recognition
We present a dictionary learning approach to compensate for the transfor...
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A Unified Approach for Modeling and Recognition of Individual Actions and Group Activities
Recognizing group activities is challenging due to the difficulties in i...
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Informationtheoretic Dictionary Learning for Image Classification
We present a twostage approach for learning dictionaries for object cla...
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Qiang Qiu
verfied profile
Assistant Professor, Electrical and Computer Engineering, Purdue University