Computer Vision Lab

 


   

[view by topic] [view by year]

 

[Unsupervised learning of image representations] [Language, vision, commonsense, and visual abstractions] [Explainaible and transparent AI] [Attributes] [Human-debugging] [Context] [Hierarchies] [Interactive computer vision] [Energy minimization] [Combining classifiers] [Other]

 


 

Unsupervised learning of image representations [back to top]



LR-GAN
J. Yang, A. Kannan, D. Batra, and D. Parikh

LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
International Conference on Learning Representations (ICLR), 2017

 

 

J. Yang, D. Parikh, and D. Batra

Joint Unsupervised Learning of Deep Representations and Image Clusters

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.



Language, vision, common sense, and visual abstractions [back to top]


   
 
negotiation
 
M. Lewis, D. Yarats, Y. N. Dauphin, D. Parikh, and D. Batra

Deal or No Deal? End-to-End Learning for Negotiation Dialogues     

arxiv.org/abs/1706.05125, 2017

 
dialog model architecture
 
J. Lu, A. Kannan, J. Yang, D. Parikh, and D. Batra

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

arxiv.org/abs/1706.01554, 2017

 
ParlAI logo
 
A. Miller, W. Feng, A. Fisch, J. Lu, D. Batra, A. Bordes, D. Parikh, and J. Weston
ParlAI: A Dialog Research Software Platform
arxiv.org/abs/1705.06476, 2017
 
cooperative learning
 
T. Batra and D. Parikh

Cooperative Learning with Visual Attributes    

arxiv.org/abs/1705.05512, 2017

 
C-VQA
 

A. Agrawal, A. Kembhavi, D. Batra, and D. Parikh

C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset

arxiv.org/abs/1704.08243, 2017

 
punny
 

A. Chandrasekaran, D. Parikh, and M. Bansal

Punny Captions: Witty Wordplay in Image Descriptions

arxiv.org/abs/1704.08224, 2017


 
ToAIM VQA
 
A. Chandrasekaran*, D. Yadav*, P. Chattopadhyay*, V. Prabhu*, and D. Parikh
* equal contribution
It Takes Two to Tango: Towards Theory of AI's Mind
arxiv.org/abs/1704.00717, 2017
 
visual dialog
 
A. Das, S. Kottur, K. Gupta, A. Singh, D. Yadav, J. Moura, D. Parikh, and D. Batra
Visual Dialog
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (Spotlight)
[www.visualdialog.org] [video
 
VQA v2.0 (balanced)
 
Y. Goyal*, T. Khot*, D. Summers-Stay, D. Batra, and D. Parikh
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering (a.k.a. The VQA v2.0 Dataset)

* equal contribution

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[project page] [video]
 
when to look
 
J. Lu*, C. Xiong*, D. Parikh, and R. Socher

* equal contribution

Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (Spotlight)
[code coming soon!]
 
context-aware captioning
 
R. Vedantam, S. Bengio, K. Murphy, D. Parikh, and G. Chechik
Context-aware Captions from Context-agnostic Supervision
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (Spotlight)
 
hierarchical co-attention
 

J. Lu, J. Yang, D. Batra, and D. Parikh

Hierarchical Question-Image Co-Attention for Visual Question Answering
Neural Information Procession Systems (NIPS), 2016
 
sort story
 
H. Agrawal, A. Chandrasekaran, D. Batra, D. Parikh and M. Bansal

Sort Story: Sorting Jumbled Images and Captions into Stories

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016

 
VQA models behavior
 
A. Agarwal, D. Batra, and D. Parikh

Analyzing the Behavior of Visual Question Answering Models

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016

 
question relevance
 
A. Ray, G. Christie, M. Bansal, D. Batra, and D. Parikh

Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016

  VQA-HAT

A. Das, H. Agrawal, C. L. Zitnick, D. Parikh, and D. Batra

Human Attention in Visual Question Answering:

Do Humans and Deep Networks Look at the Same Regions?

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016

Also presented at:
Workshop on Visualization for Deep Learning at
International Conference on Machine Learning (ICML), 2016
Best student paper
 
 
VQA occlusion
 

Y. Goyal, A. Mohapatra, D. Parikh, and D. Batra

Towards Transparent AI Systems: Interpreting Visual Question Answering Models

Workshop on Visualization for Deep Learning at
International Conference on Machine Learning (ICML), 2016

Best student paper

vqa_for_cap
X. Lin and D. Parikh

Leveraging Visual Question Answering for Image-Caption Ranking

European Conference on Computer Vision (ECCV), 2016

count

P. Chattopadhyay*, R. Vedantam*, Ramprasaath RS, D. Batra, and D. Parikh

Counting Everyday Objects in Everyday Scenes

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (Spotlight)

 
ying yang
 

P. Zhang*, Y. Goyal*, D. Summers-Stay, D. Batra, and D. Parikh

* equal contribution

Yin and Yang: Balancing and Answering Binary Visual Questions

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

 
humor
 

A. Chandrasekaran, A. Kalyan, S. Antol, M. Bansal, D. Batra, C. L. Zitnick, and D. Parikh

We Are Humor Beings: Understanding and Predicting Visual Humor

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. (Spotlight)


 
visual word2vec
 

S. Kottur*, R. Vedantam*, J. Moura, and D. Parikh

* equal contribution

Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[project page (including code)]

visual-stories

T. Huang, F. Ferraro, N. Mostafazadeh, I. Misra, J. Devlin, A. Agrawal, R. Girshick, X. He, P. Kohli, D. Batra, C. L. Zitnick, D. Parikh, L. Vanderwende, M. Galley, and M. Mitchell

Visual Storytelling
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), 2016.

[project page with dataset]

commonsense_stories

N. Mostafazadeh, N. Chambers, X. He, D. Parikh, D. Batra, L. Vanderwende, P. Kohli, and J. Allen

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), 2016. (Oral)

[project page with data and evaluation]

vqa
 

S. Antol*, A. Agrawal*, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh

* equal contribution

VQA: Visual Question Answering

International Conference on Computer Vision (ICCV), 2015.

[project page (including the VQA dataset)]


C. L. Zitnick, A. Agrawal, S. Antol, M. Mitchell, D. Batra, and D. Parikh

Measuring Machine Intelligence Through Visual Question Answering

AI Magazine (2016)


A. Agrawal*, J. Lu*, S. Antol*, M. Mitchell, C. L. Zitnick, D. Parikh, and D. Batra

* equal contribution

VQA: Visual Question Answering

Special Issue on Combined Image and Language Understanding

International Journal of Computer Vision (IJCV), 2016

clipart
 

R. Vedantam*, X. Lin*, T. Batra, C. L. Zitnick, and D. Parikh

* equal contribution

Learning Common Sense Through Visual Abstraction

International Conference on Computer Vision (ICCV), 2015.

[supplementary material] [project page (under construction)]


X. Lin and D. Parikh

Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. (Oral)

[extended abstract] [talk (video)] [project page with code, data, slides, etc.]


M. Jas and D. Parikh

Image Specificity

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. (Oral)

[extended abstract] [talk (video)] [project page with code, data, slides, etc.]


R. Vedantam, C. L. Zitnick, and D. Parikh

CIDEr: Consensus-based Image Description Evaluation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

[extended abstract] [project page with code, data, etc.]

 

 

 

S. Antol, C. L. Zitnick and D. Parikh

Zero-Shot Learning via Visual Abstraction

European Conference on Computer Vision (ECCV), 2014.

[project page]


 

C. L. Zitnick, D. Parikh and L. Vanderwende

Learning the Visual Interpretation of Sentences

International Conference on Computer Vision (ICCV), 2013.

[project page, data, slides, video, etc.]

 

C. L. Zitnick and D. Parikh

Bringing Semantics Into Focus Using Visual Abstraction

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Oral)


C. L. Zitnick, R. Vedantam and D. Parikh

Adopting Abstract Images for Semantic Scene Understanding

Special Issue on the best papers at the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016

 
[project page, data, slides, video, etc.]


Explainaible and transparent AI [back to top]

    
 
grad-cam
 
Ramprasaath RS, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra
Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization
arxiv.org/abs/1610.02391, 2016
[code, demo]
VQA-HAT

A. Das, H. Agrawal, C. L. Zitnick, D. Parikh, and D. Batra

Human Attention in Visual Question Answering:

Do Humans and Deep Networks Look at the Same Regions?

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016

Also presented at:
Workshop on Visualization for Deep Learning at
International Conference on Machine Learning (ICML), 2016
Best student paper
 
 
VQA occlusion
 
Y. Goyal, A. Mohapatra, D. Parikh, and D. Batra

Towards Transparent AI Systems: Interpreting Visual Question Answering Models

Workshop on Visualization for Deep Learning at
International Conference on Machine Learning (ICML), 2016

Best student paper

 

 

 

A. BansalA. Farhadi and D. Parikh

Towards Transparent Systems: Semantic Characterization of Failure Modes

European Conference on Computer Vision (ECCV), 2014.
[project page]
 
 

 

P. Zhang, J. Wang, A. Farhadi, M. Hebert and D. Parikh

Predicting Failures of Vision Systems

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
[project page]

 

       

      


Attributes [back to top]

   

attributes book
R. Feris, C. Lampert, and D. Parikh (Editors)

Visual Attributes (Book)

Series on Advances in Computer Vision and Pattern Recognition, Springer, 2017

[springer link]

 
urban perception
 
A. Dubey, N. Naik, D. Parikh, R. Raskar, and C. Hidalgo.
Deep Learning the City: Quantifying Urban Perception at a Global Scale
European Conference on Computer Vision (ECCV), 2016.
who to listen to
S. Lad, B. Romera Paredes, J. Valentin, Philip Torr, and D. Parikh
Knowing Who To Listen To: Prioritizing Experts from a Diverse Ensemble for Attribute Personalization
International Conference on Image Processing (ICIP), 2016.
 

 




A. Deza and D. Parikh
Understanding Image Virality
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[extended abstract] [project page with code, data, etc.]
 

 
S. Lad and D. Parikh

Interactive Guiding Semi-Supervised Clustering via Attribute-based Explanations

European Conference on Computer Vision (ECCV), 2014.

[project page]

 

 
G. Christie, A. Parkash, U. Krothapalli and D. Parikh

Predicting User Annoyance Using Visual Attributes

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

[project page]


 

D. Parikh

Visual Attributes for Enhanced Human-Machine Communication (Invited paper)

Allerton Conference on Communication, Control and Computing, 2013. (Oral)

 

 

 

N. Turakhia and D. Parikh

Attribute Dominance: What Pops Out? 

International Conference on Computer Vision (ICCV), 2013.

[project page and data] [poster]

 

 
A. Sadovnik, A. C. Gallagher, D. Parikh and T. Chen

Spoken Attributes: Mixing Binary and Relative Attributes to Say the Right Thing

International Conference on Computer Vision (ICCV), 2013.

[project page and data] [poster]

 

 
D. Parikh and K. Grauman

Implied Feedback: Learning Nuances of User Behavior in Image Search

International Conference on Computer Vision (ICCV), 2013.

[supp material] [poster]

 

 

M. Rastegari, A. Diba, D. Parikh and A. Farhadi

Multi-Attribute Queries: To Merge or Not to Merge?

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013

[poste]


A. Parkash and D. Parikh

Attributes for Classifier Feedback

European Conference on Computer Vision (ECCV), 2012 (Oral)

[slides] [talk (video)]

 

A. Biswas and D. Parikh

Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013

[poster]

 

[project page and data] [demo]

   

Demo at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 by

N. Agrawal, A. Biswas, A. Kovashka, K. Grauman and D. Parikh.

 

 

 

A. Kovashka, D. Parikh and K. Grauman

WhittleSearch: Interactive Image Search with Relative Attribute Feedback

International Journal of Computer Vision (IJCV), 2015


A. Kovashka, D. Parikh and K. Grauman

WhittleSearch: Image Search with Relative Attribute Feedback

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012

[project page and data[poster] [demo] [video]

 

Demo at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 by

N. Agrawal, A. Biswas, A. Kovashka, K. Grauman and D. Parikh.

 

 
 
K. Duan,
D. Parikh, D. Crandall and K. Grauman

Discovering Localized Attributes for Fine-grained Recognition

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012

[project page[poster]

 

 
 
 

 
 
 
P. Isola, D. Parikh, J. Xiao, A. Torralba and A. Oliva

What makes a photograph memorable?

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014.

   

D. Parikh, P. IsolaA. Torralba and A. Oliva

Understanding the Intrinsic Memorability of Images (Abstract)

Visual Sciences Society (VSS), 2012

 

P. Isola, D. ParikhA. Torralba and A. Oliva

Understanding the Intrinsic Memorability of Images

Neural Information Processing Systems (NIPS), 2011
[project page] [MIT news]
 

D. Parikh and K. Grauman

Relative Attributes

International Conference on Computer Vision (ICCV), 2011 (Oral)

Marr Prize (Best Paper Award) Winner

[project page] [data] [code[slides] [talk (video)[poster] [demos]

 

D. ParikhA. Kovashka, A. Parkash and K. Grauman

Relative Attributes for Enhanced Human-Machine Communication (Invited paper)

AAAI Conference on Artificial Intelligence (AAAI), 2012 (Oral)

      

Demos at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 by

N. Agrawal, A. Biswas, A. Kovashka, K. Grauman and D. Parikh.

 
 
D. Parikh and K. Grauman

Interactively Building a Discriminative Vocabulary of Nameable Attributes

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011

[supplementary material] [project page] [poster] [slides]

 

 

D. Parikh and K. Grauman

Interactive Discovery of Task-Specific Nameable Attributes (Abstract)

First Workshop on Fine-Grained Visual Categorization (FGVC)

held in conjunction with 

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (Best Poster Award)

[project page] [poster

 


 

Human-debugging [back to top]

 

 

 
 
X. Lin, M. Cogswell, D. Parikh and D. Batra

Propose and Re-rank Semantic Segmentation via Deep Image Classification

Big Vision workshop

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

[project page]

 

A. Bansal, A. Kowdle, D. Parikh, A. C. Gallagher and C. L. Zitnick

Which Edges Matter?

Workshop on 3D Representation and Recognition (3dRR)
International Conference on Computer Vision (ICCV), 2013.


R. Mottaghi, S. Fidler, J. Yao, R. Urtasun and D. Parikh

Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013

[poster]


R. Mottaghi, S. Fidler, A. Yuille, R. Urtasun, and D. Parikh.

Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016.

[supplementary material]

   

C. L. Zitnick and D. Parikh

The Role of Image Understanding in Contour Detection

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012

[project page] [data] [poster]

 

 
 

 
 

D. Parikh, C. L. Zitnick and T. Chen

Exploring Tiny Images: The Roles of Appearance and Contextual Information for Machine and Human Object Recognition

Pattern Analysis and Machine Intelligence (PAMI), 2012 (to appear)

D. Parikh and C. L. Zitnick

Human-Debugging of Machines

Second Workshop on Computational Social Science and the Wisdom of Crowds

Neural Information Processing Systems (NIPS), 2011

 

 
D. Parikh
Recognizing Jumbled Images: The Role of Local and Global Information in Image Classification

International Conference on Computer Vision (ICCV), 2011

[poster] [slides]

 
 

 
 
D. Parikh and C. L. Zitnick
Finding the Weakest Link in Person Detectors
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011
[project page]
[data] [poster[slides]
D. Parikh and C. L. Zitnick

The Role of Features, Algorithms and Data in Visual Recognition

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

[poster] [slides]

 

 

Context [back to top]

  

 
 
 
C. Li
, D. Parikh and T. Chen

Automatic Discovery of Groups of Objects for Scene Understanding

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012

[project page[poster]

 

  

  
C. Li, D. Parikh and T. Chen

Extracting Adaptive Contextual Cues from Unlabeled Regions

International Conference on Computer Vision (ICCV), 2011

[project page]


 

D. Parikh

Modeling Context for Image Understanding: When, For What, and How?

Ph.D. Thesis, Carnegie Mellon University, 2009
 
 
 


 
 

D. Parikh, C. L. Zitnick and T. Chen

Determining Patch Saliency Using Low-Level Context

European Conference on Computer Vision (ECCV), 2008

[poster] [slides]


 
 

D. Parikh, C. L. Zitnick and T. Chen
Exploring Tiny Images: The Roles of Appearance and Contextual Information for Machine and Human Object Recognition
Pattern Analysis and Machine Intelligence (PAMI) (to appear) 

[slides]

 

D. Parikh, C. L. Zitnick and T. Chen

From Appearance to Context-Based Recognition: Dense Labeling in Small Images

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008

[poster] [slides]

     

D. Parikh and T. Chen

Unsupervised Modeling of Objects and their Hierarchical Contextual Interactions

EURASIP Journal on Image and Video Processing, Special Issue on Patches in Vision, 2008

[slides]

  


 

Hierarchies [back to top]

 

D. Parikh, C. L. Zitnick and T. Chen

Unsupervised Learning of Hierarchical Spatial Structures in Images

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009

[poster[slides]

D. Parikh and T. Chen

Unsupervised Identification of Multiple Objects of Interest from Multiple Images: dISCOVER

Asian Conference in Computer Vision (ACCV), 2007

[poster]

 

D. Parikh and T. Chen

Hierarchical Semantics of Objects (hSOs)

IEEE International Conference in Computer Vision (ICCV), 2007

[poster[slides]

 

D.Parikh and T. Chen

Unsupervised Learning of Hierarchical Semantics of Objects (hSOs)

Beyond Patches Workshop, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007 (Best Paper Award)

[slides]

 

D. Parikh and T. Chen

Unsupervised Modeling of Objects and their Hierarchical Contextual Interactions

EURASIP Journal on Image and Video Processing, Special Issue on Patches in Vision, 2008

[slides]

 



 

Interactive computer vision [back to top]

 

D. Batra, A. Kowdle, D. Parikh, J. Luo, T. Chen

Interactive Co-segmentation of Objects in Image Collections (Book)

SpringerBriefs in Computer Science, 2011.

[springer link]

  

D. Batra, A. Kowdle, D. Parikh, J. Luo, T. Chen

Interactively Co-segmenting Topically Related Image with Intelligence Scribble Guidance

International Journal of Computer Vision (IJCV), January 2011

[project page and dataset]

  

D. Batra, A. Kowdle, D. Parikh, J. Luo, T. Chen

iCoseg: Interactive Co-segmentation with Intelligent Scribble Guidance

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010      

[poster[project page and dataset]

 

D. Batra, D. Parikh, A. Kowdle, T. Chen and J. Luo

Seed Image Selection in Interactive Cosegmentation

IEEE International Conference on Image Processing (ICIP), 2009

        

D. Batra, A. Kowdle, K. Tang, D. Parikh, J. Luo, T. Chen

Interactive Cosegmentation by Touch.

Demo at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009

[project page]

D. Batra, A. Kowdle, D. Parikh and T. Chen

Cutout-Search: Putting a name to the Picture

Workshop on Internet Vision, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009



 

Energy minimization [back to top]

           


 
 

A. Gallagher, D. Batra and D. Parikh

Inference for Order Reduction in MRFs

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011

D. Batra, A. Gallagher, D. Parikh and T. Chen

Beyond Trees: MRF Inference via Outer-Planar Decomposition

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

[poster]

 

 

Combining classifiers [back to top]

  

C. Mao, H. Lee, D. Parikh, T. Chen and S. Huang

Semi-Supervised Cotraining and Active Learning based Approach for Multi-view Intrusion Detection

ACM Symposium on Applied Computing (SAC), 2009

 

D. Parikh and T. Chen

 Data Fusion and Cost Minimization for Intrusion Detection

IEEE Transactions on Information Forensics and Security, Special Issue on Statistical Methods for Network Security and Forensics, August 2008

 

D. Parikh and T. Chen

Bringing Diverse Classifiers to Common Grounds: dtransform

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008

[slides]

 

D. Parikh and R.Polikar

An Ensemble Based Incremental Learning Approach to Data Fusion

IEEE Transactions on Systems, Man and Cybernetics, April 2007

 

D.Parikh and T. Chen

Classification-Error Cost Minimization Strategy: dCMS

IEEE Statistical Signal Processing Workshop, 2007

[poster]

 

R. Polikar, D. Parikh and S. Mandayam

Multiple Classifiers System for Multisensor Data Fusion

IEEE Proceedings on Sensors Applications Symposium, 2006

 

D. Parikh and R.Polikar

A Multiple Classifier Approach for Multisensor Data Fusion

IEEE Proceedings on Information Fusion, 2005

 

D. Parikh, M. Kim, J. Oagaro, S.Mandayam and R.Polikar

Combining Classifiers for Multisensor Data Fusion

IEEE Proceedings on Systems, Man and Cybernetics, 2004

 
 

 
  

R. Polikar, A. Topalis, D. Parikh, D. Green, J. Kounios and C. Clark

An Ensemble Based Data Fusion for Early Diagnosis of Alzheimer’s Disease

Information Fusion, Special Issue on Applications of Ensemble Methods, January 2008

 

D. Parikh, N. Stepenosky, A. Topalis, D. Green, J. Kounios, C. Clark and R.Polikar

Ensemble Based Data Fusion for Early Diagnosis of Alzheimer’s Disease

IEEE Proceedings on The Engineering in Medicine and Biology, 2005

D. Parikh, M. Kim, J. Oagaro, S.Mandayam and R.Polikar

Ensemble of Classifiers Approach for NDT Data Fusion

IEEE Proceedings on Ultrasonics, Ferroelectrics and Frequency Control, 2004

 


 

Other [back to top]

  


 
 
C. L. Zitnick and D. Parikh

Color Source Separation for Enhanced Pixel Manipulations

MSR-TR-2011-98, Microsoft Research, 2011

 
 

D. Parikh and G. Jancke

Localization and Segmentation of a 2D High Capacity Color Barcode

Workshop on Applications in Computer Vision (WACV), 2008

[slides]


 
 

 
 

D. Parikh, R. Sukthankar, T. Chen and M. Chen

Feature-based Part Retrieval for Interactive 3D Reassembly

IEEE Workshop on Applications of Computer Vision (WACV), 2007

[poster] [slides]

Y. Mehta, K. Jahan, J. Laicovsky, L. Miller, D. Parikh and A. Lozano

Evaluate the Effect of Coarse and Fine Rubber Particles on Laboratory Rutting Performance of Asphalt Concrete Mixtures

The Journal of Solid Waste Technology And Management, 2005

   

D. Parikh, Y. Mehta and K. Jahan

Evaluate the Effect of Ground Tire Rubber on Laboratory Rutting Performance of Asphalt Concrete Mixtures

Proceedings of Industrial and Hazardous Waste Conference, 2002