List of Publications

Detailed list of publications available over here

Image retrieval based on projective invariance

Rajashekhar, S. Chaudhuri and V. P. Namboodiri (2004). "Image retrieval based on projective invariance." IEEE International Conference on Image Processing Singapore, October 2004, Page 405-408

Use of Linear Diffusion in depth estimation based on defocus cue

V.P. Namboodiri and S. Chaudhuri (2004). “Use of Linear Diffusion in depth estimation based on defocus cue” Proceedings of Fourth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP) Kolkata, India. December 2004.

Shock Filters based on Implicit Cluster Separation

V.P. Namboodiri and S. Chaudhuri (2005). "Shock Filters based on Implicit Cluster Separation." Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR),San Diego June 2005, Page 1-6.

Improved Kernel-Based Object Tracking Under Occluded Scenarios

V.P. Namboodiri, A. Ghorawat and S. Chaudhuri (2006) “Improved Kernel-Based Object Tracking Under Occluded Scenarios”. In: Kalra P.K., Peleg S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg

Retrieval of images of man-made structures based on projective invariance

Rajashekhar, S. Chaudhuri and V.P. Namboodiri (2007), “Retrieval of images of man-made structures based on projective invariance”. Pattern Recognition Journal, Volume 40, Issue 1, January 2007, Pages 296-308

On defocus, diffusion and depth estimation

V.P. Namboodiri and S. Chaudhuri (2007). “On defocus, diffusion and depth estimation” Pattern Recognition Letters Volume 28, Issue 3, 1 February 2007, Pages 311-319

Super-Resolution Using Sub-band Constrained Total Variation

P. Chatterjee, V.P. Namboodiri, S. Chaudhuri (2007) “Super-Resolution Using Sub-band Constrained Total Variation” In: Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational Methods in Computer Vision SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg

Shape Recovery Using Stochastic Heat Flow

V.P. Namboodiri and S. Chaudhuri (2007) “Shape Recovery Using Stochastic Heat Flow“ Proceedings of the British Machine Vision Conference 2007, University of Warwick, UK, September 10-13, 2007

Image Restoration using Geometrically Stabilized Reverse Heat Equation

V.P. Namboodiri and S. Chaudhuri (2005). "Image Restoration using Geometrically Stabilized Reverse Heat Equation." Proceedings of IEEE International Conference on Image Processing (ICIP), San Antonio, Texas, USA, 2007, Pages IV - 413 - 416.

Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera

V.P. Namboodiri and S. Chaudhuri (2008). "Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera." Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR),Anchorage, AK, USA, June 2008, Page 1-6.

Regularized depth from defocus

V.P. Namboodiri, S. Chaudhuri and S. Hadap (2008). “Regularized depth from defocus”, Proceedings of IEEE International Conference on Image Processing (ICIP) , San Diego, CA, USA, pp. 1520-1523.

Action Recognition: A Region Based Approach

H. Bilen, V.P. Namboodiri and L. Van Gool (2011). “Action recognition: A region based approach”, 2011 IEEE Workshop on Applications of Computer Vision (WACV), Kona, HI , 2011, pp. 294-300

Object and Action Classification with Latent Variables

H. Bilen, V.P. Namboodiri and L. Van Gool (2011). “Object and Action Classification with Latent Variables”, In Jesse Hoey, Stephen McKenna and Emanuele Trucco, Proceedings of the British Machine Vision Conference, pages 17.1-17.11. BMVA Press, September 2011

Super-resolution techniques for minimally invasive surgery

V. De Smet, V.P. Namboodiri and L. Van Gool (2011). “Super-resolution techniques for minimally invasive surgery”, 6th MICCAI workshop on augmented environments for computer assisted interventions-AE-CAI 2011 , Toronto,Canada.

Systematic evaluation of super-resolution using classification

V. De Smet, V.P. Namboodiri and L. Van Gool (2011). “Systematic evaluation of super-resolution using classification”, 2011 Visual Communications and Image Processing (VCIP), Tainan, 2011, pp. 1-4.

Classification with Global, Local and Shared Features

Bilen H., Namboodiri V.P., Van Gool L.J. (2012) Classification with Global, Local and Shared Features. In: Pinz A., Pock T., Bischof H., Leberl F. (eds) Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg, pp 134-143

Nonuniform Image Patch Exemplars for Low Level Vision

V. De Smet, L. Van Gool and V. P. Namboodiri, "Nonuniform image patch exemplars for low level vision," 2013 IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, 2013, pp. 23-30.

Object and Action Classification with Latent Window Parameters

H. Bilen, V.P. Namboodiri and L. Van Gool (2014), “Object and Action Classification with Latent Window Parameters”, International Journal of Computer Vision (IJCV) Vol: 106: 237 - 251, February 2014

Object classification with adaptable regions

H. Bilen, M. Pedersoli, V. P. Namboodiri, T. Tuytelaars, L. Van Gool,“Object Classification with Adaptable Regions”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2014

Mind the gap: Subspace based hierarchical domain adaptation

A. Raj, V. P. Namboodiri, T. Tuytelaars, “Mind the Gap: Subspace based Hierarchical Domain Adaptation”, Workshop in Transfer and Multi-View Learning in Advances in Neural Information System Conference (NIPS) 27, Canada, 2014

Where is my Friend? - Person identification in Social Networks

D. Pathak, Sai Nitish S. and V. P. Namboodiri, “Where is my Friend? - Person identification in Social Networks”, Proceedings of the Eleventh IEEE International Conference on Automatic Face and Gesture Recognition (FG 2015), Ljubljana, Slovenia, 2015

Subspace alignment based domain adaptation for RCNN detector

Anant Raj, Vinay P. Namboodiri and Tinne Tuytelaars, “Subspace Alignment based Domain Adaptation for RCNN Detector”, Proceedings of British Machine Vision Conference (BMVC 2015), Swansea, UK, 2015

Adapting RANSAC SVM to Detect Outliers for Robust Classification.

Subhabrata Debnath, Anjan Banerjee and Vinay P. Namboodiri, “Adapting RANSAC SVM to detect outliers for Robust Classification”,Proceedings of British Machine Vision Conference (BMVC 2015), Swansea, UK, 2015

EDS pooling layer

P. Singh, P. Raj and V.P. Namboodiri, "EDS Pooling Layer", Journal of Image and Vision Computing, Volume 98, June 2020, 103923

Deep attributes for one-shot face recognition

A. Jadhav, V.P. Namboodiri and K.S. Venkatesh, “Deep Attributes for One-Shot Face Recog- nition”, ECCV Workshop on ‘Transfering and Adapting Source Knowledge in Computer Vision’, Amsterdam, 2016

Using Gaussian Processes to Improve Zero-Shot Learning with Relative Attributes

Y. Dolma and V.P. Namboodiri, “Gaussian Processes to Improve Zero-Shot Learning with Relative Attributes”, Proceedings of Asian Conference in Computer Vision (ACCV), Taipei, Taiwan, 2016

Contextual rnn-gans for abstract reasoning diagram generation

A. Ghosh, V. Kulharia, A. Mukerjee, V.P. Namboodiri, M. Bansal, “Contextual RNN-GANs for Abstract Reasoning Diagram Generation”, Proceedings of Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, California, USA, February 2017

Sketchsoup: Exploratory ideation using design sketches

R. Arora, I. Darolia, V.P. Namboodiri, K. Singh and A. Bousseau, “SketchSoup: Exploratory Ideation Using Design Sketches”, Computer Graphics Forum, 2017

Compact Environment-Invariant Codes for Robust Visual Place Recognition

U.Jain, V.P. Namboodiri and G. Pandey,“Supervised Hashing for Robust Visual Place Recognition”, 14th Conference on Computer and Robot Vision, Edmonton, Alberta, May 16-19, 2017

Reactive Displays for Virtual Reality

G S S Srinivas Rao, Neeraj Thakur, Vinay P. Namboodiri, “Reactive Displays for Virtual Reality”, Proceedings of 16th IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (Poster Proceedings), Nantes, France, 2017

Visual Odometry Based Omni-directional Hyperlapse

P. Rani, A. Jangid, V.P. Namboodiri and K.S. Venkatesh, “Visual Odometry based Omni-directional Hyperlapse”, Proceedings of National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2017), Mandi, India 2017

No Modes left behind: Capturing the data distribution effectively using GANs

S. Sharma and V.P. Namboodiri,”No Modes left behind: Capturing the data distribution effectively using GANs”, Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18),New Orleans, USA, February 2018

Word spotting in silent lip videos

A. Jha, V. P. Namboodiri and C. V. Jawahar, "Word Spotting in Silent Lip Videos," IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, 2018, pp. 150-159.

Unsupervised domain adaptation of deep object detectors.

Debjeet Majumdar and Vinay P. Namboodiri,”Unsupervised domain adaptation of deep object detectors.”, 26th European Symposium on Artificial Neural Networks, ESANN 2018, Bruges, Belgium, April 25-27, 2018.

Multi-agent diverse generative adversarial networks

A. Ghosh, V. Kulharia, V.P. Namboodiri, P.H.S. Torr and P. Dokania, “Multi-Agent Diverse Generative Adversarial Networks”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Salt Lake City, Utah, June 2018.

Differential Attention for Visual Question Answering

B.N. Patro and V.P. Namboodiri, “Differential Attention for Visual Question Answering”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Salt Lake City, Utah, June 2018.

Eclectic domain mixing for effective adaptation in action spaces

Jamal, A., Deodhare, D., Namboodiri, V.P., Venkatesh, K.S. “Eclectic domain mixing for effective adaptation in action spaces”, Journal on Multimedia Tools and Applications, November 2018, Volume 77, Issue 22, pp 29949–29969.

Learning semantic sentence embeddings using sequential pair-wise discriminator

B.N. Patro, V.K. Kurmi, S. Kumar and V.P. Namboodiri, “Learning semantic sentence embeddings using sequential pair-wise discriminator”, Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 2018

Monoaural Audio Source Separation Using Variational Autoencoders.

Laxmi Pandey, Anurendra Kumar and Vinay P. Namboodiri, “Monoaural Audio Source Separation Using Variational Autoencoders.”, 19th Annual Conference of the International Speech Communication Association, Interspeech 2018, Hyderabad, India, 2-6 September 2018

Deep Domain Adaptation in Action Space.

Arshad Jamal, Vinay P. Namboodiri, Dipti Deodhare and K.S. Venkatesh, “Deep Domain Adaptation in Action Space”, British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018

Deep active learning for object detection.

Soumya Roy, Asim Unmesh and Vinay P. Namboodiri, “Deep active learning for object detection”, British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018

Multimodal differential network for visual question generation

Badri N. Patro, Sandeep Kumar, Vinod K. Kurmi, Vinay P. Namboodiri,”Multimodal Differential Network for Visual Question Generation”, 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018

U-DADA: Unsupervised Deep Action Domain Adaptation

Jamal A., Namboodiri V.P., Deodhare D., Venkatesh K.S. (2019) U-DADA: Unsupervised Deep Action Domain Adaptation. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11363. Springer,

Supervised Hashing for Retrieval of Multimodal Biometric Data

Sumesh T.A., Namboodiri V., Gupta P. (2019) Supervised Hashing for Retrieval of Multimodal Biometric Data. In: Arora C., Mitra K. (eds) Computer Vision Applications. WCVA 2018. Communications in Computer and Information Science, vol 1019. Springer, Singapore

Multi-layer pruning framework for compressing single shot multibox detector

P. Singh, Manikandan R., N. Matiyali and V. P. Namboodiri, "Multi-layer pruning framework for compressing single shot multibox detector," IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, Hawaii, USA.

Stability Based Filter Pruning for Accelerating Deep CNNs

P. Singh, Manikandan V.S.R. Kadi, N. Verma and V. P. Namboodiri, "Stability Based Filter Pruning for Accelerating Deep CNNs," IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, Hawaii, USA.

Spotting words in silent speech videos: a retrieval-based approach

A. Jha, V. P. Namboodiri and C. V. Jawahar,”Spotting words in silent speech videos: a retrieval-based approach”, Journal of Machine Vision and Applications, March 2019, Volume 30, Issue 2, pp 217–229

Cross-language Speech Dependent Lip-synchronization

A. Jha, V. Voleti, V. Namboodiri and C. V. Jawahar, "Cross-language Speech Dependent Lip-synchronization," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 7140-7144.

Hetconv: Heterogeneous kernel-based convolutions for deep cnns

Pravendra Singh, Vinay Kumar Verma, Piyush Rai and Vinay P. Namboodiri,”Hetconv: Heterogeneous kernel-based convolutions for deep cnns”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Long Beach, California, June 2019.

Attending to Discriminative Certainty for Domain Adaptation

Vinod Kumar Kurmi*, Shanu Kumar* and Vinay P Namboodiri,”Attending to Discriminative Certainty for Domain Adaptation”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Long Beach, California, June 2019.

Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal

Abhishek Joshi and Vinay P. Namboodiri,”Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal”, Proceedings of International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary

Looking back at Labels: A Class based Domain Adaptation Technique

Vinod Kumar Kurmi and Vinay P. Namboodiri, “Looking back at Labels: A Class based Domain Adaptation Technique”, Proceedings of International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary

Play and Prune: Adaptive Filter Pruning for Deep Model Compression

Pravendra Singh, Vinay Kumar Verma, Piyush Rai and Vinay P. Namboodiri,”Play and Prune: Adaptive Filter Pruning for Deep Model Compression”, Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-2019)Macao, China, August 2019.

Curriculum based Dropout Discriminator for Domain Adaptation

Vinod Kumar Kurmi, Vipul Bajaj, Venkatesh K Subramanian and Vinay P Namboodiri, “Curriculum based Dropout Discriminator for Domain Adaptation”, British Machine Vision Conference 2018, BMVC 2018, Cardiff, UK, Northumbria University, Newcastle, UK, September 9-12, 2019

Towards Automatic Face-to-Face Translation

Prajwal Renukanand*, Rudrabha Mukhopadhyay*, Jerin Philip, Abhishek Jha, Vinay Namboodiri and C.V. Jawahar, “Towards Automatic Face-to-Face Translation”, 27th ACM International Conference on Multimedia (ACM-MM), Nice, France, 2019, Pages 1428–1436

U-CAM: Visual Explanation using Uncertainty based Class Activation Maps

Badri N. Patro, Mayank Lunayach, Shivansh Patel and Vinay P. Namboodiri, “U-CAM: Visual Explanation using Uncertainty based Class Activation Maps”, Proceedings of IEEE International Conference on Computer Vision (ICCV)Seoul, South Korea, October 2019.

Granular Multimodal Attention Networks for Visual Dialog

B.N. Patro, S. Patel and V.P. Namboodiri, “Granular Multimodal Attention Networks for Visual Dialog”, ICCV Workshop on Intelligent Short Videos (ISV),Seoul, Korea, 2019.

Dynamic Attention Networks for Task Oriented Grounding

S. Dasgupta, B.N. Patro, V.P. Namboodiri, “Dynamic Attention Networks for Task Oriented Grounding”, ICCV Workshop on Intelligent Short Videos (ISV),Seoul, Korea, 2019.

HetConv: Beyond Homogeneous Convolution Kernels for Deep CNNs

Pravendra Singh, Vinay Kumar Verma, Piyush Rai and Vinay P. Namboodiri, “HetConv: Beyond Homogeneous Convolution Kernels for Deep CNNs”, International Journal of Computer Vision, accepted

FALF ConvNets: Fatuous auxiliary loss based filter-pruning for efficient deep CNNs

Pravendra Singh, Vinay Sameer Raja Kadi and Vinay P.Namboodiri, “FALF ConvNets: Fatuous auxiliary loss based filter-pruning for efficient deep CNNs”, Image and Vision Computing Journal, Volume 93, January 2020, 103857

Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA

Patro, B., Anupriy & Namboodiri, V. (2020, April). Explanation vs attention: A two-player game to obtain attention for vqa. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 11848-11855).

Bridged Variational Autoencoders for Joint Modeling of Images and Attributes

Ravindra Yadav, Ashish Sardana, Vinay P Namboodiri, Rajesh M Hegde. "Bridged Variational Autoencoders for Joint Modeling of Images and Attributes." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1479-1487

Can I teach a robot to replicate a line art

R. B. Venkataramaiyer, S. Kumar, and V. P. Namboodiri (2020). "Can I teach a robot to replicate a line art ", Proceedings of 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)" Aspen, USA, Mar. 2020.

Cooperative Initialization based Deep Neural Network Training

P. Singh, M. Varshney, V. P. Namboodiri, "Cooperative Initialization based Deep Neural Network Training", IEEE Winter Conference on Applications of Computer Vision (WACV), 2020

Leveraging Filter Correlations for Deep Model Compression

P. Singh, V.K. Verma, P. Rai, V.P. Namboodiri, "Leveraging Filter Correlations for Deep Model Compression", IEEE Winter Conference on Applications of Computer Vision (WACV ’20) pages 824-833, 2020

A Network Pruning Network Approach to Deep Model Compression

V.K. Verma, P. Singh, V.P. Namboodiri, P. Rai, "A "Network Pruning Network" Approach to Deep Model Compression", IEEE Winter Conference on Applications of Computer Vision (WACV), 2020

Jointly Trained Image and Video Generation using Residual Vectors

Yatin Dandi, Aniket Das, Soumye Singhal, Vinay Namboodiri, Piyush Rai; “Jointly Trained Image and Video Generation using Residual Vectors “, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3028-3042

Deep Bayesian Network for Visual Question Generation

Patro, B., Kurmi, V., Kumar, S., & Namboodiri, V. (2020). Deep Bayesian Network for Visual Question Generation. In The IEEE Winter Conference on Applications of Computer Vision (pp. 1566-1576).

Robust Explanations for Visual Question Answering

Patro, B., Patel, S., & Namboodiri, V. (2020). Robust Explanations for Visual Question Answering. In The IEEE Winter Conference on Applications of Computer Vision (pp. 1577-1586).

Accuracy booster: Performance boosting using feature map re-calibration

Pravendra Singh, Pratik Mazumder and Vinay P. Namboodiri, “Accuracy booster: Performance boosting using feature map re-calibration”, The IEEE Winter Conference on Applications of Computer Vision, pages=884--893, 2020

Cpwc: Contextual point wise convolution for object recognition

Pravendra Singh, Pratik Mazumder and Vinay P. Namboodiri, “Cpwc: Contextual point wise convolution for object recognition”, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ,pages=4152–4156, 2020

Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning

P. Singh; V.K. Verma; P. Rai; V.P. Namboodiri,"Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning", IEEE Journal of Selected Topics in Signal Processing ( Volume: 14, Issue: 4, May 2020)

Minimizing Supervision in Multi-label Categorization

Rajat, M. Varshney, P. Singh, V.P. Namboodiri, "Minimizing Supervision in Multi-label Categorization". CVPR Workshop on Fair, Data-Efficient and Trusted Computer Vision June 2020 2020: 93-102

Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis

K. R. Prajwal, R. Mukhopadhyay, V. P. Namboodiri and C. V. Jawahar, "Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 13793-13802, doi: 10.1109/CVPR42600.2020.01381.

A Multilingual Parallel Corpora Collection Effort for Indian Languages

Shashank Siripragada, Jerin Philip, Vinay P Namboodiri, CV Jawahar, “A Multilingual Parallel Corpora Collection Effort for Indian Languages”, Proceedings of The 12th Language Resources and Evaluation Conference

SkipConv: Skip Convolution for Computationally Efficient Deep CNNs

P. Singh, V.P. Namboodiri, "SkipConv: Skip Convolution for Computationally Efficient Deep CNNs", . International Joint Conference on Neural Networks (IJCNN), 2020

Passive Batch Injection Training Technique: Boosting Network Performance by Injecting Mini-Batches from a different Data Distribution

P. Singh, P. Mazumder and V. P. Namboodiri, "Passive Batch Injection Training Technique: Boosting Network Performance by Injecting Mini-Batches from a different Data Distribution," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9206622.

Probabilistic framework for solving visual dialog

Badri N. Patro, Anupriy & Vinay P. Namboodiri, ``Probabilistic framework for solving visual dialog'', Pattern Recognition, Volume 110, February 2021, 107586.

Revisiting paraphrase question generator using pairwise discriminator

Badri N Patro, Dev Chauhan, Vinod K Kurmi, Vinay P Namboodiri,(2020)``Revisiting paraphrase question generator using pairwise discriminator'',Neurocomputing, Volume 420, 2021, pp. 149-161

SD-MTCNN: Self-Distilled Multi-Task

Ankit Jha, Awanish Kumar, Biplab Banerjee and Vinay Namboodiri, “SD-MTCNN: Self-Distilled Multi-Task CNN”, Proceedings of British Machine Vision Conference (2020),

Determinantal Point Process as an alternative to NMS

Some, Samik, Mithun Das Gupta, and Vinay P. Namboodiri. "Determinantal Point Process as an alternative to NMS." Proceedings of British Machine Vision Conference (2020), arXiv preprint arXiv:2008.11451 (2020).

A Lip Sync Expert Is All You Need For Speech To Lip Generation In The Wild

K R Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C.V. Jawahar. 2020. A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild. In Proceedings of the 28th ACM International Conference on Multimedia (MM '20). Association for Computing Machinery, New York, NY, USA, 484–492. DOI:https://doi.org/10.1145/3394171.3413532

Visually Precise Query

Dasgupta, Riddhiman and Tom, Francis and Kumar, Sudhir and Das Gupta, Mithun and Kumar, Yokesh and Patro, Badri N. and Namboodiri, Vinay P. (2020)``Visually Precise Query'', Proceedings of the 28th ACM International Conference on Multimedia, New York, NY, USA, 2020.

Stochastic Talking Face Generation Using Latent Distribution Matching

Ravindra Yadav, Ashish Sardana, Vinay P Namboodiri, Rajesh M Hegde. ”Stochastic Talking Face Generation Using Latent Distribution Matching.” In InterSpeech, Shanghai, China. October 25, 2020.

Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing

Prem Raj, Vinay P. Namboodiri, Laxmidhar Behera, “Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2020)

Uncertainty Class Activation Map (U-CAM) using Gradient Certainty method

Badri N Patro, Mayank Lunayach, Vinay P Namboodiri (2020).``Uncertainty Class Activation Map (U-CAM) using Gradient Certainty method'', IEEE Transactions on Image Processing,2020.

GIFSL-grafting based improved few-shot learning

Pratik Mazumder, Pravendra Singh and Vinay P. Namboodiri, ”GIFSL - grafting based improved few-shot learning", Journal of Image and Vision Computing, volume 104 2020 , issn = "0262-8856", doi = "https://doi.org/10.1016/j.imavis.2020.104006"

STEER: Simple Temporal Regularization For Neural ODEs

Arnab Ghosh, Harkirat Singh Behl, Emilien Dupont, Philip H. S. Torr, Vinay Namboodiri, “STEER: Simple Temporal Regularization For Neural ODEs”, Proceedings of Neural Information Processing Systems Conference (NeuRIPS) 2020

PhraseOut: A Code Mixed Data Augmentation Method for Multilingual Neural Machine Translation

B. Jasim, V.P. Namboodiri and C.V. Jawahar, "PhraseOut: A Code Mixed Data Augmentation Method for Multilingual Neural Machine Translation", International Conference on Natural Language Processing (ICON-2020)

Exploring Pair-Wise NMT for Indian Languages

K. Akella, S.H. Allu, S.S. Ragupathi, A. Singhal, Z. Khan, V.P. Namboodiri, C V Jawahar, "Exploring Pair-Wise NMT for Indian Languages", International Conference on Natural Language Processing (ICON-2020) short paper

Visual Speech Enhancement Without A Real Visual Stream

Hegde, Sindhu B., K. Prajwal, R. Mukhopadhyay, Vinay Namboodiri and C. Jawahar. “Visual Speech Enhancement Without A Real Visual Stream.” Winter Conference on Applications of Computer Vision (WACV ’21)

Domain Impression: A Source Data Free Domain Adaptation Method

Vinod K. Kurmi, Venkatesh K Subramanian, Vinay P. Namboodiri, “Domain Impression: A Source Data Free Domain Adaptation Method”, IEEE Winter Conference of Applications on Computer Vision (WACV), Virtual, 2021

Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting

Vinod K. Kurmi, Badri N. Patro, Venkatesh K Subramanian, Vinay P. Namboodiri,“Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting”, IEEE Winter Conference of Applications on Computer Vision (WACV), Virtual, 2021

AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings

Pratik Mazumder and Pravendra Singh, Kranti Kumar Parida and Vinay P. Namboodiri, “AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings”, The IEEE Winter Conference on Applications of Computer Vision, WACV 2021