About Us

Visual Cognition Laboratory Private Limited: Pioneering AI-Driven Computer Vision Solutions

Visual Cognition Laboratory Private Limited is a leading technology company specializing in the development of cutting-edge, artificially intelligent computer vision software and systems. Our innovative solutions are designed to transform industries, enhance productivity, and revolutionize the way businesses utilize computer vision technology. At Visual Cognition Laboratory, our team of experienced professionals is dedicated to pushing the boundaries of artificial intelligence and computer vision. We combine our deep domain expertise with advanced research and development methodologies to create practical, high-performance solutions that cater to the unique needs of our clients. Our extensive portfolio of AI-driven computer vision products and services encompasses a wide range of applications, including object detection and recognition, image and video analytics, pattern recognition, and augmented reality. By harnessing the power of AI, we enable businesses to unlock new opportunities, streamline operations, and stay ahead in an increasingly competitive landscape. As a trusted partner, Visual Cognition Laboratory Private Limited is committed to delivering exceptional value and customer satisfaction. Our scalable, flexible solutions are designed to grow with your business, ensuring long-term success and a strong return on investment.

Discover the future of computer vision technology with Visual Cognition Laboratory Private Limited – where innovation meets practicality.

Accolades

Award
Award
Award

To develop and deliver innovative, AI-driven computer vision solutions that empower businesses, drive industry transformation, and enhance human experiences.

To become a global leader in computer vision and artificial intelligence by creating advanced technologies that redefine the boundaries of what’s possible, improving lives, and shaping the future of digital interactions.

At Visual Cognition Laboratory, our core values center around innovation, excellence, and collaboration in computer vision and AI. We prioritize customer needs, creating tailored solutions with integrity and transparency. Embracing agility, we adapt to market demands while emphasizing social responsibility and sustainable practices for the betterment of society and the environment.

Our Research

  • Richa Sharma, Manoj Sharma,Ankit Shukla, Santanu Chaudhury ”Conditional Deep 3D-Convolutional Generative Adversarial Nets for RGB-D Generation,” Mathematical Problems in Engineering, vol. 2021, Article ID 8358314, 8 pages, 2021
  • Aakash Saboo, Manoj Sharma, ”Latent-optimization based Disease-aware Image Editing for Medical Image Augmentation” has been accepted in BMVC 2021. 
  • Kalagara Chaitanya Kumar, Aryan Lala, Ritesh Vyas, Manoj Sharma “Improved periocular recognition through blend of handcrafted and deep features”, has been accepted in CVIP-2021. 
  • Ankit Shukla, Manoj Sharma, ”Auto-Encoder Guided Attention based Network forHyperspectral Recovery from Real RGB Images” has been accepted in PReMI 2021. 
  • Kalagara Chaitanya Kumar, Aryan Lala, Ritesh Vyas, Manoj Sharma “Periocular recognition via effective textural descriptor”, 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON 2020), to be organized by MNNIT Allahabad, Prayagraj. 
  • M. Sharma, M. Makwana, A. P. Singh, A. Upadhyay, S. Chaudhury ”Gradually growing Residual and self-Attention based Dense Deep Back Projection Network for Large Scale Super-Resolution of Image” published in PReMI-2019 
  • M. Sharma, A. Ray, A. Upadhyay, M. Makwana, ”An End-to-End trainable framework for joint optimization of document enhancement and recognition”, 2019 15th IAPR International Conference on Document Analysis and Recognition published in ICDAR 2019 
  • J. Wilson, A.K. Meher, B.V. Bindu, M. Sharma. ”Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams under Concept Drift” Book Chapter Published in NeurIPS 2018 comptetion track. 
  • M. Sharma, A. Upadhyay, R. Mukhopadhyay, S. Chaudhury. “NTIRE 2019 Challenge on Video Super-Resolution”, Co-author of Team Paper 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (published in CVPRW), 2019. 
  • M. Sharma, M. Makwana, A. Upadhyay, A. P. Singh, A. Trivedi, A. Saini, S. Chaudhury. “NTIRE 2019 Challenge on Image Colorization”, Co-author of Team Paper 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (publis in CVPRW), 2019. 
  • M. Sharma, P. Mukherjee, M. Makwana, A.P. Singh, A. Trivedi, A. Upadhyay, B. Lall, S. Chaudhury. “DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks”, https://arxiv.org/abs/1904.01215.or has been accepted in PReMI 2021 
  • M. Sharma, A. Upadhyay, R. Mukhopadhyay, S. Chaudhury. “2D-3D CNN based architectures for spectral reconstruction from RGB images”, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Utah, 2018, pp. 957-964. 
  • M. Sharma, A. Upadhyay, R. Mukhopadhyay, S. Chaudhury. “IRGUN : Improved Residue based Gradual Up-Scaling Network for Single Image Super Resolution”, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Utah, 2018, pp. 947-956. 
  • M. Sharma, A. Upadhyay, R. Mukhopadhyay, S. Chaudhury. “NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images”, Co-author of Team Paper 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Utah, 2018, pp. 1042-1051.
  • M. Sharma, A. Upadhyay, R. Mukhopadhyay, S. Chaudhury. “NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results”, Co-author of Team Paper 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Utah, 2018, pp. 965-976. 
  • M. Sharma, A. Upadhyay, R. Mukhopadhyay, S. Chaudhury. “2018 PIRM Challenge on Perceptual Image Super-resolution”, Co-author of Team Paper 2018 European Conference on Computer Vision Workshop (ECCVW), Munich, 2018. 
  • M. Sharma, R. Mukhopadhyay, S. Chaudhury and B. Lall. “An End-to-End Deep Learning Framework for Super-Resolution based Inpainting”, 2017 National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG), IIT-mandi, 2017, pp. 198-208. 
  • M. Sharma, S. Chaudhury and B. Lall. “Space-Time Super-Resolution using Deep Learning based Framework”, 2017 International Conference on Pattern Recognition and Machine Intelligence (PReMI), 2017, pp. 582-590. 
  • M. Sharma, A. Ray, S. Chaudhury and B. Lall. “A Noise-Resilient SuperResolution framework to boost OCR performance”, 2017 14th IAPR International Conference on Document Analysis and Recognition ICDAR, Kyoto, 2017, pp. 466- 471. 
  • M. Sharma, S. Chaudhury and B. Lall, “Deep learning based frameworks for image super-resolution and noise-resilient super-resolution,” 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 744-751. 
  • M. Sharma, S. Chaudhury and B. Lall, “Sparse representation based classifier to assess video quality,” 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Patna, pp. 1-4. 
  • M. Sharma, R. Nath, “Multiple sub-filter using variable step size and partial update for acoustic echo cancellation,” Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on, Melaka, 2012, pp. 261-265. 
  • M. Sharma, M. Shukla and A. Kaul, “Image hiding using unitary similarity transformation,” Image Information Processing (ICIIP), 2011 International Conference on, Himachal Pradesh, 2011, pp. 1-4.

People We Work With

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