Scholar
Vinay Kumar Verma
Google Scholar ID: 7x6GZ1EAAAAJ
@Amazon, Prev: IIT-Kanpur, Duke University NC, HCU
Citations & Impact
Citations
2,071
H-index
20
i10-index
25
Publications
20
Co-authors
40
list available
Contact
Publications
Resume (English only)
Academic Achievements
- My paper titled 'Reliable or Deceptive? Investigating Gated Features for Smooth Visual Explanations in CNNs' was accepted to CVPRW-25. Another paper, 'MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering,' was accepted to NAACL-25. 'Convolutional Prompting meets Language Models for Continual Learning' was accepted to CVPR-24. 'Verse: Virtual-gradient aware streaming lifelong learning with anytime inference' was accepted to ICRA-24. 'Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning' was accepted to WACV-24. 'CoD: Coherent Detection of Entities from Images with Multiple Modalities' was accepted to WACV-24. 'Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning' was accepted to WACV-24. 'Exemplar-Free Continual Transformer with Convolutions' was accepted to ICCV-23. 'Streaming LifeLong Learning With Any-Time Inference' was accepted to ICRA-23. 'Pushing the Efficiency Limit Using Structured Sparse Convolutions' was accepted to WACV-23.
Research Experience
- I have worked as an Applied Scientist at International Machine Learning, Amazon, BLR, India; as a Post Doctoral fellow at Duke University's Department of Electrical and Computer Engineering; and as a Ph.D. scholar at IIT Kanpur, focusing on Zero-Shot Learning, Multi-label Zero-shot Learning, and Deep Model Compression.
Education
- I was a Post Doctoral fellow under the guidance of Prof. Carin at Duke University in the Department of Electrical and Computer Engineering. Before that, I was a Ph.D. scholar at the Department of Computer Science and Engineering, IIT Kanpur, where I worked under the guidance of Dr. Piyush Rai on Zero-Shot Learning, Multi-label Zero-shot Learning, and Deep Model Compression.
Background
- Currently, I am a Senior Applied Scientist at Amazon, working for the Private Brand. My research interests include Probabilistic Machine Learning, Deep Learning, Continual Learning, Few-Shot Learning, and Computer Vision.
Miscellany
- More about me can be found in my CV.
