Vinay Kumar Verma

Currently, I am Senior Applied Scientist at Amazon, working for the Private Brand. Prior to this I was Applied Scientist at International Machine Learning, Amazon, BLR, India.

After my Ph.D. I joined as a Post Doctoral fellow under the guidance of Prof. Carin at Duke University in the Department of Electrical and Computer Engineering.

Before joining Duke I was a Ph. D. scholar at the Department of Computer Science and Engineering, IIT Kanpur. In Ph. D. I worked under the guidance of Dr Piyush Rai. In my Ph. D. I have worked on Zero-Shot Learning, Multi-label Zero-shot Learning and Deep Model Compression.

My interest in research also includes Probabilistic Machine Learning, Deep Learning, Continual Learning, Few-Shot Learning and Computer Vision. More about me can be found in the CV.

Google Scholar (Please refer to this for the recent publication)

Recent News

Paper on title "Reliable or Deceptive? Investigating Gated Features for Smooth Visual Explanations in CNNs" accepted to CVPRW-25.
Paper on title "MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering" accepted to NAACL-25.
Paper on title "Convolutional Prompting meets Language Models for Continual Learning" accepted to CVPR-24.
Paper on title "Verse: Virtual-gradient aware streaming lifelong learning with anytime inference", accepted to ICRA-24.
Paper on title "Eﬃcient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning", accepted to WACV-24.
Paper on title "CoD: Coherent Detection of Entities from Images with Multiple Modalities", accepted to WACV-24.
Paper on title "Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning", accepted to WACV-24.
Paper on title "Exemplar-Free Continual Transformer with Convolutions", accepted to ICCV-23.
Paper on title "Streaming LifeLong Learning With Any-Time Inference", accepted to ICRA-23.
Paper on title "Pushing the Eﬃciency Limit Using Structured Sparse Convolutions", accepted to WACV-23.