Pranav Jeevan

Pranav Jeevan

PhD candidate

Publications

Evaluation Metric for Quality Control and Generative Models in Histopathology Images

Published in Proceedings of the 2025 IEEE International Symposium on Biomedical Imaging (ISBI), Huston TX, USA, 2025

Our study introduces ResNet-L2 (RL2), a novel metric using ResNet features and a normalizing flow for evaluating generative models in histopathology, offering reliable assessments with fewer images and quicker assessments than traditional metrics, effectively handling diverse degradation types and diffusion processes.

WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency

Published in Proceedings of the 2025 AAAI Conference on Artificial Intelligence (AAAI-25), Philadelphia, PA, USA, 2025

WaveMixSR-V2, an enhanced version of WaveMixSR, incorporating pixel shuffle and a multistage design, achieving state-of-the-art super-resolution performance on the BSD100 dataset with improved resource efficiency, lower latency, and higher throughput.

FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch

Published in Proceedings of the 2024 IEEE International Conference on Big Data (IEEE BigData), Washington, DC, USA, 2024

FLeNS combines Nesterov acceleration with adaptive Hessian sketching to achieve super-linear convergence in federated learning while significantly reducing communication overhead.

Adversarial Transport Terms for Unsupervised Domain Adaptation

Published in Proceedings of the 27th International Conference on Pattern Recognition, Kolkata, India, 2024

The new technique, ATT, enhances unsupervised domain adaptation by introducing a novel transport loss that displaces classifier outputs to reduce class confusion and improve domain-invariant representations, leading to superior UDA results on benchmark datasets.

PawFACS: Leveraging Semi-Supervised Learning for Pet Facial Action Recognition

Published in Proceedings of the 35th British Machine Vision Conference (BMVC) 2024, Glasgow, UK, 2024

My work as an intern at Samsung Research where I developed a multi-label classification system for detecting facial action units in dogs and cats using a semi-supervised pseudo-labeling strategy using small loss trick and consistency regularization

Normalizing Flow Based Metric for Image Generation

Published in Arxiv, 2024

We propose two efficient flow-based metrics, FLD and D-FLD, that provide accurate realness assessment of generated images with significantly fewer parameters and samples than FID, making them ideal for evaluating small image sets in diverse domains.

EDSNet: Efficient-DSNet for Video Summarization

Published in arxiv, 2024

EDSNet enhances the Direct-to-Summarize Network (DSNet) with resource-efficient token mixing mechanisms and alternative pooling strategies, significantly reducing computational costs while maintaining competitive performance in video summarization.

WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution

Published in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikaloa, HI, USA, 2024

WaveMixSR is a new neural network for image super-resolution that uses the WaveMix architecture, which is based on a 2D-discrete wavelet transform for spatial token-mixing, and achieves higher performance while requiring fewer resources and training data than transformer-based models.

Resource-efficient Image Inpainting

Published in Tiny Papers @ International Conference on Learning Representations (ICLR), 2023

The paper proposes a computationally-efficient WaveMix-based fully convolutional architecture for image inpainting that outperforms the current state-of-the-art models while using less parameters and lower training and evaluation times.

Convolutional Xformers for Vision

Published in Arxiv, 2022

CXV is a new convolutional transformer hybrid architecture that outperforms other architectures in image classification, especially in scenarios with limited data and GPU resources.

Resource-Efficient Hybrid X-Formers for Vision

Published in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022

We developed a resource-efficient architecture for vision by making three modifications on ViT to address their shortcomings, which significantly improved their performance and made them more accessible.