31 citations · 34 across the 9 of their papers we have counts for
6 papers · 1 filter
CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
Neeraj Anand, Samyak Jha, Udbhav Bamba +1
Despite the rapid success of Large Vision-Language Models (LVLMs), a persistent challenge is their tendency to generate hallucinated content, undermining reliability in real-world…
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning
Udbhav Bamba, Neeraj Anand, Saksham Aggarwal +2
Binarization is a powerful compression technique for neural networks, significantly reducing FLOPs, but often results in a significant drop in model performance. To address this is…
UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Deepak K. Gupta, Udbhav Bamba, Abhishek Thakur +4
Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, chal…
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations
Rishabh Tiwari, Udbhav Bamba, Arnav Chavan +1
Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss…
Rescaling CNN through Learnable Repetition of Network Parameters
Arnav Chavan, Udbhav Bamba, Rishabh Tiwari +1
Deeper and wider CNNs are known to provide improved performance for deep learning tasks. However, most such networks have poor performance gain per parameter increase. In this pape…
Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection
Suyog Jadhav, Udbhav Bamba, Arnav Chavan +2
Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation. For Semantic segmentation task, we propose…