12 citations · 19 across the 7 of their papers we have counts for
5 papers · 1 filter
Normalizing Flow-Based Metric for Image Generation
Pranav Jeevan, Neeraj Nixon, Amit Sethi
We propose two new evaluation metrics to assess realness of generated images based on normalizing flows: a simpler and efficient flow-based likelihood distance (FLD) and a more exa…
EDSNet: Efficient-DSNet for Video Summarization
Ashish Prasad, Pranav Jeevan, Amit Sethi
Current video summarization methods largely rely on transformer-based architectures, which, due to their quadratic complexity, require substantial computational resources. In this…
WaveMix: Resource-efficient Token Mixing for Images
Pranav Jeevan, Amit Sethi
Although certain vision transformer (ViT) and CNN architectures generalize well on vision tasks, it is often impractical to use them on green, edge, or desktop computing due to the…
Convolutional Xformers for Vision
Pranav Jeevan, Amit sethi
Vision transformers (ViTs) have found only limited practical use in processing images, in spite of their state-of-the-art accuracy on certain benchmarks. The reason for their limit…
Vision Xformers: Efficient Attention for Image Classification
Pranav Jeevan, Amit Sethi
Although transformers have become the neural architectures of choice for natural language processing, they require orders of magnitude more training data, GPU memory, and computati…