5 citations · 12 across the 7 of their papers we have counts for
17 papers
PROVES: Establishing Image Provenance using Semantic Signatures
Mingyang Xie, Manav Kulshrestha, Shaojie Wang +4
Modern AI tools, such as generative adversarial networks, have transformed our ability to create and modify visual data with photorealistic results. However, one of the deleterious…
Leveraging redundancy in attention with Reuse Transformers
Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit +5
Pairwise dot product-based attention allows Transformers to exchange information between tokens in an input-dependent way, and is key to their success across diverse applications i…
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation
Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain +3
State-of-the-art transformer models use pairwise dot-product based self-attention, which comes at a computational cost quadratic in the input sequence length. In this paper, we inv…
Understanding Robustness of Transformers for Image Classification
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner +3
Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (Vi…
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
Zhihao Xia, Michaël Gharbi, Federico Perazzi +2
We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments. Our goal is to produce a high-…
Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints
Ayan Chakrabarti, Roch Guérin, Chenyang Lu +1
To deploy machine learning-based algorithms for real-time applications with strict latency constraints, we consider an edge-computing setting where a subset of inputs are offloaded…