77 citations · 169 across the 26 of their papers we have counts for
6 papers · 1 filter
Caption supervision enables robust learners
Benjamin Feuer, Ameya Joshi, Chinmay Hegde
Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the…
Adversarial Token Attacks on Vision Transformers
Ameya Joshi, Gauri Jagatap, Chinmay Hegde
Vision transformers rely on a patch token based self attention mechanism, in contrast to convolutional networks. We investigate fundamental differences between these two families o…
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Gauri Jagatap, Chinmay Hegde
Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafte…
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar +1
Deep neural networks have been shown to exhibit an intriguing vulnerability to adversarial input images corrupted with imperceptible perturbations. However, the majority of adversa…
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
Rakib Hyder, Viraj Shah, Chinmay Hegde +1
The classical problem of phase retrieval arises in various signal acquisition systems. Due to the ill-posed nature of the problem, the solution requires assumptions on the structur…
Efficient Upsampling of Natural Images
Chinmay Hegde, Oncel Tuzel, Fatih Porikli
We propose a novel method of efficient upsampling of a single natural image. Current methods for image upsampling tend to produce high-resolution images with either blurry salient…