9 citations · 15 across the 11 of their papers we have counts for
12 papers
CD-RCM: Generalizable Continuous-Depth Novel View Synthesis for Reflectance Confocal Microscopy
Tooba Imtiaz, Milind Rajadhyaksha, Kivanc Kose +1
Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution "optical biopsies" of human skin \emph{in vivo} by acquiring en-face images at successive depths, fo…
PanoWorld: Geometry-Consistent Panoramic Video World Modeling
Le Jiang, Xiangyu Bai, Bishoy Galoaa +7
We present PanoWorld, a panoramic video world model that generates geometry-consistent 360 video from a single image and a caption. Existing panoramic video methods optimi…
Human Cognition in Machines: A Unified Perspective of World Models
Timothy Rupprecht, Pu Zhao, Amir Taherin +20
This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To…
LVT: Large-Scale Scene Reconstruction via Local View Transformers
Tooba Imtiaz, Lucy Chai, Kathryn Heal +4
Large transformer models are proving to be a powerful tool for 3D vision and novel view synthesis. However, the standard Transformer's well-known quadratic complexity makes it diff…
STAR: Stability-Inducing Weight Perturbation for Continual Learning
Masih Eskandar, Tooba Imtiaz, Davin Hill +2
Humans can naturally learn new and varying tasks in a sequential manner. Continual learning is a class of learning algorithms that updates its learned model as it sees new data (on…
ADAPT to Robustify Prompt Tuning Vision Transformers
Masih Eskandar, Tooba Imtiaz, Zifeng Wang +1
The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks. Many existing defenses against these attacks, such as adversarial t…
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Tooba Imtiaz, Morgan Kohler, Jared Miller +5
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal. The addition of calculated small distortion to images, for instance, can d…
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation
Jaesung Choe, Kyungdon Joo, Tooba Imtiaz +1
Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D perceptions for practical usage -- dense 3D information (stereo cameras) and highly-accurat…
CD-UAP: Class Discriminative Universal Adversarial Perturbation
Chaoning Zhang, Philipp Benz, Tooba Imtiaz +1
A single universal adversarial perturbation (UAP) can be added to all natural images to change most of their predicted class labels. It is of high practical relevance for an attack…
Double Targeted Universal Adversarial Perturbations
Philipp Benz, Chaoning Zhang, Tooba Imtiaz +1
Despite their impressive performance, deep neural networks (DNNs) are widely known to be vulnerable to adversarial attacks, which makes it challenging for them to be deployed in se…
Data from Model: Extracting Data from Non-robust and Robust Models
Philipp Benz, Chaoning Zhang, Tooba Imtiaz +1
The essence of deep learning is to exploit data to train a deep neural network (DNN) model. This work explores the reverse process of generating data from a model, attempting to re…
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
Chaoning Zhang, Philipp Benz, Tooba Imtiaz +1
A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a ve…