55 citations · 65 across the 16 of their papers we have counts for
16 papers
Modular Energy Steering for Safe Text-to-Image Generation with Foundation Models
Yaoteng Tan, Zikui Cai, M. Salman Asif
Controlling the behavior of text-to-image generative models is critical for safe and practical deployment. Existing safety approaches typically rely on model fine-tuning or curated…
EigenScore: OOD Detection using Covariance in Diffusion Models
Shirin Shoushtari, Yi Wang, Xiao Shi +2
Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems in safety-sensitive domains. Diffusion models have recently emerged as powerful…
VOccl3D: A Video Benchmark Dataset for 3D Human Pose and Shape Estimation under real Occlusions
Yash Garg, Saketh Bachu, Arindam Dutta +5
Human pose and shape (HPS) estimation methods have been extensively studied, with many demonstrating high zero-shot performance on in-the-wild images and videos. However, these met…
Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Models
Shirin Shoushtari, Edward P. Chandler, Yuanhao Wang +2
Diffusion models are widely used as priors in imaging inverse problems. However, their performance often degrades under distribution shifts between the training and test-time image…
Robust Multimodal Learning via Cross-Modal Proxy Tokens
Md Kaykobad Reza, Ameya Patil, Mashhour Solh +1
Multimodal models often experience a significant performance drop when one or more modalities are missing during inference. To address this challenge, we propose a simple yet effec…
MMP: Towards Robust Multi-Modal Learning with Masked Modality Projection
Niki Nezakati, Md Kaykobad Reza, Ameya Patil +2
Multimodal learning seeks to combine data from multiple input sources to enhance the performance of different downstream tasks. In real-world scenarios, performance can degrade sub…
Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling
Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
Diffusion models can generate a variety of high-quality images by modeling complex data distributions. Trained diffusion models can also be very effective image priors for solving…
Targeted Unlearning with Single Layer Unlearning Gradient
Zikui Cai, Yaoteng Tan, M. Salman Asif
Machine unlearning methods aim to remove sensitive or unwanted content from trained models, but typically demand extensive model updates at significant computational cost while pot…
Transform-Dependent Adversarial Attacks
Yaoteng Tan, Zikui Cai, M. Salman Asif
Deep networks are highly vulnerable to adversarial attacks, yet conventional attack methods utilize static adversarial perturbations that induce fixed mispredictions. In this work,…
Cross-Modal Safety Alignment: Is textual unlearning all you need?
Trishna Chakraborty, Erfan Shayegani, Zikui Cai +5
Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing…
Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training
Edward P. Chandler, Shirin Shoushtari, Jiaming Liu +2
Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior mo…
STRIDE: Single-video based Temporally Continuous Occlusion-Robust 3D Pose Estimation
Rohit Lal, Saketh Bachu, Yash Garg +6
The capability to accurately estimate 3D human poses is crucial for diverse fields such as action recognition, gait recognition, and virtual/augmented reality. However, a persisten…
Domain Expansion via Network Adaptation for Solving Inverse Problems
Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
Deep learning-based methods deliver state-of-the-art performance for solving inverse problems that arise in computational imaging. These methods can be broadly divided into two gro…
Parameter-efficient Multi-Task and Multi-Domain Learning using Factorized Tensor Networks
Yash Garg, Nebiyou Yismaw, Rakib Hyder +2
Multi-task and multi-domain learning methods seek to learn multiple tasks/domains, jointly or one after another, using a single unified network. The primary challenge and opportuni…
Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation
Md Kaykobad Reza, Ashley Prater-Bennette, M. Salman Asif
Multimodal learning seeks to utilize data from multiple sources to improve the overall performance of downstream tasks. It is desirable for redundancies in the data to make multimo…
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Shirin Shoushtari, Jiaming Liu, Edward P. Chandler +2
Plug-and-Play (PnP) priors is a widely-used family of methods for solving imaging inverse problems by integrating physical measurement models with image priors specified using imag…