6 papers
HAMMR-L: Noise Reduction in Quantum Outcomes Using a Richardson-Lucy Deconvolution Algorithm for Quantum State Graphs
Jake Scally, Austin Myers, Ryan Carmichael +2
Current quantum computers present significant noise, especially as circuit depth and qubit count increase. Prior work has demonstrated that erroneous outcomes exhibit some behavior…
Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging
Montasir Shams, Chashi Mahiul Islam, Shaeke Salman +2
Vision transformers (ViTs) have rapidly gained prominence in medical imaging tasks such as disease classification, segmentation, and detection due to their superior accuracy compar…
Malicious Path Manipulations via Exploitation of Representation Vulnerabilities of Vision-Language Navigation Systems
Chashi Mahiul Islam, Shaeke Salman, Montasir Shams +2
Building on the unprecedented capabilities of large language models for command understanding and zero-shot recognition of multi-modal vision-language transformers, visual language…
Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal Models
Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu
Utilizing a shared embedding space, emerging multimodal models exhibit unprecedented zero-shot capabilities. However, the shared embedding space could lead to new vulnerabilities i…
Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models
Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu +1
Transformer-based models have dominated natural language processing and other areas in the last few years due to their superior (zero-shot) performance on benchmark datasets. Howev…
Intriguing Equivalence Structures of the Embedding Space of Vision Transformers
Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu
Pre-trained large foundation models play a central role in the recent surge of artificial intelligence, resulting in fine-tuned models with remarkable abilities when measured on be…