8 citations · 16 across the 10 of their papers we have counts for
10 papers
TokenTrace: Multi-Concept Attribution through Watermarked Token Recovery
Li Zhang, Shruti Agarwal, John Collomosse +2
Generative AI models pose a significant challenge to intellectual property (IP), as they can replicate unique artistic styles and concepts without attribution. While watermarking o…
BLO-Inst: Bi-Level Optimization Based Alignment of YOLO and SAM for Robust Instance Segmentation
Li Zhang, Pengtao Xie
The Segment Anything Model has revolutionized image segmentation with its zero-shot capabilities, yet its reliance on manual prompts hinders fully automated deployment. While integ…
SteganoBackdoor: Evading Data-Poisoning Defenses via Steganographic Backdoors
Eric Xue, Ruiyi Zhang, Pengtao Xie
Transformer-based models are highly susceptible to backdoor attacks via supervised fine-tuning (SFT). To red-team existing data-poisoning defenses, prior work has increasingly focu…
Defense against Prompt Injection Attacks via Mixture of Encodings
Ruiyi Zhang, David Sullivan, Kyle Jackson +2
Large Language Models (LLMs) have emerged as a dominant approach for a wide range of NLP tasks, with their access to external information further enhancing their capabilities. Howe…
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining
Ruiyi Zhang, Sai Ashish Somayajula, Pengtao Xie
Large-scale general domain pretraining followed by downstream-specific finetuning has become a predominant paradigm in machine learning. However, discrepancies between the pretrain…
AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model
Yuchen Li, Li Zhang, Youwei Liang +1
Segment Anything Model (SAM) has gained significant recognition in the field of semantic segmentation due to its versatile capabilities and impressive performance. Despite its succ…
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
Peijia Qin, Ruiyi Zhang, Pengtao Xie
Parameter-efficient fine-tuning (PEFT) is a flexible and efficient method for adapting large language models (LLMs) to downstream tasks. Among these methods, weight-decomposed low-…
BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models
Rushi Qiang, Ruiyi Zhang, Pengtao Xie
Low-rank adaptation (LoRA) is a popular method for fine-tuning large-scale pre-trained models in downstream tasks by learning low-rank incremental matrices. Though LoRA and its var…
BLO-SAM: Bi-level Optimization Based Overfitting-Preventing Finetuning of SAM
Li Zhang, Youwei Liang, Ruiyi Zhang +2
The Segment Anything Model (SAM), a foundation model pretrained on millions of images and segmentation masks, has significantly advanced semantic segmentation, a fundamental task i…
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs
Youwei Liang, Ruiyi Zhang, Li Zhang +1
A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical research, accelerating drug discovery, enhancing our understanding of structure-activity relation…