4 papers
POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking
Zhangheng LI, Jianing Zhu, Junyuan Hong +4
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on cross-modal tasks by jointly training on large-scale textual and visual data, where privacy-sen…
SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets
Cody Simons, Dripta S. Raychaudhuri, Sk Miraj Ahmed +3
Scene understanding using multi-modal data is necessary in many applications, e.g., autonomous navigation. To achieve this in a variety of situations, existing models must be able…
A Study on Improving Realism of Synthetic Data for Machine Learning
Tingwei Shen, Ganning Zhao, Suya You
Synthetic-to-real data translation using generative adversarial learning has achieved significant success in improving synthetic data. Yet, limited studies focus on deep evaluation…
LGSQE: Lightweight Generated Sample Quality Evaluatoin
Ganning Zhao, Vasileios Magoulianitis, Suya You +1
Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample. To address this problem, a lightwe…