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Found 8 papers, 1 papers with code
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impressive performance on tasks such as natural image captioning, visual question answering (VQA), and spatial reasoning.
Robust Influence-based Training Methods for Noisy Brain MRI
In this work, we study a difficult but realistic setting of training a deep learning model on noisy MR images to classify brain tumors.
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks.
In-Context Learning Demonstration Selection via Influence Analysis
Despite its multiple benefits, ICL generalization performance is sensitive to the selected demonstrations.
Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model
In this work, we study the ability of VLMs on hateful meme detection and hateful meme correction tasks with zero-shot prompting.
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006.
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, which leaves many avenues for an adversary to exploit.
Poisoning Attacks on Fair Machine Learning
Our framework enables attackers to flexibly adjust the attack's focus on prediction accuracy or fairness and accurately quantify the impact of each candidate point to both accuracy loss and fairness violation, thus producing effective poisoning samples.
