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Found 10 papers, 2 papers with code
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models
The rapid advancement of vision-language models (VLMs) has established a new paradigm in video anomaly detection (VAD): leveraging VLMs to simultaneously detect anomalies and provide comprehendible explanations for the decisions.
Buckle Up: Robustifying LLMs at Every Customization Stage via Data Curation
To mitigate such attack, we propose an effective defensive framework utilizing data curation to revise commonsense texts and enhance their safety implication from the perspective of LLMs.
Robustifying Safety-Aligned Large Language Models through Clean Data Curation
Large language models (LLMs) are vulnerable when trained on datasets containing harmful content, which leads to potential jailbreaking attacks in two scenarios: the integration of harmful texts within crowdsourced data used for pre-training and direct tampering with LLMs through fine-tuning.
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.
Recent Advances in Predictive Modeling with Electronic Health Records
This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
Medical report generation is one of the most challenging tasks in medical image analysis.
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
To fairly evaluate the performance, we also propose three specific evaluation metrics.
