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Found 5 papers, 1 papers with code
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
Multimodal large language models (MLLMs) have shown impressive reasoning abilities, which, however, are also more vulnerable to jailbreak attacks than their LLM predecessors.
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vision-language tasks.
Leveraging per Image-Token Consistency for Vision-Language Pre-training
(2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations.
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification
The main question we address in this paper is how to scale up visual recognition of unseen classes, also known as zero-shot learning, to tens of thousands of categories as in the ImageNet-21K benchmark.
Region Semantically Aligned Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes.
