4 accepted papers
While the safety risks of image-based large language models (Image LLMs) have been extensively studied, their video-based counterparts (Video LLMs) remain critically under-examined. To systematically study this problem, we introduce \textbf{VideoSafetyEval} - the first large-scale, real-world benchm…
Multimodal LLMs (MLLMs) are increasingly deployed across diverse applications, but they pose significant safety concerns due to cross-modal interactions. To improve model safety awareness, existing methods rely on explicit-risk preference datasets and reinforcement learning guided by safety rewards.…
Multi-modal Retrieval-Augmented Generation (RAG) has become a critical method for empowering LLMs by leveraging candidate visual documents. However, current methods consider the entire document as the basic retrieval unit, introducing substantial irrelevant visual content in two ways: 1) Relevant do
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information. This structure can be defined by ontology and augmented in the classifier as a form of domain knowledge. To capture such dependencies between the labels…