Yueqi Xie

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance

2024 · arXiv (Cornell University)

The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs. This paper investigates the novel challenge of defending MLLMs against such attacks. Compared to …

Decoupled Side Information Fusion for Sequential Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Side information fusion for sequential recommendation (SR) aims to effectively leverage various side information to enhance the performance of next-item prediction. Most state-of-the-art methods build on self-attention networks and focus on exploring various solutions to …