9 citations · 14 across the 7 of their papers we have counts for
6 papers · 1 filter
Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Ruoyu Chen, Xiaoqing Guo, Kangwei Liu +6
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs. Yet, the extent to which generated token…
FaceInsight: A Multimodal Large Language Model for Face Perception
Jingzhi Li, Changjiang Luo, Ruoyu Chen +4
Recent advances in multimodal large language models (MLLMs) have demonstrated strong capabilities in understanding general visual content. However, these general-domain MLLMs perfo…
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
Ruoyu Chen, Hua Zhang, Jingzhi Li +3
The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature sp…
PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors
Xinwei Liu, Xiaojun Jia, Yuan Xun +2
Diffusion models (DMs) have advanced text-to-image (T2I) synthesis, yet their personalization capabilities raise serious privacy and copyright concerns. Malicious actors can misuse…
Interpreting Object-level Foundation Models via Visual Precision Search
Ruoyu Chen, Siyuan Liang, Jingzhi Li +5
Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. Howev…
Towards Generalized Few-Shot Open-Set Object Detection
Binyi Su, Hua Zhang, Jingzhi Li +1
Open-set object detection (OSOD) aims to detect the known categories and reject unknown objects in a dynamic world, which has achieved significant attention. However, previous appr…