2 citations · 2 across the 5 of their papers we have counts for
5 papers
FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory
Lei Wei, Xiao Peng, Xu Dong +2
Large language models deployed as autonomous agents face critical memory limitations, lacking selective forgetting mechanisms that lead to either catastrophic forgetting at context…
FCoT-VL:Advancing Text-oriented Large Vision-Language Models with Efficient Visual Token Compression
Jianjian Li, Junquan Fan, Feng Tang +6
The rapid success of Vision Large Language Models (VLLMs) often depends on the high-resolution images with abundant visual tokens, which hinders training and deployment efficiency.…
EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
Kai Chen, Yunhao Gou, Runhui Huang +28
GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language…
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Anran Wu, Luwei Xiao, Xingjiao Wu +6
Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich…
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding
Haoli Bai, Zhiguang Liu, Xiaojun Meng +9
Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-tr…