7 citations · 11 across the 6 of their papers we have counts for
6 papers
Follow-Your-Motion: Video Motion Transfer via Efficient Spatial-Temporal Decoupled Finetuning
Yue Ma, Yulong Liu, Qiyuan Zhu +8
Recently, breakthroughs in the video diffusion transformer have shown remarkable capabilities in diverse motion generations. As for the motion-transfer task, current methods mainly…
Graceful Forgetting in Generative Language Models
Chunyang Jiang, Chi-min Chan, Yiyang Cai +3
Recently, the pretrain-finetune paradigm has become a cornerstone in various deep learning areas. While in general the pre-trained model would promote both effectiveness and effici…
HiRes-LLaVA: Restoring Fragmentation Input in High-Resolution Large Vision-Language Models
Runhui Huang, Xinpeng Ding, Chunwei Wang +7
High-resolution inputs enable Large Vision-Language Models (LVLMs) to discern finer visual details, enhancing their comprehension capabilities. To reduce the training and computati…
See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI
Yulong Liu, Yongqiang Ma, Guibo Zhu +2
Deciphering visual content from functional Magnetic Resonance Imaging (fMRI) helps illuminate the human vision system. However, the scarcity of fMRI data and noise hamper brain dec…
From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs
Yulong Liu, Yunlong Yuan, Chunwei Wang +5
The distinction between humans and animals lies in the unique ability of humans to use and create tools. Tools empower humans to overcome physiological limitations, fostering the c…
BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding
Yulong Liu, Yongqiang Ma, Wei Zhou +2
Due to the lack of paired samples and the low signal-to-noise ratio of functional MRI (fMRI) signals, reconstructing perceived natural images or decoding their semantic contents fr…