1 citations · 1 across the 11 of their papers we have counts for
11 papers · 1 filter
DiT as Real-Time Rerenderer: Streaming Video Stylization with Autoregressive Diffusion Transformer
Hengye Lyu, Zisu Li, Yue Hong +4
Recent advances in video generation models has significantly accelerated video generation and related downstream tasks. Among these, video stylization holds important research valu…
Aligned Contrastive Loss for Long-Tailed Recognition
Jiali Ma, Jiequan Cui, Maeno Kazuki +4
In this paper, we propose an Aligned Contrastive Learning (ACL) algorithm to address the long-tailed recognition problem. Our findings indicate that while multi-view training boost…
Learning to Animate Images from A Few Videos to Portray Delicate Human Actions
Haoxin Li, Yingchen Yu, Qilong Wu +3
Despite recent progress, video generative models still struggle to animate static images into videos that portray delicate human actions, particularly when handling uncommon or nov…
CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
Yuan Zhou, Qingshan Xu, Jiequan Cui +4
Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed i…
Instruction Tuning-free Visual Token Complement for Multimodal LLMs
Dongsheng Wang, Jiequan Cui, Miaoge Li +3
As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inh…
Few-shot Learner Parameterization by Diffusion Time-steps
Zhongqi Yue, Pan Zhou, Richang Hong +2
Even when using large multi-modal foundation models, few-shot learning is still challenging -- if there is no proper inductive bias, it is nearly impossible to keep the nuanced cla…