1 citations · 3 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training
Kai Wang, Mingjia Shi, Yukun Zhou +6
Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a clos…
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…
Doubly Abductive Counterfactual Inference for Text-based Image Editing
Xue Song, Jiequan Cui, Hanwang Zhang +3
We study text-based image editing (TBIE) of a single image by counterfactual inference because it is an elegant formulation to precisely address the requirement: the edited image s…
Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning
Yuan Zhou, Richang Hong, Yanrong Guo +3
In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i.e., relation disentanglement, which means enhancing FSCIL via disentanglin…
Exploring Diffusion Time-steps for Unsupervised Representation Learning
Zhongqi Yue, Jiankun Wang, Qianru Sun +3
Representation learning is all about discovering the hidden modular attributes that generate the data faithfully. We explore the potential of Denoising Diffusion Probabilistic Mode…
Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior
Zike Wu, Pan Zhou, Xuanyu Yi +2
Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To…