2 citations · 2 across the 2 of their papers we have counts for
5 papers · 1 filter
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards
Honghao Chen, Xingzhou Lou, Xiaokun Feng +2
Chain of thought reasoning has demonstrated remarkable success in large language models, yet its adaptation to vision-language reasoning remains an open challenge with unclear best…
Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition
Yurong Zhang, Honghao Chen, Xinyu Zhang +2
Parameter-efficient transfer learning (PETL) is a promising task, aiming to adapt the large-scale pre-trained model to downstream tasks with a relatively modest cost. However, curr…
Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness
Honghao Chen, Yurong Zhang, Xiaokun Feng +2
Robustness is a vital aspect to consider when deploying deep learning models into the wild. Numerous studies have been dedicated to the study of the robustness of vision transforme…
PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution
Honghao Chen, Xiangxiang Chu, Yongjian Ren +2
Recently, some large kernel convnets strike back with appealing performance and efficiency. However, given the square complexity of convolution, scaling up kernels can bring about…