2 citations · 4 across the 8 of their papers we have counts for
8 papers
VideoVLA: Video Generators Can Be Generalizable Robot Manipulators
Yichao Shen, Fangyun Wei, Zhiying Du +5
Generalization in robot manipulation is essential for deploying robots in open-world environments and advancing toward artificial general intelligence. While recent Vision-Language…
VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models
Tianci Bi, Xiaoyi Zhang, Yan Lu +1
The performance of Latent Diffusion Models (LDMs) is critically dependent on the quality of their visual tokenizers. While recent works have explored incorporating Vision Foundatio…
A General Theory for Compositional Generalization
Jingwen Fu, Zhizheng Zhang, Yan Lu +1
Compositional Generalization (CG) embodies the ability to comprehend novel combinations of familiar concepts, representing a significant cognitive leap in human intellectual advanc…
Text Grouping Adapter: Adapting Pre-trained Text Detector for Layout Analysis
Tianci Bi, Xiaoyi Zhang, Zhizheng Zhang +4
Significant progress has been made in scene text detection models since the rise of deep learning, but scene text layout analysis, which aims to group detected text instances as pa…
Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement
Tao Yang, Cuiling Lan, Yan Lu +1
Disentangled representation learning strives to extract the intrinsic factors within observed data. Factorizing these representations in an unsupervised manner is notably challengi…
Closing the Gap Between the Upper Bound and the Lower Bound of Adam's Iteration Complexity
Bohan Wang, Jingwen Fu, Huishuai Zhang +2
Recently, Arjevani et al. [1] established a lower bound of iteration complexity for the first-order optimization under an -smooth condition and a bounded noise variance assumpti…
Generalization error bounds for iterative learning algorithms with bounded updates
Jingwen Fu, Nanning Zheng
This paper explores the generalization characteristics of iterative learning algorithms with bounded updates for non-convex loss functions, employing information-theoretic techniqu…
Breaking through the learning plateaus of in-context learning in Transformer
Jingwen Fu, Tao Yang, Yuwang Wang +2
In-context learning, i.e., learning from context examples, is an impressive ability of Transformer. Training Transformers to possess this in-context learning skill is computational…