6 citations · 16 across the 13 of their papers we have counts for
13 papers
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…
TrInk: Ink Generation with Transformer Network
Zezhong Jin, Shubhang Desai, Xu Chen +8
In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input…
LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Yang Sun, Zhiyong Xie, Lixin Zou +7
Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information up…
Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
Zichao Li, Shaojie He, Meng Liao +6
Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short…
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Shikun Feng, Yuyan Ni, Yan Lu +3
Molecular generation and molecular property prediction are both crucial for drug discovery, but they are often developed independently. Inspired by recent studies, which demonstrat…
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…
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…
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…
Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization
Tao Yang, Yuwang Wang, Cuiling Lan +2
Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental ab…
Learning Trajectories are Generalization Indicators
Jingwen Fu, Zhizheng Zhang, Dacheng Yin +2
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient des…
Unifying Layout Generation with a Decoupled Diffusion Model
Mude Hui, Zhizheng Zhang, Xiaoyi Zhang +3
Layout generation aims to synthesize realistic graphic scenes consisting of elements with different attributes including category, size, position, and between-element relation. It…
Test-time Batch Normalization
Tao Yang, Shenglong Zhou, Yuwang Wang +2
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of…
Visual Concepts Tokenization
Tao Yang, Yuwang Wang, Yan Lu +1
Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fiel…