28 citations · 93 across the 21 of their papers we have counts for
21 papers
Context-aware Talking Face Video Generation
Meidai Xuanyuan, Yuwang Wang, Honglei Guo +1
In this paper, we consider a novel and practical case for talking face video generation. Specifically, we focus on the scenarios involving multi-people interactions, where the talk…
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…
Shape Guided Gradient Voting for Domain Generalization
Jiaqi Xu, Yuwang Wang, Xuejin Chen
Domain generalization aims to address the domain shift between training and testing data. To learn the domain invariant representations, the model is usually trained on multiple do…
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…
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
Yuqi Han, Tao Yu, Xiaohang Yu +2
The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more on th…
Hi Sheldon! Creating Deep Personalized Characters from TV Shows
Meidai Xuanyuan, Yuwang Wang, Honglei Guo +4
Imagine an interesting multimodal interactive scenario that you can see, hear, and chat with an AI-generated digital character, who is capable of behaving like Sheldon from The Big…
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…
DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models
Tao Yang, Yuwang Wang, Yan Lv +1
Targeting to understand the underlying explainable factors behind observations and modeling the conditional generation process on these factors, we connect disentangled representat…
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…
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Dacheng Yin, Xuanchi Ren, Chong Luo +3
This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a…
Understanding Mobile GUI: from Pixel-Words to Screen-Sentences
Jingwen Fu, Xiaoyi Zhang, Yuwang Wang +3
The ubiquity of mobile phones makes mobile GUI understanding an important task. Most previous works in this domain require human-created metadata of screens (e.g. View Hierarchy) d…
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
Xiaotian Chen, Yuwang Wang, Xuejin Chen +1
Human can infer the 3D geometry of a scene from a sketch instead of a realistic image, which indicates that the spatial structure plays a fundamental role in understanding the dept…
Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
Xuanchi Ren, Tao Yang, Yuwang Wang +1
Content and style (C-S) disentanglement intends to decompose the underlying explanatory factors of objects into two independent subspaces. From the unsupervised disentanglement per…
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Xuanchi Ren, Tao Yang, Yuwang Wang +1
From the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should…
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
Tao Yang, Xuanchi Ren, Yuwang Wang +2
Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. A well-defined theoretical gu…
Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks
Wei Zhou, Qiuping Jiang, Yuwang Wang +2
Numerous image superresolution (SR) algorithms have been proposed for reconstructing high-resolution (HR) images from input images with lower spatial resolutions. However, effectiv…
Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments
Junsheng Zhou, Yuwang Wang, Kaihuai Qin +1
Recently unsupervised learning of depth from videos has made remarkable progress and the results are comparable to fully supervised methods in outdoor scenes like KITTI. However, t…
Unsupervised High-Resolution Depth Learning From Videos With Dual Networks
Junsheng Zhou, Yuwang Wang, Kaihuai Qin +1
Unsupervised depth learning takes the appearance difference between a target view and a view synthesized from its adjacent frame as supervisory signal. Since the supervisory signal…
Adversarial View-Consistent Learning for Monocular Depth Estimation
Yixuan Liu, Yuwang Wang, Shengjin Wang
This paper addresses the problem of Monocular Depth Estimation (MDE). Existing approaches on MDE usually model it as a pixel-level regression problem, ignoring the underlying geome…
Single-shot thermal ghost imaging using wavelength-division multiplexing
Chao Deng, Yuwang Wang, Jinli Suo +2
Ghost imaging (GI) is a potential imaging technique that reconstructs the target scene from its correlated measurements with a sequential of patterns. Restricted by the multi-shot…