87 citations · 719 across the 60 of their papers we have counts for
16 papers · 1 filter
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis
Yinghao Xu, Menglei Chai, Zifan Shi +8
Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of obj…
Towards Smooth Video Composition
Qihang Zhang, Ceyuan Yang, Yujun Shen +2
Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video w…
GLeaD: Improving GANs with A Generator-Leading Task
Qingyan Bai, Ceyuan Yang, Yinghao Xu +3
Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes fr…
Dimensionality-Varying Diffusion Process
Han Zhang, Ruili Feng, Zhantao Yang +7
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, con…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Deep Generative Models on 3D Representations: A Survey
Zifan Shi, Sida Peng, Yinghao Xu +3
Generative models aim to learn the distribution of observed data by generating new instances. With the advent of neural networks, deep generative models, including variational auto…
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
Zifan Shi, Yinghao Xu, Yujun Shen +3
3D-aware image synthesis aims at learning a generative model that can render photo-realistic 2D images while capturing decent underlying 3D shapes. A popular solution is to adopt t…
Improving GANs with A Dynamic Discriminator
Ceyuan Yang, Yujun Shen, Yinghao Xu +3
Discriminator plays a vital role in training generative adversarial networks (GANs) via distinguishing real and synthesized samples. While the real data distribution remains the sa…
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Ye Du, Yujun Shen, Haochen Wang +6
Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and…
A Unified Model for Multi-class Anomaly Detection
Zhiyuan You, Lei Cui, Yujun Shen +4
Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects. In this work, we present UniAD that accomplish…
Interpreting Class Conditional GANs with Channel Awareness
Yingqing He, Zhiyi Zhang, Jiapeng Zhu +2
Understanding the mechanism of generative adversarial networks (GANs) helps us better use GANs for downstream applications. Existing efforts mainly target interpreting unconditiona…
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Yuchao Wang, Haochen Wang, Yujun Shen +6
The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predict…
High-fidelity GAN Inversion with Padding Space
Qingyan Bai, Yinghao Xu, Jiapeng Zhu +3
Inverting a Generative Adversarial Network (GAN) facilitates a wide range of image editing tasks using pre-trained generators. Existing methods typically employ the latent space of…
3D-Aware Indoor Scene Synthesis with Depth Priors
Zifan Shi, Yujun Shen, Jiapeng Zhu +2
Despite the recent advancement of Generative Adversarial Networks (GANs) in learning 3D-aware image synthesis from 2D data, existing methods fail to model indoor scenes due to the…
Region-Based Semantic Factorization in GANs
Jiapeng Zhu, Yujun Shen, Yinghao Xu +2
Despite the rapid advancement of semantic discovery in the latent space of Generative Adversarial Networks (GANs), existing approaches either are limited to finding global attribut…
Few-shot Object Counting with Similarity-Aware Feature Enhancement
Zhiyuan You, Kai Yang, Wenhan Luo +3
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query ima…