78 citations · 192 across the 20 of their papers we have counts for
16 papers · 1 filter
Debiasing Text-to-Image Diffusion Models
Ruifei He, Chuhui Xue, Haoru Tan +4
Learning-based Text-to-Image (TTI) models like Stable Diffusion have revolutionized the way visual content is generated in various domains. However, recent research has shown that…
Dataset Condensation via Generative Model
David Junhao Zhang, Heng Wang, Chuhui Xue +4
Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However…
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks
David Junhao Zhang, Mutian Xu, Chuhui Xue +4
Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additiona…
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
Runyu Ding, Jihan Yang, Chuhui Xue +3
Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset. This task is challenging because…
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
Yujun Shi, Chuhui Xue, Jun Hao Liew +5
Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably, DragGAN is an interactive point-based image editing framew…
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search
Qihao Liu, Adam Kortylewski, Yutong Bai +2
Text-guided diffusion models (TDMs) are widely applied but can fail unexpectedly. Common failures include: (i) natural-looking text prompts generating images with the wrong content…
PV3D: A 3D Generative Model for Portrait Video Generation
Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew +4
Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have app…
LUMix: Improving Mixup by Better Modelling Label Uncertainty
Shuyang Sun, Jie-Neng Chen, Ruifei He +3
Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentatio…
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
Runyu Ding, Jihan Yang, Chuhui Xue +3
Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is l…
Is synthetic data from generative models ready for image recognition?
Ruifei He, Shuyang Sun, Xin Yu +5
Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applic…
1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words
Zhangzi Zhu, Chuhui Xue, Yu Hao +2
Scene text recognition has attracted increasing interest in recent years due to its wide range of applications in multilingual translation, autonomous driving, etc. In this report,…
Runner-Up Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: Cropped Word Recognition
Zhangzi Zhu, Yu Hao, Wenqing Zhang +2
This report presents our 2nd place solution to ECCV 2022 challenge on Out-of-Vocabulary Scene Text Understanding (OOV-ST) : Cropped Word Recognition. This challenge is held in the…
Contextual Text Block Detection towards Scene Text Understanding
Chuhui Xue, Jiaxing Huang, Shijian Lu +2
Most existing scene text detectors focus on detecting characters or words that only capture partial text messages due to missing contextual information. For a better understanding…
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability
Ruifei He, Shuyang Sun, Jihan Yang +2
Large-scale pre-training has been proven to be crucial for various computer vision tasks. However, with the increase of pre-training data amount, model architecture amount, and the…
Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting
Chuhui Xue, Wenqing Zhang, Yu Hao +3
Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitive…
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Yujun Shi, Kuangqi Zhou, Jian Liang +5
Class Incremental Learning (CIL) aims at learning a multi-class classifier in a phase-by-phase manner, in which only data of a subset of the classes are provided at each phase. Pre…