1.8k citations · 2.4k across the 36 of their papers we have counts for
23 papers · 1 filter
OccNet: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions
Miao Fan, Mingrui Chen, Chen Hu +1
Image matching is a fundamental and critical task in various visual applications, such as Simultaneous Localization and Mapping (SLAM) and image retrieval, which require accurate p…
A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
Xiaotao Hu, Zhewei Huang, Ailin Huang +2
The performance of video prediction has been greatly boosted by advanced deep neural networks. However, most of the current methods suffer from large model sizes and require extra…
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
Yun-Hao Cao, Peiqin Sun, Shuchang Zhou
We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different de…
OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion
Ruihang Miao, Weizhou Liu, Mingrui Chen +4
3D Semantic Scene Completion (SSC) can provide dense geometric and semantic scene representations, which can be applied in the field of autonomous driving and robotic systems. It i…
A Survey on Human Action Recognition
Zhou Shuchang
Human Action Recognition (HAR), one of the most important tasks in computer vision, has developed rapidly in the past decade and has a wide range of applications in health monitori…
One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation
Shuangkang Fang, Weixin Xu, Heng Wang +3
Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retri…
Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer
Ailin Huang, Zhewei Huang, Shuchang Zhou
This paper reports our solution for ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, which aims to generate vivid face-to-face conversation videos based on audio…
Synergistic Self-supervised and Quantization Learning
Yun-Hao Cao, Peiqin Sun, Yechang Huang +2
With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream t…
Collaborative Neural Rendering using Anime Character Sheets
Zuzeng Lin, Ailin Huang, Zhewei Huang
Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this p…
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Yang Lin, Tianyu Zhang, Peiqin Sun +2
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been deve…
Are L2 adversarial examples intrinsically different?
Mingxuan Li, Jingyuan Wang, Yufan Wu
Deep Neural Network (DDN) has achieved notable success in various tasks, including many security concerning scenarios. However, a considerable amount of work has proved its vulnera…
Learning to Paint With Model-based Deep Reinforcement Learning
Zhewei Huang, Wen Heng, Shuchang Zhou
We show how to teach machines to paint like human painters, who can use a small number of strokes to create fantastic paintings. By employing a neural renderer in model-based Deep…
Harmonic Adversarial Attack Method
Wen Heng, Shuchang Zhou, Tingting Jiang
Adversarial attacks find perturbations that can fool models into misclassifying images. Previous works had successes in generating noisy/edge-rich adversarial perturbations, at the…
Stroke-based Character Reconstruction
Zhewei Huang, Wen Heng, Yuanzheng Tao +1
Background elimination for noisy character images or character images from real scene is still a challenging problem, due to the bewildering backgrounds, uneven illumination, low r…
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Shuchang Zhou, Yuzhi Wang, He Wen +2
Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, s…
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
Shuchang Zhou, Taihong Xiao, Yi Yang +3
Object Transfiguration replaces an object in an image with another object from a second image. For example it can perform tasks like "putting exactly those eyeglasses from image A…
EAST: An Efficient and Accurate Scene Text Detector
Xinyu Zhou, Cong Yao, He Wen +4
Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging…
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
He Wen, Shuchang Zhou, Zhe Liang +4
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires l…
Scene Text Detection via Holistic, Multi-Channel Prediction
Cong Yao, Xiang Bai, Nong Sang +3
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge. However, vas…
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
Shuchang Zhou, Jia-Nan Wu, Yuxin Wu +1
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks. We use Kronecker product to exploit the lo…
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Cong Yao, Jianan Wu, Xinyu Zhou +4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability…
Multilinear Map Layer: Prediction Regularization by Structural Constraint
Shuchang Zhou, Yuxin Wu
In this paper we propose and study a technique to impose structural constraints on the output of a neural network, which can reduce amount of computation and number of parameters b…
ICDAR 2015 Text Reading in the Wild Competition
Xinyu Zhou, Shuchang Zhou, Cong Yao +2
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community. However, maj…