Search Results for author: Ding Liang

Found 36 papers, 16 papers with code

UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation

no code implementations14 Dec 2023 Zexiang Liu, Yangguang Li, Youtian Lin, Xin Yu, Sida Peng, Yan-Pei Cao, Xiaojuan Qi, Xiaoshui Huang, Ding Liang, Wanli Ouyang

Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D objects.

Text to 3D

EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion

no code implementations11 Dec 2023 Zehuan Huang, Hao Wen, Junting Dong, Yaohui Wang, Yangguang Li, Xinyuan Chen, Yan-Pei Cao, Ding Liang, Yu Qiao, Bo Dai, Lu Sheng

Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image.

SSIM

Text-to-3D with Classifier Score Distillation

no code implementations30 Oct 2023 Xin Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Song-Hai Zhang, Xiaojuan Qi

In this paper, we re-evaluate the role of classifier-free guidance in score distillation and discover a surprising finding: the guidance alone is enough for effective text-to-3D generation tasks.

Text to 3D Texture Synthesis

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

no code implementations29 Oct 2023 Nan He, Hanyu Lai, Chenyang Zhao, Zirui Cheng, Junting Pan, Ruoyu Qin, Ruofan Lu, Rui Lu, Yunchen Zhang, Gangming Zhao, Zhaohui Hou, Zhiyuan Huang, Shaoqing Lu, Ding Liang, Mingjie Zhan

Based on TeacherLM-7. 1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting.

Data Augmentation Language Modelling

VCSUM: A Versatile Chinese Meeting Summarization Dataset

1 code implementation9 May 2023 Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou, Ding Liang, Linqi Song

Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data.

Meeting Summarization Retrieval +1

Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework

no code implementations6 May 2023 Ruijia Wu, Yuhang Wang, Huafeng Shi, Zhipeng Yu, Yichao Wu, Ding Liang

In this paper, we propose the Adversarial Decoupling Augmentation Framework (ADAF), addressing these issues by targeting the image-text fusion module to enhance the defensive performance of facial privacy protection algorithms.

Denoising

ICD-Face: Intra-class Compactness Distillation for Face Recognition

no code implementations ICCV 2023 Zhipeng Yu, Jiaheng Liu, Haoyu Qin, Yichao Wu, Kun Hu, Jiayi Tian, Ding Liang

Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR).

Face Recognition Knowledge Distillation +1

Improving Robust Fairness via Balance Adversarial Training

no code implementations15 Sep 2022 ChunYu Sun, Chenye Xu, Chengyuan Yao, Siyuan Liang, Yichao Wu, Ding Liang, Xianglong Liu, Aishan Liu

Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem.

Fairness

Universal Backdoor Attacks Detection via Adaptive Adversarial Probe

no code implementations12 Sep 2022 Yuhang Wang, Huafeng Shi, Rui Min, Ruijia Wu, Siyuan Liang, Yichao Wu, Ding Liang, Aishan Liu

Most detection methods are designed to verify whether a model is infected with presumed types of backdoor attacks, yet the adversary is likely to generate diverse backdoor attacks in practice that are unforeseen to defenders, which challenge current detection strategies.

Scheduling

Action Recognition With Motion Diversification and Dynamic Selection

no code implementations TIP 2022 Peiqin Zhuang, Yu Guo, Zhipeng Yu, Luping Zhou, Lei Bai, Ding Liang, Zhiyong Wang, Yali Wang, Wanli Ouyang

To address this issue, we introduce a Motion Diversification and Selection (MoDS) module to generate diversified spatio-temporal motion features and then select the suitable motion representation dynamically for categorizing the input video.

Action Recognition Computational Efficiency

DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection

1 code implementation12 Jul 2022 Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang

Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.

object-detection Object Detection +1

Learning Locality and Isotropy in Dialogue Modeling

1 code implementation29 May 2022 Han Wu, Haochen Tan, Mingjie Zhan, Gangming Zhao, Shaoqing Lu, Ding Liang, Linqi Song

Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models.

CoupleFace: Relation Matters for Face Recognition Distillation

no code implementations12 Apr 2022 Jiaheng Liu, Haoyu Qin, Yichao Wu, Jinyang Guo, Ding Liang, Ke Xu

In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.

Face Recognition Knowledge Distillation +1

PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection

1 code implementation30 Mar 2022 Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang

Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.

object-detection Object Detection +1

Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation

no code implementations9 Dec 2021 Gang Li, Xiang Li, Yujie Wang, Shanshan Zhang, Yichao Wu, Ding Liang

Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.

Image Classification Knowledge Distillation +3

One to Transfer All: A Universal Transfer Framework for Vision Foundation Model with Few Data

no code implementations24 Nov 2021 Yujie Wang, Junqin Huang, Mengya Gao, Yichao Wu, Zhenfei Yin, Ding Liang, Junjie Yan

Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.

INTERN: A New Learning Paradigm Towards General Vision

no code implementations16 Nov 2021 Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.

CycleMLP: A MLP-like Architecture for Dense Prediction

8 code implementations ICLR 2022 Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo

We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.

Image Classification Instance Segmentation +4

PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text

1 code implementation2 May 2021 Wenhai Wang, Enze Xie, Xiang Li, Xuebo Liu, Ding Liang, Zhibo Yang, Tong Lu, Chunhua Shen

By systematically comparing with existing scene text representations, we show that our kernel representation can not only describe arbitrarily-shaped text but also well distinguish adjacent text.

Scene Text Detection Text Detection +1

Inter-class Discrepancy Alignment for Face Recognition

no code implementations2 Mar 2021 Jiaheng Liu, Yudong Wu, Yichao Wu, Zhenmao Li, Chen Ken, Ding Liang, Junjie Yan

In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.

Face Recognition

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

9 code implementations ICCV 2021 Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao

Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.

Image Classification Instance Segmentation +3

Segmenting Transparent Object in the Wild with Transformer

2 code implementations21 Jan 2021 Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, Ping Luo

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.

Object Segmentation +2

DAM: Discrepancy Alignment Metric for Face Recognition

no code implementations ICCV 2021 Jiaheng Liu, Yudong Wu, Yichao Wu, Chuming Li, Xiaolin Hu, Ding Liang, Mengyu Wang

To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.

Face Recognition

AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting

2 code implementations ECCV 2020 Wenhai Wang, Xuebo Liu, Xiaozhong Ji, Enze Xie, Ding Liang, Zhibo Yang, Tong Lu, Chunhua Shen, Ping Luo

Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection.

Language Modelling Sentence +2

PolarMask: Single Shot Instance Segmentation with Polar Representation

2 code implementations CVPR 2020 Enze Xie, Peize Sun, Xiaoge Song, Wenhai Wang, Ding Liang, Chunhua Shen, Ping Luo

In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods.

Distance regression Instance Segmentation +4

Knowledge Distillation via Route Constrained Optimization

1 code implementation ICCV 2019 Xiao Jin, Baoyun Peng, Yi-Chao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Xiaolin Hu

However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.

Face Recognition Knowledge Distillation +1

Pyramid Mask Text Detector

1 code implementation28 Mar 2019 Jingchao Liu, Xuebo Liu, Jie Sheng, Ding Liang, Xin Li, Qingjie Liu

Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically.

Clustering Instance Segmentation +4

Dynamic Multi-path Neural Network

no code implementations28 Feb 2019 Yingcheng Su, Shunfeng Zhou, Yi-Chao Wu, Tian Su, Ding Liang, Jiaheng Liu, Dixin Zheng, Yingxu Wang, Junjie Yan, Xiaolin Hu

Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.

DeepID3: Face Recognition with Very Deep Neural Networks

7 code implementations3 Feb 2015 Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang

Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.

Face Identification Face Recognition +2

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