Search Results for author: Di Xie

Found 53 papers, 18 papers with code

Learning Expressive And Generalizable Motion Features For Face Forgery Detection

no code implementations8 Mar 2024 Jingyi Zhang, Peng Zhang, Jingjing Wang, Di Xie, ShiLiang Pu

However, current sequence-based face forgery detection methods use general video classification networks directly, which discard the special and discriminative motion information for face manipulation detection.

Anomaly Detection Classification +1

Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning

1 code implementation8 Mar 2024 Hang Du, Xuejun Yan, Jingjing Wang, Di Xie, ShiLiang Pu

Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications.

point cloud upsampling

CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model

no code implementations8 Mar 2024 Pengwei Yin, Guanzhong Zeng, Jingjing Wang, Di Xie

To overcome these limitations, we propose a novel framework called CLIP-Gaze that utilizes a pre-trained vision-language model to leverage its transferable knowledge.

Diversity Domain Generalization +2

"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach

1 code implementation1 Mar 2024 Lingyu Gu, Yongqi Du, Yuan Zhang, Di Xie, ShiLiang Pu, Robert C. Qiu, Zhenyu Liao

Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging.

Model Compression Quantization

Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models

no code implementations25 Oct 2023 WeiJie Chen, Haoyu Wang, Shicai Yang, Lei Zhang, Wei Wei, Yanning Zhang, Luojun Lin, Di Xie, Yueting Zhuang

Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as the corresponding unlabeled target data.

Domain Adaptation Image Classification

Single Domain Dynamic Generalization for Iris Presentation Attack Detection

no code implementations22 May 2023 Yachun Li, Jingjing Wang, Yuhui Chen, Di Xie, ShiLiang Pu

To tackle the above issues, we propose a Single Domain Dynamic Generalization (SDDG) framework, which simultaneously exploits domain-invariant and domain-specific features on a per-sample basis and learns to generalize to various unseen domains with numerous natural images.

Diversity Domain Generalization +1

Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation

1 code implementation CVPR 2023 Hang Du, Xuejun Yan, Jingjing Wang, Di Xie, ShiLiang Pu

Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface.

1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track

no code implementations12 Jan 2023 Wei Zhao, Binbin Chen, WeiJie Chen, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang

The domain adaptation part is implemented as a Source-Free Domain Adaptation paradigm, which only uses the pre-trained model and the unlabeled target data to further optimize in a self-supervised training manner.

Domain Generalization object-detection +3

1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track

no code implementations12 Jan 2023 Yilu Guo, Xingyue Shi, WeiJie Chen, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang

In the test-time training stage, we use the pre-trained model to assign noisy label for the unlabeled target data, and propose a Label-Periodically-Updated DivideMix method for noisy label learning.

Data Augmentation Domain Generalization +2

Unsupervised Prompt Tuning for Text-Driven Object Detection

no code implementations ICCV 2023 Weizhen He, WeiJie Chen, Binbin Chen, Shicai Yang, Di Xie, Luojun Lin, Donglian Qi, Yueting Zhuang

In this paper, we delve into this problem and propose an Unsupervised Prompt Tuning framework for text-driven object detection, which is composed of two novel mean teaching mechanisms.

Data Augmentation Object +4

Attention Diversification for Domain Generalization

1 code implementation9 Oct 2022 Rang Meng, Xianfeng Li, WeiJie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Mingli Song, Di Xie, ShiLiang Pu

Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features.

Domain Generalization

Multi-Scale Wavelet Transformer for Face Forgery Detection

no code implementations8 Oct 2022 Jie Liu, Jingjing Wang, Peng Zhang, Chunmao Wang, Di Xie, ShiLiang Pu

To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection.

FBNet: Feedback Network for Point Cloud Completion

1 code implementation8 Oct 2022 Xuejun Yan, Hongyu Yan, Jingjing Wang, Hang Du, Zhihong Wu, Di Xie, ShiLiang Pu, Li Lu

The rapid development of point cloud learning has driven point cloud completion into a new era.

Point Cloud Completion

Point Cloud Upsampling via Cascaded Refinement Network

1 code implementation8 Oct 2022 Hang Du, Xuejun Yan, Jingjing Wang, Di Xie, ShiLiang Pu

In this manner, the proposed cascaded refinement network can be easily optimized without extra learning strategies.

point cloud upsampling

Semi-supervised Ranking for Object Image Blur Assessment

1 code implementation13 Jul 2022 Qiang Li, Zhaoliang Yao, Jingjing Wang, Ye Tian, Pengju Yang, Di Xie, ShiLiang Pu

Based on this dataset, we propose a method to obtain the blur scores only with the pairwise rank labels as supervision.

Object Object Recognition +1

Label Matching Semi-Supervised Object Detection

3 code implementations CVPR 2022 Binbin Chen, WeiJie Chen, Shicai Yang, Yunyi Xuan, Jie Song, Di Xie, ShiLiang Pu, Mingli Song, Yueting Zhuang

To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly.

Object object-detection +2

Slimmable Domain Adaptation

1 code implementation CVPR 2022 Rang Meng, WeiJie Chen, Shicai Yang, Jie Song, Luojun Lin, Di Xie, ShiLiang Pu, Xinchao Wang, Mingli Song, Yueting Zhuang

In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.

Domain Generalization Unsupervised Domain Adaptation

Learning Domain Adaptive Object Detection with Probabilistic Teacher

2 code implementations13 Jun 2022 Meilin Chen, WeiJie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Yunfeng Yan, Donglian Qi, Yueting Zhuang, Di Xie, ShiLiang Pu

In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter.

Object object-detection +1

Transductive CLIP with Class-Conditional Contrastive Learning

no code implementations13 Jun 2022 Junchu Huang, WeiJie Chen, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang

This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques.

Contrastive Learning Pseudo Label +1

Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning

no code implementations23 May 2022 Fanfan Ye, Liang Ma, Qiaoyong Zhong, Di Xie, ShiLiang Pu

The knowledge extracted by the delegator is then utilized to maintain the performance of the model on old tasks in incremental learning.

Class Incremental Learning Incremental Learning

KRNet: Towards Efficient Knowledge Replay

no code implementations23 May 2022 Yingying Zhang, Qiaoyong Zhong, Di Xie, ShiLiang Pu

However, the number of stored latent codes in autoencoder increases linearly with the scale of data and the trained encoder is redundant for the replaying stage.

Continual Learning Domain Adaptation

Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

1 code implementation31 Mar 2022 Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, ShiLiang Pu, De-Chuan Zhan

In this work, we propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which synthesizes fake FSCIL tasks from the base dataset.

Few-Shot Class-Incremental Learning Incremental Learning +1

Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition

1 code implementation8 Dec 2021 Kailin Xu, Fanfan Ye, Qiaoyong Zhong, Di Xie

In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations.

Action Recognition Skeleton Based Action Recognition

Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning

no code implementations6 Sep 2021 Ning Wei, Jiahua Liang, Di Xie, ShiLiang Pu

Designing optimal reward functions has been desired but extremely difficult in reinforcement learning (RL).

reinforcement-learning Reinforcement Learning (RL)

TransForensics: Image Forgery Localization with Dense Self-Attention

no code implementations ICCV 2021 Jing Hao, Zhixin Zhang, Shicai Yang, Di Xie, ShiLiang Pu

Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verification of images more difficult.

Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection

no code implementations ICCV 2021 Jinlei Hou, Yingying Zhang, Qiaoyong Zhong, Di Xie, ShiLiang Pu, Hong Zhou

Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.

Unsupervised Anomaly Detection

Modulating Localization and Classification for Harmonized Object Detection

no code implementations16 Mar 2021 Taiheng Zhang, Qiaoyong Zhong, ShiLiang Pu, Di Xie

Object detection involves two sub-tasks, i. e. localizing objects in an image and classifying them into various categories.

Classification General Classification +3

Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation

no code implementations23 Feb 2021 WeiJie Chen, Luojun Lin, Shicai Yang, Di Xie, ShiLiang Pu, Yueting Zhuang, Wenqi Ren

Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.

Self-Supervised Learning Unsupervised Domain Adaptation

MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination

no code implementations2 Oct 2020 Shengyu Zhang, Donghui Wang, Zhou Zhao, Siliang Tang, Di Xie, Fei Wu

In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance.

Diversity Generative Adversarial Network +1

Unsupervised Image Classification for Deep Representation Learning

1 code implementation20 Jun 2020 Wei-Jie Chen, ShiLiang Pu, Di Xie, Shicai Yang, Yilu Guo, Luojun Lin

Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.

Classification Clustering +13

Neural Inheritance Relation Guided One-Shot Layer Assignment Search

no code implementations28 Feb 2020 Rang Meng, Wei-Jie Chen, Di Xie, Yuan Zhang, ShiLiang Pu

In this paper, for the first time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100.

Neural Architecture Search Relation

Fast Task Adaptation for Few-Shot Learning

no code implementations25 Sep 2019 Yingying Zhang, Qiaoyong Zhong, Di Xie, ShiLiang Pu

The key lies in generalization of prior knowledge learned from large-scale base classes and fast adaptation of the classifier to novel classes.

Few-Shot Learning

All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification

3 code implementations CVPR 2019 Wei-Jie Chen, Di Xie, Yuan Zhang, ShiLiang Pu

In this family of architectures, the basic block is only composed by 1x1 convolutional layers with only a few shift operations applied to the intermediate feature maps.

General Classification Image Classification +1

Collaborative Spatio-temporal Feature Learning for Video Action Recognition

1 code implementation4 Mar 2019 Chao Li, Qiaoyong Zhong, Di Xie, ShiLiang Pu

By sharing the convolution kernels of different views, spatial and temporal features are collaboratively learned and thus benefit from each other.

Action Recognition In Videos Temporal Action Localization +1

A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks

no code implementations17 Dec 2018 Wei-Jie Chen, Yuan Zhang, Di Xie, ShiLiang Pu

A better alternative is to propagate the entire useful information to reconstruct the pruned layer instead of directly discarding the less important neurons.

Learning Incremental Triplet Margin for Person Re-identification

no code implementations17 Dec 2018 Yingying Zhang, Qiaoyong Zhong, Liang Ma, Di Xie, ShiLiang Pu

In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively.

Metric Learning Person Re-Identification

Small-scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation

no code implementations ECCV 2018 Tao Song, Leiyu Sun, Di Xie, Haiming Sun, ShiLiang Pu

A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias.

Pedestrian Detection

Extreme Network Compression via Filter Group Approximation

no code implementations ECCV 2018 Bo Peng, Wenming Tan, Zheyang Li, Shun Zhang, Di Xie, ShiLiang Pu

In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation.

General Classification Image Classification

Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

no code implementations4 Jul 2018 Tao Song, Leiyu Sun, Di Xie, Haiming Sun, ShiLiang Pu

A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias.

Pedestrian Detection

A practical convolutional neural network as loop filter for intra frame

no code implementations16 May 2018 Xiaodan Song, Jiabao Yao, Lulu Zhou, Li Wang, Xiaoyang Wu, Di Xie, ShiLiang Pu

It aims to design a single CNN model with low redundancy to adapt to decoded frames with different qualities and ensure consistency.

Multimedia

Cascade Region Proposal and Global Context for Deep Object Detection

no code implementations30 Oct 2017 Qiaoyong Zhong, Chao Li, Yingying Zhang, Di Xie, Shicai Yang, ShiLiang Pu

Deep region-based object detector consists of a region proposal step and a deep object recognition step.

Object object-detection +3

Skeleton-based Action Recognition with Convolutional Neural Networks

1 code implementation25 Apr 2017 Chao Li, Qiaoyong Zhong, Di Xie, ShiLiang Pu

Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN).

Action Classification Action Recognition +3

Mixed context networks for semantic segmentation

no code implementations19 Oct 2016 Haiming Sun, Di Xie, ShiLiang Pu

Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy.

General Classification Segmentation +1

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