Search Results for author: Shaozi Li

Found 28 papers, 16 papers with code

Selective Domain-Invariant Feature for Generalizable Deepfake Detection

no code implementations19 Mar 2024 Yingxin Lai, Guoqing Yang Yifan He, Zhiming Luo, Shaozi Li

To solve this problem, we proposed a novel framework Selective Domain-Invariant Feature (SDIF), which reduces the sensitivity to face forgery by fusing content features and styles.

DeepFake Detection Face Swapping

A Multilevel Guidance-Exploration Network and Behavior-Scene Matching Method for Human Behavior Anomaly Detection

1 code implementation7 Dec 2023 Guoqing Yang, Zhiming Luo, Jianzhe Gao, Yingxin Lai, Kun Yang, Yifan He, Shaozi Li

Human behavior anomaly detection aims to identify unusual human actions, playing a crucial role in intelligent surveillance and other areas.

Anomaly Detection

Zero-Shot Co-salient Object Detection Framework

1 code implementation11 Sep 2023 Haoke Xiao, Lv Tang, Bo Li, Zhiming Luo, Shaozi Li

Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets.

Co-Salient Object Detection Object +2

Boundary Difference Over Union Loss For Medical Image Segmentation

1 code implementation1 Aug 2023 Fan Sun, Zhiming Luo, Shaozi Li

However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation.

Image Segmentation Medical Image Segmentation +2

Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification

no code implementations20 Jun 2023 Juanjuan Weng, Zhiming Luo, Dazhen Lin, Shaozi Li

Furthermore, we conduct a comprehensive evaluation of different loss functions within consistent training frameworks, including noise-based and generator-based.

Image Classification

Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature

1 code implementation2 May 2023 Juanjuan Weng, Zhiming Luo, Dazhen Lin, Shaozi Li, Zhun Zhong

Recent research has shown that Deep Neural Networks (DNNs) are highly vulnerable to adversarial samples, which are highly transferable and can be used to attack other unknown black-box models.

Adversarial Attack

Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration

2 code implementations7 Mar 2023 Juanjuan Weng, Zhiming Luo, Zhun Zhong, Shaozi Li, Nicu Sebe

In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly obtain saturation in CE, which largely limits the transferability.

Adversarial Attack

Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization

1 code implementation IEEE Transactions on Image Processing (TIP) 2022 Jinliang Lin, Zhedong Zheng, Zhun Zhong, Zhiming Luo, Shaozi Li, Yi Yang, Nicu Sebe

Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.

Drone navigation Drone-view target localization +3

Federated and Generalized Person Re-identification through Domain and Feature Hallucinating

no code implementations5 Mar 2022 Fengxiang Yang, Zhun Zhong, Zhiming Luo, Shaozi Li, Nicu Sebe

During local training, the DFS are used to synthesize novel domain statistics with the proposed domain hallucinating, which is achieved by re-weighting DFS with random weights.

Domain Generalization Person Re-Identification

Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification

no code implementations3 Mar 2022 Yongguo Ling, Zhun Zhong, Donglin Cao, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe

In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment.

Person Re-Identification

SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification

no code implementations24 Feb 2021 Haojie Liu, Shun Ma, Daoxun Xia, Shaozi Li

In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks, which preserve the spatial structure information of features.

Person Re-Identification

Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification

1 code implementation CVPR 2021 Yuyang Zhao, Zhun Zhong, Fengxiang Yang, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe

In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.

Domain Generalization Meta-Learning +1

Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

1 code implementation3 Dec 2019 Fengxiang Yang, Ke Li, Zhun Zhong, Zhiming Luo, Xing Sun, Hao Cheng, Xiaowei Guo, Feiyue Huang, Rongrong Ji, Shaozi Li

This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively.

Clustering Miscellaneous +2

Learning to Adapt Invariance in Memory for Person Re-identification

no code implementations1 Aug 2019 Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, Yi Yang

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain.

Person Re-Identification Unsupervised Domain Adaptation

Leveraging Virtual and Real Person for Unsupervised Person Re-identification

1 code implementation5 Nov 2018 Fengxiang Yang, Zhun Zhong, Zhiming Luo, Sheng Lian, Shaozi Li

For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data.

Collaborative Filtering Style Transfer +1

Generalizing A Person Retrieval Model Hetero- and Homogeneously

1 code implementation ECCV 2018 Zhun Zhong, Liang Zheng, Shaozi Li, Yi Yang

Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras.

Person Re-Identification Person Retrieval +2

Random Erasing Data Augmentation

17 code implementations16 Aug 2017 Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).

General Classification Image Augmentation +4

Re-ranking Person Re-identification with k-reciprocal Encoding

no code implementations CVPR 2017 Zhun Zhong, Liang Zheng, Donglin Cao, Shaozi Li

Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance.

Person Re-Identification Re-Ranking +1

Re-ranking Object Proposals for Object Detection in Automatic Driving

no code implementations19 May 2016 Zhun Zhong, Mingyi Lei, Shaozi Li, Jianping Fan

In this paper, we propose a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with less proposals.

Object object-detection +3

Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching

no code implementations8 May 2016 Zhun Zhong, Songzhi Su, Donglin Cao, Shaozi Li

Secondly, we present a ground control points selection scheme according to the maximum matching confidence of each pixel.

Stereo Matching Stereo Matching Hand

Towards 3D Object Detection With Bimodal Deep Boltzmann Machines Over RGBD Imagery

no code implementations CVPR 2015 Wei Liu, Rongrong Ji, Shaozi Li

In particular, we slide a 3D detection window in the 3D point cloud to match the exemplar shape, which the lack of training data in 3D domain is conquered via (1) We collect 3D CAD models and 2D positive samples from Internet.

3D Object Detection object-detection

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