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Content and Salient Semantics Collaboration for Cloth-Changing Person Re-Identification
Qizao Wang;Xuelin Qian;Bin Li;Lifeng Chen;Yanwei Fu;Xiangyang Xue
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Cloth-changing person re-identification aims at recognizing the same person with clothing changes across non-overlapping cameras. Advanced methods either resort to identity-related auxiliary modalities (e.g., sketches, silhouettes, and keypoints) or clothing labels to mitigate the impact of clothes. However, relying on unpractical and inflexible auxiliary modalities or annotations limits their rea...Show More
Hierarchical Context Interaction and Reasoning with Transformer for Emotion Recognition
Wenxuan Wang;Chenglei Wang;Xiaomei Wang;Xuli Shen;Qing Xu;Xuelin Qian
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Emotion recognition is an important task in computer vision. However, current approaches using hard associations (e.g., element-wise addition or concatenation) suffer from information pollution. To overcome these challenges and utilize information at different scales, we present a novel Transformer-based emotion recognition (TransEmo) framework utilizing only the RGB modality as input. TransEmo is...Show More
Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack
Xuelin Qian;Wenxuan Wang;Yu-Gang Jiang;Xiangyang Xue;Yanwei Fu
Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can ...Show More
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate identity-relevant feature learning, including soft-biometrics features of shapes or gaits, and additional labels of clothing. However, this informatio...Show More
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Ke Fan;Jingshi Lei;Xuelin Qian;Miaopeng Yu;Tianjun Xiao;Tong He;Zheng Zhang;Yanwei Fu
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Video amodal segmentation is a particularly challenging task in computer vision, which requires to deduce the full shape of an object from the visible parts of it. Recently, some studies have achieved promising performance by using motion flow to integrate information across frames under a self-supervised setting. However, motion flow has a clear limitation by the two factors of moving cameras and...Show More
Coarse-to-Fine Amodal Segmentation with Shape Prior
Jianxiong Gao;Xuelin Qian;Yikai Wang;Tianjun Xiao;Tong He;Zheng Zhang;Yanwei Fu
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to-Fine Segmentation (C2F-Seg), that addresses this problem by progressively modeling the amodal segmentation. C2F-Seg initially reduces the learning space from the pixel-level image space to the vector-quantized latent sp...Show More
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models
Simian Luo;Xuelin Qian;Yanwei Fu;Yinda Zhang;Ying Tai;Zhenyu Zhang;Chengjie Wang;Xiangyang Xue
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the 3D domain for powerful shape generation, it still has two limitations: expensive computations on volumetric grids and ambiguous auto-regressive order along grid dimensions. To overcome these limitations, we propose the I...Show More
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Xiang Li;Xuelin Qian;Litian Liang;Lingjie Kong;Qiaole Dong;Jiejun Chen;Dingxia Liu;Xiuzhong Yao;Yanwei Fu
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvio...Show More
ImpDet: Exploring Implicit Fields for 3D Object Detection
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i.e., localization, dimension, and orientation. Despite its popularity and universality, such a straightforward paradigm is sensitive to slight numerical deviations, especially in localization. By exploiting the property that point clouds are naturally captured on the surface ...Show More
DST: Dynamic Substitute Training for Data-free Black-box Attack
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (14)
With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples. For data-free black box attack scenario, existing methods are inspired by the knowledge distillation, and thus usually train a substitute model to learn knowledge from the target model using generated data as input. However, the subst...Show More
M^3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging
Xuelin Qian;Huazhu Fu;Weiya Shi;Tao Chen;Yanwei Fu;Fei Shan;Xiangyang Xue
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M^3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which on...Show More
FM2u-Net: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification
Wenxuan Wang;Yanwei Fu;Xuelin Qian;Yu-Gang Jiang;Qi Tian;Xiangyang Xue
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (10)
It is challenging in learning a makeup-invariant face verification model, due to (1) insufficient makeup/non-makeup face training pairs, (2) the lack of diverse makeup faces, and (3) the significant appearance changes caused by cosmetics. To address these challenges, we propose a unified Face Morphological Multi-branch Network (FMMu-Net) for makeup-invariant face verification, which can simultaneo...Show More
When Person Re-identification Meets Changing Clothes
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (82)
Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset...Show More
Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification
Person re-identification (re-id) aims to match people across non-overlapping camera views in a public space. This is a challenging problem because the people captured in surveillance videos often wear similar clothing. Consequently, the differences in their appearance are typically subtle and only detectable at particular locations and scales. In this paper, we propose a deep re-id network (MuDeep...Show More
Multi-scale Deep Learning Architectures for Person Re-identification
2017 IEEE International Conference on Computer Vision (ICCV)
Year: 2017 | Conference Paper |
Cited by: Papers (188)
Person Re-identification (re-id) aims to match people across non-overlapping camera views in a public space. It is a challenging problem because many people captured in surveillance videos wear similar clothes. Consequently, the differences in their appearance are often subtle and only detectable at the right location and scales. Existing re-id models, particularly the recently proposed deep learn...Show More
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