Isolating Interference Factors for Robust Cloth-Changing Person Re-Identification [0.03%]
鲁棒的换衣行人重识别隔离干扰因素
De Cheng,Yubo Li,Chaowei Fang et al.
De Cheng et al.
Cloth-Changing Person Re-Identification (CC-ReID) aims to recognize individuals across camera views despite clothing variations, a crucial task for surveillance and security systems. Existing methods typically frame it as a cross-modal alig...
Fangjinhua Wang,Qingtian Zhu,Di Chang et al.
Fangjinhua Wang et al.
3D reconstruction aims to recover the dense 3D structure of a scene. It plays an essential role in various applications such as Augmented/Virtual Reality (AR/VR), autonomous driving and robotics. Leveraging multiple views of a scene capture...
Non-Gradient Hash Factor Learning for High-Dimensional and Incomplete Data Representation Learning [0.03%]
基于非梯度哈希因子学习的高维与不完整数据表示学习方法
Di Wu,Shihui Li,Yi He et al.
Di Wu et al.
High-dimensional and incomplete (HDI) data are ubiquitous in various Big Data-related industrial applications, such as drug innovation and recommender systems. Hash learning is the most efficient representation learning approach to extract ...
Like Human Rethinking: Contour Transformer AutoRegression for Referring Remote Sensing Interpretation [0.03%]
如人再思：轮廓变压器自回归在遥感解译中的应用
Jinming Chai,Licheng Jiao,Xiaoqiang Lu et al.
Jinming Chai et al.
Referring remote sensing interpretation holds significant application value in various scenarios such as ecological protection, resource exploration, and emergency management. However, referring remote sensing expression comprehension and s...
Semantic Contrast for Domain-Robust Underwater Image Quality Assessment [0.03%]
基于语义对比的鲁棒域水下图像质量评估方法
Jingchun Zhou,Chunjiang Liu,Qiuping Jiang et al.
Jingchun Zhou et al.
Underwater image quality assessment (UIQA) is hindered by complex degradation and domain shifts across aquatic environments. Existing no-reference IQA methods rely on costly and subjective mean opinion scores (MOS), which limit their genera...
Towards Enhanced Representation Learning for Single-Source Domain Generalization in LiDAR Semantic Segmentation [0.03%]
面向单源域泛化的LiDAR语义分割的增强表示学习方法研究
Hyeonseong Kim,Yoonsu Kang,Changgyoon Oh et al.
Hyeonseong Kim et al.
With the success of the 3D deep learning models, various perception technologies for autonomous driving have been developed in the LiDAR domain. While these models perform well in the trained source domain, they struggle in unseen domains w...
Zheng Zhang,Peng Zhou,Aiting Yao et al.
Zheng Zhang et al.
Subspace clustering is one of the most popular clustering methods due to its effectiveness. Although subspace clustering methods have been demonstrated to achieve promising performance, they still lack interpretability, especially when hand...
Hongbo Zhao,Fei Zhu,Bolin Ni et al.
Hongbo Zhao et al.
For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests originate at any time from both users and model owners, and the...
DyDiT++: Diffusion Transformers with Timestep and Spatial Dynamics for Efficient Visual Generation [0.03%]
DyDiT++：具有时间和空间动态的扩散变压器，用于高效的视觉生成
Wangbo Zhao,Yizeng Han,Jiasheng Tang et al.
Wangbo Zhao et al.
Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior perfor mance but suffers from substantial computational costs. Our investigations reveal that these costs primarily stem from the stat...
BlindU: Blind Machine Unlearning without Revealing Erasing Data [0.03%]
盲机器卸载学习方法 BlindU：无需擦除数据的盲机器卸载学习
Weiqi Wang,Zhiyi Tian,Chenhan Zhang et al.
Weiqi Wang et al.
Machine unlearning enables data holders to remove the contribution of their specified samples from trained models to protect their privacy. However, it is paradoxical that most unlearning methods require the unlearning requesters to firstly...