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1. Multi-View Attentive Contextualization for Multi-View 3D Object Detection

   Liu, Xianpeng; Zheng, Ce; Qian, Ming; Xue, Nan; Chen, Chen; Zhang, Zhebin; Li, Chen; Wu, Tianfu (August 2024, IEEE CVPR)

   This paper presents Multi-View Attentive Contextualization (MvACon), a simple yet effective method for improving 2D- to-3D feature lifting in query-based multi-view 3D (MV3D) object detection. Despite remarkable progress witnessed in the field of query-based MV3D object detection, prior art often suffers from either the lack of exploiting high- resolution 2D features in dense attention-based lifting, due to high computational costs, or from insufficiently dense grounding of 3D queries to multi-scale 2D features in sparse attention-based lifting. Our proposed MvACon hits the two birds with one stone using a representationally dense yet computationally sparse attentive feature contextualization scheme that is agnostic to specific 2D-to-3D feature lifting approaches. In experiments, the proposed MvACon is thoroughly tested on the nuScenes benchmark, using both the BEVFormer and its recent 3D deformable attention (DFA3D) variant, as well as the PETR, showing consistent detection performance improvement, especially in enhancing performance in location, orientation, and velocity prediction. It is also tested on the Waymo-mini benchmark using BEVFormer with similar improvement. We qualitatively and quantitatively show that global cluster-based contexts effectively encode dense scene-level contexts for MV3D object detection. The promising results of our proposed MvACon reinforces the adage in computer vision – “(contextualized) feature matters”. 

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2. A View-Adversarial Framework for Multi-View Network Embedding

   https://doi.org/10.1145/3340531.3412127  

   Fu, Dongqi; Xu, Zhe; Li, Bo; Tong, Hanghang; He, Jingrui (October 2020, The 29th ACM International Conference on Information and Knowledge Management)

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3. A View-Adversarial Framework for Multi-View Network Embedding

   Dongqi Fu, Zhe Xu (October 2020, CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge)

   null (Ed.)

   Network embedding has demonstrated effective empirical performance for various network mining tasks such as node classification, link prediction, clustering, and anomaly detection. However, most of these algorithms focus on the single-view network scenario. From a real-world perspective, one individual node can have different connectivity patterns in different networks. For example, one user can have different relationships on Twitter, Facebook, and LinkedIn due to varying user behaviors on different platforms. In this case, jointly considering the structural information from multiple platforms (i.e., multiple views) can potentially lead to more comprehensive node representations, and eliminate noises and bias from a single view. In this paper, we propose a view-adversarial framework to generate comprehensive and robust multi-view network representations named VANE, which is based on two adversarial games. The first adversarial game enhances the comprehensiveness of the node representation by discriminating the view information which is obtained from the subgraph induced by neighbors of that node. The second adversarial game improves the robustness of the node representation with the challenging of fake node representations from the generative adversarial net. We conduct extensive experiments on downstream tasks with real-world multi-view networks, which shows that our proposed VANE framework significantly outperforms other baseline methods. 

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4. Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption

   https://doi.org/10.24963/ijcai.2018/767  

   Ding, Zhengming; Shao, Ming; Fu, Yun (July 2018, IJCAI)

   Multi-view data are extensively accessible nowadays thanks to various types of features, different view-points and sensors which tend to facilitate better representation in many key applications. This survey covers the topic of robust multi-view data representation, centered around several major visual applications. First of all, we formulate a unified learning framework which is able to model most existing multi-view learning and domain adaptation in this line. Following this, we conduct a comprehensive discussion across these two problems by reviewing the algorithms along these two topics, including multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. We further present more practical challenges in multi-view data analysis. Finally, we discuss future research including incomplete, unbalance, large-scale multi-view learning. This would benefit AI community from literature review to future direction. 

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5. Consensus Guided Multi-View Clustering

   https://doi.org/10.1145/3182384  

   Liu, Hongfu; Fu, Yun (July 2018, ACM Transactions on Knowledge Discovery from Data)