Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification

• URL: http://arxiv.org/abs/2409.00755v1
• Date: Sun, 1 Sep 2024 15:48:20 GMT
• Title: Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification
• Authors: Haojian Huang, Chuanyu Qin, Zhe Liu, Kaijing Ma, Jin Chen, Han Fang, Chao Ban, Hao Sun, Zhongjiang He,
• Abstract summary: Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views. We propose a Trusted Unified Feature-NEighborhood Dynamics (TUNED) model for robust MVC. This method effectively integrates local and global feature-neighborhood (F-N) structures for robust decision-making.
• Score: 16.994115410201974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process. While Evidential Deep Learning (EDL) has been effective in addressing view uncertainty, existing methods predominantly rely on the Dempster-Shafer combination rule, which is sensitive to conflicting evidence and often neglects the critical role of neighborhood structures within multi-view data. To address these limitations, we propose a Trusted Unified Feature-NEighborhood Dynamics (TUNED) model for robust MVC. This method effectively integrates local and global feature-neighborhood (F-N) structures for robust decision-making. Specifically, we begin by extracting local F-N structures within each view. To further mitigate potential uncertainties and conflicts in multi-view fusion, we employ a selective Markov random field that adaptively manages cross-view neighborhood dependencies. Additionally, we employ a shared parameterized evidence extractor that learns global consensus conditioned on local F-N structures, thereby enhancing the global integration of multi-view features. Experiments on benchmark datasets show that our method improves accuracy and robustness over existing approaches, particularly in scenarios with high uncertainty and conflicting views. The code will be made available at https://github.com/JethroJames/TUNED.

Related papers

• Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection [4.176139684578661]
  We propose a unified model constructed from the perspective of global-view reconstruction. We incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Experimental results demonstrate the superior performance of our method on multi-view datasets.
  arXiv  Detail & Related papers  (2025-03-18T08:35:39Z)
• Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance [69.58609684008964]
  We propose BURG, a novel method for incomplete multi-view clustering with distriBution dUal-consistency Recovery Guidance. We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views. To compensate for the lack of reliable category information, we design a dual-consistency guided recovery strategy that includes intra-view alignment guided by neighbor-aware consistency and cross-view alignment guided by prototypical consistency.
  arXiv  Detail & Related papers  (2025-03-14T02:27:45Z)
• PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments [73.80718037070773]
  We present the multi-modal Pedestrian-Focused Scene dataset, rigorously annotated in semi-structured scenes with the format of nuScenes. We also propose a novel Hybrid Multi-Scale Fusion Network (HMFN) to detect pedestrians in densely populated and occluded scenarios.
  arXiv  Detail & Related papers  (2025-02-21T09:57:53Z)
• FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL [19.236153474365747]
  Existing MARL approaches often rely on the restrictive assumption that the number of entities remains constant between training and inference. In this paper, we tackle the challenge of intra-trajectory dynamic entity composition under zero-shot out-of-domain (OOD) generalization. We propose FlickerFusion, a novel OOD generalization method that acts as a universally applicable augmentation technique for MARL backbone methods.
  arXiv  Detail & Related papers  (2024-10-21T10:57:45Z)
• Evidential Deep Partial Multi-View Classification With Discount Fusion [24.139495744683128]
  We propose a novel framework called Evidential Deep Partial Multi-View Classification (EDP-MVC) We use K-means imputation to address missing views, creating a complete set of multi-view data. The potential conflicts and uncertainties within this imputed data can affect the reliability of downstream inferences.
  arXiv  Detail & Related papers  (2024-08-23T14:50:49Z)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation [70.62860473259444]
  Universal domain adaptation (UniDA) is a practical but challenging problem. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain. We propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA.
  arXiv  Detail & Related papers  (2023-12-13T03:17:34Z)
• ELFNet: Evidential Local-global Fusion for Stereo Matching [17.675146012208124]
  We introduce the textbfEvidential textbfLocal-global textbfFusion (ELF) framework for stereo matching. It endows both uncertainty estimation and confidence-aware fusion with trustworthy heads.
  arXiv  Detail & Related papers  (2023-08-01T15:51:04Z)
• Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes [51.20150148066458]
  We propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the generalizationally crafted masks. Experiments on real-world datasets prove the significant effectiveness and ability of the proposed method.
  arXiv  Detail & Related papers  (2023-04-18T13:55:24Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)
• Local-Global Associative Frame Assemble in Video Re-ID [57.7470971197962]
  Noisy and unrepresentative frames in automatically generated object bounding boxes from video sequences cause challenges in learning discriminative representations in video re-identification (Re-ID) Most existing methods tackle this problem by assessing the importance of video frames according to either their local part alignments or global appearance correlations separately. In this work, we explore jointly both local alignments and global correlations with further consideration of their mutual promotion/reinforcement.
  arXiv  Detail & Related papers  (2021-10-22T19:07:39Z)
• Locality Relationship Constrained Multi-view Clustering Framework [5.586948325488168]
  Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF) is presented. It aims to explore the diversity, geometric, consensus and complementary information among different views. LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure.
  arXiv  Detail & Related papers  (2021-07-11T15:45:10Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.