Multi-view Instance Attention Fusion Network for classification
Multi-view learning for classification has achieved a remarkable performance compared with the single-view based methods. Inspired by the instance based learning which directly regards the instance as the prior and well preserves the valuable information in different instances, a Multi-view Instance Attention Fusion Network (MvIAFN) is proposed to efficiently exploit the correlation across both instances and views. Specifically, a small number of instances from different views are first sampled as the set of templates. Given an additional instance and based on the similarities between it and the selected templates, it can be re-presented by following an attention strategy. Thanks for this strategy, the given instance is capable of preserving the additional information from the selected instances, achieving the purpose of extracting the instance-correlation. Additionally, for each sample, we not only perform the instance attention in each single view but also get the attention across multiple views, allowing us to further fuse them to obtain the fused attention for each view. Experimental results on datasets substantiate the effectiveness of our proposed method compared with state-of-the-arts.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationMULTI-VIEW LEARNINGSimilar Papers 제목 키워드 기반
VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification
Multi-view deep neural network is perhaps the most successful approach in 3D shape classification. However, the fusion of multi-view features based on max or average pooling lacks a view selection mechanism, limiting its…
3D Shape ClassificationClassificationGeneral ClassificationObject RecognitionCheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification
Medical image classification poses unique challenges due to the long-tailed distribution of diseases, the co-occurrence of diagnostic findings, and the multiple views available for each study or patient. This paper intro…
ClassificationDiagnosticimage-classificationImage Classification+2Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data
Many real-world phenomena are observed at multiple resolutions. Predictive models designed to predict these phenomena typically consider different resolutions separately. This approach might be limiting in applications w…
General ClassificationMULTI-VIEW LEARNINGSentiment AnalysisSentiment ClassificationCross-Attentive Multiview Fusion of Vision-Language Embeddings
Vision-language models have been key to the development of open-vocabulary 2D semantic segmentation. Lifting these models from 2D images to 3D scenes, however, remains a challenging problem. Existing approaches typically…
2D Semantic Segmentation3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions …
Data AugmentationNeural Rendering