مدرسة من مبادرات مؤسسة مبادرات محمد بن راشد آل مكتوم العالمية، توفر فيديوهات تعليمية في مواد العلوم والرياضات باللغة العربية متاحة مجاناً للطلبة في كافة المراحل الدراسية.

We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs.

In this article, we propose a novel label-driven view-specific fusion MVML method named L-VSM, which bypasses seeking for a shared subspace representation and instead directly encodes the feature representation of each individual view to contribute to the …

Implementation of LVSM, SOTA Large View Synthesis with Minimal 3d Inductive Bias, from Adobe Research. We will focus only on the Decoder-only architecture in this repository. This …

L-VSM: Label Driven View-Specific Fusion for Multi-View Multi-Label Classification Gengyu Lyu , Zhen Yang, Xiang Deng, Songhe Feng IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024.

2024年10月22日 · We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image tokens into a fixed number of 1D latent tokens, functioning as a fully learned scene...

e novel view synthesis from sparse-view inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image tokens into a fixed number of 1D latent tokens, function-ing as a fully learned scene representation, and decodes novel-view images from them; and (2) a decoder-only LVSM, which directly maps input images to nove...

L-VSM：用于多视图多标签分类的标签驱动的视图特定融合 IEEE Transactions on Neural Networks and Learning Systems ( IF 10.2) Pub Date : 2024-05-02, DOI: 10.1109/tnnls.2024.3390776

L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification IEEE Transactions on Neural Networks and Learning Systems ( IF 10.2) Pub Date : 5-2-2024, DOI: 10.1109/tnnls.2024.3390776

2025年1月22日 · We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs.