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Vision Transformer: Vit and its Derivatives
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Transformer, an attention-based encoder-decoder architecture, has not only revolutionized the field of natural language processing (NLP), but has also done some pioneering work in the field of computer vision (CV). Compared to convolutional neural networks (CNNs), the Vision Transformer (ViT) relies on excellent modeling capabilities to achieve very good performance on several benchmarks such as ImageNet, COCO, and ADE20k. ViT is inspired by the self-attention mechanism in natural language processing, where word embeddings are replaced with patch embeddings. This paper reviews the derivatives of ViT and the cross-applications of ViT with other fields.
Forward citations
Cited by 2 Pith papers
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When Fine-Tuning is Not Enough: Lessons from HSAD on Hybrid and Adversarial Audio Spoof Detection
A new hybrid spoofed-audio benchmark is claimed to show that fine-tuning on it reaches 97%+ accuracy, but the reported numbers are internally inconsistent.
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Beyond Residual Connections: Manifold-Constrained Hyper-Connections for Robust Speaker Representation Learning
Applying the existing manifold-constrained hyper-connection (mHC) idea to speaker verification backbones yields small consistent EER reductions on VoxCeleb1 with no added parameters.
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