人工智能论文原理图集1

1, BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding
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2, Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
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3, LongT5: Efficient Text-To-Text Transformer for Long Sequences
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4, LUKE: Deep Contextualized Entity Representations with
Entity-aware Self-attention
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5, Improving Language Understanding
by Generative Pre-Training

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6, AltCLIP: Altering the Language Encoder in CLIP for Extended
Language Capabilities
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7, VISUALBERT: A SIMPLE AND PERFORMANT
BASELINE FOR VISION AND LANGUAGE
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8, Expanding Language-Image Pretrained Models
for General Video Recognition
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9, FLAVA: A Foundational Language And Vision Alignment Model
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10, GIT: A Generative Image-to-text Transformer
for Vision and Language
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11, OCR-free Document Understanding Transformer
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12, data2vec: A General Framework for Self-supervised Learning in Speech,
Vision and Language
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13, Image Segmentation Using Text and Image Prompts
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14,Learning Transferable Visual Models From Natural Language Supervision
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15,Masked Siamese Networks
for Label-Efficient Learning
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16, Masked Autoencoders Are Scalable Vision Learners
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17, AN IMAGE IS WORTH 16X16 WORDS:
TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE
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18, VideoMAE: Masked Autoencoders are Data-Efficient
Learners for Self-Supervised Video Pre-Training
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19, Visual Attention Network
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20, Unified Perceptual Parsing for Scene
Understanding
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21, Is Space-Time Attention All You Need for Video Understanding?
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22, PubTables-1M: Towards comprehensive table extraction from unstructured
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23, Swin2SR: SwinV2 Transformer for Compressed
Image Super-Resolution and Restoration
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24, Swin Transformer V2: Scaling Up Capacity and Resolution
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25, Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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26, SegFormer: Simple and Efficient Design for Semantic
Segmentation with Transformers
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27, MetaFormer Is Actually What You Need for Vision
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28, Neighborhood Attention Transformer
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29, MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE,
AND MOBILE-FRIENDLY VISION TRANSFORMER
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30, MobileNetV2: Inverted Residuals and Linear Bottlenecks
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31, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
Applications
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32, Per-Pixel Classification is Not All You Need
for Semantic Segmentation
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33, Masked-attention Mask Transformer for Universal Image Segmentation
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34, LeViT: a Vision Transformer in ConvNet’s Clothing
for Faster Inference
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35, Global-Local Path Networks for Monocular Depth Estimation
with Vertical CutDepth
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