人工智能论文原理图集1

1, BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding

2, Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks

3, LongT5: Efficient Text-To-Text Transformer for Long Sequences

4, LUKE: Deep Contextualized Entity Representations with
Entity-aware Self-attention

5, Improving Language Understanding
by Generative Pre-Training

6, AltCLIP: Altering the Language Encoder in CLIP for Extended
Language Capabilities

7, VISUALBERT: A SIMPLE AND PERFORMANT
BASELINE FOR VISION AND LANGUAGE

8, Expanding Language-Image Pretrained Models
for General Video Recognition

9, FLAVA: A Foundational Language And Vision Alignment Model

10, GIT: A Generative Image-to-text Transformer
for Vision and Language

11, OCR-free Document Understanding Transformer

12, data2vec: A General Framework for Self-supervised Learning in Speech,
Vision and Language

13, Image Segmentation Using Text and Image Prompts


14,Learning Transferable Visual Models From Natural Language Supervision

15,Masked Siamese Networks
for Label-Efficient Learning

16, Masked Autoencoders Are Scalable Vision Learners

17, AN IMAGE IS WORTH 16X16 WORDS:
TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

18, VideoMAE: Masked Autoencoders are Data-Efficient
Learners for Self-Supervised Video Pre-Training

19, Visual Attention Network

20, Unified Perceptual Parsing for Scene
Understanding

21, Is Space-Time Attention All You Need for Video Understanding?

22, PubTables-1M: Towards comprehensive table extraction from unstructured

23, Swin2SR: SwinV2 Transformer for Compressed
Image Super-Resolution and Restoration

24, Swin Transformer V2: Scaling Up Capacity and Resolution

25, Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

26, SegFormer: Simple and Efficient Design for Semantic
Segmentation with Transformers

27, MetaFormer Is Actually What You Need for Vision


28, Neighborhood Attention Transformer


29, MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE,
AND MOBILE-FRIENDLY VISION TRANSFORMER

30, MobileNetV2: Inverted Residuals and Linear Bottlenecks

31, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
Applications

32, Per-Pixel Classification is Not All You Need
for Semantic Segmentation



33, Masked-attention Mask Transformer for Universal Image Segmentation

34, LeViT: a Vision Transformer in ConvNet’s Clothing
for Faster Inference

35, Global-Local Path Networks for Monocular Depth Estimation
with Vertical CutDepth