Papers with Code 아카이브
Image Captioning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 8
- 평가표
- 34
- 보존된 평가 기록
- 419
Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.
( Image credit: Reflective Decoding Network for Image Captioning, ICCV'19)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SatyamGaba/visual_question_answering
https://github.com/SatyamGaba/vqa
https://github.com/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation
https://github.com/ShigemichiMatsuzaki/MSPL
https://github.com/ShivamGaurUQ/Automated-hashtag-generation-using-Deep-Learning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Shivanshu-Gupta/Visual-Question-Answering
https://github.com/Shivmohith/Visual-Assistance-for-the-Blind
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/ShroukAbozeid/ImageCaptionByVoice
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/Siddharth1698/Image-Captioning-with-Deep-Learning-and-NLP
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Siddharth1698/Image-Captioning-with-Inception-LSTM
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/SinghJasdeep/Attention-on-Attention-for-VQA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/Skumarr53/Image-Caption-Generation-using-Fastai
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/SophiaYuSophiaYu/ImageCaption
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/SpencerWhitehead/vqap2
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/StatNLP/ada4asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/SuchismitaSahu1993/VQA-System
https://github.com/Sugarc0de/image_captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Surojit-KB/ARTY
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/SyogoShibuya/Chainer-Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/THUDM/KOBE
https://github.com/THUcqb/KOBE
https://github.com/ThanThoai/Visual-Question-Answering_Vietnamese
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/TheZino/Image_captioning_SATell
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/ThisuriLekamge/Stock-Price-Prediction-on-Bitcoin-trading-data-using-LSTM-with-PyTorch
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/ThomasWestfechtel/GSDE
Gradual Source Domain Expansion for Unsupervised Domain Adaptation
https://github.com/TianshuXie/Cut-Thumbnail
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TilakD/Image-Captioning
https://github.com/Trayan7/FixMatch-with-Soft-Labels
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/TreB1eN/InsightFace_Pytorch
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/Tzoulio/Large_Models_Dialogue_for_Active_Perception
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
https://github.com/V-Sense/Aesthetic-Image-Captioning-ICCVW-2019
https://github.com/Valentyn1997/semi-supervised-text-classification
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/VincentYing/Attention-on-Attention-for-VQA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/VinitSR7/Image-Caption-Generation
https://github.com/WebQnA/WebQA_Baseline
Unified Vision-Language Pre-Training for Image Captioning and VQA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.