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/kangkang59812/GraphCaption
https://github.com/kauravin/Neural-Visual-Image-Caption-Generation-using-RNNs-and-LSTMs
https://github.com/kawine/dataset_difficulty
A large annotated corpus for learning natural language inference
https://github.com/kay-ck/bsc-attack
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/kazuto1011/deeplab-pytorch
https://github.com/kboseong/RotNet
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/kdr/videorag-mrr2024
https://github.com/kekmodel/FixMatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/kelvinxu/arctic-captions
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/kenkai21/Image_Captioning
https://github.com/kenya-sk/show_attend_and_tell
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/khangt1k25/Image-Captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/king-zark/self_critical
https://github.com/kirbiyik/caption-it
https://github.com/knowlab/bi-weekly-paper-presentation
Weakly Supervised Named Entity Tagging with Learnable Logical Rules
https://github.com/koles289/udacity-Image-Captioning
https://github.com/kornia/kornia
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/kracwarlock/action-recognition-visual-attention
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/krasserm/fairseq-image-captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/laihuihui/htc
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/lastmile-ai/aiconfig
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
https://github.com/lauradhatt/Interesting-Reads
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/layumi/Seg-Uncertainty
https://github.com/leaplabthu/pseudo-q
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/leob03/Image_captionning
https://github.com/leolyj/3d-vlp
https://github.com/lexisnexis-risk-open-source/ledetection
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/libeineu/GPKD
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/libeineu/SDT-Training
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/libeineu/mmt-vqa
https://github.com/linzhiqiu/clip-flant5
https://github.com/liuch37/image-processing
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/lium-lst/nmtpy
Does Multimodality Help Human and Machine for Translation and Image Captioning?
https://github.com/liuqihan/Image-Caption
What value do explicit high level concepts have in vision to language problems?
https://github.com/liyongze/lstm_speaker_verification
Attention-Based Models for Text-Dependent Speaker Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.