https://github.com/sarapapi/fbk-fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
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/sathvik-sankruth/Data_Mining-CSE5334
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/sathvik-sankruth/Term-project
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/saubhik/chainer-image-captioning
https://github.com/sawarn69/Medical-Report-Generation-Using-X-Ray-Images
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/sayakpaul/PAWS-TF
https://github.com/sbcblab/Keras-CoDeepNEAT
https://github.com/sd2001/Auto-Image2Caption
https://github.com/sd2001/Image2Caption
https://github.com/sebastianGehrmann/tensorflow-statereader
https://github.com/sebiancoder/bertscorevisualizer
https://github.com/securade/hub
Semantic-Conditional Diffusion Networks for Image Captioning
https://github.com/sgondala/GoogleConceptualCaptioning
https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/shangeth/Vehicle_Speed_Estimation
https://github.com/shao-chi/ImageCaption
https://github.com/shaox192/NeuralGuidance
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/shawnkx/Fast-MoS
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation
https://github.com/shihaozhaozsh/lavi-bridge
https://github.com/shinmura0/Faster-Grad-CAM
https://github.com/shivam13juna/Sequence_Prediction_LSTM_CHAR
https://github.com/shivani-raul/Image-Captioning-VGG16
https://github.com/showkeyjar/chinese_im2text.pytorch
https://github.com/showlab/loveu-tgve-2023
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
https://github.com/shrutimohanty/Hcodeep
https://github.com/shuvenduroy/activefer
Analysis of Semi-Supervised Methods for Facial Expression Recognition
https://github.com/simnyatsanga/image-caption-generator
https://github.com/simon-benigeri/lstm-language-model
https://github.com/sinazarriess/zero_shot_reg
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?
https://github.com/sithu31296/image-captioning
https://github.com/skywolf829/CSE5559_Final_Project
A Comprehensive Survey of Deep Learning for Image Captioning
https://github.com/smisthzhu/attentionocr
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/snagiri/ECE285_Jarvis_ProjectA
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/songyang0716/NLP/tree/master/natural_language_inference/sentence_encoding_RNN
A large annotated corpus for learning natural language inference
https://github.com/soumik12345/Automatic-Number-Plate-Recognition
https://github.com/souvikshanku/digit-captioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.