Image Captioning
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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/jamestjw/OCR
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition
https://github.com/jaryp/pixleattack
Scratch that! An Evolution-based Adversarial Attack against Neural Networks
https://github.com/jaykshirsagar05/captionify
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/jazzsaxmafia/show_and_tell.tensorflow
https://github.com/jdai-cv/image-captioning
https://github.com/jdauba/Domain-Adaptation-SVHN-MNIST
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
https://github.com/jelifysh/Image-Captioning
https://github.com/jerinphilip/fairseq-ilmt
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/jiahuei/COMIC-Towards-A-Compact-Image-Captioning-Model-with-Attention
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
https://github.com/jianjieluo/openai-clip-feature
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
https://github.com/jielin-qiu/mm_robustness
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
https://github.com/jim-schwoebel/allie
https://github.com/jincan333/lot
https://github.com/jinjiren/recurrent-image-annotator-web-demo
Annotation Order Matters: Recurrent Image Annotator for Arbitrary Length Image Tagging
https://github.com/jis478/Tensorflow/tree/master/TF2.0/Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jis478/cutmix_tensorflow2
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jishubasak/Punny-Caption--Exploring-Image-Captioning
https://github.com/jiwanchung/vlis
https://github.com/jmhessel/pycocoevalcap
https://github.com/johnchenresearch/SSL
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/johnnylu305/deeplab-imagenet-pytorch
https://github.com/jonas-becker/text-generation
https://github.com/joykour/COCO-Dataset-2018-Stuff-Segmentation-Challenge
https://github.com/js-aguiar/wheat-object-detection
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jsaikmr/Building-a-Topic-Modeling-for-Images-using-LDA-and-Transfer-Learning
https://github.com/juergenlandauer/Maya-Challenge
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/juletx/image-caption-generation
https://github.com/jungokasai/t2r
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/jungokasai/twist_decoding
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
https://github.com/junjie1003/DL-Fairness-Study
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
https://github.com/junshutang/Make-It-3D
https://github.com/junwang23/deepdirtycodes
A Critical Review of Recurrent Neural Networks for Sequence Learning
https://github.com/kaikai-sk/char-rnn-chinese
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/kakaobrain/rq-vae-transformer
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.