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/Wentong-DST/up-down-captioner
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/Westlake-AI/openmixup
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/William-N-Havard/VGS-dataset-metadata
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
https://github.com/Yinan-Zhao/AoANet_VizWiz
https://github.com/YuhangMing/visual-attention-image-caption
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Zhangyuewei98/arctic-captions-py3
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Zhendong-Wang/arsm_image_captioning
https://github.com/Zhongying-Deng/NorMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/abcSup/captionamerica
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/abhijit-buet/VizWiz-Visua-Question-Answering-2021
https://github.com/abhijit-buet/VizWiz-Visual-Question-Answering-2021
https://github.com/abhshkdz/neural-vqa-attention
https://github.com/abril4416/kgen_vqa
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
https://github.com/ag169/Image-Captioning
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/ag17sep/Image-Captioning-Project
https://github.com/ahmetumutdurmus/zaremba
https://github.com/ahmetustun/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/airplane2230/keras_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/airsplay/lxmert
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ajamjoom/Image-Captions
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/ajaysub110/A-Neural-Compositional-Paradigm-for-Image-Captioning
https://github.com/alab-nii/eval_vl_glue
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/albertotestoni/ndq_visual_objects
https://github.com/aldraus/quilt-llava
https://github.com/alex-f1tor/Image-Caption
https://github.com/alibaba/EasyNLP/tree/master/examples/blip_retrieval
https://github.com/alibaba/graphtranslator
https://github.com/allenai/mslr-shared-task
https://github.com/allenai/pythia
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/am-sirdaniel/Image-Captioning-Model
https://github.com/amazon-research/bigdetection
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/amazon-research/exponential-moving-average-normalization
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/amazon-science/bigdetection
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/amitakamath/hard_positives
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/amitakamath/whatsup_vlms
CoCa: Contrastive Captioners are Image-Text Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.