https://github.com/JasonObeid/Chart2Text
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization
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/Jayanthipalanichamy/ElucidateTheScene
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/JoshuaPlacidi/MS-COCO-Object-Tags
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/JoshuaPlacidi/MS-COCO-Tags
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/JunnYu/x-transformers-paddle
https://github.com/KaiyangZhou/ssdg-benchmark
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/KastanDay/video-pretrained-transformer
https://github.com/Kaushal28/CutMix-Regularization-using-PyTorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/KikimorMay/MultiFace
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/Kumar-Tarun/summarization
https://github.com/KurochkinAlexey/Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction
https://github.com/LaurentVe/Automatic-image-Captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/LeadingIndiaAI/COCO-DATASET-STUFF-SEGMENTATION-CHALLENGE
https://github.com/LeeDoYup/FixMatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/Lieberk/Paddle-AoA-Captioning
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/LinXueyuanStdio/LaTeX_OCR
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/LinXueyuanStdio/LaTeX_OCR_PRO
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/LisaAnne/DCC
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
https://github.com/LisaAnne/Hallucination
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
https://github.com/Luffy03/DCA
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/Luoxd1996/DTC
Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
https://github.com/MS-P3/code7/tree/main/x_clip
https://github.com/MalvinaNikandrou/contvqa
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
https://github.com/Mind23-2/MindCode-128
Efficient Teacher: Semi-Supervised Object Detection for YOLOv5
https://github.com/Mind23-2/MindCode-34
https://github.com/Mind23-2/MindCode-93
Efficient Teacher: Semi-Supervised Object Detection for YOLOv5
https://github.com/MindCode-4/code-14/tree/main/CAJ
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/MindCode-4/code-2/tree/main/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/MindCode-4/code-4/tree/main/realm
https://github.com/MindSpore-paper-code-2/code3/tree/main/ViG
https://github.com/MindSpore-scientific-2/code-14/tree/main/lxmert
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
https://github.com/MindSpore-scientific-2/code-5/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific-2/code-9/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific/code-1/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific/code-1/tree/main/PR_Product
PR Product: A Substitute for Inner Product in Neural Networks
https://github.com/MindSpore-scientific/code-10/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.