https://github.com/FujiiKazuma/CDFIAPLSUP
공식 코드Cell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classification
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/FuxiaoLiu/VisualNews-Repository
공식 코드Visual News: Benchmark and Challenges in News Image Captioning
https://github.com/GT-RIPL/Xmodal-Ctx
공식 코드Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning
https://github.com/GeondoPark/MHSAN
공식 코드MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding
https://github.com/Gitsamshi/WeakVRD-Captioning
공식 코드https://github.com/HAWLYQ/ICECAP
공식 코드ICECAP: Information Concentrated Entity-aware Image Captioning
https://github.com/HAWLYQ/Qc-TextCap
공식 코드https://github.com/HCPLab-SYSU/Fine-Grained-Image-Captioning
공식 코드Fine-Grained Image Captioning with Global-Local Discriminative Objective
https://github.com/HHU-MMBS/plp-official-tmlr2024
공식 코드Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
https://github.com/HenryPengZou/JointMatch
공식 코드https://github.com/Henryjiepanli/Building-Guided-Pseudo-Label-Learning-for-Cross-Modal-Building-Damage-Mapping
공식 코드Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping
https://github.com/HiLab-git/SSL4MIS
공식 코드Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer
https://github.com/HoinJung/Unified-Debiaisng-VLM-SFID
공식 코드A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks
https://github.com/Hritikbansal/medmax
공식 코드MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants
https://github.com/Hrren/Beyond-Words
공식 코드Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning
https://github.com/I2-Multimedia-Lab/A2S-v3
공식 코드Unified Unsupervised Salient Object Detection via Knowledge Transfer
https://github.com/IBM/IBM_VizWiz
공식 코드Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
https://github.com/ImperialNLP/BertGen
공식 코드https://github.com/JCZ404/Semi-DETR
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/JeremyNathanJusuf/image-captioning
공식 코드https://github.com/JeremyXSC/MCN-MT
공식 코드Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification
https://github.com/Jhyun17/DeYO
공식 코드Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
https://github.com/JiachengLi1995/TALLOR
공식 코드Weakly Supervised Named Entity Tagging with Learnable Logical Rules
https://github.com/JiatongXia/Pseudo-Labeling
공식 코드https://github.com/JoshuaFeinglass/SMURF
공식 코드SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis
https://github.com/JoshuaFeinglass/TROPE
공식 코드https://github.com/Junjue-Wang/LoveDA
공식 코드LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/Junjue-Wang/LoveNAS
공식 코드LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/KaiyangZhou/Dassl.pytorch
공식 코드https://github.com/KentoNishi/Augmentation-for-LNL
공식 코드https://github.com/KhaLee2307/StrDA
공식 코드Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition
https://github.com/KovenYu/state-information
공식 코드Weakly supervised discriminative feature learning with state information for person identification
https://github.com/Kthyeon/ssfod
공식 코드Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
https://github.com/Levigty/CMCS
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/LiqunChen0606/Graph-Optimal-Transport
공식 코드https://github.com/LiyaoTang/ERDA
공식 코드All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.