https://github.com/zhishenyang/scientific_figure_captioning_dataset
공식 코드SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning
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/zhiyuan-li-john/rapsg
공식 코드Exploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation
https://github.com/zhou745/gaufuse_wstal
공식 코드Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels
https://github.com/zhougroup/BAM
공식 코드https://github.com/zhu-han/castle
공식 코드Boosting Cross-Domain Speech Recognition with Self-Supervision
https://github.com/zhu-xlab/ChatEarthNet
공식 코드ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
https://github.com/zhuang-li/factual
공식 코드FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
https://github.com/zhuemann/contextualsegmentation
공식 코드ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
https://github.com/zihangm/obj-centric-unsup-caption
공식 코드https://github.com/zihuaevan/acfmodel
공식 코드Adaptively Clustering Neighbor Elements for Image-Text Generation
https://github.com/zijian-hu/SimPLE
공식 코드SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
https://github.com/ziyangwang007/cv-ssl-mis
공식 코드https://github.com/zjuchenlong/sca-cnn
공식 코드SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning
https://github.com/zjukg/seqcsg
공식 코드Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
https://github.com/zkcys001/UDAStrongBaseline
공식 코드Group-aware Label Transfer for Domain Adaptive Person Re-identification
https://github.com/zpywhu/pointobb-v3
공식 코드PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection
https://github.com/zrguo/casp
공식 코드Bridging the Gap for Test-Time Multimodal Sentiment Analysis
https://github.com/ztangaj/gveval
공식 코드G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o
https://github.com/zwenyu/UniSSDA
공식 코드Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
https://github.com/zyrant/cpdet3d
공식 코드Learning Class Prototypes for Unified Sparse Supervised 3D Object Detection
https://github.com/zytx121/awesome-vlgfm
공식 코드https://huggingface.co/datasets/sankalpsinha77/MARVEL-40M
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://worksheets.codalab.org/worksheets/0xe3eb416773ed4883bb737662b31b4948
공식 코드Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://git.zib.de/ableich/ecg-comment-generation-public
https://github.com/1alexandra/speech
Attention-Based Models for Text-Dependent Speaker Verification
https://github.com/2023-MindSpore-1/ms-code-112
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
https://github.com/2023-MindSpore-1/ms-code-222/tree/main/SGAE
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/blip2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2
https://github.com/21-projects-for-deep-learning/image2text
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/28-08-2000/ImageCaptioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/394781865/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/A-Telfer/AugKey
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/AIPHES/emnlp19-moverscore
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
https://github.com/Aaatresh/Seeing-through-sound
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.