https://github.com/jhb86253817/stld
공식 코드Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach
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/jiahaoxxx/ssmll-d2l_mat
공식 코드Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-Attention
공식 코드COMIC: Towards A Compact Image Captioning Model with Attention
https://github.com/jiahuei/sparse-image-captioning
공식 코드https://github.com/jiajiali04/semiweeds
공식 코드Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection
https://github.com/jianhanlim/ipr-imagecaptioning
공식 코드Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection
https://github.com/jianlong-yuan/SimpleBaseline
공식 코드A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
https://github.com/jianlong-yuan/semi-mmseg
공식 코드Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
https://github.com/jiaodali/vision-matters-when-it-should
공식 코드Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
https://github.com/jiasenlu/AdaptiveAttention
공식 코드Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/jiasenlu/NeuralBabyTalk
공식 코드https://github.com/jiaw-z/dkt-stereo
공식 코드https://github.com/jihaonew/mm-instruct
공식 코드MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://github.com/jingqiangchen/concaps
공식 코드Transform, Contrast and Tell: Coherent Entity-Aware Multi-Image Captioning
https://github.com/jinnaiyuu/mbr-optimal-transport
공식 코드Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
https://github.com/jiuhaichen/florence-vl
공식 코드https://github.com/jiwoncocoder/conmatch
공식 코드ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
https://github.com/jmerullo/limber
공식 코드https://github.com/jmhessel/clipscore
공식 코드CLIPScore: A Reference-free Evaluation Metric for Image Captioning
https://github.com/jnwilliams/fuse_prompt_inversion
공식 코드FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
https://github.com/jo-wang/cal-sfda
공식 코드https://github.com/joannezhouyi/Hateful_Memes_Challenge
공식 코드https://github.com/joeyz0z/conzic
공식 코드ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
https://github.com/joeyz0z/meacap
공식 코드https://github.com/johncaged/OPT_Questioner
공식 코드Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
https://github.com/jongsuk1/avcap
공식 코드AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning
https://github.com/joniali/evaluating-fairness-discriminative-fms
공식 코드Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
https://github.com/jonnyascott/xclp
공식 코드Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning
https://github.com/joshuafeinglass/vl-detector-eval
공식 코드https://github.com/journeybench/journeybench
공식 코드JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images
https://github.com/joyhuyy1412/cadr-fixmatch
공식 코드https://github.com/jungokasai/billboard
공식 코드Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
https://github.com/jungokasai/thumb
공식 코드https://github.com/junjun-yan/st-pinn
공식 코드ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
https://github.com/junyangwang0410/knight
공식 코드From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping
https://github.com/justairr/satori-r1
공식 코드SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and Verifiable Rewards
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.