https://github.com/xulianuwa/AuxSegNet
공식 코드Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
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/xushilin1/dst-det
공식 코드DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
https://github.com/y-d-wang/rsadapter
공식 코드RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question Answering
https://github.com/yahoo/object_relation_transformer
공식 코드https://github.com/yanbei-jiang/artwork-interpretation
공식 코드KALE: An Artwork Image Captioning System Augmented with Heterogeneous Graph
https://github.com/yangbang18/multicapclip
공식 코드MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning
https://github.com/yangbang18/zeronlg
공식 코드https://github.com/yangcong356/bita
공식 코드Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning
https://github.com/yangk802/udts
공식 코드Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
https://github.com/yangxuntu/SGAE
공식 코드https://github.com/yankai-chen/easing
공식 코드https://github.com/yanliang3612/UNREAL
공식 코드UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification
https://github.com/yao-papercodes/aglrls
공식 코드https://github.com/yaodongyu/projnorm
공식 코드Predicting Out-of-Distribution Error with the Projection Norm
https://github.com/yaohungt/Demystifying_Self_Supervised_Learning
공식 코드https://github.com/yawencui/uad-ce
공식 코드Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning
https://github.com/yaxinhou/meta-expert
공식 코드A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning
https://github.com/ycyinchao/rdvp-msd
공식 코드https://github.com/yebin46/fleur
공식 코드https://github.com/yebo0216best/lps-main
공식 코드Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
https://github.com/yehli/xmodaler
공식 코드X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
https://github.com/yfyuan01/ravenea
공식 코드RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding
https://github.com/yhZhai/WSCL
공식 코드Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
https://github.com/yhlleo/DWC-GAN
공식 코드Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
https://github.com/yiming-zh/DPL
공식 코드Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
https://github.com/yinghui-li-new/dapointr
공식 코드DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion
https://github.com/yinjunbo/ssda3d
공식 코드SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
https://github.com/yk-pku/two-shot-video-object-segmentation
공식 코드https://github.com/ylsung/vl_adapter
공식 코드VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
https://github.com/yoadtew/zero-shot-image-to-text
공식 코드ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic
https://github.com/yoadtew/zero-shot-video-to-text
공식 코드https://github.com/yongliang-wu/explorecfg
공식 코드Exploring Diverse In-Context Configurations for Image Captioning
https://github.com/yoonkicho/pplr
공식 코드Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
https://github.com/ys-zong/vl-icl
공식 코드VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
https://github.com/ysymyth/ec-nl
공식 코드https://github.com/ytaek-oh/daso
공식 코드DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.