Papers with Code 아카이브
Image Captioning
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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/wooseok-shin/PrevMatch
공식 코드Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
https://github.com/wskbest/mfc-bench
공식 코드MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
https://github.com/wtliao/ImageTransformer
공식 코드https://github.com/wufeim/imagenet3d
공식 코드ImageNet3D: Towards General-Purpose Object-Level 3D Understanding
https://github.com/wuhy68/p-adapter
공식 코드p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models
https://github.com/wuli55555/issf
공식 코드Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature
https://github.com/wusiwei0410/scimmir
공식 코드SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
https://github.com/wwwfan628/da-aim
공식 코드https://github.com/wyzjack/SLA2P
공식 코드SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation
https://github.com/wzk1015/CNMT
공식 코드Confidence-aware Non-repetitive Multimodal Transformers for TextCaps
https://github.com/wzn0828/PR_Product
공식 코드PR Product: A Substitute for Inner Product in Neural Networks
https://github.com/x-coral/wda
공식 코드Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
https://github.com/x-ytong/DPA
공식 코드Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
https://github.com/xdfai/edgsp
공식 코드https://github.com/xiangdal/implicit_alignment
공식 코드Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
https://github.com/xiaochr/multimodal-augmentation-image-captioning
공식 코드Multimodal Data Augmentation for Image Captioning using Diffusion Models
https://github.com/xiaofeng-life/sfsnid
공식 코드https://github.com/xiaohulugo/t-ro2024-dali
공식 코드https://github.com/xiarho/madm
공식 코드Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xihongyang1999/dealmvc
공식 코드DealMVC: Dual Contrastive Calibration for Multi-view Clustering
https://github.com/xihongyang1999/graphlearner
공식 코드GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
https://github.com/xin-ran-w/capagent
공식 코드From Simple to Professional: A Combinatorial Controllable Image Captioning Agent
https://github.com/xingaoli/dp-hoi
공식 코드Disentangled Pre-training for Human-Object Interaction Detection
https://github.com/xinjiefan/ACMC_ICLR
공식 코드Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
https://github.com/xlhex/nlg_api_watermark
공식 코드Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
https://github.com/xmed-lab/cpr
공식 코드Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
https://github.com/xmed-lab/epl_semidg
공식 코드Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
https://github.com/xmu-xiaoma666/DTNet
공식 코드https://github.com/xmu-xiaoma666/lstnet
공식 코드https://github.com/xmuqimingxia/sp3d
공식 코드SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
https://github.com/xu-shitong/diffusion-image-captioning
공식 코드CLIP-Diffusion-LM: Apply Diffusion Model on Image Captioning
https://github.com/xu1998hz/sescore
공식 코드Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
https://github.com/xuanlinli17/autoregressive_inference
공식 코드Discovering Non-monotonic Autoregressive Orderings with Variational Inference
https://github.com/xuewyang/Fashion_Captioning
공식 코드Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.