https://github.com/neuir/m2rag
공식 코드Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
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/nhcha6/classdistributionprediction
공식 코드Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection
https://github.com/niamhbelton/ss-fewsome_disease_severity_knee_osteoarthritis
공식 코드https://github.com/nickhaidos/scenir-icml2025
공식 코드SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
https://github.com/nightrome/cocostuff
공식 코드https://github.com/nini0919/semires
공식 코드https://github.com/nini0919/sspng
공식 코드https://github.com/njucckevin/ads-cap
공식 코드ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic Corpora
https://github.com/njucckevin/knowcap
공식 코드https://github.com/nlpaueb/bio_image_caption
공식 코드https://github.com/noagarcia/phase
공식 코드Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
https://github.com/nocaps-org/updown-baseline
공식 코드https://github.com/nohtow/wtf-rl
공식 코드Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement Learning
https://github.com/nolongerprejudice/tgbi
공식 코드On Measuring Gender Bias in Translation of Gender-neutral Pronouns
https://github.com/novamind-z/ptsn
공식 코드Progressive Tree-Structured Prototype Network for End-to-End Image Captioning
https://github.com/nust-machine-intelligence-laboratory/i2crc
공식 코드https://github.com/nust-machine-intelligence-laboratory/torchsemiseg2
공식 코드https://github.com/nvlabs/prismer
공식 코드https://github.com/ofa-sys/ofa
공식 코드https://github.com/oiplab-dut/mfnet
공식 코드MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection
https://github.com/omidmn/Face-Cap
공식 코드https://github.com/omidmnezami/pick-object-attack
공식 코드Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection
https://github.com/open-mmlab/OpenSelfSup
공식 코드Delving into Inter-Image Invariance for Unsupervised Visual Representations
https://github.com/open-mmlab/mmselfsup
공식 코드Delving into Inter-Image Invariance for Unsupervised Visual Representations
https://github.com/open3da/ll3da
공식 코드LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
https://github.com/openbmb/omnilmm
공식 코드RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
https://github.com/opendatalab/h2rsvlm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendatalab/vhm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendatalab/vigc
공식 코드https://github.com/opengvlab/multi-modality-arena
공식 코드LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
https://github.com/ortonwang/plgdf
공식 코드https://github.com/ortonwang/scribblevs
공식 코드https://github.com/osmanberke/sfda-ssvep-bci
공식 코드Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
https://github.com/osu-nlp-group/seeact
공식 코드https://github.com/ovguyo/captions-in-vqa
공식 코드Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts
https://github.com/ovshake/cobra
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.