Papers with Code 아카이브
Image Captioning
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- 하위 태스크
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- 평가표
- 34
- 보존된 평가 기록
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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/guanghuixu/AnchorCaptioner
공식 코드Towards Accurate Text-based Image Captioning with Content Diversity Exploration
https://github.com/gudovskiy/al-fk-self-supervision
공식 코드Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
https://github.com/guoyang9/elip
공식 코드ELIP: Efficient Language-Image Pre-training with Fewer Vision Tokens
https://github.com/gyhandy/text2image-for-detection
공식 코드Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation
https://github.com/haantran96/wavetransformer
공식 코드https://github.com/hailanyi/cpd
공식 코드Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
https://github.com/hailanyi/virconv
공식 코드Virtual Sparse Convolution for Multimodal 3D Object Detection
https://github.com/hamadya/cldtracker
공식 코드CLDTracker: A Comprehensive Language Description for Visual Tracking
https://github.com/hamperdredes/sood
공식 코드https://github.com/hanhpt23/silvarmed
공식 코드https://github.com/hao840/adem-vl
공식 코드ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning
https://github.com/haoxuanli-thu/dbf-unet
공식 코드DBF-UNet: A Two-Stage Framework for Carotid Artery Segmentation with Pseudo-Label Generation
https://github.com/haru-zt/ddpls
공식 코드Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection
https://github.com/hasanar1f/llava-hallunication-fix
공식 코드PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model
https://github.com/hasanar1f/paint
공식 코드PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model
https://github.com/hashmatshadab/robust-llava
공식 코드https://github.com/hassyGo/NLG-RL
공식 코드Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction
https://github.com/hawlyq/infometic
공식 코드InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation
https://github.com/hayyubi/views
공식 코드https://github.com/hazel-heejeong-nam/self-supervised-causal-disentanglement
공식 코드SCADI: Self-supervised Causal Disentanglement in Latent Variable Models
https://github.com/heng-hw/spacap3d
공식 코드Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
https://github.com/hengcai-nju/3d2dct
공식 코드3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks
https://github.com/henryhzy/vl-pet
공식 코드VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control
https://github.com/hf618/dtbs
공식 코드https://github.com/hfut-cv/lmt-gp
공식 코드https://github.com/hfutml/calibration-mllm
공식 코드https://github.com/hgilles06/infashai
공식 코드Neural Fashion Image Captioning : Accounting for Data Diversity
https://github.com/hiaac-nlp/capivara
공식 코드https://github.com/hilab-git/fpl-plus
공식 코드https://github.com/hilab-git/srpl-sfda
공식 코드https://github.com/hilab-git/upl-sfda
공식 코드https://github.com/hitsz-hlt/st-w-scorer-absa
공식 코드Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction
https://github.com/hku-medai/wsi-hgnn
공식 코드Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
https://github.com/hlz0606/sspcm
공식 코드https://github.com/hmllmh/rstc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.