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/ninatu/howtocaption
https://github.com/nishimehta/Image_Captioning
https://github.com/nithishkaviyan/Show-and-Tell-Neural-Network-Image-Caption-Generator-
https://github.com/nlx-group/pix2pix-onthefly
Leveraging LLMs for On-the-Fly Instruction Guided Image Editing
https://github.com/ntusteeian/VQA_CNN-LSTM
https://github.com/nu11us/WC-MS-Celeb
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/oarriaga/neural_image_captioning
https://github.com/oneway3124/disaggregation-vrnn
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/open-dataflow/rare
Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
https://github.com/open-finance-lab/finlora
https://github.com/open-mmlab/OpenUnReID
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
https://github.com/open-mmlab/mmclassification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/openbmb/minicpm-v
https://github.com/openvinotoolkit/datumaro
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/openvinotoolkit/openvino_xai
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/oplatek/e2end
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/pacman-ctm/thesis_code
GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features
https://github.com/palatos/RISE_tf
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/patil-suraj/question_generation
https://github.com/peteanderson80/Up-Down-Captioner
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/peteanderson80/coco-caption
https://github.com/phanav/fixmatch-fastai
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/phisad/keras-shatt
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/pierrecolombo/nlg_eval_via_simi_measures
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
https://github.com/pinellolab/dna-diffusion
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
https://github.com/prasadsana/Eye_for_blind
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/prithivirajdamodaran/styleformer
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://github.com/pskrunner14/descriptor
https://github.com/ptrckhmmr/learning-to-defer-with-limited-expert-predictions
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/purvaten/punny_captions
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/puxinhe/im2txt_v3
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/pwc-1/Paper-8/tree/main/blip
https://github.com/pwc-1/Paper-9/tree/main/4/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/pwc-1/Paper-9/tree/main/4/PR_Product
PR Product: A Substitute for Inner Product in Neural Networks
https://github.com/pwc-1/Paper-9/tree/main/5/wav2vec2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.