https://github.com/hh-xiaohu/Image-augementation-pytorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Papers with Code 아카이브
Image Captioning
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- 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/hieunghia-pat/ObjectRelationTransformerAoA
https://github.com/hieunghia-pat/OpenViIC
UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioning
https://github.com/hieunghia-pat/uit-objectaoa
https://github.com/hik-lab/ssod
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/hikaruya8/lstm_model_py
https://github.com/hikvision-research/SSOD
End-to-End Semi-Supervised Object Detection with Soft Teacher
https://github.com/hiyaroy12/multitask_learning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/hjc18/language_modeling_lstm
https://github.com/hjjpku/dynamic_graph_caption
https://github.com/hlt-mt/fbk-fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/hltchkust/cantonese-asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/homebrewltd/ichigo
https://github.com/hpprc/simple-simcse-ja
A large annotated corpus for learning natural language inference
https://github.com/httttttt/ResNetAtentionBiLSTM
https://github.com/huckiyang/QuantumSpeech-QCNN
https://github.com/huckiyang/speech_quantum_dl
https://github.com/huggingface/diffusers
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/huggingface/transformers
https://github.com/husnain-ali21/image_captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/hx19940102/Image-Captioning
https://github.com/hysts/pytorch_D-RISE
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/hysts/pytorch_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/iacercalixto/butd-image-captioning
https://github.com/iesl/geometric_graph_embedding
https://github.com/ildoonet/cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/img-caption-mania/img_caption_dl_textAnalytic
Image Captioning with Attention for Smart Local Tourism using EfficientNet
https://github.com/ipavlopoulos/diagnostic_captioning
https://github.com/ishritam/Image-captioning-with-visual-attention
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/isi-nlp/Zoph_RNN
https://github.com/ivendrov/order-embedding
https://github.com/j9650/CoDeepNEAT
https://github.com/jackroos/pythia
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/jacksonchen1998/Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient
https://github.com/jacobgil/pytorch-grad-cam
https://github.com/jaleedkhan/neusire
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.