https://github.com/DeepLearnXMU/MM-DCCN
공식 코드Dynamic Context-guided Capsule Network for Multimodal Machine Translation
Papers with Code 아카이브
Multimodal Machine Translation
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Multimodal machine translation is the task of doing machine translation with multiple data sources - for example, translating "a bird is flying over water" + an image of a bird over water to German text.
<span style="color:grey; opacity: 0.6">( Image credit: Findings of the Third Shared Task on Multimodal Machine Translation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ESanchezLozano/GANnotation
공식 코드Triple consistency loss for pairing distributions in GAN-based face synthesis
https://github.com/ImperialNLP/BertGen
공식 코드https://github.com/ImperialNLP/MMT-Delib
공식 코드https://github.com/Rudrabha/LipGAN
공식 코드https://github.com/bghojogh/Pixel-Aligned-Face-Recognition
공식 코드Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
https://github.com/deeplearnxmu/bigvideo-vmt
공식 코드BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
https://github.com/devaansh100/cliptrans
공식 코드CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation
https://github.com/facebookresearch/seamless_communication
공식 코드Seamless: Multilingual Expressive and Streaming Speech Translation
https://github.com/iacercalixto/variational_mmt
공식 코드https://github.com/ictnlp/pluvr
공식 코드Neural Machine Translation with Phrase-Level Universal Visual Representations
https://github.com/ictnlp/sammt
공식 코드Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation
https://github.com/jbdel/WMT18_MNMT
공식 코드https://github.com/jiaodali/vision-matters-when-it-should
공식 코드Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
https://github.com/josiahwang/multisubs-eval
공식 코드MultiSubs: A Large-scale Multimodal and Multilingual Dataset
https://github.com/kshitij98/vita
공식 코드https://github.com/ku-nlp/video-helpful-mmt
공식 코드https://github.com/ku-nlp/visa
공식 코드VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation
https://github.com/lgcnsai/ps-kd-pytorch
공식 코드Self-Knowledge Distillation with Progressive Refinement of Targets
https://github.com/libeineu/fairseq_mmt
공식 코드https://github.com/libeineu/mmt-vqa
공식 코드https://github.com/lium-lst/nmtpy
공식 코드NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems
https://github.com/matthieufp/commute
공식 코드Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation
https://github.com/matthieufp/vgamt
공식 코드Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation
https://github.com/matthieufp/zerommt
공식 코드https://github.com/maxylee/3am
공식 코드3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
https://github.com/multi30k/dataset
공식 코드https://github.com/pengr/ikd-mmt
공식 코드Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
https://github.com/scofield7419/ummt-vsh
공식 코드https://github.com/shantipriyap/hausavqa
공식 코드HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language
https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/toshohirasawa/nmtpytorch-emb-pred
공식 코드https://github.com/xl2248/msctd
공식 코드https://github.com/yaoming95/23triplet
공식 코드Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation
https://github.com/04RR/SOTA-Vision
https://github.com/1wy/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.