https://github.com/MindCode-4/code-1/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
Papers with Code 아카이브
Visual Entailment
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Visual Entailment (VE) - is a task consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal is to predict whether the image semantically entails the text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindCode-4/code-5/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/PasserBy4/mypretraining
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
https://github.com/PasserBy4/pretraining
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
https://github.com/SDLZY/VCR_Align
https://github.com/YIKUAN8/Transformers-VQA
https://github.com/amitakamath/hard_positives
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/amitakamath/whatsup_vlms
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/facebookresearch/multimodal
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/facebookresearch/reliable_vqa
https://github.com/gchhablani/multilingual-vqa
https://github.com/huggingface/transformers
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
https://github.com/jianjieluo/openai-clip-feature
https://github.com/jielin-qiu/mm_robustness
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
https://github.com/lichengunc/pretrain-vl-data
https://github.com/lucidrains/CoCa-pytorch
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/mlfoundations/open_clip
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/modelscope/modelscope
https://github.com/salesforceairesearch/crmarena
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
https://github.com/vladsandulescu/hatefulmemes
https://github.com/xiaomin418/cfsum
https://github.com/yulong-XJTU/SimVLM
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://github.com/ziyan-xiaoyu/spatialmqa
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.