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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/HITsz-TMG/ExplainableVisualEntailment
공식 코드https://github.com/JinYuanLi0012/RiVEG
공식 코드LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
https://github.com/PhoebusSi/SAR
공식 코드Check It Again: Progressive Visual Question Answering via Visual Entailment
https://github.com/allenai/close
공식 코드I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision
https://github.com/amazon-research/mix-generation
공식 코드https://github.com/asaakyan/V-FLUTE
공식 코드Understanding Figurative Meaning through Explainable Visual Entailment
https://github.com/cambridgeltl/visual-spatial-reasoning
공식 코드https://github.com/clip-vil/CLIP-ViL
공식 코드https://github.com/fawazsammani/nlxgpt
공식 코드NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
https://github.com/iigroup/map
공식 코드MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
https://github.com/jinyuanli0012/pgim
공식 코드LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
https://github.com/kai-wen-yang/qvix
공식 코드https://github.com/khuangaf/chocolate
공식 코드Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
https://github.com/kugwzk/dide
공식 코드Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/kugwzk/distilled-dualencoder
공식 코드Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/lsplx/vetesting
공식 코드VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasing
https://github.com/mshukor/vicha
공식 코드Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
https://github.com/necla-ml/SNLI-VE
공식 코드Visual Entailment Task for Visually-Grounded Language Learning
https://github.com/ofa-sys/ofa
공식 코드https://github.com/ofa-x/ofa-x
공식 코드Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations
https://github.com/researchmm/soho
공식 코드Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
https://github.com/skrighyz/fgve
공식 코드https://github.com/skywalkerzhang/Defeasible_Visual_Entailment
공식 코드Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization
https://github.com/sohojoe/clip_visual-spatial-reasoning
공식 코드https://github.com/songrise/mope
공식 코드MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion
https://github.com/tuhinjubcse/visualmetaphors
공식 코드I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors
https://github.com/wuhy68/p-adapter
공식 코드p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models
https://github.com/yasminekaroui/clicotea
공식 코드Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages
https://github.com/zhegan27/LXMERT-AdvTrain
공식 코드Large-Scale Adversarial Training for Vision-and-Language Representation Learning
https://github.com/zhegan27/VILLA
공식 코드Large-Scale Adversarial Training for Vision-and-Language Representation Learning
https://github.com/Chaolei98/FreeZAD
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/FerryHuang/SimVLM
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://github.com/JHKim-snu/GVCCI
https://github.com/JHKim-snu/PGA
https://github.com/MindCode-4/code-1/tree/main/vilt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.