https://github.com/vmurahari3/visdial-bert
공식 코드Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
Papers with Code 아카이브
Visual Dialog
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Visual Dialog requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a follow-up question about the image, the task is to answer the question.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vmurahari3/visdial-diversity
공식 코드Improving Generative Visual Dialog by Answering Diverse Questions
https://github.com/willemsenbram/a-game-of-sorts
공식 코드https://github.com/xiaoxiaoheimei/SeqDialN
공식 코드SeqDialN: Sequential Visual Dialog Networks in Joint Visual-Linguistic Representation Space
https://github.com/yuleiniu/rva
공식 코드https://github.com/zd11024/entity_questioner
공식 코드Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
https://github.com/zd11024/spot_difference
공식 코드Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene
https://github.com/zihaow123/unimm
공식 코드Unified Multimodal Model with Unlikelihood Training for Visual Dialog
https://github.com/zilongzheng/visdial-gnn
공식 코드Reasoning Visual Dialogs with Structural and Partial Observations
https://github.com/zipengxuc/ADVSE-GuessWhat
공식 코드Answer-Driven Visual State Estimator for Goal-Oriented Visual Dialogue
https://github.com/Cloud-CV/visual-chatbot
https://github.com/Cremiy/visdial-rl
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/MindCode-4/code-8/tree/main/perceptual-score
Perceptual Score: What Data Modalities Does Your Model Perceive?
https://github.com/MindSpore-scientific/code-5/tree/main/perceptual-score
Perceptual Score: What Data Modalities Does Your Model Perceive?
https://github.com/Rabahjamal/Visual-Question-Answering
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/SkyOL5/VQA-CoAttention
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/WillSuen/VQA
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/arya46/VQA_HieCoAtt
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/batra-mlp-lab/WAY
https://github.com/batra-mlp-lab/avsd
https://github.com/batra-mlp-lab/visdial
https://github.com/batra-mlp-lab/visdial-challenge-starter-pytorch
https://github.com/batra-mlp-lab/visdial-rl
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/danielamassiceti/geneval_visdial
https://github.com/dvlab-research/MGM
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/facebookresearch/ParlAI
https://github.com/facebookresearch/corefnmn
Visual Coreference Resolution in Visual Dialog using Neural Module Networks
https://github.com/gicheonkang/prograsp
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
https://github.com/hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge
https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/karunraju/VQA
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/kdexd/lang-emerge-parlai
https://github.com/lemuria-wchen/DialogVED
https://github.com/meera1hahn/Graph_LED
https://github.com/miohana/vqa
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/nonslowrunner/Visualdialogs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.