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Visual Question Answering
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific-2/code-8/tree/main/PR_Product
https://github.com/MindSpore-scientific-2/code-8/tree/main/med-flamingo
https://github.com/MindSpore-scientific-2/code-9/tree/main/med-flamingo
https://github.com/MindSpore-scientific/code-5/tree/main/perceptual-score
Perceptual Score: What Data Modalities Does Your Model Perceive?
https://github.com/MindSpore-scientific/code-7/tree/main/LayoutLMv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MindSpore-scientific/code-8/tree/main/PR_Product
https://github.com/Mukundaram/Mukund
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Murali81/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/NormXU/ERNIE-Layout-Pytorch
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
https://github.com/One-2-3-45/One-2-3-45
https://github.com/Onysuke/chainer-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/OpenBMB/MiniCPM-o
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
https://github.com/PKU-YuanGroup/LLMBind
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/PaddlePaddle/PaddleOCR
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/PengchengAi/tf-faster-rcnn-pcai
https://github.com/PierreMarza/autonerf
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu
https://github.com/Rabahjamal/Visual-Question-Answering
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/RasmusAndersen/grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/RenzKa/simlingo
https://github.com/Rishit-dagli/Visualize.AI
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/RuizhiZhang/MManGoCam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/RuoyuChen10/objectdetection-saliency-maps
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/RylanSchaeffer/prismatic-vlms
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/SDLZY/VCR_Align
https://github.com/SDaydreamer/VisualQA_Project
https://github.com/SJTU-VLG/ImageCaption-LBPF-qy
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/SatyamGaba/visual_question_answering
https://github.com/SatyamGaba/vqa
https://github.com/ShahinSHH/COVID-FACT
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Shivanshu-Gupta/Visual-Question-Answering
https://github.com/Shivmohith/Visual-Assistance-for-the-Blind
https://github.com/Shubhambindal2017/Unoffical-Implementation-of-GRAD-CAM-with-Sanity-Checks
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/SkyOL5/VQA-CoAttention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.