Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific-2/code-14/tree/main/internlm
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/layoutlmv3
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
https://github.com/MindSpore-scientific-2/code-14/tree/main/llava
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
https://github.com/MindSpore-scientific-2/code-14/tree/main/llava_next
https://github.com/MindSpore-scientific-2/code-14/tree/main/lxmert
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
https://github.com/MindSpore-scientific-2/code-5/tree/main/med-flamingo
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/examples/language_model/chinesebert
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_reading_comprehension/DuReader-robust
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_reading_comprehension/DuReader-yesno
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
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/PaddlePaddle/Research
https://github.com/PaddlePaddle/models
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
https://github.com/PengchengAi/tf-faster-rcnn-pcai
https://github.com/PetrosStav/BioMRC_code
BIOMRC: A Dataset for Biomedical Machine Reading Comprehension
https://github.com/PierreMarza/autonerf
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
https://github.com/Pointcept/GPT4Point
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.