https://huggingface.co/datasets/RadGenome/RadGenome-ChestCT
공식 코드RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224
공식 코드https://github.com/1512159/tf-faster-rcnn-medico
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2023-MindSpore-1/ms-code-2/tree/main/OCRNet
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/sdne
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/blip2
https://github.com/2023-MindSpore-4/Code6/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cogvlm
https://github.com/2024-MindSpore-1/Code5/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2024-MindSpore-1/Code6/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/27182812/ChineseBERT_paddle
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/3d-vista/3D-VisTA
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
https://github.com/42Shawn/LLaVA-PruMerge
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
https://github.com/AI-metrics/AI-metrics
https://github.com/AMAP-ML/RealQA
https://github.com/AUCOHL/RTL-Repo
https://github.com/Adam1679/mutan-article-net
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/AhnYoungBin/Grad_cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AishwaryaAgrawal/GVQA
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
https://github.com/AlanYangYi/GayPhotoStyleRecongnizer
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlanYangYi/PhotoStyleRecongnizer
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AldrichCabrera/gradCam-smallvgg
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AleDella/mdter_eval
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/AlexKuhnle/film
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/AlexSzen/wwf_shark_id
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlphaJia/tf-faster-rcnn
https://github.com/AmenRa/rank_eval
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/AndreaCossu/Relation-Network-PyTorch
https://github.com/AndresPMD/Fine_Grained_Clf
https://github.com/Ankush96/grad-cam.tensorflow
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/BigRedT/info-ground
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.