https://github.com/saketdingliwal/Few-Shot-DST
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
Papers with Code 아카이브
Visual Question Answering (VQA)
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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/salanueva/UniVSE
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
https://github.com/salesforce/ALBEF
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/salesforce/blip
https://github.com/salesforce/pb-ovd
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/samson6460/tf-keras-gradcamplusplus
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/samson6460/tf.keras-gradcamplusplus
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/samson6460/tf_keras_gradcamplusplus
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sarahesl/pubmedclip
Overcoming Data Limitation in Medical Visual Question Answering
https://github.com/sauravmishra1710/EXPLAINABLE-AI---Skin-Cancer-Detection-explained-with-GRADCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sauravmishra1710/Malaria-Detection-Using-Deep-Learning-Techniques
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sayakpaul/swin-transformers-tf
https://github.com/scy6500/blenderbot2-client
https://github.com/sea-snell/implicit-language-q-learning
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/sgondala/GoogleConceptualCaptioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/shailaja183/vlqa
https://github.com/shailzajolly/Understanding-yesno-and-nonyesno-samples-VQA
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
https://github.com/shailzajolly/icdar_vqa
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
https://github.com/sharejing/BiPaR
https://github.com/shashankMadan-designEsthetics/ParlAICustom
https://github.com/shashidhar100/Comparison-of-gradcam-and-gradcam_plus_plus_keras
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/shawnhuang497/bird
Ferret: Refer and Ground Anything Anywhere at Any Granularity
https://github.com/shengliu66/vti
https://github.com/shihaozhaozsh/lavi-bridge
https://github.com/shijx12/shijx12.github.io
https://github.com/shikorab/tf-faster-rcnn
https://github.com/shin-ee-chen/bla
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/shmsw25/factscore
https://github.com/showlab/show-o
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
https://github.com/shubham-gupta-iitr/mmmlX
https://github.com/shucheng-liu/TensorFlow_CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/shuhewang1998/gpt-ner
https://github.com/shyhyawJou/Grad-CAM-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/shyhyawJou/GradCAM-Tensorflow
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sicara/tf-explain
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sidaw/nbsvm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.