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/mmontana/ECG-heartbeat-classification
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/modelscope/modelscope
https://github.com/moduIo/Relation-Networks
https://github.com/moh833/VQA
https://github.com/mokhalid-dev/Attention-based-VQA-model
https://github.com/moserle/TSJS-Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/mpandeydev/SDnetmod
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/msandeep27/Pytorch-Grad-Cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/myaoo18/EC601-Visual-Question-Answering
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
https://github.com/n-akram/GradCamDDD
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nahumsa/qhack21
Noise-Induced Barren Plateaus in Variational Quantum Algorithms
https://github.com/nalbarr/coursera-ai4med-course3
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nautilus261/tf-faster-rcnn
https://github.com/naver/aqm-plus
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/necotis/naaqa-acoustic-question-answering
NAAQA: A Neural Architecture for Acoustic Question Answering
https://github.com/nerdimite/neuro-symbolic-ai-soc
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
https://github.com/nerdimite/relation-network
https://github.com/neuqgz/modify-faster-rcnn-tf
https://github.com/nguyenhoa93/GradCAM_and_GuidedGradCAM_tf2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nickgkan/3d_diffuser_actor
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
https://github.com/nikolettuce/DSC180B_06_NeuralBackedDecisionTrees
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ninatu/howtocaption
https://github.com/nishidayoshikatsu/chainer-gradcam-sample
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/nlinc1905/dsilt-tsa
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/nlpdata/c3
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
https://github.com/nocaps-org/updown-baseline
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/notem/keras-alexnet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/novice03/timm-vis
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ntusteeian/VQA_CNN-LSTM
https://github.com/olivesgatech/explanatory-paradigms
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/olivesgatech/introspective-learning
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/om00839/machine-suneung
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/open-vision-language/infoseek
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
https://github.com/open-vision-language/oven
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
https://github.com/openbmb/minicpm-v
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.