https://github.com/marioskatsak/mapert
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Papers with Code 아카이브
Visual Question Answering (VQA)
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- 평가표
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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/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/matwilso/relation-networks
https://github.com/mazm13/Image-to-Tree.pytorch
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/mbzuai-oryx/unimed-clip
https://github.com/mchandak29/IQ-VQA
https://github.com/mdda/relationships-from-entity-stream
https://github.com/meiqiguo/iccv2021-atypicalitydetection
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/meliketoy/gradcam.pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/mengchuang123/vasparse-github
Evaluating Object Hallucination in Large Vision-Language Models
https://github.com/mengyingfei/faster-rcnn-tf
https://github.com/menloresearch/ichigo
https://github.com/mesnico/RelationNetworks-CLEVR
https://github.com/mgorkove/blender2_server
https://github.com/mgorkove/blenderbot2-server
https://github.com/microsoft/LLaVA-Med
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
https://github.com/microsoft/MSMARCO-Conversational-Search
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Document-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Passage-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Question-Answering
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/TREC-2019-Deep-Learning
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/eureka-ml-insights
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
https://github.com/microsoft/unicl
https://github.com/microsoft/xpretrain
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
https://github.com/milvlg/rosita
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
https://github.com/min942773/parlai_wandb
https://github.com/mineshmathew/DocVQA
https://github.com/mineshmathew/DocVQA/tree/master/BERT_baseline
https://github.com/miohana/vqa
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
https://github.com/mishajw/vocab_pie
https://github.com/mitjanikolaus/compositional-image-captioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/mitvis/vistext
https://github.com/mkhalil1998/EC601_Group_Project
https://github.com/mlfoundations/open_clip
CoCa: Contrastive Captioners are Image-Text Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.