https://github.com/facebookresearch/meru
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
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/facebookresearch/mmf
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/facebookresearch/multimodal
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/facebookresearch/reconsider
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
https://github.com/fatyin88/MCQA_project
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/fcorencoret/dynamic-rn
https://github.com/feifengwhu/question_attention
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/fitushar/3D-Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/flagopen/flagscale
https://github.com/freedombenLiu/ParlAI
https://github.com/frgfm/torch-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/frkl/active-learning
Active Learning for Visual Question Answering: An Empirical Study
https://github.com/fudan-zvg/spar
From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D
https://github.com/fulstock/mrc_nested_ner_ru
https://github.com/fuqianya/ViLBERT-Paddle
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/fuqianya/bottom-up-attention-paddle
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/fursovia/self-adj-dice
https://github.com/gangasani-anusha/Short-Story_Assignment
https://github.com/gchaperon/neural-state-machine
https://github.com/gchhablani/multilingual-vqa
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/gengchenmai/se-kge
https://github.com/gicheonkang/gst-visdial
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/gitlimlab/Relation-Network-Tensorflow
https://github.com/givenbysun/hyper-class-augmented-VQA
https://github.com/gngdb/relational
https://github.com/google-deepmind/proactive_t2i_agents
Evaluating Text-to-Visual Generation with Image-to-Text Generation
https://github.com/google-research-datasets/RxR
https://github.com/google-research/bigbird
https://github.com/gooofy/zbrain
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/gpt4life/alpagasus
https://github.com/gqa-ood/gqa-ood
Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?
https://github.com/gregor-ge/mblip
https://github.com/gshashank84/CheXNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/guilk/vlc
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/guyd1995/lra-benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.