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/virajprabhu/premise-emnlp17
The Promise of Premise: Harnessing Question Premises in Visual Question Answering
https://github.com/vladsandulescu/hatefulmemes
https://github.com/vmurahari3/visdial-diversity
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/vohoaiviet/FloorNet
FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans
https://github.com/voxmenthe/OmniNet_1
OmniNet: A unified architecture for multi-modal multi-task learning
https://github.com/vyskocj/VinVL-L
https://github.com/vztu/BVQA_Benchmark
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/vztu/VIDEVAL
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/vztu/VIDEVAL_release
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/wangzpeng/tf-faster-rcnn
https://github.com/wawaku/grad-cam-keras
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/wayveai/driving-with-llms
https://github.com/weixin-liang/metashift
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
https://github.com/wenhuchen/gnn-tabfact
NumNet: Machine Reading Comprehension with Numerical Reasoning
https://github.com/wennieWN/endernewton_tf-faster-rcnn
https://github.com/wglab/gestaltmml
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/wglab/gestaltmml-gestaltgpt
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/windweller/PragmaticVQA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/wisdomikezogwo/quilt1m
https://github.com/wsanchezdupont1/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/x-plug/mplug
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/xaviercanche/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/xiaobai714/image_caption
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/xiaoduoailab/xmodellm
Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
https://github.com/xiaomin418/cfsum
https://github.com/xinding-sys/StreamMind
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/xinpingwang/tf-faster-rcnn
https://github.com/xinwei666/mmgenerativeir
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
https://github.com/xlrshop/Parl
https://github.com/xn1997/pytorch-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/xuewyang/Fashion_Captioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/xycforgithub/Airbnb-Comparison
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
https://github.com/yaleCat/Grad-CAM-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yanchunyu71/relational-networks-paddle
https://github.com/yandex-research/sparqling-queries
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.