https://github.com/HyeonwooNoh/DPPnet
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/IS2AI/Chest-X-ray-module
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/IllgamhoDuck/ResTR
https://github.com/ImperialNLP/BertGen
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/JHKim-snu/GVCCI
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/JHKim-snu/PGA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/Jara555/CNN-for-AD-classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Jeremyyny/Value-Spectrum
https://github.com/Jmak12/Iris1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JoonSeongPark/vqa
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/JosephElHachem/GradCam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JoshuaPlacidi/MS-COCO-Object-Tags
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/JoshuaPlacidi/MS-COCO-Tags
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/JuHyun-E/Attention-ResNet50
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JuHyun-E/Attention_in_NN
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JunweiLiang/DualAttentionNetwork
Dual Attention Networks for Multimodal Reasoning and Matching
https://github.com/JunweiLiang/FVTA_memoryqa
https://github.com/KLUE-benchmark/KLUE-baseline
https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/KaihuaTang/VCTree-Scene-Graph-Generation
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/KaihuaTang/VCTree-Visual-Question-Answering
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/KarhouTam/FL-bench
Think Locally, Act Globally: Federated Learning with Local and Global Representations
https://github.com/Karim-53/SGG
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/KastanDay/video-pretrained-transformer
https://github.com/Katherinaxxx/Paper-ReadingNotes
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
https://github.com/KaveeshaSIlva/Baseline-FYP
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/KaveeshaSIlva/PathVQA
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/Kenneth-Wong/tf-faster-rcnn
https://github.com/Ketansuhaas/scope-diffusers
Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models
https://github.com/KyeongmoonKim/sb
https://github.com/LLaVA-VL/LLaVA-NeXT
https://github.com/Lightning-AI/lit-llama
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/LisaAnne/Hallucination
https://github.com/LivXue/FS-MEVQA
https://github.com/LivXue/VCIN
https://github.com/LuanAdemi/VisualGo
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.