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/itsShnik/adaptively-finetuning-transformers
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/ivegner/Multi-Memory-MAC-Network
https://github.com/jack-bo1220/awesome-remote-sensing-foundation-models
https://github.com/jackroos/pythia
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/jacobandreas/nmn2
https://github.com/jacobgil/keras-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jacobgil/pytorch-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jacobmeisel/EE269_Final_Project
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/jaehyunnn/RelationalNetwork_pytorch
https://github.com/jaykim9870/cvpr-22_loveu_unipyler
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant
https://github.com/jdai-cv/image-captioning
https://github.com/jeongukjae/KR-BERT-SimCSE
https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-Attention
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/jiahuei/COMIC-Towards-A-Compact-Image-Captioning-Model-with-Attention
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/jialinwu17/Self_Critical_VQA
Self-Critical Reasoning for Robust Visual Question Answering
https://github.com/jialinwu17/tmpimgs
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/jianguanthu/amor
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis
https://github.com/jianjieluo/openai-clip-feature
https://github.com/jiasenlu/vilbert_beta
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/jiayi-wei/vqa-tf2
https://github.com/jim-schwoebel/allie
https://github.com/jinmang2/RetroReader
https://github.com/jjgo/hyperlight
https://github.com/jmvuilla/classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jnhwkim/cbp
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/johntiger1/multitask_multimodal
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/jordan-bird/synthetic-fruit-image-generator
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jpthu17/dicosa
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
https://github.com/jpthu17/diffusionret
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
https://github.com/jules-samaran/vl-bert
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/junshutang/Make-It-3D
https://github.com/juruobenruo/DexVLA
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/jxwuyi/House3D
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.