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/sihengli99/textbind
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
https://github.com/singh-shakti94/Deep-Learning-Project
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sino30535/Weak-label-in-CT-image-segamentation
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sitloboi2012/Visualization-of-Thought
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
https://github.com/sivandoveh/dac
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
https://github.com/skmda37/shearletx
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/skunkworksai/bakllava
https://github.com/skyworkai/moe-plus-plus
https://github.com/skyworkai/moh
https://github.com/snagiri/ECE285_Jarvis_ProjectA
https://github.com/snoop2head/ehrxqa-2024
https://github.com/social-ai-studio/matk
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/songhe17/pythia-clone
https://github.com/sriharsha-sreenatha/CSCI596_FinalProject
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/sseol11/ParlAI_blender
https://github.com/sseol11/ParlAI_blender_persona
https://github.com/sshh12/multi_token
https://github.com/starsholic/loveu-cvpr22-aqtc
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant
https://github.com/straightAYiJun/vqa-attention-visualize-system
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/sts-vlcc/sts-vlcc
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
https://github.com/sunsmarterjie/chatterbox
https://github.com/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/syscv/sam-hq
SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models
https://github.com/tabtoyou/kollava
https://github.com/tanjimin/grad-cam-pytorch-light
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/tarolangner/mri-biometry
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/tashchowdhury/FloodNet-Supervised_v1.0
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/tbmoon/basic_vqa
https://github.com/teddykoker/image-forensics
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/tejas-gokhale/vqa_mutant
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
https://github.com/thanhlt998/mrc-ner
https://github.com/thaolmk54/LOGNet-VQA
https://github.com/therne/dmn-tensorflow
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/thilinicooray/Bottom-up-vqa
Modeling Relationships in Referential Expressions with Compositional Modular Networks
https://github.com/thtang/CheXNet-with-localization
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.