https://github.com/VQAssessment/FAST-VQA-and-FasterVQA
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
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/VTOOL-R1/vtool-r1
ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding
https://github.com/VegB/VLN-Transformer
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
https://github.com/VincentYing/Attention-on-Attention-for-VQA
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/WebQnA/WebQA_Baseline
Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/Wentong-DST/up-down-captioner
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/WillSuen/VQA
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/Wordbe/GradCAM3D
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/X-PLUG/mPLUG-2
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/Xaniar87/SAN_SQuAD2
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/XingruiWang/superclevr-3D-question
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
https://github.com/YanjieZe/Improved-3D-Diffusion-Policy
PointVLA: Injecting the 3D World into Vision-Language-Action Models
https://github.com/YicongHong/Entity-Graph-VLN
https://github.com/YicongHong/Recurrent-VLN-BERT
https://github.com/YitianShi/vMF-Contact
https://github.com/Youngb2n/GradCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Yui010206/CREMA
https://github.com/ZHEvent/ZHEvent.github.io
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
https://github.com/ZhangYiqun018/StickerConv
https://github.com/Zhendong-Wang/arsm_image_captioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/abhi-iyer/visual-question-answering
https://github.com/abhigoyal1997/CS-763-Project
https://github.com/abhijit-buet/VizWiz-Visua-Question-Answering-2021
https://github.com/abhijit-buet/VizWiz-Visual-Question-Answering-2021
https://github.com/abhshkdz/House3D
https://github.com/abhshkdz/neural-vqa-attention
https://github.com/abwilf/social-iq-2.0-challenge
https://github.com/achiyaj/vqa-sandbox
https://github.com/adriangoe/relational-networks-pytorch
https://github.com/aerinkim/squad_2018
https://github.com/ahjeongseo/MASN-pytorch
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
https://github.com/ahmedmagdiosman/compress-vqa
Dual Recurrent Attention Units for Visual Question Answering
https://github.com/aidc-ai/parrot
Ovis: Structural Embedding Alignment for Multimodal Large Language Model
https://github.com/ajaykrishnan23/Grad_Cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ajenningsfrankston/Dynamic-Memory-Network-Plus-master
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/albertotestoni/ndq_visual_objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.