https://github.com/WangYuxuan93/CVLUE
공식 코드CVLUE: A New Benchmark Dataset for Chinese Vision-Language Understanding Evaluation
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 76
- 보존된 평가 기록
- 725
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/Wangt-CN/VC-R-CNN
공식 코드https://github.com/WeixiongLin/PMC-CLIP
공식 코드PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
https://github.com/WisconsinAIVision/YoLLaVA
공식 코드https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2
공식 코드mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
https://github.com/XenonLamb/EditVid-QA
공식 코드Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model
https://github.com/YIKUAN8/Transformers-VQA
공식 코드https://github.com/YancyKahn/CIA
공식 코드https://github.com/Yushi-Hu/PromptCap
공식 코드https://github.com/Yushi-Hu/tifa
공식 코드TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
https://github.com/Yuuxii/Black-box-QA-Ensemling
공식 코드Black-box Model Ensembling for Textual and Visual Question Answering via Information Fusion
https://github.com/ZephyrZhuQi/ssbaseline
공식 코드Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps
https://github.com/ZhengxiangShi/StepGame
공식 코드StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
공식 코드https://github.com/abril4416/Mod-Zero-VQA
공식 코드https://github.com/abril4416/kgen_vqa
공식 코드https://github.com/abskjha/Barlow-constrained-VQA
공식 코드Barlow constrained optimization for Visual Question Answering
https://github.com/abzb1/f-clip
공식 코드https://github.com/activevisionlab/awesome-llm-3d
공식 코드https://github.com/adacheng/egothink
공식 코드EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models
https://github.com/adapter-hub/xgqa
공식 코드https://github.com/adishourya/medm
공식 코드Adapting Lightweight Vision Language Models for Radiological Visual Question Answering
https://github.com/aditya10/vlc-bert
공식 코드VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
https://github.com/adityakane2001/continual_vqa
공식 코드https://github.com/adlnlp/MultimodalLLM
공식 코드Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond
https://github.com/adlnlp/attention_vl
공식 코드https://github.com/adlnlp/msg-chart
공식 코드https://github.com/afsharshamsi/bella
공식 코드Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks
https://github.com/ag2ai/simpledoc
공식 코드SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement
https://github.com/ahmad-shirazi/AnnotMLLM
공식 코드https://github.com/ahmedmagdiosman/clevr-xai
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ahmedmagdiosman/simply-clevr-dataset
공식 코드Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI
https://github.com/ai4ce/CityWalker
공식 코드CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos
https://github.com/ai4ce/Contrastive-SPARE3D
공식 코드Self-supervised Spatial Reasoning on Multi-View Line Drawings
https://github.com/ai4ce/SPARE3D
공식 코드SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings
https://github.com/ai4colonoscopy/intelliscope
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.