Papers with Code 아카이브
Visual Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 192
Ability to understand actions and reasoning associated with any visual images
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IMvision12/keras-vision-models
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ImperialNLP/BertGen
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/JHKim-snu/GVCCI
https://github.com/JHKim-snu/PGA
https://github.com/JesusCarr/JARVIS
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/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/Karim-53/SGG
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/KiritoSSR/paddle_r2c
https://github.com/LLaVA-VL/LLaVA-NeXT
https://github.com/LiYinqi/DIVE
https://github.com/Lucas2012/ProbabilisticNeuralProgrammedNetwork
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/MS-P3/code7/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/Mehrab-Tanjim/enforce-reasoning
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/Mehrab-Tanjim/vilbert-rationalization
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/Mind23-2/MindCode-156
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/MindCode-4/code-1/tree/main/vilt
https://github.com/MindCode-4/code-1/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/MindCode-4/code-1/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/MindCode-4/code-1/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/MindCode-4/code-5/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/MindCode-4/code-5/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/MindCode-4/code-5/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/NYU-DICE-Lab/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/OML-Team/open-metric-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PasserBy4/mypretraining
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
https://github.com/PasserBy4/pretraining
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
https://github.com/Phoenix1327/ML-ZSL
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
https://github.com/RUCAIBox/Slow_Thinking_with_LLMs
Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
https://github.com/SDLZY/VCR_Align
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.