Papers with Code 아카이브
Visual Commonsense Reasoning
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Image source: Visual Commonsense Reasoning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Deanplayerljx/tab-vcr
공식 코드TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines
https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
공식 코드https://github.com/allenai/visual-reasoning-rationalization
공식 코드https://github.com/gary-code/peifg
공식 코드Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor
https://github.com/google-research/language
공식 코드Fusion of Detected Objects in Text for Visual Question Answering
https://github.com/guyyariv/vlmig
공식 코드Improving Visual Commonsense in Language Models via Multiple Image Generation
https://github.com/intellabs/vl-interpret
공식 코드VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
https://github.com/j-min/VL-T5
공식 코드https://github.com/jackroos/VL-BERT
공식 코드VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/jshilong/gpt4roi
공식 코드GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
https://github.com/lupantech/ScienceQA
공식 코드Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
https://github.com/ml-research/ILLUME
공식 코드ILLUME: Rationalizing Vision-Language Models through Human Interactions
https://github.com/neilfei/brivl-nmi
공식 코드Towards artificial general intelligence via a multimodal foundation model
https://github.com/sdlzy/arc
공식 코드Joint Answering and Explanation for Visual Commonsense Reasoning
https://github.com/showlab/all-in-one
공식 코드https://github.com/tanjatang/CAN
공식 코드Interpretable Visual Understanding with Cognitive Attention Network
https://github.com/tanjatang/DMVCR
공식 코드Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
https://github.com/thunlp/pevl
공식 코드PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models
https://github.com/togethercomputer/dragonfly
공식 코드Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models
https://github.com/umich-vl/think_visually
공식 코드https://github.com/vasr-dataset/vasr
공식 코드https://github.com/wadeyin9712/gd-vcr
공식 코드Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning
https://github.com/yehli/xmodaler
공식 코드X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
https://github.com/yuweijiang/HGL-pytorch
공식 코드Heterogeneous Graph Learning for Visual Commonsense Reasoning
https://github.com/zhegan27/LXMERT-AdvTrain
공식 코드Large-Scale Adversarial Training for Vision-and-Language Representation Learning
https://github.com/zhegan27/VILLA
공식 코드Large-Scale Adversarial Training for Vision-and-Language Representation Learning
https://github.com/ImperialNLP/BertGen
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/KiritoSSR/paddle_r2c
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/MindCode-4/code-1/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/MindCode-4/code-5/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/SDLZY/VCR_Align
https://github.com/TheShadow29/visual-commonsense-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.