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
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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/zhaochen0110/openthinkimg
공식 코드OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
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/zijizhu/proto-non-param
공식 코드Interpretable Image Classification via Non-parametric Part Prototype Learning
https://github.com/zwh1999anne/Machine-Number-Sense-Dataset
공식 코드Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning
https://github.com/zwq2018/embodied_reasoner
공식 코드Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks
https://github.com/zwq2018/multi-modal-self-instruct
공식 코드https://github.com/zzw-zwzhang/awesome-of-multimodal-dialogue-models
공식 코드Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation
https://github.com/zzxslp/som-llava
공식 코드List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
https://huggingface.co/sparks-solutions/ELAM-7B
공식 코드Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UI
https://worksheets.codalab.org/worksheets/0x0097f249cd944284a81af331093c3579
공식 코드https://github.com/01-ai/yi
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/blip2
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/falcon
https://github.com/2024-MindSpore-1/Code5/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/2024-MindSpore-1/Code6/tree/main/advanced_east
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/3d-vista/3D-VisTA
3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
https://github.com/42Shawn/LLaVA-PruMerge
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
https://github.com/AlexKuhnle/film
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/BorealisAI/PROVIDE
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
https://github.com/BorealisAI/Spatio-Temporal
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
https://github.com/CPJKU/audio_conditioned_unet
https://github.com/Cadene/recipe1m.bootstrap.pytorch
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/Chaolei98/FreeZAD
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/FerryHuang/SimVLM
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Glaciohound/VCML
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.