Papers with Code 아카이브
Text Retrieval
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Text Retrieval is the task of finding the most text result (such as an answer, paragraph, or passage) given a query (which could be a question, keywords, or any relevant text)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zyyan4/ml-clap
공식 코드https://gitlab.com/timperator/which-country-is-this
공식 코드Which Country Is This? Automatic Country Ranking of Street View Photos
https://huggingface.co/TownsWu/PEG
공식 코드https://huggingface.co/enalis/scold
공식 코드A Vision-Language Foundation Model for Leaf Disease Identification
https://github.com/0jason000/wukong
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/blip2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip
https://github.com/Alenush/sirius-spring2021-image2chat
https://github.com/AndresPMD/Pytorch-yolo-phoc
https://github.com/DreadPiratePsyopus/Pytorch-yolo-phoc
https://github.com/FreddeFrallan/Multilingual-CLIP
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/IMvision12/keras-vision-models
https://github.com/J-zin/SNUH
https://github.com/MicPie/clasp
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/MindCode-4/code-4/tree/main/rwkv
https://github.com/MindCode-4/code-5/tree/main/x_clip
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/MindSpore-scientific/code-7/tree/main/X_CLIP
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/MindSpore-scientific/code-9/tree/main/SNUH
https://github.com/MysteryVaibhav/SCAN
https://github.com/PKU-YuanGroup/LLMBind
https://github.com/PKU-YuanGroup/MoE-LLaVA
https://github.com/PKU-YuanGroup/Video-LLaVA
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/SDLZY/VCR_Align
https://github.com/THUDM/P-tuning
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
https://github.com/abhidipbhattacharyya/srl_aware_ret
https://github.com/adlnlp/attention_vl
https://github.com/alibaba/EasyNLP/tree/master/examples/blip_retrieval
https://github.com/aranciokov/fsmmda_videoretrieval
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/aranciokov/ranp
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/baai-dcai/segvol
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/basf/chemteb
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.