https://github.com/ynklab/xcodesearchnet
공식 코드Constructing Multilingual Code Search Dataset Using Neural Machine Translation
Papers with Code 아카이브
Natural Language Queries
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhiqix/nl2gql
공식 코드$R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
https://github.com/zijialewislu/cvpr2025-decafnet
공식 코드DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos
https://github.com/ziyuewu59/cca
공식 코드Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding
https://github.com/zju-ctag/codebridge
공식 코드https://github.com/zjuchenlong/wsag
공식 코드https://github.com/zwanah/DataVisT5
공식 코드DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tall
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tall
https://github.com/LisaAnne/TemporalLanguageRelease
https://github.com/MS-P3/code7/tree/main/umt5
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/MindCode-4/code-5/tree/main/umt5
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/MindSpore-paper-code-2/code3/tree/main/tall
https://github.com/UmarFarooqui/MethodNamePrediction
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/UmarFarooqui/Springboard
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/VV123/NLIDB_gradient
A Transfer-Learnable Natural Language Interface for Databases
https://github.com/VV123/SpatialNLI
A Transfer-Learnable Natural Language Interface for Databases
https://github.com/WuJie1010/TSP-PRL
https://github.com/WuJie1010/Temporally-language-grounding
https://github.com/albanie/collaborative-experts
Use What You Have: Video Retrieval Using Representations From Collaborative Experts
https://github.com/alvin-zeng/drn
https://github.com/aminatadjer/test
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/caijincen712/CE
Use What You Have: Video Retrieval Using Representations From Collaborative Experts
https://github.com/code-desc/codesc
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/csebuetnlp/CoDesc
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/czy1999/MultiTQ
https://github.com/czy1999/SubGTR
https://github.com/dheerajrajagopal/EIGEN
CURIE: An Iterative Querying Approach for Reasoning About Situations
https://github.com/eric-ai-lab/mitigate-gender-bias-in-image-search
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset
https://github.com/gabeur/mmt
https://github.com/google/seqio
How Much Knowledge Can You Pack Into the Parameters of a Language Model?
https://github.com/hbzhang/cvpr2020
Use What You Have: Video Retrieval Using Representations From Collaborative Experts
https://github.com/houzhijian/cone
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
https://github.com/jmg764/CodeSearchNet-Challenge
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
https://github.com/keloee/maskfield
Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.