Papers with Code 아카이브
Text Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 16
- 보존된 평가 기록
- 51
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/salesforce/pb-ovd
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/setu4993/convert-labse-tf-pt
https://github.com/shmsw25/GraphRetriever
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/showlab/region_learner
https://github.com/skoltech-nlp/russe_detox_2022
https://github.com/snap-research/panda-70m
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
https://github.com/tanveer81/revisionllm
RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
https://github.com/terrierteam/pyterrier_ance
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
https://github.com/terrierteam/pyterrier_dr
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
https://github.com/texttron/tevatron/tree/main/examples/repllama
https://github.com/thakur-nandan/income
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
https://github.com/towhee-io/towhee
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
https://github.com/ubc-vision/make-a-story
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
https://github.com/unitaryai/VTC
https://github.com/unsuthee/VariationalDeepSemanticHashing
https://github.com/vladsandulescu/hatefulmemes
https://github.com/wadoodabdul/clinical_ner_benchmark
https://github.com/willard-yuan/video-text-retrieval-papers
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/x-plug/mplug
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/xhluca/bm25-benchmarks
BM25S: Orders of magnitude faster lexical search via eager sparse scoring
https://github.com/xiaomin418/cfsum
https://github.com/yalesong/pvse
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
https://github.com/yejy53/ep-bev
Where am I? Cross-View Geo-localization with Natural Language Descriptions
https://github.com/ylsung/vl-merging
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
https://github.com/yuliangcai2022/clumo
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/zhaoyue-zephyrus/avion
https://github.com/zhihaozhang97/ru-ai
https://github.com/zhixinlai/llmboostmedical
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/zihanwangki/x-tc
https://gitlab.com/birder/birder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.