https://github.com/lionel-hing/bic-net
공식 코드BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 19
A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lionel-hing/vsr-net
공식 코드BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval
https://github.com/lionisakis/reproducibilitiy-lsr-long
공식 코드On the Reproducibility of Learned Sparse Retrieval Adaptations for Long Documents
https://github.com/liruihui/PointAugment
공식 코드PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
https://github.com/lisaalaz/satbot
공식 코드https://github.com/littleredxh/DREML
공식 코드https://github.com/littleredxh/EasyPositiveHardNegative
공식 코드https://github.com/littleredxh/HardNegative
공식 코드https://github.com/littleredxh/vse-gradient
공식 코드Dissecting Deep Metric Learning Losses for Image-Text Retrieval
https://github.com/littlewine/zeco2
공식 코드https://github.com/liu-yating/up-person
공식 코드UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person Retrieval
https://github.com/liuaxou/prista-net
공식 코드https://github.com/liubin06/dpl
공식 코드Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback
https://github.com/liuchenzhengyi/dimongen
공식 코드DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
https://github.com/liufh1/hsl
공식 코드Hierarchical Similarity Learning for Language-based Product Image Retrieval
https://github.com/liuji93/GTH
공식 코드Optimal Projection Guided Transfer Hashing for Image Retrieval
https://github.com/liujinyu1229/dcgmh
공식 코드https://github.com/liuliuyuan6/Exp4Fuse
공식 코드https://github.com/liunian-jay/gainrag
공식 코드GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis
https://github.com/liuyuqi123study/ranksvm_for_slr
공식 코드Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
https://github.com/liuzq09/GenerativePCA
공식 코드https://github.com/liuzq09/PRI_SPCA
공식 코드Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
https://github.com/liuzrcc/PIRE
공식 코드https://github.com/liwrui/rmarn
공식 코드Riemann-based Multi-scale Attention Reasoning Network for Text-3D Retrieval
https://github.com/lixiaotong97/dsu
공식 코드https://github.com/lixinze777/knowledge-aware-language-model-attribution
공식 코드Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
https://github.com/lixinze777/lc_vs_rag
공식 코드https://github.com/lixsh6/tencent_wsdm_cup2023
공식 코드Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
https://github.com/liyongkang123/hotflip_corpus_poisoning
공식 코드Reproducing HotFlip for Corpus Poisoning Attacks in Dense Retrieval
https://github.com/liyongqi67/ltrgr
공식 코드https://github.com/liyongqi67/minder
공식 코드https://github.com/liyuan-2000/trad
공식 코드Untrained neural network embedded Fourier phase retrieval from few measurements
https://github.com/liyunqianggyn/Deep-Unsupervised-Image-Hashing
공식 코드https://github.com/lizeyujack/oceanship
공식 코드Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition
https://github.com/lizhaoliu-Lec/DAS
공식 코드https://github.com/ljsoler/TattTRN
공식 코드TattTRN: Template Reconstruction Network for Tattoo Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.