https://github.com/wengsyx/ccgs
공식 코드Learning to Locate Visual Answer in Video Corpus Using Question
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/wengwanjiang/usdrl
공식 코드USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation
https://github.com/wengzejia1/open-vclip
공식 코드Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
https://github.com/wenhuchen/HybridQA
공식 코드MuGER$^2$: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering
https://github.com/wenhuchen/OTT-QA
공식 코드https://github.com/wenjiazh/newsquote
공식 코드https://github.com/wenzhengzhang/entqa
공식 코드https://github.com/wenzhengzhang/taco
공식 코드Improving Multitask Retrieval by Promoting Task Specialization
https://github.com/wenzhihao666/g2p2-conditional
공식 코드Prompt Tuning on Graph-augmented Low-resource Text Classification
https://github.com/wesleyzhang1991/google_landmark_retrieval_2021_2nd_place_solution
공식 코드https://github.com/wgcyeo/UniversalRAG
공식 코드UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
https://github.com/wh200720041/iscloam
공식 코드Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
https://github.com/whats2000/WeiMoCIR
공식 코드Training-free Zero-shot Composed Image Retrieval via Weighted Modality Fusion and Similarity
https://github.com/whu-zqh/uika
공식 코드Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis
https://github.com/whwu95/Cap4Video
공식 코드Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
https://github.com/whydwelledonai/perplexity-trap
공식 코드Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
https://github.com/wickstrom/clinical-self-supervised-cbir-ct-liver
공식 코드A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
https://github.com/willard-yuan/hashing-baseline-for-image-retrieval
공식 코드https://github.com/willemsenbram/reference-resolution-via-text-generation
공식 코드Resolving References in Visually-Grounded Dialogue via Text Generation
https://github.com/willemsenbram/reg-with-guiding
공식 코드https://github.com/williamgazeley/confirm
공식 코드https://github.com/williamium3000/ego-privacy
공식 코드https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/winycg/clip-kd
공식 코드https://github.com/wispcarey/autokg
공식 코드AutoKG: Efficient Automated Knowledge Graph Generation for Language Models
https://github.com/wissam-sib/dilbert
공식 코드https://github.com/wjdghks950/discern-and-answer
공식 코드Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
https://github.com/wjgancn/ptychodv
공식 코드PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction
https://github.com/wjpoom/spec
공식 코드Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
https://github.com/wjun0830/qd-detr
공식 코드Query-Dependent Video Representation for Moment Retrieval and Highlight Detection
https://github.com/wlzhang2020/reasonrag
공식 코드Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
https://github.com/wlzhao/nn-merge
공식 코드https://github.com/wojciechkusa/systematic-review-datasets
공식 코드CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews
https://github.com/woodfrog/vse_infty
공식 코드Learning the Best Pooling Strategy for Visual Semantic Embedding
https://github.com/workingcoder/MCJA
공식 코드https://github.com/wpydcr/mes-rag
공식 코드MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.