Papers with Code 아카이브
Retrieval
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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/microsoft/sammo
공식 코드https://github.com/microsoft/seed-encoder
공식 코드Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
https://github.com/microsoft/simxns
공식 코드https://github.com/microsoft/taser
공식 코드https://github.com/microsoft/uicaption
공식 코드https://github.com/microsoft/unilm
공식 코드SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
https://github.com/microsoft/unilm/blob/master/YOCO/README.md
공식 코드You Only Cache Once: Decoder-Decoder Architectures for Language Models
https://github.com/microsoft/unilm/tree/master/beit
공식 코드Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/microsoft/unilm/tree/master/vlmo
공식 코드VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
https://github.com/microsoft/wavtext5k
공식 코드https://github.com/microsoft/xpretrain
공식 코드Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions
https://github.com/mida-group/crossmodal_imgretrieval
공식 코드Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations
https://github.com/mikacuy/pointnetvlad
공식 코드PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition
https://github.com/mikejqzhang/mitigating_misalignment
공식 코드Mitigating Temporal Misalignment by Discarding Outdated Facts
https://github.com/mikelwl/fico-itr
공식 코드https://github.com/mikewangwzhl/vidil
공식 코드Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
https://github.com/mikewangwzhl/zemi
공식 코드Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
https://github.com/mikwieczorek/centroids-reid
공식 코드A Strong Baseline for Fashion Retrieval with Person Re-Identification Models
https://github.com/mila-aia/alloprof-data
공식 코드https://github.com/milkyfun0/ERVD
공식 코드ERVD: An Efficient and Robust ViT-Based Distillation Framework for Remote Sensing Image Retrieval
https://github.com/millix19/dbsa
공식 코드Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention
https://github.com/mimbres/neural-audio-fp
공식 코드Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning
https://github.com/mingdali6717/ensemble-of-retrievers
공식 코드Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
https://github.com/minghchen/carl_code
공식 코드Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning
https://github.com/mingmtc/hdr
공식 코드https://github.com/mingu6/ProbFiltersVPR
공식 코드Probabilistic Visual Place Recognition for Hierarchical Localization
https://github.com/mingu6/declarativedtw
공식 코드Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
https://github.com/mingyao1120/tr-detr
공식 코드TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection
https://github.com/mingyuan-zhang/ReMoDiffuse
공식 코드https://github.com/mingyuj666/disentangling-memory-and-reasoning
공식 코드Disentangling Memory and Reasoning Ability in Large Language Models
https://github.com/minhaeoh/raise
공식 코드RAISE: Enhancing Scientific Reasoning in LLMs via Step-by-Step Retrieval
https://github.com/minjoong507/MPGN
공식 코드Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
https://github.com/minjoong507/bm-detr
공식 코드Background-aware Moment Detection for Video Moment Retrieval
https://github.com/minju0821/musical_instrument_retrieval
공식 코드Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio
https://github.com/minogame/clip-mtob
공식 코드Design of the topology for contrastive visual-textual alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.