https://github.com/MICV-yonsei/EAGLE
공식 코드EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Papers with Code 아카이브
Sentence Pair Modeling
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Comparing two sentences and their relationship based on their internal representation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mercidaiha/IRT-Router
공식 코드IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
https://github.com/Mikexu007/PCRP
공식 코드Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
https://github.com/PStarH/RWKV-embedding
공식 코드https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0
공식 코드ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/Phrase-in-Context/eval
공식 코드PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search
https://github.com/PolymathicAI/AstroCLIP
공식 코드https://github.com/PortNLP/semantic-similarity
공식 코드Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples
https://github.com/Qidian213/Ranked_Person_ReID
공식 코드Ranking and Classification driven Feature Learning for Person Re_identification
https://github.com/QingtaoPan/DuSSS
공식 코드https://github.com/QingyongHu/SQN
공식 코드SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
https://github.com/R2MED/R2MED
공식 코드https://github.com/RakdosCC/CCRV
공식 코드Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content
https://github.com/Reza-esfandiarpoor/pseudo-shots
공식 코드Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
https://github.com/RishabhMaheshwary/hard-label-attack
공식 코드Generating Natural Language Attacks in a Hard Label Black Box Setting
https://github.com/RujingYao/LSIM
공식 코드https://github.com/SeqDM/Efficient-Embeddings
공식 코드Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe
https://github.com/Somedaywilldo/AutoGCL
공식 코드AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
https://github.com/SqueezeAILab/SqueezedAttention
공식 코드Squeezed Attention: Accelerating Long Context Length LLM Inference
https://github.com/Stareru/NeighboringDistributionDivergence
공식 코드Contextualized Semantic Distance between Highly Overlapped Texts
https://github.com/SteffenCzolbe/DeepSimRegistration
공식 코드https://github.com/TRAILab/ST-SLidR
공식 코드Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
https://github.com/TencentGameMate/gnn_cross_lingual
공식 코드Cross-lingual Text Classification with Heterogeneous Graph Neural Network
https://github.com/TickleForce/ancient-greek-datasets
공식 코드Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
https://github.com/TrustAIRLab/Conversation_Reconstruction_Attack
공식 코드https://github.com/UKPLab/sentence-transformers
공식 코드Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/VoyagerXvoyagerx/InstructSAM
공식 코드InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition
https://github.com/WeimingWen/CCRV
공식 코드Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content
https://github.com/WuXinglong-HIT/CLIPER
공식 코드Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation
https://github.com/XinshaoAmosWang/OSM_CAA_WeightedContrastiveLoss
공식 코드Deep Metric Learning by Online Soft Mining and Class-Aware Attention
https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML
공식 코드https://github.com/YDYordanov/WS-training-objectives
공식 코드Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution
https://github.com/ZiweiWangTHU/ISL
공식 코드Instance Similarity Learning for Unsupervised Feature Representation
https://github.com/ZurichNLP/20min-XD
공식 코드https://github.com/ZzZZCHS/WS-3DVG
공식 코드Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding
https://github.com/ab-iitd/clitssa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.