https://github.com/claws-lab/multimodal-robustness-xmai
공식 코드Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning
Papers with Code 아카이브
Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 11
- 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/clement25/sharedllm
공식 코드Two are better than one: Context window extension with multi-grained self-injection
https://github.com/cleopatra-itn/GeoWINE
공식 코드GeoWINE: Geolocation based Wiki, Image,News and Event Retrieval
https://github.com/cleopatra-itn/claim_detection
공식 코드https://github.com/clip-italian/clip-italian
공식 코드Contrastive Language-Image Pre-training for the Italian Language
https://github.com/clips/mfaq
공식 코드https://github.com/cloudcjf/ccl
공식 코드CCL: Continual Contrastive Learning for LiDAR Place Recognition
https://github.com/clovaai/AdamP
공식 코드AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
https://github.com/clovaai/assembled-cnn
공식 코드Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
https://github.com/clovaai/embedding-expansion
공식 코드Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning
https://github.com/clovaai/symmetrical-synthesis
공식 코드https://github.com/cluel01/clip-branches
공식 코드CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval
https://github.com/clulab/geonorm
공식 코드https://github.com/cly234/deepclustering-connr
공식 코드Contextually Affinitive Neighborhood Refinery for Deep Clustering
https://github.com/cmacdonald/pyterrier_colbert
공식 코드Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval
https://github.com/cmavro/gnn-rag
공식 코드GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
https://github.com/cmciris/romo
공식 코드https://github.com/cmisra/sangeet
공식 코드SANGEET: A XML based Open Dataset for Research in Hindustani Sangeet
https://github.com/cmu-mlsp/interview_humanssum
공식 코드Speech vs. Transcript: Does It Matter for Human Annotators in Speech Summarization?
https://github.com/cnclgithub/reconmem
공식 코드https://github.com/cnzzx/vsa
공식 코드Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines
https://github.com/coastalcph/gender-neutral-vl
공식 코드Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models
https://github.com/code-rag-bench/code-rag-bench
공식 코드https://github.com/coder-jzq/radka-css
공식 코드Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive Conversational Speech Synthesis
https://github.com/codezakh/SIMLA
공식 코드Single-Stream Multi-Level Alignment for Vision-Language Pretraining
https://github.com/cods-gcs/chatty-gen
공식 코드Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs
https://github.com/cogcomp/multi-persp-search-engine
공식 코드https://github.com/cohenjer/transsscnmf
공식 코드Semi-Supervised Convolutive NMF for Automatic Piano Transcription
https://github.com/coin-dataset/annotations
공식 코드https://github.com/coir-team/coir
공식 코드CoIR: A Comprehensive Benchmark for Code Information Retrieval Models
https://github.com/coldchair/cpret
공식 코드CPRet: A Dataset, Benchmark, and Model for Retrieval in Competitive Programming
https://github.com/colinzhaoust/pir
공식 코드Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
https://github.com/collinzrj/adversarial_decoding
공식 코드Adversarial Decoding: Generating Readable Documents for Adversarial Objectives
https://github.com/complexdata-mila/mitigatemisinfo
공식 코드Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
https://github.com/complexdata-mila/webretrieval
공식 코드Web Retrieval Agents for Evidence-Based Misinformation Detection
https://github.com/computational-imaging/DeepS3PR
공식 코드Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.