https://github.com/wengzejia1/open-vclip
공식 코드Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
Papers with Code 아카이브
Text Retrieval
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Text Retrieval is the task of finding the most text result (such as an answer, paragraph, or passage) given a query (which could be a question, keywords, or any relevant text)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/wjpoom/spec
공식 코드Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
https://github.com/woodfrog/vse_infty
공식 코드Learning the Best Pooling Strategy for Visual Semantic Embedding
https://github.com/wzhings/itmafa
공식 코드Enhancing Image-Text Matching with Adaptive Feature Aggregation
https://github.com/xfactlab/i0t
공식 코드I0T: Embedding Standardization Method Towards Zero Modality Gap
https://github.com/xhluca/bm25s
공식 코드BM25S: Orders of magnitude faster lexical search via eager sparse scoring
https://github.com/xiaomi-research/dasheng-glap
공식 코드GLAP: General contrastive audio-text pretraining across domains and languages
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xqwang14/sms-loss
공식 코드Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2024
https://github.com/xsc1234/invisible-relevance-bias
공식 코드Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
https://github.com/xuandongzhao/npprompt
공식 코드https://github.com/xuguohai/X-CLIP
공식 코드X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/xuhaike/Bi-metric-search
공식 코드https://github.com/yangbang18/clfm
공식 코드Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning
https://github.com/yangliuy/HybridNCM
공식 코드https://github.com/yasminekaroui/clicotea
공식 코드Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages
https://github.com/yeliu918/dhr
공식 코드Dense Hierarchical Retrieval for Open-Domain Question Answering
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval
공식 코드Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
https://github.com/yingift/mstar
공식 코드MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling
https://github.com/yr666666/msa
공식 코드Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval
https://github.com/ytongxie/medical-vision-and-language-tasks-and-methodologies-a-survey
공식 코드A Survey of Medical Vision-and-Language Applications and Their Techniques
https://github.com/yuxie11/R2D2
공식 코드https://github.com/yxh-y/tablerag
공식 코드TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
https://github.com/zengyan-97/cclm
공식 코드Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training
https://github.com/zhangbw17/mv-adapter
공식 코드MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
https://github.com/zhangxu0963/vsl
공식 코드Image-text Retrieval via Preserving Main Semantics of Vision
https://github.com/zhangy0822/user
공식 코드USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval
https://github.com/zhanyang-nwpu/rsvg-pytorch
공식 코드RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
https://github.com/zhegan27/LXMERT-AdvTrain
공식 코드Large-Scale Adversarial Training for Vision-and-Language Representation Learning
https://github.com/zhegan27/VILLA
공식 코드Large-Scale Adversarial Training for Vision-and-Language Representation Learning
https://github.com/ziqipang/lm4visualencoding
공식 코드Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/ziyang412/ucofia
공식 코드https://github.com/zjamie813/selfalign
공식 코드Towards Fast and Accurate Image-Text Retrieval with Self-Supervised Fine-Grained Alignment
https://github.com/zuucan/needleinahaystack-plus
공식 코드LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
https://github.com/zyf0619sjtu/DreamLIP
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.