https://github.com/AlexMoreo/tensorflow-Tex2Vis
공식 코드Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
Papers with Code 아카이브
Image Retrieval with Multi-Modal Query
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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The problem of retrieving images from a database based on a multi-modal (image- text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BruceW91/CVSE
공식 코드Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
https://github.com/Cadene/recipe1m.bootstrap.pytorch
공식 코드Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
https://github.com/ChenRocks/UNITER
공식 코드https://github.com/CrossmodalGroup/GSMN
공식 코드https://github.com/D-ST-Sword/ATD
공식 코드Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
https://github.com/DannielSilva/RS-M-CLIP
공식 코드Multilingual Vision-Language Pre-training for the Remote Sensing Domain
https://github.com/DeepLearnXMU/CMTMR
공식 코드Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
https://github.com/Delong-liu-bupt/Word4Per
공식 코드Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
https://github.com/ExplainableML/cosmos
공식 코드COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
https://github.com/HaoYang0123/Position-Focused-Attention-Network
공식 코드https://github.com/HongyangLL/M3-JEPA
공식 코드M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA framework
https://github.com/HuiChen24/IMRAM
공식 코드IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
https://github.com/IntelLabs/ScalableVectorSearch
공식 코드https://github.com/JindongGu/Awesome-Prompting-on-Vision-Language-Model
공식 코드A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/Junction4Nako/mvp_pytorch
공식 코드MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning
https://github.com/Kevinnest/MSJE
공식 코드Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
https://github.com/KunpengLi1994/VSRN
공식 코드https://github.com/LARC-CMU-SMU/ACME
공식 코드Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
https://github.com/MILVLG/mmnas
공식 코드https://github.com/MediaBrain-SJTU/GSC
공식 코드Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
https://github.com/Monoxide-Chen/uncertainty_retrieval
공식 코드Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
https://github.com/Orion-AI-Lab/GAIA
공식 코드GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image Analysis
https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_generation/unimo-text
공식 코드UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
https://github.com/Paranioar/DBL
공식 코드Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
https://github.com/Paranioar/SGRAF
공식 코드https://github.com/Shuyu-XJTU/APTM
공식 코드https://github.com/SiyuanYan1/Derm1M
공식 코드https://github.com/Sxing2/CLIP-Test-time-Counterattacks
공식 코드https://github.com/TXH-mercury/VALOR
공식 코드VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
https://github.com/Tushar-N/attributes-as-operators
공식 코드Attributes as Operators: Factorizing Unseen Attribute-Object Compositions
https://github.com/UKPLab/MMT-Retrieval
공식 코드Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
https://github.com/VoyageWang/IteRPrimE
공식 코드https://github.com/Wangt-CN/MTFN-RR-PyTorch-Code
공식 코드Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
https://github.com/XinhaoMei/audio-text_retrieval
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.