https://github.com/ZihaoWang-CV/CAMP_iccv19
공식 코드CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
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Cross-Modal Information Retrieval (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the heterogeneity gap: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.
Source: Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval
Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task
MedICaT: A Dataset of Medical Images, Captions, and Textual References
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning
Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
No Token Left Behind: Explainability-Aided Image Classification and Generation
TF-CLIP: Learning Text-free CLIP for Video-based Person Re-Identification
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
Declaration-based Prompt Tuning for Visual Question Answering
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval
Unified Lexical Representation for Interpretable Visual-Language Alignment
CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
DIME: An Online Tool for the Visual Comparison of Cross-Modal Retrieval Models
Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.