https://github.com/Riadh-B/MTFN-RR-PyTorch-Code-master
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
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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.
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Deep Visual-Semantic Alignments for Generating Image Descriptions
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.