https://github.com/AlexMoreo/tensorflow-Tex2Vis
공식 코드Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
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Cross-Modal Retrieval (CMR) is a task of retrieving items across different modalities, such as image, text, video, and audio. The core challenge of CMR is the heterogeneity gap, which arises because data from different modalities have distinct representations, making direct comparison difficult. To address this, most CMR methods focus on learning a shared latent embedding space. In this space, concepts from different modalities are projected, allowing their similarity to be measured using a distance metric.
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.
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
Multilingual Vision-Language Pre-training for the Remote Sensing Domain
Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA framework
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image Analysis
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
More Grounded Image Captioning by Distilling Image-Text Matching Model
Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.