Cross-Modal Retrieval
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Victor-Martinez-Pozos/stacked_capsule_autoencoders
https://github.com/Vigneshthanga/stackGAN-v2
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/VinitSR7/Image-Caption-Generation
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/WendellGul/DCMH
https://github.com/XLearning-SCU/2021-NeurIPS-NCR
https://github.com/XLearning-SCU/2024-TIP-CREAM
https://github.com/YIKUAN8/Transformers-VQA
https://github.com/YingZhangDUT/Cross-Modal-Projection-Learning
https://github.com/YuZiHanorz/stacked_capsule_autoencoders
https://github.com/ZenzenDatabase/CMR_audiovisual
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
https://github.com/abhidipbhattacharyya/srl_aware_ret
https://github.com/adlnlp/attention_vl
https://github.com/ai-hpc-research-team/git-mol
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
https://github.com/ai-hpc-research-team/slm4mol
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
https://github.com/aleksey-egorov/attngan
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/alexmotogna/attngan
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/alexmotogna/generatorapi
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/alibaba/EasyNLP
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
https://github.com/alibaba/EasyNLP/tree/master/examples/blip_retrieval
https://github.com/alibaba/EasyTransfer/tree/master/scripts/fashion_bert
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
https://github.com/amazon-research/mix-generation
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/ara-25/MSDS19002_Project_DLSpring2020
https://github.com/aranciokov/fsmmda_videoretrieval
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/aranciokov/ranp
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/armandvilalta/Full-network-multimodal-embeddings
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/asif-hanif/baple
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/bdsaglam/torch-scae
https://github.com/benenzhu/version0.3-scae
https://github.com/bhavinjawade/NAPReq
https://github.com/bprabhakar/text-to-image
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/camma-public/peskavlp
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
https://github.com/camma-public/surgvlp
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
https://github.com/cattidea/VinVL-Paddle
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/claws-lab/multimodal-robustness-xmai
An Empirical Study of Training End-to-End Vision-and-Language Transformers
https://github.com/cnedwards/text2mol
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.