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/leolee99/2022-neurips-daa
https://github.com/leolee99/CLIP_ITM
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/leolee99/osa
https://github.com/leosampaio/sketchformer
Sketchformer: Transformer-based Representation for Sketched Structure
https://github.com/lichengunc/pretrain-vl-data
https://github.com/lucidrains/mammut-pytorch
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
https://github.com/lucidrains/musiclm-pytorch
MuLan: A Joint Embedding of Music Audio and Natural Language
https://github.com/luzhixiu/radclip
RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-training
https://github.com/lyan62/data-curation
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/mahmoodlab/trident
https://github.com/microsoft/VSE_Gradient
Dissecting Deep Metric Learning Losses for Image-Text Retrieval
https://github.com/microsoft/unicl
https://github.com/microsoft/xpretrain
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
https://github.com/milvlg/rosita
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
https://github.com/mindspore-ai/models/tree/master/research/mm/opt
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
https://github.com/mitjanikolaus/compositional-image-captioning
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/mkhalil1998/EC601_Group_Project
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
https://github.com/mustafa1728/TA3N-Lightning
https://github.com/my-yy/learnable_pins
https://github.com/necla-ml/SNLI-VE
https://github.com/ninatu/howtocaption
https://github.com/noagarcia/context-art-classification
https://github.com/oxygenlu/ratlip
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/pathmmu-benchmark/pathmmu
https://github.com/pengfeiliheu/mumc
Self-supervised vision-language pretraining for Medical visual question answering
https://github.com/penghu-cs/RCL
https://github.com/phanideepgampa/stacked-capsule-networks
https://github.com/pharmolix/openbiomed
https://github.com/phenixace/molregpt
https://github.com/pioneerAlpha/BanglaText2ImageGeneration
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/priscillalui/StackGAN-Stories
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/pshroff04/Dual_Path_CNN
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
https://github.com/pwc-1/Paper-8/tree/main/align
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/pwc-1/Paper-8/tree/main/altclip
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.