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/qinyang79/decl
https://github.com/qxzha/UGNCL
https://github.com/rightlit/cycle-image-gan-rev
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/rmokady/clip_prefix_caption
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
https://github.com/rohitbhaskar/online-ads-repository
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/roxanasoto/AttGanESRGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/salanueva/UniVSE
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/salesforce/ALBEF
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/salesforce/blip
https://github.com/salesforce/pb-ovd
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/search-opensource-space/FashionBERT
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
https://github.com/securade/hub
Semantic-Conditional Diffusion Networks for Image Captioning
https://github.com/shin-ee-chen/bla
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/sidward14/Style-AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/sivandoveh/dac
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
https://github.com/soco-ai/SF-QA
https://github.com/souvikshanku/digit-captioning
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/stanfordmimi/merlin
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
https://github.com/taki0112/AttnGAN-Tensorflow
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/taoxugit/AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/towhee-io/towhee
https://github.com/ucsd-ml-arts/ml-art-final-jeffrey
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/vladsandulescu/hatefulmemes
https://github.com/wglab/gestaltmml
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/wglab/gestaltmml-gestaltgpt
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/willard-yuan/video-text-retrieval-papers
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/woodfrog/vse_infty
https://github.com/xiaomin418/cfsum
https://github.com/yalesong/pvse
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
https://github.com/yangzhao1230/graphtextretrieval
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
https://github.com/yaolinli/capenrich
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/yolo2233/cross-modal-hasing-playground
https://github.com/yuliangcai2022/clumo
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/ziyang412/ucofia
https://github.com/zjukg/DUET
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.