https://github.com/BruceW91/CVSE
공식 코드Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
Papers with Code 아카이브
Image-text matching
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Image-Text Matching is a subtask within Cross-Modal Retrieval (CMR) that involves establishing associations between images and corresponding textual descriptions. The goal is to retrieve an image given a textual query or, conversely, retrieve a textual description given an image query. This task is challenging due to the heterogeneity gap between image and text data representations. Image-text matching is used in applications such as content-based image search, visual question answering, and multimodal summarization.
Assessing Brittleness of Image-Text Retrieval Benchmarks from Vision-Language Models Perspective
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ChenRocks/UNITER
공식 코드https://github.com/CrossmodalGroup/GSMN
공식 코드https://github.com/HaoYang0123/Position-Focused-Attention-Network
공식 코드https://github.com/JindongGu/Awesome-Prompting-on-Vision-Language-Model
공식 코드A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/Junction4Nako/mvp_pytorch
공식 코드MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning
https://github.com/KunpengLi1994/VSRN
공식 코드https://github.com/MILVLG/mmnas
공식 코드https://github.com/Paranioar/DBL
공식 코드Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
https://github.com/Paranioar/SGRAF
공식 코드https://github.com/Shuyu-XJTU/APTM
공식 코드https://github.com/Sxing2/CLIP-Test-time-Counterattacks
공식 코드https://github.com/VoyageWang/IteRPrimE
공식 코드https://github.com/Wangt-CN/MTFN-RR-PyTorch-Code
공식 코드Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
https://github.com/YuanEZhou/Grounded-Image-Captioning
공식 코드More Grounded Image Captioning by Distilling Image-Text Matching Model
https://github.com/YulongBonjour/BrainCLIP
공식 코드https://github.com/ZhanYang-nwpu/PE-RSITR
공식 코드Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval
https://github.com/allenai/medicat
공식 코드MedICaT: A Dataset of Medical Images, Captions, and Textual References
https://github.com/amazon-science/MAGID
공식 코드MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
https://github.com/anosorae/irra
공식 코드Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval
https://github.com/apple/ml-no-token-left-behind
공식 코드No Token Left Behind: Explainability-Aided Image Classification and Generation
https://github.com/boo-18/gr-gan
공식 코드https://github.com/casia-iva-lab/filo
공식 코드https://github.com/cciiplab/dpt
공식 코드Declaration-based Prompt Tuning for Visual Question Answering
https://github.com/clip-oscope/clip-oscope
공식 코드CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
https://github.com/danaesavi/socialmedia-textimage-classification-auxlosses
공식 코드Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
https://github.com/deeplearnxmu/mm-mkp
공식 코드https://github.com/dopaminelcy/alta
공식 코드Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
https://github.com/eric-ai-lab/comclip
공식 코드ComCLIP: Training-Free Compositional Image and Text Matching
https://github.com/eric-ai-lab/dsd
공식 코드Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners
https://github.com/fudannlplab/mousi
공식 코드https://github.com/idealwhite/tdanet
공식 코드https://github.com/iigroup/map
공식 코드MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
https://github.com/ivana-13/guided_masking
공식 코드Beyond Image-Text Matching: Verb Understanding in Multimodal Transformers Using Guided Masking
https://github.com/jackroos/VL-BERT
공식 코드VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/jaeseokbyun/grit-vlp
공식 코드GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.