Cross-Modal Information Retrieval
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Cross-Modal Information Retrieval (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the heterogeneity gap: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.
Source: 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/vkhoi/cora_cvpr24
공식 코드Composing Object Relations and Attributes for Image-Text Matching
https://github.com/wangzhecheng/skyscript
공식 코드SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
https://github.com/wayne980/PolyLoss
공식 코드Universal Weighting Metric Learning for Cross-Modal Matching
https://github.com/wenchuan-zhang/patho-r1
공식 코드Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
https://github.com/winycg/clip-kd
공식 코드https://github.com/wisdomikezogwo/quilt1m
공식 코드https://github.com/wjpoom/spec
공식 코드Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
https://github.com/woodfrog/vse_infty
공식 코드Learning the Best Pooling Strategy for Visual Semantic Embedding
https://github.com/wzhings/itmafa
공식 코드Enhancing Image-Text Matching with Adaptive Feature Aggregation
https://github.com/x-plug/youku-mplug
공식 코드Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
https://github.com/xandery-geek/badcm
공식 코드BadCM: Invisible Backdoor Attack Against Cross-Modal Learning
https://github.com/xiaoyuan1996/AMFMN
공식 코드Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
https://github.com/xiaoyuan1996/galr
공식 코드Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
https://github.com/xiaoyuan1996/semanticlocalizationmetrics
공식 코드Learning to Evaluate Performance of Multi-modal Semantic Localization
https://github.com/xsc1234/invisible-relevance-bias
공식 코드Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
https://github.com/xu5zhao/bicro
공식 코드https://github.com/xurige1995/3shnet
공식 코드3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting
https://github.com/yangong23/vsenetworkslseh
공식 코드https://github.com/yaohungt/Pointwise_Dependency_Neural_Estimation
공식 코드https://github.com/yehli/xmodaler
공식 코드X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
https://github.com/yilinye/modal-fusion-map
공식 코드ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval
공식 코드Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
https://github.com/ykumards/eclip
공식 코드Improving Medical Multi-modal Contrastive Learning with Expert Annotations
https://github.com/yoadtew/zero-shot-video-to-text
공식 코드https://github.com/yujielu10/llmscore
공식 코드LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
https://github.com/yunjinli/vxp
공식 코드VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition
https://github.com/yuxie11/R2D2
공식 코드https://github.com/yxuansu/magic
공식 코드Language Models Can See: Plugging Visual Controls in Text Generation
https://github.com/zdou0830/meter
공식 코드An Empirical Study of Training End-to-End Vision-and-Language Transformers
https://github.com/zengyan-97/x-vlm
공식 코드Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
https://github.com/zengyan-97/x2-vlm
공식 코드X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
https://github.com/zhangxinsong-nlp/XFM
공식 코드https://github.com/zhangxu0963/npc
공식 코드https://github.com/zhangxu0963/vsl
공식 코드Image-text Retrieval via Preserving Main Semantics of Vision
https://github.com/zhixiongz/clip4cmr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.