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/delcayr/contextrefine-clip
공식 코드ContextRefine-CLIP for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2025
https://github.com/descriptinc/lyrebird-wav2clip
공식 코드https://github.com/donnotnormal/dcgh
공식 코드Deep Class-guided Hashing for Multi-label Cross-modal Retrieval
https://github.com/dopaminelcy/alta
공식 코드Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
https://github.com/e-bug/iglue
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/e-bug/volta
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/eanson023/rehamot
공식 코드Cross-Modal Retrieval for Motion and Text via DopTriple Loss
https://github.com/echonet/echo_CLIP
공식 코드Multimodal Foundation Models For Echocardiogram Interpretation
https://github.com/epic-kitchens/epic-kitchens-100-annotations
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-narrator
공식 코드https://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/facebookresearch/imagebind
공식 코드https://github.com/fartashf/vsepp
공식 코드VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/flagai-open/flagai
공식 코드AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/flame-chasers/tbps-clip
공식 코드https://github.com/fudannlplab/mousi
공식 코드https://github.com/ganchaofan0000/DAC
공식 코드DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction
https://github.com/gingsi/coot-videotext
공식 코드COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
https://github.com/git-disl/seje
공식 코드https://github.com/google-research-datasets/wit
공식 코드AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
https://github.com/google-research/google-research
공식 코드https://github.com/guoyang9/elip
공식 코드ELIP: Efficient Language-Image Pre-training with Fewer Vision Tokens
https://github.com/gwatkinson/jump_download
공식 코드Weakly supervised cross-modal learning in high-content screening
https://github.com/haixpham/CHEF
공식 코드CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval
https://github.com/haowang1992/DSN
공식 코드Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
https://github.com/hcplab-sysu/personsearch-ctlg
공식 코드Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search
https://github.com/hhc1997/l2rm
공식 코드Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
https://github.com/hhc1997/mscn
공식 코드Noisy Correspondence Learning with Meta Similarity Correction
https://github.com/hohsiangwu/crossmodal
공식 코드Exploring modality-agnostic representations for music classification
https://github.com/huiguanlab/dasd
공식 코드Dynamic Adapter with Semantics Disentangling for Cross-lingual Cross-modal Retrieval
https://github.com/huiguanlab/nrccr
공식 코드Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning
https://github.com/idealwhite/tdanet
공식 코드https://github.com/iigroup/map
공식 코드MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
https://github.com/ilaria-manco/muscaps
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.