SOTAVerified

Cross-Modal Retrieval

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

Papers

Showing 1–10 of 522 papers

TitleStatusHype
An analysis of vision-language models for fabric retrieval—0
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval—0
Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval—0
Multimodal Medical Image Binding via Shared Text Embeddings—0
FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models—0
ContextRefine-CLIP for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2025Code0
SA-Person: Text-Based Person Retrieval with Scene-aware Re-ranking—0
FOLIAGE: Towards Physical Intelligence World Models Via Unbounded Surface Evolution—0
EmotionRankCLAP: Bridging Natural Language Speaking Styles and Ordinal Speech Emotion via Rank-N-Contrast—0
DocMMIR: A Framework for Document Multi-modal Information RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1X2-VLM (large)Image-to-text R@198.8—Unverified
2X2-VLM (base)Image-to-text R@198.5—Unverified
3BEiT-3Image-to-text R@198—Unverified
4OmniVL (14M)Image-to-text R@197.3—Unverified
5ERNIE-ViL 2.0Image-to-text R@197.2—Unverified
6Aurora (ours, r=128)Image-to-text R@197.2—Unverified
7X-VLM (base)Image-to-text R@197.1—Unverified
8VSE-GradientImage-to-text R@197—Unverified
9ALIGNImage-to-text R@195.3—Unverified
10VASTText-to-image R@191—Unverified