Image-Text Retrieval

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1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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MSGAT: Multi-Head Spiking Graph Attention with Similarity-Space Fusion for Image-Text Retrieval
Oct 8, 2026Xintao Zong, Wenxuan Liu, Jianhao Ding +2Image-Text Retrieval Cross-Modal Retrieval
Peking University
Generative Retrieval for Unsupervised Text-Based Person Search
Sep 14, 2026Mang Ye, Yucheng Ji, Yang Bai +4Unsupervised Learning Image-Text Retrieval
School of Computer Science, Wuhan University, Wuhan, China · School of Computer Science and Technology, Soochow University, SuZhou, China · Zhipu AI, Beijing 100089, China +1
Rethinking Text-Based Image Retrieval in Specific Domain
Aug 11, 2026Jingyang Tan, Sheng Yang, Yuanpeng Chen +4Image-Text Retrieval Vision-Language Retrieval
Department of Electronic and Communication Engineering, Harbin Institute of Technology · School of Data Science, Fudan University · Independent Researcher +1
GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views
Aug 10, 2026Jiahui Cui, Yan Zhao, Kan Wei +4Vision-Language Models Fine-Grained Image Retrieval
Aerospace Information Research Institute, Chinese Academy of Sciences · Key Laboratory of Target Cognition and Application Technology (TCAT), Beijing, China · University of Chinese Academy of Sciences, Beijing, China +1
LightAIR: Lightweight Action Inversion and Riemannian Rectification for Text-based Person Anomaly Search
Aug 10, 2026Yulun Zhang, Zixu Li, Zhiwei Chen +6Representation Disentanglement Image-Text Retrieval
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen, China · Shandong University Jinan, China
DARAD: Dual Adapters and Ranking-Aware Distillation for Continual Remote Sensing Image-Text Retrieval
Aug 6, 2026Xi Chen, Xu Chen, Xiangyang Jia +3VLM Adaptation Image-Text Retrieval
School of Computer Science, Wuhan University, Wuhan 430072, China · Beijing Institute for General Artificial Intelligence (BIGAI)
A Paragraph is Worth a Thousand Captions: Rethinking Text Supervision for Vision-Language Retrieval
Aug 5, 2026Mahyar Ghazanfari, Amin Tabrizian, Arsyi Aziz +2Image-Text Retrieval Vision-Language Retrieval
The George Washington University
CXR-Retrieve: Compositional Text-to-Image Retrieval in Chest Radiography
Jul 30, 2026Tomer Erez, Moshe Kimhi, Chaim Baskin +1Contrastive Learning Medical Imaging
Technion – Israel Institute of Technology · Ben-Gurion University of the Negev
ZooClaw-FashionSigLIP2: Distilled Fine-tuning for Robust Fashion Retrieval
Jun 26, 2026Siqiao Xue, Chunxue XuVLM Robustness VLM Adaptation
SARLO-80: Worldwide Slant SAR Language Optic Dataset 80cm
Jun 18, 2026Solène Debuysère, Nicolas Trouvé, Nathan Letheule +2Image-Text Retrieval Cross-Modal Retrieval
1DEMR-ONERA – The French Aerospace Lab, Université Paris-Saclay, Palaiseau, France · 2DTIS-ONERA – The French Aerospace Lab, Université Paris-Saclay, Palaiseau, France · 3Hugging Face, London
Exploring Multi-Modal Large Language Models and Two-Stage Fine-Tuning for Fashion Image Retrieval
Jun 18, 2026Nguyen Cao Hoang, Hoang Bui Le, Nam Vo Hoang +1Multimodal Large Language Models Image-Text Retrieval
University of Science, VNU-HCM, Ho Chi Minh, Vietnam · Vietnam National University, Ho Chi Minh, Vietnam
LARE: Low-Attention Region Encoding for Text-Image Retrieval
Jun 17, 2026Abdulmalik Alquwayfili, Faisal Almeshal, Jumanah Almajnouni +8Visual Representation Learning Fine-Grained Image Retrieval
Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia.
VietFashion: Benchmarking Sketch-Text Composed Image Retrieval for Cultural Outfits
Jun 11, 2026Hoang-Nguyen Cao, Le-Hoang Bui, Dinh-Khoi Vo +2Fine-Grained Image Retrieval Image-Text Retrieval
University of Science · Ho Chi Minh City, Vietnam · Vietnam National University
Bridging the Modality Gap in Forensic Image Retrieval
Jun 10, 2026Ricardo González-Gazapo, Annette Morales-González, Yoanna Martínez-Díaz +2Multimodal IR Digital Forensics
Advanced Technologies Application Center (CENATAV), Havana, Cuba · Centro de Sistemas Complejos, Facultad de Física, Universidad de La Habana, Havana, Cuba
ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search
Jun 1, 2026Chaodong Jia, Zequn Xie, Xibei Jia +3Image-Text Retrieval Vision-Language Grounding
FashionLens: Toward Versatile Fashion Image Retrieval via Task-Adaptive Learning
May 21, 2026Haokun Wen, Xuemeng Song, Xinghao Xie +3Multi-Task Learning Image-Text Retrieval
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China · Department of Data Science, City University of Hong Kong, Hong Kong, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China +4
Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
May 6, 2026Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran +2Remote Sensing Image Understanding Image-Text Retrieval
NEC Laboratories America Princeton, NJ, USA
Fast-then-Fine: A Two-Stage Framework with Multi-Granular Representation for Cross-Modal Retrieval in Remote Sensing
Apr 22, 2026Xi Chen, Xu Chen, Xiangyang Jia +3Representation Learning Cross-Modal Alignment
School of Computer Science, Wuhan University, Wuhan, China · Beijing Institute for General Artificial Intelligence (BIGAI), Beijing, China
mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval
Apr 18, 2026Kyeong Seon Kim, Baek Seong-Eun, Lee Jung-Mok +1Multimodal IR Multimodal Large Language Models
1KAIST · 2POSTECH
SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding
Apr 17, 2026Keisuke Gomi, Keiji YanaiCross-Modal Learning Image-Text Retrieval
The University of Electro-Communications
Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment
Oct 27, 2025Hongyi Wang, Zhengjie Zhu, Junlin Hou +13Multimodal IR Computational Pathology
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China. · School of Medicine, Wake Forest University, Winston-Salem, NC, USA. +11

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MSGAT: Multi-Head Spiking Graph Attention with Similarity-Space Fusion for Image-Text Retrieval

Xintao Zong, Wenxuan Liu, Jianhao Ding, Zhaofei Yu+1

Peking University

Image-Text Retrieval Cross-Modal Retrieval Spiking Neural Networks Multimodal Fusion Graph Attention Networks

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Spiking neural networks (SNNs) offer an energy-efficient computing paradigm through sparse event-driven computation, showing great potential for efficient multimodal learning. However, applying SNNs to high-level multimodal tasks, such as image-text retrieval (ITR), remains challenging, since sparse spike representations make it difficult to capture semantic structures required for cross-modal alignment. Existing spiking ITR methods rely on local alignment and additional soft-label supervision during training, while lacking awareness of structural and multi-granularity relationships. To address these issues, we propose a Multi-head Spiking Graph Attention Network (\textbf{MSGAT}) for structural modeling and equip it with dynamic attention heads to capture complementary relational patterns and enable spike-driven graph reasoning and aggregation. However, within a two-branch multi-granularity fusion framework, the fine-grained spike representations generated by MSGAT are sparse and discrete, whereas the global representations are continuous, making conventional feature-level fusion susceptible to interference across heterogeneous representations. Therefore, we introduce \textbf{Sim-Fuse}, a similarity-space fusion alignment strategy integrating coarse- and fine-grained matching relations while avoiding direct fusion of heterogeneous representations. Experiments on Flickr30K and MSCOCO show our method outperforms ANN methods under matched settings and existing SNN retrieval baselines. Moreover, with only two time steps, our SNN achieves comparable or superior performance to its ANN counterpart while reducing theoretical module-level energy by 55%. The code is provided in the Supplementary Materials.

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