Cross-Modal Retrieval

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21 papers in the last four weeks, up 250% on the four weeks before. 0.2% of all new papers.

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Latest papers 134

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IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval
Jun 6, 2026Jiale Huang, Zixu Li, Zhiwei Chen +3Cross-Modal Alignment Cross-Modal Retrieval
Shandong University · Jinan, Shandong, China · Qilu University of Technology +1
To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection
Jun 4, 2026Erfan Loweimi, Mengjie Qian, Kate Knill +7Missing-Modality Learning Audio-Visual Understanding
University of Cambridge, UK · Southwest Jiaotong University, China · University of Surrey, UK +1
UniCA: Bi-directional Cross-Attention with Positive Similarity Loss for Robust Multi-Modal Retrieval
Jun 3, 2026Yini Huang, Wenlong ZhangMultimodal IR Cross-Modal Alignment
The Hong Kong University of Science and Technology (Guangzhou), Guangdong, China · Southern Medical University, Guangdong, China
Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)
Jun 2, 2026Jingbiao MeiMultimodal IR Multimodal Embedding
University of Cambridge · Cambridge, United Kingdom
Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
Jun 2, 2026Yang Liu, Wentao Feng, Shu-Dong Huang +2Cross-Modal Alignment Cross-Modal Retrieval
College of Computer Science, Sichuan University, China · School of Computer Sci
Mixed-Modality Dual Face-Hair Retrieval
Jun 2, 2026Quoc-Anh Bui-Huynh, Mai-Tuyen Lam, Dai-Anh-Tuan Nguyen +1Cross-Modal Alignment Cross-Modal Retrieval
Vietnam National University, Ho Chi Minh City, Vietnam · University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam
Reason-Then-Retrieve for CoVR-R with Structured Edit Prompts and Dense-Sparse Fusion
Jun 1, 2026DongQing Liu, MengShi Qi, HongWei JiCross-Modal Retrieval Video Reasoning
Beijing University of Posts and Telecommunications
Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning
Jun 1, 2026Yang Liu, Qianqian Xu, Peisong Wen +2Cross-Modal Retrieval Composed Video Retrieval
School of Computer Science and Technology, University of Chinese Academy of Sciences · State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence +2
Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification
Jun 1, 2026Karina Kvanchiani, Timur MamedovCross-Modal Representation Learning Multi-Task Learning
1Tevian, Russia · 2Lomonosov Moscow State University, Russia
R^3: Composed Video Retrieval via Reasoning-Guided Recalling and Re-ranking
May 31, 2026Zixu Li, Yupeng Hu, Zhiheng Fu +3Cross-Modal Retrieval Composed Video Retrieval
1Shandong University · 2Harbin Institute of Technology (Shenzhen)
Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R
May 31, 2026Yuyang Sun, Yongliang Wu, Xingyu Zhu +8Multimodal Reranking Cross-Modal Retrieval
Southeast University · National University of Singapore · Independent Researcher +2
Reason, Retrieve, Re-rank: A Zero-Shot Reasoning-Aware Framework for Composed Video Retrieval
May 30, 2026Ali AlaviMultimodal Reranking Cross-Modal Retrieval
The Ohio State University
CR-JEPA: Cross-Modal Joint-Embedding Predictive Learning for Remote Sensing Image Retrieval
May 30, 2026Md Aminur Hossain, Ayush V. Patel, Nitant Dube +1Cross-Modal Representation Learning Joint-Embedding Predictive Architecture
Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India
T-CLIP: Enabling Thermal Perception for Contrastive Language-Image Pretraining
May 30, 2026Tayeba Qazi, Ayush Maheshwari, Prerana Mukherjee +1Thermal Imaging Image Captioning
Indian Institute of Technology Delhi, India · NVIDIA AI Technology Center, India · Jawaharlal Nehru University, India
Beyond Classification: Dynamic Adapter Routing for Continual Multimodal Retrieval
May 29, 2026Alicja Dobrzeniecka, Filip Szatkowski, Sebastian Cygert +2Multimodal IR VLM Adaptation
NASK National Research Institute · IDEAS Research Institute Warsaw University of Technology · Warsaw University of Technology +2
Variational Adapter for Cross-modal Similarity Representation
May 29, 2026WenZhang Wei, Zhipeng Gui, Dehua Peng +2Cross-Modal Representation Learning VLM Adaptation
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China · School of Data Science and Engineering, East China Normal University, Shanghai 200062, China · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Semantic Motion Anchors: Bridging Motion and Meaning in Co-Speech Gestures
May 28, 2026Varsha Suresh, Mohammad Mahdi Abootorabi, Mohamed Salman +5Cross-Modal Alignment Human Motion Generation
1Saarland University · University of British Columbia · 4Vector Institute +2
Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
May 26, 2026Madhuri Shanbhogue, Zhe Li, Shanfeng Zhang +86Cross-Modal Learning Unified Multimodal Models
OmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation
May 26, 2026Yunze Liu, Chi-Hao Wu, Enmin Zhou +1Cross-Modal Learning Cross-Modal Knowledge Distillation
Memories.ai Research
MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models
May 25, 2026Kaixiang Chen, Pengfei Fang, Hui XueCross-Modal Learning Cross-Modal Representation Learning
School of Computer Science and Engineering, Southeast University, Nanjing 210096, China and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval
May 24, 2026Zhongtian Hua, Yi Luo, Meijia Yu +1Automated Fact-Checking Cross-Modal Retrieval
Zhengzhou University, Zhengzhou, Henan, China · Henan University of Science and Technology, Luoyang, Henan, China
Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
May 23, 2026Hao Sun, Yingyan Hou, Jiayan Guo +4Multimodal Embedding Cross-Modal Retrieval
1State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China · Aerospace Information Research Institute, Chinese Academy of Sciences · Key Laboratory of Target Cognition and Application Technology +3
What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval
May 23, 2026Xinyu Zhang, Sichao Liu, Runhao Lu +2Neural Decoding Cross-Modal Retrieval
1KTH, Sweden · 3EPFL, Switzerland · 4Karolinska Institutet, Sweden +2
TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval
May 23, 2026Yuhang Zhang, Keyan Ding, Peilin Chen +5Cross-Modal Retrieval Information Retrieval
University of Science and Technology of China · 2City University of Hong Kong · 3Zhejiang University
TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge
May 23, 2026Zixu Li, Yupeng Hu, Zhiwei Chen +4Learning to Rank Cross-Modal Retrieval
Shandong University · Harbin Institute of Technology (Shenzhen)
RE-TRIANGLE: Does TRIANGLE Enable Multimodal Alignment Beyond Cosine Similarity in Retrieval?
May 22, 2026Arijit Ghosh, Aritra Bandyopadhyay, Chiranjeev Bindra +1Cross-Modal Learning Cross-Modal Alignment
University of Amsterdam Amsterdam, The Netherlands
STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding
May 22, 2026Jiahe Meng, Weiming Zeng, Yueyang Li +5Cross-Modal Knowledge Distillation Representation Learning
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China · Department of Language Science and Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China · Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang 222002, China +1
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
MSAlign: Aligning Molecule and Mass Spectra Foundation Models for Metabolite Identification
May 19, 2026Paul Krzakala, Gabriel Melo, Camille Lançon +4Distribution Shift Cross-Modal Alignment
LTCI, Télécom Paris & CMAP, Ecole Polytechnique, Institut Polytechnique de Paris · LTCI, Télécom Paris, Institut Polytechnique de Paris · CEA, INRAE, MetaboHUB, Université Paris-Saclay +1
GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval
May 19, 2026Tiantong Fang, Xiuwei Wang, Jing Xiao +3Cross-Modal Representation Learning Geometric Representation Learning
School of Artificial Intelligence, Wuhan University · School of Artificial Intelligence and Information Engineering, Zhejiang University of Science & Technology · Hangzhou Institute of Technology, Xidian University +1

Jun 6, 2026, cs.CVJ/K move · Enter open · S save

IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval

Jiale Huang, Zixu Li, Zhiwei Chen, Zhiheng Fu+2

Shandong University · Jinan, Shandong, China · Qilu University of Technology+1

Cross-Modal Alignment Cross-Modal Retrieval Composed Video Retrieval Composed Image Retrieval

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Composed Video Retrieval (CVR) is designed to retrieve a target video that matches a reference video modified by a modification text. While existing methods explore cross-modal correspondences, they often assume modified objects appear directly in videos. However, modification texts frequently describe concepts not explicitly presented but implicitly expressed through semantically related visual cues (e.g., "cake" implying "birthday party"). Current approaches typically rely on aligning explicit feature representations within the concrete space, neglecting critical latent associations. To address this, we propose an adaptIve scheMa-ImAGery enhanced composItional NEtwork (IMAGINE). Unlike standard explicit matching, IMAGINE materializes implicit semantics (termed schema imagery) via dynamic multimodal prototypes. These prototypes capture shared latent concepts to adaptively modulate visual features, effectively injecting implicit guidance into the retrieval process. By bridging the gap between explicit visual contents and implicit retrieval intentions, IMAGINE achieves state-of-the-art performance in both CVR and Composed Image Retrieval (CIR) across three widely used benchmarks.

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