The field of multimodal learning and retrieval is rapidly advancing, with a focus on developing more effective and efficient methods for integrating and processing multiple forms of data, such as text, images, and videos. Recent research has explored the use of recurrent transformers, gradient-attention guided dual-masking, and knowledge noise mitigation frameworks to improve the accuracy and robustness of multimodal models. Additionally, there is a growing interest in applying multimodal learning to real-world applications, such as materials characterization, person retrieval, and event extraction from multimedia documents. Noteworthy papers in this area include Recurrence Meets Transformers for Universal Multimodal Retrieval, which proposes a unified retrieval model that supports multimodal queries and searches across multimodal document collections, and Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval, which advances person representation learning through synergistic improvements in data curation and model architecture.
Advances in Multimodal Learning and Retrieval
Sources
Recurrence Meets Transformers for Universal Multimodal Retrieval
Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval
A Knowledge Noise Mitigation Framework for Knowledge-based Visual Question Answering
Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization
A Multimodal RAG Framework for Housing Damage Assessment: Collaborative Optimization of Image Encoding and Policy Vector Retrieval
Property prediction for ionic liquids without prior structural knowledge using limited experimental data: A data-driven neural recommender system leveraging transfer learning
MatSKRAFT: A framework for large-scale materials knowledge extraction from scientific tables
InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
Chat-Driven Text Generation and Interaction for Person Retrieval
Benchmarking and Improving LVLMs on Event Extraction from Multimedia Documents
TFANet: Three-Stage Image-Text Feature Alignment Network for Robust Referring Image Segmentation
TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding
Chain-of-Thought Re-ranking for Image Retrieval Tasks
MARIC: Multi-Agent Reasoning for Image Classification
QuizRank: Picking Images by Quizzing VLMs
What's the Best Way to Retrieve Slides? A Comparative Study of Multimodal, Caption-Based, and Hybrid Retrieval Techniques