MVCL-DAF++: Enhancing Multimodal Intent Recognition via Prototype-Aware Contrastive Alignment and Coarse-to-Fine Dynamic Attention Fusion
arXiv:2509.17446v3 Announce Type: replace Abstract: Multimodal intent recognition (MMIR) suffers from weak semantic grounding and poor robustness under noisy or rare-class conditions. We propose MVCL-DAF++, which extends MVCL-DAF with two key modules: (1) Prototype-aware contrastive alignment, aligning instances to class-level prototypes to enhance semantic consistency; and (2) Coarse-to-fine attention fusion, integrating global modality summaries with token-level features for hierarchical cross-modal interaction. On MIntRec and MIntRec2.0, MVCL-DAF++ achieves new state-of-the-art results, imp
The continuous evolution of AI models demands increasingly robust and adaptable methods for understanding complex data, making advancements in multimodal intent recognition highly relevant.
Improved multimodal intent recognition directly enhances the capability of AI systems to understand human intention more accurately across various data types, crucial for more natural and effective human-AI interaction.
AI models will be better equipped to handle noisy or rare-class data in multimodal contexts, leading to more reliable and semantically consistent interpretations of user intent.
- · AI developers
- · NLP researchers
- · AI-driven product companies
- · SaaS providers leveraging AI
- · Legacy unimodal intent recognition systems
- · Systems highly sensitive to data noise
Enhancements in multimodal AI lead to more intuitive and effective AI assistants and intelligent interfaces.
Reduced friction in human-computer interaction could accelerate the adoption and integration of AI into daily workflows and applications.
As AI better understands intent, the potential for autonomous AI agents to perform complex tasks without explicit, step-by-step human guidance increases significantly.
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Read at arXiv cs.LG