Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model
arXiv:2606.17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains. However, existing Multi-modal Coreference Resolution (MCR) methods require training with (partially) annotated data from the target dataset before they can be applied, preventing their direct usability and raising concerns about generalization. While Vision-Language Large Models (VLLMs) with billions of parameters offer promising zero-shot capabilities, they remain largely inaccessible. Their massive size limits deployability, and many ar
The proliferation of advanced Vision-Language Large Models (VLLMs) is pushing research towards methods that make their sophisticated capabilities more accessible and deployable, addressing limitations of existing approaches.
This research outlines a method to achieve multimodal coreference resolution with zero-shot capabilities, potentially making advanced AI functionalities more practical and democratized beyond massive, inaccessible models.
The ability to deploy complex multimodal AI without extensive dataset-specific training fundamentally alters the cost and accessibility barriers for a range of AI applications that rely on image and text interpretation.
- · AI developers
- · NLP researchers
- · Edge AI providers
- · Companies relying on proprietary, training-intensive MCR solutions
- · Organizations with limited compute resources for large model training
Easier and faster deployment of AI systems requiring multimodal understanding, particularly for tasks like content analysis and intelligent assistants.
Increased adoption of multimodal AI in sectors currently constrained by training data availability and computational overhead, leading to new product categories.
Generalized AI agents become more practical, accelerating the development of autonomous systems that can interpret and act across complex data types without constant human supervision.
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Read at arXiv cs.AI