arXiv:2602.14134v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending these models to fine-grained dense prediction tasks, such as semantic segmentation and depth estimation, typically necessitates the incorporation of complex, task-specific decoders and other customizations. This architectural fragmentation increases model complexity and deviates from the generalist design of MLLMs, ultimately limiting their practicality. In this work, we challenge this paradigm by ac
The rapid advancement of Multimodal Large Language Models (MLLMs) and the increasing demand for generalized AI capabilities are pushing researchers to consolidate complex task-specific architectures.
This work represents a step towards truly generalist AI models by enabling MLLMs to perform fine-grained tasks without specialized decoders, potentially simplifying architecture and accelerating development.
Traditional specialized models for dense prediction tasks may become less necessary as general-purpose MLLMs extend their capabilities into these areas with unified architectures.
- · AI model developers
- · Cloud AI providers
- · Robotics
- · Computer vision applications
- · Developers of highly specialized dense prediction models
- · Companies relying on fragmented AI architectures
Standardized MLLM architectures become more versatile, reducing development overhead for new applications.
Accelerated deployment of AI in complex physical environments as MLLMs handle diverse perception tasks seamlessly.
The pathway to more general-purpose AI agents is significantly advanced, potentially enabling more autonomous systems on a larger scale.
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Read at arXiv cs.LG