arXiv:2509.14001v5 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples. Lightweight models often struggle in this setting due to their weak semantic priors, while large vision-language models (VLMs) offer strong object-level understanding but are too computationally demanding for real-time or on-device applications. We introduce MOCHA (Multi-modal Objects-aware Cross-arcHitecture Alignment), a distillation framework that transfers multimodal region-level knowledge from a froz
The increasing computational demands of large AI models for real-time and on-device applications are making distillation frameworks like MOCHA critical for balancing performance and efficiency.
This development addresses the fundamental trade-off between powerful but resource-intensive VLMs and lightweight, performant models, crucial for broader AI adoption in edge computing.
Personalized object detection can now be more effectively deployed in resource-constrained environments by leveraging the semantic understanding of large models without their computational burden.
- · Edge AI providers
- · Robotics
- · Consumer electronics manufacturers
- · Computer vision developers
- · Companies reliant on solely large, unoptimized models for edge applications
More sophisticated AI capabilities will become feasible on devices like smartphones, drones, and embedded systems.
This could accelerate the development and deployment of autonomous systems that require real-time object detection without constant cloud connectivity.
Increased accessibility of advanced personalized object recognition may lead to new security and privacy challenges as AI agents become more prevalent in daily life.
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Read at arXiv cs.AI