VLX-Seek: Improving VLM Fine-Grained Perception via Region Reference Instead of Coordinate Generation

VLX-Seek is an innovative model designed to enhance fine-grained perception in multimodal large models (VLMs) for real-world applications like cameras, drones, and robots, shifting focus from generating coordinate-based localization to region reference. Unlike traditional VLMs that excel in semantic understanding yet struggle with precise localization, VLX-Seek employs a novel approach by using region tokens, which allows the model to refer to specific parts of an image as language entities. This method enhances the model’s ability to perform tasks such as object detection, open-vocabulary localization, and complex referring expression comprehension by turning localization into a language-conditioned retrieval among candidate visual regions. This approach not only improves inference efficiency and accuracy but also reduces the computational demands, making it especially suitable for on-device applications where resources are limited. As a result, VLX-Seek empowers embodied systems to execute actions based on stable and accurate spatial anchors, thus bridging the gap between image understanding and actionable perception.

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