  We present GLEE in this work, an object-level foundation model for locating
and identifying objects in images and videos. Through a unified framework, GLEE
accomplishes detection, segmentation, tracking, grounding, and identification
of arbitrary objects in the open world scenario for various object perception
tasks. Adopting a cohesive learning strategy, GLEE acquires knowledge from
diverse data sources with varying supervision levels to formulate general
object representations, excelling in zero-shot transfer to new data and tasks.
Specifically, we employ an image encoder, text encoder, and visual prompter to
handle multi-modal inputs, enabling to simultaneously solve various
object-centric downstream tasks while maintaining state-of-the-art performance.
Demonstrated through extensive training on over five million images from
diverse benchmarks, GLEE exhibits remarkable versatility and improved
generalization performance, efficiently tackling downstream tasks without the
need for task-specific adaptation. By integrating large volumes of
automatically labeled data, we further enhance its zero-shot generalization
capabilities. Additionally, GLEE is capable of being integrated into Large
Language Models, serving as a foundational model to provide universal
object-level information for multi-modal tasks. We hope that the versatility
and universality of our method will mark a significant step in the development
of efficient visual foundation models for AGI systems. The model and code will
be released at https://glee-vision.github.io .
