General Object Foundation Model for Images and Videos at Scale
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 .
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Tasks
Results from the Paper
Ranked #1 on Referring Expression Segmentation on Refer-YouTube-VOS (2021 public validation) (using extra training data)
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
---|---|---|---|---|---|---|---|
Long-tail Video Object Segmentation | BURST | GLEE-Lite | HOTA (all) | 22.6 | # 1 | ||
mAP (all) | 12.6 | # 1 | |||||
HOTA (com) | 36.4 | # 1 | |||||
mAP (com) | 18.9 | # 1 | |||||
HOTA (unc) | 19.1 | # 1 | |||||
mAP (unc) | 11.0 | # 1 | |||||
Long-tail Video Object Segmentation | BURST-val | GLEE-Lite | HOTA (all) | 22.6 | # 3 | ||
mAP (all) | 12.6 | # 3 | |||||
HOTA (com) | 36.4 | # 3 | |||||
mAP (com) | 18.9 | # 3 | |||||
HOTA (unc) | 19.1 | # 3 | |||||
mAP (unc) | 11.0 | # 3 | |||||
Long-tail Video Object Segmentation | BURST-val | GLEE-Plus | HOTA (all) | 26.9 | # 2 | ||
mAP (all) | 17.2 | # 2 | |||||
HOTA (com) | 38.8 | # 2 | |||||
mAP (com) | 23.7 | # 2 | |||||
HOTA (unc) | 23.9 | # 2 | |||||
mAP (unc) | 15.5 | # 2 | |||||
Long-tail Video Object Segmentation | BURST-val | GLEE-Pro | HOTA (all) | 31.2 | # 1 | ||
mAP (all) | 19.2 | # 1 | |||||
HOTA (com) | 48.7 | # 1 | |||||
mAP (com) | 24.8 | # 1 | |||||
HOTA (unc) | 26.9 | # 1 | |||||
mAP (unc) | 17.7 | # 1 | |||||
Instance Segmentation | COCO minival | GLEE-Lite | mask AP | 48.4 | # 35 | ||
Object Detection | COCO minival | GLEE-Lite | box AP | 55.0 | # 50 | ||
Object Detection | COCO minival | GLEE-Plus | box AP | 60.4 | # 21 | ||
Object Detection | COCO minival | GLEE-Pro | box AP | 62.0 | # 14 | ||
Instance Segmentation | COCO minival | GLEE-Pro | mask AP | 54.2 | # 5 | ||
Instance Segmentation | COCO minival | GLEE-Plus | mask AP | 53.0 | # 9 | ||
Instance Segmentation | COCO test-dev | GLEE-Pro | mask AP | 54.5 | # 6 | ||
Object Detection | COCO test-dev | GLEE-Plus | box mAP | 60.6 | # 24 | ||
Object Detection | COCO test-dev | GLEE-Lite | box mAP | 54.7 | # 49 | ||
Object Detection | COCO test-dev | GLEE-Pro | box mAP | 62.3 | # 20 | ||
Instance Segmentation | COCO test-dev | GLEE-Lite | mask AP | 48.3 | # 27 | ||
Instance Segmentation | COCO test-dev | GLEE-Plus | mask AP | 53.3 | # 9 | ||
Object Detection | LVIS v1.0 val | GLEE-Pro | box AP | 55.7 | # 5 | ||
Instance Segmentation | LVIS v1.0 val | GLEE-Pro | mask AP | 49.9 | # 4 | ||
Video Instance Segmentation | OVIS validation | GLEE-Pro | mask AP | 50.4 | # 2 | ||
AP75 | 55.5 | # 2 | |||||
Referring Expression Comprehension | RefCoco+ | GLEE-Pro | Val | 82.6 | # 6 | ||
Referring Expression Comprehension | RefCOCO | GLEE-Pro | Val | 91.0 | # 4 | ||
Referring Expression Segmentation | RefCOCO | GLEE-Pro | IoU | 80.0 | # 1 | ||
Referring Expression Segmentation | RefCOCOg-val | GLEE-Pro | Overall IoU | 72.9 | # 4 | ||
Referring Expression Comprehension | RefCOCOg-val | GLEE-Pro | Accuracy | 86.4 | # 5 | ||
Referring Expression Segmentation | RefCoCo val | GLEE-Pro | Overall IoU | 80.0 | # 4 | ||
Referring Expression Segmentation | RefCOCO+ val | GLEE-Pro | Overall IoU | 69.6 | # 6 | ||
Referring Video Object Segmentation | Refer-YouTube-VOS | GLEE-Plus | J&F | 67.7 | # 2 | ||
J | 65.6 | # 2 | |||||
F | 69.7 | # 2 | |||||
Referring Video Object Segmentation | Refer-YouTube-VOS | GLEE-Pro | J&F | 70.6 | # 1 | ||
J | 68.2 | # 1 | |||||
F | 72.9 | # 1 | |||||
Referring Expression Segmentation | Refer-YouTube-VOS (2021 public validation) | GLEE-Pro | J&F | 70.6 | # 1 | ||
J | 68.2 | # 1 | |||||
F | 72.9 | # 1 | |||||
Multi-Object Tracking | TAO | GLEE-Pro | TETA | 47.2 | # 1 | ||
LocA | 66.2 | # 1 | |||||
AssocA | 46.2 | # 1 | |||||
ClsA | 29.1 | # 2 | |||||
Multi-Object Tracking | TAO | GLEE-Plus | TETA | 41.5 | # 2 | ||
LocA | 52.9 | # 4 | |||||
AssocA | 40.9 | # 2 | |||||
ClsA | 30.8 | # 1 | |||||
Multi-Object Tracking | TAO | GLEE-Lite | TETA | 40.1 | # 3 | ||
LocA | 56.3 | # 3 | |||||
AssocA | 39.9 | # 3 | |||||
ClsA | 24.1 | # 3 | |||||
Open-World Instance Segmentation | UVO | GLEE-Pro | ARmask | 72.6 | # 1 | ||
Video Instance Segmentation | YouTube-VIS validation | GLEE-Pro | mask AP | 67.4 | # 3 |