Open World Object Detection
5 papers with code • 4 benchmarks • 4 datasets
Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received.
Benchmarks
These leaderboards are used to track progress in Open World Object Detection
COCO
PASCAL VOC
PASCAL VOC 2007
UVO
Most implemented papers
Towards Open World Object Detection
Humans have a natural instinct to identify unknown object instances in their environments.
Learning Open-World Object Proposals without Learning to Classify
mcahny/object_localization_network
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In this paper, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories.
OW-DETR: Open-world Detection Transformer
In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC.
Class-agnostic Object Detection with Multi-modal Transformer
mmaaz60/mvits_for_class_agnostic_od
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This has been a long-standing question in computer vision.
Revisiting Open World Object Detection
re-owod/re-owod
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Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.
