Open World Object Detection
22 papers with code • 7 benchmarks • 5 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
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MS COCO
PASCAL VOC 2007
UVO
COCO-OOD
COCO-Mix
Most implemented papers
GOOD: Exploring Geometric Cues for Detecting Objects in an Open World
We address the task of open-world class-agnostic object detection, i. e., detecting every object in an image by learning from a limited number of base object classes.
Annealing-Based Label-Transfer Learning for Open World Object Detection
dig-beihang/allow
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CVPR 2023
To the best of our knowledge, this is the first OWOD work without manual unknown selection.
Detecting Everything in the Open World: Towards Universal Object Detection
zhenyuw16/unidetector
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CVPR 2023
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category.
Detecting the open-world objects with the help of the Brain
xiaomabufei/dowb
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We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects
went-liang/unsniffer
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CVPR 2023
The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones.
Random Boxes Are Open-world Object Detectors
scuwyh2000/randbox
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ICCV 2023
First, as the randomization is independent of the distribution of the limited known objects, the random proposals become the instrumental variable that prevents the training from being confounded by the known objects.
Unsupervised Recognition of Unknown Objects for Open-World Object Detection
Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge.
Recognize Any Regions
surrey-uplab/recognize-any-regions
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Understanding the semantics of individual regions or patches within unconstrained images, such as in open-world object detection, represents a critical yet challenging task in computer vision.
Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector
lxycopper/plu
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This is because the predictor is inevitably biased to the known categories, and fails under the shift in the appearance of the unseen categories.
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector
xiaomabufei/skdf
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Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects.
