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.

Most implemented papers

Towards Open World Object Detection

JosephKJ/OWOD CVPR 2021

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 15 Aug 2021

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

akshitac8/ow-detr 2 Dec 2021

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 22 Nov 2021

This has been a long-standing question in computer vision.

Revisiting Open World Object Detection

re-owod/re-owod 3 Jan 2022

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.