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This paper proposes Open-Set Incremental Object Detection (OSIOD), which is defined as two processes: learning from the known, and an infinite cyclical process.
Abstract—In real-world applications, detectors are expected to evolve and improve their perceptual abilities through incremental learning of the unknown.
Object Detection. Conference Paper. A Benchmark and Baseline for Open-Set Incremental Object Detection. June 2024. DOI:10.1109/IJCNN60899.2024.10650826.
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The benchmark construction is based on three core principles. First, as many training images as possible are included in every task. Second, the training images ...
Oct 15, 2024 · Open World Object Detection (OWOD) combines the features of Object Detection, Incremental Learning, and Open Set Recognition. These three ...
Open-Set Object Detection (OSOD) has emerged as a contemporary research direction to address the detection of unknown objects. Recently, few works have ...
This work introduces the Google Landmarks Dataset v2 (GLDv2), a new benchmark for large-scale, fine-grained instance recognition and image retrieval.
We release OpenLORIS-Object, which is the first real-world lifelong learning dataset for robotic vision with quantifiable environmental factors.
Aug 11, 2023 · The methods that are available in OpenOOD v1.5 have the Alias and Group attribute, where Alias is the name of the method, and Group is the category the method ...
The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition ...