Jan 3, 2022 · In this paper, we rethink the OWOD experimental setting and propose five fundamental benchmark principles to guide the OWOD benchmark ...
Oct 20, 2023 · In this paper, we revisit the OWOD problem and rethink it from benchmark, metrics, and algorithm perspectives. First, we propose five ...
Our new data division is based on COCO2017. We divide the training set into four tasks, in which each task has 20 categories. For each task, we obtained ...
Oct 20, 2023 · In this paper, we revisit the OWOD problem and rethink it from benchmark, metrics, and algorithm perspectives. First, we propose five ...
Open World Object Detection (OWOD) simulates the object detection task in the real dynamic world and bridges the gap between human and machine intelligence.
Jul 6, 2024 · ... In the OWOD objective, models are expected to incrementally learn newly discovered objects without catastrophically forgetting previously ...
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'.
Oct 22, 2024 · The era of vision-language models (VLMs) trained on web-scale datasets challenges conventional formulations of "open-world" perception.
Oct 15, 2024 · Revisiting Open World Object Detection [24] (RE-OWOD) proposed by Zhao, et al. utilizes a class-specific expelling classifier (CEC) to ...
Open world object detection aims to identify objects of unseen categories and incrementally recognize them once their annotations are provided.