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. Current OWOD models, such as ORE and OW-DETR, focus
on pseudo-labeling regions with high objectness scores as unknowns, whose
performance relies heavily on the supervision of known objects. While they can
detect the unknowns that exhibit similar features to the known objects, they
suffer from a severe label bias problem that they tend to detect all regions
(including unknown object regions) that are dissimilar to the known objects as
part of the background. To eliminate the label bias, this paper proposes a
novel approach that learns an unsupervised discriminative model to recognize
true unknown objects from raw pseudo labels generated by unsupervised region
proposal methods. The resulting model can be further refined by a
classification-free self-training method which iteratively extends pseudo
unknown objects to the unlabeled regions. Experimental results show that our
method 1) significantly outperforms the prior SOTA in detecting unknown objects
while maintaining competitive performance of detecting known object classes on
the MS COCO dataset, and 2) achieves better generalization ability on the LVIS
and Objects365 datasets.

该研究论文提出了一种新颖的方法，通过学习无监督的辨别模型来识别真实的未知对象，并通过无分类训练方法对模型进行进一步改进，实现在检测未知对象方面显著优于先前的方法，在 MS COCO 数据集上保持检测已知对象类别的竞争性表现，并在 LVIS 和 Objects365 数据集上实现更好的泛化能力。

无监督识别未知物体的开放世界物体检测

Unsupervised Recognition of Unknown Objects for Open-World Object  Detection

We introduce the Globally Normalized Autoregressive Transducer (GNAT) for
addressing the label bias problem in streaming speech recognition. Our solution
admits a tractable exact computation of the denominator for the sequence-level
normalization. Through theoretical and empirical results, we demonstrate that
by switching to a globally normalized model, the word error rate gap between
streaming and non-streaming speech-recognition models can be greatly reduced
(by more than 50\% on the Librispeech dataset). This model is developed in a
modular framework which encompasses all the common neural speech recognition
models. The modularity of this framework enables controlled comparison of
modelling choices and creation of new models.

介绍了全局归一化自回归转换器 (GNAT) 解决了流式语音识别中的标签偏见问题。我们的解决方案采用可计算的分母来实现序列级别归一化。通过理论和实证结果，我们展示了通过切换到全局归一化模型，可以极大地减少流式和非流式语音识别模型之间的词误差率差距（对于 Librispeech 数据集，减少了超过 50%）。此模型在一个模块化框架中开发，包含所有普通的神经语音识别模型。这个框架的可模块化性使得可以对建模选择进行控制比较，创造新的模型。