Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …

The open-world deployment of Machine Learning (ML) algorithms in safety-critical
applications such as autonomous vehicles needs to address a variety of ML vulnerabilities …

This paper studies the problem of object discovery--separating objects from the background
without manual labels. Existing approaches utilize appearance cues, such as color, texture …

Data imbalance, in which a plurality of the data samples come from a small proportion of
labels, poses a challenge in training deep neural networks. Unlike classification, in …

Multiple datasets and open challenges for object detection have been introduced in recent
years. To build more general and powerful object detection systems, in this paper, we …

Vanilla models for object detection and instance segmentation suffer from the heavy bias
toward detecting frequent objects in the long-tailed setting. Existing methods address this …

This paper focuses on long-tailed object detection in the semi-supervised learning setting,
which poses realistic challenges, but has rarely been studied in the literature. We propose a …

Traffic sign recognition is a crucial aspect of autonomous vehicle research, and deep
learning techniques have significantly contributed to its progress. Nevertheless, the …

Large vocabulary object detectors are often faced with the long-tailed label distributions,
seriously degrading their ability to detect rarely seen categories. On one hand, the rare …

Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects
of novel categories beyond the training vocabulary. Recent work resorts to the rich …