Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and
LAION have propelled recent dramatic progress in AI. Large neural models trained on such …

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 …

Despite the recent success of long-tailed object detection, almost all long-tailed object
detectors are developed based on the two-stage paradigm. In practice, one-stage detectors …

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation.
By randomly pasting object instances onto new background images, it creates new training …

Current 3D object detectors for autonomous driving are almost entirely trained on human-
annotated data. Although of high quality, the generation of such data is laborious and costly …

Most of the existing salient object detection (SOD) models focus on improving the overall
model performance, without explicitly explaining the discrepancy between the training and …

Major advancements have been made in the field of object detection and segmentation
recently. However, when it comes to rare categories, the state-of-the-art methods fail to …

Multimodal machine learning has achieved remarkable progress in a wide range of
scenarios. However, the reliability of multimodal learning remains largely unexplored. In this …

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 …