Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

Cross-domain object detection is challenging, because object detection model is often
vulnerable to data variance, especially to the considerable domain shift between two …

Object detection typically assumes that training and test samples are drawn from an identical
distribution, which, however, does not always hold in practice. Such a distribution mismatch …

Object detectors are usually trained with large amount of labeled data, which is expensive
and labor-intensive. Pre-trained detectors applied to unlabeled dataset always suffer from …

Unsupervised domain adaptive object detection aims to learn a robust detector in the
domain shift circumstance, where the training (source) domain is label-rich with bounding …

We propose a domain adaptation approach for object detection. We introduce a two-step
method: the first step makes the detector robust to low-level differences and the second step …

3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern
street-view benchmarks. However, LiDAR-based detectors poorly generalize across …

Cross-domain visual problems, such as image-to-image translation and domain adaptive
object detection, have attracted increasing attentions in the last few years, and also become …

Domain shift is a well known problem where a model trained on a particular domain (source)
does not perform well when exposed to samples from a different domain (target) …