6 accepted papers
Transferring knowledge learned from the labeled source domain to the raw target domain for unsupervised domain adaptation (UDA) is essential to the scalable deployment of autonomous driving systems. State-of-the-art methods in UDA often employ a key idea: utilizing joint supervision signals from bot…
Modern SLAM engines typically rely on high-end sensor rigs and robust algorithms to guarantee the high-quality requirements that self-driving cars and other complex autonomous systems require from 3D point cloud maps. Nonetheless, multiple factors can impact the reconstruction quality and it is not…
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however…
In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval. Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep…
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple…
In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) method for face recognition. Different from existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which require strong prior knowledge, our SLBFLE is an unsupervised fe…