8 accepted papers
Existing cross-modal retrieval methods typically rely on large-scale vision-language pair data. This makes it challenging to efficiently develop a cross-modal retrieval model for under-resourced languages of interest. Therefore, Cross-lingual Cross-modal Retrieval (CCR), which aims to align vision a…
Localization is a crucial prerequisite for automated valet parking, in which a vehicle is required to navigate itself in a GPS-denied parking lot. Traditional visual localization methods usually build a feature map and use it for future localizations. However, the feature map is not robust to change…
Localizing a vehicle on a prebuilt HD vector map is a prerequisite for many autonomous driving applications. Existing visual localization approaches usually require a separate local feature layer to function. The separate localization layer suffers from the robustness issue inherited from the local…
Many autonomous driving applications nowadays come along with a prebuilt vector map for routing and planning purposes. In order to localize on this map, traditional LiDAR localization methods usually require a separate localization layer to function. On one hand, the separate layer occupies large st…
This paper focuses on indoor semantic segmentation using RGB-D data. Although the commonly used deconvolution networks (DeconvNet) have achieved impressive results on this task, we find there is still room for improvements in two aspects. One is about the boundary segmentation. DeconvNet aggregates…
In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i.e. silhouette consistency, into consideration. Unlike existing approaches which usually assume the silhouettes are provided, we consider more practical scenarios and generate the silhouett…
We present a novel global stereo model designed for view interpolation. Unlike existing stereo models which only output a disparity map, our model is able to output a 3D triangular mesh, which can be directly used for view interpolation. To this aim, we partition the input stereo images into 2D tria…
This paper studies the problem of improving the top-1 accuracy of RGB-D object recognition. Despite of the impressive top-5 accuracies achieved by existing methods, their top-1 accuracies are not very satisfactory. The reasons are in two-fold: (1) existing similarity measures are sensitive to object…