69,312 research outputs found
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes
Semi-supervised Learning (SSL) has received increasing attention in
autonomous driving to relieve enormous burden for 3D annotation. In this paper,
we propose UpCycling, a novel SSL framework for 3D object detection with zero
additional raw-level point cloud: learning from unlabeled de-identified
intermediate features (i.e., smashed data) for privacy preservation. The
intermediate features do not require additional computation on autonomous
vehicles since they are naturally produced by the inference pipeline. However,
augmenting 3D scenes at a feature level turns out to be a critical issue:
applying the augmentation methods in the latest semi-supervised 3D object
detectors distorts intermediate features, which causes the pseudo-labels to
suffer from significant noise. To solve the distortion problem while achieving
highly effective SSL, we introduce hybrid pseudo labels, feature-level Ground
Truth sampling (F-GT) and Rotation (F-RoT), which safely augment unlabeled
multi-type 3D scene features and provide high-quality supervision. We implement
UpCycling on two representative 3D object detection models, SECOND-IoU and
PV-RCNN, and perform experiments on widely-used datasets (Waymo, KITTI, and
Lyft). While preserving privacy with zero raw-point scene, UpCycling
significantly outperforms the state-of-the-art SSL methods that utilize
raw-point scenes, in both domain adaptation and partial-label scenarios
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh
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