1,517 research outputs found
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models
3D building generation with low data acquisition costs, such as single
image-to-3D, becomes increasingly important. However, most of the existing
single image-to-3D building creation works are restricted to those images with
specific viewing angles, hence they are difficult to scale to general-view
images that commonly appear in practical cases. To fill this gap, we propose a
novel 3D building shape generation method exploiting point cloud diffusion
models with image conditioning schemes, which demonstrates flexibility to the
input images. By cooperating two conditional diffusion models and introducing a
regularization strategy during denoising process, our method is able to
synthesize building roofs while maintaining the overall structures. We validate
our framework on two newly built datasets and extensive experiments show that
our method outperforms previous works in terms of building generation quality.Comment: 10 pages, 6 figures, accepted to ICCVW202
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