15,443 research outputs found
Semantics-Aligned Representation Learning for Person Re-identification
Person re-identification (reID) aims to match person images to retrieve the
ones with the same identity. This is a challenging task, as the images to be
matched are generally semantically misaligned due to the diversity of human
poses and capture viewpoints, incompleteness of the visible bodies (due to
occlusion), etc. In this paper, we propose a framework that drives the reID
network to learn semantics-aligned feature representation through delicate
supervision designs. Specifically, we build a Semantics Aligning Network (SAN)
which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder
(SA-Dec) for reconstructing/regressing the densely semantics aligned full
texture image. We jointly train the SAN under the supervisions of person
re-identification and aligned texture generation. Moreover, at the decoder,
besides the reconstruction loss, we add Triplet ReID constraints over the
feature maps as the perceptual losses. The decoder is discarded in the
inference and thus our scheme is computationally efficient. Ablation studies
demonstrate the effectiveness of our design. We achieve the state-of-the-art
performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the
partial person reID dataset Partial REID. Code for our proposed method is
available at:
https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20),
code has been release
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
We propose a method for reconstructing 3D shapes from 2D sketches in the form
of line drawings. Our method takes as input a single sketch, or multiple
sketches, and outputs a dense point cloud representing a 3D reconstruction of
the input sketch(es). The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder network. The encoder
converts the sketch into a compact representation encoding shape information.
The decoder converts this representation into depth and normal maps capturing
the underlying surface from several output viewpoints. The multi-view maps are
then consolidated into a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on our experiments,
compared to other methods, such as volumetric networks, our architecture offers
several advantages, including more faithful reconstruction, higher output
surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral
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