404,674 research outputs found
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification
Gait-based person re-identification (Re-ID) is valuable for safety-critical
applications, and using only 3D skeleton data to extract discriminative gait
features for person Re-ID is an emerging open topic. Existing methods either
adopt hand-crafted features or learn gait features by traditional supervised
learning paradigms. Unlike previous methods, we for the first time propose a
generic gait encoding approach that can utilize unlabeled skeleton data to
learn gait representations in a self-supervised manner. Specifically, we first
propose to introduce self-supervision by learning to reconstruct input skeleton
sequences in reverse order, which facilitates learning richer high-level
semantics and better gait representations. Second, inspired by the fact that
motion's continuity endows temporally adjacent skeletons with higher
correlations ("locality"), we propose a locality-aware attention mechanism that
encourages learning larger attention weights for temporally adjacent skeletons
when reconstructing current skeleton, so as to learn locality when encoding
gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are
built using context vectors learned by locality-aware attention, as final gait
representations. AGEs are directly utilized to realize effective person Re-ID.
Our approach typically improves existing skeleton-based methods by 10-20%
Rank-1 accuracy, and it achieves comparable or even superior performance to
multi-modal methods with extra RGB or depth information. Our codes are
available at https://github.com/Kali-Hac/SGE-LA.Comment: Accepted at IJCAI 2020 Main Track. Sole copyright holder is IJCAI.
Codes are available at https://github.com/Kali-Hac/SGE-L
Discovering Discriminative Geometric Features with Self-Supervised Attention for Vehicle Re-Identification and Beyond
In the literature of vehicle re-identification (ReID), intensive manual
labels such as landmarks, critical parts or semantic segmentation masks are
often required to improve the performance. Such extra information helps to
detect locally geometric features as a part of representation learning for
vehicles. In contrast, in this paper, we aim to address the challenge of {\em
automatically} learning to detect geometric features as landmarks {\em with no
extra labels}. To the best of our knowledge, we are the {\em first} to
successfully learn discriminative geometric features for vehicle ReID based on
self-supervised attention. Specifically, we implement an end-to-end trainable
deep network architecture consisting of three branches: (1) a global branch as
backbone for image feature extraction, (2) an attentional branch for producing
attention masks, and (3) a self-supervised branch for regularizing the
attention learning with rotated images to locate geometric features. %Our
network design naturally leads to an end-to-end multi-task joint optimization.
We conduct comprehensive experiments on three benchmark datasets for vehicle
ReID, \ie VeRi-776, CityFlow-ReID, and VehicleID, and demonstrate our
state-of-the-art performance. %of our approach with the capability of capturing
informative vehicle parts with no corresponding manual labels. We also show the
good generalization of our approach in other ReID tasks such as person ReID and
multi-target multi-camera (MTMC) vehicle tracking. {\em Our demo code is
attached in the supplementary file.
Identification. The missing link between joint attention and imitation
In this paper we outline our hypothesis that human intersubjective engagement entails identifying with other people. We tested a prediction derived from this hypothesis that concerned the relation between a component of joint attention and a specific form of imitation. The empirical investigation involved “blind” ratings of videotapes from a recent study in which we tested matched children with and without autism for their propensity to imitate the self-/other-orientated aspects of another person's actions. The results were in keeping with three a priori predictions, as follows: (a) children with autism contrasted with control participants in spending more time looking at the objects acted upon and less time looking at the tester; (b) participants with autism showed fewer “sharing” looks toward the tester, and although they also showed fewer “checking” and “orientating” looks, they were specifically less likely to show any sharing looks; and, critically, (c) within each group, individual differences in sharing looks (only) were associated with imitation of self–other orientation. We suggest that the propensity to adopt the bodily anchored psychological stance of another person is essential to certain forms of joint attention and imitation, and that a weak tendency to identify with others is pivotal for the developmental psychopathology of autism
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