282 research outputs found
Multi-scale Deep Learning Architectures for Person Re-identification
Person Re-identification (re-id) aims to match people across non-overlapping
camera views in a public space. It is a challenging problem because many people
captured in surveillance videos wear similar clothes. Consequently, the
differences in their appearance are often subtle and only detectable at the
right location and scales. Existing re-id models, particularly the recently
proposed deep learning based ones match people at a single scale. In contrast,
in this paper, a novel multi-scale deep learning model is proposed. Our model
is able to learn deep discriminative feature representations at different
scales and automatically determine the most suitable scales for matching. The
importance of different spatial locations for extracting discriminative
features is also learned explicitly. Experiments are carried out to demonstrate
that the proposed model outperforms the state-of-the art on a number of
benchmarksComment: 9 pages, 3 figures, accepted by ICCV 201
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
A recent study finds that existing few-shot learning methods, trained on the
source domain, fail to generalize to the novel target domain when a domain gap
is observed. This motivates the task of Cross-Domain Few-Shot Learning
(CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has
not been leveraged in any way to help the learning process. Thus, we advocate
utilizing few labeled target data to guide the model learning. Technically, a
novel meta-FDMixup network is proposed. We tackle this problem mainly from two
aspects. Firstly, to utilize the source and the newly introduced target data of
two different class sets, a mixup module is re-proposed and integrated into the
meta-learning mechanism. Secondly, a novel disentangle module together with a
domain classifier is proposed to extract the disentangled domain-irrelevant and
domain-specific features. These two modules together enable our model to narrow
the domain gap thus generalizing well to the target datasets. Additionally, a
detailed feasibility and pilot study is conducted to reflect the intuitive
understanding of CD-FSL under our new setting. Experimental results show the
effectiveness of our new setting and the proposed method. Codes and models are
available at https://github.com/lovelyqian/Meta-FDMixup.Comment: Accepted by ACM Multimedia 202
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