15,419 research outputs found
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
For person re-identification, existing deep networks often focus on
representation learning. However, without transfer learning, the learned model
is fixed as is, which is not adaptable for handling various unseen scenarios.
In this paper, beyond representation learning, we consider how to formulate
person image matching directly in deep feature maps. We treat image matching as
finding local correspondences in feature maps, and construct query-adaptive
convolution kernels on the fly to achieve local matching. In this way, the
matching process and results are interpretable, and this explicit matching is
more generalizable than representation features to unseen scenarios, such as
unknown misalignments, pose or viewpoint changes. To facilitate end-to-end
training of this architecture, we further build a class memory module to cache
feature maps of the most recent samples of each class, so as to compute image
matching losses for metric learning. Through direct cross-dataset evaluation,
the proposed Query-Adaptive Convolution (QAConv) method gains large
improvements over popular learning methods (about 10%+ mAP), and achieves
comparable results to many transfer learning methods. Besides, a model-free
temporal cooccurrence based score weighting method called TLift is proposed,
which improves the performance to a further extent, achieving state-of-the-art
results in cross-dataset person re-identification. Code is available at
https://github.com/ShengcaiLiao/QAConv.Comment: This is the ECCV 2020 version, including the appendi
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
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