26 research outputs found
Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy
© 2019 IEEE. The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-The-Art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets
How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning
Shortcut learning, or `Clever Hans effect` refers to situations where a
learning agent (e.g., deep neural networks) learns spurious correlations
present in data, resulting in biased models. We focus on finding shortcuts in
deep learning based spoofing countermeasures (CMs) that predict whether a given
utterance is spoofed or not. While prior work has addressed specific data
artifacts, such as silence, no general normative framework has been explored
for analyzing shortcut learning in CMs. In this study, we propose a generic
approach to identifying shortcuts by introducing systematic interventions on
the training and test sides, including the boundary cases of `near-perfect` and
`worse than coin flip` (label flip). By using three different models, ranging
from classic to state-of-the-art, we demonstrate the presence of shortcut
learning in five simulated conditions. We analyze the results using a
regression model to understand how biases affect the class-conditional score
statistics.Comment: Interspeech 202
Spatial-Temporal Person Re-identification
Most of current person re-identification (ReID) methods neglect a
spatial-temporal constraint. Given a query image, conventional methods compute
the feature distances between the query image and all the gallery images and
return a similarity ranked table. When the gallery database is very large in
practice, these approaches fail to obtain a good performance due to appearance
ambiguity across different camera views. In this paper, we propose a novel
two-stream spatial-temporal person ReID (st-ReID) framework that mines both
visual semantic information and spatial-temporal information. To this end, a
joint similarity metric with Logistic Smoothing (LS) is introduced to integrate
two kinds of heterogeneous information into a unified framework. To approximate
a complex spatial-temporal probability distribution, we develop a fast
Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint,
the st-ReID model eliminates lots of irrelevant images and thus narrows the
gallery database. Without bells and whistles, our st-ReID method achieves
rank-1 accuracy of 98.1\% on Market-1501 and 94.4\% on DukeMTMC-reID, improving
from the baselines 91.2\% and 83.8\%, respectively, outperforming all previous
state-of-the-art methods by a large margin.Comment: AAAI 201