3,688 research outputs found
Occlusions for Effective Data Augmentation in Image Classification
Deep networks for visual recognition are known to leverage "easy to
recognise" portions of objects such as faces and distinctive texture patterns.
The lack of a holistic understanding of objects may increase fragility and
overfitting. In recent years, several papers have proposed to address this
issue by means of occlusions as a form of data augmentation. However, successes
have been limited to tasks such as weak localization and model interpretation,
but no benefit was demonstrated on image classification on large-scale
datasets. In this paper, we show that, by using a simple technique based on
batch augmentation, occlusions as data augmentation can result in better
performance on ImageNet for high-capacity models (e.g., ResNet50). We also show
that varying amounts of occlusions used during training can be used to study
the robustness of different neural network architectures.Comment: Accepted to 2019 ICCV Workshop on Interpreting and Explaining Visual
Artificial Intelligence Models (v2: corrected references
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
Occluded Person Re-identification
Person re-identification (re-id) suffers from a serious occlusion problem
when applied to crowded public places. In this paper, we propose to retrieve a
full-body person image by using a person image with occlusions. This differs
significantly from the conventional person re-id problem where it is assumed
that person images are detected without any occlusion. We thus call this new
problem the occluded person re-identitification. To address this new problem,
we propose a novel Attention Framework of Person Body (AFPB) based on deep
learning, consisting of 1) an Occlusion Simulator (OS) which automatically
generates artificial occlusions for full-body person images, and 2) multi-task
losses that force the neural network not only to discriminate a person's
identity but also to determine whether a sample is from the occluded data
distribution or the full-body data distribution. Experiments on a new occluded
person re-id dataset and three existing benchmarks modified to include
full-body person images and occluded person images show the superiority of the
proposed method.Comment: 6 pages, 7 figures, IEEE International Conference of Multimedia and
Expo 201
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