Discovery-and-Selection: Towards Optimal Multiple Instance Learning for
Weakly Supervised Object Detection
- URL: http://arxiv.org/abs/2110.09060v1
- Date: Mon, 18 Oct 2021 07:06:57 GMT
- Title: Discovery-and-Selection: Towards Optimal Multiple Instance Learning for
Weakly Supervised Object Detection
- Authors: Shiwei Zhang, Wei Ke, Lin Yang, Qixiang Ye, Xiaopeng Hong, Yihong
Gong, Tong Zhang
- Abstract summary: We propose a discoveryand-selection approach fused with multiple instance learning (DS-MIL)
Our proposed DS-MIL approach can consistently improve the baselines, reporting state-of-the-art performance.
- Score: 86.86602297364826
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Weakly supervised object detection (WSOD) is a challenging task that requires
simultaneously learn object classifiers and estimate object locations under the
supervision of image category labels. A major line of WSOD methods roots in
multiple instance learning which regards images as bags of instance and selects
positive instances from each bag to learn the detector. However, a grand
challenge emerges when the detector inclines to converge to discriminative
parts of objects rather than the whole objects. In this paper, under the
hypothesis that optimal solutions are included in local minima, we propose a
discoveryand-selection approach fused with multiple instance learning (DS-MIL),
which finds rich local minima and select optimal solutions from multiple local
minima. To implement DS-MIL, an attention module is designed so that more
context information can be captured by feature maps and more valuable proposals
can be collected during training. With proposal candidates, a re-rank module is
designed to select informative instances for object detector training.
Experimental results on commonly used benchmarks show that our proposed DS-MIL
approach can consistently improve the baselines, reporting state-of-the-art
performance.
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