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Localization with Sampling-Argmax
Jiefeng Li · Tong Chen · Ruiqi Shi · Yujing Lou · Yong-Lu Li · Cewu Lu

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @

Soft-argmax operation is commonly adopted in detection-based methods to localize the target position in a differentiable manner. However, training the neural network with soft-argmax makes the shape of the probability map unconstrained. Consequently, the model lacks pixel-wise supervision through the map during training, leading to performance degradation. In this work, we propose sampling-argmax, a differentiable training method that imposes implicit constraints to the shape of the probability map by minimizing the expectation of the localization error. To approximate the expectation, we introduce a continuous formulation of the output distribution and develop a differentiable sampling process. The expectation can be approximated by calculating the average error of all samples drawn from the output distribution. We show that sampling-argmax can seamlessly replace the conventional soft-argmax operation on various localization tasks. Comprehensive experiments demonstrate the effectiveness and flexibility of the proposed method. Code is available at https://github.com/Jeff-sjtu/sampling-argmax

Author Information

Jiefeng Li (Shanghai Jiao Tong University)
Tong Chen (Shanghai Jiao Tong University)
Ruiqi Shi (Shanghai Jiao Tong University)
Yujing Lou (Shanghai Jiao Tong University)
Yong-Lu Li (Shanghai Jiao Tong University)
Cewu Lu (Shanghai Jiao Tong University)

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