Localization with Sampling-Argmax
This work addresses a specific bottleneck in localization tasks for computer vision, offering an incremental improvement over existing methods.
The paper tackles the problem of performance degradation in detection-based localization methods due to unconstrained probability maps from soft-argmax, proposing sampling-argmax as a differentiable training method that minimizes localization error expectation, and demonstrates its effectiveness across various tasks.
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