  Image coding for machines (ICM) aims to compress images for machine analysis
using recognition models rather than human vision. Hence, in ICM, it is
important for the encoder to recognize and compress the information necessary
for the machine recognition task. There are two main approaches in learned ICM;
optimization of the compression model based on task loss, and Region of
Interest (ROI) based bit allocation. These approaches provide the encoder with
the recognition capability. However, optimization with task loss becomes
difficult when the recognition model is deep, and ROI-based methods often
involve extra overhead during evaluation. In this study, we propose a novel
training method for learned ICM models that applies auxiliary loss to the
encoder to improve its recognition capability and rate-distortion performance.
Our method achieves Bjontegaard Delta rate improvements of 27.7% and 20.3% in
object detection and semantic segmentation tasks, compared to the conventional
training method.
