  The proliferation of deep learning-based machine vision applications has
given rise to a new type of compression, so called video coding for machine
(VCM). VCM differs from traditional video coding in that it is optimized for
machine vision performance instead of human visual quality. In the feature
compression track of MPEG-VCM, multi-scale features extracted from images are
subject to compression. Recent feature compression works have demonstrated that
the versatile video coding (VVC) standard-based approach can achieve a BD-rate
reduction of up to 96% against MPEG-VCM feature anchor. However, it is still
sub-optimal as VVC was not designed for extracted features but for natural
images. Moreover, the high encoding complexity of VVC makes it difficult to
design a lightweight encoder without sacrificing performance. To address these
challenges, we propose a novel multi-scale feature compression method that
enables both the end-to-end optimization on the extracted features and the
design of lightweight encoders. The proposed model combines a learnable
compressor with a multi-scale feature fusion network so that the redundancy in
the multi-scale features is effectively removed. Instead of simply cascading
the fusion network and the compression network, we integrate the fusion and
encoding processes in an interleaved way. Our model first encodes a
larger-scale feature to obtain a latent representation and then fuses the
latent with a smaller-scale feature. This process is successively performed
until the smallest-scale feature is fused and then the encoded latent at the
final stage is entropy-coded for transmission. The results show that our model
outperforms previous approaches by at least 52% BD-rate reduction and has
$\times5$ to $\times27$ times less encoding time for object detection...
