  Learning-based image compression was shown to achieve a competitive
performance with state-of-the-art transform-based codecs. This motivated the
development of new learning-based visual compression standards such as JPEG-AI.
Of particular interest to these emerging standards is the development of
learning-based image compression systems targeting both humans and machines.
This paper is concerned with learning-based compression schemes whose
compressed-domain representations can be utilized to perform visual processing
and computer vision tasks directly in the compressed domain. In our work, we
adopt a learning-based compressed-domain classification framework for
performing visual recognition using the compressed-domain latent representation
at varying bit-rates. We propose a novel feature adaptation module integrating
a lightweight attention model to adaptively emphasize and enhance the key
features within the extracted channel-wise information. Also, we design an
adaptation training strategy to utilize the pretrained pixel-domain weights.
For comparison, in addition to the performance results that are obtained using
our proposed latent-based compressed-domain method, we also present performance
results using compressed but fully decoded images in the pixel domain as well
as original uncompressed images. The obtained performance results show that our
proposed compressed-domain classification model can distinctly outperform the
existing compressed-domain classification models, and that it can also yield
similar accuracy results with a much higher computational efficiency as
compared to the pixel-domain models that are trained using fully decoded
images.
