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An increasing share of captured images and videos are transmitted for storageand remote analysis by computer vision algorithms, rather than to be viewed byhumans. Contrary to traditional standard codecs with engineered tools, neuralnetwork based codecs can be trained end-to-end to optimally compress imageswith respect to a target rate and any given differentiable performance metric.Although it is possible to train such compression tools to achieve betterrate-accuracy performance for a particular computer vision task, it could bepractical and relevant to re-use the compressed bit-stream for multiple machinetasks. For this purpose, we introduce 'Connectors' that are inserted betweenthe decoder and the task algorithms to enable a direct transformation of thecompressed content, which was previously optimized for a specific task, tomultiple other machine tasks. We demonstrate the effectiveness of the proposedmethod by achieving significant rate-accuracy performance improvement for bothimage classification and object segmentation, using the same bit-stream,originally optimized for object detection.

End-to-end optimized image compression for multiple machine tasks