Cross Modal Compression With Variable Rate Prompt

Traditional image/video compression compresses the highly redundant visual data while preserving signal fidelity. Recently, cross modal compression (CMC) is proposed to compress the data into a compact, human-comprehensible domain (such as text) with an ultra-high compression ratio while preserving semantic fidelity for machine analysis and semantic monitoring. CMC is with a constant rate because the CMC encoder can only represent the data with a fixed grain. But in practice, variable rate is necessary due to the complicated and dynamic transmission conditions, the different storage mediums, and the diverse levels of application requirements. To deal with this problem, in this paper, we propose variable rate cross modal compression (VR-CMC), where we introduce variable rate prompt to represent the data with different grains. Variable rate prompt is composed of three strategies. Specifically, 1) target length prompt (TLP) introduces the target length into the language prompt to guide the generation of the text representation; 2) decaying EOS probability (DEP) exponentially decays the probability of the EOS token with regard to the decoding step and target length, where the EOS (end-of-sequence) token is a special token indicating the end of the text; 3) text augmentation (TA) enriches the training data and makes the text representation length more balanced when training. Experimental results show that our proposed VR-CMC can effectively control the rate in the CMC framework and achieve state-of-the-art performance on MSCOCO and IM2P datasets.