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This paper introduces Arbitrary Ratio Feature Compression (ARFC), a novel framework for compressing features to arbitrary ratios using a single model based on next-token prediction. The core of ARFC is an auto-regressive model that controls the compression ratio by adjusting the number of generated tokens during inference. To improve compressed feature quality, the framework incorporates a Mixture of Solutions (MoS) module and an Entity Relation Graph Constraint (ERGC) during training, resulting in state-of-the-art performance across various tasks and compression ratios.
Forget retraining for every compression ratio: this auto-regressive feature compressor lets you dial in any ratio you want on the fly.
Feature compression is increasingly important for improving the efficiency of downstream tasks, especially in applications involving large-scale or multi-modal data. While existing methods typically rely on dedicated models for achieving specific compression ratios, they are often limited in flexibility and generalization. In particular, retraining is necessary when adapting to a new compression ratio. To address this limitation, we propose a novel and flexible Arbitrary Ratio Feature Compression (ARFC) framework, which supports any compression ratio with a single model, eliminating the need for multiple specialized models. At its core, the Arbitrary Ratio Compressor (ARC) is an auto-regressive model that performs compression via next-token prediction. This allows the compression ratio to be controlled at inference simply by adjusting the number of generated tokens. To enhance the quality of the compressed features, two key modules are introduced. The Mixture of Solutions (MoS) module refines the compressed tokens by utilizing multiple compression results (solutions), reducing uncertainty and improving robustness. The Entity Relation Graph Constraint (ERGC) is integrated into the training process to preserve semantic and structural relationships during compression. Extensive experiments on cross-modal retrieval, image classification, and image retrieval tasks across multiple datasets demonstrate that our method consistently outperforms existing approaches at various compression ratios. Notably, in some cases, it even surpasses the performance of the original, uncompressed features. These results validate the effectiveness and versatility of ARFC for practical, resource-constrained scenarios.
