Mixed-Precision Collaborative Quantization for Fast Object Tracking

To address the non-differentiability of quantizers and inaccurate gradient propagation in training low-bit quantized tracking models, we propose a mixed-precision collaborative quantization method for fast object tracking that combines a full-precision auxiliary module and low-bit quantization blocks through parameter sharing. Specifically, our approach constructs a partial full-precision auxiliary module that receives multiple intermediate outputs from the low-bit module, allowing the parameters of the low-bit model to combine gradient information from itself and the auxiliary module via gradient averaging. Additionally, the multi-branch feature enhancement block is utilized to extract different features from different branches, enabling diverse feature representations in the low-bit quantization tracking network. Extensive experiments are conducted to validate the effectiveness of the proposed mixed-precision collaborative quantization approach compared to existing quantization methods, demonstrating the superior performance of our quantization framework on object tracking networks.