  Deep learning models, particularly those based on transformers, often employ
numerous stacked structures, which possess identical architectures and perform
similar functions. While effective, this stacking paradigm leads to a
substantial increase in the number of parameters, posing challenges for
practical applications. In today's landscape of increasingly large models,
stacking depth can even reach dozens, further exacerbating this issue. To
mitigate this problem, we introduce LORS (LOw-rank Residual Structure). LORS
allows stacked modules to share the majority of parameters, requiring a much
smaller number of unique ones per module to match or even surpass the
performance of using entirely distinct ones, thereby significantly reducing
parameter usage. We validate our method by applying it to the stacked decoders
of a query-based object detector, and conduct extensive experiments on the
widely used MS COCO dataset. Experimental results demonstrate the effectiveness
of our method, as even with a 70\% reduction in the parameters of the decoder,
our method still enables the model to achieve comparable or
