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

翻译：摘要：深度学习模型，尤其是基于Transformer的模型，常采用大量具有相同架构和相似功能的堆叠结构。尽管这种堆叠范式效果显著，但会导致参数数量大幅增加，给实际应用带来挑战。在当前模型规模日趋庞大的背景下，堆叠深度甚至可达数十层，进一步加剧了这一问题。为缓解该难题，我们提出LORS（低秩残差结构）。LORS允许堆叠模块共享大部分参数，每个模块仅需极少量独特参数即可达到甚至超越完全使用独立参数的性能，从而显著减少参数用量。我们通过将所提方法应用于基于查询的目标检测器的堆叠解码器进行验证，并在广泛使用的MS COCO数据集上开展大量实验。实验结果表明了该方法的有效性：即使将解码器参数减少70%，模型仍能获得与之相当或更优的性能。