# CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning

arXiv: https://arxiv.org/abs/2402.12736 · HTML: https://mlsanity.com/paper/2402.12736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [Parameter-Efficient Transfer Learning with Diff Pruning](https://mlsanity.com/paper/2012.07463/) (2020)
- [VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks](https://mlsanity.com/paper/2112.06825/) (2021)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning](https://mlsanity.com/paper/2206.06522/) (2022)
- [Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation](https://mlsanity.com/paper/1803.11365/) (2018)
- [Multi-Scale Positive Sample Refinement for Few-Shot Object Detection](https://mlsanity.com/paper/2007.09384/) (2020)
- [Label, Verify, Correct: A Simple Few Shot Object Detection Method](https://mlsanity.com/paper/2112.05749/) (2021)
- [Confidence-Aware Multi-Teacher Knowledge Distillation](https://mlsanity.com/paper/2201.00007/) (2021)
- [GAIA: A Transfer Learning System of Object Detection that Fits Your Needs](https://mlsanity.com/paper/2106.11346/) (2021)
- [Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation](https://mlsanity.com/paper/1708.06799/) (2017)

## Built on by (most cited first, 1 of 1)

- [Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets](https://mlsanity.com/paper/2512.14237/) (2025)

## Abstract (arXiv)

Achieving a universally high accuracy in object detection is quite challenging, and the mainstream focus in the industry currently lies on detecting specific classes of objects. However, deploying one or multiple object detection networks requires a certain amount of GPU memory for training and storage capacity for inference. This presents challenges in terms of how to effectively coordinate multiple object detection tasks under resource-constrained conditions. This paper introduces a lightweight fine-tuning strategy called Calibration side tuning, which integrates aspects of adapter tuning and side tuning to adapt the successful techniques employed in transformers for use with ResNet. The Calibration side tuning architecture that incorporates maximal transition calibration, utilizing a small number of additional parameters to enhance network performance while maintaining a smooth training process. Furthermore, this paper has conducted an analysis on multiple fine-tuning strategies and have implemented their application within ResNet, thereby expanding the research on fine-tuning strategies for object detection networks. Besides, this paper carried out extensive experiments using five benchmark datasets. The experimental results demonstrated that this method outperforms other compared state-of-the-art techniques, and a better balance between the complexity and performance of the finetune schemes is achieved.