# SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses

arXiv: https://arxiv.org/abs/1911.02559 · HTML: https://mlsanity.com/paper/1911.02559/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 30 of 36)

- [Cross-Domain Adaptive Teacher for Object Detection](https://mlsanity.com/paper/2111.13216/) (2021)
- [CLIP the Gap: A Single Domain Generalization Approach for Object Detection](https://mlsanity.com/paper/2301.05499/) (2023)
- [Vector-Decomposed Disentanglement for Domain-Invariant Object Detection](https://mlsanity.com/paper/2108.06685/) (2021)
- [Task-specific Inconsistency Alignment for Domain Adaptive Object Detection](https://mlsanity.com/paper/2203.15345/) (2022)
- [Decoupled Adaptation for Cross-Domain Object Detection](https://mlsanity.com/paper/2110.02578/) (2021)
- [PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation](https://mlsanity.com/paper/2108.07142/) (2021)
- [Self-Adversarial Disentangling for Specific Domain Adaptation](https://mlsanity.com/paper/2108.03553/) (2021)
- [Multi-Granularity Alignment Domain Adaptation for Object Detection](https://mlsanity.com/paper/2203.16897/) (2022)
- [TeST: Test-time Self-Training under Distribution Shift](https://mlsanity.com/paper/2209.11459/) (2022)
- [Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment](https://mlsanity.com/paper/2405.14497/) (2024)
- [SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2307.01750/) (2023)
- [DA-DETR: Domain Adaptive Detection Transformer with Information Fusion](https://mlsanity.com/paper/2103.17084/) (2021)
- [Domain Adaptive YOLO for One-Stage Cross-Domain Detection](https://mlsanity.com/paper/2106.13939/) (2021)
- [Multi-Source Domain Adaptation for Object Detection](https://mlsanity.com/paper/2106.15793/) (2021)
- [SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection](https://mlsanity.com/paper/2211.02213/) (2022)
- [Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection](https://mlsanity.com/paper/2204.07964/) (2022)
- [Mean Teacher DETR with Masked Feature Alignment: A Robust Domain Adaptive Detection Transformer Framework](https://mlsanity.com/paper/2310.15646/) (2023)
- [Unsupervised Domain Adaptive Detection with Network Stability Analysis](https://mlsanity.com/paper/2308.08182/) (2023)
- [Source-free Domain Adaptive Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2401.17916/) (2024)
- [MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection](https://mlsanity.com/paper/2309.01086/) (2023)
- [Attention-based Domain Adaptation for Single Stage Detectors](https://mlsanity.com/paper/2106.07283/) (2021)
- [Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment](https://mlsanity.com/paper/2301.00371/) (2023)
- [Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2402.01304/) (2024)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond](https://mlsanity.com/paper/2303.10343/) (2023)
- [Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection](https://mlsanity.com/paper/2112.08605/) (2021)
- [Universal Domain Adaptive Object Detector](https://mlsanity.com/paper/2207.01756/) (2022)
- [Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation](https://mlsanity.com/paper/2211.13409/) (2022)
- [Learning Transformations To Reduce the Geometric Shift in Object Detection](https://mlsanity.com/paper/2301.05496/) (2023)
- [Visually Similar Pair Alignment for Robust Cross-Domain Object Detection](https://mlsanity.com/paper/2504.06607/) (2025)

## Abstract (arXiv)

Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different. In this paper, we propose a gradient detach based stacked complementary losses (SCL) method that uses detection losses as the primary objective, and cuts in several auxiliary losses in different network stages accompanying with gradient detach training to learn more discriminative representations. We argue that the prior methods mainly leverage more loss functions for training but ignore the interaction of different losses and also the compatible training strategy (gradient detach updating in our work). Thus, our proposed method is a more syncretic adaptation learning process. We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a significant margin. For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.