Multi-Adversarial Faster-RCNN for Unrestricted Object Detection

By Zhenwei He, Lei Zhang. IEEE International Conference on Computer Vision, 2019

356 citations recorded by the source corpus.

Conventional object detection methods essentially suppose that the training and testing data are collected from a restricted target domain with expensive labeling cost. For alleviating the problem of domain dependency and cumbersome labeling, this paper proposes to detect objects in unrestricted environment by leveraging domain knowledge trained from an auxiliary source domain with sufficient labels. Specifically, we propose a multi-adversarial Faster-RCNN (MAF) framework for unrestricted object detection, which inherently addresses domain disparity minimization for domain adaptation in feature representation. The paper merits are in three-fold: 1) With the idea that object detectors often becomes domain incompatible when image distribution resulted domain disparity appears, we propose a hierarchical domain feature alignment module, in which multiple adversarial domain classifier submodules for layer-wise domain feature confusion are designed; 2) An information invariant scale reduction module (SRM) for hierarchical feature map resizing is proposed for promoting the training efficiency of adversarial domain adaptation; 3) In order to improve the domain adaptability, the aggregated proposal features with detection results are feed into a proposed weighted gradient reversal layer (WGRL) for characterizing hard confused domain samples. We evaluate our MAF on unrestricted tasks including Cityscapes, KITTI, Sim10k, etc. and the experiments show the state-of-the-art performance over the existing detectors.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f401f5067c35ae034cc6aedb9bbc876ebb5b570a

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Citations in the corpus

Spatial Attention Pyramid Network for Unsupervised Domain Adaptation (2020, neutral): “6 MAF [14] 28.”
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation (2020, methodology): “To address DA in object detection, [126, 129] applied multi-level domain alignment with adversarial training, and Chen et al.”
Cross-Domain Adaptive Teacher for Object Detection (2021, methodology): “Researchers have proposed various methods, such as domain classifier and adversarial learning [10], to address the task of cross-domain adaptation in object detection [2, 3, 14, 32, 39, 42, 44].”
Harmonizing Transferability and Discriminability for Adapting Object Detectors (2020, methodology): “Methods L G LFM CI AP on car Source Only [42] ✗ ✗ ✗ ✗ 34.6 DA-Faster [7] ✓ ✓ ✗ ✗ 38.9 SWDA [44] ✓ ✓ ✗ ✗ 40.1 MAF [18] ✓ ✓ ✗ ✗ 41.1 HTCN ✓ ✓ ✓ ✓ 42.5 Influence of IOU threshold.”
Exploring Categorical Regularization for Domain Adaptive Object Detection (2020, methodology): “Thanks to the flexibility of Faster R-CNN, recently, it is widely adapted for domain adaptive object detection [1, 28, 41, 12] with adversarial training [5].”
Unbiased Mean Teacher for Cross-domain Object Detection (2020, methodology): “Besides the baselines compared previously, we further include DA-Faster [6], PF [40], SCDA [56], MAF [17], WD [52], MTOR [1], iFAN [57] and CDN [44] for comparison.”