2023-04-05 更新
Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition
Authors:Bowen Peng, Jianyue Xie, Bo Peng, Li Liu
The deep neural networks (DNNs) have freed the synthetic aperture radar automatic target recognition (SAR ATR) from expertise-based feature designing and demonstrated superiority over conventional solutions. There has been shown the unique deficiency of ground vehicle benchmarks in shapes of strong background correlation results in DNNs overfitting the clutter and being non-robust to unfamiliar surroundings. However, the gap between fixed background model training and varying background application remains underexplored. Inspired by contrastive learning, this letter proposes a solution called Contrastive Feature Alignment (CFA) aiming to learn invariant representation for robust recognition. The proposed method contributes a mixed clutter variants generation strategy and a new inference branch equipped with channel-weighted mean square error (CWMSE) loss for invariant representation learning. In specific, the generation strategy is delicately designed to better attract clutter-sensitive deviation in feature space. The CWMSE loss is further devised to better contrast this deviation and align the deep features activated by the original images and corresponding clutter variants. The proposed CFA combines both classification and CWMSE losses to train the model jointly, which allows for the progressive learning of invariant target representation. Extensive evaluations on the MSTAR dataset and six DNN models prove the effectiveness of our proposal. The results demonstrated that the CFA-trained models are capable of recognizing targets among unfamiliar surroundings that are not included in the dataset, and are robust to varying signal-to-clutter ratios.
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Multi-Level Contrastive Learning for Dense Prediction Task
Authors:Qiushan Guo, Yizhou Yu, Yi Jiang, Jiannan Wu, Zehuan Yuan, Ping Luo
In this work, we present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for dense prediction tasks. Our method is motivated by the three key factors in detection: localization, scale consistency and recognition. To explicitly encode absolute position and scale information, we propose a novel pretext task that assembles multi-scale images in a montage manner to mimic multi-object scenarios. Unlike the existing image-level self-supervised methods, our method constructs a multi-level contrastive loss that considers each sub-region of the montage image as a singleton. Our method enables the neural network to learn regional semantic representations for translation and scale consistency while reducing pre-training epochs to the same as supervised pre-training. Extensive experiments demonstrate that MCL consistently outperforms the recent state-of-the-art methods on various datasets with significant margins. In particular, MCL obtains 42.5 AP$^\mathrm{bb}$ and 38.3 AP$^\mathrm{mk}$ on COCO with the 1x schedule fintuning, when using Mask R-CNN with R50-FPN backbone pre-trained with 100 epochs. In comparison to MoCo, our method surpasses their performance by 4.0 AP$^\mathrm{bb}$ and 3.1 AP$^\mathrm{mk}$. Furthermore, we explore the alignment between pretext task and downstream tasks. We extend our pretext task to supervised pre-training, which achieves a similar performance to self-supervised learning. This result demonstrates the importance of the alignment between pretext task and downstream tasks, indicating the potential for wider applicability of our method beyond self-supervised settings.
PDF Technical report
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