2022-09-01 更新
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
Authors:Midhun Vayyat, Jaswin Kasi, Anuraag Bhattacharya, Shuaib Ahmed, Rahul Tallamraju
In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a student-teacher learning paradigm, that makes use of pseudo-labels generated from the target domain by the teacher network. More specifically, we extract a multi-level fused-feature map from the encoder, and apply contrastive loss across different classes and different domains, via source-target mixing of images. We consistently improve performance on various feature encoder architectures and for different domain adaptation datasets in semantic segmentation. Furthermore, we introduce a learned-weighted contrastive loss to improve upon on a state-of-the-art multi-resolution training approach in UDA. We produce state-of-the-art results on GTA $\rightarrow$ Cityscapes (74.4 mIOU, +0.6) and Synthia $\rightarrow$ Cityscapes (67.2 mIOU, +1.4) datasets. CLUDA effectively demonstrates contrastive learning in UDA as a generic method, which can be easily integrated into any existing UDA for semantic segmentation tasks. Please refer to the supplementary material for the details on implementation.
PDF Contrastive learning
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Few-shot Adaptive Object Detection with Cross-Domain CutMix
Authors:Yuzuru Nakamura, Yasunori Ishii, Yuki Maruyama, Takayoshi Yamashita
In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive. Therefore, existing large-scale datasets are used for pre-training. However, conventional transfer learning and domain adaptation cannot bridge the domain gap when the target domain differs significantly from the source domain. We propose a data synthesis method that can solve the large domain gap problem. In this method, a part of the target image is pasted onto the source image, and the position of the pasted region is aligned by utilizing the information of the object bounding box. In addition, we introduce adversarial learning to discriminate whether the original or the pasted regions. The proposed method trains on a large number of source images and a few target domain images. The proposed method achieves higher accuracy than conventional methods in a very different domain problem setting, where RGB images are the source domain, and thermal infrared images are the target domain. Similarly, the proposed method achieves higher accuracy in the cases of simulation images to real images.
PDF Yuzuru Nakamura and Yasunori Ishii are equal contribution
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Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
Authors:Jianlong Yuan, Jinchao Ge, Qi Qian, Zhibin Wang, Fan Wang, Yifan Liu
Consistency regularization has been widely studied in recent semi-supervised semantic segmentation methods. Remarkable performance has been achieved, benefiting from image, feature, and network perturbations. To make full use of these perturbations, in this work, we propose a new consistency regularization framework called mutual knowledge distillation (MKD). We innovatively introduce two auxiliary mean-teacher models based on the consistency regularization method. More specifically, we use the pseudo label generated by one mean teacher to supervise the other student network to achieve a mutual knowledge distillation between two branches. In addition to using image-level strong and weak augmentation, we also employ feature augmentation considering implicit semantic distributions to add further perturbations to the students. The proposed framework significantly increases the diversity of the training samples. Extensive experiments on public benchmarks show that our framework outperforms previous state-of-the-art(SOTA) methods under various semi-supervised settings. Code is available at: https://github.com/jianlong-yuan/semi-mmseg.
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PanorAMS: Automatic Annotation for Detecting Objects in Urban Context
Authors:Inske Groenen, Stevan Rudinac, Marcel Worring
Large collections of geo-referenced panoramic images are freely available for cities across the globe, as well as detailed maps with location and meta-data on a great variety of urban objects. They provide a potentially rich source of information on urban objects, but manual annotation for object detection is costly, laborious and difficult. Can we utilize such multimedia sources to automatically annotate street level images as an inexpensive alternative to manual labeling? With the PanorAMS framework we introduce a method to automatically generate bounding box annotations for panoramic images based on urban context information. Following this method, we acquire large-scale, albeit noisy, annotations for an urban dataset solely from open data sources in a fast and automatic manner. The dataset covers the City of Amsterdam and includes over 14 million noisy bounding box annotations of 22 object categories present in 771,299 panoramic images. For many objects further fine-grained information is available, obtained from geospatial meta-data, such as building value, function and average surface area. Such information would have been difficult, if not impossible, to acquire via manual labeling based on the image alone. For detailed evaluation, we introduce an efficient crowdsourcing protocol for bounding box annotations in panoramic images, which we deploy to acquire 147,075 ground-truth object annotations for a subset of 7,348 images, the PanorAMS-clean dataset. For our PanorAMS-noisy dataset, we provide an extensive analysis of the noise and how different types of noise affect image classification and object detection performance. We make both datasets, PanorAMS-noisy and PanorAMS-clean, benchmarks and tools presented in this paper openly available.
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Noisy Inliers Make Great Outliers: Out-of-Distribution Object Detection with Noisy Synthetic Outliers
Authors:Samuel Wilson, Tobias Fischer, Feras Dayoub, Niko Sünderhauf
Many high-performing works on out-of-distribution (OOD) detection use real or synthetically generated outlier data to regularise model confidence; however, they often require retraining of the base network or specialised model architectures. Our work demonstrates that Noisy Inliers Make Great Outliers (NIMGO) in the challenging field of OOD object detection. We hypothesise that synthetic outliers need only be minimally perturbed variants of the in-distribution (ID) data in order to train a discriminator to identify OOD samples — without expensive retraining of the base network. To test our hypothesis, we generate a synthetic outlier set by applying an additive-noise perturbation to ID samples at the image or bounding-box level. An auxiliary feature monitoring multilayer perceptron (MLP) is then trained to detect OOD feature representations using the perturbed ID samples as a proxy. During testing, we demonstrate that the auxiliary MLP distinguishes ID samples from OOD samples at a state-of-the-art level, reducing the false positive rate by more than 20\% (absolute) over the previous state-of-the-art on the OpenImages dataset. Extensive additional ablations provide empirical evidence in support of our hypothesis.
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Dynamic boxes fusion strategy in object detection
Authors:Zhijiang Wan, Shichang Liu, Manyu Li
Object detection on microscopic scenarios is a popular task. As microscopes always have variable magnifications, the object can vary substantially in scale, which burdens the optimization of detectors. Moreover, different situations of camera focusing bring in the blurry images, which leads to great challenge of distinguishing the boundaries between objects and background. To solve the two issues mentioned above, we provide bags of useful training strategies and extensive experiments on Chula-ParasiteEgg-11 dataset, bring non-negligible results on ICIP 2022 Challenge: Parasitic Egg Detection and Classification in Microscopic Images, further more, we propose a new box selection strategy and an improved boxes fusion method for multi-model ensemble, as a result our method wins 1st place(mIoU 95.28%, mF1Score 99.62%), which is also the state-of-the-art method on Chula-ParasiteEgg-11 dataset.
PDF Inconsistent data
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AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection
Authors:Maximilian Menke, Thomas Wenzel, Andreas Schwung
Object detection networks have reached an impressive performance level, yet a lack of suitable data in specific applications often limits it in practice. Typically, additional data sources are utilized to support the training task. In these, however, domain gaps between different data sources pose a challenge in deep learning. GAN-based image-to-image style-transfer is commonly applied to shrink the domain gap, but is unstable and decoupled from the object detection task. We propose AWADA, an Attention-Weighted Adversarial Domain Adaptation framework for creating a feedback loop between style-transformation and detection task. By constructing foreground object attention maps from object detector proposals, we focus the transformation on foreground object regions and stabilize style-transfer training. In extensive experiments and ablation studies, we show that AWADA reaches state-of-the-art unsupervised domain adaptation object detection performance in the commonly used benchmarks for tasks such as synthetic-to-real, adverse weather and cross-camera adaptation.
PDF 10 Pages, 4 Figures
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Effective Image Tampering Localization via Semantic Segmentation Network
Authors:Haochen Zhu, Gang Cao, Mo Zhao
With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low accuracy and robustness. Note that the tampered regions are typically semantic objects, in this letter we propose an effective image tampering localization scheme based on deep semantic segmentation network. ConvNeXt network is used as an encoder to learn better feature representation. The multi-scale features are then fused by Upernet decoder for achieving better locating capability. Combined loss and effective data augmentation are adopted to ensure effective model training. Extensive experimental results confirm that localization performance of our proposed scheme outperforms other state-of-the-art ones.
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