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COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation

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COMET: Contrastive Mean Teacher for Online Source-Free Unive...

International Joint Conference on Neural Networks (IJCNN)

作者： Pascal Schlachter Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In real-world applications, there is often a domain shift (distribution change) from training to test data. This observation recently resulted in the development of test-time adaptation (TTA). It aims to adapt a pre-trained source model to the test data without requiring access to the source data. Thereby, most existing works are so far limited to the closed-set assumption, i.e. there is no category shift (class change) between source and target domain. We argue that in a realistic open-world setting a category shift can appear in addition to a domain shift. This means, individual source classes may not appear in the target domain anymore, samples of new unknown classes may be part of the target domain or even both at the same time. Moreover, in many real-world scenarios the test data is not accessible all at once in form of a dataset but arrives sequentially as a stream of batches which require an immediate prediction. Hence, TTA must be applied in an online manner. To the best of our knowledge, the combination of these aspects, i.e. online source-free universal domain adaptation (online SF-UniDA), has not been studied yet despite its practical relevance. In this paper, we are the first ones to tackle this challenging task. We introduce a Contrastive Mean Teacher (COMET) tailored to this novel scenario. It applies a contrastive loss to rebuild a feature space where the samples of known classes build distinct clusters and the samples of new classes separate well from them. It is complemented by an entropy loss which ensures that the classifier output has a small entropy for samples of known classes and a large entropy for samples of new classes to be easily detected and rejected as unknown. To provide the losses with reliable pseudo labels, they are embedded into a mean teacher (MT) framework. We evaluate our method across two datasets and all category shifts to set an initial benchmark for online SF-UniDA. Thereby, COMET yields state-of-the-art performance and prov

关键词： Comets Training Adaptation models Codes Neural networks Benchmark testing Entropy

COMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation

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arXiv 2024年

作者： Schlachter, Pascal Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Germany

关键词： Statistical tests

Indoor Positioning Based on Active Radar Sensing and Passive Reflectors: Reflector Placement Optimization 13

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Indoor Positioning Based on Active Radar Sensing and Passive...

13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Hinderer, Sven Schlachter, Pascal Yuy, Zhibin Wuy, Xiaofeng Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany Huawei Technologies Duesseldorf GmbH Munich Research Center Munich Germany

ISBN: (纸本)9798350320114

We extend our work on a novel indoor positioning system (IPS) for autonomous mobile robots (AMRs) based on radar sensing of local, passive radar reflectors. Through the combination of simple reflectors and a single-channel frequency modulated continuous wave (FMCW) radar, high positioning accuracy at low system cost can be achieved. Further, a multi-objective (MO) particle swarm optimization (PSO) algorithm is presented that optimizes the 2D placement of radar reflectors in complex room settings. © 2023 IEEE.

关键词： autonomous mobile robot indoor positioning system particle swarm optimization passive reflector radar

Introducing Intermediate Domains for Effective Self-Training during Test-Time

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Introducing Intermediate Domains for Effective Self-Training...

International Joint Conference on Neural Networks (IJCNN)

作者： Robert A. Marsden Mario Döbler Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Experiencing domain shifts during test-time is nearly inevitable in practice and likely results in a severe performance degradation. To overcome this issue, test-time adaptation continues to update the initial source model after deployment. A promising direction are methods based on self-training which have been shown to be well suited for gradual domain adaptation, since reliable pseudo-labels can be provided. In this work, we address two problems that exist when applying self-training in the setting of test-time adaptation. First, adapting a model to long test sequences that contain multiple domains can lead to error accumulation. Second, naturally, not all shifts are gradual in practice. To tackle these challenges, we introduce GTTA. By creating artificial intermediate domains that divide the current domain shift into a more gradual one, effective self-training through high quality pseudo-labels can be performed. To create the intermediate domains, we propose two independent variations: mixup and light-weight style transfer. We demonstrate the effectiveness of our approach on the continual and gradual corruption benchmarks, as well as ImageNet-R. To further investigate gradual shifts in the context of urban scene segmentation, we publish a benchmark: CarlaTTA. It enables the exploration of several non-stationary domain shifts. 1

关键词： Degradation Adaptation models Image segmentation Neural networks Benchmark testing Reliability

A SEMI-PAIRED APPROACH FOR LABEL-TO-IMAGE TRANSLATION

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arXiv 2023年

作者： Eskandar, George Zhang, Shuai Abdelsamad, Mohamed Youssef, Mark Guo, Diandian Yang, Bin University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany

Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition tasks alleviating the need for large amounts of labeled data, yet it remains understudied in image-to-image translation (I2I) tasks. In this work, we introduce the first semi-supervised (semi-paired) framework for label-to-image translation, a challenging subtask of I2I which generates photorealistic images from semantic label maps. In the semi-paired setting, the model has access to a small set of paired data and a larger set of unpaired images and labels. Instead of using geometrical transformations as a pretext task like previous works, we leverage an input reconstruction task by exploiting the conditional discriminator on the paired data as a reverse generator. We propose a training algorithm for this shared network, and we present a rare classes sampling algorithm to focus on under-represented classes. Experiments on 3 standard benchmarks show that the proposed model outperforms state-of-the-art unsupervised and semi-supervised approaches, as well as some fully supervised approaches while using a much smaller number of paired samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

A Semi-Paired Approach for Label-to-Image Translation

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A Semi-Paired Approach for Label-to-Image Translation

IEEE International Conference on Image processing

作者： George Eskandar Shuai Zhang Mohamed Abdelsamad Mark Youssef Diandian Guo Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany

关键词：

WAVELET-BASED UNSUPERVISED LABEL-TO-IMAGE TRANSLATION

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arXiv 2023年

作者： Eskandar, George Abdelsamad, Mohamed Armanious, Karim Zhang, Shuai Yang, Bin University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany

Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a semantic layout is used to generate a photorealistic image. State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge amount of paired data to accomplish this task while generic unpaired image-to-image translation frameworks underperform in comparison, because they color-code semantic layouts and learn correspondences in appearance instead of semantic content. Starting from the assumption that a high quality generated image should be segmented back to its semantic layout, we propose a new Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised segmentation loss and whole image wavelet based discrimination. Furthermore, in order to match the high-frequency distribution of real images, a novel generator architecture in the wavelet domain is proposed. We test our methodology on 3 challenging datasets and demonstrate its ability to bridge the performance gap between paired and unpaired models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

SVS-GAN: Leveraging GANs for Semantic Video Synthesis

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arXiv 2024年

作者： Seyam, Khaled M. Wiederer, Julian Braun, Markus Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Germany Mercedes-Benz AG Stuttgart Germany

In recent years, there has been a growing interest in Semantic Image Synthesis (SIS) through the use of Generative Adversarial Networks (GANs) and diffusion models. This field has seen innovations such as the implementation of specialized loss functions tailored for this task, diverging from the more general approaches in Image-to-Image (I2I) translation. While the concept of Semantic Video Synthesis (SVS)-the generation of temporally coherent, realistic sequences of images from semantic maps-is newly formalized in this paper, some existing methods have already explored aspects of this field. Most of these approaches rely on generic loss functions designed for video-to-video translation or require additional data to achieve temporal coherence. In this paper, we introduce the SVS-GAN, a framework specifically designed for SVS, featuring a custom architecture and loss functions. Our approach includes a triple-pyramid generator that utilizes SPADE blocks. Additionally, we employ a U-Net-based network for the image discriminator, which performs semantic segmentation for the OASIS loss. Through this combination of tailored architecture and objective engineering, our framework aims to bridge the existing gap between SIS and SVS, outperforming current state-of-the-art models on datasets like Cityscapes and KITTI-360. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Automatic Localization of an Ultrasound Probe with the Help of Magnetic Sensors

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Current Directions in Biomedical Engineering 2022年第2期8卷 317-320页

作者： Bald, Christin Bergholz, Robert Schmidt, Gerhard Digital Signal Processing and System Theory Institute of Electrical Engineering and Information Technology Faculty of Engineering Christian-Albrechts-Universität zu Kiel Kaiserstraße 2 Kiel Germany Digital Signal Processing and System Theory Institute of Electrical Engineering and Information Technology Faculty of Engineering Christian-Albrechts-Universität zu Kiel Kiel Germany Pediatric Surgery Faculty of Medicine Christian-Albrechts-Universität zu Kiel Kiel Germany

Ultrasound measurements are a widely used instrument in clinical practice. For later traceability of the images, the position (and orientation) of the ultrasound probe must be recorded during the measurement. Until now this has to be done manually by the physician. An easier and more accurate approach would be the automatic tracking of the ultrasound probe. This contribution shows a first approach for automatically localizing the ultrasonic head during measurement. The proposed method is based on coils surrounding the patient bed and a 3D magnetic sensor placed on the ultrasound head. Besides some pre- and postprocessing steps, the proposed localization algorithm is based on trilateration followed by a least mean squares approach for refinement of the estimation. In a first proof-of-concept measurement with fixed positions and orientations of the ultrasound head a mean accuracy of 2.85 cm and 8.94 was achieved. Additionally, a measurement with a moving ultrasound head is presented to demonstrate the real-time capability of the system. Finally, future steps for improving the automatic measurement are discussed, including a graphical user interface for the physician and the use of magnetoelectric sensors for measurement. © 2022 The Author(s), published by De Gruyter.

关键词： Ultrasonics