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33rd British machine vision Conference Proceedings, BMVC 2022

作者： Scherer, Sebastian Schön, Robin Lienhart, Rainer University of Augsburg Machine Learning and Computer Vision Lab Augsburg Germany

Semi-supervised learning (SSL) can reduce the need for large labelled datasets by incorporating unlabelled data into the training. This is particularly interesting for semantic segmentation, where labelling data is very costly and time-consuming. Current SSL approaches use an initially supervised trained model to generate predictions for unlabelled images, called pseudo-labels, which are subsequently used for training a new model from scratch. Since the predictions usually do not come from an error-free neural network, they are naturally full of errors. However, training with partially incorrect labels often reduce the final model performance. Thus, it is crucial to manage errors/noise of pseudo-labels wisely. In this work, we use three mechanisms to control pseudo-label noise and errors: (1) We construct a solid base framework by mixing images with cow-patterns on unlabelled images to reduce the negative impact of wrong pseudo-labels. Nevertheless, wrong pseudo-labels still have a negative impact on the performance. Therefore, (2) we propose a simple and effective loss weighting scheme for pseudo-labels defined by the feedback of the model trained on these pseudo-labels. This allows us to soft-weight the pseudo-label training examples based on their determined confidence score during training. (3) We also study the common practice to ignore pseudo-labels with low confidence and empirically analyse the influence and effect of pseudo-labels with different confidence ranges on SSL and the contribution of pseudo-label filtering to the achievable performance gains. We show that our method performs superior to state of-the-art alternatives on various datasets. Furthermore, we show that our findings also transfer to other tasks such as human pose estimation. Our code is available at https://***/ChristmasFan/SSL_Denoising_Segmentation. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

关键词： Semantics

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EVALUATING THE SEARCH PHASE OF NEURAL ARCHITECTURE SEARCH 8

EVALUATING THE SEARCH PHASE OF NEURAL ARCHITECTURE SEARCH

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8th International Conference on learning Representations, ICLR 2020

作者： Yu, Kaicheng Sciuto, Christian Jaggi, Martin Musat, Claudiu Salzmann, Mathieu Computer vision lab EPFL Switzerland Daskell Machine learning and optimization lab EPFL Switzerland Swisscom Digital Lab Switzerland

Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based on their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we propose to evaluate the NAS search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the state-of-the-art NAS algorithms perform similarly to the random policy;(ii) the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process. We believe that our evaluation framework will be key to designing NAS strategies that consistently discover architectures superior to random ones. © 2020 8th International Conference on learning Representations, ICLR 2020. All rights reserved.

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Image congealing via efficient feature selection

Image congealing via efficient feature selection

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2012 IEEE Workshop on the Applications of Computer vision, WACV 2012

作者： Xue, Ya Liu, Xiaoming Machine Learning Lab GE Global Research United States Computer Vision Lab GE Global Research United States

ISBN: (纸本)9781467302333

Congealing for an image ensemble is a joint alignment process to rectify images in the spatial domain such that the aligned images are as similar to each other as possible. Fruitful congealing algorithms were applied to various object classes and medical applications. However, relatively little effort has been taken in the direction of compact and effective feature representations for each image. To remedy this problem, the least-square-based congealing framework is extended by incorporating an unsupervised feature selection algorithm, which substantially removes feature redundancy and leads to a more efficient congealing with even higher accuracy. Furthermore, our novel feature selection algorithm itself is an independent contribution. It is not explicitly linked to the congealing algorithm and can be directly applied to other learning tasks. Extensive experiments are conducted for both the feature selection and congealing algorithms. © 2012 IEEE.

关键词： Medical applications

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VidHarm: A Clip Based Dataset for Harmful Content Detection 26

VidHarm: A Clip Based Dataset for Harmful Content Detection

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Edstedt, Johan Berg, Amanda Felsberg, Michael Karlsson, Johan Benavente, Francisca Novak, Anette Pihlgren, Gustav Grund Linkoping Univ Comp Vision Lab Linkoping Sweden Statens Medierad Stockholm Sweden Lulea Univ Technol EISLAB Machine Learning Lulea Sweden

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Automatically identifying harmful content in video is an important task with a wide range of applications. However, there is a lack of professionally labeled open datasets available. In this work VidHarm, an open dataset of 3589 video clips from film trailers annotated by professionals, is presented. An analysis of the dataset is performed, revealing among other things the relation between clip and trailer level annotations. Audiovisual models are trained on the dataset and an in-depth study of modeling choices conducted. The results show that performance is greatly improved by combining the visual and audio modality, pre-training on large-scale video recognition datasets, and class balanced sampling. Lastly, biases of the trained models are investigated using discrimination probing. VidHarm is openly available, and further details are available at the webpage https://***/

关键词： Visualization Annotations Pattern recognition Task analysis

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Connecting language and vision to actions 56

Connecting language and vision to actions

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56th Annual Meeting of the Association for Computational Linguistics, ACL 2018

作者： Anderson, Peter Das, Abhishek Wu, Qi Australian National University Australian Centre for Robotic Vision Australia Georgia Tech Machine Learning and Perception Lab United States University of Adelaide Australian Centre for Robotic Vision Australia

ISBN: (纸本)9781948087667

A long-term goal of AI research is to build intelligent agents that can see the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and act in a physical or embodied environment. To this end, recent advances at the intersection of language and vision have made incredible progress - from being able to generate natural language descriptions of images/videos, to answering questions about them, to even holding free-form conversations about visual content! However, while these agents can passively describe images or answer (a sequence of) questions about them, they cannot act in the world (what if I cannot answer a question from my current view, or I am asked to move or manipulate something?). Thus, the challenge now is to extend this progress in language and vision to embodied agents that take actions and actively interact with their visual environments. © 2018 Association for Computational Linguistics

关键词： Intelligent agents

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SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects 16th

SESAME: Semantic Editing of Scenes by Adding, Manipulating o...

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16th European Conference on Computer vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Kastanis, Iason Van Gool, Luc Timofte, Radu Computer Vision Lab ETH Zurich Zürich Switzerland Robotics and Machine Learning CSEM SA Alpnach Switzerland PSI ESAT KU Leuven Leuven Belgium

ISBN: (纸本)9783030585419

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Recognition of Freely Selected Keypoints on Human Limbs

arXiv

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arXiv 2022年

作者： Ludwig, Katja Kienzle, Daniel Lienhart, Rainer Machine Learning and Computer Vision Lab University of Augsburg Germany

Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually annotated and the model needs to be retrained. Our approach leverages the vision Transformer architecture to extend the capability of the model to detect arbitrary keypoints on the limbs of persons. We propose two different approaches to encode the desired keypoints. (1) Each keypoint is defined by its position along the line between the two enclosing keypoints from the fixed set and its relative distance between this line and the edge of the limb. (2) Keypoints are defined as coordinates on a norm pose. Both approaches are based on the TokenPose [12] architecture, while the keypoint tokens that correspond to the fixed keypoints are replaced with our novel module. Experiments show that our approaches achieve similar results to TokenPose on the fixed keypoints and are capable of detecting arbitrary keypoints on the limbs. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers

arXiv

引用

arXiv 2022年

作者： Einfalt, Moritz Ludwig, Katja Lienhart, Rainer Machine Learning and Computer Vision Lab University of Augsburg Germany

The state-of-the-art for monocular 3D human pose estimation in videos is dominated by the paradigm of 2D-to-3D pose uplifting. While the uplifting methods themselves are rather efficient, the true computational complexity depends on the per-frame 2D pose estimation. In this paper, we present a Transformer-based pose uplifting scheme that can operate on temporally sparse 2D pose sequences but still produce temporally dense 3D pose estimates. We show how masked token modeling can be utilized for temporal upsampling within Transformer blocks. This allows to decouple the sampling rate of input 2D poses and the target frame rate of the video and drastically decreases the total computational complexity. Additionally, we explore the option of pre-training on large motion capture archives, which has been largely neglected so far. We evaluate our method on two popular benchmark datasets: Human3.6M and MPI-INF-3DHP. With an MPJPE of 45.0 mm and 46.9 mm, respectively, our proposed method can compete with the state-of-the-art while reducing inference time by a factor of 12. This enables real-time throughput with variable consumer hardware in stationary and mobile applications. We release our code and models at https://***/goldbricklemon/uplift-upsample-3dhpe. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational complexity

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Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks

arXiv

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arXiv 2022年

作者： Ludwig, Katja Kienzle, Daniel Lorenz, Julian Lienhart, Rainer Machine Learning and Computer Vision Lab University of Augsburg Germany

Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotations are very costly. This paper proposes a method to detect arbitrary keypoints on the limbs and skis of professional ski jumpers that requires a few, only partly correct segmentation masks during training. Our model is based on the vision Transformer architecture with a special design for the input tokens to query for the desired keypoints. Since we use segmentation masks only to generate ground truth labels for the freely selectable keypoints, partly correct segmentation masks are sufficient for our training procedure. Hence, there is no need for costly hand-annotated segmentation masks. We analyze different training techniques for freely selected and standard keypoints, including pseudo labels, and show in our experiments that only a few partly correct segmentation masks are sufficient for learning to detect arbitrary keypoints on limbs and skis. Copyright © 2022, The Authors. All rights reserved.

关键词： Sports

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EARP: Integration with Entity Attribute and Relation Path for Event Knowledge Graph Representation learning

EARP: Integration with Entity Attribute and Relation Path fo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu, Ze Zhou, Hao He, Ting Wang, Huazhen Huaqiao Univ Coll Comp Sci & Technol Xiamen Peoples R China Fujian Prov Univ Key Lab Comp Vision & Machine Learning Huaqiao Univ Xiamen Peoples R China

ISBN: (纸本)9781665488679

Event knowledge graph (EKG) as a special case of knowledge graph (KG) can realize the goal of event prediction, and has been proved useful in medical diagnosis and intelligent recommendation. To successfully build an EKG, knowledge representation learning is often required to compute the semantic links of entities and relationships in a low-dimensional space and solve the data sparsity issue in knowledge acquisition, fusion and reasoning. This paper proposes a new EKG representation learning model featuring the integration of event entity attributes and relation paths. By utilizing the knowledge of entity attribute, which contains entity type and entity description, and the knowledge about relation paths, the entity initial vector is obtained by multiplying entity semantic vector, entity description representation vector and entity type representation vector, and the representation of relation path is obtained according to the relation between event pairs, a translation-based model framework is used to integrate and train all vectors to obtain the entity learning vector and the relation learning vector. our method can generate more expressive learning representations, and consequently, enhance the inference performance of EKG. Experiments on publicly available real-world EKG datasets show that our method achieves better performance than the state-of-the-art models on two typical tasks.

关键词： event knowledge graph representation learning link prediction triple classification multi-step path

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