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arXiv 2022年

作者： Xu, Yonghao Yu, Weikang Ghamisi, Pedram Kopp, Michael Hochreiter, Sepp Vienna1030 Austria Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping58183 Sweden Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Machine Learning Group Freiberg09599 Germany ELLIS Unit Linz and LIT AI Lab Institute for Machine Learning Johannes Kepler University Linz4040 Austria

The synthesis of high-resolution remote sensing images based on text descriptions has great potential in many practical application scenarios. Although deep neural networks have achieved great success in many important remote sensing tasks, generating realistic remote sensing images from text descriptions is still very difficult. To address this challenge, we propose a novel text-to-image modern Hopfield network (Txt2Img-MHN). The main idea of Txt2Img-MHN is to conduct hierarchical prototype learning on both text and image embeddings with modern Hopfield layers. Instead of directly learning concrete but highly diverse text-image joint feature representations for different semantics, Txt2Img-MHN aims to learn the most representative prototypes from text-image embeddings, achieving a coarse-to-fine learning strategy. These learned prototypes can then be utilized to represent more complex semantics in the text-to-image generation task. To better evaluate the realism and semantic consistency of the generated images, we further conduct zero-shot classification on real remote sensing data using the classification model trained on synthesized images. Despite its simplicity, we find that the overall accuracy in the zero-shot classification may serve as a good metric to evaluate the ability to generate an image from text. Extensive experiments on the benchmark remote sensing text-image dataset demonstrate that the proposed Txt2Img-MHN can generate more realistic remote sensing images than existing methods. Code and pre-trained models are available online (https://***/YonghaoXu/Txt2Img-MHN). Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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nnDetection: A Self-configuring Method for Medical Object Detection

arXiv

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arXiv 2021年

作者： Baumgartner, Michael Jäger, Paul F. Isensee, Fabian Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Interactive Machine Learning Group German Cancer Research Center Germany HIP Applied Computer Vision Lab. German Cancer Research Center Germany Pattern Analysis and Learning Group Heidelberg University Hospital Germany

Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection. The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art. We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://***/MIC-DKFZ/nnDetection. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis

Overcoming Rare-Language Discrimination in Multi-Lingual Sen...

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IEEE International Conference on Big Data

作者： Jasmin Lampert Christoph H. Lampert Competence Unit Data Science & Artificial Intelligence AIT Austrian Institute of Technology Vienna Austria Machine Learning and Computer Vision Group Institute of Science and Technology Austria (IST Austria) Klosterneuburg Austria

ISBN: (纸本)9781665445993

The digitalization of almost all aspects of our everyday lives has led to unprecedented amounts of data being freely available on the Internet. In particular social media platforms provide rich sources of user-generated data, though typically in unstructured form, and with high diversity, such as written in many different *** identifying meaningful information in such big data resources and extracting it efficiently is one of the ongoing challenges of our time. A common step for this is sentiment analysis, which forms the foundation for tasks such as opinion mining or trend prediction. Unfortunately, publicly available tools for this task are almost exclusively available for English-language texts. Consequently, a large fraction of the Internet users, who do not communicate in English, are ignored in automatized studies, a phenomenon called rare-language *** this work we propose a technique to overcome this problem by a truly multi-lingual model, which can be trained automatically without linguistic knowledge or even the ability to read the many target languages. The main step is to combine self-annotation, specifically the use of emoticons as a proxy for labels, with multi-lingual sentence *** evaluate our method we curated several large datasets from data obtained via the free Twitter streaming API. The results show that our proposed multi-lingual training is able to achieve sentiment predictions at the same quality level for rare languages as for frequent ones, and in particular clearly better than what mono-lingual training achieves on the same data. 1

关键词： Training Sentiment analysis Social networking (online) Soft sensors Blogs Predictive models Big Data

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VICE: Variational Interpretable Concept Embeddings

arXiv

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arXiv 2022年

作者： Muttenthaler, Lukas Zheng, Charles Y. McClure, Patrick Vandermeulen, Robert A. Hebart, Martin N. Pereira, Francisco Machine Learning Group Technische Universität Berlin BIFOLD Berlin Germany Machine Learning Team FMRI Facility National Institute of Mental Health BethesdaMD United States Department of Computer Science Naval Postgraduate School MontereyCA United States Vision and Computational Cognition Group MPI for Human Cognitive and Brain Sciences Leipzig Germany The Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany The National Institute of Mental Health BethesdaMD United States

A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior. © 2022, CC BY-SA.

关键词： Embeddings

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MOT20: A benchmark for multi object tracking in crowded scenes

arXiv

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arXiv 2020年

作者： Dendorfer, Patrick Rezatofighi, Hamid Milan, Anton Shi, Javen Cremers, Daniel Reid, Ian Roth, Stefan Schindler, Konrad Leal-Taixé, Laura Dynamic Vision and Learning Group at TUM Munich Germany Australian Institute for Machine Learning School of Computer Science University of Adelaide Amazon Berlin Germany Photogrammetry and Remote Sensing Group ETH Zurich Switzerland Computer Vision Group at TUM Munich Germany Department of Computer Science Technische Universität Darmstadt Germany

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15 [5], MOT16 [8], and MOT17 [8] have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our MOT20 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4th BMTT MOT Challenge Workshop at the computer vision and Pattern Recognition Conference (CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for multiple object tracking when handling extremely crowded scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision

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GP-CONVCNP: Better generalization for convolutional conditional neural processes on time series data

arXiv

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arXiv 2021年

作者： Petersen, Jens Köhler, Gregor Zimmerer, David Isensee, Fabian Jäger, Paul F. Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIP Applied Computer Vision Lab Division of Medical Image Computing German Cancer Research Center Interactive Machine Learning Group German Cancer Research Center

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Processes (CONVCNP), have shown remarkable improvement in performance over prior art, but we find that they sometimes struggle to generalize when applied to time series data. In particular, they are not robust to distribution shifts and fail to extrapolate observed patterns into the future. By incorporating a Gaussian Process into the model, we are able to remedy this and at the same time improve performance within distribution. As an added benefit, the Gaussian Process reintroduces the possibility to sample from the model, a key feature of other members in the NP family. © 2021, CC BY.

关键词： Convolution

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Predicting the pathogenicity of protein coding mutations using Natural Language Processing

Predicting the pathogenicity of protein coding mutations usi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Naeem Rehmat Hammad Farooq Sanjay Kumar Sibt ul Hussain Hammad Naveed Computational Biology Research Lab (***) National University of Computer & Emerging Sciences Recognition Vision and Learning research group (ReVeaL) National University of Computer & Emerging Sciences

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that contribute to tumor growth from neutral ones is extremely challenging and is currently carried out manually. This manual annotation is very cumbersome and expensive in terms of time and money. In this study, we introduce a novel method "NLP-SNPPred" to read scientific literature and learn the implicit features that cause certain variations to be pathogenic. Precisely, our method ingests the bio-medical literature and produces its vector representation via exploiting state of the art NLP methods like sent2vec, word2vec and tf-idf. These representations are then fed to machine learning predictors to identify the pathogenic versus neutral variations. Our best model (NLPSNPPred) trained on OncoKB and evaluated on several publicly available benchmark datasets, outperformed state of the art function prediction methods. Our results show that NLP can be used effectively in predicting functional impact of protein coding variations with minimal complementary biological features. Moreover, encoding biological knowledge into the right representations, combined with machine learning methods can help in automating manual efforts. A free to use web-server is available at http://***.

关键词： Feature extraction Machine learning Natural language processing Training Cancer Tools Data models

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LEDNet: Deep Network for Single Image Haze Removal 2018

LEDNet: Deep Network for Single Image Haze Removal

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Proceedings of the 11th Indian Conference on computer vision, Graphics and Image Processing

作者： Akshay Dudhane Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar India Learning Affect and Semantic Image Analysis Group Indian Institute of Technology Ropar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (Tc) of the scene. Estimation of accurate Tc is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the Tc. We, on the other hand, propose integration of local and global approaches to learn both point level and object level Tc. The proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level Tc using parallel convolutional filters and spatial invariance filtering. The second stage comprises of a two level encoder-decoder architecture which anticipates the object level Tc. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal transmission map CNN

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On the Identification of Self-Adjoint Linear Time-Varying State Models

引用

IFAC-PapersOnLine 2018年第15期51卷 251-256页

作者： Rapisarda, P. Vision Learning and Control Group School of Electronics and Computer Science University of Southampton United Kingdom

A novel approach to the identification of linear time-varying (LTV) systems is illustrated, based on the concept of duality. Generically, if N input-output trajectories (uk, yk), k = 1,…,N of a self-adjoint LTV system are known, then the duality relation can be used to derive state trajectories xk, k = 1,… N corresponding to such data. Such state trajectories are computed by factorizing a matrix directly constructed from input-output data of the primal and the dual system. From such input-state-output trajectories an “unfalsified” linear time-varying model can be obtained solving a system of functional equations. © 2018

关键词： Trajectories

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Spatial and Colour Opponency in Anatomically Constrained Deep Networks

arXiv

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arXiv 2019年

作者： Harris, Ethan Mihai, Daniela Hare, Jonathon Vision Learning and Control Group Electronics and Computer Science University of Southampton

Colour vision has long fascinated scientists, who have sought to understand both the physiology of the mechanics of colour vision and the psychophysics of colour perception. We consider representations of colour in anatomically constrained convolutional deep neural networks. Following ideas from neuroscience, we classify cells in early layers into groups relating to their spectral and spatial functionality. We show the emergence of single and double opponent cells in our networks and characterise how the distribution of these cells changes under the constraint of a retinal bottleneck. Our experiments not only open up a new understanding of how deep networks process spatial and colour information, but also provide new tools to help understand the black box of deep learning. The code for all experiments is avaialable at https://***/ecs-vlc/opponency. Copyright © 2019, The Authors. All rights reserved.

关键词： Color vision

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