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VICE: Variational Interpretable Concept Embeddings

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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

Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis

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

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

nnDetection: A Self-configuring Method for Medical Object Detection

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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

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

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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

MOT20: A benchmark for multi object tracking in crowded scenes

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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

Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

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Efficient Deep Models for Real-Time 4K Image Super-Resolutio...

2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Conde, Marcos V. Zamfir, Eduard Timofte, Radu Motilla, Daniel Liu, Cen Zhang, Zexin Peng, Yunbo Lin, Yue Guo, Jiaming Zou, Xueyi Chen, Yuyi Liu, Yi Hao, Jia Yan, Youliang Zhang, Yuanfan Li, Gen Sun, Lei Kong, Lingshun Bai, Haoran Pan, Jinshan Dong, Jiangxin Tang, Jinhui Ayazoglu, Mustafa Bilecen, Bahri Batuhan Li, Mingxi Zhang, Yuhang Fan, Xianjun Sheng, Yankai Sun, Long Liu, Zibin Gou, Weiran Li, Shaoqing Yi, Ziyao Xiang, Yan Kong, Dehui Xu, Ke Gankhuyag, Ganzorig Yoon, Kihwan Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Zhou, Zhou Chao, Jiahao Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Chen, Chengpeng Guo, Zichao Park, Anjin Liu, Yuqing Jia, Qi Yu, Hongyuan Yin, Xuanwu Zuo, Kunlong Zhang, Dongyang Fu, Ting Cheng, Zhengxue Zhu, Shiai Zhou, Dajiang Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Shao, Ben Zheng, Shaolong Yin, Daheng Chen, Baijun Liu, Mengyang Nistor, Marian-Sergiu Chen, Yi-Chung Huang, Zhi-Kai Chiang, Yuan-Chun Chen, Wei-Ting Yang, Hao-Hsiang Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Vo, Tu Yan, Qingsen Zhu, Yun Su, Jinqiu Zhang, Yanning Zhang, Cheng Luo, Jiaying Cho, Youngsun Lee, Nakyung Computer Vision Lab CAIDAS IFI University of Würzburg Germany Sony Interactive Entertainment CA United States Huawei Technologies Co. Ltd. China NetEase Games AI Lab Nanjing University of Science and Technology China Tencent China Attrsense Korea Republic of Sanechips Co Ltd Ant Group China East China Normal University China Shopee Dalian University of Technology Xiaomi Inc. China China Zhejiang Dahua Technology Co. Ltd. China Multimedia Department Xiaomi Inc. China Korea Photonic Technology Institute Korea Republic of School of Computer Science and Engineering Southeast University China University Al. I. Cuza Iasi Romania Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Northwestern Polytechnical University China KC Machine Learning Lab CJ OliveNetworks AI Research

ISBN: (纸本)9798350302493

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR. © 2023 IEEE.

关键词： Program processors

ScanMix: learning from Severe Label Noise via Semantic Clustering and Semi-Supervised learning

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arXiv 2021年

作者： Sachdeva, Ragav Cordeiro, Filipe Rolim Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom School of Computer Science Australian Institute for Machine Learning Australia Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitive robustness to non-severe label noise problems, in comparison to the state of the art (SOTA) methods. ScanMix is based on the expectation maximisation framework, where the E-step estimates the latent variable to cluster the training images based on their appearance and classification results, and the M-step optimises the SSL classification and learns effective feature representations via semantic clustering. We present a theoretical result that shows the correctness and convergence of ScanMix, and an empirical result that shows that ScanMix has SOTA results on CIFAR-10/-100 (with symmetric, asymmetric and semantic label noise), Red Mini-ImageNet (from the Controlled Noisy Web Labels), Clothing1M and Webvision. In all benchmarks with severe label noise, our results are competitive to the current SOTA. © 2021, CC BY.

关键词： Semantics

LongReMix: Robust learning with High Confidence Samples in a Noisy Label Environment

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arXiv 2021年

作者： Cordeiro, Filipe R. Sachdeva, Ragav Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo School of Computer Science Australian Institute for Machine Learning Australia Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

State-of-the-art noisy-label learning algorithms rely on an unsupervised learning to classify training samples as clean or noisy, followed by a semi-supervised learning (SSL) that minimises the empirical vicinal risk using a labelled set formed by samples classified as clean, and an unlabelled set with samples classified as noisy. The classification accuracy of such noisy-label learning methods depends on the precision of the unsupervised classification of clean and noisy samples, and the robustness of SSL to small clean sets. We address these points with a new noisy-label training algorithm, called LongReMix, which improves the precision of the unsupervised classification of clean and noisy samples and the robustness of SSL to small clean sets with a two-stage learning process. The stage one of LongReMix finds a small but precise high-confidence clean set, and stage two augments this high-confidence clean set with new clean samples and oversamples the clean data to increase the robustness of SSL to small clean sets. We test LongReMix on CIFAR-10 and CIFAR-100 with introduced synthetic noisy labels, and the real-world noisy-label benchmarks CNWL (Red Mini-ImageNet), Webvision, Clothing1M, and Food101-N. The results show that our LongReMix produces significantly better classification accuracy than competing approaches, particularly in high noise rate problems. Furthermore, our approach achieves state-of-the-art performance in most datasets. The code is available at https://***/filipe-research/LongReMix. © 2021, CC BY.

关键词： Deep learning

CVPR19 Tracking and Detection Challenge: How crowded can it get?

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arXiv 2019年

作者： Dendorfer, Patrick Rezatofighi, Hamid Milan, Anton Shi, Javen Cremers, Daniel Reid, Ian Roth, Stefan Schindler, Konrad Leal-Taixa, Laura Dynamic Vision and Learning Group at Tum Munich Germany Australian Institute for Machine Learning and School of Computer Science at University of Adelaide. Amazon Berlin Germany Photogrammetry and Remote Sensing Group at 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 CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the computer vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： computer vision