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9498585d-85b6-4851-b936-8de8e7d1b293 | gmss-graph-based-multi-task-self-supervised | 2205.01030 | null | https://arxiv.org/abs/2205.01030v1 | https://arxiv.org/pdf/2205.01030v1.pdf | GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition | Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks. By learning from multiple tasks simultaneously, GMSS can find a representation that captures all of the tasks thereby decreasing the chance of overfitting on the original task, i.e., emotion recognition task. In particular, the spatial jigsaw puzzle task aims to capture the intrinsic spatial relationships of different brain regions. Considering the importance of frequency information in EEG emotional signals, the goal of the frequency jigsaw puzzle task is to explore the crucial frequency bands for EEG emotion recognition. To further regularize the learned features and encourage the network to learn inherent representations, contrastive learning task is adopted in this work by mapping the transformed data into a common feature space. The performance of the proposed GMSS is compared with several popular unsupervised and supervised methods. Experiments on SEED, SEED-IV, and MPED datasets show that the proposed model has remarkable advantages in learning more discriminative and general features for EEG emotional signals. | ['Wenming Zheng', 'Guangming Shi', 'Yi Niu', 'Yijin Zhou', 'Youshuo Ji', 'Hao Wu', 'Boxun Fu', 'Fu Li', 'Ji Chen', 'Yang Li'] | 2022-04-12 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [ 1.38046890e-01 -1.75952017e-01 1.33311570e-01 -4.83309925e-01
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341c8586-bc39-4d6e-b5b8-5d10246c81a8 | semantic-white-balance-semantic-color | 1802.00153 | null | https://arxiv.org/abs/1802.00153v5 | https://arxiv.org/pdf/1802.00153v5.pdf | Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network | The goal of computational color constancy is to preserve the perceptive colors of objects under different lighting conditions by removing the effect of color casts caused by the scene's illumination. With the rapid development of deep learning based techniques, significant progress has been made in image semantic segmentation. In this work, we exploit the semantic information together with the color and spatial information of the input image in order to remove color casts. We train a convolutional neural network (CNN) model that learns to estimate the illuminant color and gamma correction parameters based on the semantic information of the given image. Experimental results show that feeding the CNN with the semantic information leads to a significant improvement in the results by reducing the error by more than 40%. | ['Mahmoud Afifi'] | 2018-02-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 3.21564764e-01 -4.57786471e-01 3.36507231e-01 -5.31256318e-01
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dec4e46f-41ef-48a7-bab9-bba2cfda254a | transsleep-transitioning-aware-attention | 2203.12590 | null | https://arxiv.org/abs/2203.12590v1 | https://arxiv.org/pdf/2203.12590v1.pdf | TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging | Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine learning as well as deep learning architectures for sleep staging. However, two key challenges hinder the practical use of these architectures: effectively capturing salient waveforms in sleep signals and correctly classifying confusing stages in transitioning epochs. In this study, we propose a novel deep neural network structure, TransSleep, that captures distinctive local temporal patterns and distinguishes confusing stages using two auxiliary tasks. In particular, TransSleep adopts an attention-based multi-scale feature extractor module to capture salient waveforms; a stage-confusion estimator module with a novel auxiliary task, epoch-level stage classification, to estimate confidence scores for identifying confusing stages; and a context encoder module with the other novel auxiliary task, stage-transition detection, to represent contextual relationships across neighboring epochs. Results show that TransSleep achieves promising performance in automatic sleep staging. The validity of TransSleep is demonstrated by its state-of-the-art performance on two publicly available datasets, Sleep-EDF and MASS. Furthermore, we performed ablations to analyze our results from different perspectives. Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep learning-based sleep staging. | ['Heung-Il Suk', 'Eunjin Jeon', 'Wonjun Ko', 'Jauen Phyo'] | 2022-03-22 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 4.05102670e-02 -4.08836305e-01 -3.26638699e-01 -6.15212083e-01
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ad504a4d-607c-493d-99f3-9a605b469f1a | decentralized-vehicle-coordination-the | 2209.08763 | null | https://arxiv.org/abs/2209.08763v2 | https://arxiv.org/pdf/2209.08763v2.pdf | Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset | Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersecting paths in absence of a central coordinator. We learn from common sense that, for a vehicle to navigate through such understructured environments, the driver must understand and conform to the implicit "social etiquette" observed by nearby drivers. To study this implicit driving protocol, we collect the Berkeley DeepDrive Drone dataset. The dataset contains 1) a set of aerial videos recording understructured driving, 2) a collection of images and annotations to train vehicle detection models, and 3) a kit of development scripts for illustrating typical usages. We believe that the dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and, of secondary interest, for computer vision in remote sensing settings. | ['Alexandre Bayen', 'Trevor Darrell', 'Christopher Chou', 'Jiamu Zhang', 'Jiawei Lu', 'Chenhui Hao', 'Minjune Hwang', 'Dequan Wang', 'Fangyu Wu'] | 2022-09-19 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-3.07103200e-03 3.90488803e-01 8.68140012e-02 -5.05128264e-01
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357fa228-4150-4894-9fb4-bc056a414dd0 | benchmarking-robustness-in-neural-radiance | 2301.04075 | null | https://arxiv.org/abs/2301.04075v1 | https://arxiv.org/pdf/2301.04075v1.pdf | Benchmarking Robustness in Neural Radiance Fields | Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, current approaches to NeRF-based models rely on clean images with accurate camera calibration, which can be difficult to obtain in the real world, where data is often subject to corruption and distortion. In this work, we provide the first comprehensive analysis of the robustness of NeRF-based novel view synthesis algorithms in the presence of different types of corruptions. We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models. Furthermore, we analyze the robustness of the feature encoder in generalizable methods, which synthesize images using neural features extracted via convolutional neural networks or transformers, and find that it only contributes marginally to robustness. Finally, we reveal that standard data augmentation techniques, which can significantly improve the robustness of recognition models, do not help the robustness of NeRF-based models. We hope that our findings will attract more researchers to study the robustness of NeRF-based approaches and help to improve their performance in the real world. | ['Cihang Xie', 'Alan Yuille', 'Junbo Li', 'Angtian Wang', 'Chen Wang'] | 2023-01-10 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 2.66499698e-01 -3.06384176e-01 2.50099391e-01 -2.48714030e-01
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4d4122fd-cc76-40f1-900b-898b2fa3e8f2 | hybrids-of-constraint-based-and-noise-based | 2306.08765 | null | https://arxiv.org/abs/2306.08765v1 | https://arxiv.org/pdf/2306.08765v1.pdf | Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series | Constraint-based and noise-based methods have been proposed to discover summary causal graphs from observational time series under strong assumptions which can be violated or impossible to verify in real applications. Recently, a hybrid method (Assaad et al, 2021) that combines these two approaches, proved to be robust to assumption violation. However, this method assumes that the summary causal graph is acyclic, but cycles are common in many applications. For example, in ecological communities, there may be cyclic relationships between predator and prey populations, creating feedback loops. Therefore, this paper presents two new frameworks for hybrids of constraint-based and noise-based methods that can discover summary causal graphs that may or may not contain cycles. For each framework, we provide two hybrid algorithms which are experimentally tested on simulated data, realistic ecological data, and real data from various applications. Experiments show that our hybrid approaches are robust and yield good results over most datasets. | ['Wilfried Thuiller', 'Eric Gaussier', 'Emilie Devijver', 'Julyan Arbel', 'Daria Bystrova', 'Charles K. Assaad'] | 2023-06-14 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.08656862e-01 -2.59766310e-01 -1.42071381e-01 1.52769417e-01
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11d446ac-fcc5-4513-b87e-1b3620f790b4 | etpnav-evolving-topological-planning-for | 2304.03047 | null | https://arxiv.org/abs/2304.03047v2 | https://arxiv.org/pdf/2304.03047v2.pdf | ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments | Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. It becomes increasingly crucial in the field of embodied AI, with potential applications in autonomous navigation, search and rescue, and human-robot interaction. In this paper, we propose to address a more practical yet challenging counterpart setting - vision-language navigation in continuous environments (VLN-CE). To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav performs online topological mapping of environments by self-organizing predicted waypoints along a traversed path, without prior environmental experience. It privileges the agent to break down the navigation procedure into high-level planning and low-level control. Concurrently, ETPNav utilizes a transformer-based cross-modal planner to generate navigation plans based on topological maps and instructions. The plan is then performed through an obstacle-avoiding controller that leverages a trial-and-error heuristic to prevent navigation from getting stuck in obstacles. Experimental results demonstrate the effectiveness of the proposed method. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets, respectively. Our code is available at https://github.com/MarSaKi/ETPNav. | ['Liang Wang', 'Keji He', 'Yan Huang', 'Zun Wang', 'Wenguan Wang', 'Hanqing Wang', 'Dong An'] | 2023-04-06 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 7.40478113e-02 2.68350631e-01 3.98793519e-01 -2.36059159e-01
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d5ce0542-5c46-4bc9-a4c5-554108f71284 | what-is-essential-for-unseen-goal | 2305.18882 | null | https://arxiv.org/abs/2305.18882v2 | https://arxiv.org/pdf/2305.18882v2.pdf | What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? | Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully offline datasets. In addition to being conservative within the dataset, the generalization ability to achieve unseen goals is another fundamental challenge for offline GCRL. However, to the best of our knowledge, this problem has not been well studied yet. In this paper, we study out-of-distribution (OOD) generalization of offline GCRL both theoretically and empirically to identify factors that are important. In a number of experiments, we observe that weighted imitation learning enjoys better generalization than pessimism-based offline RL method. Based on this insight, we derive a theory for OOD generalization, which characterizes several important design choices. We then propose a new offline GCRL method, Generalizable Offline goAl-condiTioned RL (GOAT), by combining the findings from our theoretical and empirical studies. On a new benchmark containing 9 independent identically distributed (IID) tasks and 17 OOD tasks, GOAT outperforms current state-of-the-art methods by a large margin. | ['Tong Zhang', 'Chongjie Zhang', 'Hao Hu', 'Xiaoteng Ma', 'Yong Lin', 'Rui Yang'] | 2023-05-30 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.66700715e-01 9.68182236e-02 -3.63283485e-01 -2.83665329e-01
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3ae12208-5b85-40ec-ab0e-12d2aeec5f26 | a-call-for-more-rigor-in-unsupervised-cross | 2004.14958 | null | https://arxiv.org/abs/2004.14958v1 | https://arxiv.org/pdf/2004.14958v1.pdf | A Call for More Rigor in Unsupervised Cross-lingual Learning | We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them. An existing rationale for such research is based on the lack of parallel data for many of the world's languages. However, we argue that a scenario without any parallel data and abundant monolingual data is unrealistic in practice. We also discuss different training signals that have been used in previous work, which depart from the pure unsupervised setting. We then describe common methodological issues in tuning and evaluation of unsupervised cross-lingual models and present best practices. Finally, we provide a unified outlook for different types of research in this area (i.e., cross-lingual word embeddings, deep multilingual pretraining, and unsupervised machine translation) and argue for comparable evaluation of these models. | ['Mikel Artetxe', 'Sebastian Ruder', 'Gorka Labaka', 'Eneko Agirre', 'Dani Yogatama'] | 2020-04-30 | a-call-for-more-rigor-in-unsupervised-cross-1 | https://aclanthology.org/2020.acl-main.658 | https://aclanthology.org/2020.acl-main.658.pdf | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-2.19256148e-01 -1.19930334e-01 -6.27023697e-01 -5.69578767e-01
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a87b24af-df71-46ba-a8eb-b6757f770a80 | when-bioprocess-engineering-meets-machine | 2209.01083 | null | https://arxiv.org/abs/2209.01083v2 | https://arxiv.org/pdf/2209.01083v2.pdf | When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development | Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community. | ['Thorben Werner', 'Mariano Nicolas Cruz-Bournazou', 'Peter Neubauer', 'Lars Schmidt-Thieme', 'Randolf Scholz', 'Ernesto Martinez', 'Maxim Borisyak', 'Katharina Paulick', 'Marie-Therese Schermeyer', 'Jong Woo Kim', 'Stefan Born', 'Nghia Duong-Trung'] | 2022-09-02 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 5.19941509e-01 2.39141613e-01 -4.98902099e-03 -1.64160863e-01
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cfad41af-3038-4c4d-800d-629c143e9b7b | visual-boundary-knowledge-translation-for | 2108.00379 | null | https://arxiv.org/abs/2108.00379v1 | https://arxiv.org/pdf/2108.00379v1.pdf | Visual Boundary Knowledge Translation for Foreground Segmentation | When confronted with objects of unknown types in an image, humans can effortlessly and precisely tell their visual boundaries. This recognition mechanism and underlying generalization capability seem to contrast to state-of-the-art image segmentation networks that rely on large-scale category-aware annotated training samples. In this paper, we make an attempt towards building models that explicitly account for visual boundary knowledge, in hope to reduce the training effort on segmenting unseen categories. Specifically, we investigate a new task termed as Boundary Knowledge Translation (BKT). Given a set of fully labeled categories, BKT aims to translate the visual boundary knowledge learned from the labeled categories, to a set of novel categories, each of which is provided only a few labeled samples. To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators. The segmentation network, combined with a boundary-aware self-supervised mechanism, is devised to conduct foreground segmentation, while the two discriminators work together in an adversarial manner to ensure an accurate segmentation of the novel categories under light supervision. Exhaustive experiments demonstrate that, with only tens of labeled samples as guidance, Trans-Net achieves close results on par with fully supervised methods. | ['Mingli Song', 'Xiangtong Du', 'Yajie Liu', 'Xiang Wang', 'Xinchao Wang', 'Lechao Cheng', 'Zunlei Feng'] | 2021-08-01 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 7.40812123e-01 5.82605183e-01 -1.96821317e-01 -5.78386009e-01
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5bf2e0a0-062c-4ad5-9c9a-4b1fefbfd6ac | exploring-stereovision-based-3-d-scene | 1902.06255 | null | http://arxiv.org/abs/1902.06255v1 | http://arxiv.org/pdf/1902.06255v1.pdf | Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality | Three-dimensional (3-D) scene reconstruction is one of the key techniques in
Augmented Reality (AR), which is related to the integration of image processing
and display systems of complex information. Stereo matching is a computer
vision based approach for 3-D scene reconstruction. In this paper, we explore
an improved stereo matching network, SLED-Net, in which a Single Long
Encoder-Decoder is proposed to replace the stacked hourglass network in PSM-Net
for better contextual information learning. We compare SLED-Net to
state-of-the-art methods recently published, and demonstrate its superior
performance on Scene Flow and KITTI2015 test sets. | ['Guang-Yu Nie', 'Cong Wang', 'Yongtian Wang', 'Yun Liu', 'Yue Liu'] | 2019-02-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 2.80442744e-01 -3.70489389e-01 -6.43358603e-02 -2.93465406e-01
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5dc2c7aa-7e21-4492-9f11-0a07731a9d96 | conditional-sound-generation-using-neural | 2107.09998 | null | https://arxiv.org/abs/2107.09998v3 | https://arxiv.org/pdf/2107.09998v3.pdf | Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning | Deep generative models have recently achieved impressive performance in speech and music synthesis. However, compared to the generation of those domain-specific sounds, generating general sounds (such as siren, gunshots) has received less attention, despite their wide applications. In previous work, the SampleRNN method was considered for sound generation in the time domain. However, SampleRNN is potentially limited in capturing long-range dependencies within sounds as it only back-propagates through a limited number of samples. In this work, we propose a method for generating sounds via neural discrete time-frequency representation learning, conditioned on sound classes. This offers an advantage in efficiently modelling long-range dependencies and retaining local fine-grained structures within sound clips. We evaluate our approach on the UrbanSound8K dataset, compared to SampleRNN, with the performance metrics measuring the quality and diversity of generated sounds. Experimental results show that our method offers comparable performance in quality and significantly better performance in diversity. | ['Wenwu Wang', 'Mark D. Plumbley', 'Qiushi Huang', 'Jinzheng Zhao', 'Turab Iqbal', 'Xubo Liu'] | 2021-07-21 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.68116754e-02 -3.82967591e-01 3.13697308e-01 -1.12769626e-01
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5d4db664-030c-4b1b-b2f3-313fabc421df | rethinking-neural-operations-for-diverse | 2103.15798 | null | https://arxiv.org/abs/2103.15798v2 | https://arxiv.org/pdf/2103.15798v2.pdf | Rethinking Neural Operations for Diverse Tasks | An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users to discover the right neural operations given data from their specific domain. We introduce a search space of operations called XD-Operations that mimic the inductive bias of standard multi-channel convolutions while being much more expressive: we prove that it includes many named operations across multiple application areas. Starting with any standard backbone such as ResNet, we show how to transform it into a search space over XD-operations and how to traverse the space using a simple weight-sharing scheme. On a diverse set of tasks -- solving PDEs, distance prediction for protein folding, and music modeling -- our approach consistently yields models with lower error than baseline networks and often even lower error than expert-designed domain-specific approaches. | ['Ameet Talwalkar', 'Christopher Ré', 'Liam Li', 'Tri Dao', 'Mikhail Khodak', 'Nicholas Roberts'] | 2021-03-29 | null | http://proceedings.neurips.cc/paper/2021/hash/84fdbc3ac902561c00871c9b0c226756-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/84fdbc3ac902561c00871c9b0c226756-Paper.pdf | neurips-2021-12 | ['music-modeling'] | ['music'] | [ 3.26985747e-01 6.62467629e-02 -4.74145487e-02 -5.08927047e-01
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1aaab332-b110-4c2c-b537-f5c335385bd5 | fast-bayesian-optimization-of-needle-in-a | 2208.13771 | null | https://arxiv.org/abs/2208.13771v2 | https://arxiv.org/pdf/2208.13771v2.pdf | Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI) | Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. For example, only $0.82\%$ out of $146$k total materials in the open-access Materials Project database have a negative Poisson's ratio. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence to a global optimum or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI. ZoMBI actively extracts knowledge from the previously best-performing evaluated experiments to iteratively zoom in the sampling search bounds towards the global optimum "needle" and then prunes the memory of low-performing historical experiments to accelerate compute times by reducing the algorithm time complexity from $O(n^3)$ to $O(\phi^3)$ for $\phi$ forward experiments per activation, which trends to a constant $O(1)$ over several activations. Additionally, ZoMBI implements two custom adaptive acquisition functions to further guide the sampling of new experiments toward the global optimum. We validate the algorithm's optimization performance on three real-world datasets exhibiting Needle-in-a-Haystack and further stress-test the algorithm's performance on an additional 174 analytical datasets. The ZoMBI algorithm demonstrates compute time speed-ups of 400x compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3x more highly optimized than those discovered by similar methods MiP-EGO, TuRBO, and HEBO. | ['Tonio Buonassisi', 'Qianxiao Li', 'Zekun Ren', 'Alexander E. Siemenn'] | 2022-08-26 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 2.72513293e-02 -5.21074951e-01 -8.51540715e-02 -2.50361919e-01
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776e998c-0898-462b-bb3d-e039069e3e7b | an-adaptive-graph-learning-method-for | null | null | https://www.nature.com/articles/s42256-022-00501-8 | https://www.researchsquare.com/article/rs-1172418/v1.pdf?c=1656084202000 | An adaptive graph learning method for automated molecular interactions and properties predictions | Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work takes a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency. | ['Xiaojun Yao', 'Shengyu Zhang', 'Huanxiang Liu', 'Qifeng Bai', 'Jiaxian Yan', 'Dejun Jiang', 'Yanan Tian', 'Shuo Liu', 'Pengyong Li', 'Xiaorui Wang', 'Xiaoqing Gong', 'Ruiqiang Lu', 'Chang-Yu Hsieh', 'Yuquan Li'] | 2022-06-23 | null | null | null | nature-machine-intelligence-2022-6 | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.49848545e-01 -2.00197950e-01 -5.39242208e-01 -3.70906949e-01
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10812015-3fb8-4d5f-b0f7-78bbf10d638b | end-to-end-complex-valued-multidilated | 2110.00745 | null | https://arxiv.org/abs/2110.00745v3 | https://arxiv.org/pdf/2110.00745v3.pdf | End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression | Echo and noise suppression is an integral part of a full-duplex communication system. Many recent acoustic echo cancellation (AEC) systems rely on a separate adaptive filtering module for linear echo suppression and a neural module for residual echo suppression. However, not only do adaptive filtering modules require convergence and remain susceptible to changes in acoustic environments, but this two-stage framework also often introduces unnecessary delays to the AEC system when neural modules are already capable of both linear and nonlinear echo suppression. In this paper, we exploit the offset-compensating ability of complex time-frequency masks and propose an end-to-end complex-valued neural network architecture. The building block of the proposed model is a pseudocomplex extension based on the densely-connected multidilated DenseNet (D3Net) building block, resulting in a very small network of only 354K parameters. The architecture utilized the multi-resolution nature of the D3Net building blocks to eliminate the need for pooling, allowing the network to extract features using large receptive fields without any loss of output resolution. We also propose a dual-mask technique for joint echo and noise suppression with simultaneous speech enhancement. Evaluation on both synthetic and real test sets demonstrated promising results across multiple energy-based metrics and perceptual proxies. | ['Bin Ma', 'Shengkui Zhao', 'Woon-Seng Gan', 'Thi Ngoc Tho Nguyen', 'Karn N. Watcharasupat'] | 2021-10-02 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.36064523e-01 -1.31793484e-01 7.53025413e-01 -3.59939247e-01
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22977478-142b-4fe2-9615-964d56e0781a | augstatic-a-light-weight-image-augmentation | null | null | https://www.jetir.org/view?paper=JETIR2205199 | https://www.jetir.org/papers/JETIR2205199.pdf | AugStatic - A Light-Weight Image Augmentation Library | The rapid exponential increase in the data led to an abrupt mix of various data types, leading to a deficiency of helpful information. Creating new data with the existing different types of data are presented in this paper. Augmentation is adding up or modifying the dataset with extra data. There are many types of augmentation done for various kinds of datasets. Augmentation has been widely used in multiple pre-processing steps of diverse machine learning pipelines. Many libraries or packages are made for augmentation called augmentation libraries. There are many salient features that each library supports. This paper seeks to enhance the library that makes the AugStatic library much more lightweight and efficient. AugStatic is a custom-built image augmentation library with lower computation costs and more extraordinary salient features compared to other image augmentation libraries. This framework can be used for NumPy arrays and tensors too. | ['Dr. Venkateswara Rao Gurrala', 'Allena Venkata Sai Abhishek'] | 2022-05-01 | null | null | null | journal-of-emerging-technologies-and | ['image-manipulation-detection', 'image-smoothing', 'image-matting', 'image-variation', 'image-stitching', 'image-augmentation', 'image-manipulation', 'image-morphing', 'roi-based-image-generation', 'image-cropping', 'detecting-image-manipulation', 'data-visualization', 'data-visualization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 8.59419778e-02 -2.00577796e-01 3.18770915e-01 -6.62581682e-01
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e5a870dd-f885-4eca-b252-dee719007f28 | relaxed-transformer-decoders-for-direct | 2102.01894 | null | https://arxiv.org/abs/2102.01894v3 | https://arxiv.org/pdf/2102.01894v3.pdf | Relaxed Transformer Decoders for Direct Action Proposal Generation | Temporal action proposal generation is an important and challenging task in video understanding, which aims at detecting all temporal segments containing action instances of interest. The existing proposal generation approaches are generally based on pre-defined anchor windows or heuristic bottom-up boundary matching strategies. This paper presents a simple and efficient framework (RTD-Net) for direct action proposal generation, by re-purposing a Transformer-alike architecture. To tackle the essential visual difference between time and space, we make three important improvements over the original transformer detection framework (DETR). First, to deal with slowness prior in videos, we replace the original Transformer encoder with a boundary attentive module to better capture long-range temporal information. Second, due to the ambiguous temporal boundary and relatively sparse annotations, we present a relaxed matching scheme to relieve the strict criteria of single assignment to each groundtruth. Finally, we devise a three-branch head to further improve the proposal confidence estimation by explicitly predicting its completeness. Extensive experiments on THUMOS14 and ActivityNet-1.3 benchmarks demonstrate the effectiveness of RTD-Net, on both tasks of temporal action proposal generation and temporal action detection. Moreover, due to its simplicity in design, our framework is more efficient than previous proposal generation methods, without non-maximum suppression post-processing. The code and models are made available at https://github.com/MCG-NJU/RTD-Action. | ['Gangshan Wu', 'LiMin Wang', 'Jiaqi Tang', 'Jing Tan'] | 2021-02-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Tan_Relaxed_Transformer_Decoders_for_Direct_Action_Proposal_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Tan_Relaxed_Transformer_Decoders_for_Direct_Action_Proposal_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 0.571864 0.04715557 -0.5085007 -0.15007949 -0.7634664 -0.32628378
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407191a7-4233-4ff6-aecb-56d769e16f4f | objective-surgical-skills-assessment-and-tool | 2212.04448 | null | https://arxiv.org/abs/2212.04448v1 | https://arxiv.org/pdf/2212.04448v1.pdf | Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge | Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021. | ['Anthony Jarc', 'Stefanie Speidel', 'Danail Stoyanov', 'Lena Maier-Hein', 'Evangelos Mazomenos', 'Jinfan Zhou', 'Yueming Jin', 'Dimitris Psychogyios', 'Emanuele Colleoni', 'Satoshi Kondo', 'Max Berniker', 'Ziheng Wang', 'Xi Liu', 'Kiran Bhattacharyya', 'Aneeq Zia'] | 2022-12-08 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-2.22892240e-01 1.45721629e-01 -4.22224432e-01 -1.74695909e-01
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3e236d3d-2f81-482d-b297-2b0245c9823b | multi-level-graph-matching-networks-for-deep | null | null | https://openreview.net/forum?id=65_RUwah5kr | https://openreview.net/pdf?id=65_RUwah5kr | Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning | While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to graph similarity learning. Recent works have considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich cross-level interactions (e.g., between nodes of a graph and the other whole graph). In this paper, we propose a Multi-level Graph Matching Network (MGMN) for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs. Furthermore, to bridge the gap of the lack of standard graph similarity learning benchmark, we have created and collected a set of datasets for both graph-graph classification and regression tasks with different sizes in order to evaluate the robustness of models. Our comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baselines on these graph similarity learning benchmarks. | ['Shouling Ji', 'Chunming Wu', 'Alex X. Liu', 'Fangli Xu', 'Tengfei Ma', 'Saizhuo Wang', 'Lingfei Wu', 'Xiang Ling'] | 2021-01-01 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-2.65155882e-02 3.07020098e-01 -1.49874195e-01 -2.60673672e-01
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68e7846f-1ba1-402d-b159-0757f0a998c0 | exploring-body-texture-from-mmw-images-for | 2203.15618 | null | https://arxiv.org/abs/2203.15618v1 | https://arxiv.org/pdf/2203.15618v1.pdf | Exploring Body Texture from mmW Images for Person Recognition | Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this work we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained Convolutional Neural Networks (CNN) models. First, we analyze the individual performance of three mmW body parts, concluding that: i) mmW torso region is more discriminative than mmW face and the whole body, ii) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this work, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported. | ['V. M. Patel', 'F. Alonso-Fernandez', 'R. Vera-Rodrıguez', 'J. Fierrez', 'E. Gonzalez-Sosa'] | 2022-03-27 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.20935841e-02 8.88623148e-02 6.92906678e-02 -3.45536113e-01
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3eb9031e-08f1-452f-8233-5e99e4def33f | modeling-molecular-structures-with-intrinsic | 2302.12255 | null | https://arxiv.org/abs/2302.12255v1 | https://arxiv.org/pdf/2302.12255v1.pdf | Modeling Molecular Structures with Intrinsic Diffusion Models | Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by studying the structure that they take in 3D space. However, a fundamental challenge with this approach has been the dynamic nature of these particles, which forces us to model not a single but a whole distribution of structures for every molecular system. This thesis proposes Intrinsic Diffusion Modeling, a novel approach to this problem based on combining diffusion generative models with scientific knowledge about the flexibility of biological complexes. The knowledge of these degrees of freedom is translated into the definition of a manifold over which the diffusion process is defined. This manifold significantly reduces the dimensionality and increases the smoothness of the generation space allowing for significantly faster and more accurate generative processes. We demonstrate the effectiveness of this approach on two fundamental tasks at the basis of computational chemistry and biology: molecular conformer generation and molecular docking. In both tasks, we construct the first deep learning method to outperform traditional computational approaches achieving an unprecedented level of accuracy for scalable programs. | ['Gabriele Corso'] | 2023-02-23 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.17582656e-01 -1.66515261e-01 -2.35478450e-02 4.71608266e-02
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e9474c41-d36b-4a02-ad87-92704e9e6eb0 | high-dimensional-classification-for-brain | 1504.02800 | null | http://arxiv.org/abs/1504.02800v1 | http://arxiv.org/pdf/1504.02800v1.pdf | High-Dimensional Classification for Brain Decoding | Brain decoding involves the determination of a subject's cognitive state or
an associated stimulus from functional neuroimaging data measuring brain
activity. In this setting the cognitive state is typically characterized by an
element of a finite set, and the neuroimaging data comprise voluminous amounts
of spatiotemporal data measuring some aspect of the neural signal. The
associated statistical problem is one of classification from high-dimensional
data. We explore the use of functional principal component analysis, mutual
information networks, and persistent homology for examining the data through
exploratory analysis and for constructing features characterizing the neural
signal for brain decoding. We review each approach from this perspective, and
we incorporate the features into a classifier based on symmetric multinomial
logistic regression with elastic net regularization. The approaches are
illustrated in an application where the task is to infer, from brain activity
measured with magnetoencephalography (MEG), the type of video stimulus shown to
a subject. | ['Jiguo Cao', 'Nicole Croteau', 'Ryan Budney', 'Farouk S. Nathoo'] | 2015-04-10 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 4.26198423e-01 -3.99396345e-02 2.57309899e-02 -7.28946626e-01
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c247db44-c37b-40f2-bde9-e354403f6c51 | adaptpose-cross-dataset-adaptation-for-3d | 2112.11593 | null | https://arxiv.org/abs/2112.11593v2 | https://arxiv.org/pdf/2112.11593v2.pdf | AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation | This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by improving the diversity of the training data. We argue that diversity alone is not sufficient and that the characteristics of the training data need to be adapted to those of the new dataset such as camera viewpoint, position, human actions, and body size. To this end, we propose AdaptPose, an end-to-end framework that generates synthetic 3D human motions from a source dataset and uses them to fine-tune a 3D pose estimator. AdaptPose follows an adversarial training scheme. From a source 3D pose the generator generates a sequence of 3D poses and a camera orientation that is used to project the generated poses to a novel view. Without any 3D labels or camera information AdaptPose successfully learns to create synthetic 3D poses from the target dataset while only being trained on 2D poses. In experiments on the Human3.6M, MPI-INF-3DHP, 3DPW, and Ski-Pose datasets our method outperforms previous work in cross-dataset evaluations by 14% and previous semi-supervised learning methods that use partial 3D annotations by 16%. | ['Z. Jane Wang', 'Rabab Ward', 'Helge Rhodin', 'Bastian Wandt', 'Mohsen Gholami'] | 2021-12-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [ 2.07682535e-01 2.13949814e-01 2.73284055e-02 -4.71970648e-01
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2e44053f-124e-4d98-9708-59634fed5ccf | dan-danish-nested-named-entities-and-lexical | 2105.11301 | null | https://arxiv.org/abs/2105.11301v1 | https://arxiv.org/pdf/2105.11301v1.pdf | DaN+: Danish Nested Named Entities and Lexical Normalization | This paper introduces DaN+, a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. We empirically assess three strategies to model the two-layer Named Entity Recognition (NER) task. We compare transfer capabilities from German versus in-language annotation from scratch. We examine language-specific versus multilingual BERT, and study the effect of lexical normalization on NER. Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexical normalization are the most beneficial on the least canonical data. Our results also show that an out-of-domain setup remains challenging, while performance on news plateaus quickly. This highlights the importance of cross-domain evaluation of cross-lingual transfer. | ['Rob van der Goot', 'Kristian Nørgaard Jensen', 'Barbara Plank'] | 2021-05-24 | null | https://aclanthology.org/2020.coling-main.583 | https://aclanthology.org/2020.coling-main.583.pdf | coling-2020-8 | ['lexical-normalization'] | ['natural-language-processing'] | [-4.07388091e-01 -7.52707720e-02 -3.36743295e-01 -4.52829987e-01
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e73c216a-e4f1-47f5-8781-a94d044ce5d6 | weakly-supervised-cloud-detection-with-fixed | 2111.11879 | null | https://arxiv.org/abs/2111.11879v1 | https://arxiv.org/pdf/2111.11879v1.pdf | Weakly-Supervised Cloud Detection with Fixed-Point GANs | The detection of clouds in satellite images is an essential preprocessing task for big data in remote sensing. Convolutional neural networks (CNNs) have greatly advanced the state-of-the-art in the detection of clouds in satellite images, but existing CNN-based methods are costly as they require large amounts of training images with expensive pixel-level cloud labels. To alleviate this cost, we propose Fixed-Point GAN for Cloud Detection (FCD), a weakly-supervised approach. Training with only image-level labels, we learn fixed-point translation between clear and cloudy images, so only clouds are affected during translation. Doing so enables our approach to predict pixel-level cloud labels by translating satellite images to clear ones and setting a threshold to the difference between the two images. Moreover, we propose FCD+, where we exploit the label-noise robustness of CNNs to refine the prediction of FCD, leading to further improvements. We demonstrate the effectiveness of our approach on the Landsat-8 Biome cloud detection dataset, where we obtain performance close to existing fully-supervised methods that train with expensive pixel-level labels. By fine-tuning our FCD+ with just 1% of the available pixel-level labels, we match the performance of fully-supervised methods. | ['Ira Assent', 'Joachim Nyborg'] | 2021-11-23 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.67056102e-01 -2.36477703e-01 2.08610177e-01 -4.29323524e-01
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9fcadbf0-a6f2-4cc8-956e-36534d7f4aff | wish-you-were-here-context-aware-human | 2005.10663 | null | https://arxiv.org/abs/2005.10663v1 | https://arxiv.org/pdf/2005.10663v1.pdf | Wish You Were Here: Context-Aware Human Generation | We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene. Our method involves three subnetworks: the first generates the semantic map of the new person, given the pose of the other persons in the scene and an optional bounding box specification. The second network renders the pixels of the novel person and its blending mask, based on specifications in the form of multiple appearance components. A third network refines the generated face in order to match those of the target person. Our experiments present convincing high-resolution outputs in this novel and challenging application domain. In addition, the three networks are evaluated individually, demonstrating for example, state of the art results in pose transfer benchmarks. | ['Oran Gafni', 'Lior Wolf'] | 2020-05-21 | wish-you-were-here-context-aware-human-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['pose-transfer'] | ['computer-vision'] | [ 6.32233381e-01 5.31816006e-01 4.48670864e-01 -5.20008087e-01
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f98561a9-48a6-4b60-b486-ce936886a5e0 | darknet-traffic-classification-and | 2206.06371 | null | https://arxiv.org/abs/2206.06371v1 | https://arxiv.org/pdf/2206.06371v1.pdf | Darknet Traffic Classification and Adversarial Attacks | The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities. This research aims to improve darknet traffic detection by assessing Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Auxiliary-Classifier Generative Adversarial Networks (AC-GAN) for classification of such traffic and the underlying application types. We find that our RF model outperforms the state-of-the-art machine learning techniques used in prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our RF classifier, we obfuscate select application type classes to simulate realistic adversarial attack scenarios. We demonstrate that our best-performing classifier can be defeated by such attacks, and we consider ways to deal with such adversarial attacks. | ['Mark Stamp', 'Nhien Rust-Nguyen'] | 2022-06-12 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.24176735e-01 1.83583647e-02 -2.49588132e-01 -9.97421741e-02
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6546f84b-538f-47bb-a0ea-b2c1f16c4d3f | cmvae-causal-meta-vae-for-unsupervised-meta | 2302.09731 | null | https://arxiv.org/abs/2302.09731v1 | https://arxiv.org/pdf/2302.09731v1.pdf | CMVAE: Causal Meta VAE for Unsupervised Meta-Learning | Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks. However, existing approaches may be misled by the context-bias (e.g. background) from the training data. In this paper, we abstract the unsupervised meta-learning problem into a Structural Causal Model (SCM) and point out that such bias arises due to hidden confounders. To eliminate the confounders, we define the priors are \textit{conditionally} independent, learn the relationships between priors and intervene on them with casual factorization. Furthermore, we propose Causal Meta VAE (CMVAE) that encodes the priors into latent codes in the causal space and learns their relationships simultaneously to achieve the downstream few-shot image classification task. Results on toy datasets and three benchmark datasets demonstrate that our method can remove the context-bias and it outperforms other state-of-the-art unsupervised meta-learning algorithms because of bias-removal. Code is available at \url{https://github.com/GuodongQi/CMVAE} | ['Huimin Yu', 'Guodong Qi'] | 2023-02-20 | null | null | null | null | ['few-shot-image-classification', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 4.23691899e-01 2.90003657e-01 -6.82387888e-01 -4.88395274e-01
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b76af7c4-fb6b-4305-8095-e0ff1552483e | on-interference-rejection-using-riemannian | 2301.03399 | null | https://arxiv.org/abs/2301.03399v1 | https://arxiv.org/pdf/2301.03399v1.pdf | On Interference-Rejection using Riemannian Geometry for Direction of Arrival Estimation | We consider the problem of estimating the direction of arrival of desired acoustic sources in the presence of multiple acoustic interference sources. All the sources are located in noisy and reverberant environments and are received by a microphone array. We propose a new approach for designing beamformers based on the Riemannian geometry of the manifold of Hermitian positive definite matrices. Specifically, we show theoretically that incorporating the Riemannian mean of the spatial correlation matrices into frequently-used beamformers gives rise to beam patterns that reject the directions of interference sources and result in a higher signal-to-interference ratio. We experimentally demonstrate the advantages of our approach in designing several beamformers in the presence of simultaneously active multiple interference sources. | ['Ronen Talmon', 'Amitay Bar'] | 2023-01-09 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.47400558e-01 -1.45019948e-01 9.74971294e-01 -8.22253302e-02
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823e266a-6bc8-48d2-ae30-237e06160794 | segdiscover-visual-concept-discovery-via | 2204.10926 | null | https://arxiv.org/abs/2204.10926v1 | https://arxiv.org/pdf/2204.10926v1.pdf | SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation | Visual concept discovery has long been deemed important to improve interpretability of neural networks, because a bank of semantically meaningful concepts would provide us with a starting point for building machine learning models that exhibit intelligible reasoning process. Previous methods have disadvantages: either they rely on labelled support sets that incorporate human biases for objects that are "useful," or they fail to identify multiple concepts that occur within a single image. We reframe the concept discovery task as an unsupervised semantic segmentation problem, and present SegDiscover, a novel framework that discovers semantically meaningful visual concepts from imagery datasets with complex scenes without supervision. Our method contains three important pieces: generating concept primitives from raw images, discovering concepts by clustering in the latent space of a self-supervised pretrained encoder, and concept refinement via neural network smoothing. Experimental results provide evidence that our method can discover multiple concepts within a single image and outperforms state-of-the-art unsupervised methods on complex datasets such as Cityscapes and COCO-Stuff. Our method can be further used as a neural network explanation tool by comparing results obtained by different encoders. | ['Cynthia Rudin', 'Zhi Chen', 'Haiyang Huang'] | 2022-04-22 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.02531350e-01 7.59131491e-01 -1.82355881e-01 -6.75421894e-01
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e3ad0c93-ac93-4e31-92fd-2bd951a92930 | cultural-aware-machine-learning-based | 2304.06953 | null | https://arxiv.org/abs/2304.06953v1 | https://arxiv.org/pdf/2304.06953v1.pdf | Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy | Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in deciding whether to take the vaccine, especially from the cultural point of view. To obtain these goals, we design a novel culture-aware machine learning (ML) model, based on our new data collection, for predicting vaccination willingness. We further analyze the most important features which contribute to the ML model's predictions using advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the key factors that most likely impact the vaccine adoption decisions. Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most. Our results also show that cultural characteristics, rumors, and political affiliation are associated with increased vaccine rejection. | ['My T. Thai', 'Huan Chen', 'Sylvia Chan-Olmsted', 'Raed Alharbi'] | 2023-04-14 | null | null | null | null | ['culture'] | ['speech'] | [-1.01963796e-01 2.49743000e-01 -1.02118790e+00 -3.93226683e-01
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e5cfa607-7a90-4edd-95af-5638abb261a0 | sceneflowfields-multi-frame-matching | 1902.10099 | null | https://arxiv.org/abs/1902.10099v2 | https://arxiv.org/pdf/1902.10099v2.pdf | SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation | State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we push the limits of scene flow estimation. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader. | ['Oliver Wasenmüller', 'René Schuster', 'Didier Stricker', 'Christian Unger', 'Georg Kuschk'] | 2019-02-26 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.06861703e-01 -4.91384506e-01 -2.48372272e-01 -2.09157355e-02
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e8acd350-64aa-479c-8424-ad55f7d785de | deepar-probabilistic-forecasting-with | 1704.04110 | null | http://arxiv.org/abs/1704.04110v3 | http://arxiv.org/pdf/1704.04110v3.pdf | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this paper we propose DeepAR, a methodology for producing accurate
probabilistic forecasts, based on training an auto regressive recurrent network
model on a large number of related time series. We demonstrate how by applying
deep learning techniques to forecasting, one can overcome many of the
challenges faced by widely-used classical approaches to the problem. We show
through extensive empirical evaluation on several real-world forecasting data
sets accuracy improvements of around 15% compared to state-of-the-art methods. | ['Valentin Flunkert', 'Jan Gasthaus', 'David Salinas'] | 2017-04-13 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-2.50669003e-01 -1.89506292e-01 -7.11249709e-02 -5.50777674e-01
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1d58dc60-5757-4f7d-92dc-0fe49175a5ff | towards-feature-space-adversarial-attack-1 | 2004.12385 | null | https://arxiv.org/abs/2004.12385v2 | https://arxiv.org/pdf/2004.12385v2.pdf | Towards Feature Space Adversarial Attack | We propose a new adversarial attack to Deep Neural Networks for image classification. Different from most existing attacks that directly perturb input pixels, our attack focuses on perturbing abstract features, more specifically, features that denote styles, including interpretable styles such as vivid colors and sharp outlines, and uninterpretable ones. It induces model misclassfication by injecting imperceptible style changes through an optimization procedure. We show that our attack can generate adversarial samples that are more natural-looking than the state-of-the-art unbounded attacks. The experiment also supports that existing pixel-space adversarial attack detection and defense techniques can hardly ensure robustness in the style related feature space. | ['Qiu-Ling Xu', 'Xiangyu Zhang', 'Siyuan Cheng', 'Guanhong Tao'] | 2020-04-26 | null | https://openreview.net/forum?id=S1eqj1SKvr | https://openreview.net/pdf?id=S1eqj1SKvr | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 7.63116777e-01 1.96206659e-01 4.50806320e-01 -4.01977032e-01
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225f963a-f548-4c78-981b-d44fdf97f836 | reiterative-domain-aware-multi-target | 2109.00919 | null | https://arxiv.org/abs/2109.00919v2 | https://arxiv.org/pdf/2109.00919v2.pdf | Reiterative Domain Aware Multi-Target Adaptation | Most domain adaptation methods focus on single-source-single-target adaptation settings. Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains. To build a multi-target classifier, it is important to have: a feature extractor that generalizes well across domains; and effective aggregation of features from the labeled source and different unlabeled target domains. Towards the first, we use the recently popular Transformer as a feature extraction backbone. Towards the second, we use a co-teaching-based approach using a dual-classifier head, one of which is based on the graph neural network. The proposed approach uses a sequential adaptation strategy that adapts one domain at a time starting from the target domains that are more similar to the source, assuming that the network finds it easier to adapt to such target domains. After adapting on each target, samples with a softmax-based confidence score greater than a threshold are added to the pseudo-source, thus aggregating knowledge from different domains. However, softmax is not entirely trustworthy as a confidence score and may generate a high score for unreliable samples if trained for many iterations. To mitigate this effect, we adopt a reiterative approach, where we reduce target adaptation iterations, however, reiterate multiple times over the target domains. The experimental evaluation on the Office-Home, Office-31 and DomainNet datasets shows significant improvement over the existing methods. We have achieved 10.7$\%$ average improvement in Office-Home dataset over the state-of-art methods. | ['Xiao Xiang Zhu', 'Nasrullah Sheikh', 'Shan Zhao', 'Sudipan Saha'] | 2021-08-26 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.92441338e-01 1.99833378e-01 -3.04885775e-01 -7.25319326e-01
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5c89b276-3a99-4766-b0e1-0c15e9fc9162 | visual-relationship-detection-with-relative | 1911.00713 | null | https://arxiv.org/abs/1911.00713v1 | https://arxiv.org/pdf/1911.00713v1.pdf | Visual Relationship Detection with Relative Location Mining | Visual relationship detection, as a challenging task used to find and distinguish the interactions between object pairs in one image, has received much attention recently. In this work, we propose a novel visual relationship detection framework by deeply mining and utilizing relative location of object-pair in every stage of the procedure. In both the stages, relative location information of each object-pair is abstracted and encoded as auxiliary feature to improve the distinguishing capability of object-pairs proposing and predicate recognition, respectively; Moreover, one Gated Graph Neural Network(GGNN) is introduced to mine and measure the relevance of predicates using relative location. With the location-based GGNN, those non-exclusive predicates with similar spatial position can be clustered firstly and then be smoothed with close classification scores, thus the accuracy of top $n$ recall can be increased further. Experiments on two widely used datasets VRD and VG show that, with the deeply mining and exploiting of relative location information, our proposed model significantly outperforms the current state-of-the-art. | ['Hao Zhou', 'Chuanping Hu', 'Chongyang Zhang'] | 2019-11-02 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.00113252e-01 7.34117534e-03 -4.39342529e-01 -3.96961153e-01
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159c7533-4efa-4653-80b0-c7c217ab0824 | spherical-formulation-of-geometric-motion | 2104.12404 | null | https://arxiv.org/abs/2104.12404v1 | https://arxiv.org/pdf/2104.12404v1.pdf | Spherical formulation of geometric motion segmentation constraints in fisheye cameras | We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automoated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery. | ['Ciaran Eising', 'Letizia Mariotti'] | 2021-04-26 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-6.28726855e-02 -1.32049471e-01 -1.62618414e-01 -3.06668043e-01
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e86fd509-1664-4bf9-9fa7-d74a33150f11 | multi-armed-bandits-with-generalized | 2303.00620 | null | https://arxiv.org/abs/2303.00620v1 | https://arxiv.org/pdf/2303.00620v1.pdf | Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards | Decision-making problems of sequential nature, where decisions made in the past may have an impact on the future, are used to model many practically important applications. In some real-world applications, feedback about a decision is delayed and may arrive via partial rewards that are observed with different delays. Motivated by such scenarios, we propose a novel problem formulation called multi-armed bandits with generalized temporally-partitioned rewards. To formalize how feedback about a decision is partitioned across several time steps, we introduce $\beta$-spread property. We derive a lower bound on the performance of any uniformly efficient algorithm for the considered problem. Moreover, we provide an algorithm called TP-UCB-FR-G and prove an upper bound on its performance measure. In some scenarios, our upper bound improves upon the state of the art. We provide experimental results validating the proposed algorithm and our theoretical results. | ['Pratik Gajane', 'Nina Verbeeke', 'Luc Siecker', 'Tobias Sagis', 'Rik Litjens', 'Ronald C. van den Broek'] | 2023-03-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.08867633e-01 5.89477159e-02 -6.23731911e-01 -3.21455002e-01
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dd38ed55-2088-479b-a03a-676c02e58eb3 | e-inu-simulating-a-quadruped-robot-with | 2301.00964 | null | https://arxiv.org/abs/2301.00964v1 | https://arxiv.org/pdf/2301.00964v1.pdf | e-Inu: Simulating A Quadruped Robot With Emotional Sentience | Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work. | ['Annapurna Jonnalagadda', 'Firuz Kamalov', 'S. Anitha', 'Aswani Kumar Cherukuri', 'Sibi Chakkaravarthy S', 'Jatin Karthik Tripathy', 'Abhiruph Chakravarty'] | 2023-01-03 | null | null | null | null | ['video-emotion-detection'] | ['computer-vision'] | [-2.17839524e-01 4.06524718e-01 4.62282419e-01 -4.77054901e-02
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78679ffb-efad-4a49-a5b8-dea22424ef4f | circa-comprehensible-online-system-in-support | 2210.05440 | null | https://arxiv.org/abs/2210.05440v1 | https://arxiv.org/pdf/2210.05440v1.pdf | CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis | Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. Regardless of the numerous proposed solutions for lung X-rays, none of them is a product that can be used in the clinic. Five different datasets (POLCOVID, AIforCOVID, COVIDx, NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training; 1 050 images were used as a hold-out test set, and 44 247 as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR. Each image class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 network based on processed CXRs and a dense neural network on radiomic features. The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity and NPV than PPV. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1. A similar performance was obtained on the independent dataset for normal and COVID-19 class subtypes. Seventy-six percent of COVID-19 patients wrongly classified as normal cases were annotated by radiologists as with no signs of disease. Finally, we developed the online service (https://circa.aei.polsl.pl) to provide access to fast diagnosis support tools. | ['Joanna Polanska', 'Michal Marczyk', 'POLCOVID Study Group', 'Edyta Szurowska', 'Barbara Gizycka', 'Gabriela Zapolska', 'Krzysztof Simon', 'Robert Flisiak', 'Malgorzata Pawlowska', 'Mateusz Nowak', 'Andrzej Cieszanowski', 'Grzegorz Przybylski', 'Tadeusz Popiela', 'Jerzy Walecki', 'Magdalena Sliwinska', 'Katarzyna Gruszczynska', 'Jerzy Jaroszewicz', 'Pawel Foszner', 'Joanna Tobiasz', 'Marek Socha', 'Aleksandra Suwalska', 'Wojciech Prazuch'] | 2022-10-11 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.61080882e-01 -1.34917572e-01 -1.56171143e-01 -6.99933171e-02
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d822bc54-0672-415c-a8fe-afa41f77bc17 | attention-mechanism-based-cognition-level | 2204.08027 | null | https://arxiv.org/abs/2204.08027v2 | https://arxiv.org/pdf/2204.08027v2.pdf | Attention Mechanism based Cognition-level Scene Understanding | Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real world. The VCR task, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge, is a cognition-level scene understanding task. The VCR task has aroused researchers' interest due to its wide range of applications, including visual question answering, automated vehicle systems, and clinical decision support. Previous approaches to solving the VCR task generally rely on pre-training or exploiting memory with long dependency relationship encoded models. However, these approaches suffer from a lack of generalizability and losing information in long sequences. In this paper, we propose a parallel attention-based cognitive VCR network PAVCR, which fuses visual-textual information efficiently and encodes semantic information in parallel to enable the model to capture rich information for cognition-level inference. Extensive experiments show that the proposed model yields significant improvements over existing methods on the benchmark VCR dataset. Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning. | ['Wenbin Zhang', 'Chris Brown', 'Peng Xu', 'Israat Haque', 'Vasile Palade', 'Kea Turner', 'Eirini Ntoutsi', 'Tai Le Quy', 'Xuejiao Tang'] | 2022-04-17 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.41388100e-01 9.43506658e-02 -1.44876376e-01 -3.57537866e-01
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92379fd8-7188-4f25-899c-7837105769f2 | poserac-pose-saliency-transformer-for | 2303.08450 | null | https://arxiv.org/abs/2303.08450v2 | https://arxiv.org/pdf/2303.08450v2.pdf | PoseRAC: Pose Saliency Transformer for Repetitive Action Counting | This paper presents a significant contribution to the field of repetitive action counting through the introduction of a new approach called Pose Saliency Representation. The proposed method efficiently represents each action using only two salient poses instead of redundant frames, which significantly reduces the computational cost while improving the performance. Moreover, we introduce a pose-level method, PoseRAC, which is based on this representation and achieves state-of-the-art performance on two new version datasets by using Pose Saliency Annotation to annotate salient poses for training. Our lightweight model is highly efficient, requiring only 20 minutes for training on a GPU, and infers nearly 10x faster compared to previous methods. In addition, our approach achieves a substantial improvement over the previous state-of-the-art TransRAC, achieving an OBO metric of 0.56 compared to 0.29 of TransRAC. The code and new dataset are available at https://github.com/MiracleDance/PoseRAC for further research and experimentation, making our proposed approach highly accessible to the research community. | ['Yuexian Zou', 'Xuxin Cheng', 'Ziyu Yao'] | 2023-03-15 | null | null | null | null | ['repetitive-action-counting'] | ['computer-vision'] | [ 3.68734986e-01 7.43068457e-02 -3.96654546e-01 1.89903826e-02
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2d14bbe4-33c2-481e-bb9b-bed859d410e4 | touchless-palmprint-recognition-based-on-3d | 2103.02167 | null | https://arxiv.org/abs/2103.02167v3 | https://arxiv.org/pdf/2103.02167v3.pdf | Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement | With the growing demand for hand hygiene and convenience of use, palmprint recognition with touchless manner made a great development recently, providing an effective solution for person identification. Despite many efforts that have been devoted to this area, it is still uncertain about the discriminative ability of the contactless palmprint, especially for large-scale datasets. To tackle the problem, in this paper, we build a large-scale touchless palmprint dataset containing 2334 palms from 1167 individuals. To our best knowledge, it is the largest contactless palmprint image benchmark ever collected with regard to the number of individuals and palms. Besides, we propose a novel deep learning framework for touchless palmprint recognition named 3DCPN (3D Convolution Palmprint recognition Network) which leverages 3D convolution to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of Gabor filter is embedded into the first layer for enhancement of curve feature extraction. With a well-designed ensemble scheme,low-level 3D features are then convolved to extract high-level features. Finally on the top, we set a region-based loss function to strengthen the discriminative ability of both global and local descriptors. To demonstrate the superiority of our method, extensive experiments are conducted on our dataset and other popular databases TongJi and IITD, where the results show the proposed 3DCPN achieves state-of-the-art or comparable performances. | ['David Zhang', 'Wei Jia', 'Jinxing Li', 'Dandan Fan', 'Xu Liang', 'Zhaoqun Li'] | 2021-03-03 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 9.12974924e-02 -8.69573712e-01 -5.91312498e-02 -2.07820088e-01
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d51e027a-9f33-47aa-8321-f87c5dfb3941 | photometric-redshift-estimation-with | 2202.09964 | null | https://arxiv.org/abs/2202.09964v1 | https://arxiv.org/pdf/2202.09964v1.pdf | Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods | Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses. In this work, we investigate two major forms of biases, i.e., class-dependent residuals and mode collapse, in a case study of estimating photometric redshifts as a classification problem using Convolutional Neural Networks (CNNs) and galaxy images with spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multi-channel outputs, balancing the training data and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshifts or photometric redshifts, and the biases with respect to these two definitions are incompatible and should be treated in a split way. We suggest that resolving biases in the spectroscopic space is a prerequisite for resolving biases in the photometric space. Experiments show that our methods possess a better capability in controlling biases compared to benchmark methods, and exhibit robustness under varying implementing and training conditions provided with high-quality data. Our methods have promises for future cosmological surveys that require a good constraint of biases, and may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance trade-off and the demand on sufficient statistics suggest the need for developing better methodologies and optimizing data usage strategies. | ['O. Ilbert', 'S. Arnouts', 'R. Ait Ouahmed', 'M. Treyer', 'J. Pasquet', 'D. Fouchez', 'Q. Lin'] | 2022-02-21 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [ 9.67851952e-02 -4.18646157e-01 1.06363788e-01 -8.47843051e-01
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813be88f-22b2-439e-8d6c-9b4046fdf850 | adversarial-image-perturbation-for-privacy | 1703.09471 | null | http://arxiv.org/abs/1703.09471v2 | http://arxiv.org/pdf/1703.09471v2.pdf | Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective | Users like sharing personal photos with others through social media. At the
same time, they might want to make automatic identification in such photos
difficult or even impossible. Classic obfuscation methods such as blurring are
not only unpleasant but also not as effective as one would expect. Recent
studies on adversarial image perturbations (AIP) suggest that it is possible to
confuse recognition systems effectively without unpleasant artifacts. However,
in the presence of counter measures against AIPs, it is unclear how effective
AIP would be in particular when the choice of counter measure is unknown. Game
theory provides tools for studying the interaction between agents with
uncertainties in the strategies. We introduce a general game theoretical
framework for the user-recogniser dynamics, and present a case study that
involves current state of the art AIP and person recognition techniques. We
derive the optimal strategy for the user that assures an upper bound on the
recognition rate independent of the recogniser's counter measure. Code is
available at https://goo.gl/hgvbNK. | ['Seong Joon Oh', 'Mario Fritz', 'Bernt Schiele'] | 2017-03-28 | adversarial-image-perturbation-for-privacy-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Oh_Adversarial_Image_Perturbation_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Oh_Adversarial_Image_Perturbation_ICCV_2017_paper.pdf | iccv-2017-10 | ['person-recognition'] | ['computer-vision'] | [ 3.88904363e-01 3.32147837e-01 4.14645344e-01 1.21831506e-01
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553b8b4a-5b6c-4a01-9ad0-010d799b85a7 | global-selector-a-new-benchmark-dataset-and | 2106.01263 | null | https://arxiv.org/abs/2106.01263v5 | https://arxiv.org/pdf/2106.01263v5.pdf | Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems | Sample-and-rank is a key decoding strategy for modern generation-based dialogue systems. It helps achieve diverse and high-quality responses by selecting an answer from a small pool of generated candidates. The current state-of-the-art ranking methods mainly use an encoding paradigm called Cross-Encoder, which separately encodes each context-candidate pair and ranks the candidates according to their fitness scores. However, Cross-Encoder repeatedly encodes the same lengthy context for each candidate, resulting in high computational costs. Poly-Encoder addresses the above problems by reducing the interaction between context and candidates, but with a price of performance drop. In this work, we develop a new paradigm called Uni-Encoder, that keeps the full attention over each pair as in Cross-Encoder while only encoding the context once, as in Poly-Encoder. Uni-Encoder encodes all the candidates with the context in one forward pass. We use the same positional embedding for all candidates to ensure they are treated equally and design a new attention mechanism to avoid confusion. Our Uni-Encoder can simulate other ranking paradigms using different attention and response concatenation methods. Extensive experiments show that our proposed paradigm achieves new state-of-the-art results on four benchmark datasets with high computational efficiency. For instance, it improves R10@1 by 2.9% with an approximately 4X faster inference speed on the Ubuntu V2 dataset. | ['Leyang Cui', 'Pengfei Fang', 'Haofei Yu', 'Zhenzhong Lan', 'Hongliang He', 'Chiyu Song'] | 2021-06-02 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 1.97110668e-01 -1.03593148e-01 -2.95885563e-01 -4.70618993e-01
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08e5c6d9-e788-4826-a7f0-a852e8dc5a0a | 3d-graph-convolutional-networks-with-temporal | 1903.00919 | null | http://arxiv.org/abs/1903.00919v1 | http://arxiv.org/pdf/1903.00919v1.pdf | 3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting | Spatio-temporal prediction plays an important role in many application areas
especially in traffic domain. However, due to complicated spatio-temporal
dependency and high non-linear dynamics in road networks, traffic prediction
task is still challenging. Existing works either exhibit heavy training cost or
fail to accurately capture the spatio-temporal patterns, also ignore the
correlation between distant roads that share the similar patterns. In this
paper, we propose a novel deep learning framework to overcome these issues: 3D
Temporal Graph Convolutional Networks (3D-TGCN). Two novel components of our
model are introduced. (1) Instead of constructing the road graph based on
spatial information, we learn it by comparing the similarity between time
series for each road, thus providing a spatial information free framework. (2)
We propose an original 3D graph convolution model to model the spatio-temporal
data more accurately. Empirical results show that 3D-TGCN could outperform
state-of-the-art baselines. | ['Mengzhang Li', 'Jiyong Zhang', 'Bing Yu', 'Zhanxing Zhu'] | 2019-03-03 | null | null | null | null | ['4d-spatio-temporal-semantic-segmentation'] | ['computer-vision'] | [-3.48239005e-01 -3.83833498e-01 -3.22948009e-01 -3.79547983e-01
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76aef325-6e57-4eae-83bb-6cfba5925b22 | cloze-evaluation-for-deeper-understanding-of-1 | null | null | https://aclanthology.org/2022.csrr-1.2 | https://aclanthology.org/2022.csrr-1.2.pdf | Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian | Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (2016) in evaluating story understanding in Indonesian, by constructing a four-sentence story with one correct ending and one incorrect ending. To investigate commonsense knowledge acquisition in language models, we experimented with: (1) a classification task to predict the correct ending; and (2) a generation task to complete the story with a single sentence. We investigate these tasks in two settings: (i) monolingual training and ii) zero-shot cross-lingual transfer between Indonesian and English. | ['Jey Han Lau', 'Timothy Baldwin', 'Fajri Koto'] | null | null | null | null | csrr-acl-2022-5 | ['cloze-test'] | ['natural-language-processing'] | [ 5.71746588e-01 1.48923501e-01 3.74139324e-02 -2.56059229e-01
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e9b97670-0bba-49df-a522-968360d54e85 | ensemble-sequence-level-training-for | 1808.10592 | null | http://arxiv.org/abs/1808.10592v1 | http://arxiv.org/pdf/1808.10592v1.pdf | Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report | This paper describes multimodal machine translation systems developed jointly
by Oregon State University and Baidu Research for WMT 2018 Shared Task on
multimodal translation. In this paper, we introduce a simple approach to
incorporate image information by feeding image features to the decoder side. We
also explore different sequence level training methods including scheduled
sampling and reinforcement learning which lead to substantial improvements. Our
systems ensemble several models using different architectures and training
methods and achieve the best performance for three subtasks: En-De and En-Cs in
task 1 and (En+De+Fr)-Cs task 1B. | ['Renjie Zheng', 'Mingbo Ma', 'Yilin Yang', 'Liang Huang'] | 2018-08-31 | ensemble-sequence-level-training-for-1 | https://aclanthology.org/W18-6443 | https://aclanthology.org/W18-6443.pdf | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.77835208e-01 6.28600940e-02 -3.82698357e-01 -4.26408380e-01
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a2c5b086-2b0d-4fe3-b809-2923b70dda1f | visual-cue-integration-for-small-target | 1903.07546 | null | http://arxiv.org/abs/1903.07546v1 | http://arxiv.org/pdf/1903.07546v1.pdf | Visual Cue Integration for Small Target Motion Detection in Natural Cluttered Backgrounds | The robust detection of small targets against cluttered background is
important for future artificial visual systems in searching and tracking
applications. The insects' visual systems have demonstrated excellent ability
to avoid predators, find prey or identify conspecifics - which always appear as
small dim speckles in the visual field. Build a computational model of the
insects' visual pathways could provide effective solutions to detect small
moving targets. Although a few visual system models have been proposed, they
only make use of small-field visual features for motion detection and their
detection results often contain a number of false positives. To address this
issue, we develop a new visual system model for small target motion detection
against cluttered moving backgrounds. Compared to the existing models, the
small-field and wide-field visual features are separately extracted by two
motion-sensitive neurons to detect small target motion and background motion.
These two types of motion information are further integrated to filter out
false positives. Extensive experiments showed that the proposed model can
outperform the existing models in terms of detection rates. | ['Huatian Wang', 'Hongxin Wang', 'Qinbing Fu', 'Shigang Yue', 'Jigen Peng'] | 2019-03-18 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.77013397e-01 -9.70925033e-01 5.29496977e-03 -4.60526068e-03
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84b684e8-4f17-4878-9f73-067a4c46f3f1 | swinfir-revisiting-the-swinir-with-fast | 2208.11247 | null | https://arxiv.org/abs/2208.11247v2 | https://arxiv.org/pdf/2208.11247v2.pdf | SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution | Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, advances like SwinIR adopts the window-based and local attention strategy to balance the performance and computational overhead, which restricts employing large receptive fields to capture global information and establish long dependencies in the early layers. To further improve the efficiency of capturing global information, in this work, we propose SwinFIR to extend SwinIR by replacing Fast Fourier Convolution (FFC) components, which have the image-wide receptive field. We also revisit other advanced techniques, i.e, data augmentation, pre-training, and feature ensemble to improve the effect of image reconstruction. And our feature ensemble method enables the performance of the model to be considerably enhanced without increasing the training and testing time. We applied our algorithm on multiple popular large-scale benchmarks and achieved state-of-the-art performance comparing to the existing methods. For example, our SwinFIR achieves the PSNR of 32.83 dB on Manga109 dataset, which is 0.8 dB higher than the state-of-the-art SwinIR method. | ['Zhezhu Jin', 'Xiaobing Wang', 'Shizhuo Liu', 'Feiyu Huang', 'Dafeng Zhang'] | 2022-08-24 | null | null | null | null | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 7.69877806e-02 -5.80724418e-01 1.12736166e-01 -2.09111303e-01
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609f9abb-fd00-4690-a14f-cdcbeffc9dad | explainable-recommender-with-geometric | 2305.05331 | null | https://arxiv.org/abs/2305.05331v1 | https://arxiv.org/pdf/2305.05331v1.pdf | Explainable Recommender with Geometric Information Bottleneck | Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours. | ['Yulan He', 'Kun Zhang', 'Menghan Wang', 'Lin Gui', 'Hanqi Yan'] | 2023-05-09 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 1.65270209e-01 9.80446517e-01 -4.43095624e-01 -7.46956348e-01
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3d7f0585-07e4-4526-9eee-8f0284eb1f0d | a-multi-task-joint-framework-for-real-time | 2012.06418 | null | https://arxiv.org/abs/2012.06418v1 | https://arxiv.org/pdf/2012.06418v1.pdf | A Multi-task Joint Framework for Real-time Person Search | Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult to achieve real-time in real-world applications. To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively. For the person detection module, we proposed the YOLOv5-GS model, which is trained with person dataset. It combines the advantages of the Ghostnet and the Squeeze-and-Excitation (SE) block, and improves the speed and accuracy. For the feature extraction module, we design the Model Adaptation Architecture (MAA), which could select different network according to the number of people. It could balance the relationship between accuracy and speed. For identity comparison, we propose a Three Dimension (3D) Pooled Table and a matching strategy to improve identification accuracy. On the condition of 1920*1080 resolution video and 500 IDs table, the identification rate (IR) and frames per second (FPS) achieved by our method could reach 93.6% and 25.7, | ['Guangqiang Yin', 'Chunyu Wang', 'Jie Liang', 'Kangning Yin', 'Ye Li'] | 2020-12-11 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.53782517e-01 -9.34648216e-01 1.44425169e-01 -1.99242979e-01
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614dc53a-9db9-4813-8c76-fdb12509792c | riffled-independence-for-ranked-data | null | null | http://papers.nips.cc/paper/3775-riffled-independence-for-ranked-data | http://papers.nips.cc/paper/3775-riffled-independence-for-ranked-data.pdf | Riffled Independence for Ranked Data | Representing distributions over permutations can be a daunting task due to the fact that the number of permutations of n objects scales factorially in n. One recent way that has been used to reduce storage complexity has been to exploit probabilistic independence, but as we argue, full independence assumptions impose strong sparsity constraints on distributions and are unsuitable for modeling rankings. We identify a novel class of independence structures, called riffled independence, which encompasses a more expressive family of distributions while retaining many of the properties necessary for performing efficient inference and reducing sample complexity. In riffled independence, one draws two permutations independently, then performs the riffle shuffle, common in card games, to combine the two permutations to form a single permutation. In ranking, riffled independence corresponds to ranking disjoint sets of objects independently, then interleaving those rankings. We provide a formal introduction and present algorithms for using riffled independence within Fourier-theoretic frameworks which have been explored by a number of recent papers. | ['Carlos Guestrin', 'Jonathan Huang'] | 2009-12-01 | null | null | null | neurips-2009-12 | ['card-games'] | ['playing-games'] | [ 4.50827241e-01 -2.91395694e-01 -9.05710682e-02 -3.38818312e-01
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caa6156e-214f-412c-9a14-54112727f4b2 | fable-fabric-anomaly-detection-automation | 2306.10089 | null | https://arxiv.org/abs/2306.10089v1 | https://arxiv.org/pdf/2306.10089v1.pdf | FABLE : Fabric Anomaly Detection Automation Process | Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to detect anomaly notwithstanding some specific industrial scenario requiring even less specific training and therefore a generalization for anomaly detection. The obvious use case is the fabric anomaly detection, where we have to deal with a really wide range of colors and types of textile and a stoppage of the production line for training could not be considered. In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process during the domain-generalized anomaly detection. Combining the ability to generalize and the learning process offer a fast and precise anomaly detection and segmentation. The main contributions of this paper are the following: A domain-generalization texture anomaly detection method achieving the state-of-the-art performances, a fast specific training on good samples extracted by the proposed method, a self-evaluation method based on custom defect creation and an automatic detection of already seen fabric to prevent re-training. | ['Mahmoud Soua', 'Hichem Snoussi', 'Simon Thomine'] | 2023-06-16 | null | null | null | null | ['defect-detection', 'domain-generalization', 'anomaly-detection', 'specificity'] | ['computer-vision', 'methodology', 'methodology', 'natural-language-processing'] | [ 4.21337783e-01 -2.14693509e-02 5.49268663e-01 -4.04568166e-01
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b56f8aeb-d557-4808-8f66-be7f7a0f305a | treedqn-learning-to-minimize-branch-and-bound | 2306.05905 | null | https://arxiv.org/abs/2306.05905v1 | https://arxiv.org/pdf/2306.05905v1.pdf | TreeDQN: Learning to minimize Branch-and-Bound tree | Combinatorial optimization problems require an exhaustive search to find the optimal solution. A convenient approach to solving combinatorial optimization tasks in the form of Mixed Integer Linear Programs is Branch-and-Bound. Branch-and-Bound solver splits a task into two parts dividing the domain of an integer variable, then it solves them recursively, producing a tree of nested sub-tasks. The efficiency of the solver depends on the branchning heuristic used to select a variable for splitting. In the present work, we propose a reinforcement learning method that can efficiently learn the branching heuristic. We view the variable selection task as a tree Markov Decision Process, prove that the Bellman operator adapted for the tree Markov Decision Process is contracting in mean, and propose a modified learning objective for the reinforcement learning agent. Our agent requires less training data and produces smaller trees compared to previous reinforcement learning methods. | ['Alexander Kostin', 'Dmitry Sorokin'] | 2023-06-09 | null | null | null | null | ['combinatorial-optimization', 'variable-selection'] | ['methodology', 'methodology'] | [ 3.39746028e-01 5.60188591e-01 -8.68522584e-01 -1.40008628e-01
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db2e2957-8bd4-4657-9b2d-771b2b932ffd | log-based-anomaly-detection-without-log | 2108.01955 | null | https://arxiv.org/abs/2108.01955v3 | https://arxiv.org/pdf/2108.01955v3.pdf | Log-based Anomaly Detection Without Log Parsing | Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vocabulary) words, and 2) semantic misunderstandings. The log parsing errors could cause the loss of important information for anomaly detection. To address the limitations of existing methods, we propose NeuralLog, a novel log-based anomaly detection approach that does not require log parsing. NeuralLog extracts the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Our experimental results show that the proposed approach can effectively understand the semantic meaning of log messages and achieve accurate anomaly detection results. Overall, NeuralLog achieves F1-scores greater than 0.95 on four public datasets, outperforming the existing approaches. | ['Hongyu Zhang', 'Van-Hoang Le'] | 2021-08-04 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [ 8.12664777e-02 -3.46586317e-01 -1.47088706e-01 -4.85137790e-01
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31e09361-8d85-43fc-8198-93376a000a5c | heuristic-free-optimization-of-force | 2207.07524 | null | https://arxiv.org/abs/2207.07524v1 | https://arxiv.org/pdf/2207.07524v1.pdf | Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments | In both industrial and service domains, a central benefit of the use of robots is their ability to quickly and reliably execute repetitive tasks. However, even relatively simple peg-in-hole tasks are typically subject to stochastic variations, requiring search motions to find relevant features such as holes. While search improves robustness, it comes at the cost of increased runtime: More exhaustive search will maximize the probability of successfully executing a given task, but will significantly delay any downstream tasks. This trade-off is typically resolved by human experts according to simple heuristics, which are rarely optimal. This paper introduces an automatic, data-driven and heuristic-free approach to optimize robot search strategies. By training a neural model of the search strategy on a large set of simulated stochastic environments, conditioning it on few real-world examples and inverting the model, we can infer search strategies which adapt to the time-variant characteristics of the underlying probability distributions, while requiring very few real-world measurements. We evaluate our approach on two different industrial robots in the context of spiral and probe search for THT electronics assembly. | ['Michael Beetz', 'Rainer Jäkel', 'Darko Katic', 'Benjamin Alt'] | 2022-07-15 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 3.08963388e-01 6.13931799e-03 1.16561547e-01 -1.05519705e-01
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8e211fb9-4af7-41ba-99fe-c2e3480bcb96 | whats-in-a-name-answer-equivalence-for-open | null | null | https://aclanthology.org/2021.emnlp-main.757 | https://aclanthology.org/2021.emnlp-main.757.pdf | What’s in a Name? Answer Equivalence For Open-Domain Question Answering | A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation. | ['Jordan Boyd-Graber', 'Chen Zhao', 'Chenglei Si'] | null | null | null | null | emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [-3.46905701e-02 7.93131948e-01 -2.15584233e-01 -5.83120763e-01
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ab8f7bef-98a9-43eb-b27b-bbc907d9d8f2 | gated-dilated-networks-for-lung-nodule | 1901.00120 | null | https://arxiv.org/abs/1901.00120v2 | https://arxiv.org/pdf/1901.00120v2.pdf | Gated-Dilated Networks for Lung Nodule Classification in CT scans | Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and challenging task. In this study, we propose a novel CNN architecture called Gated-Dilated (GD) networks to classify nodules as malignant or benign. Unlike previous studies, the GD network uses multiple dilated convolutions instead of max-poolings to capture the scale variations. Moreover, the GD network has a Context-Aware sub-network that analyzes the input features and guides the features to a suitable dilated convolution. We evaluated the proposed network on more than 1,000 CT scans from the LIDC-LDRI dataset. Our proposed network outperforms state-of-the-art baseline models including Multi-Crop, Resnet, and Densenet, with an AUC of >0.95. Compared to the baseline models, the GD network improves the classification accuracies of mid-range sized nodules. Furthermore, we observe a relationship between the size of the nodule and the attention signal generated by the Context-Aware sub-network, which validates our new network architecture. | ['Mundher Al-Shabi', 'Maxine Tan', 'Hwee Kuan Lee'] | 2019-01-01 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.39770450e-02 1.99343279e-01 -3.53237480e-01 -2.34180138e-01
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eaed4825-9b43-4b6a-9eb1-3b789007b0bb | jpeg-artifact-correction-using-denoising | 2209.11888 | null | https://arxiv.org/abs/2209.11888v2 | https://arxiv.org/pdf/2209.11888v2.pdf | JPEG Artifact Correction using Denoising Diffusion Restoration Models | Diffusion models can be used as learned priors for solving various inverse problems. However, most existing approaches are restricted to linear inverse problems, limiting their applicability to more general cases. In this paper, we build upon Denoising Diffusion Restoration Models (DDRM) and propose a method for solving some non-linear inverse problems. We leverage the pseudo-inverse operator used in DDRM and generalize this concept for other measurement operators, which allows us to use pre-trained unconditional diffusion models for applications such as JPEG artifact correction. We empirically demonstrate the effectiveness of our approach across various quality factors, attaining performance levels that are on par with state-of-the-art methods trained specifically for the JPEG restoration task. | ['Michael Elad', 'Stefano Ermon', 'Jiaming Song', 'Bahjat Kawar'] | 2022-09-23 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 4.39782470e-01 -1.55517772e-01 4.37028073e-02 -1.94056198e-01
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18cabd58-82f1-4f87-9791-6e97254ef6a9 | ressl-relational-self-supervised-learning | 2107.09282 | null | https://arxiv.org/abs/2107.09282v2 | https://arxiv.org/pdf/2107.09282v2.pdf | ReSSL: Relational Self-Supervised Learning with Weak Augmentation | Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduced a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations. Moreover, to boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. Experimental results show that our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency. Code is available at \url{https://github.com/KyleZheng1997/ReSSL}. | ['Chang Xu', 'Xiaogang Wang', 'ChangShui Zhang', 'Chen Qian', 'Fei Wang', 'Shan You', 'Mingkai Zheng'] | 2021-07-20 | null | http://proceedings.neurips.cc/paper/2021/hash/14c4f36143b4b09cbc320d7c95a50ee7-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/14c4f36143b4b09cbc320d7c95a50ee7-Paper.pdf | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [-1.82727799e-02 9.11355019e-02 -5.02114952e-01 -3.88045311e-01
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0a6259e7-dc6e-4ed9-91ac-904edcb198ae | adversarial-text-to-image-synthesis-a-review | 2101.09983 | null | https://arxiv.org/abs/2101.09983v2 | https://arxiv.org/pdf/2101.09983v2.pdf | Adversarial Text-to-Image Synthesis: A Review | With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last years regarding visual realism, diversity, and semantic alignment. However, the field still faces several challenges that require further research efforts such as enabling the generation of high-resolution images with multiple objects, and developing suitable and reliable evaluation metrics that correlate with human judgement. In this review, we contextualize the state of the art of adversarial text-to-image synthesis models, their development since their inception five years ago, and propose a taxonomy based on the level of supervision. We critically examine current strategies to evaluate text-to-image synthesis models, highlight shortcomings, and identify new areas of research, ranging from the development of better datasets and evaluation metrics to possible improvements in architectural design and model training. This review complements previous surveys on generative adversarial networks with a focus on text-to-image synthesis which we believe will help researchers to further advance the field. | ['Andreas Dengel', 'Jörn Hees', 'Federico Raue', 'Tobias Hinz', 'Stanislav Frolov'] | 2021-01-25 | null | null | null | null | ['adversarial-text', 'conditional-image-generation'] | ['adversarial', 'computer-vision'] | [ 7.00198054e-01 1.74054950e-01 1.01861112e-01 -4.64708418e-01
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f9c80a5e-7526-4498-a9aa-8acb586af6c7 | fedadmm-a-federated-primal-dual-algorithm | 2203.15104 | null | https://arxiv.org/abs/2203.15104v1 | https://arxiv.org/pdf/2203.15104v1.pdf | FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation | Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its ability to accommodate heterogeneity in client compute and storage resources, non-i.i.d. data assumptions, and data privacy. Our contribution is to offer a new federated learning algorithm, FedADMM, for solving non-convex composite optimization problems with non-smooth regularizers. We prove converges of FedADMM for the case when not all clients are able to participate in a given communication round under a very general sampling model. | ['James Anderson', 'Siddartha Marella', 'Han Wang'] | 2022-03-28 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.95803893e-01 -1.83800161e-01 -3.48581135e-01 -5.24110496e-01
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85e50d1a-4c85-468c-ad16-4f6c7d5c3753 | kgi-an-integrated-framework-for-knowledge | 2204.03985 | null | https://arxiv.org/abs/2204.03985v2 | https://arxiv.org/pdf/2204.03985v2.pdf | KGI: An Integrated Framework for Knowledge Intensive Language Tasks | In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking. Moreover, given a user query, we show how the output from these different models can be combined to cross-examine the outputs of each other. Particularly, we show how accuracy in dialogue can be improved using the question answering model. We are also releasing all models used in the demo as a contribution of this paper. A short video demonstrating the system is available at https://ibm.box.com/v/emnlp2022-demo. | ['Nandana Mihindukulasooriya', 'Alfio Gliozzo', 'Gaetano Rossiello', 'Michael Glass', 'Md Faisal Mahbub Chowdhury'] | 2022-04-08 | null | null | null | null | ['zero-shot-slot-filling', 'passage-retrieval', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.86823618e-02 9.78989899e-01 6.13377728e-02 -1.31679416e-01
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9e3c1c71-16c7-4ead-aded-ad57a5b0b4bf | subspace-clustering-via-optimal-direction | 1706.03860 | null | http://arxiv.org/abs/1706.03860v4 | http://arxiv.org/pdf/1706.03860v4.pdf | Subspace Clustering via Optimal Direction Search | This letter presents a new spectral-clustering-based approach to the subspace
clustering problem. Underpinning the proposed method is a convex program for
optimal direction search, which for each data point d finds an optimal
direction in the span of the data that has minimum projection on the other data
points and non-vanishing projection on d. The obtained directions are
subsequently leveraged to identify a neighborhood set for each data point. An
alternating direction method of multipliers framework is provided to
efficiently solve for the optimal directions. The proposed method is shown to
notably outperform the existing subspace clustering methods, particularly for
unwieldy scenarios involving high levels of noise and close subspaces, and
yields the state-of-the-art results for the problem of face clustering using
subspace segmentation. | ['Mostafa Rahmani', 'George Atia'] | 2017-06-12 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.62612215e-01 -2.35313162e-01 -3.88615519e-01 -5.52947298e-02
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0583a5e0-bf62-404f-ada9-90094f5661bd | scene-graph-generation-a-comprehensive-survey | 2201.00443 | null | https://arxiv.org/abs/2201.00443v2 | https://arxiv.org/pdf/2201.00443v2.pdf | Scene Graph Generation: A Comprehensive Survey | Deep learning techniques have led to remarkable breakthroughs in the field of generic object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. Although this is a challenging task, the community has proposed a lot of SGG approaches and achieved good results. In this paper, we provide a comprehensive survey of recent achievements in this field brought about by deep learning techniques. We review 138 representative works that cover different input modalities, and systematically summarize existing methods of image-based SGG from the perspective of feature extraction and fusion. We attempt to connect and systematize the existing visual relationship detection methods, to summarize, and interpret the mechanisms and the strategies of SGG in a comprehensive way. Finally, we finish this survey with deep discussions about current existing problems and future research directions. This survey will help readers to develop a better understanding of the current research status and ideas. | ['Mohammed Bennamoun', 'Syed Afaq Ali Shah', 'Qiguang Miao', 'Xia Zhao', 'Mingtao Feng', 'Peiyi Shen', 'Haoran Hou', 'Yixuan Dang', 'Youliang Jiang', 'Liang Zhang', 'Guangming Zhu'] | 2022-01-03 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.20660639e-01 1.40963709e-02 -1.44522190e-01 -4.81690496e-01
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4f4d0dd6-397d-42e0-83e7-a539496d4ced | general-transformation-for-consistent-online | 2306.07163 | null | https://arxiv.org/abs/2306.07163v1 | https://arxiv.org/pdf/2306.07163v1.pdf | General Transformation for Consistent Online Approximation Algorithms | We introduce a transformation framework that can be utilized to develop online algorithms with low $\epsilon$-approximate regret in the random-order model from offline approximation algorithms. We first give a general reduction theorem that transforms an offline approximation algorithm with low average sensitivity to an online algorithm with low $\epsilon$-approximate regret. We then demonstrate that offline approximation algorithms can be transformed into a low-sensitivity version using a coreset construction method. To showcase the versatility of our approach, we apply it to various problems, including online $(k,z)$-clustering, online matrix approximation, and online regression, and successfully achieve polylogarithmic $\epsilon$-approximate regret for each problem. Moreover, we show that in all three cases, our algorithm also enjoys low inconsistency, which may be desired in some online applications. | ['Yuichi Yoshida', 'Jing Dong'] | 2023-06-12 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.17296943e-01 3.92314315e-01 -1.91374540e-01 -1.98161110e-01
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11a6c53d-6a31-408d-b783-60ebfb68685f | combining-human-parsing-with-analytical | 2207.14243 | null | https://arxiv.org/abs/2207.14243v1 | https://arxiv.org/pdf/2207.14243v1.pdf | Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification | Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning and particularly Deep Learning (DL) has become the main re-id tool that allowed researches to achieve unprecedented accuracy levels on benchmark datasets. However, there is a known problem of poor generalization of DL models. That is, models trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training. To address this issue, we present a model without trainable parameters which shows great potential for high generalization. It combines a fully analytical feature extraction and similarity ranking scheme with DL-based human parsing used to obtain the initial subregion classification. We show that such combination to a high extent eliminates the drawbacks of existing analytical methods. We use interpretable color and texture features which have human-readable similarity measures associated with them. To verify the proposed method we conduct experiments on Market1501 and CUHK03 datasets achieving competitive rank-1 accuracy comparable with that of DL-models. Most importantly we show that our method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to transfer learning tasks. It is significantly higher than previously reported 30-50% transfer accuracy. We discuss the potential ways of adding new features to further improve the model. We also show the advantage of interpretable features for constructing human-generated queries from verbal description to conduct search without a query image. | ['Nikita Gabdullin'] | 2022-07-28 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [-2.41959602e-01 -1.20059013e-01 -1.46585137e-01 -5.79406381e-01
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f58bb7ab-4c83-4644-a18e-9238e67ee60c | 3d-cinemagraphy-from-a-single-image | 2303.05724 | null | https://arxiv.org/abs/2303.05724v1 | https://arxiv.org/pdf/2303.05724v1.pdf | 3D Cinemagraphy from a Single Image | We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emergence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthesize novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method. | ['Guosheng Lin', 'Ke Xian', 'Jianming Zhang', 'Huiqiang Sun', 'Zhiguo Cao', 'Xingyi Li'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_3D_Cinemagraphy_From_a_Single_Image_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-animation'] | ['computer-vision'] | [ 3.21519196e-01 1.14213765e-01 2.87556738e-01 2.75138561e-02
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01aaa343-fd74-4211-b465-2447c2e375de | lamp-hq-a-large-scale-multi-pose-high-quality | 1912.07809 | null | https://arxiv.org/abs/1912.07809v2 | https://arxiv.org/pdf/1912.07809v2.pdf | LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition | Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recognition has attracted considerable attention in the computer vision community because of its convenience and adaptability in practical applications. Various deep learning-based methods have been proposed and substantially increased the recognition performance, but the lack of NIR-VIS training samples leads to the difficulty of the model training process. In this paper, we propose a new Large-Scale Multi-Pose High-Quality NIR-VIS database LAMP-HQ containing 56,788 NIR and 16,828 VIS images of 573 subjects with large diversities in pose, illumination, attribute, scene and accessory. We furnish a benchmark along with the protocol for NIR-VIS face recognition via generation on LAMP-HQ, including Pixel2Pixel, CycleGAN, and ADFL. Furthermore, we propose a novel exemplar-based variational spectral attention network to produce high-fidelity VIS images from NIR data. A spectral conditional attention module is introduced to reduce the domain gap between NIR and VIS data and then improve the performance of NIR-VIS heterogeneous face recognition on various databases including the LAMP-HQ. | ['Zhen Lei', 'Huaibo Huang', 'Haoxue Wu', 'Ran He', 'Aijing Yu'] | 2019-12-17 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 2.88705498e-01 -5.34172654e-01 1.73605412e-01 -5.16562343e-01
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ccf7df5f-da28-493c-b8a3-351e47c77462 | targeted-collapse-regularized-autoencoder-for | 2306.12627 | null | https://arxiv.org/abs/2306.12627v1 | https://arxiv.org/pdf/2306.12627v1.pdf | Targeted collapse regularized autoencoder for anomaly detection: black hole at the center | Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the autoencoder on normal training data, anomalous inputs will exhibit a significant reconstruction error. Consequently, this enables a clear differentiation between normal and anomalous samples. In practice, however, it is observed that autoencoders can generalize beyond the normal class and achieve a small reconstruction error on some of the anomalous samples. To improve the performance, various techniques propose additional components and more sophisticated training procedures. In this work, we propose a remarkably straightforward alternative: instead of adding neural network components, involved computations, and cumbersome training, we complement the reconstruction loss with a computationally light term that regulates the norm of representations in the latent space. The simplicity of our approach minimizes the requirement for hyperparameter tuning and customization for new applications which, paired with its permissive data modality constraint, enhances the potential for successful adoption across a broad range of applications. We test the method on various visual and tabular benchmarks and demonstrate that the technique matches and frequently outperforms alternatives. We also provide a theoretical analysis and numerical simulations that help demonstrate the underlying process that unfolds during training and how it can help with anomaly detection. This mitigates the black-box nature of autoencoder-based anomaly detection algorithms and offers an avenue for further investigation of advantages, fail cases, and potential new directions. | ['Iman Soltani Bozchalooi', 'Dimitar Filev', 'Rajesh Gupta', 'Devesh Upadhyay', 'Huanyi Shui', 'Amin Ghafourian'] | 2023-06-22 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 7.17627779e-02 -1.13121688e-01 1.77688748e-01 -2.87382811e-01
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b5bfa3d2-7a42-46cc-a930-53907b8b3649 | improved-prediction-of-soil-properties-with | 2002.04312 | null | https://arxiv.org/abs/2002.04312v1 | https://arxiv.org/pdf/2002.04312v1.pdf | Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra | Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%. | ['Saulo Martiello Mastelini', 'Everton Jose Santana', 'Sylvio Barbon Jr', 'Felipe Rodrigues dos Santos', 'Fabio Luiz Melquiades'] | 2020-02-11 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.41012055e-01 -3.56709033e-01 -2.28898779e-01 -2.58651488e-02
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8dc883e5-d656-4460-9d88-f5a86220fa67 | a-deep-learning-approach-for-fetal-qrs | null | null | https://iopscience.iop.org/article/10.1088/1361-6579/aab297/meta | https://iopscience.iop.org/article/10.1088/1361-6579/aab297/meta | A deep learning approach for fetal QRS complex detection | Objective: Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide
more additional clinical information for detecting and diagnosing fetal diseases. We propose and
demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals
by using a convolutional neural network (CNN) model. The main objective is to investigate whether
reliable fetal QRS complex detection performance can still be obtained from features of singlechannel NI-FECG signals, without canceling maternal ECG (MECG) signals. Approach: A deep
learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a
of the PhysioNet/Computing in Cardiology Challenge database. The sample entropy method is
used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis.
Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before
they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall,
F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex
detection. Main results: The proposed deep learning method can achieve relatively high precision
(75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known
pattern classification methods, namely KNN, naive Bayes and SVM. Significance: The proposed
deep learning method can attain reliable fetal QRS complex detection performance from the raw
NI-FECG signals without canceling MECG signals. In addition, the influence of different activation
functions and signal quality assessment on classification performance are evaluated, and results
show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification
performance is obtained with the signal quality assessment step in this study. | ['Xuemei Guo and GuoliWang', 'Lijuan Liao', 'Wei Zhong'] | 2018-04-20 | null | null | null | physiol-meas-39-2018-045004-9pp-2018-4 | ['qrs-complex-detection'] | ['medical'] | [ 1.97262704e-01 5.96095696e-02 1.24100320e-01 -4.50215101e-01
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6e47121a-0487-476b-931e-2feed4378984 | automatic-semantic-modeling-for-structural | 2212.10915 | null | https://arxiv.org/abs/2212.10915v1 | https://arxiv.org/pdf/2212.10915v1.pdf | Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base | A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known. | ['Zaiwen Feng', 'Keqing He', 'Hongyu Zhang', 'Wolfgang Mayer', 'Jiakang Xu'] | 2022-12-21 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 1.41269088e-01 6.31134391e-01 -5.94447792e-01 -5.17920494e-01
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b2a94e9e-d35f-4201-bcdc-a3ab455d9a68 | fg-net-fast-large-scale-lidar-point | 2012.09439 | null | https://arxiv.org/abs/2012.09439v2 | https://arxiv.org/pdf/2012.09439v2.pdf | FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling | This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and outlier filtering method is designed to facilitate subsequent high-level tasks. For effective understanding purpose, we propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling, in which the local feature relationships and geometric patterns can be fully exploited. For the efficiency issue, we put forward an inverse density sampling operation and a feature pyramid based residual learning strategy to save the computational cost and memory consumption respectively. Extensive experiments on real-world challenging datasets demonstrated that our approaches outperform state-of-the-art approaches in terms of accuracy and efficiency. Moreover, weakly supervised transfer learning is also conducted to demonstrate the generalization capacity of our method. | ['Ben M. Chen', 'Feng Lin', 'Zhi Gao', 'Kangcheng Liu'] | 2020-12-17 | null | null | null | null | ['3d-part-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-4.20794994e-01 -5.42901576e-01 1.52861103e-01 -3.30675423e-01
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b170094b-510d-41c9-9348-15e66e29f53e | instance-based-inductive-deep-transfer | 1802.05934 | null | http://arxiv.org/abs/1802.05934v1 | http://arxiv.org/pdf/1802.05934v1.pdf | Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing | Supervised learning models are typically trained on a single dataset and the
performance of these models rely heavily on the size of the dataset, i.e.,
amount of data available with the ground truth. Learning algorithms try to
generalize solely based on the data that is presented with during the training.
In this work, we propose an inductive transfer learning method that can augment
learning models by infusing similar instances from different learning tasks in
the Natural Language Processing (NLP) domain. We propose to use instance
representations from a source dataset, \textit{without inheriting anything}
from the source learning model. Representations of the instances of
\textit{source} \& \textit{target} datasets are learned, retrieval of relevant
source instances is performed using soft-attention mechanism and
\textit{locality sensitive hashing}, and then, augmented into the model during
training on the target dataset. Our approach simultaneously exploits the local
\textit{instance level information} as well as the macro statistical viewpoint
of the dataset. Using this approach we have shown significant improvements for
three major news classification datasets over the baseline. Experimental
evaluations also show that the proposed approach reduces dependency on labeled
data by a significant margin for comparable performance. With our proposed
cross dataset learning procedure we show that one can achieve
competitive/better performance than learning from a single dataset. | ['K. M. Annervaz', 'Ambedkar Dukkipati', 'Somnath Basu Roy Chowdhury'] | 2018-02-16 | instance-based-inductive-deep-transfer-1 | https://aclanthology.org/D19-6120 | https://aclanthology.org/D19-6120.pdf | ws-2019-11 | ['news-classification'] | ['natural-language-processing'] | [ 3.58948618e-01 2.86501616e-01 -5.08960783e-01 -7.00648069e-01
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96d2da24-cd53-4ff4-b12f-ad8e9a6bbfc5 | local-aggregation-for-unsupervised-learning | 1903.12355 | null | http://arxiv.org/abs/1903.12355v2 | http://arxiv.org/pdf/1903.12355v2.pdf | Local Aggregation for Unsupervised Learning of Visual Embeddings | Unsupervised approaches to learning in neural networks are of substantial
interest for furthering artificial intelligence, both because they would enable
the training of networks without the need for large numbers of expensive
annotations, and because they would be better models of the kind of
general-purpose learning deployed by humans. However, unsupervised networks
have long lagged behind the performance of their supervised counterparts,
especially in the domain of large-scale visual recognition. Recent developments
in training deep convolutional embeddings to maximize non-parametric instance
separation and clustering objectives have shown promise in closing this gap.
Here, we describe a method that trains an embedding function to maximize a
metric of local aggregation, causing similar data instances to move together in
the embedding space, while allowing dissimilar instances to separate. This
aggregation metric is dynamic, allowing soft clusters of different scales to
emerge. We evaluate our procedure on several large-scale visual recognition
datasets, achieving state-of-the-art unsupervised transfer learning performance
on object recognition in ImageNet, scene recognition in Places 205, and object
detection in PASCAL VOC. | ['Alex Lin Zhai', 'Daniel Yamins', 'Chengxu Zhuang'] | 2019-03-29 | local-aggregation-for-unsupervised-learning-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhuang_Local_Aggregation_for_Unsupervised_Learning_of_Visual_Embeddings_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhuang_Local_Aggregation_for_Unsupervised_Learning_of_Visual_Embeddings_ICCV_2019_paper.pdf | iccv-2019-10 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.61622182e-01 8.39523450e-02 -2.00357996e-02 -8.42846096e-01
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25683ef5-da26-4536-9ea0-e538a135c5a1 | greedy-approximate-projection-for-magnetic | 1807.06912 | null | http://arxiv.org/abs/1807.06912v2 | http://arxiv.org/pdf/1807.06912v2.pdf | Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes | In quantitative Magnetic Resonance Imaging, traditional methods suffer from
the so-called Partial Volume Effect (PVE) due to spatial resolution
limitations. As a consequence of PVE, the parameters of the voxels containing
more than one tissue are not correctly estimated. Magnetic Resonance
Fingerprinting (MRF) is not an exception. The existing methods addressing PVE
are neither scalable nor accurate. We propose to formulate the recovery of
multiple tissues per voxel as a nonconvex constrained least-squares
minimisation problem. To solve this problem, we develop a memory efficient,
greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our
method adaptively finds the regions of interest on the manifold of fingerprints
defined by the MRF sequence. We generalise our method to compensate for phase
errors appearing in the model, using an alternating minimisation approach. We
show, through simulations on synthetic data with PVE, that our algorithm
outperforms state-of-the-art methods. Our approach is validated on the EUROSPIN
phantom and on in vivo datasets. | [] | 2018-11-28 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.78580838e-01 7.99992681e-02 1.85287327e-01 -2.95829624e-01
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ebf2eecf-cd87-43c7-800c-786e305059d8 | grounded-language-learning-fast-and-slow | 2009.01719 | null | https://arxiv.org/abs/2009.01719v4 | https://arxiv.org/pdf/2009.01719v4.pdf | Grounded Language Learning Fast and Slow | Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning. Here, we show that an embodied agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can exhibit similar one-shot word learning when trained with conventional reinforcement learning algorithms. After a single introduction to a novel object via continuous visual perception and a language prompt ("This is a dax"), the agent can re-identify the object and manipulate it as instructed ("Put the dax on the bed"). In doing so, it seamlessly integrates short-term, within-episode knowledge of the appropriate referent for the word "dax" with long-term lexical and motor knowledge acquired across episodes (i.e. "bed" and "putting"). We find that, under certain training conditions and with a particular memory writing mechanism, the agent's one-shot word-object binding generalizes to novel exemplars within the same ShapeNet category, and is effective in settings with unfamiliar numbers of objects. We further show how dual-coding memory can be exploited as a signal for intrinsic motivation, stimulating the agent to seek names for objects that may be useful for later executing instructions. Together, the results demonstrate that deep neural networks can exploit meta-learning, episodic memory and an explicitly multi-modal environment to account for 'fast-mapping', a fundamental pillar of human cognitive development and a potentially transformative capacity for agents that interact with human users. | ['Felix Hill', 'Stephen Clark', 'Olivier Tieleman', 'Tamara von Glehn', 'Hamza Merzic', 'Nathaniel Wong'] | 2020-09-03 | null | https://openreview.net/forum?id=wpSWuz_hyqA | https://openreview.net/pdf?id=wpSWuz_hyqA | iclr-2021-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.55620113e-01 6.31615072e-02 6.20878302e-03 7.55448341e-02
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11601902-1afb-45a5-be4f-4286f028bce5 | image-completion-with-heterogeneously | 2211.03700 | null | https://arxiv.org/abs/2211.03700v1 | https://arxiv.org/pdf/2211.03700v1.pdf | Image Completion with Heterogeneously Filtered Spectral Hints | Image completion with large-scale free-form missing regions is one of the most challenging tasks for the computer vision community. While researchers pursue better solutions, drawbacks such as pattern unawareness, blurry textures, and structure distortion remain noticeable, and thus leave space for improvement. To overcome these challenges, we propose a new StyleGAN-based image completion network, Spectral Hint GAN (SH-GAN), inside which a carefully designed spectral processing module, Spectral Hint Unit, is introduced. We also propose two novel 2D spectral processing strategies, Heterogeneous Filtering and Gaussian Split that well-fit modern deep learning models and may further be extended to other tasks. From our inclusive experiments, we demonstrate that our model can reach FID scores of 3.4134 and 7.0277 on the benchmark datasets FFHQ and Places2, and therefore outperforms prior works and reaches a new state-of-the-art. We also prove the effectiveness of our design via ablation studies, from which one may notice that the aforementioned challenges, i.e. pattern unawareness, blurry textures, and structure distortion, can be noticeably resolved. Our code will be open-sourced at: https://github.com/SHI-Labs/SH-GAN. | ['Humphrey Shi', 'Yadong Mu', 'Andranik Sargsyan', 'Vahram Tadevosyan', 'Shant Navasardyan', 'Xingqian Xu'] | 2022-11-07 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.70623738e-01 7.21958093e-03 1.22383341e-01 -2.11550176e-01
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d7bfdb1d-347b-4e0b-93f0-0b42a284d36d | barycenters-of-natural-images-constrained-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Simon_Barycenters_of_Natural_Images__Constrained_Wasserstein_Barycenters_for_Image_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Simon_Barycenters_of_Natural_Images__Constrained_Wasserstein_Barycenters_for_Image_CVPR_2020_paper.pdf | Barycenters of Natural Images Constrained Wasserstein Barycenters for Image Morphing | Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in each image in the transition. To obtain a smooth and straightforward transition, one may adopt the well-known Wasserstein Barycenter Problem (WBP). While this approach guarantees minimal changes under the Wasserstein metric, the resulting images might seem unnatural. In this work, we propose a novel approach for image morphing that possesses all three desired properties. To this end, we define a constrained variant of the WBP that enforces the intermediate images to satisfy an image prior. We describe an algorithm that solves this problem and demonstrate it using the sparse prior and generative adversarial networks.
| [' Aviad Aberdam', 'Dror Simon'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-morphing'] | ['computer-vision'] | [ 5.69100916e-01 3.77516121e-01 2.51802385e-01 -2.33149186e-01
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e41d7629-c3d8-45bf-80d1-d3249cb3ae2d | similarity-metric-learning-for-rgb-infrared | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.pdf | Similarity Metric Learning for RGB-Infrared Group Re-Identification | Group re-identification (G-ReID) aims to re-identify a group of people that is observed from non-overlapping camera systems. The existing literature has mainly addressed RGB-based problems, but RGB-infrared (RGB-IR) cross-modality matching problem has not been studied yet. In this paper, we propose a metric learning method Closest Permutation Matching (CPM) for RGB-IR G-ReID. We model each group as a set of single-person features which are extracted by MPANet, then we propose the metric Closest Permutation Distance (CPD) to measure the similarity between two sets of features. CPD is invariant with order changes of group members so that it solves the layout change problem in G-ReID. Furthermore, we introduce the problem of G-ReID without person labels. In the weak-supervised case, we design the Relation-aware Module (RAM) that exploits visual context and relations among group members to produce a modality-invariant order of features in each group, with which group member features within a set can be sorted to form a robust group representation against modality change. To support the study on RGB-IR G-ReID, we construct a new large-scale RGB-IR G-ReID dataset CM-Group. The dataset contains 15,440 RGB images and 15,506 infrared images of 427 groups and 1,013 identities. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed models and the complexity of CM-Group. The code and dataset are available at: https://github.com/WhollyOat/CM-Group. | ['JianHuang Lai', 'Jianghao Xiong'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.49231458e-02 -4.35724616e-01 5.57588041e-02 -4.93867934e-01
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89c01871-4da2-4a5f-9d7c-6f776c7bd9f7 | stacked-semantic-guided-network-for-zero-shot | 1904.01971 | null | https://arxiv.org/abs/1904.01971v2 | https://arxiv.org/pdf/1904.01971v2.pdf | Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is a task of cross-domain image retrieval from a natural image gallery with free-hand sketch under a zero-shot scenario. Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image. However, the intrinsic visual sparsity and large intra-class variance of the sketch make the learning of the conditional decoder more difficult and hence achieve unsatisfactory retrieval performance. In this paper, we propose a novel stacked semantic-guided network to address the unique characteristics of sketches in ZS-SBIR. Specifically, we devise multi-layer feature fusion networks that incorporate different intermediate feature representation information in a deep neural network to alleviate the intrinsic sparsity of sketches. In order to improve visual knowledge transfer from seen to unseen classes, we elaborate a coarse-to-fine conditional decoder that generates coarse-grained category-specific corresponding features first (taking auxiliary semantic information as conditional input) and then generates fine-grained instance-specific corresponding features (taking sketch representation as conditional input). Furthermore, regression loss and classification loss are utilized to preserve the semantic and discriminative information of the synthesized features respectively. Extensive experiments on the large-scale Sketchy dataset and TU-Berlin dataset demonstrate that our proposed approach outperforms state-of-the-art methods by more than 20\% in retrieval performance. | ['DaCheng Tao', 'Xinxu Xu', 'Cheng Deng', 'Wei Liu', 'Hao Wang', 'Xinbo Gao'] | 2019-04-03 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.36632961e-01 -4.34786648e-01 -2.89690733e-01 -3.37524593e-01
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3c6d9f7c-5830-4c34-96db-24e8174a61d5 | image-reconstruction-from-events-why-learn-it | 2112.06242 | null | https://arxiv.org/abs/2112.06242v3 | https://arxiv.org/pdf/2112.06242v3.pdf | Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow | Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously. Recovering brightness from events is appealing since the reconstructed images inherit the high dynamic range (HDR) and high-speed properties of events; hence they can be used in many robotic vision applications and to generate slow-motion HDR videos. However, state-of-the-art methods tackle this problem by training an event-to-image Recurrent Neural Network (RNN), which lacks explainability and is difficult to tune. In this work we show, for the first time, how tackling the combined problem of motion and brightness estimation leads us to formulate event-based image reconstruction as a linear inverse problem that can be solved without training an image reconstruction RNN. Instead, classical and learning-based regularizers are used to solve the problem and remove artifacts from the reconstructed images. The experiments show that the proposed approach generates images with visual quality on par with state-of-the-art methods despite only using data from a short time interval. State-of-the-art results are achieved using an image denoising Convolutional Neural Network (CNN) as the regularization function. The proposed regularized formulation and solvers have a unifying character because they can be applied also to reconstruct brightness from the second derivative. Additionally, the formulation is attractive because it can be naturally combined with super-resolution, motion-segmentation and color demosaicing. Code is available at https://github.com/tub-rip/event_based_image_rec_inverse_problem | ['Guillermo Gallego', 'Anthony Yezzi', 'Zelin Zhang'] | 2021-12-12 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.87793529e-01 -6.96481168e-02 2.20139697e-01 -2.83750415e-01
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fe41defa-2a9d-4688-99c2-e4e3553553d8 | decomposed-diffusion-models-for-high-quality | 2303.08320 | null | https://arxiv.org/abs/2303.08320v3 | https://arxiv.org/pdf/2303.08320v3.pdf | VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation | A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation. | ['Jingren Zhou', 'Tieniu Tan', 'Deli Zhao', 'Yujun Shen', 'Liang Wang', 'Yan Huang', 'Yingya Zhang', 'Dayou Chen', 'Zhengxiong Luo'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_VideoFusion_Decomposed_Diffusion_Models_for_High-Quality_Video_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_VideoFusion_Decomposed_Diffusion_Models_for_High-Quality_Video_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation'] | ['computer-vision'] | [ 3.31835836e-01 -5.50324284e-02 9.87450704e-02 -8.04376900e-02
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4a51fc82-e441-4099-b6a0-2266de1bb264 | myfood-a-food-segmentation-and-classification | 2012.03087 | null | https://arxiv.org/abs/2012.03087v1 | https://arxiv.org/pdf/2012.03087v1.pdf | MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring | The absence of food monitoring has contributed significantly to the increase in the population's weight. Due to the lack of time and busy routines, most people do not control and record what is consumed in their diet. Some solutions have been proposed in computer vision to recognize food images, but few are specialized in nutritional monitoring. This work presents the development of an intelligent system that classifies and segments food presented in images to help the automatic monitoring of user diet and nutritional intake. This work shows a comparative study of state-of-the-art methods for image classification and segmentation, applied to food recognition. In our methodology, we compare the FCN, ENet, SegNet, DeepLabV3+, and Mask RCNN algorithms. We build a dataset composed of the most consumed Brazilian food types, containing nine classes and a total of 1250 images. The models were evaluated using the following metrics: Intersection over Union, Sensitivity, Specificity, Balanced Precision, and Positive Predefined Value. We also propose an system integrated into a mobile application that automatically recognizes and estimates the nutrients in a meal, assisting people with better nutritional monitoring. The proposed solution showed better results than the existing ones in the market. The dataset is publicly available at the following link http://doi.org/10.5281/zenodo.4041488 | ['Valmir Macario', 'Filipe R. Cordeiro', 'Charles N. C. Freitas'] | 2020-12-05 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-1.69927984e-01 -2.11851388e-01 -4.23652768e-01 -3.85798037e-01
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5c8cd72d-c213-4f23-90be-a5578fbb625c | table-retrieval-may-not-necessitate-table | 2205.09843 | null | https://arxiv.org/abs/2205.09843v1 | https://arxiv.org/pdf/2205.09843v1.pdf | Table Retrieval May Not Necessitate Table-specific Model Design | Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work has designed special models and training paradigms for table-related tasks such as table-based question answering and table retrieval. Though effective, they add complexity in both modeling and data acquisition compared to generic text solutions and obscure which elements are truly beneficial. In this work, we focus on the task of table retrieval, and ask: "is table-specific model design necessary for table retrieval, or can a simpler text-based model be effectively used to achieve a similar result?" First, we perform an analysis on a table-based portion of the Natural Questions dataset (NQ-table), and find that structure plays a negligible role in more than 70% of the cases. Based on this, we experiment with a general Dense Passage Retriever (DPR) based on text and a specialized Dense Table Retriever (DTR) that uses table-specific model designs. We find that DPR performs well without any table-specific design and training, and even achieves superior results compared to DTR when fine-tuned on properly linearized tables. We then experiment with three modules to explicitly encode table structures, namely auxiliary row/column embeddings, hard attention masks, and soft relation-based attention biases. However, none of these yielded significant improvements, suggesting that table-specific model design may not be necessary for table retrieval. | ['Graham Neubig', 'Eric Nyberg', 'Zhengbao Jiang', 'Zhiruo Wang'] | 2022-05-19 | null | https://aclanthology.org/2022.suki-1.5 | https://aclanthology.org/2022.suki-1.5.pdf | naacl-suki-2022-7 | ['hard-attention', 'natural-questions', 'table-retrieval'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-3.72548960e-02 6.56808019e-02 -8.69747028e-02 -3.28344524e-01
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c015a232-4e22-4fcb-9fa4-e4557e891470 | altered-topological-structure-of-the-brain | 2304.05908 | null | https://arxiv.org/abs/2304.05908v1 | https://arxiv.org/pdf/2304.05908v1.pdf | Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis | Childhood maltreatment may adversely affect brain development and consequently behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter structure in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in global topology of the brain white-matter structural covariance network for child participants. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing sample, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly demonstrates that TDA can be used as a baseline framework to model altered topological structures of the brain. | ['Seth Pollak', 'Richard Davidson', 'Andrew Alexander', 'Thomas Burns', 'Jamie Hanson', 'Moo K. Chung', 'Tahmineh Azizi'] | 2023-04-12 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-4.86411341e-02 1.59346551e-01 2.48031974e-01 -3.12147558e-01
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8c08a69d-dbaf-4034-b49b-864aca6af4e4 | a-survey-of-trustworthy-federated-learning | 2302.10637 | null | https://arxiv.org/abs/2302.10637v1 | https://arxiv.org/pdf/2302.10637v1.pdf | A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy | Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint. | ['Irwin King', 'Zenglin Xu', 'Jinglong Luo', 'Dun Zeng', 'Yifei Zhang'] | 2023-02-21 | null | null | null | null | ['drug-discovery'] | ['medical'] | [-3.06586981e-01 3.69452219e-03 -1.83713540e-01 -3.70633811e-01
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a80de58d-1fd5-4f2b-8564-73053487257c | neural-document-expansion-for-ad-hoc | 2012.14005 | null | https://arxiv.org/abs/2012.14005v1 | https://arxiv.org/pdf/2012.14005v1.pdf | Neural document expansion for ad-hoc information retrieval | Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. However, this approach needs a large amount of in-domain training data. In this paper, we show that this neural document expansion approach can be effectively adapted to standard IR tasks, where labels are scarce and many long documents are present. | ['Andrew Arnold', 'Cheng Tang'] | 2020-12-27 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 3.18812340e-01 3.90932150e-03 -4.11187291e-01 -3.34522724e-01
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a1f2a1b7-f36b-41bb-8abc-16332fc7ee85 | multitask-learning-for-network-traffic | 1906.05248 | null | https://arxiv.org/abs/1906.05248v2 | https://arxiv.org/pdf/1906.05248v2.pdf | Multitask Learning for Network Traffic Classification | Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach. | ['Shahbaz Rezaei', 'Xin Liu'] | 2019-06-12 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.92247760e-02 -5.84323168e-01 -5.92377305e-01 -7.80294776e-01
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c7213099-63dc-4cf9-8a7d-bb6447c78f62 | dialogue-policies-for-learning-board-games | null | null | https://aclanthology.org/2020.sigdial-1.41 | https://aclanthology.org/2020.sigdial-1.41.pdf | Dialogue Policies for Learning Board Games through Multimodal Communication | This paper presents MDP policy learning for agents to learn strategic behavior–how to play board games–during multimodal dialogues. Policies are trained offline in simulation, with dialogues carried out in a formal language. The agent has a temporary belief state for the dialogue, and a persistent knowledge store represented as an extensive-form game tree. How well the agent learns a new game from a dialogue with a simulated partner is evaluated by how well it plays the game, given its dialogue-final knowledge state. During policy training, we control for the simulated dialogue partner’s level of informativeness in responding to questions. The agent learns best when its trained policy matches the current dialogue partner’s informativeness. We also present a novel data collection for training natural language modules. Human subjects who engaged in dialogues with a baseline system rated the system’s language skills as above average. Further, results confirm that human dialogue partners also vary in their informativeness. | ['Rebecca Passonneau', 'Alan Wagner', 'Sweekar Sudhakara', 'Aishan Liu', 'Ali Ayub', 'Maryam Zare'] | null | null | null | null | sigdial-acl-2020-7 | ['board-games'] | ['playing-games'] | [-1.80205107e-01 1.09416389e+00 -1.26851812e-01 -3.29758883e-01
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af0c26fb-c529-4786-9b8c-b2c6f270fa70 | part-guided-relational-transformers-for-fine | 2212.13685 | null | https://arxiv.org/abs/2212.13685v1 | https://arxiv.org/pdf/2212.13685v1.pdf | Part-guided Relational Transformers for Fine-grained Visual Recognition | Fine-grained visual recognition is to classify objects with visually similar appearances into subcategories, which has made great progress with the development of deep CNNs. However, handling subtle differences between different subcategories still remains a challenge. In this paper, we propose to solve this issue in one unified framework from two aspects, i.e., constructing feature-level interrelationships, and capturing part-level discriminative features. This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing. The part discovery module efficiently discovers the discriminative regions which are highly-corresponded to the gradient descent procedure. Then the second feature transformation module builds correlations within the global embedding and multiple part embedding, enhancing spatial interactions among semantic pixels. Moreover, our proposed approach does not rely on additional part branches in the inference time and reaches state-of-the-art performance on 3 widely-used fine-grained object recognition benchmarks. Experimental results and explainable visualizations demonstrate the effectiveness of our proposed approach. The code can be found at https://github.com/iCVTEAM/PART. | ['Yonghong Tian', 'Xiaowu Chen', 'Jia Li', 'Yifan Zhao'] | 2022-12-28 | null | null | null | null | ['fine-grained-visual-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.72582781e-02 -3.59969139e-01 -9.74728018e-02 -5.08309782e-01
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a30ad240-a6d8-41f1-93e3-3ec331a8ccae | scene-labeling-using-gated-recurrent-units | 1611.07485 | null | http://arxiv.org/abs/1611.07485v2 | http://arxiv.org/pdf/1611.07485v2.pdf | Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning | Recurrent neural network (RNN), as a powerful contextual dependency modeling
framework, has been widely applied to scene labeling problems. However, this
work shows that directly applying traditional RNN architectures, which unfolds
a 2D lattice grid into a sequence, is not sufficient to model structure
dependencies in images due to the "impact vanishing" problem. First, we give an
empirical analysis about the "impact vanishing" problem. Then, a new RNN unit
named Recurrent Neural Network with explicit long range conditioning (RNN-ELC)
is designed to alleviate this problem. A novel neural network architecture is
built for scene labeling tasks where one of the variants of the new RNN unit,
Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used
to model multi scale contextual dependencies in images. We validate the use of
GRU-ELC units with state-of-the-art performance on three standard scene
labeling datasets. Comprehensive experiments demonstrate that the new GRU-ELC
unit benefits scene labeling problem a lot as it can encode longer contextual
dependencies in images more effectively than traditional RNN units. | ['Suya You', 'Kevin Zhou', 'Weiyue Wang', 'Qiangui Huang', 'Ulrich Neumann'] | 2016-11-22 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 5.35818160e-01 -8.83353967e-03 4.49356958e-02 -4.28953350e-01
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b49eae0c-024f-46d7-bf24-87450b014b8a | revealing-single-frame-bias-for-video-and | 2206.03428 | null | https://arxiv.org/abs/2206.03428v1 | https://arxiv.org/pdf/2206.03428v1.pdf | Revealing Single Frame Bias for Video-and-Language Learning | Training an effective video-and-language model intuitively requires multiple frames as model inputs. However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more frames. In this work, we explore single-frame models for video-and-language learning. On a diverse set of video-and-language tasks (including text-to-video retrieval and video question answering), we show the surprising result that, with large-scale pre-training and a proper frame ensemble strategy at inference time, a single-frame trained model that does not consider temporal information can achieve better performance than existing methods that use multiple frames for training. This result reveals the existence of a strong "static appearance bias" in popular video-and-language datasets. Therefore, to allow for a more comprehensive evaluation of video-and-language models, we propose two new retrieval tasks based on existing fine-grained action recognition datasets that encourage temporal modeling. Our code is available at https://github.com/jayleicn/singularity | ['Mohit Bansal', 'Tamara L. Berg', 'Jie Lei'] | 2022-06-07 | null | null | null | null | ['video-question-answering', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.42225623e-01 -4.39209640e-01 -4.69473094e-01 -4.71551180e-01
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bac117fd-ff20-49ac-a73a-1b83448ccc06 | pairwise-geometric-matching-for-large-scale | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Li_Pairwise_Geometric_Matching_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Li_Pairwise_Geometric_Matching_2015_CVPR_paper.pdf | Pairwise Geometric Matching for Large-Scale Object Retrieval | Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency. | ['Xinchao Li', 'Martha Larson', 'Alan Hanjalic'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['geometric-matching'] | ['computer-vision'] | [ 9.64801162e-02 -3.70998830e-01 -5.43387160e-02 -2.54279166e-01
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e361b4ee-af62-420b-a660-108a4f9bfe63 | latent-filter-scaling-for-multimodal | 1812.09877 | null | http://arxiv.org/abs/1812.09877v3 | http://arxiv.org/pdf/1812.09877v3.pdf | Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation | In multimodal unsupervised image-to-image translation tasks, the goal is to
translate an image from the source domain to many images in the target domain.
We present a simple method that produces higher quality images than current
state-of-the-art while maintaining the same amount of multimodal diversity.
Previous methods follow the unconditional approach of trying to map the latent
code directly to a full-size image. This leads to complicated network
architectures with several introduced hyperparameters to tune. By treating the
latent code as a modifier of the convolutional filters, we produce multimodal
output while maintaining the traditional Generative Adversarial Network (GAN)
loss and without additional hyperparameters. The only tuning required by our
method controls the tradeoff between variability and quality of generated
images. Furthermore, we achieve disentanglement between source domain content
and target domain style for free as a by-product of our formulation. We perform
qualitative and quantitative experiments showing the advantages of our method
compared with the state-of-the art on multiple benchmark image-to-image
translation datasets. | ['Peter Wonka', 'Yazeed Alharbi', 'Neil Smith'] | 2018-12-24 | latent-filter-scaling-for-multimodal-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Alharbi_Latent_Filter_Scaling_for_Multimodal_Unsupervised_Image-To-Image_Translation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Alharbi_Latent_Filter_Scaling_for_Multimodal_Unsupervised_Image-To-Image_Translation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 6.01455629e-01 3.45161617e-01 1.34281009e-01 -4.21619684e-01
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3.98472458e-01 -3.39577228e-01 -1.87305644e-01 7.02157915e-01] | [11.693009376525879, -0.36958858370780945] |
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