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9273f84a-ada6-4b8d-9ace-d95ed37769b4 | towards-deep-symbolic-reinforcement-learning | 1609.05518 | null | http://arxiv.org/abs/1609.05518v2 | http://arxiv.org/pdf/1609.05518v2.pdf | Towards Deep Symbolic Reinforcement Learning | Deep reinforcement learning (DRL) brings the power of deep neural networks to
bear on the generic task of trial-and-error learning, and its effectiveness has
been convincingly demonstrated on tasks such as Atari video games and the game
of Go. However, contemporary DRL systems inherit a number of shortcomings from
the current generation of deep learning techniques. For example, they require
very large datasets to work effectively, entailing that they are slow to learn
even when such datasets are available. Moreover, they lack the ability to
reason on an abstract level, which makes it difficult to implement high-level
cognitive functions such as transfer learning, analogical reasoning, and
hypothesis-based reasoning. Finally, their operation is largely opaque to
humans, rendering them unsuitable for domains in which verifiability is
important. In this paper, we propose an end-to-end reinforcement learning
architecture comprising a neural back end and a symbolic front end with the
potential to overcome each of these shortcomings. As proof-of-concept, we
present a preliminary implementation of the architecture and apply it to
several variants of a simple video game. We show that the resulting system --
though just a prototype -- learns effectively, and, by acquiring a set of
symbolic rules that are easily comprehensible to humans, dramatically
outperforms a conventional, fully neural DRL system on a stochastic variant of
the game. | ['Murray Shanahan', 'Kai Arulkumaran', 'Marta Garnelo'] | 2016-09-18 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-9.34233144e-03 2.06046924e-01 3.87594439e-02 -2.58885980e-01
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1fad0795-e082-44b3-9e6b-5c17fd40171e | mask-scalar-prediction-for-improving-robust | 2204.12092 | null | https://arxiv.org/abs/2204.12092v1 | https://arxiv.org/pdf/2204.12092v1.pdf | Mask scalar prediction for improving robust automatic speech recognition | Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints and the resulting distortion that the frontend processing introduces in speech. Time-frequency masking based approaches have been shown to work well, but they need additional hyper-parameters to scale the mask to limit speech distortion. Such mask scalars are typically hand-tuned and chosen conservatively. In this work, we present a technique to predict mask scalars using an ASR-based loss in an end-to-end fashion, with minimal increase in the overall model size and complexity. We evaluate the approach on two robust ASR tasks: multichannel enhancement in the presence of speech and non-speech noise, and acoustic echo cancellation (AEC). Results show that the presented algorithm consistently improves word error rate (WER) without the need for any additional tuning over strong baselines that use hand-tuned hyper-parameters: up to 16% for multichannel enhancement in noisy conditions, and up to 7% for AEC. | ['Yuma Koizumi', 'Nathan Howard', 'Sankaran Panchapagesan', 'James Walker', 'Arun Narayanan'] | 2022-04-26 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.66131651e-01 3.67841311e-02 4.93393153e-01 -5.44554591e-01
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138e424c-f399-42ac-a84a-36dc40e724fd | tag-based-attention-guided-bottom-up-approach | 2204.10765 | null | https://arxiv.org/abs/2204.10765v1 | https://arxiv.org/pdf/2204.10765v1.pdf | Tag-Based Attention Guided Bottom-Up Approach for Video Instance Segmentation | Video Instance Segmentation is a fundamental computer vision task that deals with segmenting and tracking object instances across a video sequence. Most existing methods typically accomplish this task by employing a multi-stage top-down approach that usually involves separate networks to detect and segment objects in each frame, followed by associating these detections in consecutive frames using a learned tracking head. In this work, however, we introduce a simple end-to-end trainable bottom-up approach to achieve instance mask predictions at the pixel-level granularity, instead of the typical region-proposals-based approach. Unlike contemporary frame-based models, our network pipeline processes an input video clip as a single 3D volume to incorporate temporal information. The central idea of our formulation is to solve the video instance segmentation task as a tag assignment problem, such that generating distinct tag values essentially separates individual object instances across the video sequence (here each tag could be any arbitrary value between 0 and 1). To this end, we propose a novel spatio-temporal tagging loss that allows for sufficient separation of different objects as well as necessary identification of different instances of the same object. Furthermore, we present a tag-based attention module that improves instance tags, while concurrently learning instance propagation within a video. Evaluations demonstrate that our method provides competitive results on YouTube-VIS and DAVIS-19 datasets, and has minimum run-time compared to other state-of-the-art performance methods. | ['Mubarak Shah', 'Jyoti Kini'] | 2022-04-22 | null | null | null | null | ['video-instance-segmentation', 'temporal-tagging'] | ['computer-vision', 'natural-language-processing'] | [ 4.58983570e-01 1.43006351e-02 -3.78507197e-01 -3.76113564e-01
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628a22c0-e159-4b24-9f64-9d836d067326 | data-efficient-training-of-cnns-and | 2303.02095 | null | https://arxiv.org/abs/2303.02095v2 | https://arxiv.org/pdf/2303.02095v2.pdf | Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective | Coreset selection is among the most effective ways to reduce the training time of CNNs, however, only limited is known on how the resultant models will behave under variations of the coreset size, and choice of datasets and models. Moreover, given the recent paradigm shift towards transformer-based models, it is still an open question how coreset selection would impact their performance. There are several similar intriguing questions that need to be answered for a wide acceptance of coreset selection methods, and this paper attempts to answer some of these. We present a systematic benchmarking setup and perform a rigorous comparison of different coreset selection methods on CNNs and transformers. Our investigation reveals that under certain circumstances, random selection of subsets is more robust and stable when compared with the SOTA selection methods. We demonstrate that the conventional concept of uniform subset sampling across the various classes of the data is not the appropriate choice. Rather samples should be adaptively chosen based on the complexity of the data distribution for each class. Transformers are generally pretrained on large datasets, and we show that for certain target datasets, it helps to keep their performance stable at even very small coreset sizes. We further show that when no pretraining is done or when the pretrained transformer models are used with non-natural images (e.g. medical data), CNNs tend to generalize better than transformers at even very small coreset sizes. Lastly, we demonstrate that in the absence of the right pretraining, CNNs are better at learning the semantic coherence between spatially distant objects within an image, and these tend to outperform transformers at almost all choices of the coreset size. | ['Irtiza Hasan', 'Deepak K. Gupta', 'Dilip K. Prasad', 'Animesh Gupta'] | 2023-03-03 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.66167790e-01 -1.36199579e-01 -1.26940340e-01 -3.14051360e-01
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e51e298a-7d21-4de9-9f78-1c07ddfe1385 | probing-model-signal-awareness-via-prediction | 2011.14934 | null | https://arxiv.org/abs/2011.14934v2 | https://arxiv.org/pdf/2011.14934v2.pdf | Probing Model Signal-Awareness via Prediction-Preserving Input Minimization | This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture the correct vulnerability signals to produce their predictions. Our prediction-preserving input minimization (P2IM) approach systematically reduces the original source code to a minimal snippet which a model needs to maintain its prediction. The model's reliance on incorrect signals is then uncovered when the vulnerability in the original code is missing in the minimal snippet, both of which the model however predicts as being vulnerable. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR). We apply P2IM on three different neural network architectures across multiple datasets. The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric, highlighting that the models are presumably picking up a lot of noise or dataset nuances while learning their vulnerability detection logic. Although the drop in model performance may be perceived as an adversarial attack, but this isn't P2IM's objective. The idea is rather to uncover the signal-awareness of a black-box model in a data-driven manner via controlled queries. SAR's purpose is to measure the impact of task-agnostic model training, and not to suggest a shortcoming in the Recall metric. The expectation, in fact, is for SAR to match Recall in the ideal scenario where the model truly captures task-specific signals. | ['Yunhui Zheng', 'Sahil Suneja', 'Jim Laredo', 'Alessandro Morari', 'Yufan Zhuang'] | 2020-11-25 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 5.85608482e-01 3.13251048e-01 -1.29689306e-01 -2.17267171e-01
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11725bbb-4360-4f80-a141-aade11f6228a | third-party-aligner-for-neural-word | 2211.04198 | null | https://arxiv.org/abs/2211.04198v1 | https://arxiv.org/pdf/2211.04198v1.pdf | Third-Party Aligner for Neural Word Alignments | Word alignment is to find translationally equivalent words between source and target sentences. Previous work has demonstrated that self-training can achieve competitive word alignment results. In this paper, we propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. Specifically, source word and target word of each word pair aligned by the third-party aligner are trained to be close neighbors to each other in the contextualized embedding space when fine-tuning a pre-trained cross-lingual language model. Experiments on the benchmarks of various language pairs show that our approach can surprisingly do self-correction over the third-party supervision by finding more accurate word alignments and deleting wrong word alignments, leading to better performance than various third-party word aligners, including the currently best one. When we integrate all supervisions from various third-party aligners, we achieve state-of-the-art word alignment performances, with averagely more than two points lower alignment error rates than the best third-party aligner. We released our code at https://github.com/sdongchuanqi/Third-Party-Supervised-Aligner. | ['Min Zhang', 'Yuqi Zhang', 'Xiangyu Duan', 'Chuanqi Dong', 'Jinpeng Zhang'] | 2022-11-08 | null | null | null | null | ['word-alignment'] | ['natural-language-processing'] | [ 8.44058618e-02 8.45555365e-02 -4.14731115e-01 -5.40765405e-01
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1a67c887-e751-4683-9dc3-90c22cc90a8d | robust-detection-and-attribution-of-climate | 2212.04905 | null | https://arxiv.org/abs/2212.04905v1 | https://arxiv.org/pdf/2212.04905v1.pdf | Robust detection and attribution of climate change under interventions | Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change. | ['Reto Knutti', 'Guillaume Obozinski', 'Nicolai Meinshausen', 'Sebastian Sippel', 'Enikő Székely'] | 2022-12-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.73585892e-01 -1.23776168e-01 -3.20127487e-01 -1.55552626e-01
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401808bf-5fcf-46c0-8892-c7bc2d6cf522 | efficient-parallel-split-learning-over | 2303.15991 | null | https://arxiv.org/abs/2303.15991v3 | https://arxiv.org/pdf/2303.15991v3.pdf | Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks | The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple client devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training and reduces the dimension of local gradients for back propagation (BP) via last-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at client devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization. | ['Yuguang Fang', 'Kaibin Huang', 'Yue Gao', 'Xianhao Chen', 'Yiqin Deng', 'Guangyu Zhu', 'Zheng Lin'] | 2023-03-26 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 4.71132435e-02 -1.55805618e-01 -5.19558191e-01 -6.04247451e-01
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c2fff7de-ab37-41de-bdda-12dbb24e32a9 | learning-policies-for-social-network | 1907.11625 | null | https://arxiv.org/abs/1907.11625v5 | https://arxiv.org/pdf/1907.11625v5.pdf | Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling | A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling algorithms; these methods sample nodes and their neighbours in a carefully constructed order and choose opinion leaders from this discovered network to maximize influence spread in the (unknown) complete network. In this work, we propose a reinforcement learning framework for network discovery that automatically learns useful node and graph representations that encode important structural properties of the network. At training time, the method identifies portions of the network such that the nodes selected from this sampled subgraph can effectively influence nodes in the complete network. The realization of such transferable network structure based adaptable policies is attributed to the meticulous design of the framework that encodes relevant node and graph signatures driven by an appropriate reward scheme. We experiment with real-world social networks from four different domains and show that the policies learned by our RL agent provide a 10-36% improvement over the current state-of-the-art method. | ['Priyesh Vijayan', 'Balaraman Ravindran', 'Harshavardhan Kamarthi', 'Bryan Wilder', 'Milind Tambe'] | 2019-07-08 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.42697525e-01 9.74444270e-01 -5.46149254e-01 -1.88906655e-01
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66fc6169-be48-4c67-a7a0-040c56809442 | an-adversarial-generative-network-designed | null | null | https://www.mdpi.com/2072-4292/14/18/4619 | https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.mdpi.com%2F2072-4292%2F14%2F18%2F4619%2Fpdf&data=05%7C01%7Cnlandro%40uninsubria.it%7C9403cb44323448a0e66908da9a123570%7C9252ed8bdffc401c86ca6237da9991fa%7C0%7C0%7C637991700433335562%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=QOIPJDP651wRimcvwiatPirmKtrdVottLXRuHNCGXSY%3D&reserved=0 | An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of Mars | In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DTM) and thus obtaining a 3D spatial mapping, which is necessary for a better analysis of planetary surfaces. However, the entire reconstruction process performed with the stereo-matching algorithm can be time consuming and can generate many artifacts. Coupled with the lack of adequate stereo coverage, it can pose a significant obstacle to 3D planetary mapping. Recently, many deep learning architectures have been proposed for monocular depth estimation, which aspires to predict the third dimension given a single 2D image, with considerable advantages thanks to the simplification of the reconstruction problem, leading to a significant increase in interest in deep models for the generation of super-resolution images and DTM estimation. In this paper, we combine these last two concepts into a single end-to-end model and introduce a new generative adversarial network solution that estimates the DTM at 4× resolution from a single monocular image, called SRDiNet (super-resolution depth image network). Furthermore, we introduce a sub-network able to apply a refinement using interpolated input images to better enhance the fine details of the final product, and we demonstrate the effectiveness of its benefits through three different versions of the proposal: SRDiNet with GAN approach, SRDiNet without adversarial network, and SRDiNet without the refinement learned network plus GAN approach. The results of Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) are reported, applying the best model along all Oxia Planum tiles and releasing a 3D product enhanced by 4×. | ['Mattia Gatti', 'Emanuele Simioni', 'Claudio Pernechele', 'Nicola Landro', 'Gabriele Cremonese', 'Cristina Re', 'Ignazio Gallo', 'Riccardo La Grassa'] | 2022-08-15 | null | null | null | remote-sensing-2022-8 | ['stereo-matching-1'] | ['computer-vision'] | [ 4.35642540e-01 4.34899002e-01 3.33498389e-01 -9.75646675e-02
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03b7e580-1603-4551-96f8-1d096815c9ea | fine-grained-age-estimation-in-the-wild-with | 1805.10445 | null | https://arxiv.org/abs/1805.10445v2 | https://arxiv.org/pdf/1805.10445v2.pdf | Fine-Grained Age Estimation in the wild with Attention LSTM Networks | Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the agesensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network. | ['Xingfang Yuan', 'Zhenbing Zhao', 'Zhanyu Ma', 'Na Liu', 'Ke Zhang', 'Ce Gao', 'Xinyao Guo'] | 2018-05-26 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-1.94393739e-01 -1.03506602e-01 1.21742971e-02 -7.34023452e-01
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88208b02-9a06-46c4-94be-e88d9167f338 | 3d-human-action-recognition-with-siamese-lstm | 1807.02131 | null | http://arxiv.org/abs/1807.02131v1 | http://arxiv.org/pdf/1807.02131v1.pdf | 3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning | This paper proposes a new 3D Human Action Recognition system as a two-phase
system: (1) Deep Metric Learning Module which learns a similarity metric
between two 3D joint sequences using Siamese-LSTM networks; (2) A Multiclass
Classification Module that uses the output of the first module to produce the
final recognition output. This model has several advantages: the first module
is trained with a larger set of data because it uses many combinations of
sequence pairs.Our deep metric learning module can also be trained
independently of the datasets, which makes our system modular and
generalizable. We tested the proposed system on standard and newly introduced
datasets that showed us that initial results are promising. We will continue
developing this system by adding more sophisticated LSTM blocks and by
cross-training between different datasets. | ['Yusuf Sinan Akgul', 'Seyma Yucer'] | 2018-07-05 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 7.82664269e-02 -2.53101319e-01 -5.57412617e-02 -6.52639627e-01
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90157feb-3601-4aef-9fb1-68561ced7454 | denoise-and-contrast-for-category-agnostic | 2103.16671 | null | https://arxiv.org/abs/2103.16671v1 | https://arxiv.org/pdf/2103.16671v1.pdf | Denoise and Contrast for Category Agnostic Shape Completion | In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region around it. Local and global information are encoded in a combined embedding. A denoising pretext task provides the network with the needed local cues, decoupled from the high-level semantics and naturally shared over multiple classes. On the other hand, contrastive learning maximizes the agreement between variants of the same shape with different missing portions, thus producing a representation which captures the global appearance of the shape. The combined embedding inherits category-agnostic properties from the chosen pretext tasks. Differently from existing approaches, this allows to better generalize the completion properties to new categories unseen at training time. Moreover, while decoding the obtained joint representation, we better blend the reconstructed missing part with the partial shape by paying attention to its known surrounding region and reconstructing this frame as auxiliary objective. Our extensive experiments and detailed ablation on the ShapeNet dataset show the effectiveness of each part of the method with new state of the art results. Our quantitative and qualitative analysis confirms how our approach is able to work on novel categories without relying neither on classification and shape symmetry priors, nor on adversarial training procedures. | ['Tatiana Tommasi', 'Enrico Magli', 'Giulia Fracastoro', 'Diego Valsesia', 'Antonio Alliegro'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Alliegro_Denoise_and_Contrast_for_Category_Agnostic_Shape_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-2.17565857e-02 5.84376216e-01 4.15849574e-02 -2.93851316e-01
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0fac0ed4-1162-443b-b075-b6d7667d5435 | on-handling-catastrophic-forgetting-for | 2302.09310 | null | https://arxiv.org/abs/2302.09310v1 | https://arxiv.org/pdf/2302.09310v1.pdf | On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge | Human activity recognition (HAR) has been a classic research problem. In particular, with recent machine learning (ML) techniques, the recognition task has been largely investigated by companies and integrated into their products for customers. However, most of them apply a predefined activity set and conduct the learning process on the cloud, hindering specific personalizations from end users (i.e., edge devices). Even though recent progress in Incremental Learning allows learning new-class data on the fly, the learning process is generally conducted on the cloud, requiring constant data exchange between cloud and edge devices, thus leading to data privacy issues. In this paper, we propose PILOTE, which pushes the incremental learning process to the extreme edge, while providing reliable data privacy and practical utility, e.g., low processing latency, personalization, etc. In particular, we consider the practical challenge of extremely limited data during the incremental learning process on edge, where catastrophic forgetting is required to be handled in a practical way. We validate PILOTE with extensive experiments on human activity data collected from mobile sensors. The results show PILOTE can work on edge devices with extremely limited resources while providing reliable performance. | ['Hakim Hacid', 'George Arvanitakis', 'Jingwei Zuo'] | 2023-02-18 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.89665690e-01 -4.86375429e-02 -4.03065681e-01 -2.33122230e-01
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6653cddb-b815-465d-827a-9a2db6a3e040 | extracting-candidate-factors-affecting-long | 2103.06446 | null | https://arxiv.org/abs/2103.06446v1 | https://arxiv.org/pdf/2103.06446v1.pdf | Extracting candidate factors affecting long-term trends of student abilities across subjects | Long-term student achievement data provide useful information to formulate the research question of what types of student skills would impact future trends across subjects. However, few studies have focused on long-term data. This is because the criteria of examinations vary depending on their designers; additionally, it is difficult for the same designer to maintain the coherence of the criteria of examinations beyond grades. To solve this inconsistency issue, we propose a novel approach to extract candidate factors affecting long-term trends across subjects from long-term data. Our approach is composed of three steps: Data screening, time series clustering, and causal inference. The first step extracts coherence data from long-term data. The second step groups the long-term data by shape and value. The third step extracts factors affecting the long-term trends and validates the extracted variation factors using two or more different data sets. We then conducted evaluation experiments with student achievement data from five public elementary schools and four public junior high schools in Japan. The results demonstrate that our approach extracts coherence data, clusters long-term data into interpretable groups, and extracts candidate factors affecting academic ability across subjects. Subsequently, our approach formulates a hypothesis and turns archived achievement data into useful information. | ['Atsushi Yoshikawa', 'Hiroki Kuno', 'Satoshi Takahashi'] | 2021-03-11 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-4.54257339e-01 -4.87118751e-01 -5.65843105e-01 -5.19025803e-01
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5caaf88d-532b-472a-9363-97f54b316dbb | causal-dependence-plots-for-interpretable | 2303.04209 | null | https://arxiv.org/abs/2303.04209v2 | https://arxiv.org/pdf/2303.04209v2.pdf | Causal Dependence Plots | Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how one variable--an outcome--depends on changes in another variable--a predictor--$\textit{along with any consequent causal changes in other predictor variables}$. Crucially, CDPs differ from standard methods based on holding other predictors constant or assuming they are independent. CDPs make use of an auxiliary causal model because causal conclusions require causal assumptions. With simulations and real data experiments, we show CDPs can be combined in a modular way with methods for causal learning or sensitivity analysis. Since people often think causally about input-output dependence, CDPs can be powerful tools in the xAI or interpretable machine learning toolkit and contribute to applications like scientific machine learning and algorithmic fairness. | ['Sakina Hansen', 'Lucius E. J. Bynum', 'Joshua R. Loftus'] | 2023-03-07 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.47358844e-01 2.22781777e-01 -6.34616315e-01 -6.27746224e-01
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8c006142-7544-4854-a6ba-ee14c0bb6ed6 | a-multi-task-approach-to-learning | null | null | https://aclanthology.org/P18-2035 | https://aclanthology.org/P18-2035.pdf | A Multi-task Approach to Learning Multilingual Representations | We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario. | ['Shrikanth Narayanan', 'Karan Singla', 'Dogan Can'] | 2018-07-01 | null | null | null | acl-2018-7 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-5.17200708e-01 -3.42626721e-01 -6.00065947e-01 -6.12950444e-01
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dd6cfc87-0a2c-45a8-8518-3f7ded2977c8 | madiff-offline-multi-agent-learning-with | 2305.17330 | null | https://arxiv.org/abs/2305.17330v1 | https://arxiv.org/pdf/2305.17330v1.pdf | MADiff: Offline Multi-agent Learning with Diffusion Models | Diffusion model (DM), as a powerful generative model, recently achieved huge success in various scenarios including offline reinforcement learning, where the policy learns to conduct planning by generating trajectory in the online evaluation. However, despite the effectiveness shown for single-agent learning, it remains unclear how DMs can operate in multi-agent problems, where agents can hardly complete teamwork without good coordination by independently modeling each agent's trajectories. In this paper, we propose MADiff, a novel generative multi-agent learning framework to tackle this problem. MADiff is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple diffusion agents. To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller, which includes opponent modeling and can be used for multi-agent trajectory prediction. MADiff takes advantage of the powerful generative ability of diffusion while well-suited in modeling complex multi-agent interactions. Our experiments show the superior performance of MADiff compared to baseline algorithms in a range of multi-agent learning tasks. | ['Weinan Zhang', 'Stefano Ermon', 'Yong Yu', 'Minkai Xu', 'Bingyi Kang', 'Liyuan Mao', 'Minghuan Liu', 'Zhengbang Zhu'] | 2023-05-27 | null | null | null | null | ['trajectory-prediction', 'offline-rl'] | ['computer-vision', 'playing-games'] | [-6.53650701e-01 3.41533124e-01 -1.35169297e-01 2.89238364e-01
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31bf6d77-0b2a-4f12-a893-bbcaca9b7153 | 3d-siamrpn-an-end-to-end-learning-method-for | 2108.05630 | null | https://arxiv.org/abs/2108.05630v1 | https://arxiv.org/pdf/2108.05630v1.pdf | 3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud | 3D single object tracking is a key issue for autonomous following robot, where the robot should robustly track and accurately localize the target for efficient following. In this paper, we propose a 3D tracking method called 3D-SiamRPN Network to track a single target object by using raw 3D point cloud data. The proposed network consists of two subnetworks. The first subnetwork is feature embedding subnetwork which is used for point cloud feature extraction and fusion. In this subnetwork, we first use PointNet++ to extract features of point cloud from template and search branches. Then, to fuse the information of features in the two branches and obtain their similarity, we propose two cross correlation modules, named Pointcloud-wise and Point-wise respectively. The second subnetwork is region proposal network(RPN), which is used to get the final 3D bounding box of the target object based on the fusion feature from cross correlation modules. In this subnetwork, we utilize the regression and classification branches of a region proposal subnetwork to obtain proposals and scores, thus get the final 3D bounding box of the target object. Experimental results on KITTI dataset show that our method has a competitive performance in both Success and Precision compared to the state-of-the-art methods, and could run in real-time at 20.8 FPS. Additionally, experimental results on H3D dataset demonstrate that our method also has good generalization ability and could achieve good tracking performance in a new scene without re-training. | ['Sebastian Scherer', 'Yubo Cui', 'Sifan Zhou', 'Zheng Fang'] | 2021-08-12 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-4.99012142e-01 -4.33356166e-01 -3.19723263e-02 7.29066953e-02
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d9bbbb06-a397-4dbb-9367-3f00b04111f0 | banditsum-extractive-summarization-as-a | 1809.09672 | null | https://arxiv.org/abs/1809.09672v3 | https://arxiv.org/pdf/1809.09672v3.pdf | BanditSum: Extractive Summarization as a Contextual Bandit | In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document. | ['Herke van Hoof', 'Eric Crawford', 'Yikang Shen', 'Jackie Chi Kit Cheung', 'Yue Dong'] | 2018-09-25 | banditsum-extractive-summarization-as-a-1 | https://aclanthology.org/D18-1409 | https://aclanthology.org/D18-1409.pdf | emnlp-2018-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.58008075e-01 4.80307132e-01 -8.74508023e-01 -2.72193700e-01
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cccbe072-0958-4969-a84d-59b11b823590 | applying-naive-bayes-classification-to-google | 1608.08574 | null | http://arxiv.org/abs/1608.08574v1 | http://arxiv.org/pdf/1608.08574v1.pdf | Applying Naive Bayes Classification to Google Play Apps Categorization | There are over one million apps on Google Play Store and over half a million
publishers. Having such a huge number of apps and developers can pose a
challenge to app users and new publishers on the store. Discovering apps can be
challenging if apps are not correctly published in the right category, and, in
turn, reduce earnings for app developers. Additionally, with over 41 categories
on Google Play Store, deciding on the right category to publish an app can be
challenging for developers due to the number of categories they have to choose
from. Machine Learning has been very useful, especially in classification
problems such sentiment analysis, document classification and spam detection.
These strategies can also be applied to app categorization on Google Play Store
to suggest appropriate categories for app publishers using details from their
application.
In this project, we built two variations of the Naive Bayes classifier using
open metadata from top developer apps on Google Play Store in other to classify
new apps on the store. These classifiers are then evaluated using various
evaluation methods and their results compared against each other. The results
show that the Naive Bayes algorithm performs well for our classification
problem and can potentially automate app categorization for Android app
publishers on Google Play Store | ['Babatunde Olabenjo'] | 2016-08-30 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-2.25836545e-01 -1.49743855e-01 -5.01515448e-01 -2.61948049e-01
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0d695f6a-2212-44b3-9c22-44360bd4ed7a | facing-the-hard-problems-in-fgvc | 2006.13190 | null | https://arxiv.org/abs/2006.13190v2 | https://arxiv.org/pdf/2006.13190v2.pdf | Facing the Hard Problems in FGVC | In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully analyzes the performance of recent SOTA methods, quantitatively, but more importantly, qualitatively. We show that these models universally struggle with certain "hard" images, while also making complementary mistakes. We underscore the importance of such analysis, and demonstrate that combining complementary models can improve accuracy on the popular CUB-200 dataset by over 5%. In addition to detailed analysis and characterization of the errors made by these SOTA methods, we provide a clear set of recommended directions for future FGVC researchers. | ['Andrew Merrill', 'Matt Gwilliam', 'Connor Anderson', 'Adam Teuscher', 'Ryan Farrell'] | 2020-06-23 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 3.97664830e-02 -4.86184776e-01 -1.32790416e-01 -3.17293435e-01
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26545c6d-e6c9-45df-9b84-527b48a21213 | drug-drug-interaction-extraction-via | 1705.03261 | null | http://arxiv.org/abs/1705.03261v2 | http://arxiv.org/pdf/1705.03261v2.pdf | Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers | Drug-drug interaction (DDI) is a vital information when physicians and
pharmacists intend to co-administer two or more drugs. Thus, several DDI
databases are constructed to avoid mistakenly combined use. In recent years,
automatically extracting DDIs from biomedical text has drawn researchers'
attention. However, the existing work utilize either complex feature
engineering or NLP tools, both of which are insufficient for sentence
comprehension. Inspired by the deep learning approaches in natural language
processing, we propose a recur- rent neural network model with multiple
attention layers for DDI classification. We evaluate our model on 2013 SemEval
DDIExtraction dataset. The experiments show that our model classifies most of
the drug pairs into correct DDI categories, which outperforms the existing NLP
or deep learning methods. | ['Qingbo Wu', 'Shasha Li', 'Jie Yu', 'Zibo Yi'] | 2017-05-09 | null | null | null | null | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 1.01855643e-01 -1.00779168e-01 -5.25421262e-01 -5.27552187e-01
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ce46139d-9ef0-4da5-864b-6dcd4cfbb44c | rmssinger-realistic-music-score-based-singing | 2305.10686 | null | https://arxiv.org/abs/2305.10686v1 | https://arxiv.org/pdf/2305.10686v1.pdf | RMSSinger: Realistic-Music-Score based Singing Voice Synthesis | We are interested in a challenging task, Realistic-Music-Score based Singing Voice Synthesis (RMS-SVS). RMS-SVS aims to generate high-quality singing voices given realistic music scores with different note types (grace, slur, rest, etc.). Though significant progress has been achieved, recent singing voice synthesis (SVS) methods are limited to fine-grained music scores, which require a complicated data collection pipeline with time-consuming manual annotation to align music notes with phonemes. Furthermore, these manual annotation destroys the regularity of note durations in music scores, making fine-grained music scores inconvenient for composing. To tackle these challenges, we propose RMSSinger, the first RMS-SVS method, which takes realistic music scores as input, eliminating most of the tedious manual annotation and avoiding the aforementioned inconvenience. Note that music scores are based on words rather than phonemes, in RMSSinger, we introduce word-level modeling to avoid the time-consuming phoneme duration annotation and the complicated phoneme-level mel-note alignment. Furthermore, we propose the first diffusion-based pitch modeling method, which ameliorates the naturalness of existing pitch-modeling methods. To achieve these, we collect a new dataset containing realistic music scores and singing voices according to these realistic music scores from professional singers. Extensive experiments on the dataset demonstrate the effectiveness of our methods. Audio samples are available at https://rmssinger.github.io/. | ['Zhou Zhao', 'Huadai Liu', 'Chenye Cui', 'Rongjie Huang', 'Zhenhui Ye', 'Jinglin Liu', 'Jinzheng He'] | 2023-05-18 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 5.56251891e-02 -2.93254256e-01 5.83757795e-02 2.16723442e-01
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1691c742-b542-4b3e-93b8-f75ab85fe67d | sketch-to-text-generation-toward-contextual | null | null | https://aclanthology.org/W16-6607 | https://aclanthology.org/W16-6607.pdf | Sketch-to-Text Generation: Toward Contextual, Creative, and Coherent Composition | null | ['Yejin Choi'] | 2016-09-01 | null | null | null | ws-2016-9 | ['sketch-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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726af6c2-3be9-48c0-b8da-3b052b67d44c | minimizing-fuzzy-interpretations-in-fuzzy | 2303.11438 | null | https://arxiv.org/abs/2303.11438v1 | https://arxiv.org/pdf/2303.11438v1.pdf | Minimizing Fuzzy Interpretations in Fuzzy Description Logics by Using Crisp Bisimulations | The problem of minimizing finite fuzzy interpretations in fuzzy description logics (FDLs) is worth studying. For example, the structure of a fuzzy/weighted social network can be treated as a fuzzy interpretation in FDLs, where actors are individuals and actions are roles. Minimizing the structure of a fuzzy/weighted social network makes it more compact, thus making network analysis tasks more efficient. In this work, we study the problem of minimizing a finite fuzzy interpretation in a FDL by using the largest crisp auto-bisimulation. The considered FDLs use the Baaz projection operator and their semantics is specified using an abstract algebra of fuzzy truth values, which can be any linear and complete residuated lattice. We provide an efficient algorithm with a complexity of $O((m \log{l} + n) \log{n})$ for minimizing a given finite fuzzy interpretation $\mathcal{I}$, where $n$ is the size of the domain of $\mathcal{I}$, $m$ is number of nonzero instances of atomic roles of $\mathcal{I}$ and $l$ is the number of different fuzzy values used for instances of atomic roles of $\mathcal{I}$. We prove that the fuzzy interpretation returned by the algorithm is minimal among the ones that preserve fuzzy TBoxes and ABoxes under certain conditions. | ['Linh Anh Nguyen'] | 2023-03-13 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [ 2.28323147e-01 5.47149360e-01 1.08608894e-01 -5.88364661e-01
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33660e78-ae64-4d48-bf83-24af8b1bde78 | warwick-image-forensics-dataset-for-device | 2004.10469 | null | https://arxiv.org/abs/2004.10469v2 | https://arxiv.org/pdf/2004.10469v2.pdf | Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics | Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community. | ['Chang-Tsun Li', 'Yujue Zhou', 'Yijun Quan', 'Li Li'] | 2020-04-22 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 7.14525640e-01 -7.65276551e-01 -6.41200542e-02 -2.55722612e-01
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bab59edf-905d-4a38-af57-6781c2d2bade | language-models-are-unsupervised-multitask | null | null | https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf | https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf | Language Models are Unsupervised Multitask Learners | Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically
approached with supervised learning on taskspecific datasets. We demonstrate that language
models begin to learn these tasks without any explicit supervision when trained on a new dataset
of millions of webpages called WebText. When
conditioned on a document plus questions, the answers generated by the language model reach 55
F1 on the CoQA dataset - matching or exceeding
the performance of 3 out of 4 baseline systems
without using the 127,000+ training examples.
The capacity of the language model is essential
to the success of zero-shot task transfer and increasing it improves performance in a log-linear
fashion across tasks. Our largest model, GPT-2,
is a 1.5B parameter Transformer that achieves
state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting
but still underfits WebText. Samples from the
model reflect these improvements and contain coherent paragraphs of text. These findings suggest
a promising path towards building language processing systems which learn to perform tasks from
their naturally occurring demonstrations. | ['Jeffrey Wu', 'Rewon Child', 'Ilya Sutskever', 'David Luan', 'Alec Radford', 'Dario Amodei'] | 2019-02-14 | null | null | null | preprint-2019-2 | ['multi-task-language-understanding'] | ['methodology'] | [ 3.14117014e-01 4.85305578e-01 -3.09291512e-01 -4.30970311e-01
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9ceaa338-8d85-4089-846e-dfe79b509233 | runne-2022-shared-task-recognizing-nested | 2205.11159 | null | https://arxiv.org/abs/2205.11159v1 | https://arxiv.org/pdf/2205.11159v1.pdf | RuNNE-2022 Shared Task: Recognizing Nested Named Entities | The RuNNE Shared Task approaches the problem of nested named entity recognition. The annotation schema is designed in such a way, that an entity may partially overlap or even be nested into another entity. This way, the named entity "The Yermolova Theatre" of type "organization" houses another entity "Yermolova" of type "person". We adopt the Russian NEREL dataset for the RuNNE Shared Task. NEREL comprises news texts written in the Russian language and collected from the Wikinews portal. The annotation schema includes 29 entity types. The nestedness of named entities in NEREL reaches up to six levels. The RuNNE Shared Task explores two setups. (i) In the general setup all entities occur more or less with the same frequency. (ii) In the few-shot setup the majority of entity types occur often in the training set. However, some of the entity types are have lower frequency, being thus challenging to recognize. In the test set the frequency of all entity types is even. This paper reports on the results of the RuNNE Shared Task. Overall the shared task has received 156 submissions from nine teams. Half of the submissions outperform a straightforward BERT-based baseline in both setups. This paper overviews the shared task setup and discusses the submitted systems, discovering meaning insights for the problem of nested NER. The links to the evaluation platform and the data from the shared task are available in our github repository: https://github.com/dialogue-evaluation/RuNNE. | ['Elena Tutubalina', 'Vladimir Ivanov', 'Tatiana Batura', 'Igor Rozhkov', 'Natalia Loukachevitch', 'Maxim Zmeev', 'Ekaterina Artemova'] | 2022-05-23 | null | null | null | null | ['dialogue-evaluation', 'nested-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-3.82456362e-01 3.65227878e-01 2.94214189e-02 -2.09446132e-01
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bb7a301c-3a6f-4a53-947d-f4a91b613a92 | attentional-pyramid-pooling-of-salient-visual | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Peng_Attentional_Pyramid_Pooling_of_Salient_Visual_Residuals_for_Place_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Peng_Attentional_Pyramid_Pooling_of_Salient_Visual_Residuals_for_Place_Recognition_ICCV_2021_paper.pdf | Attentional Pyramid Pooling of Salient Visual Residuals for Place Recognition | The core of visual place recognition (VPR) lies in how to identify task-relevant visual cues and embed them into discriminative representations. Focusing on these two points, we propose a novel encoding strategy named Attentional Pyramid Pooling of Salient Visual Residuals (APPSVR). It incorporates three types of attention modules to model the saliency of local features in individual, spatial and cluster dimensions respectively. (1) To inhibit task-irrelevant local features, a semantic-reinforced local weighting scheme is employed for local feature refinement; (2) To leverage the spatial context, an attentional pyramid structure is constructed to adaptively encode regional features according to their relative spatial saliency; (3) To distinguish the different importance of visual clusters to the task, a parametric normalization is proposed to adjust their contribution to image descriptor generation. Experiments demonstrate APPSVR outperforms the existing techniques and achieves a new state-of-the-art performance on VPR benchmark datasets. The visualization shows the saliency map learned in a weakly supervised manner is largely consistent with human cognition. | ['Danwei Wang', 'Heshan Li', 'Jun Zhang', 'Guohao Peng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['visual-place-recognition'] | ['computer-vision'] | [ 2.60918289e-01 -2.83690453e-01 -3.75076920e-01 -2.99130172e-01
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cd2cedbe-c9b8-4dbc-b20c-5f2dacfc6c61 | end-to-end-audio-strikes-back-boosting | 2204.11479 | null | https://arxiv.org/abs/2204.11479v5 | https://arxiv.org/pdf/2204.11479v5.pdf | End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network | While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, fine-tuned from large datasets. By utilizing the inherited lightweight nature of audio and novel audio augmentations, we were able to present an efficient end-to-end network with strong generalization ability. Experiments on a variety of sound classification sets demonstrate the effectiveness and robustness of our approach, by achieving state-of-the-art results in various settings. Public code is available at: \href{https://github.com/Alibaba-MIIL/AudioClassfication}{this http url} | ['Asaf Noy', 'Gilad Sharir', 'Tal Ridnik', 'Gadi Zimerman', 'Avi Gazneli'] | 2022-04-25 | null | null | null | null | ['environmental-sound-classification', 'sound-classification', 'keyword-spotting'] | ['audio', 'audio', 'speech'] | [ 1.49971351e-01 -2.25310937e-01 -1.20392509e-01 -4.01758879e-01
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9fc73a78-52fe-4f70-b34e-87dec6b129ca | cultural-and-geographical-influences-on-image | null | null | https://aclanthology.org/2021.naacl-main.19 | https://aclanthology.org/2021.naacl-main.19.pdf | Cultural and Geographical Influences on Image Translatability of Words across Languages | Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across languages, translations can be identified via images associated with words in different languages that have a high degree of visual similarity. However, translating via images has been shown to improve upon text-only models only marginally. To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i.e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity. In addition, in line with previous works that show images help more in translating concrete words, we found that concrete words have improved image translatability compared to abstract ones. | ['Derry Tanti Wijaya', 'Chris Callison-Burch', 'Mohammad Sadegh Rasooli', 'Isidora Tourni', 'Nikzad Khani'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['multimodal-machine-translation', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.22814842e-01 -1.81554213e-01 -1.79220706e-01 -3.05437744e-01
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2a8024ff-6f1c-426f-9857-d7e4569424ff | l-co-net-learned-condensation-optimization | 2004.11253 | null | https://arxiv.org/abs/2004.11253v1 | https://arxiv.org/pdf/2004.11253v1.pdf | L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI | In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool) and 90.0% (LV Myocardium) with five-fold cross-validation and yielded similar clinical parameters as those estimated from the ground truth segmentation data. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning, and guidance applications. | ['S. M. Kamrul Hasan', 'Cristian A. Linte'] | 2020-04-21 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.26897955e-01 2.64258176e-01 -1.18928395e-01 -4.47365582e-01
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c3efef07-4f96-4d8d-a70e-5088e75b7459 | every-pixel-counts-joint-learning-of-geometry | 1810.06125 | null | https://arxiv.org/abs/1810.06125v2 | https://arxiv.org/pdf/1810.06125v2.pdf | Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding | Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progress recently. Current state-of-the-art (SoTA) methods treat the two tasks independently. One typical assumption of the existing depth estimation methods is that the scenes contain no independent moving objects. while object moving could be easily modeled using optical flow. In this paper, we propose to address the two tasks as a whole, i.e. to jointly understand per-pixel 3D geometry and motion. This eliminates the need of static scene assumption and enforces the inherent geometrical consistency during the learning process, yielding significantly improved results for both tasks. We call our method as "Every Pixel Counts++" or "EPC++". Specifically, during training, given two consecutive frames from a video, we adopt three parallel networks to predict the camera motion (MotionNet), dense depth map (DepthNet), and per-pixel optical flow between two frames (OptFlowNet) respectively. The three types of information are fed into a holistic 3D motion parser (HMP), and per-pixel 3D motion of both rigid background and moving objects are disentangled and recovered. Comprehensive experiments were conducted on datasets with different scenes, including driving scenario (KITTI 2012 and KITTI 2015 datasets), mixed outdoor/indoor scenes (Make3D) and synthetic animation (MPI Sintel dataset). Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods. Code will be available at: https://github.com/chenxuluo/EPC. | ['Yang Wang', 'Wei Xu', 'Peng Wang', 'Ram Nevatia', 'Chenxu Luo', 'Alan Yuille', 'Zhenheng Yang'] | 2018-10-14 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-6.78896829e-02 -2.66308159e-01 -1.32058084e-01 -2.25418851e-01
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f0d82eef-191a-4020-98bb-ec594bc232c1 | reflective-decoding-unsupervised-paraphrasing-1 | 2010.08566 | null | https://arxiv.org/abs/2010.08566v4 | https://arxiv.org/pdf/2010.08566v4.pdf | Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models | Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions: forward and backward. First, in the contextualization step, we use LMs to generate ensembles of past and future contexts which collectively capture the input (e.g. the source sentence for paraphrasing). Second, in the reflection step, we condition on these "context ensembles", generating outputs that are compatible with them. Comprehensive empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding surpasses multiple supervised baselines on various metrics including human evaluation. | ['Yejin Choi', 'Jena Hwang', 'Chandra Bhagavatula', 'Ari Holtzman', 'Ximing Lu', 'Peter West'] | 2020-10-16 | reflective-decoding-unsupervised-paraphrasing | https://aclanthology.org/2021.acl-long.114 | https://aclanthology.org/2021.acl-long.114.pdf | acl-2021-5 | ['conditional-text-generation', 'text-infilling'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.17733896e-01 3.01514596e-01 -3.46693814e-01 -3.93585861e-01
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53fbb2cc-61fe-41aa-b4c3-a3ae8442717e | motionmixer-mlp-based-3d-human-body-pose | 2207.00499 | null | https://arxiv.org/abs/2207.00499v1 | https://arxiv.org/pdf/2207.00499v1.pdf | MotionMixer: MLP-based 3D Human Body Pose Forecasting | In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained spatial dependencies of the body joints. The interaction of the body joints over time is then modelled by a temporal MLP. The spatial-temporal mixed features are finally aggregated and decoded to obtain the future motion. To calibrate the influence of each time step in the pose sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. Our code is available at: https://github.com/MotionMLP/MotionMixer | ['Vasileios Belagiannis', 'Klaus Dietmayer', 'Ulrich Kressel', 'Adrian Holzbock', 'Arij Bouazizi'] | 2022-07-01 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-1.07950285e-01 -1.86666362e-02 -2.60209262e-01 -3.28917861e-01
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dc33d5ab-96bd-4474-a649-a9fe584b33c5 | the-alzheimer-s-disease-prediction-of | 2002.03419 | null | https://arxiv.org/abs/2002.03419v2 | https://arxiv.org/pdf/2002.03419v2.pdf | The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up | We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials. | ['Alex Diaz-Papkovich', 'Sach Mukherjee', 'Steven Kiddle', 'Zhiyue Huang', 'James Howlett', 'Steven M. Hill', 'Paul Manser', 'Christina Rabe', 'Keli Liu', 'Vikram Venkatraghavan', 'Lauge Sorensen', 'Sebastien Ourselin', 'Mads Nielsen', 'Mostafa M. Ghazi', 'Denisa Rimocea', 'Raluca Pop', 'Alex Kelner', 'Ionut Buciuman', 'Deqiang Qiu', 'Shiyang Chen', 'Ke Qi', 'Gang Chen', 'B. T. Thomas Yeo', 'Jiashi Feng', 'Nanbo Sun', 'Minh Nguyen', 'Hector Corrada Bravo', 'Timothy Wood', 'Aya Ismail', 'Jose G. Tamez-Pena', 'Hongtu Zhu', 'Kaixian Yu', 'Tengfei Li', 'Huiling Liao', 'Mariya Cohen', 'Dan Halbersberg', 'Yarden Levy', 'Aviv Nahon', 'Michael C. Donohue', 'Wesley K. Thompson', 'Dan Li', 'Pascal Lu', 'Manon Ansart', 'Marcin Salaterski', 'Arman Eshaghi', 'Michael W. Weiner', 'Arthur W. Toga', 'Esther E. Bron', 'Neil P. Oxtoby', 'Kruti Pandya', 'John Gallacher', 'Javier de Velasco Oriol', 'Jacob Vogel', 'Frederik Barkhof', 'Cynthia M. Stonnington', 'Simon R. White', 'Robert Ciszek', 'Murat Bilgel', 'Leon Aksman', 'Karol Estrada', 'Jimit Doshi', 'Jianfeng Wu', 'Igor Koval', 'Brian D. M. Tom', 'Aristeidis Sotiras', 'Angela Tam', 'Anais Rouanet', 'Alexandra L. Young', 'Yalin Wang', 'William Engels', 'Terry J. Lyons', 'Stanley Durrleman', 'Samuel Iddi', 'Razvan V. Marinescu', 'Polina Golland', 'Paul Moore', 'Noel Faux', 'Jussi Tohka', 'Joseph Cole', 'Guray Erus', 'Emmanuel Jammeh', 'Edgar E. V. Clemente', 'Daniel C. Alexander', 'Bruno Jedynak', 'Bernd Taschler', 'Andrew Doyle', 'Andre Altmann', 'Suman Sedai', 'Nick C. Fox', 'Vivek Devadas', 'Veronika Lunina', 'Tina Toni', 'Stefan Klein', 'Lars Lau Raket', 'Clementine Fourrier', 'Christos Davatzikos'] | 2020-02-09 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [-2.00365454e-01 -6.25701100e-02 -1.03051439e-01 -5.67724943e-01
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739a67ff-0753-4199-b3ba-827162178625 | explainable-models-via-compression-of-tree | 2206.07904 | null | https://arxiv.org/abs/2206.07904v1 | https://arxiv.org/pdf/2206.07904v1.pdf | Explainable Models via Compression of Tree Ensembles | Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of PLMs -- interpretability. In this paper we consider the problem of compressing a large set of learned trees into a single explainable model. To this effect, we propose CoTE -- Compression of Tree Ensembles -- that produces a single small decision list as a compressed representation. CoTE first converts the trees to decision lists and then performs the combination and compression with the aid of the original training set. An experimental evaluation demonstrates the effectiveness of CoTE in several benchmark relational data sets. | ['Prasad Tadepalli', 'Roni Khardon', 'Saket Joshi', 'Sriraam Natarajan', 'Siwen Yan'] | 2022-06-16 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.28038990e-01 5.52157104e-01 -4.18258011e-01 -5.85616052e-01
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e0eccb06-5dd2-4580-b9e6-ec8382cf2e06 | relevance-topic-model-for-unstructured-social | null | null | http://papers.nips.cc/paper/4979-relevance-topic-model-for-unstructured-social-group-activity-recognition | http://papers.nips.cc/paper/4979-relevance-topic-model-for-unstructured-social-group-activity-recognition.pdf | Relevance Topic Model for Unstructured Social Group Activity Recognition | Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a relevance topic model" for jointly learning meaningful mid-level representations upon bag-of-words (BoW) video representations and a classifier with sparse weights. In our approach, sparse Bayesian learning is incorporated into an undirected topic model (i.e., Replicated Softmax) to discover topics which are relevant to video classes and suitable for prediction. Rectified linear units are utilized to increase the expressive power of topics so as to explain better video data containing complex contents and make variational inference tractable for the proposed model. An efficient variational EM algorithm is presented for model parameter estimation and inference. Experimental results on the Unstructured Social Activity Attribute dataset show that our model achieves state of the art performance and outperforms other supervised topic model in terms of classification accuracy, particularly in the case of a very small number of labeled training videos." | ['Liang Wang', 'Fang Zhao', 'Tieniu Tan', 'Yongzhen Huang'] | 2013-12-01 | null | null | null | neurips-2013-12 | ['group-activity-recognition'] | ['computer-vision'] | [ 2.97720253e-01 2.68030822e-01 -7.63041377e-01 -4.82349426e-01
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97bcdd0d-8bbd-4d2c-ba9b-0b9fb7956256 | nearest-subspace-search-in-the-signed | 2110.05606 | null | https://arxiv.org/abs/2110.05606v2 | https://arxiv.org/pdf/2110.05606v2.pdf | Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification | This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT). The proposed method exploits certain linearization properties of the SCDT to render the problem easier to solve in the SCDT space. The method uses the nearest subspace search technique in the SCDT domain to provide a non-iterative, effective, and simple to implement classification algorithm. Experiments show that the proposed technique outperforms the state-of-the-art neural networks using a very low number of training samples and is also robust to out-of-distribution examples on simulated data. We also demonstrate the efficacy of the proposed technique in real-world applications by applying it to an ECG classification problem. The python code implementing the proposed classifier can be found in PyTransKit (https://github.com/rohdelab/PyTransKit). | ['Gustavo K. Rohde', 'Shiying Li', 'Yan Zhuang', 'Mohammad Shifat-E-Rabbi', 'Abu Hasnat Mohammad Rubaiyat'] | 2021-10-11 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.89099038e-01 -2.88013667e-01 -1.20259803e-02 -5.00807226e-01
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ffcf99b8-6277-4e07-86b1-9dab6f50c0dd | sinai-voting-system-for-twitter-sentiment | null | null | https://aclanthology.org/S14-2100 | https://aclanthology.org/S14-2100.pdf | SINAI: Voting System for Twitter Sentiment Analysis | null | ["L. Alfonso Ure{\\~n}a-L{\\'o}pez", "Salud Mar{\\'\\i}a Jim{\\'e}nez-Zafra", "Eugenio Mart{\\'\\i}nez-C{\\'a}mara", 'Maite Martin'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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2e0087f8-767b-475f-a6cd-522b2f971f9f | collaborative-learning-for-hand-and-object | 2204.13062 | null | https://arxiv.org/abs/2204.13062v1 | https://arxiv.org/pdf/2204.13062v1.pdf | Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution | Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality. Existing approaches for hand and object reconstruction require explicitly defined physical constraints and known objects, which limits its application domains. Our algorithm is agnostic to object models, and it learns the physical rules governing hand-object interaction. This requires automatically inferring the shapes and physical interaction of hands and (potentially unknown) objects. We seek to approach this challenging problem by proposing a collaborative learning strategy where two-branches of deep networks are learning from each other. Specifically, we transfer hand mesh information to the object branch and vice versa for the hand branch. The resulting optimisation (training) problem can be unstable, and we address this via two strategies: (i) attention-guided graph convolution which helps identify and focus on mutual occlusion and (ii) unsupervised associative loss which facilitates the transfer of information between the branches. Experiments using four widely-used benchmarks show that our framework achieves beyond state-of-the-art accuracy in 3D pose estimation, as well as recovers dense 3D hand and object shapes. Each technical component above contributes meaningfully in the ablation study. | ['Hyung Jin Chang', 'Ales Leonardis', 'Kwang In Kim', 'Tze Ho Elden Tse'] | 2022-04-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tse_Collaborative_Learning_for_Hand_and_Object_Reconstruction_With_Attention-Guided_Graph_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tse_Collaborative_Learning_for_Hand_and_Object_Reconstruction_With_Attention-Guided_Graph_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-pose-estimation', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.15657142e-02 1.17242806e-01 -6.51889145e-02 -9.20295641e-02
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8db488e9-1065-4c59-8083-8d916bfea903 | a-fair-loss-function-for-network-pruning | 2211.10285 | null | https://arxiv.org/abs/2211.10285v1 | https://arxiv.org/pdf/2211.10285v1.pdf | A Fair Loss Function for Network Pruning | Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance. In this paper, we introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using biased classifiers for facial classification and skin-lesion classification tasks demonstrate that the proposed method is a simple and effective tool that can enable existing pruning methods to be used in fairness sensitive contexts. | ['Alexander Wong', 'Robbie Meyer'] | 2022-11-18 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.32588845e-01 3.41495901e-01 -5.55297971e-01 -8.94664586e-01
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f834ab7b-6057-45e7-a49d-6085be58f581 | event-collapse-in-contrast-maximization | 2207.04007 | null | https://arxiv.org/abs/2207.04007v2 | https://arxiv.org/pdf/2207.04007v2.pdf | Event Collapse in Contrast Maximization Frameworks | Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prior works have largely ignored the issue or proposed workarounds, it is imperative to analyze this phenomenon in detail. Our work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space-time deformation based on differential geometry and physics. We experimentally show on publicly available datasets that the proposed metrics mitigate event collapse and do not harm well-posed warps. To the best of our knowledge, regularizers based on the proposed metrics are the only effective solution against event collapse in the experimental settings considered, compared with other methods. We hope that this work inspires further research to tackle more complex warp models. | ['Guillermo Gallego', 'Yoshimitsu Aoki', 'Shintaro Shiba'] | 2022-07-08 | null | null | null | null | ['event-based-vision', 'event-based-motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.48015812e-01 -2.17146024e-01 1.59357056e-01 -2.67791543e-02
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effbdf87-a220-42bf-9914-af0e25069858 | mixhop-higher-order-graph-convolution | 1905.00067 | null | https://arxiv.org/abs/1905.00067v3 | https://arxiv.org/pdf/1905.00067v3.pdf | MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operators, by repeatedly mixing feature representations of neighbors at various distances. Mixhop requires no additional memory or computational complexity, and outperforms on challenging baselines. In addition, we propose sparsity regularization that allows us to visualize how the network prioritizes neighborhood information across different graph datasets. Our analysis of the learned architectures reveals that neighborhood mixing varies per datasets. | ['Hrayr Harutyunyan', 'Sami Abu-El-Haija', 'Bryan Perozzi', 'Amol Kapoor', 'Aram Galstyan', 'Nazanin Alipourfard', 'Greg Ver Steeg', 'Kristina Lerman'] | 2019-04-30 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-8.63913000e-02 2.30827495e-01 -5.31412601e-01 -5.99724591e-01
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bb69abb7-4909-4de0-bfa1-fd62deb3ef62 | multi-label-hate-speech-and-abusive-language | null | null | https://aclanthology.org/W19-3506 | https://aclanthology.org/W19-3506.pdf | Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter | Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time. | ['Indra Budi', 'Muhammad Okky Ibrohim'] | 2019-08-01 | null | null | null | ws-2019-8 | ['abuse-detection'] | ['natural-language-processing'] | [ 1.70625627e-01 -3.13024521e-01 -1.20769799e-01 -3.33771139e-01
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2fade9dc-96bf-43d6-be8a-5b8e6613c816 | serc-syntactic-and-semantic-sequence-based | 2111.02265 | null | https://arxiv.org/abs/2111.02265v2 | https://arxiv.org/pdf/2111.02265v2.pdf | SERC: Syntactic and Semantic Sequence based Event Relation Classification | Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are closely related and influence each other. So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. We use the syntactic structure of the text for identifying temporal and causal relations between two events from the text. We extract parts-of-speech tag sequence, dependency tag sequence and word sequence from the text. We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features. Evaluation of our model on four popular datasets yields promising results for temporal and causal relation classification. | ['Sumit Bhatia', 'Raghava Mutharaju', 'Kritika Venkatachalam'] | 2021-11-03 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 2.83496410e-01 -2.07737744e-01 -5.76183379e-01 -8.22030485e-01
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d3875ac1-41e0-4617-b729-176195214e0e | comparative-study-on-the-effects-of-noise-in | 2306.01110 | null | https://arxiv.org/abs/2306.01110v2 | https://arxiv.org/pdf/2306.01110v2.pdf | Comparative Study on the Effects of Noise in ML-Based Anxiety Detection | Wearable health devices are ushering in a new age of continuous and noninvasive remote monitoring. One application of this technology is in anxiety detection. Many advancements in anxiety detection have happened in controlled lab settings, but noise prevents these advancements from generalizing to real-world conditions. We seek to progress the field by studying how noise impacts model performance and developing models that are robust to noisy, real-world conditions and, hence, attuned to the commotion of everyday life. In this study we look to investigate why and how previous methods have failed. Using the wearable stress and affect detection (WESAD) dataset, we compare the effect of various intensities of noise on machine learning models classifying levels of physiological arousal in the three-class classification problem: baseline vs. stress vs. amusement. Before introducing noise, our baseline model performance reaches 98.7%, compared to Schmidt 2018's 80.3%. We discuss potential sources of this discrepancy in results through a careful evaluation of feature extraction and model architecture choices. Finally, after the introduction of noise, we provide a thorough analysis of the effect of noise on each model architecture. | ['Elizabeth Hsiao-Wecksler', 'Abdul Alkurdi', 'Samuel Schapiro'] | 2023-06-01 | null | null | null | null | ['anxiety-detection'] | ['medical'] | [ 3.11835706e-01 -2.43152589e-01 8.91213194e-02 -6.07135534e-01
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4f8a9750-2895-46e0-953a-7e6f2dcad7c9 | terminology-localization-guidelines-for-the | null | null | https://aclanthology.org/L14-1130 | https://aclanthology.org/L14-1130.pdf | Terminology localization guidelines for the national scenario | This paper presents a set of principles and practical guidelines for terminology work in the national scenario to ensure a harmonized approach in term localization. These linguistic principles and guidelines are elaborated by the Terminology Commission in Latvia in the domain of Information and Communication Technology (ICT). We also present a novel approach in a corpus-based selection and an evaluation of the most frequently used terms. Analysis of the terms proves that, in general, in the normative terminology work in Latvia localized terms are coined according to these guidelines. We further evaluate how terms included in the database of official terminology are adopted in the general use such as newspaper articles, blogs, forums, websites etc. Our evaluation shows that in a non-normative context the official terminology faces a strong competition from other variations of localized terms. Conclusions and recommendations from lexical analysis of localized terms are provided. We hope that presented guidelines and approach in evaluation will be useful to terminology institutions, regulative authorities and researchers in different countries that are involved in the national terminology work. | ['Andrejs Vasi{\\c{l}}jevs', 'M{\\=a}rcis Pinnis', 'Iveta Kei{\\v{s}}a', 'Ilze Ilzi{\\c{n}}a', 'Juris Borzovs'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-1.35528743e-01 -1.67338312e-01 -9.26857740e-02 1.46340340e-01
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6d980754-b9dc-4073-add1-a8f971a57db2 | exact-complete-and-universal-continuous-time | cond-mat/9703200 | null | https://arxiv.org/abs/cond-mat/9703200v2 | https://arxiv.org/pdf/cond-mat/9703200v2.pdf | Exact, Complete, and Universal Continuous-Time Worldline Monte Carlo Approach to the Statistics of Discrete Quantum Systems | We show how the worldline quantum Monte Carlo procedure, which usually relies on an artificial time discretization, can be formulated directly in continuous time, rendering the scheme exact. For an arbitrary system with discrete Hilbert space, none of the configuration update procedures contain small parameters. We find that the most effective update strategy involves the motion of worldline discontinuities (both in space and time), i.e., the evaluation of the Green's function. Being based on local updates only, our method nevertheless allows one to work with the grand canonical ensemble and non-zero winding numbers, and to calculate any dynamic correlation function as easily as expectation values of, e.g., total energy. The principles found for the update in continuous time generalize to any continuous variables in the space of discrete virtual transitions, and in principle also make it possible to simulate continuous systems exactly. | ['I. S. Tupitsyn', 'B. V. Svistunov', "N. V. Prokof'ev"] | 1997-03-24 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-3.56650725e-02 -2.69933015e-01 3.92683715e-01 4.84250225e-02
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fac4e09b-0423-4b3e-ad35-5bf953a14e41 | factored-action-spaces-in-deep-reinforcement | null | null | https://openreview.net/forum?id=naSAkn2Xo46 | https://openreview.net/pdf?id=naSAkn2Xo46 | Factored Action Spaces in Deep Reinforcement Learning | Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms. An existing approach consists in splitting the action space into smaller components and choosing either independently or sequentially actions in each dimension. This approach led to astonishing results for the StarCraft and Dota 2 games, however it remains underexploited and understudied. In this paper, we name this approach Factored Actions Reinforcement Learning (FARL) and study both its theoretical impact and practical use. Notably, we provide a theoretical analysis of FARL on the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) algorithms and evaluate these agents in different classes of problems. We show that FARL is a very versatile and efficient approach to combinatorial and continuous control problems. | ['Olivier Sigaud', 'Karim Beguir', 'Nicolas Perrin', 'Alexandre Laterre', 'Louis Monier', 'Jean-Baptiste Sevestre', 'Valentin Macé', 'Thomas Pierrot'] | 2021-01-01 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-1.35815337e-01 9.88631770e-02 -4.81008470e-01 4.02705342e-01
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a8e8069b-7447-4542-90e9-d84b5108d533 | enhancing-pure-pixel-identification | 1406.5286 | null | http://arxiv.org/abs/1406.5286v1 | http://arxiv.org/pdf/1406.5286v1.pdf | Enhancing Pure-Pixel Identification Performance via Preconditioning | In this paper, we analyze different preconditionings designed to enhance
robustness of pure-pixel search algorithms, which are used for blind
hyperspectral unmixing and which are equivalent to near-separable nonnegative
matrix factorization algorithms. Our analysis focuses on the successive
projection algorithm (SPA), a simple, efficient and provably robust algorithm
in the pure-pixel algorithm class. Recently, a provably robust preconditioning
was proposed by Gillis and Vavasis (arXiv:1310.2273) which requires the
resolution of a semidefinite program (SDP) to find a data points-enclosing
minimum volume ellipsoid. Since solving the SDP in high precisions can be time
consuming, we generalize the robustness analysis to approximate solutions of
the SDP, that is, solutions whose objective function values are some
multiplicative factors away from the optimal value. It is shown that a high
accuracy solution is not crucial for robustness, which paves the way for faster
preconditionings (e.g., based on first-order optimization methods). This first
contribution also allows us to provide a robustness analysis for two other
preconditionings. The first one is pre-whitening, which can be interpreted as
an optimal solution of the same SDP with additional constraints. We analyze
robustness of pre-whitening which allows us to characterize situations in which
it performs competitively with the SDP-based preconditioning. The second one is
based on SPA itself and can be interpreted as an optimal solution of a
relaxation of the SDP. It is extremely fast while competing with the SDP-based
preconditioning on several synthetic data sets. | ['Wing-Kin Ma', 'Nicolas Gillis'] | 2014-06-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.64934611e-01 -3.53054404e-02 1.59844160e-01 3.28039676e-01
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5881e370-468a-48d2-add6-6d888d34962a | multiple-instance-ensembling-for-paranasal | 2303.17915 | null | https://arxiv.org/abs/2303.17915v1 | https://arxiv.org/pdf/2303.17915v1.pdf | Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus | Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS. | ['Alexander Schlaefer', 'Anna Sophie Hoffmann', 'Christian Betz', 'Dennis Eggert', 'Bastian Cheng', 'Marvin Petersen', 'Elina Petersen', 'Dirk Beyersdorff', 'Benjamin Tobias Becker', 'Finn Behrendt', 'Debayan Bhattacharya'] | 2023-03-31 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 2.55455524e-01 3.54852140e-01 6.56185597e-02 -2.04695627e-01
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fc812f14-fdca-476e-a379-f26d99731363 | mixnet-for-generalized-face-presentation | 2010.13246 | null | https://arxiv.org/abs/2010.13246v1 | https://arxiv.org/pdf/2010.13246v1.pdf | MixNet for Generalized Face Presentation Attack Detection | The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as \textit{MixNet} to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm. | ['Richa Singh', 'Mayank Vatsa', 'Akshay Agarwal', 'Sushant Kumar Singh', 'Nilay Sanghvi'] | 2020-10-25 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.54059845e-01 -3.93876523e-01 1.09223146e-02 -2.53894448e-01
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3892b2e8-f78e-4624-825b-cb506f2b9ae3 | the-role-of-the-vagus-nerve-during-fetal | 2106.01756 | null | https://arxiv.org/abs/2106.01756v1 | https://arxiv.org/pdf/2106.01756v1.pdf | The role of the vagus nerve during fetal development and its relationship with the environment | The autonomic nervous system (ANS) regulatory capacity begins before birth as the sympathetic and parasympathetic activity contributes significantly to the fetus' development. Several studies have shown how vagus nerve is involved in many vital processes during fetal, perinatal and postnatal life: from the regulation of inflammation through the anti-inflammatory cholinergic pathway, which may affect the functioning of each organ, to the production of hormones involved in bioenergetic metabolism. In addition, the vagus nerve has been recognized as the primary afferent pathway capable of transmitting information to the brain from every organ of the body. Therefore, this hypothesis paper aims to review the development of ANS during fetal and perinatal life, focusing particularly on the vagus nerve, to identify possible "critical windows" that could impact its maturation. These "critical windows" could help clinicians know when to monitor fetuses to effectively assess the developmental status of both ANS and specifically the vagus nerve. In addition, this paper will focus on which factors (i.e. fetal characteristics and behaviors, maternal lifestyle and pathologies, placental health and dysfunction, labor, incubator conditions, and drug exposure) may have an impact on the development of the vagus during the above-mentioned "critical window" and how. This analysis could help clinicians and stakeholders define precise guidelines for improving the management of fetuses and newborns, particularly to reduce the potential adverse environmental impacts on ANS development that may lead to persistent long-term consequences. Since the development of ANS and the vagus influence have been shown to be reflected in cardiac variability, this paper will rely in particular on studies using fetal heart rate variability (fHRV) to monitor the continued growth and health of both animal and human fetuses. | ['Andrea Manzotti', 'Marco Chiera', 'Stefano Vecchi', 'Chiara Viglione', 'Marta C. Antonelli', 'Martin G. Frasch', 'Francesco Cerritelli'] | 2021-06-03 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 7.50140771e-02 1.48594573e-01 -4.73349899e-01 9.74181760e-03
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bbf90c17-630b-4cc3-b0ec-86e73b568600 | sl3d-self-supervised-self-labeled-3d | 2210.16810 | null | https://arxiv.org/abs/2210.16810v3 | https://arxiv.org/pdf/2210.16810v3.pdf | SL3D: Self-supervised-Self-labeled 3D Recognition | Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of these results rely on manually collecting densely annotated real-world 3D data, which is highly time-consuming and expensive to obtain, limiting the scalability of 3D recognition tasks. Thus, we study unsupervised 3D recognition and propose a Self-supervised-Self-Labeled 3D Recognition (SL3D) framework. SL3D simultaneously solves two coupled objectives, i.e., clustering and learning feature representation to generate pseudo-labeled data for unsupervised 3D recognition. SL3D is a generic framework and can be applied to solve different 3D recognition tasks, including classification, object detection, and semantic segmentation. Extensive experiments demonstrate its effectiveness. Code is available at https://github.com/fcendra/sl3d. | ['Xiaojuan Qi', 'Jiajun Shen', 'Lan Ma', 'Fernando Julio Cendra'] | 2022-10-30 | null | null | null | null | ['unsupervised-3d-semantic-segmentation'] | ['computer-vision'] | [-6.46017566e-02 -1.36861518e-01 -3.56961638e-01 -5.63353717e-01
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1dd8cb87-7fb3-4d33-84c3-66f7ae60f25e | an-improved-model-ensembled-of-different | 2305.17156 | null | https://arxiv.org/abs/2305.17156v1 | https://arxiv.org/pdf/2305.17156v1.pdf | An Improved Model Ensembled of Different Hyper-parameter Tuned Machine Learning Algorithms for Fetal Health Prediction | Fetal health is a critical concern during pregnancy as it can impact the well-being of both the mother and the baby. Regular monitoring and timely interventions are necessary to ensure the best possible outcomes. While there are various methods to monitor fetal health in the mother's womb, the use of artificial intelligence (AI) can improve the accuracy, efficiency, and speed of diagnosis. In this study, we propose a robust ensemble model called ensemble of tuned Support Vector Machine and ExtraTrees (ETSE) for predicting fetal health. Initially, we employed various data preprocessing techniques such as outlier rejection, missing value imputation, data standardization, and data sampling. Then, seven machine learning (ML) classifiers including Support Vector Machine (SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Decision Tree (DT), Random Forest (RF), ExtraTrees (ET), and K-Neighbors were implemented. These models were evaluated and then optimized by hyperparameter tuning using the grid search technique. Finally, we analyzed the performance of our proposed ETSE model. The performance analysis of each model revealed that our proposed ETSE model outperformed the other models with 100% precision, 100% recall, 100% F1-score, and 99.66% accuracy. This indicates that the ETSE model can effectively predict fetal health, which can aid in timely interventions and improve outcomes for both the mother and the baby. | ['Sharmin Akter', 'Md. Simul Hasan Talukder'] | 2023-05-26 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 1.54491112e-01 6.26896992e-02 -3.10763091e-01 -6.02606535e-01
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9dc1f82b-27cf-4546-8bd0-929290081ef4 | augmented-understanding-and-automated | 2007.08710 | null | https://arxiv.org/abs/2007.08710v1 | https://arxiv.org/pdf/2007.08710v1.pdf | Augmented Understanding and Automated Adaptation of Curation Rules | Over the past years, there has been many efforts to curate and increase the added value of the raw data. Data curation has been defined as activities and processes an analyst undertakes to transform the raw data into contextualized data and knowledge. Data curation enables decision-makers and data analyst to extract value and derive insight from the raw data. However, to curate the raw data, an analyst needs to carry out various curation tasks including, extraction linking, classification, and indexing, which are error-prone, tedious and challenging. Besides, deriving insight require analysts to spend a long period of time to scan and analyze the curation environments. This problem is exacerbated when the curation environment is large, and the analyst needs to curate a varied and comprehensive list of data. To address these challenges, in this dissertation, we present techniques, algorithms and systems for augmenting analysts in curation tasks. We propose: ~(1) a feature-based and automated technique for curating the raw data. ~(2) We propose an autonomic approach for adapting data curation rules. ~(3) We provide a solution to augment users in formulating their preferences while curating data in large scale information spaces. ~(4) We implement a set of APIs for automating the basic curation tasks, including Named Entity extraction, POS tags, classification, and etc. | ['Alireza Tabebordbar'] | 2020-07-17 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 2.04415351e-01 -6.53188850e-04 7.56111071e-02 -6.84863448e-01
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1ad169b7-8025-43dc-a2c8-ab823ada8b9b | design-analysis-and-application-of-a | 1702.00158 | null | http://arxiv.org/abs/1702.00158v1 | http://arxiv.org/pdf/1702.00158v1.pdf | Design, Analysis and Application of A Volumetric Convolutional Neural Network | The design, analysis and application of a volumetric convolutional neural
network (VCNN) are studied in this work. Although many CNNs have been proposed
in the literature, their design is empirical. In the design of the VCNN, we
propose a feed-forward K-means clustering algorithm to determine the filter
number and size at each convolutional layer systematically. For the analysis of
the VCNN, the cause of confusing classes in the output of the VCNN is explained
by analyzing the relationship between the filter weights (also known as anchor
vectors) from the last fully-connected layer to the output. Furthermore, a
hierarchical clustering method followed by a random forest classification
method is proposed to boost the classification performance among confusing
classes. For the application of the VCNN, we examine the 3D shape
classification problem and conduct experiments on a popular ModelNet40 dataset.
The proposed VCNN offers the state-of-the-art performance among all
volume-based CNN methods. | ['C. -C. Jay Kuo', 'Xiaqing Pan', 'Yueru Chen'] | 2017-02-01 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-3.08748186e-01 -2.48259380e-02 7.78005794e-02 -3.11314493e-01
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88ecb568-0b63-49a3-8178-4e35aed39da4 | rethinking-feature-based-knowledge | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Rethinking_Feature-Based_Knowledge_Distillation_for_Face_Recognition_CVPR_2023_paper.pdf | Rethinking Feature-Based Knowledge Distillation for Face Recognition | With the continual expansion of face datasets, feature-based distillation prevails for large-scale face recognition. In this work, we attempt to remove identity supervision in student training, to spare the GPU memory from saving massive class centers. However, this naive removal leads to inferior distillation result. We carefully inspect the performance degradation from the perspective of intrinsic dimension, and argue that the gap in intrinsic dimension, namely the intrinsic gap, is intimately connected to the infamous capacity gap problem. By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation. Remarkably, the proposed reverse distillation creates universally student-friendly teacher that demonstrates outstanding student improvement. We further enhance its effectiveness by designing a student proxy to better bridge the intrinsic gap. As a result, the proposed method surpasses state-of-the-art distillation techniques with identity supervision on various face recognition benchmarks, and the improvements are consistent across different teacher-student pairs. | ['Sungjoo Suh', 'Ran Yang', 'Min Yang', 'Ji-won Baek', 'Seungju Han', 'Hui Li', 'Zidong Guo', 'Jingzhi Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-recognition'] | ['computer-vision'] | [-1.81538668e-02 -1.20683961e-01 -1.35471433e-01 -4.12302464e-01
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3df909b4-ac56-4f57-a53d-a344ffb94b13 | deep-learning-based-human-pose-estimation-a | 2012.13392 | null | https://arxiv.org/abs/2012.13392v5 | https://arxiv.org/pdf/2012.13392v5.pdf | Deep Learning-Based Human Pose Estimation: A Survey | Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 250 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. A regularly updated project page is provided: \url{https://github.com/zczcwh/DL-HPE} | ['Sijie Zhu', 'Chen Chen', 'Mubarak Shah', 'Nasser Kehtarnavaz', 'Ju Shen', 'Taojiannan Yang', 'Wenhan Wu', 'Ce Zheng'] | 2020-12-24 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.67997569e-01 1.75671577e-02 -4.92024809e-01 -1.71621621e-01
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07c1a0cf-0ef5-4927-95f1-3efa34846441 | modal-features-for-image-texture | 2005.01928 | null | https://arxiv.org/abs/2005.01928v1 | https://arxiv.org/pdf/2005.01928v1.pdf | Modal features for image texture classification | Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features. These new features are called modal features. Initially aiming to decompose a signal into a modal basis built from a vibration mechanics problem, the DMD projection is applied to images in order to extract modal features with two approaches. The first one, called full scale DMD, consists in exploiting directly the decomposition resulting coordinates as features. The second one, called filtering DMD, consists in using the DMD modes as filters to obtain features through a local transformation process. Experiments are performed on image texture classification tasks including several widely used data bases, compared to several classic feature extraction methods. We show that the DMD approach achieves good classification performances, comparable to the state of the art techniques, with a lower extraction time. | ['Thomas Lacombe', 'Maurice Pillet', 'Hugues Favreliere'] | 2020-05-05 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.89034259e-01 -2.59254515e-01 1.37188062e-01 -1.20043650e-01
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ab4f28c5-af11-4b82-be0a-53e481dbe119 | textual-analogy-parsing-whats-shared-and | 1809.02700 | null | http://arxiv.org/abs/1809.02700v1 | http://arxiv.org/pdf/1809.02700v1.pdf | Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts | To understand a sentence like "whereas only 10% of White Americans live at or
below the poverty line, 28% of African Americans do" it is important not only
to identify individual facts, e.g., poverty rates of distinct demographic
groups, but also the higher-order relations between them, e.g., the disparity
between them. In this paper, we propose the task of Textual Analogy Parsing
(TAP) to model this higher-order meaning. The output of TAP is a frame-style
meaning representation which explicitly specifies what is shared (e.g., poverty
rates) and what is compared (e.g., White Americans vs. African Americans, 10%
vs. 28%) between its component facts. Such a meaning representation can enable
new applications that rely on discourse understanding such as automated chart
generation from quantitative text. We present a new dataset for TAP, baselines,
and a model that successfully uses an ILP to enforce the structural constraints
of the problem. | ['Christopher D. Manning', 'Dan Jurafsky', 'Percy Liang', 'Matthew Lamm', 'Arun Tejasvi Chaganty'] | 2018-09-07 | textual-analogy-parsing-whats-shared-and-1 | https://aclanthology.org/D18-1008 | https://aclanthology.org/D18-1008.pdf | emnlp-2018-10 | ['textual-analogy-parsing'] | ['natural-language-processing'] | [ 2.25247949e-01 5.44309556e-01 -6.57420456e-01 -7.19097137e-01
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e40d4639-7af0-410f-927d-4756e75486e2 | canet-a-context-aware-network-for-shadow | 2108.09894 | null | https://arxiv.org/abs/2108.09894v1 | https://arxiv.org/pdf/2108.09894v1.pdf | CANet: A Context-Aware Network for Shadow Removal | In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential matching pairs of shadow and non-shadow patches. Combined with the potential contextual relationships between shadow and non-shadow regions, our well-designed contextual feature transfer (CFT) mechanism can transfer contextual information from non-shadow to shadow regions at different scales. With the reconstructed feature maps, we remove shadows at L and A/B channels separately. At Stage-II, we use an encoder-decoder to refine current results and generate the final shadow removal results. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes. Extensive experimental results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts. | ['Chunxia Xiao', 'Ling Zhang', 'Chengjiang Long', 'Zipei Chen'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_CANet_A_Context-Aware_Network_for_Shadow_Removal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_CANet_A_Context-Aware_Network_for_Shadow_Removal_ICCV_2021_paper.pdf | iccv-2021-1 | ['shadow-removal', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 9.75748479e-01 -5.11359349e-02 3.19767475e-01 -6.44777298e-01
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1ac78f4c-11c7-4726-a19f-679565f65268 | what-makes-an-effective-scalarising-function | 2104.04790 | null | https://arxiv.org/abs/2104.04790v1 | https://arxiv.org/pdf/2104.04790v1.pdf | What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation? | Performing multi-objective Bayesian optimisation by scalarising the objectives avoids the computation of expensive multi-dimensional integral-based acquisition functions, instead of allowing one-dimensional standard acquisition functions\textemdash such as Expected Improvement\textemdash to be applied. Here, two infill criteria based on hypervolume improvement\textemdash one recently introduced and one novel\textemdash are compared with the multi-surrogate Expected Hypervolume Improvement. The reasons for the disparities in these methods' effectiveness in maximising the hypervolume of the acquired Pareto Front are investigated. In addition, the effect of the surrogate model mean function on exploration and exploitation is examined: careful choice of data normalisation is shown to be preferable to the exploration parameter commonly used with the Expected Improvement acquisition function. Finally, the effectiveness of all the methodological improvements defined here is demonstrated on a real-world problem: the optimisation of a wind turbine blade aerofoil for both aerodynamic performance and structural stiffness. With effective scalarisation, Bayesian optimisation finds a large number of new aerofoil shapes that strongly dominate standard designs. | ['Wei Yu', 'Alma Rahat', 'Tinkle Chugh', 'Clym Stock-Williams'] | 2021-04-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.33381271e-01 1.48845986e-01 2.88598359e-01 2.57282890e-02
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c9184376-2598-44db-a442-e65f50de6a14 | open-retrieval-conversational-machine-reading | 2102.08633 | null | https://arxiv.org/abs/2102.08633v3 | https://arxiv.org/pdf/2102.08633v3.pdf | Open-Retrieval Conversational Machine Reading | In conversational machine reading, systems need to interpret natural language rules, answer high-level questions such as "May I qualify for VA health care benefits?", and ask follow-up clarification questions whose answer is necessary to answer the original question. However, existing works assume the rule text is provided for each user question, which neglects the essential retrieval step in real scenarios. In this work, we propose and investigate an open-retrieval setting of conversational machine reading. In the open-retrieval setting, the relevant rule texts are unknown so that a system needs to retrieve question-relevant evidence from a collection of rule texts, and answer users' high-level questions according to multiple retrieved rule texts in a conversational manner. We propose MUDERN, a Multi-passage Discourse-aware Entailment Reasoning Network which extracts conditions in the rule texts through discourse segmentation, conducts multi-passage entailment reasoning to answer user questions directly, or asks clarification follow-up questions to inquiry more information. On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin. In addition, we conduct in-depth analyses to provide new insights into this new setting and our model. | ['Michael R. Lyu', 'Chien-Sheng Wu', 'Irwin King', 'Jingjing Li', 'Yifan Gao'] | 2021-02-17 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 6.04903638e-01 8.41727078e-01 -5.09978890e-01 -3.67061287e-01
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87341114-4ed7-412f-ab17-a2f5a6fb096b | a-backbone-replaceable-fine-tuning-network | 2010.09501 | null | https://arxiv.org/abs/2010.09501v2 | https://arxiv.org/pdf/2010.09501v2.pdf | A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment | Heatmap regression based face alignment has achieved prominent performance on static images. However, the stability and accuracy are remarkably discounted when applying the existing methods on dynamic videos. We attribute the degradation to random noise and motion blur, which are common in videos. The temporal information is critical to address this issue yet not fully considered in the existing works. In this paper, we visit the video-oriented face alignment problem in two perspectives: detection accuracy prefers lower error for a single frame, and detection consistency forces better stability between adjacent frames. On this basis, we propose a Jitter loss function that leverages temporal information to suppress inaccurate as well as jittered landmarks. The Jitter loss is involved in a novel framework with a fine-tuning ConvLSTM structure over a backbone replaceable network. We further demonstrate that accurate and stable landmarks are associated with different regions with overlaps in a canonical coordinate, based on which the proposed Jitter loss facilitates the optimization process during training. The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods. Generally, it can swiftly convert a landmark detector for facial images to a better-performing one for videos without retraining the entire model. | ['Shihong Xia', 'Zihao Zhang', 'Zhenfeng Fan', 'Yingjie Guo', 'Xu sun'] | 2020-10-19 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 3.56654674e-02 -2.70744711e-01 -6.70199543e-02 -5.44763863e-01
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ca7582ab-28c5-4c6a-bbea-bd5f1d58a5b0 | faq-retrieval-using-query-question-similarity | 1905.02851 | null | https://arxiv.org/abs/1905.02851v2 | https://arxiv.org/pdf/1905.02851v2.pdf | FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance | Frequently Asked Question (FAQ) retrieval is an important task where the objective is to retrieve an appropriate Question-Answer (QA) pair from a database based on a user's query. We propose a FAQ retrieval system that considers the similarity between a user's query and a question as well as the relevance between the query and an answer. Although a common approach to FAQ retrieval is to construct labeled data for training, it takes annotation costs. Therefore, we use a traditional unsupervised information retrieval system to calculate the similarity between the query and question. On the other hand, the relevance between the query and answer can be learned by using QA pairs in a FAQ database. The recently-proposed BERT model is used for the relevance calculation. Since the number of QA pairs in FAQ page is not enough to train a model, we cope with this issue by leveraging FAQ sets that are similar to the one in question. We evaluate our approach on two datasets. The first one is localgovFAQ, a dataset we construct in a Japanese administrative municipality domain. The second is StackExchange dataset, which is the public dataset in English. We demonstrate that our proposed method outperforms baseline methods on these datasets. | ['Sadao Kurohashi', 'Ribeka Tanaka', 'Wataru Sakata', 'Tomohide Shibata'] | 2019-05-08 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [-2.05602199e-01 -2.23202199e-01 -2.90317964e-02 -5.03005743e-01
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5ab7b4d3-d154-4626-be3f-3a3230289c75 | benchmark-platform-for-ultra-fine-grained | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Benchmark_Platform_for_Ultra-Fine-Grained_Visual_Categorization_Beyond_Human_Performance_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Benchmark_Platform_for_Ultra-Fine-Grained_Visual_Categorization_Beyond_Human_Performance_ICCV_2021_paper.pdf | Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance | Deep learning methods have achieved remarkable success in fine-grained visual categorization. Such successful categorization at sub-ordinate level, e.g., different animal or plant species, however relies heavily on the visual differences that human can observe and the ground-truths are labelled on the basis of such human visual observation. In contrast, few research has been done for visual categorization at the ultra-fine-grained level, i.e., a granularity where even human experts can hardly identify the visual differences or are not yet able to give affirmative labels by inferring observed pattern differences. This paper reports our efforts towards mitigating this research gap. We introduce the ultra-fine-grained (UFG) image dataset, a large collection of 47,114 images from 3,526 categories. All the images in the proposed UFG image dataset are grouped into categories with different confirmed cultivar names. In addition, we perform an extensive evaluation of state-of-the-art fine-grained classification methods on the proposed UFG image dataset as comparative baselines. The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI). The dataset is available online at https://github.com/XiaohanYu-GU/Ultra-FGVC. | ['Shengwu Xiong', 'Xiaohui Yuan', 'Yongsheng Gao', 'Yang Zhao', 'Xiaohan Yu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.50744319e-01 -9.95363444e-02 -3.20846409e-01 -3.96750987e-01
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dc343f7f-04f8-42ad-b83c-c363b84b8de5 | optimizing-filter-size-in-convolutional | 1707.08630 | null | http://arxiv.org/abs/1707.08630v2 | http://arxiv.org/pdf/1707.08630v2.pdf | Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition | Recognizing facial action units (AUs) during spontaneous facial displays is a
challenging problem. Most recently, Convolutional Neural Networks (CNNs) have
shown promise for facial AU recognition, where predefined and fixed convolution
filter sizes are employed. In order to achieve the best performance, the
optimal filter size is often empirically found by conducting extensive
experimental validation. Such a training process suffers from expensive
training cost, especially as the network becomes deeper.
This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the
filter sizes and weights of all convolutional layers are learned simultaneously
from the training data along with learning convolution filters. Specifically,
the filter size is defined as a continuous variable, which is optimized by
minimizing the training loss. Experimental results on two AU-coded spontaneous
databases have shown that the proposed OFS-CNN is capable of estimating optimal
filter size for varying image resolution and outperforms traditional CNNs with
the best filter size obtained by exhaustive search. The OFS-CNN also beats the
CNN using multiple filter sizes and more importantly, is much more efficient
during testing with the proposed forward-backward propagation algorithm. | ['Xiao-Feng Wang', "James O'Reilly", 'Zibo Meng', 'Zhiyuan Li', 'Yan Tong', 'Shizhong Han', 'Jie Cai'] | 2017-07-26 | optimizing-filter-size-in-convolutional-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Han_Optimizing_Filter_Size_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Han_Optimizing_Filter_Size_CVPR_2018_paper.pdf | cvpr-2018-6 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 3.36763799e-01 3.68174389e-02 8.12662989e-02 -5.06563008e-01
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96f036e8-0fb3-4ab7-b9ac-5f445ba76544 | non-intrusive-electrical-appliances | 1911.13257 | null | https://arxiv.org/abs/1911.13257v1 | https://arxiv.org/pdf/1911.13257v1.pdf | Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors | Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device's energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumption of individual appliances apart from the aggregated power rating, the spotting of individual appliances' energy usages by NILM will not only provide consumers the feedback of appliance-specific energy usage but also lead to the changes of their consumption behavior which facilitate energy conservation. B Neenan et al. [2] have demonstrated that direct individual appliance-specific energy usage signals lead to consumers' behavioral changes which improves energy efficiency by as much as 15%. Upon disaggregation of an energy signal, the signal needs to be classified according to the appropriate appliance. Hence, the goal of this paper is to disaggregate total energy consumption data to individual appliance signature and then classify appliance-specific energy loads using a prominent supervised classification method known as K-Nearest Neighbors (KNN). To perform this operation we have used a publicly accessible dataset of power signals from several houses known as the REDD dataset. Before applying KNN, data is preprocessed for each device. Then KNN is applied to check whether their energy consumption signature is separable or not. KNN is applied with K=5. | ['Mohammad Mahmudur Rahman Khan', 'Md. Abu Bakr Siddique', 'Shadman Sakib'] | 2019-11-22 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.71036109e-01 -2.04594433e-01 -3.86982292e-01 -4.47699279e-01
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4b347f43-d2e3-4d6e-8c8f-6d9141e28a68 | e-ner-evidential-deep-learning-for | 2305.17854 | null | https://arxiv.org/abs/2305.17854v1 | https://arxiv.org/pdf/2305.17854v1.pdf | E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition | Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities. | ['Bingzhe Wu', 'Zhe Liu', 'Zhirui Zhang', 'Lemao Liu', 'Haotian Wang', 'Minlie Huang', 'Shiwan Zhao', 'Mengting Hu', 'Zhen Zhang'] | 2023-05-29 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-6.30194664e-01 3.20069522e-01 -1.38343588e-01 -4.53479409e-01
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09669827-04e2-4950-8586-21dfc3539490 | explainable-machine-learning-control-robust | 2001.10056 | null | https://arxiv.org/abs/2001.10056v1 | https://arxiv.org/pdf/2001.10056v1.pdf | Explainable Machine Learning Control -- robust control and stability analysis | Recently, the term explainable AI became known as an approach to produce models from artificial intelligence which allow interpretation. Since a long time, there are models of symbolic regression in use that are perfectly explainable and mathematically tractable: in this contribution we demonstrate how to use symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In previous publications, network control was achieved by automatized machine learning control using genetic programming. Here, we focus on the subsequent analysis of the analytical expressions which result from the machine learning. In particular, we use AUTO to analyze the stability properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable models over less accessible neural networks. | ['Markus Abel', 'Thomas Isele', 'Markus Quade'] | 2020-01-23 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.68467301e-01 7.37909615e-01 -6.55351356e-02 -9.04366225e-02
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215538a5-3b4c-45ec-8add-0d15ac05159e | fantrack-3d-multi-object-tracking-with | 1905.02843 | null | https://arxiv.org/abs/1905.02843v1 | https://arxiv.org/pdf/1905.02843v1.pdf | FANTrack: 3D Multi-Object Tracking with Feature Association Network | We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. Our solution learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. We evaluate our approach on the challenging KITTI dataset and show competitive results. Our code is available at https://git.uwaterloo.ca/wise-lab/fantrack. | ['Erkan Baser', 'Krzysztof Czarnecki', 'Prarthana Bhattacharyya', 'Venkateshwaran Balasubramanian'] | 2019-05-07 | null | null | null | null | ['online-multi-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.10522762e-01 -5.09369671e-01 -8.08647722e-02 -2.75780290e-01
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e764adc1-ae27-4a5d-b0c1-34a9d2f964fa | near-real-time-distributed-state-estimation | 2207.11117 | null | https://arxiv.org/abs/2207.11117v1 | https://arxiv.org/pdf/2207.11117v1.pdf | Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks | Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays. | ['Dejan Vukobratovic', 'Mirjana Maksimovic', 'Dragisa Miskovic', 'Merim Dzaferagic', 'Darijo Raca', 'Mirsad Cosovic', 'Miodrag Forcan', 'Ognjen Kundacina'] | 2022-07-22 | null | null | null | null | ['energy-management'] | ['time-series'] | [-7.52668202e-01 3.41795027e-01 -3.61042082e-01 -1.76746666e-01
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741c429f-49e4-4f41-85e6-834c9b10aed3 | live-speech-portraits-real-time | 2109.10595 | null | https://arxiv.org/abs/2109.10595v2 | https://arxiv.org/pdf/2109.10595v2.pdf | Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation | To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space. In the second stage, we learn facial dynamics and motions from the projected audio features. The predicted motions include head poses and upper body motions, where the former is generated by an autoregressive probabilistic model which models the head pose distribution of the target person. Upper body motions are deduced from head poses. In the final stage, we generate conditional feature maps from previous predictions and send them with a candidate image set to an image-to-image translation network to synthesize photorealistic renderings. Our method generalizes well to wild audio and successfully synthesizes high-fidelity personalized facial details, e.g., wrinkles, teeth. Our method also allows explicit control of head poses. Extensive qualitative and quantitative evaluations, along with user studies, demonstrate the superiority of our method over state-of-the-art techniques. | ['Xun Cao', 'Jinxiang Chai', 'Yuanxun Lu'] | 2021-09-22 | null | null | null | null | ['talking-head-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.38818026e-01 5.77458918e-01 3.24839920e-01 -5.66863775e-01
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34e764da-4f89-451a-a1dd-cb99097e78dd | from-query-tools-to-causal-architects | 2306.16902 | null | https://arxiv.org/abs/2306.16902v1 | https://arxiv.org/pdf/2306.16902v1.pdf | From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data | Large Language Models (LLMs) exhibit exceptional abilities for causal analysis between concepts in numerous societally impactful domains, including medicine, science, and law. Recent research on LLM performance in various causal discovery and inference tasks has given rise to a new ladder in the classical three-stage framework of causality. In this paper, we advance the current research of LLM-driven causal discovery by proposing a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning. To make LLM more than a query tool and to leverage its power in discovering natural and new laws of causality, we integrate the valuable LLM expertise on existing causal mechanisms into statistical analysis of objective data to build a novel and practical baseline for causal structure learning. We introduce a universal set of prompts designed to extract causal graphs from given variables and assess the influence of LLM prior causality on recovering causal structures from data. We demonstrate the significant enhancement of LLM expertise on the quality of recovered causal structures from data, while also identifying critical challenges and issues, along with potential approaches to address them. As a pioneering study, this paper aims to emphasize the new frontier that LLMs are opening for classical causal discovery and inference, and to encourage the widespread adoption of LLM capabilities in data-driven causal analysis. | ['Huanhuan Chen', 'Xiangyu Wang', 'Lyvzhou Chen', 'Taiyu Ban'] | 2023-06-29 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.20431048e-01 3.58585536e-01 -1.15955400e+00 -4.14724439e-01
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ce6d3d5a-859c-499a-9d3a-0755ba828b7d | orthogonal-annotation-benefits-barely | 2303.13090 | null | https://arxiv.org/abs/2303.13090v1 | https://arxiv.org/pdf/2303.13090v1.pdf | Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation | Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks. Experimental results on three benchmark datasets validated our effectiveness in performance and efficiency in annotation. For example, with only 10 annotated slices, our method reaches a Dice up to 86.93% on KiTS19 dataset. | ['Yang Gao', 'Yinghuan Shi', 'Qian Yu', 'Lei Qi', 'Shumeng Li', 'Heng Cai'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.29762679e-01 3.72416168e-01 -4.44838464e-01 -6.19765222e-01
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1cc58f6b-67c2-4c11-9422-1d388086ed96 | rethinking-planar-homography-estimation-using | null | null | https://link.springer.com/chapter/10.1007/978-3-030-20876-9_36 | https://eprints.qut.edu.au/126933/ | Rethinking Planar Homography Estimation Using Perspective Fields | Planar homography estimation refers to the problem of computing a bijective linear mapping of pixels between two images. While this problem has been studied with convolutional neural networks (CNNs), existing methods simply regress the location of the four corners using a dense layer preceded by a fully-connected layer. This vector representation damages the spatial structure of the corners since they have a clear spatial order. Moreover, four points are the minimum required to compute the homography, and so such an approach is susceptible to perturbation. In this paper, we propose a conceptually simple, reliable, and general framework for homography estimation. In contrast to previous works, we formulate this problem as a perspective field (PF), which models the essence of the homography - pixel-to-pixel bijection. The PF is naturally learned by the proposed fully convolutional residual network, PFNet, to keep the spatial order of each pixel. Moreover, since every pixels’ displacement can be obtained from the PF, it enables robust homography estimation by utilizing dense correspondences. Our experiments demonstrate the proposed method outperforms traditional correspondence-based approaches and state-of-the-art CNN approaches in terms of accuracy while also having a smaller network size. In addition, the new parameterization of this task is general and can be implemented by any fully convolutional network (FCN) architecture. | ['Simon Denman', 'Rui Zeng', 'Clinton Fookes', 'Sridha Sridharan'] | 2019-05-26 | null | null | null | accv-2018-2019-5 | ['homography-estimation'] | ['computer-vision'] | [ 2.66629457e-01 7.33914152e-02 -9.22694430e-02 -1.14446461e-01
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c9b03785-b455-48a3-a4c7-a1a4b813daf4 | towards-few-shot-inductive-link-prediction-on | 2307.01204 | null | https://arxiv.org/abs/2307.01204v1 | https://arxiv.org/pdf/2307.01204v1.pdf | Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach | Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance. | ['Chen Gong', 'Quoc Viet Hung Nguyen', 'Shirui Pan', 'Linhao Luo', 'Zicheng Zhao'] | 2023-06-26 | null | null | null | null | ['inductive-link-prediction', 'link-prediction', 'knowledge-graphs'] | ['graphs', 'graphs', 'knowledge-base'] | [-1.35946423e-01 8.30262601e-01 -8.03433418e-01 -4.75472748e-01
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d48f9f14-b6fa-4fbc-a96c-9a90ac466611 | rethinking-multiple-instance-learning-for | 2307.02249 | null | https://arxiv.org/abs/2307.02249v1 | https://arxiv.org/pdf/2307.02249v1.pdf | Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need | Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggregate instance features into a bag feature through attention mechanisms and then train a bag classifier, where the attention scores can be used for instance-level classification. However, the pseudo instance labels constructed by the former usually contain a lot of noise, and the attention scores constructed by the latter are not accurate enough, both of which affect their performance. In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks. To this end, we propose an instance-level weakly supervised contrastive learning algorithm for the first time under the MIL setting to effectively learn instance feature representation. We also propose an accurate pseudo label generation method through prototype learning. We then develop a joint training strategy for weakly supervised contrastive learning, prototype learning, and instance classifier training. Extensive experiments and visualizations on four datasets demonstrate the powerful performance of our method. Codes will be available. | ['Zhijian Song', 'Manning Wang', 'Xiaoyuan Luo', 'Yingfan Ma', 'Linhao Qu'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'classification-1', 'multiple-instance-learning', 'pseudo-label'] | ['computer-vision', 'methodology', 'methodology', 'methodology', 'miscellaneous'] | [ 5.52014709e-01 1.59227893e-01 -6.02945745e-01 -5.87257743e-01
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c253adbc-1673-4add-8054-ebd06e063c27 | bernnet-learning-arbitrary-graph-spectral | 2106.10994 | null | https://arxiv.org/abs/2106.10994v3 | https://arxiv.org/pdf/2106.10994v3.pdf | BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | Many representative graph neural networks, e.g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined filter weights or learns them without necessary constraints, which may lead to oversimplified or ill-posed filters. To overcome these issues, we propose BernNet, a novel graph neural network with theoretical support that provides a simple but effective scheme for designing and learning arbitrary graph spectral filters. In particular, for any filter over the normalized Laplacian spectrum of a graph, our BernNet estimates it by an order-$K$ Bernstein polynomial approximation and designs its spectral property by setting the coefficients of the Bernstein basis. Moreover, we can learn the coefficients (and the corresponding filter weights) based on observed graphs and their associated signals and thus achieve the BernNet specialized for the data. Our experiments demonstrate that BernNet can learn arbitrary spectral filters, including complicated band-rejection and comb filters, and it achieves superior performance in real-world graph modeling tasks. Code is available at https://github.com/ivam-he/BernNet. | ['Hongteng Xu', 'Zengfeng Huang', 'Zhewei Wei', 'Mingguo He'] | 2021-06-21 | null | http://proceedings.neurips.cc/paper/2021/hash/76f1cfd7754a6e4fc3281bcccb3d0902-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/76f1cfd7754a6e4fc3281bcccb3d0902-Paper.pdf | neurips-2021-12 | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.85642973e-01 1.26073569e-01 -1.61101729e-01 -9.78371128e-02
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3b2db1e1-b053-45e0-a6cf-91b5de543e9a | the-s-hock-dataset-analyzing-crowds-at-the | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Conigliaro_The_S-Hock_Dataset_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Conigliaro_The_S-Hock_Dataset_2015_CVPR_paper.pdf | The S-Hock Dataset: Analyzing Crowds at the Stadium | The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people "interested in watching something specific that they came to see". This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-Hock), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research. | ['Chiara Bassetti', 'Paolo Rota', 'Nicola Conci', 'Marco Cristani', 'Nicu Sebe', 'Francesco Setti', 'Davide Conigliaro'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['head-pose-estimation'] | ['computer-vision'] | [-6.51229501e-01 7.66045973e-02 3.76183838e-01 -1.45529613e-01
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08f6a4e5-9194-474b-aebd-49b7b4896434 | event-cameras-contrast-maximization-and | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Stoffregen_Event_Cameras_Contrast_Maximization_and_Reward_Functions_An_Analysis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Stoffregen_Event_Cameras_Contrast_Maximization_and_Reward_Functions_An_Analysis_CVPR_2019_paper.pdf | Event Cameras, Contrast Maximization and Reward Functions: An Analysis | Event cameras asynchronously report timestamped changes in pixel intensity and offer advantages over conventional raster scan cameras in terms of low-latency, low redundancy sensing and high dynamic range. In recent years, much of research in event based vision has been focused on performing tasks such as optic flow estimation, moving object segmentation, feature tracking, camera rotation estimation and more, through contrast maximization. In contrast maximization, events are warped along motion trajectories whose parameters depend on the quantity being estimated, to some time t_ref. The parameters are then scored by some reward function of the accumulated events at t_ref. The versatility of this approach has lead to a flurry of research in recent years, but no in-depth study of the reward chosen during optimization has yet been made. In this work we examine the choice of reward used in contrast maximization, propose a classification of different rewards and show how a reward can be constructed that is more robust to noise and aperture uncertainty. We validate our work experimentally by predicting optical flow and comparing to ground-truth data.
| [' Lindsay Kleeman', 'Timo Stoffregen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['event-based-vision'] | ['computer-vision'] | [ 4.03098643e-01 -4.95511830e-01 -3.80941108e-02 -3.51482809e-01
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4f3f6796-9c30-41c1-b1c8-2aa80f588206 | translating-a-visual-lego-manual-to-a-machine | 2207.12572 | null | https://arxiv.org/abs/2207.12572v1 | https://arxiv.org/pdf/2207.12572v1.pdf | Translating a Visual LEGO Manual to a Machine-Executable Plan | We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We formulate this problem as a sequential prediction task: at each step, our model reads the manual, locates the components to be added to the current shape, and infers their 3D poses. This task poses the challenge of establishing a 2D-3D correspondence between the manual image and the real 3D object, and 3D pose estimation for unseen 3D objects, since a new component to be added in a step can be an object built from previous steps. To address these two challenges, we present a novel learning-based framework, the Manual-to-Executable-Plan Network (MEPNet), which reconstructs the assembly steps from a sequence of manual images. The key idea is to integrate neural 2D keypoint detection modules and 2D-3D projection algorithms for high-precision prediction and strong generalization to unseen components. The MEPNet outperforms existing methods on three newly collected LEGO manual datasets and a Minecraft house dataset. | ['Jiajun Wu', 'Chin-Yi Cheng', 'Jiayuan Mao', 'Yunzhi Zhang', 'Ruocheng Wang'] | 2022-07-25 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.20088646e-01 1.99759737e-01 2.09210720e-02 -3.15813273e-01
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6d1c4406-ea16-4dcb-b944-b0611263c791 | a-three-way-knot-privacy-fairness-and | 2306.15567 | null | https://arxiv.org/abs/2306.15567v1 | https://arxiv.org/pdf/2306.15567v1.pdf | A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics | As the frontier of machine learning applications moves further into human interaction, multiple concerns arise regarding automated decision-making. Two of the most critical issues are fairness and data privacy. On the one hand, one must guarantee that automated decisions are not biased against certain groups, especially those unprotected or marginalized. On the other hand, one must ensure that the use of personal information fully abides by privacy regulations and that user identities are kept safe. The balance between privacy, fairness, and predictive performance is complex. However, despite their potential societal impact, we still demonstrate a poor understanding of the dynamics between these optimization vectors. In this paper, we study this three-way tension and how the optimization of each vector impacts others, aiming to inform the future development of safe applications. In light of claims that predictive performance and fairness can be jointly optimized, we find this is only possible at the expense of data privacy. Overall, experimental results show that one of the vectors will be penalized regardless of which of the three we optimize. Nonetheless, we find promising avenues for future work in joint optimization solutions, where smaller trade-offs are observed between the three vectors. | ['Luís Antunes', 'Nuno Moniz', 'Tânia Carvalho'] | 2023-06-27 | null | null | null | null | ['fairness', 'fairness', 'decision-making'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [ 3.08660030e-01 2.39993170e-01 -4.32179809e-01 -5.28954089e-01
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49828b5f-5a5e-4451-9851-30a772473ae1 | gaanet-ghost-auto-anchor-network-for | 2305.03425 | null | https://arxiv.org/abs/2305.03425v1 | https://arxiv.org/pdf/2305.03425v1.pdf | GAANet: Ghost Auto Anchor Network for Detecting Varying Size Drones in Dark | The usage of drones has tremendously increased in different sectors spanning from military to industrial applications. Despite all the benefits they offer, their misuse can lead to mishaps, and tackling them becomes more challenging particularly at night due to their small size and low visibility conditions. To overcome those limitations and improve the detection accuracy at night, we propose an object detector called Ghost Auto Anchor Network (GAANet) for infrared (IR) images. The detector uses a YOLOv5 core to address challenges in object detection for IR images, such as poor accuracy and a high false alarm rate caused by extended altitudes, poor lighting, and low image resolution. To improve performance, we implemented auto anchor calculation, modified the conventional convolution block to ghost-convolution, adjusted the input channel size, and used the AdamW optimizer. To enhance the precision of multiscale tiny object recognition, we also introduced an additional extra-small object feature extractor and detector. Experimental results in a custom IR dataset with multiple classes (birds, drones, planes, and helicopters) demonstrate that GAANet shows improvement compared to state-of-the-art detectors. In comparison to GhostNet-YOLOv5, GAANet has higher overall mean average precision (mAP@50), recall, and precision around 2.5\%, 2.3\%, and 1.4\%, respectively. The dataset and code for this paper are available as open source at https://github.com/ZeeshanKaleem/GhostAutoAnchorNet. | ['Abbas Jamalipour', 'Yansha Deng', 'Zeeshan Kaleem', 'Maham Misbah', 'Misha Urooj Khan'] | 2023-05-05 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-5.16657978e-02 -3.89477253e-01 3.76659691e-01 7.12246224e-02
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08694a17-b6ac-4f54-88dd-345a46b622cc | semi-supervised-neural-machine-translation-1 | 2304.00557 | null | https://arxiv.org/abs/2304.00557v1 | https://arxiv.org/pdf/2304.00557v1.pdf | Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages | The advent of deep learning has led to a significant gain in machine translation. However, most of the studies required a large parallel dataset which is scarce and expensive to construct and even unavailable for some languages. This paper presents a simple yet effective method to tackle this problem for low-resource languages by augmenting high-quality sentence pairs and training NMT models in a semi-supervised manner. Specifically, our approach combines the cross-entropy loss for supervised learning with KL Divergence for unsupervised fashion given pseudo and augmented target sentences derived from the model. We also introduce a SentenceBERT-based filter to enhance the quality of augmenting data by retaining semantically similar sentence pairs. Experimental results show that our approach significantly improves NMT baselines, especially on low-resource datasets with 0.46--2.03 BLEU scores. We also demonstrate that using unsupervised training for augmented data is more efficient than reusing the ground-truth target sentences for supervised learning. | ['Dien Dinh', 'Long Nguyen', 'Giang Nguyen', 'Thang M. Pham', 'Viet H. Pham'] | 2023-04-02 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.96745092e-01 -3.09337483e-04 -2.39882916e-01 -6.48228467e-01
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348eaa7c-0883-49e6-bf8f-78a15e8be74a | kieglfn-a-unified-acne-grading-framework-on | null | null | http://dx.doi.org/10.1016/j.cmpb.2022.106911 | http://dx.doi.org/10.1016/j.cmpb.2022.106911 | KIEGLFN: A unified acne grading framework on face images | Grading the severity level is an extremely important procedure for correct diagnoses and personalized treatment schemes for acne. However, the acne grading criteria are not unified in the medical field. This work aims to develop an acne diagnosis system that can be generalized to various criteria. Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed. It imitates the global estimation of the dermatologist diagnosis in two steps. First, an adaptive image preprocessing method effectively filters meaningless information and enhances key information. Next, an innovative network structure fuses global deep features with local features to simulate the dermatologists’ comparison of local skin and global observation. In addition, a transfer fine-tuning strategy is proposed to transfer prior knowledge on one criterion to another criterion, which effectively improves the framework performance in case of insufficient data. Results: The Preprocessing method effectively filters meaningless areas and improves the performance of downstream models.The framework reaches accuracies of 84.52% and 59.35% on two datasets separately. Conclusions: The application of the framework on acne grading exceeds the state-of-the-art method by 1.71%, reaches the diagnostic level of a professional dermatologist and the transfer fine-tuning strategy improves the accuracy of 6.5% on the small data. | ['Gongning Luo', 'Bingmei Liu', 'Xue Cheng', 'Haiyan You', 'Yi Guan', 'Dongxin Chen', 'Zhaoyang Ma', 'Jingchi Jiang', 'Yi Lin'] | 2022-06-01 | null | null | null | computer-methods-and-programs-in-biomedicine-4 | ['acne-severity-grading'] | ['medical'] | [ 1.95040911e-01 -2.81171203e-01 -1.08291797e-01 -3.04403126e-01
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f8fa257a-4c2e-4092-b774-3205e140dad4 | blind-image-deconvolution-using-student-s-t | 2006.14780 | null | https://arxiv.org/abs/2006.14780v1 | https://arxiv.org/pdf/2006.14780v1.pdf | Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity | In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilizes the structural information by coupling Student's-t image prior with overlapping group sparsity. The proposed method resulted in an effective blind deconvolution algorithm that outperforms other state-of-the-art algorithms. | ['Deokyoung Kang', 'Suk I. Yoo', 'In S. Jeon'] | 2020-06-26 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.24675760e-01 -4.83463109e-01 4.59706903e-01 -1.67912379e-01
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812a4024-9de3-42b2-b2f8-11f3218b37c8 | medical-image-enhancement-using-histogram | 2003.06615 | null | https://arxiv.org/abs/2003.06615v1 | https://arxiv.org/pdf/2003.06615v1.pdf | Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification | MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses difficult; hence better localization of image pixels is required. Histogram Equalization techniques help to enhance the image so that it gives an improved visual quality and a well defined problem. The contrast and brightness is enhanced in such a way that it does not lose its original information and the brightness is preserved. We compare the different equalization techniques in this paper; the techniques are critically studied and elaborated. They are also tabulated to compare various parameters present in the image. In addition we have also segmented and extracted the tumor part out of the brain using K-means algorithm. For classification and feature extraction the method used is Support Vector Machine (SVM). The main goal of this research work is to help the medical field with a light of image processing. | ['Sakshi Patel', 'Rajesh Kumar Muthu', 'Bharath K P'] | 2020-03-14 | null | null | null | null | ['medical-image-enhancement'] | ['computer-vision'] | [ 4.32269275e-01 -1.06865875e-01 -9.16649923e-02 -3.28073710e-01
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ebb5a41a-f51e-4f09-825c-d7db62f158c4 | approxdet-content-and-contention-aware | 2010.10754 | null | https://arxiv.org/abs/2010.10754v1 | https://arxiv.org/pdf/2010.10754v1.pdf | ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles | Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous transportation. With an ever-growing number of powerful client devices, there is incentive to move these heavy video analytics workloads from the cloud to mobile devices to achieve low latency and real-time processing and to preserve user privacy. However, most video analytic systems are heavyweight and are trained offline with some pre-defined latency or accuracy requirements. This makes them unable to adapt at runtime in the face of three types of dynamism -- the input video characteristics change, the amount of compute resources available on the node changes due to co-located applications, and the user's latency-accuracy requirements change. In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios. To achieve this, we introduce a multi-branch object detection kernel (layered on Faster R-CNN), which incorporates a data-driven modeling approach on the performance metrics, and a latency SLA-driven scheduler to pick the best execution branch at runtime. We couple this kernel with approximable video object tracking algorithms to create an end-to-end video object detection system. We evaluate ApproxDet on a large benchmark video dataset and compare quantitatively to AdaScale and YOLOv3. We find that ApproxDet is able to adapt to a wide variety of contention and content characteristics and outshines all baselines, e.g., it achieves 52% lower latency and 11.1% higher accuracy over YOLOv3. | ['Saurabh Bagchi', 'Yin Li', 'Somali Chaterji', 'Subrata Mitra', 'Jayoung Lee', 'Pengcheng Wang', 'Chen-Lin Zhang', 'ran Xu'] | 2020-10-21 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-3.19622427e-01 -7.42066324e-01 -4.59395051e-01 -3.52164209e-01
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ae977c5b-8d5a-4de9-bb69-221f617f1018 | score-level-multi-cue-fusion-for-sign | 2009.14139 | null | https://arxiv.org/abs/2009.14139v1 | https://arxiv.org/pdf/2009.14139v1.pdf | Score-level Multi Cue Fusion for Sign Language Recognition | Sign Languages are expressed through hand and upper body gestures as well as facial expressions. Therefore, Sign Language Recognition (SLR) needs to focus on all such cues. Previous work uses hand-crafted mechanisms or network aggregation to extract the different cue features, to increase SLR performance. This is slow and involves complicated architectures. We propose a more straightforward approach that focuses on training separate cue models specializing on the dominant hand, hands, face, and upper body regions. We compare the performance of 3D Convolutional Neural Network (CNN) models specializing in these regions, combine them through score-level fusion, and use the weighted alternative. Our experimental results have shown the effectiveness of mixed convolutional models. Their fusion yields up to 19% accuracy improvement over the baseline using the full upper body. Furthermore, we include a discussion for fusion settings, which can help future work on Sign Language Translation (SLT). | ['Ahmet Alp Kındıroğlu', 'Oğulcan Özdemir', 'Çağrı Gökçe', 'Lale Akarun'] | 2020-09-29 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.42568398e-01 -6.81167021e-02 -3.77937078e-01 -5.06526351e-01
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481d1cee-41fd-499d-96b3-986db24d6d1a | enhancing-code-classification-by-mixup-based | 2210.03003 | null | https://arxiv.org/abs/2210.03003v2 | https://arxiv.org/pdf/2210.03003v2.pdf | MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation | Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance. Collecting such data is often not hard, but the labeling process is notoriously laborious. The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise. Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP. However, existing data augmentation approaches in code analysis adopt simple methods, such as data transformation and adversarial example generation, thus bringing limited performance superiority. In this paper, we propose a data augmentation approach MIXCODE that aims to effectively supplement valid training data, inspired by the recent advance named Mixup in computer vision. Specifically, we first utilize multiple code refactoring methods to generate transformed code that holds consistent labels with the original data. Then, we adapt the Mixup technique to mix the original code with the transformed code to augment the training data. We evaluate MIXCODE on two programming languages (Java and Python), two code tasks (problem classification and bug detection), four benchmark datasets (JAVA250, Python800, CodRep1, and Refactory), and seven model architectures (including two pre-trained models CodeBERT and GraphCodeBERT). Experimental results demonstrate that MIXCODE outperforms the baseline data augmentation approach by up to 6.24% in accuracy and 26.06% in robustness. | ['Jianjun Zhao', 'Zhenya Zhang', 'Yves Le Traon', 'Mike Papadakis', 'Maxime Cordy', 'Yuejun Guo', 'Qiang Hu', 'Zeming Dong'] | 2022-10-06 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 1.45796373e-01 -8.90238360e-02 -3.27941060e-01 -3.02559316e-01
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f40d3e86-5d30-4af0-be6c-8ccc216f7c5e | stochastic-modeling-for-learnable-human-pose | 2110.00280 | null | https://arxiv.org/abs/2110.00280v3 | https://arxiv.org/pdf/2110.00280v3.pdf | Generalizable Human Pose Triangulation | We address the problem of generalizability for multi-view 3D human pose estimation. The standard approach is to first detect 2D keypoints in images and then apply triangulation from multiple views. Even though the existing methods achieve remarkably accurate 3D pose estimation on public benchmarks, most of them are limited to a single spatial camera arrangement and their number. Several methods address this limitation but demonstrate significantly degraded performance on novel views. We propose a stochastic framework for human pose triangulation and demonstrate a superior generalization across different camera arrangements on two public datasets. In addition, we apply the same approach to the fundamental matrix estimation problem, showing that the proposed method can successfully apply to other computer vision problems. The stochastic framework achieves more than 8.8% improvement on the 3D pose estimation task, compared to the state-of-the-art, and more than 30% improvement for fundamental matrix estimation, compared to a standard algorithm. | ['Tomislav Pribanić', 'Tomislav Petković', 'David Bojanić', 'Kristijan Bartol'] | 2021-10-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [ 6.95364773e-02 -2.71228492e-01 5.87757155e-02 -3.90843526e-02
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18b12f95-8ed7-452e-9357-adb7ac44e53e | prom-a-phrase-level-copying-mechanism-with | 2305.06647 | null | https://arxiv.org/abs/2305.06647v1 | https://arxiv.org/pdf/2305.06647v1.pdf | PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization | Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work proposes PROM, a new PhRase-level cOpying Mechanism that enhances attention on n-grams, which can be applied to zero-shot summarization with pre-training. PROM adds an indicator layer to explicitly pick up tokens in n-gram that can be copied from the source, and calculates an auxiliary loss for the copying prediction. Empirical studies show that PROM makes significant improvements in fine-tuning on benchmarks. In zero-shot setting, PROM is utilized in the self-supervised pre-training on raw corpora and provides new general baselines on a wide range of summarization datasets. Further analysis shows that PROM performs more reasonable copying and contributes to faithfulness. | ['Nan Duan', 'Hai Zhao', 'Pengcheng He', 'Yeyun Gong', 'Xinbei Ma'] | 2023-05-11 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.60244125e-01 3.39580625e-01 -7.10642993e-01 -1.47453472e-01
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dc29a7f9-aff0-4c48-9a3d-aeea5b25ea25 | self-supervised-learning-of-event-guided | 2306.15507 | null | https://arxiv.org/abs/2306.15507v1 | https://arxiv.org/pdf/2306.15507v1.pdf | Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames | This paper makes the first attempt to tackle the challenging task of recovering arbitrary frame rate latent global shutter (GS) frames from two consecutive rolling shutter (RS) frames, guided by the novel event camera data. Although events possess high temporal resolution, beneficial for video frame interpolation (VFI), a hurdle in tackling this task is the lack of paired GS frames. Another challenge is that RS frames are susceptible to distortion when capturing moving objects. To this end, we propose a novel self-supervised framework that leverages events to guide RS frame correction and VFI in a unified framework. Our key idea is to estimate the displacement field (DF) non-linear dense 3D spatiotemporal information of all pixels during the exposure time, allowing for the reciprocal reconstruction between RS and GS frames as well as arbitrary frame rate VFI. Specifically, the displacement field estimation (DFE) module is proposed to estimate the spatiotemporal motion from events to correct the RS distortion and interpolate the GS frames in one step. We then combine the input RS frames and DF to learn a mapping for RS-to-GS frame interpolation. However, as the mapping is highly under-constrained, we couple it with an inverse mapping (i.e., GS-to-RS) and RS frame warping (i.e., RS-to-RS) for self-supervision. As there is a lack of labeled datasets for evaluation, we generate two synthetic datasets and collect a real-world dataset to train and test our method. Experimental results show that our method yields comparable or better performance with prior supervised methods. | ['Lin Wang', 'Guoqiang Liang', 'Yunfan Lu'] | 2023-06-27 | null | null | null | null | ['self-supervised-learning', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 5.31444013e-01 -3.85992825e-01 -1.63362414e-01 -3.92063260e-01
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191f3d83-05d5-4f6e-a082-296b58df3dbc | linear-relaxations-for-finding-diverse | null | null | http://papers.nips.cc/paper/6500-linear-relaxations-for-finding-diverse-elements-in-metric-spaces | http://papers.nips.cc/paper/6500-linear-relaxations-for-finding-diverse-elements-in-metric-spaces.pdf | Linear Relaxations for Finding Diverse Elements in Metric Spaces | Choosing a diverse subset of a large collection of points in a metric space is a fundamental problem, with applications in feature selection, recommender systems, web search, data summarization, etc. Various notions of diversity have been proposed, tailored to different applications. The general algorithmic goal is to find a subset of points that maximize diversity, while obeying a cardinality (or more generally, matroid) constraint. The goal of this paper is to develop a novel linear programming (LP) framework that allows us to design approximation algorithms for such problems. We study an objective known as {\em sum-min} diversity, which is known to be effective in many applications, and give the first constant factor approximation algorithm. Our LP framework allows us to easily incorporate additional constraints, as well as secondary objectives. We also prove a hardness result for two natural diversity objectives, under the so-called {\em planted clique} assumption. Finally, we study the empirical performance of our algorithm on several standard datasets. We first study the approximation quality of the algorithm by comparing with the LP objective. Then, we compare the quality of the solutions produced by our method with other popular diversity maximization algorithms. | ['Mehrdad Ghadiri', 'Vahab Mirrokni', 'Aditya Bhaskara', 'Ola Svensson'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['data-summarization'] | ['miscellaneous'] | [ 1.23688228e-01 -1.85148213e-02 -3.05155128e-01 -2.34928623e-01
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9f531191-44d7-4de3-a708-7fd8cc58b57b | audio-content-analysis | 2101.00132 | null | https://arxiv.org/abs/2101.00132v1 | https://arxiv.org/pdf/2101.00132v1.pdf | Audio Content Analysis | Preprint for a book chapter introducing Audio Content Analysis. With a focus on Music Information Retrieval systems, this chapter defines musical audio content, introduces the general process of audio content analysis, and surveys basic approaches to audio content analysis. The various tasks in Audio Content Analysis are categorized into three classes: music transcription, music performance analysis, and music identification and categorization. The examples for music transcription systems include music key detection, fundamental frequency detection, and music structure detection. Music performance analysis systems feature an overview of beat and tempo detection approaches as well as music performance assessment. The covered music classification systems are audio fingerprinting, music genre classification, and music emotion recognition. The chapter concludes with a discussion and current challenges in the field and a speculation on future perspectives. | ['Alexander Lerch'] | 2021-01-01 | null | null | null | null | ['genre-classification', 'music-emotion-recognition', 'music-classification', 'music-transcription'] | ['computer-vision', 'music', 'music', 'music'] | [ 5.91609597e-01 -5.63569129e-01 -1.97871909e-01 1.64503276e-01
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7b796040-508c-48df-8096-5fe8555a4fa2 | interpreting-outliers-localized-logistic | 1702.06354 | null | http://arxiv.org/abs/1702.06354v1 | http://arxiv.org/pdf/1702.06354v1.pdf | Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation | We propose an inlier-based outlier detection method capable of both
identifying the outliers and explaining why they are outliers, by identifying
the outlier-specific features. Specifically, we employ an inlier-based outlier
detection criterion, which uses the ratio of inlier and test probability
densities as a measure of plausibility of being an outlier. For estimating the
density ratio function, we propose a localized logistic regression algorithm.
Thanks to the locality of the model, variable selection can be
outlier-specific, and will help interpret why points are outliers in a
high-dimensional space. Through synthetic experiments, we show that the
proposed algorithm can successfully detect the important features for outliers.
Moreover, we show that the proposed algorithm tends to outperform existing
algorithms in benchmark datasets. | ['Makoto Yamada', 'Samuel Kaski', 'Song Liu'] | 2017-02-21 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-4.97818202e-01 -2.68368363e-01 1.33032799e-02 -2.27934033e-01
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7.12170184e-01 -8.66613030e-01 -1.00419271e+00 -4.30981457e-01] | [7.569726943969727, 2.674499988555908] |
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