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1f212f5e-6300-443c-9477-23511fb2f55f | classification-by-sparse-additive-models | 2212.01792 | null | https://arxiv.org/abs/2212.01792v1 | https://arxiv.org/pdf/2212.01792v1.pdf | Classification by sparse additive models | We consider (nonparametric) sparse additive models (SpAM) for classification. The design of a SpAM classifier is based on minimizing the logistic loss with a sparse group Lasso/Slope-type penalties on the coefficients of univariate components' expansions in orthonormal series (e.g., Fourier or wavelets). The resulting classifier is inherently adaptive to the unknown sparsity and smoothness. We show that it is nearly-minimax (up to log-factors) within the entire range of analytic, Sobolev and Besov classes, and illustrate its performance on the real-data example. | ['Felix Abramovich'] | 2022-12-04 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 9.70059782e-02 -9.20612589e-02 -4.04682487e-01 -5.19896567e-01
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d561b7e6-faa8-45f3-a8e6-9b0f31269bdb | texture-cnn-for-histopathological-image | 1905.12005 | null | https://arxiv.org/abs/1905.12005v1 | https://arxiv.org/pdf/1905.12005v1.pdf | Texture CNN for Histopathological Image Classification | Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues. | ['Luiz E. S. de Oliveira', 'Alceu de S. Britto Jr.', 'Jonathan de Matos', 'Alessandro L. Koerich'] | 2019-05-28 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [-3.78623977e-02 2.21969858e-01 -1.74575135e-01 -4.75544155e-01
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c7e948d6-508a-4285-84ae-a48ddab9e882 | fitvid-overfitting-in-pixel-level-video | 2106.13195 | null | https://arxiv.org/abs/2106.13195v1 | https://arxiv.org/pdf/2106.13195v1.pdf | FitVid: Overfitting in Pixel-Level Video Prediction | An agent that is capable of predicting what happens next can perform a variety of tasks through planning with no additional training. Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks. This makes predicting the future frames of a video, conditioned on the observed past and potentially future actions, an interesting task which remains exceptionally challenging despite many recent advances. Existing video prediction models have shown promising results on simple narrow benchmarks but they generate low quality predictions on real-life datasets with more complicated dynamics or broader domain. There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions. In this paper, we argue that the inefficient use of parameters in the current video models is the main reason for underfitting. Therefore, we introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks while having similar parameter count as the current state-of-the-art models. We analyze the consequences of overfitting, illustrating how it can produce unexpected outcomes such as generating high quality output by repeating the training data, and how it can be mitigated using existing image augmentation techniques. As a result, FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics. | ['Dumitru Erhan', 'Chelsea Finn', 'Sergey Levine', 'Suraj Nair', 'Mohammad Taghi Saffar', 'Mohammad Babaeizadeh'] | 2021-06-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.34126920e-01 1.65623263e-01 -3.23524833e-01 -2.07057983e-01
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372ab0a0-fa6b-4441-a968-7694807da9dd | scaling-densities-for-improved-density-ratio | null | null | https://openreview.net/forum?id=vdbidlOkeF0 | https://openreview.net/pdf?id=vdbidlOkeF0 | Scaling Densities For Improved Density Ratio Estimation | Estimating the discrepancy between two densities ($p$ and $q$) is central to machine learning. Most frequently used methods for the quantification of this discrepancy capture it as a function of the ratio of the densities $p/q$. In practice, closed-form expressions for these densities or their ratio are rarely available. As such, estimating density ratios accurately using only samples from $p$ and $q$ is of high significance and has led to a flurry of recent work in this direction. Among these, binary classification based density ratio estimators have shown great promise and have been extremely successful in specialized domains. However, estimating the density ratio using a binary classifier, when the samples from the densities are well separated, remains challenging. In this work, we first show that the state-of-the-art solutions for such well-separated cases have limited applicability, may suffer from theoretical inconsistencies or lack formal guarantees and therefore perform poorly in the general case. We then present an alternative framework for density ratio estimation that is motivated by the scaled-Bregman divergence. Our proposal is to scale the densities $p$ and $q$ by another density $m$ and estimate $\log p/q$ as $\log p/m - \log q/m$. We show that if the scaling measures are constructed such that they overlap with $p$ and $q$, then a single multi-class logistic regression can be trained to accurately recover $p/m$ and $q/m$ on samples from $p, q$ and $m$. We formally justify our method with the scaled-Bregman theorem and show that it does not suffer from the issues that plague the existing solutions. | ['Michael U. Gutmann', 'Kai Xu', 'Benjamin Rhodes', 'Seungwook Han', 'Akash Srivastava'] | 2021-09-29 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-1.63690582e-01 -1.47855714e-01 -2.74548620e-01 -2.63604879e-01
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106029b3-a517-44fd-a12e-a6995492191f | starmap-for-category-agnostic-keypoint-and | 1803.09331 | null | http://arxiv.org/abs/1803.09331v2 | http://arxiv.org/pdf/1803.09331v2.pdf | StarMap for Category-Agnostic Keypoint and Viewpoint Estimation | Semantic keypoints provide concise abstractions for a variety of visual
understanding tasks. Existing methods define semantic keypoints separately for
each category with a fixed number of semantic labels in fixed indices. As a
result, this keypoint representation is in-feasible when objects have a varying
number of parts, e.g. chairs with varying number of legs. We propose a
category-agnostic keypoint representation, which combines a multi-peak heatmap
(StarMap) for all the keypoints and their corresponding features as 3D
locations in the canonical viewpoint (CanViewFeature) defined for each
instance. Our intuition is that the 3D locations of the keypoints in canonical
object views contain rich semantic and compositional information. Using our
flexible representation, we demonstrate competitive performance in keypoint
detection and localization compared to category-specific state-of-the-art
methods. Moreover, we show that when augmented with an additional depth channel
(DepthMap) to lift the 2D keypoints to 3D, our representation can achieve
state-of-the-art results in viewpoint estimation. Finally, we show that our
category-agnostic keypoint representation can be generalized to novel
categories. | ['Qi-Xing Huang', 'Linjie Luo', 'Xingyi Zhou', 'Arjun Karpur'] | 2018-03-25 | starmap-for-category-agnostic-keypoint-and-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xingyi_Zhou_Category-Agnostic_Semantic_Keypoint_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xingyi_Zhou_Category-Agnostic_Semantic_Keypoint_ECCV_2018_paper.pdf | eccv-2018-9 | ['viewpoint-estimation'] | ['computer-vision'] | [-2.19837710e-01 -1.26746282e-01 -2.81217903e-01 -3.23037386e-01
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2d43c595-2048-4090-a9c7-16219b9e075e | minif2f-a-cross-system-benchmark-for-formal | 2109.00110 | null | https://arxiv.org/abs/2109.00110v2 | https://arxiv.org/pdf/2109.00110v2.pdf | MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics | We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses. We report baseline results using GPT-f, a neural theorem prover based on GPT-3 and provide an analysis of its performance. We intend for miniF2F to be a community-driven effort and hope that our benchmark will help spur advances in neural theorem proving. | ['Stanislas Polu', 'Jesse Michael Han', 'Kunhao Zheng'] | 2021-08-31 | minif2f-a-cross-system-benchmark-for-formal-1 | https://openreview.net/forum?id=9ZPegFuFTFv | https://openreview.net/pdf?id=9ZPegFuFTFv | iclr-2022-4 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-7.59041831e-02 4.72753674e-01 -2.07540050e-01 -2.71692216e-01
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ecee4ff3-dcd4-4273-9cfd-7cf9f07c9191 | learning-alignment-for-multimodal-emotion | 1909.05645 | null | https://arxiv.org/abs/1909.05645v2 | https://arxiv.org/pdf/1909.05645v2.pdf | Learning Alignment for Multimodal Emotion Recognition from Speech | Speech emotion recognition is a challenging problem because human convey emotions in subtle and complex ways. For emotion recognition on human speech, one can either extract emotion related features from audio signals or employ speech recognition techniques to generate text from speech and then apply natural language processing to analyze the sentiment. Further, emotion recognition will be beneficial from using audio-textual multimodal information, it is not trivial to build a system to learn from multimodality. One can build models for two input sources separately and combine them in a decision level, but this method ignores the interaction between speech and text in the temporal domain. In this paper, we propose to use an attention mechanism to learn the alignment between speech frames and text words, aiming to produce more accurate multimodal feature representations. The aligned multimodal features are fed into a sequential model for emotion recognition. We evaluate the approach on the IEMOCAP dataset and the experimental results show the proposed approach achieves the state-of-the-art performance on the dataset. | ['HUI ZHANG', 'Haiyang Xu', 'Yun Wang', 'Xiangang Li', 'Kun Han', 'Yiping Peng'] | 2019-09-06 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.59272808e-01 1.71820801e-02 1.32734433e-01 -6.31046295e-01
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323d1b72-26ce-4ada-befa-719654806f7a | learnable-behavior-control-breaking-atari | 2305.05239 | null | https://arxiv.org/abs/2305.05239v1 | https://arxiv.org/pdf/2305.05239v1.pdf | Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection | The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which collect samples with diverse behaviors derived from a population of different exploratory policies. Adaptive policy selection has been adopted for behavior control. However, the behavior selection space is largely limited by the predefined policy population, which further limits behavior diversity. In this paper, we propose a general framework called Learnable Behavioral Control (LBC) to address the limitation, which a) enables a significantly enlarged behavior selection space via formulating a hybrid behavior mapping from all policies; b) constructs a unified learnable process for behavior selection. We introduce LBC into distributed off-policy actor-critic methods and achieve behavior control via optimizing the selection of the behavior mappings with bandit-based meta-controllers. Our agents have achieved 10077.52% mean human normalized score and surpassed 24 human world records within 1B training frames in the Arcade Learning Environment, which demonstrates our significant state-of-the-art (SOTA) performance without degrading the sample efficiency. | ['Shu-Tao Xia', 'Hao Wang', 'Jiangcheng Zhu', 'Bin Wang', 'Jianye Hao', 'Yuecheng Liu', 'Yuzheng Zhuang', 'Jiajun Fan'] | 2023-05-09 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-3.32957953e-01 -9.47522745e-02 -5.76187670e-01 6.72911257e-02
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6c9d0202-bed4-4815-8b44-057070247425 | ninjadesc-content-concealing-visual | 2112.12785 | null | https://arxiv.org/abs/2112.12785v2 | https://arxiv.org/pdf/2112.12785v2.pdf | NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning | In the light of recent analyses on privacy-concerning scene revelation from visual descriptors, we develop descriptors that conceal the input image content. In particular, we propose an adversarial learning framework for training visual descriptors that prevent image reconstruction, while maintaining the matching accuracy. We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors. The experimental results demonstrate that the visual descriptors obtained with our method significantly deteriorate the image reconstruction quality with minimal impact on correspondence matching and camera localization performance. | ['Daniel DeTone', 'Chris Sweeney', 'Krystian Mikolajczyk', 'Vassileios Balntas', 'Eddy Ilg', 'Tianwei Shen', 'Tsun-Yi Yang', 'Vincent Lee', 'Hyo Jin Kim', 'Tony Ng'] | 2021-12-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['camera-localization'] | ['computer-vision'] | [ 7.01435030e-01 2.43123472e-01 -1.66624516e-01 -1.32390320e-01
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84151bee-4e0e-493f-92fb-13e9c3854826 | accelerating-self-imitation-learning-from | 2212.03562 | null | https://arxiv.org/abs/2212.03562v1 | https://arxiv.org/pdf/2212.03562v1.pdf | Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble | Deep reinforcement learning (DRL) provides a new way to generate robot control policy. However, the process of training control policy requires lengthy exploration, resulting in a low sample efficiency of reinforcement learning (RL) in real-world tasks. Both imitation learning (IL) and learning from demonstrations (LfD) improve the training process by using expert demonstrations, but imperfect expert demonstrations can mislead policy improvement. Offline to Online reinforcement learning requires a lot of offline data to initialize the policy, and distribution shift can easily lead to performance degradation during online fine-tuning. To solve the above problems, we propose a learning from demonstrations method named A-SILfD, which treats expert demonstrations as the agent's successful experiences and uses experiences to constrain policy improvement. Furthermore, we prevent performance degradation due to large estimation errors in the Q-function by the ensemble Q-functions. Our experiments show that A-SILfD can significantly improve sample efficiency using a small number of different quality expert demonstrations. In four Mujoco continuous control tasks, A-SILfD can significantly outperform baseline methods after 150,000 steps of online training and is not misled by imperfect expert demonstrations during training. | ['Chao Li'] | 2022-12-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.52911031e-01 5.60640506e-02 -3.98719877e-01 1.85319281e-03
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c6fc838a-ee2d-4f93-9e85-8d216022fecf | unsupervised-image-classification-for-deep | 2006.11480 | null | https://arxiv.org/abs/2006.11480v2 | https://arxiv.org/pdf/2006.11480v2.pdf | Unsupervised Image Classification for Deep Representation Learning | Deep clustering against self-supervised learning is a very important and promising direction for unsupervised visual representation learning since it requires little domain knowledge to design pretext tasks. However, the key component, embedding clustering, limits its extension to the extremely large-scale dataset due to its prerequisite to save the global latent embedding of the entire dataset. In this work, we aim to make this framework more simple and elegant without performance decline. We propose an unsupervised image classification framework without using embedding clustering, which is very similar to standard supervised training manner. For detailed interpretation, we further analyze its relation with deep clustering and contrastive learning. Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method. Furthermore, the experiments on transfer learning benchmarks have verified its generalization to other downstream tasks, including multi-label image classification, object detection, semantic segmentation and few-shot image classification. | ['Wei-Jie Chen', 'Shicai Yang', 'ShiLiang Pu', 'Luojun Lin', 'Yilu Guo', 'Di Xie'] | 2020-06-20 | null | null | null | null | ['image-clustering', 'multi-label-image-classification', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.71496317e-01 1.31864682e-01 -3.90639395e-01 -4.67609555e-01
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9921c870-5095-4d87-8e8e-099610456677 | emergence-of-implicit-filter-sparsity-in | null | null | https://openreview.net/forum?id=rylVvNS3hE | https://openreview.net/pdf?id=rylVvNS3hE | Emergence of Implicit Filter Sparsity in Convolutional Neural Networks | We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay. Through an extensive empirical study (Anonymous, 2019) we hypothesize the mechanism be hind the sparsification process. We find that the interplay of various phenomena influences the strength of L2 and weight decay regularizers, leading the supposedly non sparsity inducing regularizers to induce filter sparsity. In this workshop article we summarize some of our key findings and experiments, and present additional results on modern network architectures such as ResNet-50. | ['Christian Theobalt', 'Kwang In Kim', 'Dushyant Mehta'] | 2019-05-17 | null | null | null | icml-workshop-deep-phenomen-2019-6 | ['l2-regularization'] | ['methodology'] | [-6.27222657e-02 4.97449994e-01 -4.01793480e-01 -6.67739809e-01
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22f014f7-274d-4706-bc04-a0a6e1216b36 | natural-language-generation-and-understanding | 2307.02503 | null | https://arxiv.org/abs/2307.02503v1 | https://arxiv.org/pdf/2307.02503v1.pdf | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review | This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI's Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple's Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process. | ['Chee Wei Tan', 'Siu Wai Ho', 'Ching Nam Hang', 'Shangxin Guo', 'Man Fai Wong'] | 2023-07-04 | null | null | null | null | ['code-generation', 'code-translation', 'defect-detection', 'text-generation'] | ['computer-code', 'computer-code', 'computer-vision', 'natural-language-processing'] | [ 1.36946931e-01 3.71959567e-01 -1.90331236e-01 -2.46227086e-01
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1b4a9705-ab19-442b-b863-f7ed8c3f459e | retrospective-analysis-of-sars-cov-2-omicron | null | null | https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-022-07821-5 | https://bmcinfectdis.biomedcentral.com/counter/pdf/10.1186/s12879-022-07821-5.pdf | Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model | Background: SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS-CoV-2 infection incidences in several countries. At the start of December 2021 the first omicron cases were reported in France.
Methods: In this paper we investigate the spreading potential of this novel variant relatively to the delta variant that was also in circulation in France at that time. Using a dynamic multi-variant model accounting for cross-immunity through a status-based approach, we analyze screening data reported by Santé Publique France over 13 metropolitan French regions between 1st of December 2021 and the 30th of January 2022. During the investigated period, the delta variant was replaced by omicron in all metropolitan regions in approximately three weeks. The analysis conducted retrospectively allows us to consider the whole replacement time window and compare regions with different times of omicron introduction and baseline levels of variants' transmission potential. As large uncertainties regarding cross-immunity among variants persist, uncertainty analyses were carried out to assess its impact on our estimations.
Results: Assuming that 80% of the population was immunized against delta, a cross delta/omicron cross-immunity of 25% and an omicron generation time of 3.5 days, the relative strength of omicron to delta, expressed as the ratio of their respective reproduction rates, [Formula: see text], was found to range between 1.51 and 1.86 across regions. Uncertainty analysis on epidemiological parameters led to [Formula: see text] ranging from 1.57 to 2.34 on average over the metropolitan French regions, weighted by population size.
Conclusions: Upon introduction, omicron spread rapidly through the French territory and showed a high fitness relative to delta. We documented considerable geographical heterogeneities on the spreading dynamics. The historical reconstruction of variant emergence dynamics provide valuable ground knowledge to face future variant emergence events. | ['Lulla Opatowski', 'Chiara Poletto', 'Benjamin Roche', 'Elisabeta Vergu', 'Thomas Haschka'] | 2022-11-03 | null | null | null | bmc-infectious-diseases-2022-11 | ['epidemiology'] | ['medical'] | [ 1.30228102e-01 -3.06619167e-01 1.98000968e-01 3.03618819e-01
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465b795e-ccee-43eb-a448-41266d9d046d | easyportrait-face-parsing-and-portrait | 2304.13509 | null | https://arxiv.org/abs/2304.13509v2 | https://arxiv.org/pdf/2304.13509v2.pdf | EasyPortrait - Face Parsing and Portrait Segmentation Dataset | Recently, due to COVID-19 and the growing demand for remote work, video conferencing apps have become especially widespread. The most valuable features of video chats are real-time background removal and face beautification. While solving these tasks, computer vision researchers face the problem of having relevant data for the training stage. There is no large dataset with high-quality labeled and diverse images of people in front of a laptop or smartphone camera to train a lightweight model without additional approaches. To boost the progress in this area, we provide a new image dataset, EasyPortrait, for portrait segmentation and face parsing tasks. It contains 20,000 primarily indoor photos of 8,377 unique users, and fine-grained segmentation masks separated into 9 classes. Images are collected and labeled from crowdsourcing platforms. Unlike most face parsing datasets, in EasyPortrait, the beard is not considered part of the skin mask, and the inside area of the mouth is separated from the teeth. These features allow using EasyPortrait for skin enhancement and teeth whitening tasks. This paper describes the pipeline for creating a large-scale and clean image segmentation dataset using crowdsourcing platforms without additional synthetic data. Moreover, we trained several models on EasyPortrait and showed experimental results. Proposed dataset and trained models are publicly available. | ['Sofia Kirillova', 'Karina Kvanchiani', 'Alexander Kapitanov'] | 2023-04-26 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.72633666e-01 1.35423213e-01 2.68478878e-02 -6.74508691e-01
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132e51da-0b02-4e84-bfb2-5674037f3db8 | surface-defect-detection-and-evaluation-for | 2203.09580 | null | https://arxiv.org/abs/2203.09580v1 | https://arxiv.org/pdf/2203.09580v1.pdf | Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning | Detecting and evaluating surface coating defects is important for marine vessel maintenance. Currently, the assessment is carried out manually by qualified inspectors using international standards and their own experience. Automating the processes is highly challenging because of the high level of variation in vessel type, paint surface, coatings, lighting condition, weather condition, paint colors, areas of the vessel, and time in service. We present a novel deep learning-based pipeline to detect and evaluate the percentage of corrosion, fouling, and delamination on the vessel surface from normal photographs. We propose a multi-stage image processing framework, including ship section segmentation, defect segmentation, and defect classification, to automatically recognize different types of defects and measure the coverage percentage on the ship surface. Experimental results demonstrate that our proposed pipeline can objectively perform a similar assessment as a qualified inspector. | ['Vishal Monga', 'James Z. Wang', 'Kareem Metwaly', 'Li Yu'] | 2022-03-17 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-2.80345622e-02 -3.33997369e-01 8.37058008e-01 -3.29285949e-01
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13321129-4df3-4602-8cb0-fc6872ced3c2 | knowledge-and-keywords-augmented-abstractive | null | null | https://aclanthology.org/2021.newsum-1.3 | https://aclanthology.org/2021.newsum-1.3.pdf | Knowledge and Keywords Augmented Abstractive Sentence Summarization | In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowledge structure. Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework. Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure. Automatic and human evaluations demonstrate that KAS achieves the best performances. | ['Shuo Guan'] | null | null | null | null | emnlp-newsum-2021-11 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 1.79771945e-01 1.25432551e-01 -6.48792982e-01 -1.39105365e-01
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b5fd2443-1b6d-4cc2-8900-c510ce81d803 | when-color-constancy-goes-wrong-correcting | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Afifi_When_Color_Constancy_Goes_Wrong_Correcting_Improperly_White-Balanced_Images_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Afifi_When_Color_Constancy_Goes_Wrong_Correcting_Improperly_White-Balanced_Images_CVPR_2019_paper.pdf | When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images | This paper focuses on correcting a camera image that has been improperly white-balanced. This situation occurs when a camera's auto white balance fails or when the wrong manual white-balance setting is used. Even after decades of computational color constancy research, there are no effective solutions to this problem. The challenge lies not in identifying what the correct white balance should have been, but in the fact that the in-camera white-balance procedure is followed by several camera-specific nonlinear color manipulations that make it challenging to correct the image's colors in post-processing. This paper introduces the first method to explicitly address this problem. Our method is enabled by a dataset of over 65,000 pairs of incorrectly white-balanced images and their corresponding correctly white-balanced images. Using this dataset, we introduce a k-nearest neighbor strategy that is able to compute a nonlinear color mapping function to correct the image's colors. We show our method is highly effective and generalizes well to camera models not in the training set.
| [' Michael S. Brown', ' Scott Cohen', ' Brian Price', 'Mahmoud Afifi'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['color-constancy'] | ['computer-vision'] | [ 5.34362555e-01 -4.79872167e-01 1.14376336e-01 -3.23698163e-01
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c6fe1157-e15a-4dde-9ae5-a78b77741db1 | cv4code-sourcecode-understanding-via-visual | 2205.08585 | null | https://arxiv.org/abs/2205.08585v1 | https://arxiv.org/pdf/2205.08585v1.pdf | CV4Code: Sourcecode Understanding via Visual Code Representations | We present CV4Code, a compact and effective computer vision method for sourcecode understanding. Our method leverages the contextual and the structural information available from the code snippet by treating each snippet as a two-dimensional image, which naturally encodes the context and retains the underlying structural information through an explicit spatial representation. To codify snippets as images, we propose an ASCII codepoint-based image representation that facilitates fast generation of sourcecode images and eliminates redundancy in the encoding that would arise from an RGB pixel representation. Furthermore, as sourcecode is treated as images, neither lexical analysis (tokenisation) nor syntax tree parsing is required, which makes the proposed method agnostic to any particular programming language and lightweight from the application pipeline point of view. CV4Code can even featurise syntactically incorrect code which is not possible from methods that depend on the Abstract Syntax Tree (AST). We demonstrate the effectiveness of CV4Code by learning Convolutional and Transformer networks to predict the functional task, i.e. the problem it solves, of the source code directly from its two-dimensional representation, and using an embedding from its latent space to derive a similarity score of two code snippets in a retrieval setup. Experimental results show that our approach achieves state-of-the-art performance in comparison to other methods with the same task and data configurations. For the first time we show the benefits of treating sourcecode understanding as a form of image processing task. | ['Sean J. Moran', 'Fran Silavong', 'Rohan Saphal', 'Lili Tao', 'Ruibo Shi'] | 2022-05-11 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 3.30788225e-01 -1.69201680e-02 -8.01217183e-02 -3.44652623e-01
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d38ffa14-92f6-4412-a0df-4f153789288f | using-morphological-knowledge-in-open | null | null | https://aclanthology.org/N18-1130 | https://aclanthology.org/N18-1130.pdf | Using Morphological Knowledge in Open-Vocabulary Neural Language Models | Languages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically na{\"\i}ve: they must discover that words exist and are delimited by spaces{---}basic linguistic facts that are built in to the structure of word-based models. We introduce an open-vocabulary language model that incorporates more sophisticated linguistic knowledge by predicting words using a mixture of three generative processes: (1) by generating words as a sequence of characters, (2) by directly generating full word forms, and (3) by generating words as a sequence of morphemes that are combined using a hand-written morphological analyzer. Experiments on Finnish, Turkish, and Russian show that our model outperforms character sequence models and other strong baselines on intrinsic and extrinsic measures. Furthermore, we show that our model learns to exploit morphological knowledge encoded in the analyzer, and, as a byproduct, it can perform effective unsupervised morphological disambiguation. | ['Chris Dyer', 'Austin Matthews', 'Graham Neubig'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.95592242e-01 4.02206868e-01 -2.30598435e-01 -3.24546874e-01
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0c5217bd-2ca8-48fe-8c42-90eb543ea94e | tabfact-a-large-scale-dataset-for-table-based | 1909.02164 | null | https://arxiv.org/abs/1909.02164v5 | https://arxiv.org/pdf/1909.02164v5.pdf | TabFact: A Large-scale Dataset for Table-based Fact Verification | The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains under-explored. This paper specifically aims to study the fact verification given semi-structured data as evidence. To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED. TabFact is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA). Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification. LPA parses statements into programs and executes them against the tables to obtain the returned binary value for verification. Both methods achieve similar accuracy but still lag far behind human performance. We also perform a comprehensive analysis to demonstrate great future opportunities. The data and code of the dataset are provided in \url{https://github.com/wenhuchen/Table-Fact-Checking}. | ['Yunkai Zhang', 'William Yang Wang', 'Wenhu Chen', 'Hongmin Wang', 'Shiyang Li', 'Jianshu Chen', 'Hong Wang', 'Xiyou Zhou'] | 2019-09-05 | null | https://openreview.net/forum?id=rkeJRhNYDH | https://openreview.net/pdf?id=rkeJRhNYDH | iclr-2020-1 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 8.28939155e-02 3.96142721e-01 -8.75721991e-01 -5.73578894e-01
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4b5c3693-ebdf-469e-8e97-4aa554766114 | self-similarity-driven-scale-invariant | 2302.12986 | null | https://arxiv.org/abs/2302.12986v1 | https://arxiv.org/pdf/2302.12986v1.pdf | Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search | Weakly supervised person search aims to jointly detect and match persons with only bounding box annotations. Existing approaches typically focus on improving the features by exploring relations of persons. However, scale variation problem is a more severe obstacle and under-studied that a person often owns images with different scales (resolutions). On the one hand, small-scale images contain less information of a person, thus affecting the accuracy of the generated pseudo labels. On the other hand, the similarity of cross-scale images is often smaller than that of images with the same scale for a person, which will increase the difficulty of matching. In this paper, we address this problem by proposing a novel one-step framework, named Self-similarity driven Scale-invariant Learning (SSL). Scale invariance can be explored based on the self-similarity prior that it shows the same statistical properties of an image at different scales. To this end, we introduce a Multi-scale Exemplar Branch to guide the network in concentrating on the foreground and learning scale-invariant features by hard exemplars mining. To enhance the discriminative power of the features in an unsupervised manner, we introduce a dynamic multi-label prediction which progressively seeks true labels for training. It is adaptable to different types of unlabeled data and serves as a compensation for clustering based strategy. Experiments on PRW and CUHK-SYSU databases demonstrate the effectiveness of our method. | ['Zhen Lei', 'Guo-Jun Qi', 'Jinlin Wu', 'Yang Yang', 'Benzhi Wang'] | 2023-02-25 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.12033650e-01 -3.58196765e-01 -5.99857271e-02 -6.08116984e-01
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e7716ea0-ad23-46a1-a7e1-87c8ef39e9cb | distributed-mpc-with-data-driven-estimation | 2202.14014 | null | https://arxiv.org/abs/2202.14014v2 | https://arxiv.org/pdf/2202.14014v2.pdf | Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control | This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance matrix $\mathbf{Y}$ to let it be time-varying and unknown a priori. The prevalent control designs are either (a) centralized, providing optimal solutions but less scalable and susceptible to single-point failures/attacks, or (b) decentralized or localized, having increased scalability and attack resilience but are suboptimal. The proposed distributed solution offers the attractive features of both methodologies, where neighboring nodes share state information to attain a globally optimal solution. In addition, the presented framework provides a data-driven estimation of Y to circumvent the challenging issue of acquiring accurate knowledge of the line impedance (required to form Y). We first introduce the centralized version of the predictive voltage control problem and then transfer it to a distributed version. The distributed version is solved via the alternating direction method of multipliers (ADMM), using only local measurements and communication leveraging the graph structure of the power system. The proposed framework is resilient to prediction uncertainty, modeling error, and communication link failure, and the in-built redundancy within the proposed framework supports anomaly detection in cyberattacks. We validate the proposed methodology for IEEE-30 bus, IEEE-57 bus transmission systems, and IEEE-123 bus distribution systems. | ['Ratnesh Kumar', 'Ramij R. Hossain'] | 2022-02-28 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.76991373e-01 4.04918678e-02 -9.21453014e-02 1.62522599e-01
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8b77e062-9a08-4291-b0d2-c8ce2dd4f937 | roarnet-a-robust-3d-object-detection-based-on | 1811.03818 | null | http://arxiv.org/abs/1811.03818v1 | http://arxiv.org/pdf/1811.03818v1.pdf | RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement | We present RoarNet, a new approach for 3D object detection from a 2D image
and 3D Lidar point clouds. Based on two-stage object detection framework with
PointNet as our backbone network, we suggest several novel ideas to improve 3D
object detection performance. The first part of our method, RoarNet_2D,
estimates the 3D poses of objects from a monocular image, which approximates
where to examine further, and derives multiple candidates that are
geometrically feasible. This step significantly narrows down feasible 3D
regions, which otherwise requires demanding processing of 3D point clouds in a
huge search space. Then the second part, RoarNet_3D, takes the candidate
regions and conducts in-depth inferences to conclude final poses in a recursive
manner. Inspired by PointNet, RoarNet_3D processes 3D point clouds directly
without any loss of data, leading to precise detection. We evaluate our method
in KITTI, a 3D object detection benchmark. Our result shows that RoarNet has
superior performance to state-of-the-art methods that are publicly available.
Remarkably, RoarNet also outperforms state-of-the-art methods even in settings
where Lidar and camera are not time synchronized, which is practically
important for actual driving environments. RoarNet is implemented in Tensorflow
and publicly available with pre-trained models. | ['Kiwoo Shin', 'Youngwook Paul Kwon', 'Masayoshi Tomizuka'] | 2018-11-09 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [-2.52544492e-01 -4.48062479e-01 -1.29624099e-01 -4.59482186e-02
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68dcb85f-487f-42ec-9375-f413056060e1 | optmsm-optimizing-multi-scenario-modeling-for | 2306.13382 | null | https://arxiv.org/abs/2306.13382v1 | https://arxiv.org/pdf/2306.13382v1.pdf | OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction | A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them simultaneously. Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-specific information from input features, and ii) a scenario-specific prediction module that serves each scenario based on these representations. However, most of these approaches primarily focus on improving the former module and neglect the latter module. This can result in challenges such as increased model parameter size, training difficulty, and performance bottlenecks for each scenario. To address these issues, we propose a novel framework called OptMSM (\textbf{Opt}imizing \textbf{M}ulti-\textbf{S}cenario \textbf{M}odeling). First, we introduce a simplified yet effective scenario-enhanced learning module to alleviate the aforementioned challenges. Specifically, we partition the input features into scenario-specific and scenario-shared features, which are mapped to specific information embedding encodings and a set of shared information embeddings, respectively. By imposing an orthogonality constraint on the shared information embeddings to facilitate the disentanglement of shared information corresponding to each scenario, we combine them with the specific information embeddings to obtain multi-functional representations. Second, we introduce a scenario-specific hypernetwork in the scenario-specific prediction module to capture interactions within each scenario more effectively, thereby alleviating the performance bottlenecks. Finally, we conduct extensive offline experiments and an online A/B test to demonstrate the effectiveness of OptMSM. | ['Xiuqiang He', 'Dugang Liu', 'Fuyuan Lyu', 'Yuwen Fu', 'Yang Qiao', 'Xing Tang'] | 2023-06-23 | null | null | null | null | ['disentanglement', 'click-through-rate-prediction'] | ['methodology', 'miscellaneous'] | [ 1.08698994e-01 -4.54040945e-01 -4.00907725e-01 -3.65499854e-01
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c059db7f-e50f-4af3-9f7b-94cd91e1432c | kgs-causal-discovery-using-knowledge-guided | 2304.05493 | null | https://arxiv.org/abs/2304.05493v1 | https://arxiv.org/pdf/2304.05493v1.pdf | KGS: Causal Discovery Using Knowledge-guided Greedy Equivalence Search | Learning causal relationships solely from observational data provides insufficient information about the underlying causal mechanism and the search space of possible causal graphs. As a result, often the search space can grow exponentially for approaches such as Greedy Equivalence Search (GES) that uses a score-based approach to search the space of equivalence classes of graphs. Prior causal information such as the presence or absence of a causal edge can be leveraged to guide the discovery process towards a more restricted and accurate search space. In this study, we present KGS, a knowledge-guided greedy score-based causal discovery approach that uses observational data and structural priors (causal edges) as constraints to learn the causal graph. KGS is a novel application of knowledge constraints that can leverage any of the following prior edge information between any two variables: the presence of a directed edge, the absence of an edge, and the presence of an undirected edge. We extensively evaluate KGS across multiple settings in both synthetic and benchmark real-world datasets. Our experimental results demonstrate that structural priors of any type and amount are helpful and guide the search process towards an improved performance and early convergence. | ['Md Osman Gani', 'Uzma Hasan'] | 2023-04-11 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.79912078e-01 2.21496001e-01 -7.45654106e-01 -4.16672707e-01
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e09a1d8f-8a47-427f-babb-0faebebaef02 | linguistically-enriched-and-context-aware | 2101.06514 | null | https://arxiv.org/abs/2101.06514v1 | https://arxiv.org/pdf/2101.06514v1.pdf | Linguistically-Enriched and Context-Aware Zero-shot Slot Filling | Slot filling is identifying contiguous spans of words in an utterance that correspond to certain parameters (i.e., slots) of a user request/query. Slot filling is one of the most important challenges in modern task-oriented dialog systems. Supervised learning approaches have proven effective at tackling this challenge, but they need a significant amount of labeled training data in a given domain. However, new domains (i.e., unseen in training) may emerge after deployment. Thus, it is imperative that these models seamlessly adapt and fill slots from both seen and unseen domains -- unseen domains contain unseen slot types with no training data, and even seen slots in unseen domains are typically presented in different contexts. This setting is commonly referred to as zero-shot slot filling. Little work has focused on this setting, with limited experimental evaluation. Existing models that mainly rely on context-independent embedding-based similarity measures fail to detect slot values in unseen domains or do so only partially. We propose a new zero-shot slot filling neural model, LEONA, which works in three steps. Step one acquires domain-oblivious, context-aware representations of the utterance word by exploiting (a) linguistic features; (b) named entity recognition cues; (c) contextual embeddings from pre-trained language models. Step two fine-tunes these rich representations and produces slot-independent tags for each word. Step three exploits generalizable context-aware utterance-slot similarity features at the word level, uses slot-independent tags, and contextualizes them to produce slot-specific predictions for each word. Our thorough evaluation on four diverse public datasets demonstrates that our approach consistently outperforms the SOTA models by 17.52%, 22.15%, 17.42%, and 17.95% on average for unseen domains on SNIPS, ATIS, MultiWOZ, and SGD datasets, respectively. | ['Vagelis Hristidis', 'Fuad Jamour', 'A. B. Siddique'] | 2021-01-16 | null | null | null | null | ['zero-shot-slot-filling'] | ['natural-language-processing'] | [ 1.74382627e-01 1.10434569e-01 -4.87394720e-01 -5.88740468e-01
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9dcde38e-4a0d-4ceb-8730-4263ca769a9c | artificial-intelligence-for-breast-cancer | 2110.00942 | null | https://arxiv.org/abs/2110.00942v1 | https://arxiv.org/pdf/2110.00942v1.pdf | Artificial Intelligence For Breast Cancer Detection: Trends & Directions | In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis. | ['Unaiza Sajid', 'Sheeraz Arif', 'Rizwan Ahmed Khan', 'Shahid Munir Shah'] | 2021-10-03 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.82733011e-01 3.05776060e-01 -2.95328140e-01 -5.82860887e-01
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b8913610-2c78-4f39-8ef7-56ac09a8ed9d | causal-and-counterfactual-views-of-missing | 2210.05558 | null | https://arxiv.org/abs/2210.05558v1 | https://arxiv.org/pdf/2210.05558v1.pdf | Causal and counterfactual views of missing data models | It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this paper, we consider the implications of the converse view: that missing data problems are a form of causal inference. We make explicit how the missing data problem of recovering the complete data law from the observed law can be viewed as identification of a joint distribution over counterfactual variables corresponding to values had we (possibly contrary to fact) been able to observe them. Drawing analogies with causal inference, we show how identification assumptions in missing data can be encoded in terms of graphical models defined over counterfactual and observed variables. We review recent results in missing data identification from this viewpoint. In doing so, we note interesting similarities and differences between missing data and causal identification theories. | ['James Robins', 'Ilya Shpitser', 'Rohit Bhattacharya', 'Razieh Nabi'] | 2022-10-11 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 8.40378404e-01 5.84545374e-01 -9.14705098e-01 -6.90592051e-01
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2fac6fef-c639-41fa-96fa-cd52320f729a | vital-vision-transformer-neural-networks-for | 2302.09443 | null | https://arxiv.org/abs/2302.09443v1 | https://arxiv.org/pdf/2302.09443v1.pdf | VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization | Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones. Unfortunately, the heterogeneity of wireless transceivers across diverse smartphones carried by users has been shown to reduce the accuracy and reliability of localization algorithms. In this paper, we propose a novel framework based on vision transformer neural networks called VITAL that addresses this important challenge. Experiments indicate that VITAL can reduce the uncertainty created by smartphone heterogeneity while improving localization accuracy from 41% to 68% over the best-known prior works. We also demonstrate the generalizability of our approach and propose a data augmentation technique that can be integrated into most deep learning-based localization frameworks to improve accuracy. | ['Sudeep Pasricha', 'Saideep Tiku', 'Danish Gufran'] | 2023-02-18 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.71033904e-01 -6.48138821e-02 -3.81954134e-01 -3.39626938e-01
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01636e6e-b0e5-4fac-8ef5-1cf54693ddb4 | federated-multi-target-domain-adaptation | 2108.07792 | null | https://arxiv.org/abs/2108.07792v1 | https://arxiv.org/pdf/2108.07792v1.pdf | Federated Multi-Target Domain Adaptation | Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Within this new Federated Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance of exiting domain adaptation methods and propose an effective DualAdapt method to address the new challenges. Extensive experimental results on image classification and semantic segmentation tasks demonstrate that our method achieves high accuracy, incurs minimal communication cost, and requires low computational resources on client devices. | ['Ming-Hsuan Yang', 'Yukun Zhu', 'Hang Qi', 'Yin Cui', 'Boqing Gong', 'Chun-Han Yao'] | 2021-08-17 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.33059481e-01 4.85014059e-02 -3.33685696e-01 -8.60991001e-01
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c0095344-358e-47d5-8ae9-2985a51884be | geometric-moment-invariants-to-motion-blur | 2101.08647 | null | https://arxiv.org/abs/2101.08647v2 | https://arxiv.org/pdf/2101.08647v2.pdf | Geometric Moment Invariants to Motion Blur | In this paper, we focus on removing interference of motion blur by the derivation of motion blur invariants.Unlike earlier work, we don't restore any blurred image. Based on geometric moment and mathematical model of motion blur, we prove that geometric moments of blurred image and original image are linearly related. Depending on this property, we can analyse whether an existing moment-based feature is invariant to motion blur. Surprisingly, we find some geometric moment invariants are invariants to not only spatial transform but also motion blur. Meanwhile, we test invariance and robustness of these invariants using synthetic and real blur image datasets. And the results show these invariants outperform some widely used blur moment invariants and non-moment image features in image retrieval, classification and template matching. | ['Hanlin Mo.', 'Hongxiang Hao.', 'Hua Li'] | 2021-01-21 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 4.23045978e-02 -6.76146507e-01 1.69785231e-01 -1.14884578e-01
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ef8e17ef-5e42-42ee-b2b2-37e958bbc3ce | incorporating-deep-syntactic-and-semantic | 2306.02078 | null | https://arxiv.org/abs/2306.02078v1 | https://arxiv.org/pdf/2306.02078v1.pdf | Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN | Recently, it is quite common to integrate Chinese sequence labeling results to enhance syntactic and semantic parsing. However, little attention has been paid to the utility of hierarchy and structure information encoded in syntactic and semantic features for Chinese sequence labeling tasks. In this paper, we propose a novel framework to encode syntactic structure features and semantic information for Chinese sequence labeling tasks with graph convolutional networks (GCN). Experiments on five benchmark datasets, including Chinese word segmentation and part-of-speech tagging, demonstrate that our model can effectively improve the performance of Chinese labeling tasks. | ['Qi Su', 'Jun Wang', 'Xuemei Tang'] | 2023-06-03 | null | null | null | null | ['part-of-speech-tagging', 'semantic-parsing', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.38307303e-01 -3.56546529e-02 -1.59852713e-01 -7.38243818e-01
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dc518d94-8625-4d9f-8c47-0e6d0cd71830 | ji-yu-zi-ci-ji-bie-ci-xiang-liang-he-zhi-zhen | null | null | https://aclanthology.org/2020.ccl-1.43 | https://aclanthology.org/2020.ccl-1.43.pdf | 基于子词级别词向量和指针网络的朝鲜语句子排序(Korean Sentence Ordering Based on Sub Word Level Word Vector and Pointer Network) | 句子排序是多文档摘要系统和机器阅读理解中重要的任务之一,排序的质量将直接 影响摘要和答案的连贯性与可读性。因此,本文采用在中英文上大规模使用的深度 学习方法,同时结合朝鲜语词语形态变化丰富的特点,提出了一种基于子词级别词 向量和指针网络的朝鲜语句子排序模型,其目的是解决传统方法无法挖掘深层语义 信息问题。 本文提出基于形态素拆分的词向量训练方法(MorV),同时对比子词n元 词向量训练方法(SG),得到朝鲜语词向量;采用了两种句向量方法:基于卷积神经网 络(CNN)、基于长短时记忆网络(LSTM),结合指针网络分别进行实验。结果表明本文 采用MorV和LSTM的句向量结合方法可以更好地捕获句子间的语义逻辑关系,提升句 子排序的效果。 关键词: 词向量 ;形态素拆分 ;指针网络 ;句子排序 | ['Xiaoqing Xie', 'Xiaodong Yan'] | null | null | null | null | ccl-2020-10 | ['sentence-ordering'] | ['natural-language-processing'] | [-9.05410528e-01 -9.62505817e-01 3.39770824e-01 5.83569884e-01
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32a6442e-a826-4c6c-bf77-d545f2d75307 | real-time-anomaly-detection-and-feature | null | null | https://ieeexplore.ieee.org/abstract/document/9426191 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9426191 | Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video | The intelligent surveillance system urgently needs the real-time machine recognition of abnormal events to solve
the extremely uneven human supervision resource and digital cameras. Besides, the number of anomaly types that real-time
machine monitoring could recognize has not met the need. This paper presents a fast and robust methodology for real-time
anomaly detection under different scenarios. We created the Video-Energy-Vector(VEV) to significantly reduce the dimension of feature maps while maintaining the spatial-temporal information. We applied the proposed method on different computer vision features to evaluate the effectiveness of common features to different types of abnormal events based on SVM. Also, we adopted the voting model among different features, which significantly increased the performance. Further More, the small video size to be trained guaranteed the real-time efficiency. The result of the modified UCF-Crime Dataset has proved that our approach has achieved robust results and had the generalization ability on new anomaly types. | ['Yajun Fang', 'Jingyuan Chen', 'Ruoyu Xue'] | 2021-05-11 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 9.18863267e-02 -5.25840819e-01 2.77182221e-01 -4.42454547e-01
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b969256a-4b4d-4492-ae23-4e40765e4fc2 | unsupervised-neural-aspect-extraction-with | null | null | https://www.ijcai.org/proceedings/2019/712 | https://www.ijcai.org/proceedings/2019/0712.pdf | Unsupervised Neural Aspect Extraction with Sememes | Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts. To enhance the performance on aspect extraction, leveraging lexical semantic resources is a possible solution to such challenge. In this paper, we present an unsupervised neural framework that leverages sememes to enhance lexical semantics. The overall framework is analogous to an autoenoder which reconstructs sentence representations and learns aspects by latent variables. Two models that form sentence representations are proposed by exploiting sememes via (1) a hierarchical attention; (2) a context-enhanced attention. Experiments on two real-world datasets demonstrate the validity and the effectiveness of our models, which significantly outperforms existing baselines. | ['Dong Yu', 'Qing He', 'Xiaopeng Yang', 'Jinyao Li', 'Yan Song', 'Xiang Ao', 'Ling Luo'] | 2019-08-01 | null | null | null | ijcai-2019-8 | ['aspect-term-extraction-and-sentiment', 'aspect-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23152751e-01 4.20265406e-01 -6.05388582e-01 -5.63458383e-01
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6719336a-9d49-457f-abe8-9dd8ebe64efe | multimodal-machine-translation-with-embedding | 1904.00639 | null | http://arxiv.org/abs/1904.00639v1 | http://arxiv.org/pdf/1904.00639v1.pdf | Multimodal Machine Translation with Embedding Prediction | Multimodal machine translation is an attractive application of neural machine
translation (NMT). It helps computers to deeply understand visual objects and
their relations with natural languages. However, multimodal NMT systems suffer
from a shortage of available training data, resulting in poor performance for
translating rare words. In NMT, pretrained word embeddings have been shown to
improve NMT of low-resource domains, and a search-based approach is proposed to
address the rare word problem. In this study, we effectively combine these two
approaches in the context of multimodal NMT and explore how we can take full
advantage of pretrained word embeddings to better translate rare words. We
report overall performance improvements of 1.24 METEOR and 2.49 BLEU and
achieve an improvement of 7.67 F-score for rare word translation. | ['Hayahide Yamagishi', 'Mamoru Komachi', 'Yukio Matsumura', 'Tosho Hirasawa'] | 2019-04-01 | multimodal-machine-translation-with-embedding-1 | https://aclanthology.org/N19-3012 | https://aclanthology.org/N19-3012.pdf | naacl-2019-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.34959888e-01 -1.63766757e-01 -4.46459174e-01 -7.92412460e-02
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09f75008-3c21-48cd-bf3b-c00662da0ebc | cot-unsupervised-domain-adaptation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.pdf | COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge transfer, aligning the distribution between source and target domain from a global perspective is widely adopted in UDA. Recent researchers further point out the importance of local-level alignment and propose to construct instance-pair alignment by leveraging on Optimal Transport (OT) theory. However, existing OT-based UDA approaches are limited to handling class imbalance challenges and introduce a heavy computation overhead when considering a large-scale training situation. To cope with two aforementioned issues, we propose a Clustering-based Optimal Transport (COT) algorithm, which formulates the alignment procedure as an Optimal Transport problem and constructs a mapping between clustering centers in the source and target domain via an end-to-end manner. With this alignment on clustering centers, our COT eliminates the negative effect caused by class imbalance and reduces the computation cost simultaneously. Empirically, our COT achieves state-of-the-art performance on several authoritative benchmark datasets. | ['Baigui Sun', 'Zhipeng Zhou', 'Yang Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 1.19796246e-01 -1.71781391e-01 -5.62225103e-01 -5.63544393e-01
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34ab5a68-5837-4fbe-b85f-8d1c26b7222f | dense-color-constancy-with-effective-edge | 1911.07163 | null | https://arxiv.org/abs/1911.07163v2 | https://arxiv.org/pdf/1911.07163v2.pdf | ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities | As a novel method eliminating chromatic aberration on objects, computational color constancy has becoming a fundamental prerequisite for many computer vision applications. Among algorithms performing this task, the learning-based ones have achieved great success in recent years. However, they fail to fully consider the spatial information of images, leaving plenty of room for improvement of the accuracy of illuminant estimation. In this paper, by exploiting the spatial information of images, we propose a color constancy algorithm called Attention Dense Color Constancy (ADCC) using convolutional neural network (CNN). Specifically, based on the 2D log-chrominance histograms of the input images as well as their specially augmented ones, ADCC estimates the illuminant with a self-attention DenseNet. The augmented images help to tell apart the edge gradients, edge pixels and non-edge ones in log-histogram, which contribute significantly to the feature extraction and color-ambiguity elimination, thereby advancing the accuracy of illuminant estimation. Simulations and experiments on benchmark datasets demonstrate that the proposed algorithm is effective for illuminant estimation compared to the state-of-the-art methods. Thus, ADCC offers great potential in promoting applications of smart cities, such as smart camera, where color is an important factor for distinguishing objects. | ['Neal N. Xiong', 'Yilang Zhang', 'Jian Wang', 'Zheng Wei', 'Xin Yuan'] | 2019-11-17 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.27251612e-02 -8.74951124e-01 1.07026540e-01 -2.57399321e-01
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6a05201e-4fb3-4581-87d3-d42a3753071f | performance-preserving-event-log-sampling-for | 2301.07624 | null | https://arxiv.org/abs/2301.07624v1 | https://arxiv.org/pdf/2301.07624v1.pdf | Performance-Preserving Event Log Sampling for Predictive Monitoring | Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy. | ['Wil M. P. van der Aalst', 'Sebastiaan J. van Zelst', 'Marco Pegoraro', 'Gyunam Park', 'Mozhgan Vazifehdoostirani', 'Mohammadreza Fani Sani'] | 2023-01-18 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 6.98513150e-01 2.74759233e-01 -2.60163963e-01 -1.94304392e-01
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b730e53f-f81a-414d-82dc-622fb02d2638 | sead-end-to-end-text-to-sql-generation-with-1 | null | null | https://openreview.net/forum?id=Dgx157I8729 | https://openreview.net/pdf?id=Dgx157I8729 | SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising | On the WikiSQL benchmark, most methods tackle the challenge of text-to-SQL with predefined sketch slots and build sophisticated sub-tasks to fill these slots. Though achieving promising results, these methods suffer from over-complex model structure. In this paper, we present a simple yet effective approach that enables auto-regressive sequence-to-sequence model to robust text-to-SQL generation. Instead of formulating the task of text-to-SQL as slot-filling, we propose to train sequence-to-sequence model with Schema-aware Denoising (SeaD), which consists of two denoising objectives that train model to either recover input or predict output from two novel erosion and shuffle noises. These model-agnostic denoising objectives act as the auxiliary tasks for structural data modeling during sequence-to-sequence generation. In addition, we propose a clause-sensitive execution guided (EG) decoding strategy to overcome the limitation of EG decoding for generative model. The experiments show that the proposed method improves the performance of sequence-to-sequence model in both schema linking and grammar correctness and establishes new state-of-the-art on WikiSQL benchmark. Our work indicates that the capacity of sequence-to-sequence model for text-to-SQL may have been under-estimated and could be enhanced by specialized denoising task. | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['text-to-sql', 'slot-filling'] | ['computer-code', 'natural-language-processing'] | [ 6.93133712e-01 2.68584400e-01 9.09253284e-02 -5.86080194e-01
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ee31096f-4940-4ea7-bff4-eab10b04bc4b | de-gan-a-conditional-generative-adversarial-1 | 2010.08764 | null | https://arxiv.org/abs/2010.08764v1 | https://arxiv.org/pdf/2010.08764v1.pdf | DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement | Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. | ['Yousri Kessentini', 'Mohamed Ali Souibgui'] | 2020-10-17 | de-gan-a-conditional-generative-adversarial | https://ieeexplore.ieee.org/document/9187695 | https://ieeexplore.ieee.org/document/9187695 | null | ['document-enhancement'] | ['computer-vision'] | [ 7.34324157e-01 -2.46895716e-01 4.28990811e-01 -6.52366430e-02
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43c43002-0042-4716-9409-622b833720aa | accurate-prediction-using-triangular-type-2 | 2109.05461 | null | https://arxiv.org/abs/2109.05461v1 | https://arxiv.org/pdf/2109.05461v1.pdf | Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression | Many works have been done to handle the uncertainties in the data using type 1 fuzzy regression. Few type 2 fuzzy regression works used interval type 2 for indeterminate modeling using type 1 fuzzy membership. The current survey proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data. The triangular secondary membership function is used instead of widely used interval type models. In the proposed model, vagueness in primary and secondary fuzzy sets is minimized and also, a specified x-plane of observed value is included in the same {\alpha}- plane of the predicted value. Complex calculations of the type-2 fuzzy (T2F) model are simplified by reducing three dimensional type-2 fuzzy set (3DT2FS) into two dimensional interval type-2 fuzzy (2DIT2F) models. The current survey presents a new regression model of T2F by considering the more general form of T2F membership functions and thus avoids high complexity. The performance of the developed model is evaluated using the TAIEX and COVID-19 forecasting datasets. Our developed model reached the highest performance as compared to the other state-of-art techniques. Our developed method is ready to be tested with more uncertain data and has the potential to use to predict the weather and stock prediction. | ['Abbas Khosravi', 'Majid Halaji', 'Roohallah Alizadehsani', 'Parisa Moridian', 'Narges Shafaei', 'Afshin Shoeibi', 'Assef Zare'] | 2021-09-12 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.12281656e-01 -1.00116111e-01 -1.15801260e-01 -6.52753890e-01
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59239fb7-1c46-4b32-a8f9-6ac16df0b3c0 | self-corrective-perturbations-for-semantic | 1703.07928 | null | http://arxiv.org/abs/1703.07928v2 | http://arxiv.org/pdf/1703.07928v2.pdf | Self corrective Perturbations for Semantic Segmentation and Classification | Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of research. In this work, we present an intriguing behavior: pre-trained
CNNs can be made to improve their predictions by structurally perturbing the
input. We observe that these perturbations - referred as Guided Perturbations -
enable a trained network to improve its prediction performance without any
learning or change in network weights. We perform various ablative experiments
to understand how these perturbations affect the local context and feature
representations. Furthermore, we demonstrate that this idea can improve
performance of several existing approaches on semantic segmentation and scene
labeling tasks on the PASCAL VOC dataset and supervised classification tasks on
MNIST and CIFAR10 datasets. | ['Arpit Jain', 'Ser Nam Lim', 'Swami Sankaranarayanan'] | 2017-03-23 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 7.15428054e-01 3.21056157e-01 -9.59093962e-03 -7.84448326e-01
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af592dcc-570c-42b4-a534-119cc48e4d0c | two-stream-transformer-architecture-for-long | 2208.01753 | null | https://arxiv.org/abs/2208.01753v1 | https://arxiv.org/pdf/2208.01753v1.pdf | Two-Stream Transformer Architecture for Long Video Understanding | Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention and lack of inductive bias, transformers are resource intensive and suffer from data inefficiencies. Long form video understanding tasks amplify data and memory efficiency problems in transformers making current approaches unfeasible to implement on data or memory restricted domains. This paper introduces an efficient Spatio-Temporal Attention Network (STAN) which uses a two-stream transformer architecture to model dependencies between static image features and temporal contextual features. Our proposed approach can classify videos up to two minutes in length on a single GPU, is data efficient, and achieves SOTA performance on several long video understanding tasks. | ['Andrew Gilbert', 'Jon Weinbren', 'Edward Fish'] | 2022-08-02 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 1.96360365e-01 -4.27464217e-01 -2.98757553e-01 -2.23855406e-01
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c80f734b-9b9c-496d-819f-52fc07188f07 | local-interpretability-of-random-forests-for | 2303.16506 | null | https://arxiv.org/abs/2303.16506v1 | https://arxiv.org/pdf/2303.16506v1.pdf | Local Interpretability of Random Forests for Multi-Target Regression | Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in machine learning, especially when it can directly impact human well-being. Although model-agnostic techniques exist for multi-target regression, specific techniques tailored to random forest models are not available. To address this issue, we propose a technique that provides rule-based interpretations for instances made by a random forest model for multi-target regression, influenced by a recent model-specific technique for random forest interpretability. The proposed technique was evaluated through extensive experiments and shown to offer competitive interpretations compared to state-of-the-art techniques. | ['Grigorios Tsoumakas', 'Ioannis Mollas', 'Nikolaos Mylonas', 'Avraam Bardos'] | 2023-03-29 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 7.61152685e-01 6.25790954e-01 -9.27473187e-01 -6.84988379e-01
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74ae388a-b093-43a8-a6d8-c7203322eb3f | style-a-video-agile-diffusion-for-arbitrary | 2305.05464 | null | https://arxiv.org/abs/2305.05464v1 | https://arxiv.org/pdf/2305.05464v1.pdf | Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer | Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and the necessary computational resources for training, directly applying these models for video stylization remains difficult. Also, given that the noise addition process on the input content is random and destructive, fulfilling the style transfer task's content preservation criteria is challenging. This paper proposes a zero-shot video stylization method named Style-A-Video, which utilizes a generative pre-trained transformer with an image latent diffusion model to achieve a concise text-controlled video stylization. We improve the guidance condition in the denoising process, establishing a balance between artistic expression and structure preservation. Furthermore, to decrease inter-frame flicker and avoid the formation of additional artifacts, we employ a sampling optimization and a temporal consistency module. Extensive experiments show that we can attain superior content preservation and stylistic performance while incurring less consumption than previous solutions. Code will be available at https://github.com/haha-lisa/Style-A-Video. | ['WeiMing Dong', 'Yuxin Zhang', 'Nisha Huang'] | 2023-05-09 | null | null | null | null | ['style-transfer', 'video-style-transfer'] | ['computer-vision', 'computer-vision'] | [ 2.63503551e-01 -3.07994455e-01 -6.66443929e-02 -1.01481855e-01
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c3810c31-de96-4190-a386-d7029b5ca88f | star-a-session-based-time-aware-recommender | 2211.06394 | null | https://arxiv.org/abs/2211.06394v1 | https://arxiv.org/pdf/2211.06394v1.pdf | STAR: A Session-Based Time-Aware Recommender System | Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Modern SBRs utilize deep neural networks to map users' current interest(s) during an ongoing session to a latent space so that their next preference can be predicted. Although state-of-art SBR models achieve satisfactory results, most focus on studying the sequence of events inside sessions while ignoring temporal details of those events. In this paper, we examine the potential of session temporal information in enhancing the performance of SBRs, conceivably by reflecting the momentary interests of anonymous users or their mindset shifts during sessions. We propose the STAR framework, which utilizes the time intervals between events within sessions to construct more informative representations for items and sessions. Our mechanism revises session representation by embedding time intervals without employing discretization. Empirical results on Yoochoose and Diginetica datasets show that the suggested method outperforms the state-of-the-art baseline models in Recall and MRR criteria. | ['Saman Haratizadeh', 'Reza Yeganegi'] | 2022-11-11 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-9.46805254e-02 -1.55008465e-01 -7.19604611e-01 -7.89160728e-01
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bcae5771-6e28-4055-9704-325ee4b42180 | hats-a-hierarchical-graph-attention-network | 1908.07999 | null | https://arxiv.org/abs/1908.07999v3 | https://arxiv.org/pdf/1908.07999v3.pdf | HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction | Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods. | ['Sang-Hoon Lee', 'Minbyul Jeong', 'Raehyun Kim', 'Jaewoo Kang', 'Chan Ho So', 'Jinkyu Kim'] | 2019-08-07 | null | null | null | null | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-4.64780986e-01 6.75263926e-02 -7.24393308e-01 -2.72429168e-01
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3f7fcecc-9e07-45aa-b225-507e1e3c878a | hierarchical-control-in-islanded-dc | 1910.05107 | null | https://arxiv.org/abs/1910.05107v2 | https://arxiv.org/pdf/1910.05107v2.pdf | Hierarchical Control in Islanded DC Microgrids with Flexible Structures | Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines. However, a comprehensive analysis of all the layers put together is often missing. In this work, we remedy this limitation by setting out a top-to-bottom hierarchical control architecture. Decentralized voltage controllers attached to DGUs form our primary layer. Governed by an MPC--based Energy Management System (EMS), our tertiary layer generates optimal power references and decision variables for DGUs. In particular, decision variables can turn DGUs ON/OFF and select their operation modes. An intermediary secondary layer translates EMS power references into appropriate voltage signals required by the primary layer. More specifically, to provide a voltage solution, the secondary layer solves an optimization problem embedding power-flow equations shown to be always solvable. Since load voltages are not directly enforced, their uniqueness is necessary for DGUs to produce reference powers handed down by the EMS. To this aim, we deduce a novel uniqueness condition based only on local load parameters. Our control framework, besides being applicable for generic DCmG topologies, can accommodate topological changes caused by EMS commands. Its functioning is validated via simulations on a modified 16-bus DC system. | ['Giancarlo Ferrari-Trecate', 'Riccardo Scattolini', 'Alessio La Bella', 'Pulkit Nahata'] | 2019-10-11 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.89115632e-01 2.22010940e-01 -2.54737109e-01 2.49998108e-01
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5b6461c5-cf2c-44c6-850e-93cdacdf0011 | graph-neural-networks-for-text-classification | 2304.11534 | null | https://arxiv.org/abs/2304.11534v2 | https://arxiv.org/pdf/2304.11534v2.pdf | Graph Neural Networks for Text Classification: A Survey | Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can be naturally cast into a graph, which captures words, documents, and corpus global features. In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks. We discuss each of these methods in detail, dealing with the graph construction mechanisms and the graph-based learning process. As well as the technological survey, we look at issues behind and future directions addressed in text classification using graph neural networks. We also cover datasets, evaluation metrics, and experiment design and present a summary of published performance on the publicly available benchmarks. Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey. | ['Soyeon Caren Han', 'Yihao Ding', 'Kunze Wang'] | 2023-04-23 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 1.47937357e-01 1.37697950e-01 -5.41048706e-01 -4.32286739e-01
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c84cc6b2-b448-4d0b-823e-ae8e6301801a | adaboost-neural-network-and-cyclopean-view | null | null | https://www.researchgate.net/publication/338455423_AdaBoost_neural_network_and_cyclopean_view_for_no-reference_stereoscopic_image_quality_assessment | https://www.researchgate.net/publication/338455423_AdaBoost_neural_network_and_cyclopean_view_for_no-reference_stereoscopic_image_quality_assessment | Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment | Stereoscopic imaging has been widely used in many fields. In many scenarios, stereo images quality could be affected by various degradations, such as asymmetric distortion. Accordingly, to guarantee the best quality of experience, robust and accurate reference-less metrics are required for quality assessment of stereoscopic content. Most existing stereo no-reference Image Quality Assessment (IQA) models are not consistent with asymmetrical distortions. This paper presents a new no-reference stereoscopic image quality assessment metric using a human visual system (HVS) modeling and an advanced machine-learning algorithm. The proposed approach consists of two stages. In the first stage, cyclopean image is constructed considering the presence of binocular rivalry in order to cover the asymmetrically distorted part. In the second stage, gradient magnitude, relative gradient magnitude, and gradient orientation are extracted. These are used as a predictive source of information for the quality. In order to obtain the best overall performance against different databases, Adaptive Boosting (AdaBoost) idea of machine learning combined with artificial neural network model has been adopted. The benchmark LIVE 3D phase-I, phase-II, and IRCCyN/IVC 3D databases have been used to evaluate the performance of the proposed approach. Experimental results have demonstrated that the proposed metric performance achieves high consistency with subjective assessment and outperforms the blind stereo IQA over various types of distortion. | ['Zianou Ahmed seghir', 'Fella Hachouf', 'Oussama Messai'] | 2020-03-13 | null | null | null | signal-processing-image-communication-2020-3 | ['image-quality-estimation', 'blind-image-quality-assessment', 'stereoscopic-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.67995363e-01 -7.18564987e-01 -5.47326496e-03 -2.34592125e-01
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87a7de6a-2f09-4149-b20a-bc86c92a558e | training-free-neural-active-learning-with | 2306.04454 | null | https://arxiv.org/abs/2306.04454v1 | https://arxiv.org/pdf/2306.04454v1.pdf | Training-Free Neural Active Learning with Initialization-Robustness Guarantees | Existing neural active learning algorithms have aimed to optimize the predictive performance of neural networks (NNs) by selecting data for labelling. However, other than a good predictive performance, being robust against random parameter initializations is also a crucial requirement in safety-critical applications. To this end, we introduce our expected variance with Gaussian processes (EV-GP) criterion for neural active learning, which is theoretically guaranteed to select data points which lead to trained NNs with both (a) good predictive performances and (b) initialization robustness. Importantly, our EV-GP criterion is training-free, i.e., it does not require any training of the NN during data selection, which makes it computationally efficient. We empirically demonstrate that our EV-GP criterion is highly correlated with both initialization robustness and generalization performance, and show that it consistently outperforms baseline methods in terms of both desiderata, especially in situations with limited initial data or large batch sizes. | ['Bryan Kian Hsiang Low', 'See-Kiong Ng', 'Jasraj Singh', 'Zhongxiang Dai', 'Apivich Hemachandra'] | 2023-06-07 | null | null | null | null | ['active-learning', 'gaussian-processes', 'active-learning'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 3.08838218e-01 1.75371334e-01 -3.52278054e-01 -3.80055726e-01
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b490b887-cb0a-419c-a5e7-0560f74c4972 | looking-for-change-roll-the-dice-and-demand | 2009.02062 | null | https://arxiv.org/abs/2009.02062v2 | https://arxiv.org/pdf/2009.02062v2.pdf | Looking for change? Roll the Dice and demand Attention | Change detection, i.e. identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a reliable deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, new attention modules, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity, that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new spatial and channel convolution Attention layer (the FracTAL). The new attention layer, designed specifically for vision tasks, is memory efficient, thus suitable for use in all levels of deep convolutional networks. Based on these, we introduce two new efficient self-contained feature extraction convolution units. We validate the performance of these feature extraction building blocks on the CIFAR10 reference data and compare the results with standard ResNet modules. Further, we introduce a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. Our network moves away from any notion of subtraction of feature layers for identifying change. We validate our approach by showing excellent performance and achieving state of the art score (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets. | ['François Waldner', 'Foivos I. Diakogiannis', 'Peter Caccetta'] | 2020-09-04 | null | null | null | null | ['change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing'] | ['miscellaneous', 'miscellaneous'] | [ 4.94719177e-01 -4.64499235e-01 5.81149638e-01 -3.73278886e-01
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7521c42d-1c97-415c-a29c-ed3c2479c3b1 | cgintrinsics-better-intrinsic-image | 1808.08601 | null | http://arxiv.org/abs/1808.08601v3 | http://arxiv.org/pdf/1808.08601v3.pdf | CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering | Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we present \ICG, a new, large-scale dataset of physically-based rendered images
of scenes with full ground truth decompositions. The rendering process we use
is carefully designed to yield high-quality, realistic images, which we find to
be crucial for this problem domain. We also propose a new end-to-end training
method that learns better decompositions by leveraging \ICG, and optionally IIW
and SAW, two recent datasets of sparse annotations on real-world images.
Surprisingly, we find that a decomposition network trained solely on our
synthetic data outperforms the state-of-the-art on both IIW and SAW, and
performance improves even further when IIW and SAW data is added during
training. Our work demonstrates the suprising effectiveness of
carefully-rendered synthetic data for the intrinsic images task. | ['Zhengqi Li', 'Noah Snavely'] | 2018-08-26 | cgintrinsics-better-intrinsic-image-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhengqi_Li_CGIntrinsics_Better_Intrinsic_ECCV_2018_paper.pdf | eccv-2018-9 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 3.93253744e-01 2.63537824e-01 1.62281871e-01 -1.55099392e-01
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736a49e0-f59d-4e3a-ac9c-cd50ac2e2ae7 | dialbert-a-hierarchical-pre-trained-model-for | 2004.03760 | null | https://arxiv.org/abs/2004.03760v2 | https://arxiv.org/pdf/2004.03760v2.pdf | DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement | Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to. We propose a new model, named Dialogue BERT (DialBERT), which integrates local and global semantics in a single stream of messages to disentangle the conversations that mixed together. We employ BERT to capture the matching information in each utterance pair at the utterance-level, and use a BiLSTM to aggregate and incorporate the context-level information. With only a 3% increase in parameters, a 12% improvement has been attained in comparison to BERT, based on the F1-Score. The model achieves a state-of-the-art result on the a new dataset proposed by IBM and surpasses previous work by a substantial margin. | ['Zhen-Hua Ling', 'Jia-Chen Gu', 'Xiaodan Zhu', 'Quan Liu', 'Tianda Li', 'Zhiming Su', 'Si Wei'] | 2020-04-08 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 2.47705385e-01 3.44866484e-01 -1.71986237e-01 -6.33638859e-01
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24a1704a-b840-43e3-a812-6d92228b07b3 | vision-matters-when-it-should-sanity-checking | 2109.03415 | null | https://arxiv.org/abs/2109.03415v1 | https://arxiv.org/pdf/2109.03415v1.pdf | Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models | Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all. We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as Multi30K, where the translations of image captions were prepared without actually showing the images to human translators. In this paper, we present a qualitative study that examines the role of datasets in stimulating the leverage of visual modality and we propose methods to highlight the importance of visual signals in the datasets which demonstrate improvements in reliance of models on the source images. Our findings suggest the research on effective MMT architectures is currently impaired by the lack of suitable datasets and careful consideration must be taken in creation of future MMT datasets, for which we also provide useful insights. | ['Rico Sennrich', 'Duygu Ataman', 'Jiaoda Li'] | 2021-09-08 | null | https://aclanthology.org/2021.emnlp-main.673 | https://aclanthology.org/2021.emnlp-main.673.pdf | emnlp-2021-11 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.68620741e-01 1.90509439e-01 -7.79426470e-02 -2.27791041e-01
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b04e3ab2-0b24-4db4-8191-20b4fc28c75c | complementary-classifier-induced-partial | 2305.09897 | null | https://arxiv.org/abs/2305.09897v1 | https://arxiv.org/pdf/2305.09897v1.pdf | Complementary Classifier Induced Partial Label Learning | In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set (a.k.a. complementary labels), which accurately indicates a set of labels that do not belong to a sample. In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional PLL classifier, to eliminate the false-positive labels in the candidate label set. Besides, we assume the feature space and the label space share the same local topological structure captured by a dynamic graph, and use it to assist disambiguation. Extensive experimental results validate the superiority of the proposed approach against state-of-the-art PLL methods on 4 controlled UCI data sets and 6 real-world data sets, and reveal the usefulness of complementary learning in PLL. The code has been released in the link https://github.com/Chongjie-Si/PL-CL. | ['Min-Ling Zhang', 'Chongjie Si', 'Yuheng Jia'] | 2023-05-17 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.69506505e-01 6.54818863e-02 -4.70336735e-01 -1.41640037e-01
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7d233b70-2849-4e74-8c05-11de143f1803 | ita-an-energy-efficient-attention-and-softmax | 2307.03493 | null | https://arxiv.org/abs/2307.03493v2 | https://arxiv.org/pdf/2307.03493v2.pdf | ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers | Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and audio processing. However, the efficient hardware acceleration of transformer models poses new challenges due to their high arithmetic intensities, large memory requirements, and complex dataflow dependencies. In this work, we propose ITA, a novel accelerator architecture for transformers and related models that targets efficient inference on embedded systems by exploiting 8-bit quantization and an innovative softmax implementation that operates exclusively on integer values. By computing on-the-fly in streaming mode, our softmax implementation minimizes data movement and energy consumption. ITA achieves competitive energy efficiency with respect to state-of-the-art transformer accelerators with 16.9 TOPS/W, while outperforming them in area efficiency with 5.93 TOPS/mm$^2$ in 22 nm fully-depleted silicon-on-insulator technology at 0.8 V. | ['Luca Benini', 'Angelo Garofalo', 'Victor J. B. Jung', 'Tim Fischer', 'Gianna Paulin', 'Moritz Scherer', 'Gamze İslamoğlu'] | 2023-07-07 | null | null | null | null | ['quantization'] | ['methodology'] | [ 2.68545866e-01 1.84498563e-01 -6.39423072e-01 -5.09256124e-01
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a25ea0ae-9a02-4a7b-8128-7b30d0bb2199 | instructvid2vid-controllable-video-editing | 2305.12328 | null | https://arxiv.org/abs/2305.12328v1 | https://arxiv.org/pdf/2305.12328v1.pdf | InstructVid2Vid: Controllable Video Editing with Natural Language Instructions | We present an end-to-end diffusion-based method for editing videos with human language instructions, namely $\textbf{InstructVid2Vid}$. Our approach enables the editing of input videos based on natural language instructions without any per-example fine-tuning or inversion. The proposed InstructVid2Vid model combines a pretrained image generation model, Stable Diffusion, with a conditional 3D U-Net architecture to generate time-dependent sequence of video frames. To obtain the training data, we incorporate the knowledge and expertise of different models, including ChatGPT, BLIP, and Tune-a-Video, to synthesize video-instruction triplets, which is a more cost-efficient alternative to collecting data in real-world scenarios. To improve the consistency between adjacent frames of generated videos, we propose the Frame Difference Loss, which is incorporated during the training process. During inference, we extend the classifier-free guidance to text-video input to guide the generated results, making them more related to both the input video and instruction. Experiments demonstrate that InstructVid2Vid is able to generate high-quality, temporally coherent videos and perform diverse edits, including attribute editing, change of background, and style transfer. These results highlight the versatility and effectiveness of our proposed method. Code is released in $\href{https://github.com/BrightQin/InstructVid2Vid}{InstructVid2Vid}$. | ['Yueting Zhuang', 'Tat-Seng Chua', 'Siliang Tang', 'Juncheng Li', 'Bosheng Qin'] | 2023-05-21 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 2.32820749e-01 -2.67200708e-01 -1.95525438e-02 -3.59274834e-01
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8cb0e1e5-71ee-42a4-a8af-161c25320f0b | neural-insights-for-digital-marketing-content | 2302.01416 | null | https://arxiv.org/abs/2302.01416v3 | https://arxiv.org/pdf/2302.01416v3.pdf | Neural Insights for Digital Marketing Content Design | In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement. However, creating successful marketing content is a manual and time-consuming process that lacks clear guiding principles. This paper seeks to close the loop between content creation and online experimentation by offering marketers AI-driven actionable insights based on historical data to improve their creative process. We present a neural-network-based system that scores and extracts insights from a marketing content design, namely, a multimodal neural network predicts the attractiveness of marketing contents, and a post-hoc attribution method generates actionable insights for marketers to improve their content in specific marketing locations. Our insights not only point out the advantages and drawbacks of a given current content, but also provide design recommendations based on historical data. We show that our scoring model and insights work well both quantitatively and qualitatively. | ['Houssam Nassif', 'Ricardo Henao', 'Shreya Chakrabarti', 'Tanner Fiez', 'Yuan Li', 'Fanjie Kong'] | 2023-02-02 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.37852693e-01 2.88854569e-01 -6.66296661e-01 -6.14445329e-01
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2d2b8e06-1727-419d-b1f5-e6971f35a830 | multi-dimensional-frequency-dynamic | 2302.09256 | null | https://arxiv.org/abs/2302.09256v2 | https://arxiv.org/pdf/2302.09256v2.pdf | Multi-dimensional frequency dynamic convolution with confident mean teacher for sound event detection | Recently, convolutional neural networks (CNNs) have been widely used in sound event detection (SED). However, traditional convolution is deficient in learning time-frequency domain representation of different sound events. To address this issue, we propose multi-dimensional frequency dynamic convolution (MFDConv), a new design that endows convolutional kernels with frequency-adaptive dynamic properties along multiple dimensions. MFDConv utilizes a novel multi-dimensional attention mechanism with a parallel strategy to learn complementary frequency-adaptive attentions, which substantially strengthen the feature extraction ability of convolutional kernels. Moreover, in order to promote the performance of mean teacher, we propose the confident mean teacher to increase the accuracy of pseudo-labels from the teacher and train the student with high confidence labels. Experimental results show that the proposed methods achieve 0.470 and 0.692 of PSDS1 and PSDS2 on the DESED real validation dataset. | ['Pengyuan Zhang', 'Xueshuai Zhang', 'Shengchang Xiao'] | 2023-02-18 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [-2.23319352e-01 -4.23795134e-01 2.56977916e-01 -3.18008691e-01
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8f0a173f-5b68-4af4-9866-dda5ef5f143c | relative-volume-constraints-for-single-view | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Toppe_Relative_Volume_Constraints_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Toppe_Relative_Volume_Constraints_2013_CVPR_paper.pdf | Relative Volume Constraints for Single View 3D Reconstruction | We introduce the concept of relative volume constraints in order to account for insufficient information in the reconstruction of 3D objects from a single image. The key idea is to formulate a variational reconstruction approach with shape priors in form of relative depth profiles or volume ratios relating object parts. Such shape priors can easily be derived either from a user sketch or from the object's shading profile in the image. They can handle textured or shadowed object regions by propagating information. We propose a convex relaxation of the constrained optimization problem which can be solved optimally in a few seconds on graphics hardware. In contrast to existing single view reconstruction algorithms, the proposed algorithm provides substantially more flexibility to recover shape details such as self-occlusions, dents and holes, which are not visible in the object silhouette. | ['Claudia Nieuwenhuis', 'Eno Toppe', 'Daniel Cremers'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 4.54452515e-01 3.06345135e-01 1.70250744e-01 -2.29268774e-01
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db2b52f6-cdc4-4e74-b291-5ebb3bfe0683 | an-artifcial-life-approach-to-studying-niche | 1907.12812 | null | https://arxiv.org/abs/1907.12812v1 | https://arxiv.org/pdf/1907.12812v1.pdf | An artifcial life approach to studying niche differentiation in soundscape ecology | Artificial life simulations are an important tool in the study of ecological phenomena that can be difficult to examine directly in natural environments. Recent work has established the soundscape as an ecologically important resource and it has been proposed that the differentiation of animal vocalizations within a soundscape is driven by the imperative of intraspecies communication. The experiments in this paper test that hypothesis in a simulated soundscape in order to verify the feasibility of intraspecies communication as a driver of acoustic niche differentiation. The impact of intraspecies communication is found to be a significant factor in the division of a soundscape's frequency spectrum when compared to simulations where the need to identify signals from conspecifics does not drive the evolution of signalling. The method of simulating the effects of interspecies interactions on the soundscape is positioned as a tool for developing artificial life agents that can inhabit and interact with physical ecosystems and soundscapes. | ['Laura Beloff', 'David Kadish', 'Sebastian Risi'] | 2019-07-30 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.11012238e-01 -5.78942180e-01 8.18453372e-01 1.32384717e-01
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23af0c6d-432c-4c3b-a090-2d06ca0eab60 | hierarchical-filtering-with-online-learned | 2210.12807 | null | https://arxiv.org/abs/2210.12807v1 | https://arxiv.org/pdf/2210.12807v1.pdf | Hierarchical Filtering with Online Learned Priors for ECG Denoising | Electrocardiographic signals (ECG) are used in many healthcare applications, including at-home monitoring of vital signs. These applications often rely on wearable technology and provide low quality ECG signals. Although many methods have been proposed for denoising the ECG to boost its quality and enable clinical interpretation, these methods typically fall short for ECG data obtained with wearable technology, because of either their limited tolerance to noise or their limited flexibility to capture ECG dynamics. This paper presents HKF, a hierarchical Kalman filtering method, that leverages a patient-specific learned structured prior of the ECG signal, and integrates it into a state space model to yield filters that capture both intra- and inter-heartbeat dynamics. HKF is demonstrated to outperform previously proposed methods such as the model-based Kalman filter and data-driven autoencoders, in ECG denoising task in terms of mean-squared error, making it a suitable candidate for application in extramural healthcare settings. | ['Rik Vullings', 'Ruud J. G. van Sloun', 'Nir Shlezinger', 'Guy Revach', 'Timur Locher'] | 2022-10-23 | null | null | null | null | ['ecg-denoising'] | ['medical'] | [ 4.69702214e-01 -1.31184801e-01 2.94300824e-01 -4.34973985e-01
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22f34bf5-f68c-4e76-804a-e6e2123065e0 | dbms-ku-at-semeval-2019-task-9-exploring | null | null | https://aclanthology.org/S19-2208 | https://aclanthology.org/S19-2208.pdf | DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion | This paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums. To deal with this task, we explore several machine learning approaches, i.e., Random Forest (RF), Logistic Regression (LR), Multinomial Naive Bayes (MNB), Linear Support Vector Classification (LSVC), Sublinear Support Vector Classification (SSVC), Convolutional Neural Network (CNN), and Variable Length Chromosome Genetic Algorithm-Naive Bayes (VLCGA-NB). Our system obtains reasonable results of F1-Score 0.47 and 0.37 on the evaluation data in Subtask A and Subtask B, respectively. In particular, our obtained results outperform the baseline in Subtask A. Interestingly, the results seem to show that our system could perform well in classifying Non-suggestion class. | ['Tirana Fatyanosa', 'Masayoshi Aritsugi', 'Al Hafiz Akbar Maulana Siagian'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-1.74205169e-01 2.76635885e-01 -5.77899396e-01 -6.35740399e-01
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6c31c5b5-b2ff-4574-9cef-3a18d578d3f0 | cross-camera-convolutional-color-constancy | 2011.11890 | null | https://arxiv.org/abs/2011.11890v6 | https://arxiv.org/pdf/2011.11890v6.pdf | Cross-Camera Convolutional Color Constancy | We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camera previously unseen during training. C5 is a hypernetwork-like extension of the convolutional color constancy (CCC) approach: C5 learns to generate the weights of a CCC model that is then evaluated on the input image, with the CCC weights dynamically adapted to different input content. Unlike prior cross-camera color constancy models, which are usually designed to be agnostic to the spectral properties of test-set images from unobserved cameras, C5 approaches this problem through the lens of transductive inference: additional unlabeled images are provided as input to the model at test time, which allows the model to calibrate itself to the spectral properties of the test-set camera during inference. C5 achieves state-of-the-art accuracy for cross-camera color constancy on several datasets, is fast to evaluate (~7 and ~90 ms per image on a GPU or CPU, respectively), and requires little memory (~2 MB), and thus is a practical solution to the problem of calibration-free automatic white balance for mobile photography. | ['Francois Bleibel', 'Yun-Ta Tsai', 'Chloe LeGendre', 'Jonathan T. Barron', 'Mahmoud Afifi'] | 2020-11-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Afifi_Cross-Camera_Convolutional_Color_Constancy_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Afifi_Cross-Camera_Convolutional_Color_Constancy_ICCV_2021_paper.pdf | iccv-2021-1 | ['color-constancy'] | ['computer-vision'] | [ 4.26493913e-01 -2.55587637e-01 -2.89469417e-02 -4.46164221e-01
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34bce104-32c1-4778-aca5-2e5df560fa98 | aspectual-type-and-temporal-relation | null | null | https://aclanthology.org/E12-1027 | https://aclanthology.org/E12-1027.pdf | Aspectual Type and Temporal Relation Classification | null | ["Ant{\\'o}nio Branco", 'Francisco Costa'] | 2012-04-01 | null | null | null | eacl-2012-4 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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52deefeb-a5d4-4bf9-9e0c-8505437edf7c | inference-via-sparse-coding-in-a-hierarchical | 2108.01548 | null | https://arxiv.org/abs/2108.01548v3 | https://arxiv.org/pdf/2108.01548v3.pdf | Inference via Sparse Coding in a Hierarchical Vision Model | Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology. But how the level of sparsity contributes to performance on visual tasks is not well understood. In this work, sparse coding has been integrated into an existing hierarchical V2 model (Hosoya and Hyv\"arinen, 2015), but replacing its independent component analysis (ICA) with an explicit sparse coding in which the degree of sparsity can be controlled. After training, the sparse coding basis functions with a higher degree of sparsity resembled qualitatively different structures, such as curves and corners. The contributions of the models were assessed with image classification tasks, specifically tasks associated with mid-level vision including figure-ground classification, texture classification, and angle prediction between two line stimuli. In addition, the models were assessed in comparison to a texture sensitivity measure that has been reported in V2 (Freeman et al., 2013), and a deleted-region inference task. The results from the experiments show that while sparse coding performed worse than ICA at classifying images, only sparse coding was able to better match the texture sensitivity level of V2 and infer deleted image regions, both by increasing the degree of sparsity in sparse coding. Higher degrees of sparsity allowed for inference over larger deleted image regions. The mechanism that allows for this inference capability in sparse coding is described here. | ['Odelia Schwartz', 'Luis Sanchez-Giraldo', 'Joshua Bowren'] | 2021-08-03 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.14127957e-03 1.93898290e-01 -1.34726807e-01 -2.85407573e-01
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9b219dea-09f9-4dc9-a379-d69ddcbb8d72 | cross-lingual-transfer-learning-for-semantic | null | null | https://aclanthology.org/2020.clib-1.8 | https://aclanthology.org/2020.clib-1.8.pdf | Cross-lingual Transfer Learning for Semantic Role Labeling in Russian | This work is devoted to semantic role labeling (SRL) task in Russian. We investigate the role of transfer learning strategies between English FrameNet and Russian FrameBank corpora. We perform experiments with embeddings obtained from various types of multilingual language models, including BERT, XLM-R, MUSE, and LASER. For evaluation, we use a Russian FrameBank dataset. As source data for transfer learning, we experimented with the full version of FrameNet and the reduced dataset with a smaller number of semantic roles identical to FrameBank. Evaluation results demonstrate that BERT embeddings show the best transfer capabilities. The model with pretraining on the reduced English SRL data and fine-tuning on the Russian SRL data show macro-averaged F1-measure of 79.8%, which is above our baseline of 78.4%. | ['Alexander Kirillovich', 'Elena Tutubalina', 'Ilseyar Alimova'] | null | null | null | null | clib-2020-9 | ['semantic-role-labeling', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-1.53489426e-01 5.79967320e-01 -3.29091519e-01 -4.59488094e-01
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5cfce382-1dd2-448d-8a79-909db710351b | an-effective-unconstrained-correlation-filter | 1603.07800 | null | http://arxiv.org/abs/1603.07800v1 | http://arxiv.org/pdf/1603.07800v1.pdf | An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition | In this paper, an effective unconstrained correlation filter called Uncon-
strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to
robust face recognition. Compared with the conventional correlation filters in
Class-dependence Feature Analysis (CFA), UOOTF improves the overall performance
for unseen patterns by removing the hard constraints on the origin correlation
outputs during the filter design. To handle non-linearly separable
distributions between different classes, we further develop a non- linear
extension of UOOTF based on the kernel technique. The kernel ex- tension of
UOOTF allows for higher flexibility of the decision boundary due to a wider
range of non-linearity properties. Experimental results demon- strate the
effectiveness of the proposed unconstrained correlation filter and its
kernelization in the task of face recognition. | ['Cuihua Li', 'Chenhui Yang', 'Bineng Zhong', 'Yan Yan', 'Hanzi Wang'] | 2016-03-25 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-1.82132367e-02 -4.55359936e-01 1.43788485e-02 -4.41993535e-01
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49e7bb06-acff-4525-95df-785bd5b10955 | alejandro-mosquera-at-semeval-2021-task-1 | null | null | https://aclanthology.org/2021.semeval-1.68 | https://aclanthology.org/2021.semeval-1.68.pdf | Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction | This paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) shared task was ranked 3rd out of 48 systems for sub-task 1 and achieved Pearson correlation coefficients of 0.779 and 0.809 for single words and multi-word expressions respectively. The conclusion is that a combination of lexical, contextual and semantic features can still produce strong baselines when compared against human judgement. | ['Alejandro Mosquera'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-2.85125017e-01 -2.09534630e-01 -2.82566220e-01 -5.60593903e-01
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107ae901-de93-4c8a-8bb0-2eadd8f7d832 | wikides-a-wikipedia-based-dataset-for | 2209.13101 | null | https://arxiv.org/abs/2209.13101v1 | https://arxiv.org/pdf/2209.13101v1.pdf | WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs | As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text summarization. In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization. The dataset consists of over 80k English samples on 6987 topics. We set up a two-phase summarization method - description generation (Phase I) and candidate ranking (Phase II) - as a strong approach that relies on transfer and contrastive learning. For description generation, T5 and BART show their superiority compared to other small-scale pre-trained models. By applying contrastive learning with the diverse input from beam search, the metric fusion-based ranking models outperform the direct description generation models significantly up to 22 ROUGE in topic-exclusive split and topic-independent split. Furthermore, the outcome descriptions in Phase II are supported by human evaluation in over 45.33% chosen compared to 23.66% in Phase I against the gold descriptions. In the aspect of sentiment analysis, the generated descriptions cannot effectively capture all sentiment polarities from paragraphs while doing this task better from the gold descriptions. The automatic generation of new descriptions reduces the human efforts in creating them and enriches Wikidata-based knowledge graphs. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions. Finally, we expect WikiDes to be a useful dataset for related works in capturing salient information from short paragraphs. The curated dataset is publicly available at: https://github.com/declare-lab/WikiDes. | ['Alexander Gelbukh', 'Soujanya Poria', 'Newton Howard', 'Lotfollah Najjar', 'Amir Hussain', 'Navonil Majumder', 'Abu Bakar Siddiqur Rahman', 'Hoang Thang Ta'] | 2022-09-27 | null | null | null | null | ['extreme-summarization'] | ['natural-language-processing'] | [ 6.32549301e-02 5.20714521e-01 -5.24951518e-01 -3.40244733e-02
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c9d3bcc3-dacd-43ac-b863-9e28e8a30bd9 | multi-step-prediction-of-occupancy-grid-maps | 1812.09395 | null | http://arxiv.org/abs/1812.09395v3 | http://arxiv.org/pdf/1812.09395v3.pdf | Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks | We investigate the multi-step prediction of the drivable space, represented
by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that
accurate multi-step prediction of the drivable space can efficiently improve
path planning and navigation resulting in safe, comfortable and optimum paths
in autonomous driving. We train a variety of Recurrent Neural Network (RNN)
based architectures on the OGM sequences from the KITTI dataset. The results
demonstrate significant improvement of the prediction accuracy using our
proposed difference learning method, incorporating motion related features,
over the state of the art. We remove the egomotion from the OGM sequences by
transforming them into a common frame. Although in the transformed sequences
the KITTI dataset is heavily biased toward static objects, by learning the
difference between subsequent OGMs, our proposed method provides accurate
prediction over both the static and moving objects. | ['Nima Mohajerin', 'Mohsen Rohani'] | 2018-12-21 | multi-step-prediction-of-occupancy-grid-maps-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mohajerin_Multi-Step_Prediction_of_Occupancy_Grid_Maps_With_Recurrent_Neural_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mohajerin_Multi-Step_Prediction_of_Occupancy_Grid_Maps_With_Recurrent_Neural_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['prediction-of-occupancy-grid-maps'] | ['computer-vision'] | [ 5.40646613e-02 9.55454037e-02 -1.62804067e-01 -8.58274043e-01
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c0b89d0f-df25-4d9c-a5b8-4aef3a9a3767 | table-search-using-a-deep-contextualized | 2005.09207 | null | https://arxiv.org/abs/2005.09207v2 | https://arxiv.org/pdf/2005.09207v2.pdf | Table Search Using a Deep Contextualized Language Model | Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex syntactic word relations. In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval. We investigate how to encode table content considering the table structure and input length limit of BERT. We also propose an approach that incorporates features from prior literature on table retrieval and jointly trains them with BERT. In experiments on public datasets, we show that our best approach can outperform the previous state-of-the-art method and BERT baselines with a large margin under different evaluation metrics. | ['Zhiyu Chen', 'Jeff Heflin', 'Yinan Xu', 'Mohamed Trabelsi', 'Brian D. Davison'] | 2020-05-19 | null | null | null | null | ['table-retrieval', 'table-search'] | ['natural-language-processing', 'natural-language-processing'] | [-4.58467044e-02 5.59528582e-02 -6.58690035e-01 -5.25845170e-01
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a01dea65-cd5b-476e-a36d-06ec926ac454 | memory-augmented-online-video-anomaly | 2302.10719 | null | https://arxiv.org/abs/2302.10719v1 | https://arxiv.org/pdf/2302.10719v1.pdf | Memory-augmented Online Video Anomaly Detection | The ability to understand the surrounding scene is of paramount importance for Autonomous Vehicles (AVs). This paper presents a system capable to work in a real time guaranteed response times and online fashion, giving an immediate response to the arise of anomalies surrounding the AV, exploiting only the videos captured by a dash-mounted camera. Our architecture, called MOVAD, relies on two main modules: a short-term memory to extract information related to the ongoing action, implemented by a Video Swin Transformer adapted to work in an online scenario, and a long-term memory module that considers also remote past information thanks to the use of a Long-Short Term Memory (LSTM) network. We evaluated the performance of our method on Detection of Traffic Anomaly (DoTA) dataset, a challenging collection of dash-mounted camera videos of accidents. After an extensive ablation study, MOVAD is able to reach an AUC score of 82.11%, surpassing the current state-of-the-art by +2.81 AUC. Our code will be available on https://github.com/IMPLabUniPr/movad/tree/icip | ['Andrea Prati', 'Massimo Bertozzi', 'Tomaso Fontanini', 'Vittorio Bernuzzi', 'Leonardo Rossi'] | 2023-02-21 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 2.42943168e-02 5.00754081e-02 1.93157017e-01 -3.71842951e-01
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13e89272-5db3-4afa-9d4b-3a9d6c17ecce | task-related-self-supervised-learning-for | 2105.04951 | null | https://arxiv.org/abs/2105.04951v2 | https://arxiv.org/pdf/2105.04951v2.pdf | Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection | Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism(TSLCD) is proposed to eliminate it. The main contributions include: (1) the task-related self-supervised learning module is introduced to extract spatial features more effectively. (2) a hard-sample-mining loss function is applied to pay more attention to the hard-to-classify samples. (3) a smooth mechanism is utilized to remove some of pseudo-changes and noise. Experiments on four remote sensing change detection datasets reveal that the proposed TSLCD method achieves the state-of-the-art for change detection task. | ['Yuan Yuan', 'Zhiyu Jiang', 'Zhinan Cai'] | 2021-05-11 | null | null | null | null | ['change-detection-for-remote-sensing-images'] | ['miscellaneous'] | [ 2.72914529e-01 -5.43358803e-01 -3.78584713e-02 -4.18131083e-01
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107e67f3-89dc-4467-bc7d-b90d600f6cb4 | additive-poisson-process-learning-intensity-1 | null | null | https://openreview.net/forum?id=voEpzgY8gsT | https://openreview.net/pdf?id=voEpzgY8gsT | Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Poisson Processes | We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in Poisson processes using projections into lower-dimensional space. Our model combines the techniques in information geometry to model higher-order interactions on a statistical manifold and in generalized additive models to use lower-dimensional projections to overcome the effects from the curse of dimensionality. Our approach solves a convex optimization problem by minimizing the KL divergence from a sample distribution in lower-dimensional projections to the distribution modeled by an intensity function in the Poisson process. Our empirical results show that our model is able to use samples observed in the lower dimensional space to estimate the higher-order intensity function with extremely sparse observations. | ['Mahito Sugiyama', 'Lamiae Azizi', 'Feng Zhou', 'Simon Luo'] | 2021-09-29 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.08767949e-01 1.17557257e-01 3.01093727e-01 -1.03220463e-01
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0d7b5c26-96c8-40b5-a632-513cbee94246 | training-a-fully-convolutional-neural-network | 1706.08948 | null | http://arxiv.org/abs/1706.08948v2 | http://arxiv.org/pdf/1706.08948v2.pdf | Training a Fully Convolutional Neural Network to Route Integrated Circuits | We present a deep, fully convolutional neural network that learns to route a
circuit layout net with appropriate choice of metal tracks and wire class
combinations. Inputs to the network are the encoded layouts containing spatial
location of pins to be routed. After 15 fully convolutional stages followed by
a score comparator, the network outputs 8 layout layers (corresponding to 4
route layers, 3 via layers and an identity-mapped pin layer) which are then
decoded to obtain the routed layouts. We formulate this as a binary
segmentation problem on a per-pixel per-layer basis, where the network is
trained to correctly classify pixels in each layout layer to be 'on' or 'off'.
To demonstrate learnability of layout design rules, we train the network on a
dataset of 50,000 train and 10,000 validation samples that we generate based on
certain pre-defined layout constraints. Precision, recall and $F_1$ score
metrics are used to track the training progress. Our network achieves
$F_1\approx97\%$ on the train set and $F_1\approx92\%$ on the validation set.
We use PyTorch for implementing our model. Code is made publicly available at
https://github.com/sjain-stanford/deep-route . | ['Sambhav R. Jain', 'Kye Okabe'] | 2017-06-27 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 2.32525066e-01 1.44278094e-01 -3.74035567e-01 -7.44700134e-01
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1cfedc34-9b4c-4557-8d9b-1876cf10bf4f | unlocking-high-accuracy-differentially | 2204.13650 | null | https://arxiv.org/abs/2204.13650v2 | https://arxiv.org/pdf/2204.13650v2.pdf | Unlocking High-Accuracy Differentially Private Image Classification through Scale | Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method for deep learning, realizes this protection by injecting noise during training. However previous works have found that DP-SGD often leads to a significant degradation in performance on standard image classification benchmarks. Furthermore, some authors have postulated that DP-SGD inherently performs poorly on large models, since the norm of the noise required to preserve privacy is proportional to the model dimension. In contrast, we demonstrate that DP-SGD on over-parameterized models can perform significantly better than previously thought. Combining careful hyper-parameter tuning with simple techniques to ensure signal propagation and improve the convergence rate, we obtain a new SOTA without extra data on CIFAR-10 of 81.4% under (8, 10^{-5})-DP using a 40-layer Wide-ResNet, improving over the previous SOTA of 71.7%. When fine-tuning a pre-trained NFNet-F3, we achieve a remarkable 83.8% top-1 accuracy on ImageNet under (0.5, 8*10^{-7})-DP. Additionally, we also achieve 86.7% top-1 accuracy under (8, 8 \cdot 10^{-7})-DP, which is just 4.3% below the current non-private SOTA for this task. We believe our results are a significant step towards closing the accuracy gap between private and non-private image classification. | ['Borja Balle', 'Samuel L. Smith', 'Jamie Hayes', 'Leonard Berrada', 'Soham De'] | 2022-04-28 | null | null | null | null | ['image-classification-with-dp'] | ['computer-vision'] | [ 1.61967129e-01 1.93916366e-01 2.53074281e-02 -4.82602984e-01
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ba8ad2ee-eba7-4b58-89c5-8a281fbf25ef | clustering-evolving-data-using-kernel-based | 1411.5988 | null | http://arxiv.org/abs/1411.5988v1 | http://arxiv.org/pdf/1411.5988v1.pdf | Clustering evolving data using kernel-based methods | In this thesis, we propose several modelling strategies to tackle evolving
data in different contexts. In the framework of static clustering, we start by
introducing a soft kernel spectral clustering (SKSC) algorithm, which can
better deal with overlapping clusters with respect to kernel spectral
clustering (KSC) and provides more interpretable outcomes. Afterwards, a whole
strategy based upon KSC for community detection of static networks is proposed,
where the extraction of a high quality training sub-graph, the choice of the
kernel function, the model selection and the applicability to large-scale data
are key aspects. This paves the way for the development of a novel clustering
algorithm for the analysis of evolving networks called kernel spectral
clustering with memory effect (MKSC), where the temporal smoothness between
clustering results in successive time steps is incorporated at the level of the
primal optimization problem, by properly modifying the KSC formulation. Later
on, an application of KSC to fault detection of an industrial machine is
presented. Here, a smart pre-processing of the data by means of a proper
windowing operation is necessary to catch the ongoing degradation process
affecting the machine. In this way, in a genuinely unsupervised manner, it is
possible to raise an early warning when necessary, in an online fashion.
Finally, we propose a new algorithm called incremental kernel spectral
clustering (IKSC) for online learning of non-stationary data. This ambitious
challenge is faced by taking advantage of the out-of-sample property of kernel
spectral clustering (KSC) to adapt the initial model, in order to tackle
merging, splitting or drifting of clusters across time. Real-world applications
considered in this thesis include image segmentation, time-series clustering,
community detection of static and evolving networks. | ['Rocco Langone'] | 2014-11-20 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 2.89768904e-01 -2.21417814e-01 2.20623687e-01 5.23405932e-02
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318eb2be-d9ef-4c04-88b5-ec20d4bc38bf | grounding-consistency-distilling-spatial | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Diomataris_Grounding_Consistency_Distilling_Spatial_Common_Sense_for_Precise_Visual_Relationship_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Diomataris_Grounding_Consistency_Distilling_Spatial_Common_Sense_for_Precise_Visual_Relationship_ICCV_2021_paper.pdf | Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection | Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet. Most SGGs silently exploit datasets' bias on relationships' context, i.e. its subject and object, to improve recall and neglect spatial and visual evidence, e.g. having seen a glut of data for person wearing shirt, they are overconfident that every person is wearing every shirt. Such imprecise predictions are mainly ascribed to the lack of negative examples for most relationships, fact that obstructs models from meaningfully learning predicates, even those which have ample positive examples. We first present an in-depth investigation of the context bias issue to showcase that all examined state-of-the-art SGGs share the above vulnerabilities. In response, we propose a semi-supervised scheme that forces predicted triplets to be grounded consistently back to the image, in a closed-loop manner. The developed spatial common sense can be then distilled to a student SGG and substantially enhance its spatial reasoning ability. This Grounding Consistency Distillation (GCD) approach is model-agnostic and profits from the superfluous unlabeled samples to retain the valuable context information and avert memorization of annotations. Furthermore, we ascertain that current metrics disregard unlabeled samples, rendering themselves incapable of reflecting context bias, then we mine and incorporate during evaluation hard-negatives to reformulate precision as a reliable metric. Extensive experimental comparisons exhibit large quantitative - up to 70% relative precision boost on VG200 dataset - and qualitative improvements to prove the significance of our GCD method and our metrics towards refocusing graph generation as a core aspect of scene understanding. Code available at https://github.com/deeplab-ai/grounding-consistent-vrd. | ['Petros Maragos', 'Vassilis Pitsikalis', 'Nikolaos Gkanatsios', 'Markos Diomataris'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['visual-relationship-detection'] | ['computer-vision'] | [ 6.20365739e-01 6.99168861e-01 -1.22211732e-01 -5.04114270e-01
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d25d32ae-5468-4abf-9708-fed492488781 | team-noconflict-at-case-2021-task-1 | null | null | https://aclanthology.org/2021.case-1.20 | https://aclanthology.org/2021.case-1.20.pdf | Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection | An ever-increasing amount of text, in the form of social media posts and news articles, gives rise to new challenges and opportunities for the automatic extraction of socio-political events. In this paper, we present our submission to the Shared Tasks on Socio-Political and Crisis Events Detection, Task 1, Multilingual Protest News Detection, Subtask 2, Event Sentence Classification, of CASE @ ACL-IJCNLP 2021. In our submission, we utilize the RoBERTa model with additional pretraining, and achieve the best F1 score of 0.8532 in event sentence classification in English and the second-best F1 score of 0.8700 in Portuguese via simple translation. We analyze the failure cases of our model. We also conduct an ablation study to show the effect of choosing the right pretrained language model, adding additional training data and data augmentation. | ['Niklas Stoehr', 'Tiancheng Hu'] | null | null | null | null | acl-case-2021-8 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.09928258e-01 2.00707912e-01 1.18865639e-01 -3.42691869e-01
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a8ed3c17-782a-4070-a13f-779d3711baf7 | cooperative-self-training-for-multi-target | 2210.01578 | null | https://arxiv.org/abs/2210.01578v1 | https://arxiv.org/pdf/2210.01578v1.pdf | Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation | In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions. To address MTDA, we propose a self-training strategy that employs pseudo-labels to induce cooperation among multiple domain-specific classifiers. We employ feature stylization as an efficient way to generate image views that forms an integral part of self-training. Additionally, to prevent the network from overfitting to noisy pseudo-labels, we devise a rectification strategy that leverages the predictions from different classifiers to estimate the quality of pseudo-labels. Our extensive experiments on numerous settings, based on four different semantic segmentation datasets, validate the effectiveness of the proposed self-training strategy and show that our method outperforms state-of-the-art MTDA approaches. Code available at: https://github.com/Mael-zys/CoaST | ['Stéphane Lathuilière', 'Elisa Ricci', 'Hongtao Lu', 'Subhankar Roy', 'Yangsong Zhang'] | 2022-10-04 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.58963394e-01 4.19042349e-01 -3.25640261e-01 -6.03599131e-01
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aea33a45-72ad-4e66-a920-20d45ea00507 | l3cube-hindbert-and-devbert-pre-trained-bert | 2211.11418 | null | https://arxiv.org/abs/2211.11418v4 | https://arxiv.org/pdf/2211.11418v4.pdf | L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages | The monolingual Hindi BERT models currently available on the model hub do not perform better than the multi-lingual models on downstream tasks. We present L3Cube-HindBERT, a Hindi BERT model pre-trained on Hindi monolingual corpus. Further, since Indic languages, Hindi and Marathi share the Devanagari script, we train a single model for both languages. We release DevBERT, a Devanagari BERT model trained on both Marathi and Hindi monolingual datasets. We evaluate these models on downstream Hindi and Marathi text classification and named entity recognition tasks. The HindBERT and DevBERT-based models show significant improvements over multi-lingual MuRIL, IndicBERT, and XLM-R. Based on these observations we also release monolingual BERT models for other Indic languages Kannada, Telugu, Malayalam, Tamil, Gujarati, Assamese, Odia, Bengali, and Punjabi. These models are shared at https://huggingface.co/l3cube-pune . | ['Raviraj Joshi'] | 2022-11-21 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-8.10430825e-01 7.33333156e-02 -2.10876800e-02 -6.95462227e-01
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f6b80120-82e5-47ac-ae70-9811dc4ff5bb | a-study-on-bias-and-fairness-in-deep-speaker | 2303.08026 | null | https://arxiv.org/abs/2303.08026v1 | https://arxiv.org/pdf/2303.08026v1.pdf | A Study on Bias and Fairness In Deep Speaker Recognition | With the ubiquity of smart devices that use speaker recognition (SR) systems as a means of authenticating individuals and personalizing their services, fairness of SR systems has becomes an important point of focus. In this paper we study the notion of fairness in recent SR systems based on 3 popular and relevant definitions, namely Statistical Parity, Equalized Odds, and Equal Opportunity. We examine 5 popular neural architectures and 5 commonly used loss functions in training SR systems, while evaluating their fairness against gender and nationality groups. Our detailed experiments shed light on this concept and demonstrate that more sophisticated encoder architectures better align with the definitions of fairness. Additionally, we find that the choice of loss functions can significantly impact the bias of SR models. | ['Ali Etemad', 'Amirhossein Hajavi'] | 2023-03-14 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 8.33974220e-03 1.14133418e-01 -7.23838329e-01 -1.00541198e+00
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c86d2326-16f9-4419-828e-7490868a2315 | learning-cross-lingual-mappings-for-data | 2306.08577 | null | https://arxiv.org/abs/2306.08577v1 | https://arxiv.org/pdf/2306.08577v1.pdf | Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition | Exploiting cross-lingual resources is an effective way to compensate for data scarcity of low resource languages. Recently, a novel multilingual model fusion technique has been proposed where a model is trained to learn cross-lingual acoustic-phonetic similarities as a mapping function. However, handcrafted lexicons have been used to train hybrid DNN-HMM ASR systems. To remove this dependency, we extend the concept of learnable cross-lingual mappings for end-to-end speech recognition. Furthermore, mapping models are employed to transliterate the source languages to the target language without using parallel data. Finally, the source audio and its transliteration is used for data augmentation to retrain the target language ASR. The results show that any source language ASR model can be used for a low-resource target language recognition followed by proposed mapping model. Furthermore, data augmentation results in a relative gain up to 5% over baseline monolingual model. | ['Thomas Hain', 'Muhammad Umar Farooq'] | 2023-06-14 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.48698241e-01 9.99316014e-03 -2.47180521e-01 -4.99877691e-01
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c6386015-3c66-4a5b-829a-c3bf0882daf5 | robust-backdoor-attack-with-visible-semantic | 2306.00816 | null | https://arxiv.org/abs/2306.00816v1 | https://arxiv.org/pdf/2306.00816v1.pdf | Robust Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers | Deep neural networks (DNNs) can be manipulated to exhibit specific behaviors when exposed to specific trigger patterns, without affecting their performance on normal samples. This type of attack is known as a backdoor attack. Recent research has focused on designing invisible triggers for backdoor attacks to ensure visual stealthiness. These triggers have demonstrated strong attack performance even under backdoor defense, which aims to eliminate or suppress the backdoor effect in the model. However, through experimental observations, we have noticed that these carefully designed invisible triggers are often susceptible to visual distortion during inference, such as Gaussian blurring or environmental variations in real-world scenarios. This phenomenon significantly undermines the effectiveness of attacks in practical applications. Unfortunately, this issue has not received sufficient attention and has not been thoroughly investigated. To address this limitation, we propose a novel approach called the Visible, Semantic, Sample-Specific, and Compatible trigger (VSSC-trigger), which leverages a recent powerful image method known as the stable diffusion model. In this approach, a text trigger is utilized as a prompt and combined with a benign image. The resulting combination is then processed by a pre-trained stable diffusion model, generating a corresponding semantic object. This object is seamlessly integrated with the original image, resulting in a new realistic image, referred to as the poisoned image. Extensive experimental results and analysis validate the effectiveness and robustness of our proposed attack method, even in the presence of visual distortion. We believe that the new trigger proposed in this work, along with the proposed idea to address the aforementioned issues, will have significant prospective implications for further advancements in this direction. | ['Baoyuan Wu', 'Yanbo Fan', 'Yong Zhang', 'Li Liu', 'Zihao Zhu', 'Hongrui Chen', 'Ruotong Wang'] | 2023-06-01 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 5.38792968e-01 -1.73536927e-01 8.65521058e-02 9.90269259e-02
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661f54a0-57c4-4abe-b130-8df683a5c2e3 | the-twins-corpus-of-museum-visitor-questions | null | null | https://aclanthology.org/L12-1339 | https://aclanthology.org/L12-1339.pdf | The Twins Corpus of Museum Visitor Questions | The Twins corpus is a collection of utterances spoken in interactions with two virtual characters who serve as guides at the Museum of Science in Boston. The corpus contains about 200,000 spoken utterances from museum visitors (primarily children) as well as from trained handlers who work at the museum. In addition to speech recordings, the corpus contains the outputs of speech recognition performed at the time of utterance as well as the system interpretation of the utterances. Parts of the corpus have been manually transcribed and annotated for question interpretation. The corpus has been used for improving performance of the museum characters and for a variety of research projects, such as phonetic-based Natural Language Understanding, creation of conversational characters from text resources, dialogue policy learning, and research on patterns of user interaction. It has the potential to be used for research on children's speech and on language used when talking to a virtual human. | ['David Traum', 'Athanasios Katsamanis', 'Ron artstein', 'Priti Aggarwal', 'Jillian Gerten', 'Shrikanth Narayanan', 'Angela Nazarian'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [ 2.88431078e-01 6.49373114e-01 2.08083928e-01 -9.01220143e-01
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2ad7e5ae-625c-449d-95ef-e11ce77297e3 | cognitive-semantic-communication-systems-1 | 2303.08546 | null | https://arxiv.org/abs/2303.08546v1 | https://arxiv.org/pdf/2303.08546v1.pdf | Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation | Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods of existing semantic communication frameworks are inexplicable and inflexible, which limits the achievable performance. In this paper, to tackle this issue, a knowledge graph is exploited to develop semantic communication systems. Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. Moreover, a simple, general, and interpretable semantic alignment algorithm for semantic information detection is proposed. Furthermore, an effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph. Additionally, the pre-trained model is fine-tuned to recover semantic information. For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users by matching the knowledge level between the source and the destination. Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive semantic communication systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability. Finally, we present realistic single-user and multi-user cognitive semantic communication systems results by building a software-defined radio prototype system. | ['Naofal Al-Dhahir', 'Rose Qingyang Hu', 'Qihui Wu', 'Lu Yuan', 'Ming Xu', 'Yihao Li', 'Fuhui Zhou'] | 2023-03-15 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.54762125e-01 3.05049419e-01 -1.32588685e-01 -3.97362143e-01
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a182c91e-54f9-4b24-a766-2621b2b8e4a1 | singing-voice-synthesis-based-on | 1904.06868 | null | https://arxiv.org/abs/1904.06868v2 | https://arxiv.org/pdf/1904.06868v2.pdf | Singing voice synthesis based on convolutional neural networks | The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. In these systems, the relationship between musical score feature sequences and acoustic feature sequences extracted from singing voices is modeled by DNNs. Then, an acoustic feature sequence of an arbitrary musical score is output in units of frames by the trained DNNs, and a natural trajectory of a singing voice is obtained by using a parameter generation algorithm. As singing voices contain rich expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs. An acoustic feature sequence is generated in units of segments that consist of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Experimental results in a subjective listening test show that the proposed architecture can synthesize natural sounding singing voices. | ['Keiichi Tokuda', 'Keiichiro Oura', 'Kei Hashimoto', 'Yoshihiko Nankaku', 'Kazuhiro Nakamura'] | 2019-04-15 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-6.08453304e-02 -2.92285025e-01 2.68817842e-01 -2.08895415e-01
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946e19e2-792a-4b7c-892e-dd6377b86315 | high-quality-automatic-voice-over-with | 2306.17005 | null | https://arxiv.org/abs/2306.17005v1 | https://arxiv.org/pdf/2306.17005v1.pdf | High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units | The goal of Automatic Voice Over (AVO) is to generate speech in sync with a silent video given its text script. Recent AVO frameworks built upon text-to-speech synthesis (TTS) have shown impressive results. However, the current AVO learning objective of acoustic feature reconstruction brings in indirect supervision for inter-modal alignment learning, thus limiting the synchronization performance and synthetic speech quality. To this end, we propose a novel AVO method leveraging the learning objective of self-supervised discrete speech unit prediction, which not only provides more direct supervision for the alignment learning, but also alleviates the mismatch between the text-video context and acoustic features. Experimental results show that our proposed method achieves remarkable lip-speech synchronization and high speech quality by outperforming baselines in both objective and subjective evaluations. Code and speech samples are publicly available. | ['Haizhou Li', 'Mingyang Zhang', 'Berrak Sisman', 'Junchen Lu'] | 2023-06-29 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 2.27034017e-01 1.66141659e-01 -4.10270244e-01 -3.70975286e-01
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ddab2ff1-adc2-4ce9-aa30-76b26db2857b | representational-efficiency-outweighs-action | 1807.07134 | null | http://arxiv.org/abs/1807.07134v1 | http://arxiv.org/pdf/1807.07134v1.pdf | Representational efficiency outweighs action efficiency in human program induction | The importance of hierarchically structured representations for tractable
planning has long been acknowledged. However, the questions of how people
discover such abstractions and how to define a set of optimal abstractions
remain open. This problem has been explored in cognitive science in the problem
solving literature and in computer science in hierarchical reinforcement
learning. Here, we emphasize an algorithmic perspective on learning
hierarchical representations in which the objective is to efficiently encode
the structure of the problem, or, equivalently, to learn an algorithm with
minimal length. We introduce a novel problem-solving paradigm that links
problem solving and program induction under the Markov Decision Process (MDP)
framework. Using this task, we target the question of whether humans discover
hierarchical solutions by maximizing efficiency in number of actions they
generate or by minimizing the complexity of the resulting representation and
find evidence for the primacy of representational efficiency. | ['Michael Chang', 'David D. Bourgin', 'Thomas L. Griffiths', 'Sophia Sanborn'] | 2018-07-18 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.41391587e-01 6.61982119e-01 -1.81696191e-01 -5.30602299e-02
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4b55b3dc-888c-45b5-8181-9586c3ab3acd | svitt-temporal-learning-of-sparse-video-text | 2304.08809 | null | https://arxiv.org/abs/2304.08809v1 | https://arxiv.org/pdf/2304.08809v1.pdf | SViTT: Temporal Learning of Sparse Video-Text Transformers | Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size; the curse of dimensionality for multi-frame modeling; and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://svcl.ucsd.edu/projects/svitt. | ['Nuno Vasconcelos', 'Subarna Tripathi', 'Kyle Min', 'Yi Li'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-text-retrieval'] | ['computer-vision'] | [-3.99441384e-02 -2.32069679e-02 -5.96318126e-01 -1.73942044e-01
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77c6660c-4c22-4520-9ee4-740f3b12986c | cuts-high-dimensional-causal-discovery-from | 2305.05890 | null | https://arxiv.org/abs/2305.05890v1 | https://arxiv.org/pdf/2305.05890v1.pdf | CUTS+: High-dimensional Causal Discovery from Irregular Time-series | Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling. | ['Qionghai Dai', 'Kunlun He', 'Jinli Suo', 'Zongren Li', 'Tingxiong Xiao', 'Lianglong Li', 'Yuxiao Cheng'] | 2023-05-10 | null | null | null | null | ['causal-discovery', 'irregular-time-series'] | ['knowledge-base', 'time-series'] | [ 6.22085966e-02 1.24945059e-01 -6.63558006e-01 6.63108900e-02
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5d4b48c6-70f1-49f2-ba12-b50db49476f3 | urban-geobim-construction-by-integrating | 2304.11719 | null | https://arxiv.org/abs/2304.11719v2 | https://arxiv.org/pdf/2304.11719v2.pdf | Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models | Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction. | ['Lei Luo', 'Zhiyi He', 'Puzuo Wang', 'Wei Yao', 'Jie Shao'] | 2023-04-23 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-3.77343386e-01 -2.40484491e-01 1.21433109e-01 -4.02160585e-01
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b9b77be6-e686-49b8-9ddc-5e5fb38e5e9b | exploring-sentiment-analysis-techniques-in | 2305.14842 | null | https://arxiv.org/abs/2305.14842v1 | https://arxiv.org/pdf/2305.14842v1.pdf | Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review | Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. Over the past decade, SA has gained significant popularity in the field of Natural Language Processing (NLP). With the widespread use of social media and online platforms, SA has become crucial for companies to gather customer feedback and shape their marketing strategies. Additionally, researchers rely on SA to analyze public sentiment on various topics. In this particular research study, a comprehensive survey was conducted to explore the latest trends and techniques in SA. The survey encompassed a wide range of methods, including lexicon-based, graph-based, network-based, machine learning, deep learning, ensemble-based, rule-based, and hybrid techniques. The paper also addresses the challenges and opportunities in SA, such as dealing with sarcasm and irony, analyzing multi-lingual data, and addressing ethical concerns. To provide a practical case study, Twitter was chosen as one of the largest online social media platforms. Furthermore, the researchers shed light on the diverse application areas of SA, including social media, healthcare, marketing, finance, and politics. The paper also presents a comparative and comprehensive analysis of existing trends and techniques, datasets, and evaluation metrics. The ultimate goal is to offer researchers and practitioners a systematic review of SA techniques, identify existing gaps, and suggest possible improvements. This study aims to enhance the efficiency and accuracy of SA processes, leading to smoother and error-free outcomes. | ['Karthick Prasad Gunasekaran'] | 2023-05-24 | null | null | null | null | ['marketing', 'sentiment-analysis'] | ['miscellaneous', 'natural-language-processing'] | [-4.20203470e-02 -4.47127484e-02 -4.57021862e-01 -3.27259213e-01
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2aa61a86-a9cc-4993-ad56-8a8ca8889c19 | on-the-stability-of-fine-tuning-bert | 2006.04884 | null | https://arxiv.org/abs/2006.04884v3 | https://arxiv.org/pdf/2006.04884v3.pdf | On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: training the same model with multiple random seeds can result in a large variance of the task performance. Previous literature (Devlin et al., 2019; Lee et al., 2020; Dodge et al., 2020) identified two potential reasons for the observed instability: catastrophic forgetting and small size of the fine-tuning datasets. In this paper, we show that both hypotheses fail to explain the fine-tuning instability. We analyze BERT, RoBERTa, and ALBERT, fine-tuned on commonly used datasets from the GLUE benchmark, and show that the observed instability is caused by optimization difficulties that lead to vanishing gradients. Additionally, we show that the remaining variance of the downstream task performance can be attributed to differences in generalization where fine-tuned models with the same training loss exhibit noticeably different test performance. Based on our analysis, we present a simple but strong baseline that makes fine-tuning BERT-based models significantly more stable than the previously proposed approaches. Code to reproduce our results is available online: https://github.com/uds-lsv/bert-stable-fine-tuning. | ['Maksym Andriushchenko', 'Marius Mosbach', 'Dietrich Klakow'] | 2020-06-08 | null | https://openreview.net/forum?id=nzpLWnVAyah | https://openreview.net/pdf?id=nzpLWnVAyah | iclr-2021-1 | ['misconceptions'] | ['miscellaneous'] | [-3.25270563e-01 2.01107487e-02 -1.14018463e-01 -2.36429229e-01
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a4bba25a-fb4d-482d-9e6d-d6b6f27a8279 | revisiting-the-roles-of-text-in-text-games | null | null | https://openreview.net/forum?id=F_9GY8mIRSw | https://openreview.net/pdf?id=F_9GY8mIRSw | Revisiting the Roles of “Text” in Text Games | Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges. However, recent work has questioned the necessity of NLU by showing random text hashes could perform decently. In this paper, we pursue a fine-grained investigation into the roles of text in the face of different RL challenges, and reconcile that semantic and non-semantic language representations could be complementary rather than contrasting. Concretely, we propose a simple scheme to extract relevant contextual information into an approximate state hash as extra input for an RNN-based text agent. Such a lightweight plug-in achieves competitive performance with state-of-the-art text agents using advanced NLU techniques such as knowledge graph and passage retrieval, suggesting non-NLU methods might suffice to tackle the challenge of partial observability. However, if we remove RNN encoders and use approximate or even ground-truth state hash alone, the model performs miserably, which confirms the importance of semantic function approximation to tackle the challenge of combinatorially large observation and action spaces. Our findings and analysis provide new insights for designing better text game task setups and agents. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.40335724e-01 6.64339483e-01 -1.50708109e-01 2.04265624e-01
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44c7a03f-5a8d-4d0a-a971-c1d88ccf848c | hyperpocket-generative-point-cloud-completion | 2102.05973 | null | https://arxiv.org/abs/2102.05973v1 | https://arxiv.org/pdf/2102.05973v1.pdf | HyperPocket: Generative Point Cloud Completion | Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlusions. Therefore, completing such partial representations remains a fundamental challenge of many computer vision applications. Most of the existing approaches aim to solve this problem by learning to reconstruct individual 3D objects in a synthetic setup of an uncluttered environment, which is far from a real-life scenario. In this work, we reformulate the problem of point cloud completion into an object hallucination task. Thus, we introduce a novel autoencoder-based architecture called HyperPocket that disentangles latent representations and, as a result, enables the generation of multiple variants of the completed 3D point clouds. We split point cloud processing into two disjoint data streams and leverage a hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the missing object parts. As a result, the generated point clouds are not only smooth but also plausible and geometrically consistent with the scene. Our method offers competitive performances to the other state-of-the-art models, and it enables a~plethora of novel applications. | ['Tomasz Trzciński', 'Jacek Tabor', 'Łukasz Struski', 'Sławomir Tadeja', 'Diana Janik', 'Marcin Mazur', 'Artur Kasymov', 'Przemysław Spurek'] | 2021-02-11 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-3.60308290e-02 1.39693007e-01 1.64082244e-01 -3.27425063e-01
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-6.65269554e-01 -7.36557841e-01 -6.21759713e-01 5.45081019e-01
5.02279997e-01 -9.07353282e-01 -1.44511700e-01 -6.31306246e-02] | [8.385064125061035, -3.502208948135376] |
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