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c5d1461e-6438-4683-b7e0-b8f39ed1b1ea | sounding-video-generator-a-unified-framework | 2303.16541 | null | https://arxiv.org/abs/2303.16541v1 | https://arxiv.org/pdf/2303.16541v1.pdf | Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation | As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sounding video generation and propose the Sounding Video Generator (SVG), a unified framework for generating realistic videos along with audio signals. Specifically, we present the SVG-VQGAN to transform visual frames and audio melspectrograms into discrete tokens. SVG-VQGAN applies a novel hybrid contrastive learning method to model inter-modal and intra-modal consistency and improve the quantized representations. A cross-modal attention module is employed to extract associated features of visual frames and audio signals for contrastive learning. Then, a Transformer-based decoder is used to model associations between texts, visual frames, and audio signals at token level for auto-regressive sounding video generation. AudioSetCap, a human annotated text-video-audio paired dataset, is produced for training SVG. Experimental results demonstrate the superiority of our method when compared with existing textto-video generation methods as well as audio generation methods on Kinetics and VAS datasets. | ['Jing Liu', 'Xinxin Zhu', 'Sihan Chen', 'Weining Wang', 'Jiawei Liu'] | 2023-03-29 | null | null | null | null | ['audio-generation', 'video-generation'] | ['audio', 'computer-vision'] | [ 3.02232981e-01 -1.52970433e-01 9.98295844e-02 5.14662564e-02
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6dadb733-6c99-4530-8ba0-dcd26aafb19a | differences-in-boundary-behavior-in-the-3d | 2306.03987 | null | https://arxiv.org/abs/2306.03987v1 | https://arxiv.org/pdf/2306.03987v1.pdf | Differences in boundary behavior in the 3D vertex and Voronoi models | An important open question in the modeling of biological tissues is how to identify the right scale for coarse-graining, or equivalently, the right number of degrees of freedom. For confluent biological tissues, both vertex and Voronoi models, which differ only in their representation of the degrees of freedom, have effectively been used to predict behavior, including fluid-solid transitions and cell tissue compartmentalization, which are important for biological function. However, recent work in 2D has hinted that there may be differences between the two models in systems with heterotypic interfaces between two tissue types, and there is a burgeoning interest in 3D tissue models. Therefore, we compare the geometric structure and dynamic sorting behavior in mixtures of two cell types in both 3D vertex and Voronoi models. We find that while the cell shape indices exhibit similar trends in both models, the registration between cell centers and cell orientation at the boundary are significantly different between the two models. We demonstrate that these macroscopic differences are caused by changes to the cusp-like restoring forces introduced by the different representations of the degrees of freedom at the boundary, and that the Voronoi model is more strongly constrained by forces that are an artifact of the way the degrees of freedom are represented. This suggests that vertex models may be more appropriate for 3D simulations of tissues with heterotypic contacts. | ['M. Lisa Manning', 'Tao Zhang', 'Elizabeth Lawson-Keister'] | 2023-06-06 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.65251771e-02 -4.02822904e-02 4.77303788e-02 4.52358574e-01
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7c32657a-125d-4d76-a0d5-57a2af4e91a0 | deep-contextualized-word-representations | 1802.05365 | null | http://arxiv.org/abs/1802.05365v2 | http://arxiv.org/pdf/1802.05365v2.pdf | Deep contextualized word representations | We introduce a new type of deep contextualized word representation that
models both (1) complex characteristics of word use (e.g., syntax and
semantics), and (2) how these uses vary across linguistic contexts (i.e., to
model polysemy). Our word vectors are learned functions of the internal states
of a deep bidirectional language model (biLM), which is pre-trained on a large
text corpus. We show that these representations can be easily added to existing
models and significantly improve the state of the art across six challenging
NLP problems, including question answering, textual entailment and sentiment
analysis. We also present an analysis showing that exposing the deep internals
of the pre-trained network is crucial, allowing downstream models to mix
different types of semi-supervision signals. | ['Luke Zettlemoyer', 'Christopher Clark', 'Kenton Lee', 'Matthew E. Peters', 'Matt Gardner', 'Mohit Iyyer', 'Mark Neumann'] | 2018-02-15 | deep-contextualized-word-representations-1 | https://aclanthology.org/N18-1202 | https://aclanthology.org/N18-1202.pdf | naacl-2018-6 | ['conversational-response-selection', 'citation-intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.17319652e-01 9.15665403e-02 -3.39089900e-01 -6.19980395e-01
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275d3c90-6b42-486f-a483-aeaeab04f5c6 | category-level-6d-object-pose-estimation-with | 2212.04632 | null | https://arxiv.org/abs/2212.04632v2 | https://arxiv.org/pdf/2212.04632v2.pdf | Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation | In this paper, we propose a novel 3D graph convolution based pipeline for category-level 6D pose and size estimation from monocular RGB-D images. The proposed method leverages an efficient 3D data augmentation and a novel vector-based decoupled rotation representation. Specifically, we first design an orientation-aware autoencoder with 3D graph convolution for latent feature learning. The learned latent feature is insensitive to point shift and size thanks to the shift and scale-invariance properties of the 3D graph convolution. Then, to efficiently decode the rotation information from the latent feature, we design a novel flexible vector-based decomposable rotation representation that employs two decoders to complementarily access the rotation information. The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task. Finally, we propose a 3D deformation mechanism to increase the generalization ability of the pipeline. Extensive experiments show that the proposed pipeline achieves state-of-the-art performance on category-level tasks. Further, the experiments demonstrate that the proposed rotation representation is more suitable for the pose estimation tasks than other rotation representations. | ['Ales Leonardis', 'Jinming Duan', 'Linlin Shen', 'Hyung Jin Chang', 'Zhongqun Zhang', 'Xi Jia', 'Wei Chen'] | 2022-12-09 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-2.87301242e-01 -1.25766367e-01 -3.37685317e-01 -3.52274507e-01
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b31e54fc-c1cc-47d2-9d7b-8ecfe01a9bcb | using-self-training-to-improve-back | 2006.02876 | null | https://arxiv.org/abs/2006.02876v3 | https://arxiv.org/pdf/2006.02876v3.pdf | Enhanced back-translation for low resource neural machine translation using self-training | Improving neural machine translation (NMT) models using the back-translations of the monolingual target data (synthetic parallel data) is currently the state-of-the-art approach for training improved translation systems. The quality of the backward system - which is trained on the available parallel data and used for the back-translation - has been shown in many studies to affect the performance of the final NMT model. In low resource conditions, the available parallel data is usually not enough to train a backward model that can produce the qualitative synthetic data needed to train a standard translation model. This work proposes a self-training strategy where the output of the backward model is used to improve the model itself through the forward translation technique. The technique was shown to improve baseline low resource IWSLT'14 English-German and IWSLT'15 English-Vietnamese backward translation models by 11.06 and 1.5 BLEUs respectively. The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation by 2.7 BLEU. | ['Bashir Shehu Galadanci', 'Abubakar Isa', 'Idris Abdulmumin'] | 2020-06-04 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.00840759e-01 2.11892530e-01 -2.49387160e-01 -4.75644350e-01
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b45a695b-e2f5-471e-89d4-9647cf1baeb4 | initial-results-for-pairwise-causal-discovery | 2212.01279 | null | https://arxiv.org/abs/2212.01279v1 | https://arxiv.org/pdf/2212.01279v1.pdf | Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow | Pairwise Causal Discovery is the task of determining causal, anticausal, confounded or independence relationships from pairs of variables. Over the last few years, this challenging task has promoted not only the discovery of novel machine learning models aimed at solving the task, but also discussions on how learning the causal direction of variables may benefit machine learning overall. In this paper, we show that Quantitative Information Flow (QIF), a measure usually employed for measuring leakages of information from a system to an attacker, shows promising results as features for the task. In particular, experiments with real-world datasets indicate that QIF is statistically tied to the state of the art. Our initial results motivate further inquiries on how QIF relates to causality and what are its limitations. | ['Flavio Figueiredo', 'Felipe Giori'] | 2022-12-02 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.68331736e-01 -3.47239785e-02 -4.55350578e-01 -2.94458628e-01
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e5122aac-30f5-4f28-86bf-bb536797c010 | unsupervised-traffic-scene-generation-with | 2303.08473 | null | https://arxiv.org/abs/2303.08473v1 | https://arxiv.org/pdf/2303.08473v1.pdf | Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs | Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to real-world data. This is especially true in autonomous driving scenarios, which represent a critical aspect for overcoming utilizing synthetic data for training neural networks. We propose a method based on domain-invariant scene representation to directly synthesize traffic scene imagery without rendering. Specifically, we rely on synthetic scene graphs as our internal representation and introduce an unsupervised neural network architecture for realistic traffic scene synthesis. We enhance synthetic scene graphs with spatial information about the scene and demonstrate the effectiveness of our approach through scene manipulation. | ['Federico Tombari', 'Nassir Navab', 'Rachid Ellouze', 'Artem Savkin'] | 2023-03-15 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 9.28636789e-01 3.31275463e-01 2.33606353e-01 -4.46101099e-01
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22177242-2027-47c4-9c03-c5e981f74111 | how-attentive-are-graph-attention-networks | 2105.14491 | null | https://arxiv.org/abs/2105.14491v3 | https://arxiv.org/pdf/2105.14491v3.pdf | How Attentive are Graph Attention Networks? | Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GAT, every node attends to its neighbors given its own representation as the query. However, in this paper we show that GAT computes a very limited kind of attention: the ranking of the attention scores is unconditioned on the query node. We formally define this restricted kind of attention as static attention and distinguish it from a strictly more expressive dynamic attention. Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data. To remove this limitation, we introduce a simple fix by modifying the order of operations and propose GATv2: a dynamic graph attention variant that is strictly more expressive than GAT. We perform an extensive evaluation and show that GATv2 outperforms GAT across 11 OGB and other benchmarks while we match their parametric costs. Our code is available at https://github.com/tech-srl/how_attentive_are_gats . GATv2 is available as part of the PyTorch Geometric library, the Deep Graph Library, and the TensorFlow GNN library. | ['Eran Yahav', 'Uri Alon', 'Shaked Brody'] | 2021-05-30 | how-attentive-are-graph-attention-networks-1 | https://openreview.net/forum?id=F72ximsx7C1 | https://openreview.net/pdf?id=F72ximsx7C1 | iclr-2022-4 | ['graph-property-prediction'] | ['graphs'] | [-2.20774099e-01 3.38805497e-01 -2.72540420e-01 -2.40183622e-01
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8d6f09d2-f01b-4940-b37f-b173440f4900 | woodbury-transformations-for-deep-generative | 2002.12229 | null | https://arxiv.org/abs/2002.12229v3 | https://arxiv.org/pdf/2002.12229v3.pdf | Woodbury Transformations for Deep Generative Flows | Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling. The core requirement for this advantage is that they are constructed using functions that can be efficiently inverted and for which the determinant of the function's Jacobian can be efficiently computed. Researchers have introduced various such flow operations, but few of these allow rich interactions among variables without incurring significant computational costs. In this paper, we introduce Woodbury transformations, which achieve efficient invertibility via the Woodbury matrix identity and efficient determinant calculation via Sylvester's determinant identity. In contrast with other operations used in state-of-the-art normalizing flows, Woodbury transformations enable (1) high-dimensional interactions, (2) efficient sampling, and (3) efficient likelihood evaluation. Other similar operations, such as 1x1 convolutions, emerging convolutions, or periodic convolutions allow at most two of these three advantages. In our experiments on multiple image datasets, we find that Woodbury transformations allow learning of higher-likelihood models than other flow architectures while still enjoying their efficiency advantages. | ['You Lu', 'Bert Huang'] | 2020-02-27 | null | http://proceedings.neurips.cc/paper/2020/hash/3fb04953d95a94367bb133f862402bce-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/3fb04953d95a94367bb133f862402bce-Paper.pdf | neurips-2020-12 | ['normalising-flows'] | ['methodology'] | [-1.14815079e-01 -1.99792355e-01 -7.13808239e-02 -3.29631299e-01
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b4ba5bda-97d9-4b64-8179-3fdfc976a95c | deep-clustering-survival-machines-with | 2301.11826 | null | https://arxiv.org/abs/2301.11826v3 | https://arxiv.org/pdf/2301.11826v3.pdf | Deep Clustering Survival Machines with Interpretable Expert Distributions | Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance. | ['Yong Fan', 'Hao Zheng', 'Zhen Zhou', 'Zhicheng Jiao', 'Hongming Li', 'BoJian Hou'] | 2023-01-27 | null | null | null | null | ['survival-analysis', 'deep-clustering', 'deep-clustering'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-3.94753724e-01 -1.68315127e-01 -4.93671000e-01 -6.92994416e-01
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070af0c2-05b8-4ba3-9ba7-bd546404d254 | depth-aware-object-segmentation-and-grasp | 2111.11114 | null | https://arxiv.org/abs/2111.11114v1 | https://arxiv.org/pdf/2111.11114v1.pdf | Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks | In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate gripper. We introduce depth-aware Coordinate Convolution (CoordConv), a method to increase accuracy for point proposal based object instance segmentation in complex scenes without adding any additional network parameters or computation complexity. Depth-aware CoordConv uses depth data to extract prior information about the location of an object to achieve highly accurate object instance segmentation. These resulting segmentation masks, combined with predicted grasp candidates, lead to a complete scene description for grasping using a parallel-plate gripper. We evaluate the accuracy of grasp detection and instance segmentation on challenging robotic picking datasets, namely Sil\'eane and OCID_grasp, and show the benefit of joint grasp detection and segmentation on a real-world robotic picking task. | ['Friedrich Fraundorfer', 'Stephan Weiss', 'Rohit Dhakate', 'Christoph Böhm', 'Stefan Ainetter'] | 2021-11-22 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.15755990e-01 -6.89828098e-02 1.92259535e-01 -5.21591246e-01
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b06db2b6-6d6c-4abd-8b6d-97712f63859b | factor-augmented-regularized-model-for-hazard | 2210.01067 | null | https://arxiv.org/abs/2210.01067v1 | https://arxiv.org/pdf/2210.01067v1.pdf | Factor-Augmented Regularized Model for Hazard Regression | A prevalent feature of high-dimensional data is the dependence among covariates, and model selection is known to be challenging when covariates are highly correlated. To perform model selection for the high-dimensional Cox proportional hazards model in presence of correlated covariates with factor structure, we propose a new model, Factor-Augmented Regularized Model for Hazard Regression (FarmHazard), which builds upon latent factors that drive covariate dependence and extends Cox's model. This new model generates procedures that operate in two steps by learning factors and idiosyncratic components from high-dimensional covariate vectors and then using them as new predictors. Cox's model is a widely used semi-parametric model for survival analysis, where censored data and time-dependent covariates bring additional technical challenges. We prove model selection consistency and estimation consistency under mild conditions. We also develop a factor-augmented variable screening procedure to deal with strong correlations in ultra-high dimensional problems. Extensive simulations and real data experiments demonstrate that our procedures enjoy good performance and achieve better results on model selection, out-of-sample C-index and screening than alternative methods. | ['Jianqing Fan', 'Pierre Bayle'] | 2022-10-03 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 1.77378982e-01 -4.07789141e-01 -6.79632246e-01 -5.67456365e-01
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3267af87-2d5e-4054-833e-12c5fa7f7289 | atrial-fibrillation-detection-using-rr | 2302.07648 | null | https://arxiv.org/abs/2302.07648v1 | https://arxiv.org/pdf/2302.07648v1.pdf | Atrial Fibrillation Detection Using RR-Intervals for Application in Photoplethysmographs | Atrial Fibrillation is a common form of irregular heart rhythm that can be very dangerous. Our primary goal is to analyze Atrial Fibrillation data within ECGs to develop a model based only on RR-Intervals, or the length between heart-beats, to create a real time classification model for Atrial Fibrillation to be implemented in common heart-rate monitors on the market today. Physionet's MIT-BIH Atrial Fibrillation Database \cite{goldberger2000physiobank} and 2017 Challenge Database \cite{clifford2017af} were used to identify patterns of Atrial Fibrillation and test classification models on. These two datasets are very different. The MIT-BIH database contains long samples taken with a medical grade device, which is not useful for simulating a consumer device, but is useful for Atrial Fibrillation pattern detection. The 2017 Challenge database includes short ($<60sec$) samples taken with a portable device and reveals many of the challenges of Atrial Fibrillation classification in a real-time device. We developed multiple SVM models with three sets of extracted features as predictor variables which gave us moderately high accuracies with low computational intensity. With robust filtering techniques already applied in many Photoplethysmograph-based consumer heart-rate monitors, this method can be used to develop a reliable real time model for Atrial Fibrillation detection in consumer-grade heart-rate monitors. | ['Yishi Wang', 'Georgia Smith'] | 2023-02-13 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 2.64736891e-01 -3.11187178e-01 5.10116667e-02 -1.36084527e-01
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31c24fa5-6dd9-4543-8275-aff4a85f1d91 | robust-head-pose-estimation-based-on | 1603.09732 | null | http://arxiv.org/abs/1603.09732v3 | http://arxiv.org/pdf/1603.09732v3.pdf | Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions | Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods. | ['Silèye Ba', 'Antoine Deleforge', 'Vincent Drouard', 'Radu Horaud', 'Georgios Evangelidis'] | 2016-03-31 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.41549909e-01 2.07693025e-01 -1.22414790e-01 -8.65440190e-01
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3d9efea2-ad38-4c51-9deb-8481173ca215 | imbalanced-multi-label-classification-for | 2306.07046 | null | https://arxiv.org/abs/2306.07046v1 | https://arxiv.org/pdf/2306.07046v1.pdf | Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces | In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance, Classifier Chains, and Label Powerset. The results show that fine tuned BERT outperforms the other three methods by a significant margin, achieving high values of accuracy, F1 Score, Precision, and Recall. Binary Relevance also performs well on this dataset, while Classifier Chains and Label Powerset demonstrate relatively poor performance. These findings highlight the effectiveness of fine tuned BERT for multi label text classification tasks, and suggest that it may be a useful tool for businesses seeking to analyze complex and multifaceted texts. | ['Christophe Cruz', 'Muhammad Arslan'] | 2023-06-12 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-3.08930017e-02 -2.45289430e-01 -9.17102277e-01 -5.45312822e-01
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265fb321-7297-4749-a2dc-9b4acdcea86e | tempeval-3-evaluating-events-time-expressions | 1206.5333 | null | http://arxiv.org/abs/1206.5333v2 | http://arxiv.org/pdf/1206.5333v2.pdf | TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations | We describe the TempEval-3 task which is currently in preparation for the
SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on
temporal information processing. TempEval-3 follows on from previous TempEval
events, incorporating: a three-part task structure covering event, temporal
expression and temporal relation extraction; a larger dataset; and single
overall task quality scores. | ['James Allen', 'Naushad UzZaman', 'Marc Verhagen', 'Leon Derczynski', 'Hector Llorens', 'James Pustejovsky'] | 2012-06-22 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-5.92178181e-02 1.01751581e-01 -4.31853354e-01 -6.61115289e-01
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ad9c3382-565e-4ed5-ab47-ac2b04ce41ae | ismallnet-densely-nested-network-with-label | 2210.16561 | null | https://arxiv.org/abs/2210.16561v2 | https://arxiv.org/pdf/2210.16561v2.pdf | iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection | Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detection. On the one hand, to fully exploit the shape information of small targets, we decouple the original labeled ground-truth (GT) map into an interior map and a boundary one. The GT map, in collaboration with the two additional maps, tackles the unbalanced distribution of small object boundaries. On the other hand, two key modules are delicately designed and incorporated into the proposed network to boost the overall performance. First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information. Second, we develop an interior-boundary fusion module to integrate multi-granularity information. Experiments on NUAA-SIRST and NUDT-SIRST clearly show the superiority of iSmallNet over 11 state-of-the-art detectors. | ['Mingqiang Wei', 'Haoran Xie', 'Jie Qin', 'Peng Li', 'Yongzhen Wang', 'Zhiheng Hu'] | 2022-10-29 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.14168525e-01 -4.98715639e-02 1.49708584e-01 -2.90512294e-01
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6d63700c-5dde-4239-99c8-11e66f9c4ad1 | comparative-study-of-parameter-selection-for | 2302.00592 | null | https://arxiv.org/abs/2302.00592v1 | https://arxiv.org/pdf/2302.00592v1.pdf | Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation | Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimation. | ['Pratheepan Yogarajah', 'Pradeepa Samarasinghe', 'Shyam Reyal', 'Nuwan Kodagoda', 'Asiri Lindamulage'] | 2022-12-28 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-8.83234069e-02 6.49083912e-01 -8.84801894e-02 -8.32435906e-01
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6eb8942c-0b73-4dcb-8e2c-9866ece0037a | a-sparse-sampling-sensor-front-end-ic-for-low | 2208.06698 | null | https://arxiv.org/abs/2208.06698v1 | https://arxiv.org/pdf/2208.06698v1.pdf | A Sparse Sampling Sensor Front-end IC for Low Power Continuous SpO$_2$ \& HR Monitoring | Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD) and LEDs are commonly used as the interface devices in PPG sensors; however, using of flexible organic devices can enhance the sensor conformality and reduce the cost of fabrication. In most PPG sensors, most of system power consumption is concentrated in powering LEDs, traditionally consuming mWs. Using organic devices further increases this power demand since these devices exhibit larger parasitic capacitances and typically need higher drive voltages. This work presents a sensor IC for continuous SpO$_2$ and HR monitoring that features an on-chip reconstruction-free sparse sampling algorithm to reduce the overall system power consumption by $\sim$70\% while maintaining the accuracy of the output information. The designed frontend is compatible with a wide range of devices from silicon PDs to organic PDs with parasitic capacitances up to 10 nF. Implemented in a 40 nm HV CMOS process, the chip occupies 2.43 mm$^2$ and consumes 49.7 $\mu$W and 15.2 $\mu$W of power in continuous and sparse sampling modes respectively. The performance of the sensor IC has been verified \textit{in vivo} with both types of devices and the results are compared against a clinical grade reference. Less than 1 bpm and 1\% mean absolute errors were achieved in both continuous and sparse modes of operation. | ['Rikky Muller', 'Ana Claudia Arias', 'Jonathan Ting', 'Cem Yalcin', 'Jasmine Jan', 'Sina Faraji Alamouti'] | 2022-08-13 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 4.77078974e-01 4.12695706e-01 8.29466339e-03 -1.96722016e-01
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ba96f94b-d25e-4f6a-b176-2f6f3d8f791f | expats-a-toolkit-for-explainable-automated | 2104.03364 | null | https://arxiv.org/abs/2104.03364v1 | https://arxiv.org/pdf/2104.03364v1.pdf | EXPATS: A Toolkit for Explainable Automated Text Scoring | Automated text scoring (ATS) tasks, such as automated essay scoring and readability assessment, are important educational applications of natural language processing. Due to their interpretability of models and predictions, traditional machine learning (ML) algorithms based on handcrafted features are still in wide use for ATS tasks. Practitioners often need to experiment with a variety of models (including deep and traditional ML ones), features, and training objectives (regression and classification), although modern deep learning frameworks such as PyTorch require deep ML expertise to fully utilize. In this paper, we present EXPATS, an open-source framework to allow its users to develop and experiment with different ATS models quickly by offering flexible components, an easy-to-use configuration system, and the command-line interface. The toolkit also provides seamless integration with the Language Interpretability Tool (LIT) so that one can interpret and visualize models and their predictions. We also describe two case studies where we build ATS models quickly with minimal engineering efforts. The toolkit is available at \url{https://github.com/octanove/expats}. | ['Masato Hagiwara', 'Hitoshi Manabe'] | 2021-04-07 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-4.02174622e-01 5.02468925e-03 -9.81379375e-02 -6.46054506e-01
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9df156fe-90f9-417c-bace-e4b267a9d962 | stl-based-synthesis-of-feedback-controllers | 2212.01022 | null | https://arxiv.org/abs/2212.01022v1 | https://arxiv.org/pdf/2212.01022v1.pdf | STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning | Deep Reinforcement Learning (DRL) has the potential to be used for synthesizing feedback controllers (agents) for various complex systems with unknown dynamics. These systems are expected to satisfy diverse safety and liveness properties best captured using temporal logic. In RL, the reward function plays a crucial role in specifying the desired behaviour of these agents. However, the problem of designing the reward function for an RL agent to satisfy complex temporal logic specifications has received limited attention in the literature. To address this, we provide a systematic way of generating rewards in real-time by using the quantitative semantics of Signal Temporal Logic (STL), a widely used temporal logic to specify the behaviour of cyber-physical systems. We propose a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation. We evaluate our STL-based reinforcement learning mechanism on several complex continuous control benchmarks and compare our STL semantics with those available in the literature in terms of their efficacy in synthesizing the controller agent. Experimental results establish our new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning. | ['Indranil Saha', 'Nikhil Kumar Singh'] | 2022-12-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 8.40084478e-02 2.18479797e-01 -4.64157552e-01 -4.65434082e-02
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c595ea1d-4b4a-4c7f-b2e8-6d828b4f9016 | contextual-gradient-scaling-for-few-shot | 2110.10353 | null | https://arxiv.org/abs/2110.10353v1 | https://arxiv.org/pdf/2110.10353v1.pdf | Contextual Gradient Scaling for Few-Shot Learning | Model-agnostic meta-learning (MAML) is a well-known optimization-based meta-learning algorithm that works well in various computer vision tasks, e.g., few-shot classification. MAML is to learn an initialization so that a model can adapt to a new task in a few steps. However, since the gradient norm of a classifier (head) is much bigger than those of backbone layers, the model focuses on learning the decision boundary of the classifier with similar representations. Furthermore, gradient norms of high-level layers are small than those of the other layers. So, the backbone of MAML usually learns task-generic features, which results in deteriorated adaptation performance in the inner-loop. To resolve or mitigate this problem, we propose contextual gradient scaling (CxGrad), which scales gradient norms of the backbone to facilitate learning task-specific knowledge in the inner-loop. Since the scaling factors are generated from task-conditioned parameters, gradient norms of the backbone can be scaled in a task-wise fashion. Experimental results show that CxGrad effectively encourages the backbone to learn task-specific knowledge in the inner-loop and improves the performance of MAML up to a significant margin in both same- and cross-domain few-shot classification. | ['Byung Cheol Song', 'SeungHyun Lee', 'Sanghyuk Lee'] | 2021-10-20 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 1.03344560e-01 -2.18558505e-01 -2.72976458e-01 -5.22565544e-01
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8888b53d-42b9-4779-9172-bdd28d0838a2 | challenges-in-generalization-in-open-domain | 2109.01156 | null | https://arxiv.org/abs/2109.01156v3 | https://arxiv.org/pdf/2109.01156v3.pdf | Challenges in Generalization in Open Domain Question Answering | Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. However, it is unclear which aspects of novel questions make them challenging. Drawing upon studies on systematic generalization, we introduce and annotate questions according to three categories that measure different levels and kinds of generalization: training set overlap, compositional generalization (comp-gen), and novel-entity generalization (novel-entity). When evaluating six popular parametric and non-parametric models, we find that for the established Natural Questions and TriviaQA datasets, even the strongest model performance for comp-gen/novel-entity is 13.1/5.4% and 9.6/1.5% lower compared to that for the full test set -- indicating the challenge posed by these types of questions. Furthermore, we show that whilst non-parametric models can handle questions containing novel entities relatively well, they struggle with those requiring compositional generalization. Lastly, we find that key question difficulty factors are: cascading errors from the retrieval component, frequency of question pattern, and frequency of the entity. | ['Pontus Stenetorp', 'Sebastian Riedel', 'Patrick Lewis', 'Linqing Liu'] | 2021-09-02 | null | https://aclanthology.org/2022.findings-naacl.155 | https://aclanthology.org/2022.findings-naacl.155.pdf | findings-naacl-2022-7 | ['triviaqa', 'systematic-generalization'] | ['miscellaneous', 'reasoning'] | [-9.07583311e-02 2.89278537e-01 2.18398079e-01 -4.28017288e-01
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8067ba3e-4590-47f5-a523-b77150c75558 | benefits-of-semantics-on-web-service | 1305.0191 | null | http://arxiv.org/abs/1305.0191v1 | http://arxiv.org/pdf/1305.0191v1.pdf | Benefits of Semantics on Web Service Composition from a Complex Network Perspective | The number of publicly available Web services (WS) is continuously growing,
and in parallel, we are witnessing a rapid development in semantic-related web
technologies. The intersection of the semantic web and WS allows the
development of semantic WS. In this work, we adopt a complex network
perspective to perform a comparative analysis of the syntactic and semantic
approaches used to describe WS. From a collection of publicly available WS
descriptions, we extract syntactic and semantic WS interaction networks. We
take advantage of tools from the complex network field to analyze them and
determine their properties. We show that WS interaction networks exhibit some
of the typical characteristics observed in real-world networks, such as short
average distance between nodes and community structure. By comparing syntactic
and semantic networks through their properties, we show the introduction of
semantics in WS descriptions should improve the composition process. | ['Jean-François Santucci', 'Vincent Labatut', 'Chantal Cherifi'] | 2013-05-01 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-9.36415792e-02 2.75313675e-01 7.58174211e-02 -5.50482392e-01
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f1f8bb44-80c4-4a1a-8ffa-bf1212c76113 | large-ai-model-based-semantic-communications | 2307.03492 | null | https://arxiv.org/abs/2307.03492v1 | https://arxiv.org/pdf/2307.03492v1.pdf | Large AI Model-Based Semantic Communications | Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model provides new solutions to overcome above issues. Here, we propose a large AI model-based SC framework (LAM-SC) specifically designed for image data, where we first design the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semantic-aware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the significance of the large AI model-based KB development in future SC paradigms. | ['Xiaohu You', 'Cunhua Pan', 'Kun Yang', 'Kezhi Wang', 'Li Dong', 'Yubo Peng', 'Feibo Jiang'] | 2023-07-07 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [ 2.15195268e-01 1.83771282e-01 -3.55766356e-01 -3.56276393e-01
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0f7716d9-35b0-437a-9b1e-6b4db6370f79 | visualizing-convolutional-neural-networks-to | 1809.03851 | null | http://arxiv.org/abs/1809.03851v1 | http://arxiv.org/pdf/1809.03851v1.pdf | Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification | Because of their state-of-the-art performance in computer vision, CNNs are
becoming increasingly popular in a variety of fields, including medicine.
However, as neural networks are black box function approximators, it is
difficult, if not impossible, for a medical expert to reason about their
output. This could potentially result in the expert distrusting the network
when he or she does not agree with its output. In such a case, explaining why
the CNN makes a certain decision becomes valuable information. In this paper,
we try to open the black box of the CNN by inspecting and visualizing the
learned feature maps, in the field of dermatology. We show that, to some
extent, CNNs focus on features similar to those used by dermatologists to make
a diagnosis. However, more research is required for fully explaining their
output. | ['Bert Vankeirsbilck', 'Pieter Van Molle', 'Pieter Simoens', 'Bart Dhoedt', 'Tim Verbelen', 'Miguel De Strooper'] | 2018-09-11 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.61320297e-02 6.10527456e-01 -8.24442431e-02 -3.38400036e-01
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69380c33-ef7c-40a7-beb7-54d301d14cc0 | beyond-pick-and-place-tackling-robotic | 2110.06192 | null | https://arxiv.org/abs/2110.06192v2 | https://arxiv.org/pdf/2110.06192v2.pdf | Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes | We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple "pick-and-place" solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer. Our learned policies can efficiently handle multiple object combinations in the real world and exhibit a large variety of stacking skills. In a large experimental study, we investigate what choices matter for learning such general vision-based agents in simulation, and what affects optimal transfer to the real robot. We then leverage data collected by such policies and improve upon them with offline RL. A video and a blog post of our work are provided as supplementary material. | ['Francesco Nori', 'Raia Hadsell', 'Martin Riedmiller', 'Federico Casarini', 'Stefano Saliceti', 'Antoine Laurens', 'Michael Neunert', 'Rae Jeong', 'Akhil Raju', 'Jose Enrique Chen', 'Claudio Fantacci', 'David Khosid', 'Nimrod Gileadi', 'Abbas Abdolmaleki', 'Arunkumar Byravan', 'Jost Tobias Springenberg', 'Konstantinos Bousmalis', 'Thomas Lampe', 'Yuxiang Zhou', 'Coline Devin', 'Alex X. Lee'] | 2021-10-12 | null | null | null | null | ['skill-generalization', 'skill-mastery'] | ['robots', 'robots'] | [-9.15607885e-02 3.42978351e-02 1.01191200e-01 -1.32078752e-01
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5a4294ee-f68f-45ec-ae1a-047db0570f88 | what-can-you-do-with-a-rock-affordance | 1703.03429 | null | http://arxiv.org/abs/1703.03429v1 | http://arxiv.org/pdf/1703.03429v1.pdf | What can you do with a rock? Affordance extraction via word embeddings | Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose. | ['Daniel Ricks', 'Nancy Fulda', 'David Wingate', 'Ben Murdoch'] | 2017-03-09 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 1.55421078e-01 7.93409199e-02 -3.69568259e-01 -8.20024684e-02
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512c9520-c873-442b-bd22-1cb0342e8f5a | traffic-sign-detection-and-classification-in | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf | Traffic-Sign Detection and Classification in the Wild | Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. We call this benchmark Tsinghua-Tencent 100K. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify traffic-signs. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available. | ['Shimin Hu', 'Baoli Li', 'Xiaolei Huang', 'SongHai Zhang', 'Dun Liang', 'Zhe Zhu'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['traffic-sign-detection'] | ['computer-vision'] | [ 3.91454905e-01 -5.44534326e-01 -1.54248178e-01 -4.11286980e-01
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c300f699-ebf2-4a1b-bd30-16813c26b139 | cv-hazop-introducing-test-data-validation-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zendel_CV-HAZOP_Introducing_Test_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zendel_CV-HAZOP_Introducing_Test_ICCV_2015_paper.pdf | CV-HAZOP: Introducing Test Data Validation for Computer Vision | Test data plays an important role in computer vision (CV) but is plagued by two questions: Which situations should be covered by the test data and have we tested enough to reach a conclusion? In this paper we propose a new solution answering these questions using a standard procedure devised by the safety community to validate complex systems: The Hazard and Operability Analysis (HAZOP). It is designed to systematically search and identify difficult, performance-decreasing situations and aspects. We introduce a generic CV model that creates the basis for the hazard analysis and, for the first time, apply an extensive HAZOP to the CV domain. The result is a publicly available checklist with more than 900 identified individual hazards. This checklist can be used to evaluate existing test datasets by quantifying the amount of covered hazards. We evaluate our approach by first analyzing and annotating the popular stereo vision test datasets Middlebury and KITTI. Second, we compare the performance of six popular stereo matching algorithms at the identified hazards from our checklist with their average performance and show, as expected, a clear negative influence of the hazards. The presented approach is a useful tool to evaluate and improve test datasets and creates a common basis for future dataset designs. | ['Markus Murschitz', 'Wolfgang Herzner', 'Oliver Zendel', 'Martin Humenberger'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 9.64338630e-02 -6.61117882e-02 4.48333174e-01 -2.01046944e-01
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fe7b527a-7e94-4c73-adf9-b3d8c443a4b9 | unfused-unsupervised-finetuning-using-self | 2303.05668 | null | https://arxiv.org/abs/2303.05668v2 | https://arxiv.org/pdf/2303.05668v2.pdf | UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation | In this paper, we introduce UnFuSeD, a novel approach to leverage self-supervised learning and reduce the need for large amounts of labeled data for audio classification. Unlike prior works, which directly fine-tune a self-supervised pre-trained encoder on a target dataset, we use the encoder to generate pseudo-labels for unsupervised fine-tuning before the actual fine-tuning step. We first train an encoder using a novel self-supervised learning algorithm (SSL) on an unlabeled audio dataset. Then, we use that encoder to generate pseudo-labels on our target task dataset via clustering the extracted representations. These pseudo-labels are then used to guide self-distillation on a randomly initialized model, which we call unsupervised fine-tuning. Finally, the resultant encoder is then fine-tuned on our target task dataset. Through UnFuSeD, we propose the first system that moves away from generic SSL paradigms in literature, which pre-train and fine-tune the same encoder, and present a novel self-distillation-based system to leverage SSL pre-training for low-resource audio classification. In practice, UnFuSeD achieves state-of-the-art results on the LAPE Benchmark, significantly outperforming all our baselines. Additionally, UnFuSeD allows us to achieve this at a 40% reduction in the number of parameters over the previous state-of-the-art system. We make all our codes publicly available. | ['Dinesh Manocha', 'S. Umesh', 'Sreyan Ghosh', 'Ashish Seth'] | 2023-03-10 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 5.13723195e-01 9.87897217e-02 -1.10610567e-01 -6.37860775e-01
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fa8f3c9d-07bf-4bb1-a4c8-49b52f7fb1f4 | learning-and-using-the-arrow-of-time | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wei_Learning_and_Using_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Learning_and_Using_CVPR_2018_paper.pdf | Learning and Using the Arrow of Time | We seek to understand the arrow of time in videos -- what makes videos look like they are playing forwards or backwards? Can we visualize the cues? Can the arrow of time be a supervisory signal useful for activity analysis? To this end, we build three large-scale video datasets and apply a learning-based approach to these tasks. To learn the arrow of time efficiently and reliably, we design a ConvNet suitable for extended temporal footprints and for class activation visualization, and study the effect of artificial cues, such as cinematographic conventions, on learning. Our trained model achieves state-of-the-art performance on large-scale real-world video datasets. Through cluster analysis and localization of important regions for the prediction, we examine learned visual cues that are consistent among many samples and show when and where they occur. Lastly, we use the trained ConvNet for two applications: self-supervision for action recognition, and video forensics -- determining whether Hollywood film clips have been deliberately reversed in time, often used as special effects. | ['Andrew Zisserman', 'Donglai Wei', 'William T. Freeman', 'Joseph J. Lim'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-forensics', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.01826900e-01 -2.34292358e-01 -4.63328242e-01 -4.59881246e-01
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643ad215-fcdf-4d82-bf29-dbcc22996043 | bird-s-eye-view-panoptic-segmentation-using | 2108.03227 | null | https://arxiv.org/abs/2108.03227v3 | https://arxiv.org/pdf/2108.03227v3.pdf | Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images | Bird's-Eye-View (BEV) maps have emerged as one of the most powerful representations for scene understanding due to their ability to provide rich spatial context while being easy to interpret and process. Such maps have found use in many real-world tasks that extensively rely on accurate scene segmentation as well as object instance identification in the BEV space for their operation. However, existing segmentation algorithms only predict the semantics in the BEV space, which limits their use in applications where the notion of object instances is also critical. In this work, we present the first BEV panoptic segmentation approach for directly predicting dense panoptic segmentation maps in the BEV, given a single monocular image in the frontal view (FV). Our architecture follows the top-down paradigm and incorporates a novel dense transformer module consisting of two distinct transformers that learn to independently map vertical and flat regions in the input image from the FV to the BEV. Additionally, we derive a mathematical formulation for the sensitivity of the FV-BEV transformation which allows us to intelligently weight pixels in the BEV space to account for the varying descriptiveness across the FV image. Extensive evaluations on the KITTI-360 and nuScenes datasets demonstrate that our approach exceeds the state-of-the-art in the PQ metric by 3.61 pp and 4.93 pp respectively. | ['Abhinav Valada', 'Nikhil Gosala'] | 2021-08-06 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.03580427e-01 -3.75623643e-01 5.25978133e-02 -6.45238519e-01
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f0b60d52-c77c-4ba9-a78e-f41f07f645c3 | incorporating-global-visual-features-into-1 | null | null | https://aclanthology.org/D17-1105 | https://aclanthology.org/D17-1105.pdf | Incorporating Global Visual Features into Attention-based Neural Machine Translation. | We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate translations into English and German, how different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal NMT models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set. | ['Qun Liu', 'Iacer Calixto'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['video-description'] | ['computer-vision'] | [ 3.44008178e-01 2.48078778e-01 -1.14654727e-01 -1.63634345e-01
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315e1eec-6843-4516-880b-079a35b54d60 | provable-robustness-for-streaming-models-with | 2303.16308 | null | https://arxiv.org/abs/2303.16308v1 | https://arxiv.org/pdf/2303.16308v1.pdf | Provable Robustness for Streaming Models with a Sliding Window | The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to be independent and model performance is measured as an expectation over the input distribution. Robustness certificates are derived for individual input instances with the assumption that the model is evaluated on each instance separately. However, in many deep learning applications such as online content recommendation and stock market analysis, models use historical data to make predictions. Robustness certificates based on the assumption of independent input samples are not directly applicable in such scenarios. In this work, we focus on the provable robustness of machine learning models in the context of data streams, where inputs are presented as a sequence of potentially correlated items. We derive robustness certificates for models that use a fixed-size sliding window over the input stream. Our guarantees hold for the average model performance across the entire stream and are independent of stream size, making them suitable for large data streams. We perform experiments on speech detection and human activity recognition tasks and show that our certificates can produce meaningful performance guarantees against adversarial perturbations. | ['Soheil Feizi', 'Vinu Sankar Sadasivan', 'Aounon Kumar'] | 2023-03-28 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 5.35010457e-01 5.94998188e-02 -3.13973486e-01 -1.55850887e-01
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c8c92913-a592-4458-ad42-ecd76fe6af2d | gain-missing-data-imputation-using-generative | 1806.02920 | null | http://arxiv.org/abs/1806.02920v1 | http://arxiv.org/pdf/1806.02920v1.pdf | GAIN: Missing Data Imputation using Generative Adversarial Nets | We propose a novel method for imputing missing data by adapting the
well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call
our method Generative Adversarial Imputation Nets (GAIN). The generator (G)
observes some components of a real data vector, imputes the missing components
conditioned on what is actually observed, and outputs a completed vector. The
discriminator (D) then takes a completed vector and attempts to determine which
components were actually observed and which were imputed. To ensure that D
forces G to learn the desired distribution, we provide D with some additional
information in the form of a hint vector. The hint reveals to D partial
information about the missingness of the original sample, which is used by D to
focus its attention on the imputation quality of particular components. This
hint ensures that G does in fact learn to generate according to the true data
distribution. We tested our method on various datasets and found that GAIN
significantly outperforms state-of-the-art imputation methods. | ['James Jordon', 'Mihaela van der Schaar', 'Jinsung Yoon'] | 2018-06-07 | gain-missing-data-imputation-using-generative-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2025 | http://proceedings.mlr.press/v80/yoon18a/yoon18a.pdf | icml-2018-7 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 4.02032584e-01 4.09175128e-01 -1.74020201e-01 -3.49279284e-01
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dd2616f3-eb48-41e5-9b55-350fb67df29a | support-set-based-cross-supervision-for-video | 2108.10576 | null | https://arxiv.org/abs/2108.10576v1 | https://arxiv.org/pdf/2108.10576v1.pdf | Support-Set Based Cross-Supervision for Video Grounding | Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to learn the complicated multi-modal relations by only architecture designing in fact. In this paper, we introduce a novel Support-set Based Cross-Supervision (Sscs) module which can improve existing methods during training phase without extra inference cost. The proposed Sscs module contains two main components, i.e., discriminative contrastive objective and generative caption objective. The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts. Due to the co-existence of some visual entities in both ground-truth and background intervals, i.e., mutual exclusion, naively contrastive learning is unsuitable to video grounding. We address the problem by boosting the cross-supervision with the support-set concept, which collects visual information from the whole video and eliminates the mutual exclusion of entities. Combined with the original objectives, Sscs can enhance the abilities of multi-modal relation modeling for existing approaches. We extensively evaluate Sscs on three challenging datasets, and show that our method can improve current state-of-the-art methods by large margins, especially 6.35% in terms of R1@0.5 on Charades-STA. | ['Xinbo Gao', 'Mingqian Tang', 'Ziyuan Huang', 'Xiaomeng Li', 'De Cheng', 'Shiwei Zhang', 'Nannan Wang', 'Xinpeng Ding'] | 2021-08-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ding_Support-Set_Based_Cross-Supervision_for_Video_Grounding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ding_Support-Set_Based_Cross-Supervision_for_Video_Grounding_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-grounding'] | ['computer-vision'] | [ 0.1442402 0.12616742 -0.37134168 -0.32012948 -0.8201831 -0.15437023
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90b9766a-7701-4249-8db5-ea3e3212bae7 | uncertainty-detection-in-eeg-neural-decoding | 2201.00627 | null | https://arxiv.org/abs/2201.00627v2 | https://arxiv.org/pdf/2201.00627v2.pdf | Uncertainty Detection and Reduction in Neural Decoding of EEG Signals | EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given the significant variance and noise in EEG signals. Previous works on EEG analysis mainly focus on the exploration of noise pattern in the source signal, while the uncertainty during the decoding process is largely unexplored. Automatically detecting and reducing such decoding uncertainty is important for BCI motor imagery applications such as robotic arm control etc. In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process. It utilized a combination of dropout oriented method and Bayesian neural network for uncertainty estimation to incorporate both the uncertainty in the input signal and the uncertainty in the model parameters. We further proposed a data augmentation based approach for uncertainty reduction. The model can be integrated into current widely used EEG neural decoders without change of architecture. We performed extensive experiments for uncertainty estimation and its reduction in both intra-subject EEG decoding and cross-subject EEG decoding on two public motor imagery datasets, where the proposed model achieves significant improvement both on the quality of estimated uncertainty and the effectiveness of uncertainty reduction. | ['Hui Yang', 'Sargur N. Srihari', 'Sheng Liu', 'Zhenyi Wang', 'Tiehang Duan'] | 2021-12-28 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.93649852e-01 6.57046288e-02 5.86089790e-01 -6.45875633e-01
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6863d4ed-003a-4eb1-8032-bf77e7591996 | dgraph-a-large-scale-financial-dataset-for | 2207.03579 | null | https://arxiv.org/abs/2207.03579v4 | https://arxiv.org/pdf/2207.03579v4.pdf | DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection | Graph Anomaly Detection (GAD) has recently become a hot research spot due to its practicability and theoretical value. Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work. Thus, this paper present DGraph, a real-world dynamic graph in the finance domain. DGraph overcomes many limitations of current GAD datasets. It contains about 3M nodes, 4M dynamic edges, and 1M ground-truth nodes. We provide a comprehensive observation of DGraph, revealing that anomalous nodes and normal nodes generally have different structures, neighbor distribution, and temporal dynamics. Moreover, it suggests that unlabeled nodes are also essential for detecting fraudsters. Furthermore, we conduct extensive experiments on DGraph. Observation and experiments demonstrate that DGraph is propulsive to advance GAD research and enable in-depth exploration of anomalous nodes. | ['Michalis Vazirgiannis', 'Lei Chen', 'Jiarong Xu', 'Zhisheng Zhang', 'Chunping Wang', 'Yang Wang', 'Yang Yang', 'Xuanwen Huang'] | 2022-06-30 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [-3.12410980e-01 1.26856729e-01 -3.29953939e-01 -1.19452439e-02
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1d74e5fa-090f-4e23-8c30-4a71b85bce7b | improvements-in-remote-cardiopulmonary | null | null | http://affect.media.mit.edu/pdfs/14.McDuff_etal_Improvements.pdf | http://affect.media.mit.edu/pdfs/14.McDuff_etal_Improvements.pdf | Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera | Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing. However, traditional RGB cameras have limited frequency resolution. We present results of PPG measurements from a novel five band camera and show that alternate frequency bands, in particular an orange band, allowed physiological measurements much more highly correlated with an FDA approved contact PPG sensor.
In a study with participants (n = 10) at rest and under stress, correlations of over 0.92 (p < 0.01) were obtained for heart rate, breathing rate, and heart rate variability measurements. In addition, the remotely measured heart rate variability spectrograms
closely matched those from the contact approach. The best results were obtained using a combination of cyan, green, and orange
(CGO) bands; incorporating red and blue channel observations did not improve performance. In short, RGB is not optimal for this
problem: CGO is better. Incorporating alternative color channel sensors should not increase the cost of such cameras dramatically. | ['R', 'and Picard', 'S.', 'Gontarek', 'D.', 'McDuff'] | 2014-05-14 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability'] | ['medical', 'medical'] | [ 1.17930360e-01 -2.43754506e-01 1.68196782e-01 -3.69830847e-01
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73b5d808-db6e-48ac-b52b-e210bc1a3df4 | toward-connecting-speech-acts-and-search | 2305.04858 | null | https://arxiv.org/abs/2305.04858v1 | https://arxiv.org/pdf/2305.04858v1.pdf | Toward Connecting Speech Acts and Search Actions in Conversational Search Tasks | Conversational search systems can improve user experience in digital libraries by facilitating a natural and intuitive way to interact with library content. However, most conversational search systems are limited to performing simple tasks and controlling smart devices. Therefore, there is a need for systems that can accurately understand the user's information requirements and perform the appropriate search activity. Prior research on intelligent systems suggested that it is possible to comprehend the functional aspect of discourse (search intent) by identifying the speech acts in user dialogues. In this work, we automatically identify the speech acts associated with spoken utterances and use them to predict the system-level search actions. First, we conducted a Wizard-of-Oz study to collect data from 75 search sessions. We performed thematic analysis to curate a gold standard dataset -- containing 1,834 utterances and 509 system actions -- of human-system interactions in three information-seeking scenarios. Next, we developed attention-based deep neural networks to understand natural language and predict speech acts. Then, the speech acts were fed to the model to predict the corresponding system-level search actions. We also annotated a second dataset to validate our results. For the two datasets, the best-performing classification model achieved maximum accuracy of 90.2% and 72.7% for speech act classification and 58.8% and 61.1%, respectively, for search act classification. | ['Chirag Shah', 'Satanu Ghosh', 'Souvick Ghosh'] | 2023-05-08 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.16819531e-01 3.75561059e-01 -1.69340581e-01 -5.16737401e-01
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bf6a6dfc-c114-4068-852a-e7a0465dda4e | stg-mtl-scalable-task-grouping-for-multi-task | 2307.03374 | null | https://arxiv.org/abs/2307.03374v1 | https://arxiv.org/pdf/2307.03374v1.pdf | STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map | Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one, and some groupings might produce performance degradation due to negative interference between tasks. Furthermore, existing solutions are severely suffering from scalability issues, limiting any practical application. In our paper, we propose a new data-driven method that addresses these challenges and provides a scalable and modular solution for classification task grouping based on hand-crafted features, specifically Data Maps, which capture the training behavior for each classification task during the MTL training. We experiment with the method demonstrating its effectiveness, even on an unprecedented number of tasks (up to 100). | ['Mohamed ElHelw', 'Mustafa Elattar', 'Abubakar Abid', 'Ammar Sherif'] | 2023-07-07 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.45437258e-01 -5.77841401e-01 -2.29378328e-01 -2.95731843e-01
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17108744-347e-44e4-8c25-09d0a8bddf8b | translating-embeddings-in-document-for | null | null | https://openreview.net/forum?id=BHUCb6_xBde | https://openreview.net/pdf?id=BHUCb6_xBde | Translating Embeddings in Document for Modeling Multi-relational Graphs | Document-level relation extraction (RE) aims at extracting heterogeneous relational graphs upon entities in document, which has been handled with graph neural networks (GNNs) and pre-trained language models (PLMs) effectively.
However, a crucial problem is that most GNNs adopt a task-independent pseudo graph convoluation which cause the over-parameterization.
Is this paper, we argue that excessive parameters are unnecessary, and further propose a novel light-weight model named \textbf{TransGCN}. Specifically, we obtain the representation of entities in a document through a PLM encoder and construct our transmission-based graph convolutional network (GCN) on them. Unlike the previous methods that require storing the parameters of GNNs, our transmission-based GCN is performed with message passing constrained with the transmitting scores, which can be calculated by knowledge graph embedding models. In this way, we reduce the number of parameters by about half compared to the SOTA model.
We conduct experiments on DocRED, which is a large-scale human-annotated document RE dataset. The results show that we outperform the state-of-the-art model with only half amount of parameters. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.10574672e-02 5.76101959e-01 -2.16577142e-01 -1.66749880e-01
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46a33434-25ab-4db2-a9db-b4ed2fa4bf12 | dynamic-full-field-optical-coherence | 2301.12888 | null | https://arxiv.org/abs/2301.12888v1 | https://arxiv.org/pdf/2301.12888v1.pdf | Dynamic Full-Field Optical Coherence Tomography module adapted to commercial microscopes for longitudinal in vitro cell culture study | Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organisation as well as cell shape, motility and division. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening. | ['Kate Grieve', 'Olivier Thouvenin', 'Sacha Reichman', 'Olivier Goureau', 'Serge Picaud', 'Valerie Forster', 'Amélie Slembrouck-Brec', 'Marilou Clémençon', 'Jeremy Brogard', 'Salvatore Azzollini', 'Tual Monfort'] | 2023-01-30 | null | null | null | null | ['culture'] | ['speech'] | [ 3.16921204e-01 -2.14211032e-01 3.92727852e-01 1.89956933e-01
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55e9ab69-bf5e-4bb5-9db6-27f1070ef90d | on-hallucination-and-predictive-uncertainty | 2103.15025 | null | https://arxiv.org/abs/2103.15025v1 | https://arxiv.org/pdf/2103.15025v1.pdf | On Hallucination and Predictive Uncertainty in Conditional Language Generation | Despite improvements in performances on different natural language generation tasks, deep neural models are prone to hallucinating facts that are incorrect or nonexistent. Different hypotheses are proposed and examined separately for different tasks, but no systematic explanations are available across these tasks. In this study, we draw connections between hallucinations and predictive uncertainty in conditional language generation. We investigate their relationship in both image captioning and data-to-text generation and propose a simple extension to beam search to reduce hallucination. Our analysis shows that higher predictive uncertainty corresponds to a higher chance of hallucination. Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties. It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant. | ['William Yang Wang', 'Yijun Xiao'] | 2021-03-28 | null | https://aclanthology.org/2021.eacl-main.236 | https://aclanthology.org/2021.eacl-main.236.pdf | eacl-2021-2 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 1.02332957e-01 8.99967968e-01 1.54941007e-02 -4.59481746e-01
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e48db7cc-94ca-4b35-b921-4a43a58d79f0 | motion-based-camera-localization-system-in | 2012.01690 | null | https://arxiv.org/abs/2012.01690v3 | https://arxiv.org/pdf/2012.01690v3.pdf | Motion-based Camera Localization System in Colonoscopy Videos | Optical colonoscopy is an essential diagnostic and prognostic tool for many gastrointestinal diseases, including cancer screening and staging, intestinal bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel disease assessment. Automated assessment of colonoscopy is of interest considering the subjectivity present in qualitative human interpretations of colonoscopy findings. Localization of the camera is essential to interpreting the meaning and context of findings for diseases evaluated by colonoscopy. In this study, we propose a camera localization system to estimate the relative location of the camera and classify the colon into anatomical segments. The camera localization system begins with non-informative frame detection and removal. Then a self-training end-to-end convolutional neural network is built to estimate the camera motion, where several strategies are proposed to improve its robustness and generalization on endoscopic videos. Using the estimated camera motion a camera trajectory can be derived and a relative location index calculated. Based on the estimated location index, anatomical colon segment classification is performed by constructing a colon template. The proposed motion estimation algorithm was evaluated on an external dataset containing the ground truth for camera pose. The experimental results show that the performance of the proposed method is superior to other published methods. The relative location index estimation and anatomical region classification were further validated using colonoscopy videos collected from routine clinical practice. This validation yielded an average accuracy in classification of 0.754, which is substantially higher than the performances obtained using location indices built from other methods. | ['Zijun Gao', 'Kayvan Najarian', 'Jonathan Gryak', 'Ryan W. Stidham', 'Heming Yao'] | 2020-12-03 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 5.89958914e-02 -1.00042380e-01 -2.60524541e-01 -7.01183900e-02
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3beeaf57-c520-4a69-8698-867469eace4d | no-reference-quality-assessment-of | null | null | https://www.mdpi.com/2313-433X/8/6/173 | https://www.mdpi.com/2313-433X/8/6/173 | No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features | With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings. | ['Domonkos Varga'] | 2022-06-19 | null | null | null | journal-of-imaging-2022-6 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.69625551e-01 -8.47338021e-01 1.98706940e-01 -4.16630924e-01
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7ee98526-ccb1-496d-b809-69af41431675 | shattering-the-agent-environment-interface | 2305.11455 | null | https://arxiv.org/abs/2305.11455v1 | https://arxiv.org/pdf/2305.11455v1.pdf | Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models | A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward model to emulate human feedback, distinct from the language model itself. This reward model is then coupled with policy-gradient methods to dramatically improve the alignment between language model outputs and desired responses. In this work, we adopt a novel perspective wherein a pre-trained language model is itself simultaneously a policy, reward function, and transition function. An immediate consequence of this is that reward learning and language model fine-tuning can be performed jointly and directly, without requiring any further downstream policy optimization. While this perspective does indeed break the traditional agent-environment interface, we nevertheless maintain that there can be enormous statistical benefits afforded by bringing to bear traditional algorithmic concepts from reinforcement learning. Our experiments demonstrate one concrete instance of this through efficient exploration based on the representation and resolution of epistemic uncertainty. In order to illustrate these ideas in a transparent manner, we restrict attention to a simple didactic data generating process and leave for future work extension to systems of practical scale. | ['Benjamin Van Roy', 'Dilip Arumugam', 'Shi Dong', 'Wanqiao Xu'] | 2023-05-19 | null | null | null | null | ['policy-gradient-methods', 'efficient-exploration'] | ['methodology', 'methodology'] | [ 6.90969452e-02 6.56485677e-01 -1.45761296e-01 -2.02747971e-01
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6476b834-0670-42bd-b2f1-fe8311e548f7 | linear-covariance-loss-for-end-to-end | 2303.11516 | null | https://arxiv.org/abs/2303.11516v1 | https://arxiv.org/pdf/2303.11516v1.pdf | Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation | Most modern image-based 6D object pose estimation methods learn to predict 2D-3D correspondences, from which the pose can be obtained using a PnP solver. Because of the non-differentiable nature of common PnP solvers, these methods are supervised via the individual correspondences. To address this, several methods have designed differentiable PnP strategies, thus imposing supervision on the pose obtained after the PnP step. Here, we argue that this conflicts with the averaging nature of the PnP problem, leading to gradients that may encourage the network to degrade the accuracy of individual correspondences. To address this, we derive a loss function that exploits the ground truth pose before solving the PnP problem. Specifically, we linearize the PnP solver around the ground-truth pose and compute the covariance of the resulting pose distribution. We then define our loss based on the diagonal covariance elements, which entails considering the final pose estimate yet not suffering from the PnP averaging issue. Our experiments show that our loss consistently improves the pose estimation accuracy for both dense and sparse correspondence based methods, achieving state-of-the-art results on both Linemod-Occluded and YCB-Video. | ['Mathieu Salzmann', 'Yinlin Hu', 'Fulin Liu'] | 2023-03-21 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.33413002e-01 2.13366151e-01 -1.09637819e-01 -2.33808711e-01
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d1cce1fc-12e0-44d7-93f2-8c1e6d6e5d0f | rediscovering-the-slavic-continuum-in | 2010.11973 | null | https://arxiv.org/abs/2010.11973v1 | https://arxiv.org/pdf/2010.11973v1.pdf | Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification | Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness and/or non-linguists' perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability between languages in our study to be the best predictor of the language representation similarity. | ['Dietrich Klakow', 'Bernd Möbius', 'Tania Avgustinova', 'Jacek Kudera', 'Badr M. Abdullah'] | 2020-10-22 | null | https://aclanthology.org/2020.vardial-1.12 | https://aclanthology.org/2020.vardial-1.12.pdf | vardial-coling-2020-12 | ['spoken-language-identification'] | ['speech'] | [-3.87021273e-01 -1.48944125e-01 -2.49015361e-01 -5.01073718e-01
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423b0e4c-fdfc-40d6-b0bb-7d08f8e2f1bd | co-optimizing-distributed-energy-resources-in | 2208.09781 | null | https://arxiv.org/abs/2208.09781v2 | https://arxiv.org/pdf/2208.09781v2.pdf | Co-optimizing Distributed Energy Resources in Linear Complexity under Net Energy Metering | The co-optimization of behind-the-meter distributed energy resources is considered for prosumers under the net energy metering tariff. The distributed energy resources considered include renewable generations, flexible demands, and battery energy storage systems. An energy management system schedules the consumptions and battery storage based on locally available stochastic renewables by maximizing the expected operation surplus under the net energy metering tariff. A stochastic dynamic programming formulation is introduced for which structural properties of the dynamic optimization are derived. A closed-form optimal myopic co-optimization algorithm is proposed, which achieves optimality when the storage capacity constraints are nonbinding. The proposed co-optimization algorithm has linear computation complexity and can be implemented in a decentralized fashion. The performance of the myopic co-optimization algorithm and economic benefits of the optimal co-optimization policy to prosumers and grid operations are evaluated in numerical simulations. | ['Qing Zhao', 'Lang Tong', 'Ahmed S. Alahmed'] | 2022-08-21 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.80200672e-01 8.78923535e-02 -6.01041019e-01 -2.23406944e-02
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9ea172e0-4874-4a64-81d3-f1e4efd5fa2b | code-execution-with-pre-trained-language | 2305.05383 | null | https://arxiv.org/abs/2305.05383v1 | https://arxiv.org/pdf/2305.05383v1.pdf | Code Execution with Pre-trained Language Models | Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, we investigate how well pre-trained models can understand and perform code execution. We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution, which challenges existing models such as Codex. We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension. We evaluate CodeExecutor on code execution and show its promising performance and limitations. We also demonstrate its potential benefits for code intelligence tasks such as zero-shot code-to-code search and text-to-code generation. Our analysis provides insights into the learning and generalization abilities of pre-trained models for code execution. | ['Nan Duan', 'Neel Sundaresan', 'Shengyu Fu', 'Alexey Svyatkovskiy', 'Daxin Jiang', 'Weizhu Chen', 'Shuai Lu', 'Chenxiao Liu'] | 2023-05-08 | null | null | null | null | ['text-to-code-generation', 'code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-vision'] | [ 2.64975041e-01 -7.22486619e-03 -5.07886350e-01 -5.26322722e-01
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4642f8aa-b681-43f6-b948-09b7871fc550 | metamax-improved-open-set-deep-neural | 2211.10872 | null | https://arxiv.org/abs/2211.10872v1 | https://arxiv.org/pdf/2211.10872v1.pdf | MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration | Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to address open-set recognition by calibrating the predictive scores of a standard closed-set classification network. In this paper we present MetaMax, a more effective post-processing technique that improves upon contemporary methods by directly modeling class activation vectors. MetaMax removes the need for computing class mean activation vectors (MAVs) and distances between a query image and a class MAV as required in OpenMax. Experimental results show that MetaMax outperforms OpenMax and is comparable in performance to other state-of-the-art approaches. | ['William J. Beksi', 'Nolan B. Gutierrez', 'Zongyao Lyu'] | 2022-11-20 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.92781550e-01 1.53733507e-01 -2.22642615e-01 -7.37621546e-01
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ed1d8edf-4ae7-459e-b383-4db144a844a9 | neural-architecture-search-for-effective | 2303.09639 | null | https://arxiv.org/abs/2303.09639v1 | https://arxiv.org/pdf/2303.09639v1.pdf | Neural Architecture Search for Effective Teacher-Student Knowledge Transfer in Language Models | Large pre-trained language models have achieved state-of-the-art results on a variety of downstream tasks. Knowledge Distillation (KD) of a smaller student model addresses their inefficiency, allowing for deployment in resource-constraint environments. KD however remains ineffective, as the student is manually selected from a set of existing options already pre-trained on large corpora, a sub-optimal choice within the space of all possible student architectures. This paper proposes KD-NAS, the use of Neural Architecture Search (NAS) guided by the Knowledge Distillation process to find the optimal student model for distillation from a teacher, for a given natural language task. In each episode of the search process, a NAS controller predicts a reward based on a combination of accuracy on the downstream task and latency of inference. The top candidate architectures are then distilled from the teacher on a small proxy set. Finally the architecture(s) with the highest reward is selected, and distilled on the full downstream task training set. When distilling on the MNLI task, our KD-NAS model produces a 2 point improvement in accuracy on GLUE tasks with equivalent GPU latency with respect to a hand-crafted student architecture available in the literature. Using Knowledge Distillation, this model also achieves a 1.4x speedup in GPU Latency (3.2x speedup on CPU) with respect to a BERT-Base Teacher, while maintaining 97% performance on GLUE Tasks (without CoLA). We also obtain an architecture with equivalent performance as the hand-crafted student model on the GLUE benchmark, but with a 15% speedup in GPU latency (20% speedup in CPU latency) and 0.8 times the number of parameters | ['Bishwaranjan Bhattacharjee', 'Yousef El-Kurdi', 'Rameswar Panda', 'Michele Merler', 'Takuma Udagawa', 'Aashka Trivedi'] | 2023-03-16 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-7.67397061e-02 2.51893878e-01 -1.85036406e-01 -1.46065846e-01
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b5aa0d1c-fcd8-429d-9814-baadb4e7f36c | multi-label-product-categorization-using | 1907.00420 | null | https://arxiv.org/abs/1907.00420v2 | https://arxiv.org/pdf/1907.00420v2.pdf | Multi-Label Product Categorization Using Multi-Modal Fusion Models | In this study, we investigated multi-modal approaches using images, descriptions, and titles to categorize e-commerce products on Amazon. Specifically, we examined late fusion models, where the modalities are fused at the decision level. Products were each assigned multiple labels, and the hierarchy in the labels were flattened and filtered. For our individual baseline models, we modified a CNN architecture to classify the description and title, and then modified Keras' ResNet-50 to classify the images, achieving $F_1$ scores of 77.0%, 82.7%, and 61.0%, respectively. In comparison, our tri-modal late fusion model can classify products more effectively than single modal models can, improving the $F_1$ score to 88.2%. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label classification problems. | ['Pasawee Wirojwatanakul', 'Artit Wangperawong'] | 2019-06-30 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 6.85878992e-02 -9.72998813e-02 -4.11131799e-01 -4.75129038e-01
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8e154058-a854-476e-9f5a-74925d1f96ed | iiitt-dravidian-codemix-fire2021 | 2111.07906 | null | https://arxiv.org/abs/2111.07906v1 | https://arxiv.org/pdf/2111.07906v1.pdf | IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages | Sentiment analysis of social media posts and comments for various marketing and emotional purposes is gaining recognition. With the increasing presence of code-mixed content in various native languages, there is a need for ardent research to produce promising results. This research paper bestows a tiny contribution to this research in the form of sentiment analysis of code-mixed social media comments in the popular Dravidian languages Kannada, Tamil and Malayalam. It describes the work for the shared task conducted by Dravidian-CodeMix at FIRE 2021 by employing pre-trained models like ULMFiT and multilingual BERT fine-tuned on the code-mixed dataset, transliteration (TRAI) of the same, English translations (TRAA) of the TRAI data and the combination of all the three. The results are recorded in this research paper where the best models stood 4th, 5th and 10th ranks in the Tamil, Kannada and Malayalam tasks respectively. | ['Senthil Kumar B', 'Bharathi B', 'Karthik Puranik'] | 2021-11-15 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-4.68632579e-01 -2.83943892e-01 -1.41368136e-01 -3.70309949e-01
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e6f39d36-1a2e-42ac-8485-c3d18559f53b | dynamically-updating-event-representations | null | null | https://aclanthology.org/2020.findings-emnlp.121 | https://aclanthology.org/2020.findings-emnlp.121.pdf | Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning | Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two strong transfer learning baselines on both the English and Japanese data. | ['Sadao Kurohashi', 'Ichiro Kobayashi', 'Masayuki Asahara', 'Fei Cheng'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['temporal-relation-classification'] | ['natural-language-processing'] | [-2.43860468e-01 4.14497778e-02 -6.54351175e-01 -2.81593174e-01
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03b205f9-a080-4b14-972e-8f46f4f0f8f2 | deepstack-expert-level-artificial | 1701.01724 | null | http://arxiv.org/abs/1701.01724v3 | http://arxiv.org/pdf/1701.01724v3.pdf | DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker | Artificial intelligence has seen several breakthroughs in recent years, with
games often serving as milestones. A common feature of these games is that
players have perfect information. Poker is the quintessential game of imperfect
information, and a longstanding challenge problem in artificial intelligence.
We introduce DeepStack, an algorithm for imperfect information settings. It
combines recursive reasoning to handle information asymmetry, decomposition to
focus computation on the relevant decision, and a form of intuition that is
automatically learned from self-play using deep learning. In a study involving
44,000 hands of poker, DeepStack defeated with statistical significance
professional poker players in heads-up no-limit Texas hold'em. The approach is
theoretically sound and is shown to produce more difficult to exploit
strategies than prior approaches. | ['Nolan Bard', 'Viliam Lisý', 'Neil Burch', 'Matej Moravčík', 'Michael Bowling', 'Kevin Waugh', 'Trevor Davis', 'Martin Schmid', 'Dustin Morrill', 'Michael Johanson'] | 2017-01-06 | null | null | null | null | ['game-of-poker'] | ['playing-games'] | [-3.16841424e-01 4.96543467e-01 -4.68130440e-01 6.97572716e-03
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f3055732-0c30-45c7-9fa4-b0e835484e7f | visual-motion-analysis-of-the-player-s-finger | 2303.12697 | null | https://arxiv.org/abs/2303.12697v1 | https://arxiv.org/pdf/2303.12697v1.pdf | Visual motion analysis of the player's finger | This work is about the extraction of the motion of fingers, in their three articulations, of a keyboard player from a video sequence. The relevance of the problem involves several aspects, in fact, the extraction of the movements of the fingers may be used to compute the keystroke efficiency and individual joint contributions, as showed by Werner Goebl and Caroline Palmer in the paper 'Temporal Control and Hand Movement Efficiency in Skilled Music Performance'. Those measures are directly related to the precision in timing and force measures. A very good approach to the hand gesture recognition problem has been presented in the paper ' Real-Time Hand Gesture Recognition Using Finger Segmentation'. Detecting the keys pressed on a keyboard is a task that can be complex because of the shadows that can degrade the quality of the result and possibly cause the detection of not pressed keys. Among the several approaches that already exist, a great amount of them is based on the subtraction of frames in order to detect the movements of the keys caused by their pressure. Detecting the keys that are pressed could be useful to automatically evaluate the performance of a pianist or to automatically write sheet music of the melody that is being played. | ['Marco Costanzo'] | 2023-02-24 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.62394780e-01 -4.99316335e-01 8.75098929e-02 3.31365734e-01
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10cb75e2-bec8-4d38-9ce4-8781d89d1e14 | audioclip-extending-clip-to-image-text-and | 2106.13043 | null | https://arxiv.org/abs/2106.13043v1 | https://arxiv.org/pdf/2106.13043v1.pdf | AudioCLIP: Extending CLIP to Image, Text and Audio | In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models. In this work, we present an extension of the CLIP model that handles audio in addition to text and images. Our proposed model incorporates the ESResNeXt audio-model into the CLIP framework using the AudioSet dataset. Such a combination enables the proposed model to perform bimodal and unimodal classification and querying, while keeping CLIP's ability to generalize to unseen datasets in a zero-shot inference fashion. AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90.07% on the UrbanSound8K and 97.15% on the ESC-50 datasets. Further it sets new baselines in the zero-shot ESC-task on the same datasets 68.78% and 69.40%, respectively). Finally, we also assess the cross-modal querying performance of the proposed model as well as the influence of full and partial training on the results. For the sake of reproducibility, our code is published. | ['Andreas Dengel', 'Jörn Hees', 'Federico Raue', 'Andrey Guzhov'] | 2021-06-24 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.57218531e-01 -4.01958227e-01 3.02840352e-01 -2.94981837e-01
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30681068-f98e-482d-815b-ce84da37cbda | more-informed-random-sample-consensus | 2011.09116 | null | https://arxiv.org/abs/2011.09116v1 | https://arxiv.org/pdf/2011.09116v1.pdf | More Informed Random Sample Consensus | Random sample consensus (RANSAC) is a robust model-fitting algorithm. It is widely used in many fields including image-stitching and point cloud registration. In RANSAC, data is uniformly sampled for hypothesis generation. However, this uniform sampling strategy does not fully utilize all the information on many problems. In this paper, we propose a method that samples data with a L\'{e}vy distribution together with a data sorting algorithm. In the hypothesis sampling step of the proposed method, data is sorted with a sorting algorithm we proposed, which sorts data based on the likelihood of a data point being in the inlier set. Then, hypotheses are sampled from the sorted data with L\'{e}vy distribution. The proposed method is evaluated on both simulation and real-world public datasets. Our method shows better results compared with the uniform baseline method. | ['YangQuan Chen', 'Guoxiang Zhang'] | 2020-11-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.97456901e-02 -5.81645250e-01 -9.26164091e-02 -3.08303714e-01
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09aa3939-3f99-4297-b2b9-af58328a4043 | learning-to-generate-code-comments-from-class | 2103.13426 | null | https://arxiv.org/abs/2103.13426v2 | https://arxiv.org/pdf/2103.13426v2.pdf | Learning to Generate Code Comments from Class Hierarchies | Descriptive code comments are essential for supporting code comprehension and maintenance. We propose the task of automatically generating comments for overriding methods. We formulate a novel framework which accommodates the unique contextual and linguistic reasoning that is required for performing this task. Our approach features: (1) incorporating context from the class hierarchy; (2) conditioning on learned, latent representations of specificity to generate comments that capture the more specialized behavior of the overriding method; and (3) unlikelihood training to discourage predictions which do not conform to invariant characteristics of the comment corresponding to the overridden method. Our experiments show that the proposed approach is able to generate comments for overriding methods of higher quality compared to prevailing comment generation techniques. | ['Milos Gligoric', 'Junyi Jessy Li', 'Raymond J. Mooney', 'Pengyu Nie', 'Sheena Panthaplackel', 'Jiyang Zhang'] | 2021-03-24 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 6.62894666e-01 6.42667055e-01 -1.83270857e-01 -4.99275953e-01
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34f5ccfe-efe1-4bbe-835b-f79373642374 | fair-k-centers-via-maximum-matching | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | Fair k-Centers via Maximum Matching | The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent history. In data summarization, where a much smaller subset of a data set is chosen to represent the whole of the data, fairness can be introduced by guaranteeing each "demographic group" a specific portion of the representative subset. Specifically, this paper examines this fair variant of the k-centers problem, where a subset of the data with cardinality k is chosen to minimize distance to the rest of the data. Previous papers working on this problem presented both a 3-approximation algorithm with a super-linear runtime and a linear-time algorithm whose approximation factor is exponential in the number of demographic groups. This paper combines the best parts of each algorithm , by presenting a linear-time algorithm with a guaranteed 3-approximation factor, and provides empirical evidence of both the algorithm's runtime and effectiveness. | ['Thy Nguyen', 'Huy Nguyen', 'Matthew Jones'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4221-Paper.pdf | icml-2020-1 | ['data-summarization'] | ['miscellaneous'] | [ 3.71724889e-02 4.39811826e-01 -6.11489296e-01 -5.06375432e-01
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4e1d602a-e279-484a-be7a-578ab7a1d8de | de-biasing-facial-detection-system-using-vae | 2204.09556 | null | https://arxiv.org/abs/2204.09556v1 | https://arxiv.org/pdf/2204.09556v1.pdf | De-biasing facial detection system using VAE | Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society. There are many reasons behind a system being biased. The bias can be due to the algorithm we are using for our problem or may be due to the dataset we are using, having some features over-represented in it. In the face detection system bias due to the dataset is majorly seen. Sometimes models learn only features that are over-represented in data and ignore rare features from data which results in being biased toward those over-represented features. In real life, these biased systems are dangerous to society. The proposed approach uses generative models which are best suited for learning underlying features(latent variables) from the dataset and by using these learned features models try to reduce the threats which are there due to bias in the system. With the help of an algorithm, the bias present in the dataset can be removed. And then we train models on two datasets and compare the results. | ['Prof. Tanuja Pattanshetti', 'Kajal Kumbharkar', 'Siddhant V. Kandge', 'Vedant V. Kandge'] | 2022-04-16 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 3.08775187e-01 2.40258917e-01 -2.20510304e-01 -4.54522222e-01
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0501b2bd-5c9c-40b7-9820-7c6e07e49d14 | resolution-robust-large-mask-inpainting-with | 2109.07161 | null | https://arxiv.org/abs/2109.07161v2 | https://arxiv.org/pdf/2109.07161v2.pdf | Resolution-robust Large Mask Inpainting with Fourier Convolutions | Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function. To alleviate this issue, we propose a new method called large mask inpainting (LaMa). LaMa is based on i) a new inpainting network architecture that uses fast Fourier convolutions (FFCs), which have the image-wide receptive field; ii) a high receptive field perceptual loss; iii) large training masks, which unlocks the potential of the first two components. Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g. completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter&time costs than the competitive baselines. The code is available at \url{https://github.com/saic-mdal/lama}. | ['Victor Lempitsky', 'Kiwoong Park', 'Harshith Goka', 'Naejin Kong', 'Aleksei Silvestrov', 'Arsenii Ashukha', 'Anastasia Remizova', 'Anton Mashikhin', 'Elizaveta Logacheva', 'Roman Suvorov'] | 2021-09-15 | null | null | null | null | ['seeing-beyond-the-visible'] | ['computer-vision'] | [ 2.64751822e-01 1.02061266e-02 -9.85263381e-03 -4.82333452e-02
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204f487e-addb-419c-8c17-4deb976915a5 | modselect-automatic-modality-selection-for | 2208.09414 | null | https://arxiv.org/abs/2208.09414v1 | https://arxiv.org/pdf/2208.09414v1.pdf | ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization | Modality selection is an important step when designing multimodal systems, especially in the case of cross-domain activity recognition as certain modalities are more robust to domain shift than others. However, selecting only the modalities which have a positive contribution requires a systematic approach. We tackle this problem by proposing an unsupervised modality selection method (ModSelect), which does not require any ground-truth labels. We determine the correlation between the predictions of multiple unimodal classifiers and the domain discrepancy between their embeddings. Then, we systematically compute modality selection thresholds, which select only modalities with a high correlation and low domain discrepancy. We show in our experiments that our method ModSelect chooses only modalities with positive contributions and consistently improves the performance on a Synthetic-to-Real domain adaptation benchmark, narrowing the domain gap. | ['Rainer Stiefelhagen', 'David Schneider', 'Alina Roitberg', 'Zdravko Marinov'] | 2022-08-19 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 3.96845847e-01 -1.47502214e-01 -3.25640410e-01 -3.51481616e-01
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37d817e3-4716-4ee3-99e7-eb315e11f832 | adversarial-examples-in-deep-learning-for | 2009.11911 | null | https://arxiv.org/abs/2009.11911v1 | https://arxiv.org/pdf/2009.11911v1.pdf | Adversarial Examples in Deep Learning for Multivariate Time Series Regression | Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safety-critical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance. | ['Gautam Raj Mode', 'Khaza Anuarul Hoque'] | 2020-09-24 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 4.49332632e-02 -2.93032289e-01 1.22139826e-02 3.27172987e-02
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1797759f-368f-4304-bd71-a9814b9654a7 | a-robust-and-generalized-framework-for | 2105.10651 | null | https://arxiv.org/abs/2105.10651v1 | https://arxiv.org/pdf/2105.10651v1.pdf | A Robust and Generalized Framework for Adversarial Graph Embedding | Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs. However, most existing methods usually randomly select the negative samples from the original graph to enhance the training data without considering the noise. In addition, most of these methods only focus on the explicit graph structures and cannot fully capture complex semantics of edges such as various relationships or asymmetry. In order to address these issues, we propose a robust and generalized framework for adversarial graph embedding based on generative adversarial networks. Inspired by generative adversarial network, we propose a robust and generalized framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution, and enables the discriminator and generator to jointly learn each node's robust and generalized representation. Based on this framework, we propose three models to handle three types of graph data and derive the corresponding optimization algorithms, i.e., UG-AGE and DG-AGE for undirected and directed homogeneous graphs, respectively, and HIN-AGE for heterogeneous information networks. Extensive experiments show that our methods consistently and significantly outperform existing state-of-the-art methods across multiple graph mining tasks, including link prediction, node classification, and graph reconstruction. | ['Lifang He', 'Philip S. Yu', 'Qingyun Sun', 'Shijie Zhu', 'Senzhang Wang', 'Hao Peng', 'Xingcheng Fu', 'JianXin Li'] | 2021-05-22 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 1.61947221e-01 4.11350608e-01 -3.21363419e-01 -1.64249748e-01
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683e9c87-9b15-496b-bc77-a9b710fddccd | a-groupwise-multilinear-correspondence | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Bolkart_A_Groupwise_Multilinear_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Bolkart_A_Groupwise_Multilinear_ICCV_2015_paper.pdf | A Groupwise Multilinear Correspondence Optimization for 3D Faces | Multilinear face models are widely used to model the space of human faces with expressions. For databases of 3D human faces of different identities performing multiple expressions, these statistical shape models decouple identity and expression variations. To compute a high-quality multilinear face model, the quality of the registration of the database of 3D face scans used for training is essential. Meanwhile, a multilinear face model can be used as an effective prior to register 3D face scans, which are typically noisy and incomplete. Inspired by the minimum description length approach, we propose the first method to jointly optimize a multilinear model and the registration of the 3D scans used for training. Given an initial registration, our approach fully automatically improves the registration by optimizing an objective function that measures the compactness of the multilinear model, resulting in a sparse model. We choose a continuous representation for each face shape that allows to use a quasi-Newton method in parameter space for optimization. We show that our approach is computationally significantly more efficient and leads to correspondences of higher quality than existing methods based on linear statistical models. This allows us to evaluate our approach on large standard 3D face databases and in the presence of noisy initializations. | ['Stefanie Wuhrer', 'Timo Bolkart'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['3d-face-modeling'] | ['computer-vision'] | [-1.67964593e-01 1.02986231e-01 -8.94551054e-02 -6.03256881e-01
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8a71de7c-75b4-4da7-9c5a-f3877e54429e | facies-classification-from-well-logs-using-an | 1706.00613 | null | http://arxiv.org/abs/1706.00613v1 | http://arxiv.org/pdf/1706.00613v1.pdf | Facies classification from well logs using an inception convolutional network | The idea to use automated algorithms to determine geological facies from well
logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent
and dramatic increase in research in the field of machine learning makes it a
good time to revisit the topic. Following an exercise proposed by Dubois et al.
(2007) and Hall (2016) we employ a modern type of deep convolutional network,
called \textit{inception network} (Szegedy et al., 2015), to tackle the
supervised classification task and we discuss the methodological limits of such
problem as well as further research opportunities. | ['Mathieu Rodriguez', 'Matthias Delescluse', 'Janis Keuper', 'Valentin Tschannen'] | 2017-06-02 | null | null | null | null | ['facies-classification'] | ['miscellaneous'] | [-6.00534193e-02 3.04464549e-01 5.90716302e-02 -4.26702231e-01
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159052dc-26be-476c-a4fd-d2a9832cfc68 | rethinking-style-transformer-by-energy-based | null | null | https://openreview.net/forum?id=rqONayCTQKl | https://openreview.net/pdf?id=rqONayCTQKl | Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model | Style control, content preservation, and fluency determine the quality of text style transfer models. To train on a nonparallel corpus, several existing approaches aim to deceive the style discriminator with an adversarial loss. However, adversarial training significantly degrades fluency compared to the other two metrics. In this work, we explain this phenomenon with the energy-based interpretation and leverage a pretrained language model to improve fluency. Specifically, we propose a novel approach of applying the pretrained language model to the text style transfer framework by restructuring the discriminator and the model itself, allowing the generator and the discriminator to also take advantage of the power of the pretrained model. We evaluate our model on four public benchmarks Amazon, Yelp, GYAFC, and Civil Comments and achieve state-of-the-art performance on the overall metrics. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.11644918e-01 9.34019499e-03 -2.22963821e-02 -4.15955037e-01
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26d81d5d-12f9-47a0-96cd-f031c231b33e | tackling-the-cocktail-fork-problem-for | 2212.07327 | null | https://arxiv.org/abs/2212.07327v1 | https://arxiv.org/pdf/2212.07327v1.pdf | Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks | Emulating the human ability to solve the cocktail party problem, i.e., focus on a source of interest in a complex acoustic scene, is a long standing goal of audio source separation research. Much of this research investigates separating speech from noise, speech from speech, musical instruments from each other, or sound events from each other. In this paper, we focus on the cocktail fork problem, which takes a three-pronged approach to source separation by separating an audio mixture such as a movie soundtrack or podcast into the three broad categories of speech, music, and sound effects (SFX - understood to include ambient noise and natural sound events). We benchmark the performance of several deep learning-based source separation models on this task and evaluate them with respect to simple objective measures such as signal-to-distortion ratio (SDR) as well as objective metrics that better correlate with human perception. Furthermore, we thoroughly evaluate how source separation can influence downstream transcription tasks. First, we investigate the task of activity detection on the three sources as a way to both further improve source separation and perform transcription. We formulate the transcription tasks as speech recognition for speech and audio tagging for music and SFX. We observe that, while the use of source separation estimates improves transcription performance in comparison to the original soundtrack, performance is still sub-optimal due to artifacts introduced by the separation process. Therefore, we thoroughly investigate how remixing of the three separated source stems at various relative levels can reduce artifacts and consequently improve the transcription performance. We find that remixing music and SFX interferences at a target SNR of 17.5 dB reduces speech recognition word error rate, and similar impact from remixing is observed for tagging music and SFX content. | ['Jonathan Le Roux', 'Zhong-Qiu Wang', 'Aswin Shanmugam Subramanian', 'Gordon Wichern', 'Darius Petermann'] | 2022-12-14 | null | null | null | null | ['audio-tagging', 'audio-source-separation', 'activity-detection'] | ['audio', 'audio', 'computer-vision'] | [ 4.23052132e-01 -5.17627060e-01 1.79923043e-01 1.17837086e-01
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399c58df-8c73-4055-961e-a05b02b6a708 | instructions-and-guide-causal-insights-for | 2208.12610 | null | https://arxiv.org/abs/2208.12610v2 | https://arxiv.org/pdf/2208.12610v2.pdf | NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education | In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests. | ['Cheng Zhang', 'Joel Jennings', 'Nick Pawlowski', 'Simon Woodhead', 'Craig Barton', 'Zichao Wang', 'Digory Smith', 'Wenbo Gong'] | 2022-08-17 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 3.16414088e-02 3.29028010e-01 1.21337429e-01 -2.61096627e-01
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fbe3ac12-3ca7-4f66-af4f-8d0ccce39aa9 | subsampling-generative-adversarial-networks | 1909.10670 | null | https://arxiv.org/abs/1909.10670v5 | https://arxiv.org/pdf/1909.10670v5.pdf | Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss | Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods---Discriminator Rejection Sampling (DRS) and Metropolis-Hastings GAN (MH-GAN)---were recently proposed, and their effectiveness in improving GANs was demonstrated on multiple datasets. However, DRS and MH-GAN are based on discriminator based density ratio estimation (DRE) methods, so they may not work well if the discriminator in the trained GAN is far from optimal. Moreover, they do not apply to some GANs (e.g., MMD-GAN). In this paper, we propose a novel Softplus (SP) loss for DRE. Based on it, we develop a sample-based DRE method in a feature space learned by a specially designed and pre-trained ResNet-34 (DRE-F-SP). We derive the rate of convergence of a density ratio model trained under the SP loss. Then, we propose three different density ratio subsampling methods (DRE-F-SP+RS, DRE-F-SP+MH, and DRE-F-SP+SIR) for GANs based on DRE-F-SP. Our subsampling methods do not rely on the optimality of the discriminator and are suitable for all types of GANs. We empirically show our subsampling approach can substantially outperform DRS and MH-GAN on a synthetic dataset and the CIFAR-10 dataset, using multiple GANs. | ['Xin Ding', 'Z. Jane Wang', 'William J. Welch'] | 2019-09-24 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 8.73836875e-02 -3.56035330e-03 -1.55491987e-02 -2.80547500e-01
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5d404ea0-aa9e-4911-b8c0-47a66b407dc7 | convolutional-recurrent-neural-networks-for-1 | 1609.04243 | null | http://arxiv.org/abs/1609.04243v3 | http://arxiv.org/pdf/1609.04243v3.pdf | Convolutional Recurrent Neural Networks for Music Classification | We introduce a convolutional recurrent neural network (CRNN) for music
tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local
feature extraction and recurrent neural networks for temporal summarisation of
the extracted features. We compare CRNN with three CNN structures that have
been used for music tagging while controlling the number of parameters with
respect to their performance and training time per sample. Overall, we found
that CRNNs show a strong performance with respect to the number of parameter
and training time, indicating the effectiveness of its hybrid structure in
music feature extraction and feature summarisation. | ['Kyunghyun Cho', 'Keunwoo Choi', 'Mark Sandler', 'George Fazekas'] | 2016-09-14 | null | null | null | null | ['music-classification'] | ['music'] | [ 1.47166073e-01 -1.29612964e-02 -3.07103425e-01 8.95456523e-02
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fea23833-0eaa-4306-8c6a-b425a91e8b86 | knowsemlm-a-knowledge-infused-semantic | null | null | https://aclanthology.org/K19-1051 | https://aclanthology.org/K19-1051.pdf | KnowSemLM: A Knowledge Infused Semantic Language Model | Story understanding requires developing expectations of what events come next in text. Prior knowledge {--} both statistical and declarative {--} is essential in guiding such expectations. While existing semantic language models (SemLM) capture event co-occurrence information by modeling event sequences as semantic frames, entities, and other semantic units, this paper aims at augmenting them with causal knowledge (i.e., one event is likely to lead to another). Such knowledge is modeled at the frame and entity level, and can be obtained either statistically from text or stated declaratively. The proposed method, KnowSemLM, infuses this knowledge into a semantic LM by joint training and inference, and is shown to be effective on both the event cloze test and story/referent prediction tasks. | ['Dan Roth', 'Haoruo Peng', 'Qiang Ning'] | 2019-11-01 | null | null | null | conll-2019-11 | ['cloze-test'] | ['natural-language-processing'] | [ 3.69735092e-01 5.38995206e-01 -5.45292616e-01 -7.31117606e-01
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92daea72-517d-4491-90c8-b301dd42a923 | aspect-based-sentiment-analysis-as-machine | null | null | https://aclanthology.org/2022.coling-1.217 | https://aclanthology.org/2022.coling-1.217.pdf | Aspect-based Sentiment Analysis as Machine Reading Comprehension | Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance. | ['Hai Zhao', 'Yifei Yang'] | null | null | null | null | coling-2022-10 | ['aspect-based-sentiment-analysis', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23025584e-01 2.43104592e-01 -2.33362228e-01 -9.46522057e-01
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3c381de6-b1fb-4636-8f3d-e18a6e1f1bb0 | hard-non-monotonic-attention-for-character | 1808.10024 | null | https://arxiv.org/abs/1808.10024v2 | https://arxiv.org/pdf/1808.10024v2.pdf | Hard Non-Monotonic Attention for Character-Level Transduction | Character-level string-to-string transduction is an important component of various NLP tasks. The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different alphabets. Recent approaches have used sequence-to-sequence models with an attention mechanism to learn which parts of the input string the model should focus on during the generation of the output string. Both soft attention and hard monotonic attention have been used, but hard non-monotonic attention has only been used in other sequence modeling tasks such as image captioning and has required a stochastic approximation to compute the gradient. In this work, we introduce an exact, polynomial-time algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings, showing that hard attention models can be viewed as neural reparameterizations of the classical IBM Model 1. We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention. | ['Pamela Shapiro', 'Shijie Wu', 'Ryan Cotterell'] | 2018-08-29 | hard-non-monotonic-attention-for-character-1 | https://aclanthology.org/D18-1473 | https://aclanthology.org/D18-1473.pdf | emnlp-2018-10 | ['hard-attention'] | ['methodology'] | [ 1.06285596e+00 1.98140576e-01 -1.14760250e-01 -4.23548609e-01
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9ba82f0e-302a-4316-ba92-176caf261a0a | leverage-points-in-modality-shifts-comparing | 2306.02348 | null | https://arxiv.org/abs/2306.02348v1 | https://arxiv.org/pdf/2306.02348v1.pdf | Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations | Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models. While different embeddings exhibit different applicability and performance on downstream tasks, little is known about the systematic representation differences attributed to the visual modality. Our paper compares word embeddings from three vision-and-language models (CLIP, OpenCLIP and Multilingual CLIP) and three text-only models, with static (FastText) as well as contextual representations (multilingual BERT; XLM-RoBERTa). This is the first large-scale study of the effect of visual grounding on language representations, including 46 semantic parameters. We identify meaning properties and relations that characterize words whose embeddings are most affected by the inclusion of visual modality in the training data; that is, points where visual grounding turns out most important. We find that the effect of visual modality correlates most with denotational semantic properties related to concreteness, but is also detected for several specific semantic classes, as well as for valence, a sentiment-related connotational property of linguistic expressions. | ['Denis Paperno', 'Lisa Bylinina', 'Aleksey Tikhonov'] | 2023-06-04 | null | null | null | null | ['visual-grounding', 'word-embeddings'] | ['computer-vision', 'methodology'] | [-2.93737262e-01 -3.42922732e-02 -2.92264581e-01 -3.91861081e-01
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be235c20-bd0a-403c-8adf-2b70d85f49b6 | few-shot-dialogue-summarization-via-skeleton | 2305.12077 | null | https://arxiv.org/abs/2305.12077v1 | https://arxiv.org/pdf/2305.12077v1.pdf | Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer | In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massive annotated data from other downstream tasks and then performed prompt transfer in prompt tuning so as to enable cross-task knowledge transfer. However, existing general-purpose prompt transfer techniques lack consideration for dialogue-specific information. In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information. To automatically extract dialogue skeletons as supervised training data for skeleton generation, we design a novel approach with perturbation-based probes requiring neither annotation effort nor domain knowledge. Training the model on such skeletons can also help preserve model capability during prompt transfer. Our method significantly outperforms existing baselines. In-depth analyses demonstrate the effectiveness of our method in facilitating cross-task knowledge transfer in few-shot dialogue summarization. | ['Mark Riedl', 'Ani Nenkova', 'Kanak Mahadik', 'Ruiyi Zhang', 'Handong Zhao', 'Junda Wu', 'Haoliang Wang', 'Tong Yu', 'Kaige Xie'] | 2023-05-20 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 0.48066258 0.5848817 -0.29713798 -0.34262708 -1.2591066 -0.5243595
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bc9c938a-12a6-4079-b682-1ef3ee6ad8ab | a-survey-on-deep-learning-for-skin-lesion | 2206.00356 | null | https://arxiv.org/abs/2206.00356v3 | https://arxiv.org/pdf/2206.00356v3.pdf | A Survey on Deep Learning for Skin Lesion Segmentation | Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online at https://github.com/sfu-mial/skin-lesion-segmentation-survey. | ['Catarina Barata', 'Alceu Bissoto', 'Kumar Abhishek', 'Ghassan Hamarneh', 'M. Emre Celebi', 'Eduardo Valle', 'Sandra Avila', 'Zahra Mirikharaji'] | 2022-06-01 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.86124659e-01 3.34008560e-02 -3.23834062e-01 -9.20213163e-02
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dd95a0f0-3878-4261-ac48-0a4beae08abe | benchmarking-aggression-identification-in | null | null | https://aclanthology.org/W18-4401 | https://aclanthology.org/W18-4401.pdf | Benchmarking Aggression Identification in Social Media | In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. For this task, the participants were provided with a dataset of 15,000 aggression-annotated Facebook Posts and Comments each in Hindi (in both Roman and Devanagari script) and English for training and validation. For testing, two different sets - one from Facebook and another from a different social media - were provided. A total of 130 teams registered to participate in the task, 30 teams submitted their test runs, and finally 20 teams also sent their system description paper which are included in the TRAC workshop proceedings. The best system obtained a weighted F-score of 0.64 for both Hindi and English on the Facebook test sets, while the best scores on the surprise set were 0.60 and 0.50 for English and Hindi respectively. The results presented in this report depict how challenging the task is. The positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest in this topic. | ['Marcos Zampieri', 'Atul Kr. Ojha', 'Shervin Malmasi', 'Ritesh Kumar'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-4.42068368e-01 2.63108313e-01 3.40483129e-01 -4.21999842e-01
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1a5c64bd-241c-4168-9065-4ce0c729fc35 | graph-anomaly-detection-with-unsupervised | 2210.09535 | null | https://arxiv.org/abs/2210.09535v2 | https://arxiv.org/pdf/2210.09535v2.pdf | Graph Anomaly Detection with Unsupervised GNNs | Graph-based anomaly detection finds numerous applications in the real-world. Thus, there exists extensive literature on the topic that has recently shifted toward deep detection models due to advances in deep learning and graph neural networks (GNNs). A vast majority of prior work focuses on detecting node/edge/subgraph anomalies within a single graph, with much less work on graph-level anomaly detection in a graph database. This work aims to fill two gaps in the literature: We (1) design GLAM, an end-to-end graph-level anomaly detection model based on GNNs, and (2) focus on unsupervised model selection, which is notoriously hard due to lack of any labels, yet especially critical for deep NN based models with a long list of hyper-parameters. Further, we propose a new pooling strategy for graph-level embedding, called MMD-pooling, that is geared toward detecting distribution anomalies which has not been considered before. Through extensive experiments on 15 real-world datasets, we show that (i) GLAM outperforms node-level and two-stage (i.e. not end-to-end) baselines, and (ii) model selection picks a significantly more effective model than expectation (i.e. average) -- without using any labels -- among candidates with otherwise large variation in performance. | ['Leman Akoglu', 'Arvind Srinivasan', 'Saurabh Sawlani', 'Lingxiao Zhao'] | 2022-10-18 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.56798437e-01 1.66294098e-01 1.64314434e-02 -6.26580492e-02
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814226dc-f945-4d11-94ea-a1fa4fb73023 | museclir-a-multiple-senses-and-cross-lingual | null | null | https://aclanthology.org/2022.coling-1.96 | https://aclanthology.org/2022.coling-1.96.pdf | MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset | This paper addresses a deficiency in existing cross-lingual information retrieval (CLIR) datasets and provides a robust evaluation of CLIR systems’ disambiguation ability. CLIR is commonly tackled by combining translation and traditional IR. Due to translation ambiguity, the problem of ambiguity is worse in CLIR than in monolingual IR. But existing auto-generated CLIR datasets are dominated by searches for named entity mentions, which does not provide a good measure for disambiguation performance, as named entity mentions can often be transliterated across languages and tend not to have multiple translations. Therefore, we introduce a new evaluation dataset (MuSeCLIR) to address this inadequacy. The dataset focusses on polysemous common nouns with multiple possible translations. MuSeCLIR is constructed from multilingual Wikipedia and supports searches on documents written in European (French, German, Italian) and Asian (Chinese, Japanese) languages. We provide baseline statistical and neural model results on MuSeCLIR which show that MuSeCLIR has a higher requirement on the ability of systems to disambiguate query terms. | ['David Weir', 'Julie Weeds', 'Wing Yan Li'] | null | null | null | null | coling-2022-10 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.43994141e-01 -2.34978318e-01 -6.14111900e-01 -7.74855018e-02
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90a46c1b-77cb-43c7-9a70-c10200cbfa66 | ustc-nelslip-at-semeval-2023-task-2 | 2305.02517 | null | https://arxiv.org/abs/2305.02517v1 | https://arxiv.org/pdf/2305.02517v1.pdf | USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER | This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at both the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to XLM-R with a gazetteer built from Wikidata, and shows great generalization ability across different tracks. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task. | ['Xiaoyi Zhao', 'Quan Liu', 'Zhen-Hua Ling', 'Jiajun Qi', 'Jia-Chen Gu', 'Jun-Yu Ma'] | 2023-05-04 | null | null | null | null | ['named-entity-recognition-ner', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-3.71467084e-01 6.68658540e-02 1.17783345e-01 -6.16402268e-01
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130509a5-a37a-4000-8326-cc02c96f31a5 | recurrent-trend-predictive-neural-network-for | null | null | https://ieeexplore.ieee.org/document/9451553 | https://ieeexplore.ieee.org/document/9451553 | Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection | We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall- τ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications. | ['Osman Yildiz', 'Cüneyt Güzeliş', 'Mert Nakıp'] | 2021-06-10 | null | null | null | ieee-access-2021-6 | ['fire-detection', 'time-series-regression'] | ['time-series', 'time-series'] | [ 2.59151459e-01 -3.11790138e-01 -1.99396700e-01 -2.80702382e-01
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c429ffd9-2d81-415c-9fdd-1691fcfd3eef | rethinking-video-vits-sparse-video-tubes-for | 2212.03229 | null | https://arxiv.org/abs/2212.03229v1 | https://arxiv.org/pdf/2212.03229v1.pdf | Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning | We present a simple approach which can turn a ViT encoder into an efficient video model, which can seamlessly work with both image and video inputs. By sparsely sampling the inputs, the model is able to do training and inference from both inputs. The model is easily scalable and can be adapted to large-scale pre-trained ViTs without requiring full finetuning. The model achieves SOTA results and the code will be open-sourced. | ['Anelia Angelova', 'Weicheng Kuo', 'AJ Piergiovanni'] | 2022-12-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Piergiovanni_Rethinking_Video_ViTs_Sparse_Video_Tubes_for_Joint_Image_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision'] | [ 1.72923997e-01 2.19354749e-01 -4.61225748e-01 -3.93001318e-01
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0cc8afaa-df1b-4341-a357-918aaae00d93 | distribution-aligned-multimodal-and-multi | 2006.01431 | null | https://arxiv.org/abs/2006.01431v1 | https://arxiv.org/pdf/2006.01431v1.pdf | Distribution Aligned Multimodal and Multi-Domain Image Stylization | Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously. In this paper, we propose a unified framework for multimodal and multi-domain style transfer with the support of both exemplar-based reference and randomly sampled guidance. The key component of our method is a novel style distribution alignment module that eliminates the explicit distribution gaps between various style domains and reduces the risk of mode collapse. The multimodal diversity is ensured by either guidance from multiple images or random style code, while the multi-domain controllability is directly achieved by using a domain label. We validate our proposed framework on painting style transfer with a variety of different artistic styles and genres. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate that our method can generate high-quality results of multi-domain styles and multimodal instances with reference style guidance or random sampled style. | ['Wei-Ming Dong', 'Minxuan Lin', 'Fan Tang', 'Xiao Li', 'Chongyang Ma', 'Changsheng Xu'] | 2020-06-02 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 4.65551764e-01 -2.87227839e-01 2.74742711e-02 1.09714679e-02
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0564f40c-7fe6-4920-8b19-2f1b8835d58e | exif-as-language-learning-cross-modal | 2301.04647 | null | https://arxiv.org/abs/2301.04647v4 | https://arxiv.org/pdf/2301.04647v4.pdf | EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata | We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions "zero shot" by clustering the visual embeddings for all of the patches within an image. | ['Andrew Owens', 'Ayush Shrivastava', 'Chenhao Zheng'] | 2023-01-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_EXIF_As_Language_Learning_Cross-Modal_Associations_Between_Images_and_Camera_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-forensics'] | ['computer-vision'] | [ 1.69686407e-01 1.48037225e-01 -4.81732339e-01 -6.12531185e-01
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e08c7ed0-2755-4f3e-9f9b-77e7eab72054 | ffdnet-toward-a-fast-and-flexible-solution | 1710.04026 | null | http://arxiv.org/abs/1710.04026v2 | http://arxiv.org/pdf/1710.04026v2.pdf | FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising | Due to the fast inference and good performance, discriminative learning
methods have been widely studied in image denoising. However, these methods
mostly learn a specific model for each noise level, and require multiple models
for denoising images with different noise levels. They also lack flexibility to
deal with spatially variant noise, limiting their applications in practical
denoising. To address these issues, we present a fast and flexible denoising
convolutional neural network, namely FFDNet, with a tunable noise level map as
the input. The proposed FFDNet works on downsampled sub-images, achieving a
good trade-off between inference speed and denoising performance. In contrast
to the existing discriminative denoisers, FFDNet enjoys several desirable
properties, including (i) the ability to handle a wide range of noise levels
(i.e., [0, 75]) effectively with a single network, (ii) the ability to remove
spatially variant noise by specifying a non-uniform noise level map, and (iii)
faster speed than benchmark BM3D even on CPU without sacrificing denoising
performance. Extensive experiments on synthetic and real noisy images are
conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The
results show that FFDNet is effective and efficient, making it highly
attractive for practical denoising applications. | ['WangMeng Zuo', 'Lei Zhang', 'Kai Zhang'] | 2017-10-11 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 1.19430110e-01 -8.33353698e-01 2.13817924e-01 -3.25615942e-01
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d384c582-4b7f-4cc8-9bfd-91b460496011 | lens-to-lens-bokeh-effect-transformation | null | null | https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/html/Conde_Lens-to-Lens_Bokeh_Effect_Transformation._NTIRE_2023_Challenge_Report_CVPRW_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/papers/Conde_Lens-to-Lens_Bokeh_Effect_Transformation._NTIRE_2023_Challenge_Report_CVPRW_2023_paper.pdf | Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report | We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical properties. The aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens setups. To the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation. | ['JiXiang Niu', 'Yiqing Xu', 'Baoliang Chen', 'Yuxuan Zhao', 'Thomas B. Schön', 'Jens Sjölund', 'Zheng Zhao', 'Fredrik K. Gustafsson', 'Ziwei Luo', 'Vishal Monga', 'Amirsaeed Yazdani', 'Trung Hoang', 'Haichuan Zhang', 'Siyuan Lai', 'Wenyi Lian', 'Zhihao Yang', 'Xinchao Wang', 'Michael Bi Mi', 'Yongcheng Jing', 'Songhua Liu', 'Xingyi Yang', 'Zigeng Chen', 'Chaowei Liu', 'Ke Xian', 'Zhiguo Cao', 'Liao Shen', 'Huiqiang Sun', 'Xianrui Luo', 'Chengxin Liu', 'Zhiyu Pan', 'Juewen Peng', 'Fan Wang', 'Jinlong Wu', 'Dafeng Zhang', 'Xiangyu Kong', 'Radu Timofte', 'Tom E. Bishop', 'Tim Seizinger', 'Manuel Kolmet', 'Marcos V. Conde'] | 2023-06-01 | null | null | null | cvprw-2023-6 | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 4.30285156e-01 -6.43809885e-03 3.21563035e-01 -4.13316816e-01
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240e5379-fd42-4de4-b5de-40d514878901 | wukong-reader-multi-modal-pre-training-for | 2212.09621 | null | https://arxiv.org/abs/2212.09621v1 | https://arxiv.org/pdf/2212.09621v1.pdf | Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding | Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability. | ['Qun Liu', 'Xin Jiang', 'Jiansheng Wei', 'Lu Hou', 'Liangwei Wang', 'Rongfu Zheng', 'Nian Xie', 'Shuang Liu', 'Wentao Li', 'Xiaojun Meng', 'Zhiguang Liu', 'Haoli Bai'] | 2022-12-19 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 1.51756898e-01 -4.63404715e-01 -4.57723171e-01 -3.44112217e-01
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75e3caf7-8321-4707-9ca8-e6433148c7f1 | saliendet-a-saliency-based-feature | 2305.06940 | null | https://arxiv.org/abs/2305.06940v2 | https://arxiv.org/pdf/2305.06940v2.pdf | SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving | Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder autonomous vehicles from driving beyond the operational domain. To addresss this issue, we propose a saliency-based OD algorithm (SalienDet) to detect unknown objects. Our SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation. Moreover, we design a dataset relabeling approach to differentiate the unknown objects from all objects in training sample set to achieve Open-World Detection. To validate the performance of SalienDet, we evaluate SalienDet on KITTI, nuScenes, and BDD datasets, and the result indicates that it outperforms existing algorithms for unknown object detection. Notably, SalienDet can be easily adapted for incremental learning in open-world detection tasks. The project page is \url{https://github.com/dingmike001/SalienDet-Open-Detection.git}. | ['Azim Eskandarian', 'Ce Zhang', 'Ning Ding'] | 2023-05-11 | null | null | null | null | ['object-proposal-generation', 'incremental-learning'] | ['computer-vision', 'methodology'] | [-1.78534746e-01 1.26845419e-01 -2.87484795e-01 -2.29281962e-01
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7219c313-eea6-465d-81d7-11a76887b2c0 | conversational-memory-network-for-emotion | null | null | https://aclanthology.org/N18-1193 | https://aclanthology.org/N18-1193.pdf | Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos | Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose a deep neural framework, termed Conversational Memory Network (CMN), which leverages contextual information from the conversation history. In particular, CMN uses multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. These memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show a significant improvement of 3 − 4{\%} in accuracy over the state of the art. | ['Louis-Philippe Morency', 'Erik Cambria', 'Soujanya Poria', 'Roger Zimmermann', 'Devamanyu Hazarika', 'Amir Zadeh'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.36420250e-01 1.01460077e-01 -1.16670519e-01 -7.20038116e-01
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2188cc46-d696-4a39-b5c0-9775c6666d36 | exploring-word-segmentation-and-medical | null | null | https://aclanthology.org/2021.bionlp-1.23 | https://aclanthology.org/2021.bionlp-1.23.pdf | Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts | Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs. One challenge to these tasks is the lack of medical domain datasets with high-quality annotations, especially medical-related tags that reveal the characteristics of Chinese EMRs. In this paper, we collected a Chinese EMR corpus, namely, ACEMR, with human annotations for Chinese word segmentation and EMR-related tags. On the ACEMR corpus, we run well-known models (i.e., BiLSTM, BERT, and ZEN) and existing state-of-the-art systems (e.g., WMSeg and TwASP) for CWS and medical concept recognition. Experimental results demonstrate the necessity of building a dedicated medical dataset and show that models that leverage extra resources achieve the best performance for both tasks, which provides certain guidance for future studies on model selection in the medical domain. | ['Yan Song', 'Xiang Wan', 'Song Wu', 'Tsung-Hui Chang', 'Yuanhe Tian', 'Yang Liu'] | null | null | null | null | naacl-bionlp-2021-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 6.12240791e-01 5.51148131e-02 -2.81488687e-01 -3.68811846e-01
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2ed9999f-fbb4-44eb-845f-b1c6b8babc05 | weakly-supervised-video-moment-retrieval-via | 1911.08199 | null | https://arxiv.org/abs/1911.08199v3 | https://arxiv.org/pdf/1911.08199v3.pdf | Weakly-Supervised Video Moment Retrieval via Semantic Completion Network | Video moment retrieval is to search the moment that is most relevant to the given natural language query. Existing methods are mostly trained in a fully-supervised setting, which requires the full annotations of temporal boundary for each query. However, manually labeling the annotations is actually time-consuming and expensive. In this paper, we propose a novel weakly-supervised moment retrieval framework requiring only coarse video-level annotations for training. Specifically, we devise a proposal generation module that aggregates the context information to generate and score all candidate proposals in one single pass. We then devise an algorithm that considers both exploitation and exploration to select top-K proposals. Next, we build a semantic completion module to measure the semantic similarity between the selected proposals and query, compute reward and provide feedbacks to the proposal generation module for scoring refinement. Experiments on the ActivityCaptions and Charades-STA demonstrate the effectiveness of our proposed method. | ['Qi. Wang', 'Huasheng Liu', 'Zhijie Lin', 'Zhou Zhao', 'Zhu Zhang'] | 2019-11-19 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.41701773e-01 -3.02849352e-01 -6.30291641e-01 -5.61004400e-01
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fa1e554e-a56e-41d8-870b-d6ce54472115 | koniq-10k-an-ecologically-valid-database-for | 1910.06180 | null | https://arxiv.org/abs/1910.06180v2 | https://arxiv.org/pdf/1910.06180v2.pdf | KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment | Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512x384). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image. | ['Vlad Hosu', 'Tamas Sziranyi', 'Hanhe Lin', 'Dietmar Saupe'] | 2019-10-14 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-3.18810105e-01 -2.82484621e-01 3.08730125e-01 -2.38262385e-01
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2f5fa5d1-77f1-4db2-9ba5-2751b9f4edae | improving-cross-lingual-word-embeddings-by | 1808.08780 | null | http://arxiv.org/abs/1808.08780v1 | http://arxiv.org/pdf/1808.08780v1.pdf | Improving Cross-Lingual Word Embeddings by Meeting in the Middle | Cross-lingual word embeddings are becoming increasingly important in
multilingual NLP. Recently, it has been shown that these embeddings can be
effectively learned by aligning two disjoint monolingual vector spaces through
linear transformations, using no more than a small bilingual dictionary as
supervision. In this work, we propose to apply an additional transformation
after the initial alignment step, which moves cross-lingual synonyms towards a
middle point between them. By applying this transformation our aim is to obtain
a better cross-lingual integration of the vector spaces. In addition, and
perhaps surprisingly, the monolingual spaces also improve by this
transformation. This is in contrast to the original alignment, which is
typically learned such that the structure of the monolingual spaces is
preserved. Our experiments confirm that the resulting cross-lingual embeddings
outperform state-of-the-art models in both monolingual and cross-lingual
evaluation tasks. | ['Steven Schockaert', 'Luis Espinosa-Anke', 'Jose Camacho-Collados', 'Yerai Doval'] | 2018-08-27 | improving-cross-lingual-word-embeddings-by-1 | https://aclanthology.org/D18-1027 | https://aclanthology.org/D18-1027.pdf | emnlp-2018-10 | ['multilingual-nlp'] | ['natural-language-processing'] | [-3.32284302e-01 -1.49706721e-01 -4.59318310e-01 -2.16759980e-01
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4b64909d-1174-4e96-ad0e-f6bfc9ad57db | netgpt-generative-pretrained-transformer-for | 2304.09513 | null | https://arxiv.org/abs/2304.09513v2 | https://arxiv.org/pdf/2304.09513v2.pdf | NetGPT: Generative Pretrained Transformer for Network Traffic | All data on the Internet are transferred by network traffic, thus accurately modeling network traffic can help improve network services quality and protect data privacy. Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as application classification, attack detection and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model NetGPT for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. With diverse traffic datasets from encrypted software, DNS, private industrial protocols and cryptocurrency mining, expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks on traffic datasets, and outperform state-of-the-art baselines by a wide margin. | ['Yujun Zhang', 'Yequan Wang', 'Chungang Lin', 'Xuying Meng'] | 2023-04-19 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.99225563e-01 -4.56509501e-01 -4.28426176e-01 -5.35269022e-01
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dda06661-9e73-4cb2-ad51-e75adc3f2d48 | a-survey-on-using-gaze-behaviour-for-natural | 2112.15471 | null | https://arxiv.org/abs/2112.15471v2 | https://arxiv.org/pdf/2112.15471v2.pdf | A Survey on Using Gaze Behaviour for Natural Language Processing | Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour is a costly task, both in terms of time and money. Hence, in this paper, we focus on research done to alleviate the need for recording gaze behaviour at run time. We also mention different eye tracking corpora in multiple languages, which are currently available and can be used in natural language processing. We conclude our paper by discussing applications in a domain - education - and how learning gaze behaviour can help in solving the tasks of complex word identification and automatic essay grading. | ['Pushpak Bhattacharyya', 'Abhijit Mishra', 'Diptesh Kanojia', 'Sandeep Mathias'] | 2021-12-21 | null | null | null | null | ['complex-word-identification'] | ['natural-language-processing'] | [ 2.55975157e-01 2.94411361e-01 1.35493532e-01 -3.29279333e-01
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4.35831219e-01 -7.29635775e-01 -1.72696277e-01 4.12863284e-01] | [11.15761661529541, 9.337653160095215] |
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