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5f181d65-ae02-4235-b67a-8015612d9b15 | dependency-induction-through-the-lens-of | 2109.09790 | null | https://arxiv.org/abs/2109.09790v1 | https://arxiv.org/pdf/2109.09790v1.pdf | Dependency Induction Through the Lens of Visual Perception | Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar induction. However, as compared to dependency grammars, constituency grammars do not provide a straightforward way to incorporate visual information without enforcing language-specific heuristics. In this paper, we propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based heuristic to jointly learn constituency-structure and dependency-structure grammars. Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50\% as compared to state-of-the-art models trained on pure text. Next, we propose an extension of our model that leverages both word concreteness and visual semantic role labels in constituency and dependency parsing. Our experiments show that the proposed extension outperforms the current state-of-the-art visually grounded models in constituency parsing even with a smaller grammar size. | ['Graham Neubig', 'Yonatan Bisk', 'Xinyu Wang', 'Junxian He', 'Hao Zhu', 'Shruti Rijhwani', 'Ruisi Su'] | 2021-09-20 | null | https://aclanthology.org/2021.conll-1.2 | https://aclanthology.org/2021.conll-1.2.pdf | conll-emnlp-2021-11 | ['constituency-grammar-induction', 'constituency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.02798367e-02 6.98367000e-01 -1.78203613e-01 -2.76172340e-01
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5e784b2c-df81-49d7-b1d1-1e9bfbf40192 | uncertainty-aware-temporal-self-learning-uats | 2104.03840 | null | https://arxiv.org/abs/2104.03840v1 | https://arxiv.org/pdf/2104.03840v1.pdf | Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond | Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentation of TZ, PZ, distal prostatic urethra (DPU) and the anterior fibromuscular stroma (AFS), the task becomes more challenging and has not yet been solved at the level of human performance. One reason might be the insufficient amount of labeled data for supervised training. Therefore, we propose to apply a semi-supervised learning (SSL) technique named uncertainty-aware temporal self-learning (UATS) to overcome the expensive and time-consuming manual ground truth labeling. We combine the SSL techniques temporal ensembling and uncertainty-guided self-learning to benefit from unlabeled images, which are often readily available. Our method significantly outperforms the supervised baseline and obtained a Dice coefficient (DC) of up to 78.9% , 87.3%, 75.3%, 50.6% for TZ, PZ, DPU and AFS, respectively. The obtained results are in the range of human inter-rater performance for all structures. Moreover, we investigate the method's robustness against noise and demonstrate the generalization capability for varying ratios of labeled data and on other challenging tasks, namely the hippocampus and skin lesion segmentation. UATS achieved superiority segmentation quality compared to the supervised baseline, particularly for minimal amounts of labeled data. | ['Marko Rak', 'Christian Hansen', 'Sebastian Stober', 'Martin Schostak', 'Daniel Schindele', 'Suhita Ghosh', 'Anneke Meyer'] | 2021-04-08 | null | null | null | null | ['prostate-zones-segmentation', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 4.32305306e-01 5.50623953e-01 -5.07244349e-01 -3.90367389e-01
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f6ff249a-ad06-4f0d-b262-b435a08cd84d | predicting-camera-viewpoint-improves-cross | 2004.03143 | null | https://arxiv.org/abs/2004.03143v1 | https://arxiv.org/pdf/2004.03143v1.pdf | Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation | Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets and its effect on cross-dataset generalization across a compendium of 5 pose datasets. We specifically focus on systematic differences in the distribution of camera viewpoints relative to a body-centered coordinate frame. Based on this observation, we propose an auxiliary task of predicting the camera viewpoint in addition to pose. We find that models trained to jointly predict viewpoint and pose systematically show significantly improved cross-dataset generalization. | ['Daeyun Shin', 'Charless C. Fowlkes', 'Zhe Wang'] | 2020-04-07 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 3.42871808e-02 -4.50867712e-01 -3.42852831e-01 -5.83260298e-01
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017f6fce-d855-4462-b0e3-e4fd43f59082 | learning-a-spatio-temporal-embedding-for-1 | 1912.08969 | null | https://arxiv.org/abs/1912.08969v1 | https://arxiv.org/pdf/1912.08969v1.pdf | Learning a Spatio-Temporal Embedding for Video Instance Segmentation | We present a novel embedding approach for video instance segmentation. Our method learns a spatio-temporal embedding integrating cues from appearance, motion, and geometry; a 3D causal convolutional network models motion, and a monocular self-supervised depth loss models geometry. In this embedding space, video-pixels of the same instance are clustered together while being separated from other instances, to naturally track instances over time without any complex post-processing. Our network runs in real-time as our architecture is entirely causal - we do not incorporate information from future frames, contrary to previous methods. We show that our model can accurately track and segment instances, even with occlusions and missed detections, advancing the state-of-the-art on the KITTI Multi-Object and Tracking Dataset. | ['Anthony Hu', 'Alex Kendall', 'Roberto Cipolla'] | 2019-12-19 | null | https://openreview.net/forum?id=HyxTJxrtvr | https://openreview.net/pdf?id=HyxTJxrtvr | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.39044933e-02 6.70193210e-02 -4.43842143e-01 -2.97296643e-01
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e049835a-f87f-4f50-8e6d-faffa55494fd | dense-dual-path-network-for-real-time | 2010.10778 | null | https://arxiv.org/abs/2010.10778v1 | https://arxiv.org/pdf/2010.10778v1.pdf | Dense Dual-Path Network for Real-time Semantic Segmentation | Semantic segmentation has achieved remarkable results with high computational cost and a large number of parameters. However, real-world applications require efficient inference speed on embedded devices. Most previous works address the challenge by reducing depth, width and layer capacity of network, which leads to poor performance. In this paper, we introduce a novel Dense Dual-Path Network (DDPNet) for real-time semantic segmentation under resource constraints. We design a light-weight and powerful backbone with dense connectivity to facilitate feature reuse throughout the whole network and the proposed Dual-Path module (DPM) to sufficiently aggregate multi-scale contexts. Meanwhile, a simple and effective framework is built with a skip architecture utilizing the high-resolution feature maps to refine the segmentation output and an upsampling module leveraging context information from the feature maps to refine the heatmaps. The proposed DDPNet shows an obvious advantage in balancing accuracy and speed. Specifically, on Cityscapes test dataset, DDPNet achieves 75.3% mIoU with 52.6 FPS for an input of 1024 X 2048 resolution on a single GTX 1080Ti card. Compared with other state-of-the-art methods, DDPNet achieves a significant better accuracy with a comparable speed and fewer parameters. | ['Feilin Liu', 'Junqiao Zhao', 'Yan Wu', 'Xinneng Yang'] | 2020-10-21 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.83881417e-01 -5.87517582e-02 -1.63812473e-01 -3.77690285e-01
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e72d4670-5c72-4c57-9e10-1fcfba0d89ff | limited-angle-tomographic-reconstruction-of | 2007.10734 | null | https://arxiv.org/abs/2007.10734v1 | https://arxiv.org/pdf/2007.10734v1.pdf | Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning | Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. Recently, it was shown that one effective way to learn the priors for strongly scattering yet highly structured 3D objects, e.g. layered and Manhattan, is by a static neural network [Goy et al, Proc. Natl. Acad. Sci. 116, 19848-19856 (2019)]. Here, we present a radically different approach where the collection of raw images from multiple angles is viewed analogously to a dynamical system driven by the object-dependent forward scattering operator. The sequence index in angle of illumination plays the role of discrete time in the dynamical system analogy. Thus, the imaging problem turns into a problem of nonlinear system identification, which also suggests dynamical learning as better fit to regularize the reconstructions. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the fundamental building block. Through comprehensive comparison of several quantitative metrics, we show that the dynamic method improves upon previous static approaches with fewer artifacts and better overall reconstruction fidelity. | ['George Barbastathis', 'Alexandre Goy', 'Iksung Kang'] | 2020-07-21 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 5.46223164e-01 -4.34344746e-02 6.39668345e-01 -4.53470916e-01
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5d95b031-f7fe-49a0-a5e7-6325ad5d392e | decamouflage-a-framework-to-detect-image | 2010.03735 | null | https://arxiv.org/abs/2010.03735v1 | https://arxiv.org/pdf/2010.03735v1.pdf | Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks | As an essential processing step in computer vision applications, image resizing or scaling, more specifically downsampling, has to be applied before feeding a normally large image into a convolutional neural network (CNN) model because CNN models typically take small fixed-size images as inputs. However, image scaling functions could be adversarially abused to perform a newly revealed attack called image-scaling attack, which can affect a wide range of computer vision applications building upon image-scaling functions. This work presents an image-scaling attack detection framework, termed as Decamouflage. Decamouflage consists of three independent detection methods: (1) rescaling, (2) filtering/pooling, and (3) steganalysis. While each of these three methods is efficient standalone, they can work in an ensemble manner not only to improve the detection accuracy but also to harden potential adaptive attacks. Decamouflage has a pre-determined detection threshold that is generic. More precisely, as we have validated, the threshold determined from one dataset is also applicable to other different datasets. Extensive experiments show that Decamouflage achieves detection accuracy of 99.9\% and 99.8\% in the white-box (with the knowledge of attack algorithms) and the black-box (without the knowledge of attack algorithms) settings, respectively. To corroborate the efficiency of Decamouflage, we have also measured its run-time overhead on a personal PC with an i5 CPU and found that Decamouflage can detect image-scaling attacks in milliseconds. Overall, Decamouflage can accurately detect image scaling attacks in both white-box and black-box settings with acceptable run-time overhead. | ['Surya Nepal', 'Hyoungshick Kim', 'Muhammad Ejaz Ahmed', 'Yifeng Zheng', 'Yansong Gao', 'Alsharif Abuadbba', 'Bedeuro Kim'] | 2020-10-08 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 6.58058763e-01 -1.97448328e-01 2.72058696e-01 -5.81536256e-02
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e6fb52b0-e1f5-4d23-9d4b-3a69a9ae4465 | few-shot-document-level-relation-extraction | 2205.02048 | null | https://arxiv.org/abs/2205.02048v2 | https://arxiv.org/pdf/2205.02048v2.pdf | Few-Shot Document-Level Relation Extraction | We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-of-the-above (NOTA) distributions. Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on two existing supervised learning data sets, DocRED and sciERC. We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation. We find FSDLRE to be a challenging setting with interesting new characteristics such as the ability to sample NOTA instances from the support set. The data, code, and trained models are available online (https://github.com/nicpopovic/FREDo). | ['Michael Färber', 'Nicholas Popovic'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.421 | https://aclanthology.org/2022.naacl-main.421.pdf | naacl-2022-7 | ['few-shot-relation-classification', 'document-level-relation-extraction', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.30154610e-01 3.24374437e-01 -6.88563824e-01 -3.68845671e-01
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8df42c26-9b12-4536-b3e6-38cd33d1351b | group-fairness-in-adaptive-submodular | 2207.03364 | null | https://arxiv.org/abs/2207.03364v2 | https://arxiv.org/pdf/2207.03364v2.pdf | Group Equality in Adaptive Submodular Maximization | In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings. It has been shown that the utility function of many machine learning applications, including data summarization, influence maximization in social networks, and personalized recommendation, satisfies the property of submodularity. Hence, maximizing a submodular function subject to various constraints can be found at the heart of many of those applications. On a high level, submodular maximization aims to select a group of most representative items (e.g., data points). However, the design of most existing algorithms does not incorporate the fairness constraint, leading to under- or over-representation of some particular groups. This motivates us to study the submodular maximization problem with group equality, where we aim to select a group of items to maximize a (possibly non-monotone) submodular utility function subject to a group equality constraint. To this end, we develop the first constant-factor approximation algorithm for this problem. The design of our algorithm is robust enough to be extended to solving the submodular maximization problem under a more complicated adaptive setting. Moreover, we further extend our study to incorporating a global cardinality constraint. | ['Jing Yuan', 'Shaojie Tang'] | 2022-07-07 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.23901674e-01 4.25758511e-01 -6.92500591e-01 -3.38136822e-01
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d965e9e9-d6fe-4f9d-9a17-c8996f5f8e36 | what-does-transformer-learn-about-source-code | 2207.08466 | null | https://arxiv.org/abs/2207.08466v1 | https://arxiv.org/pdf/2207.08466v1.pdf | What does Transformer learn about source code? | In the field of source code processing, the transformer-based representation models have shown great powerfulness and have achieved state-of-the-art (SOTA) performance in many tasks. Although the transformer models process the sequential source code, pieces of evidence show that they may capture the structural information (\eg, in the syntax tree, data flow, control flow, \etc) as well. We propose the aggregated attention score, a method to investigate the structural information learned by the transformer. We also put forward the aggregated attention graph, a new way to extract program graphs from the pre-trained models automatically. We measure our methods from multiple perspectives. Furthermore, based on our empirical findings, we use the automatically extracted graphs to replace those ingenious manual designed graphs in the Variable Misuse task. Experimental results show that the semantic graphs we extracted automatically are greatly meaningful and effective, which provide a new perspective for us to understand and use the information contained in the model. | ['Zhi Jin', 'Ge Li', 'Kechi Zhang'] | 2022-07-18 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 1.69117033e-01 5.08309305e-01 -4.49872166e-01 -2.81561673e-01
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ddee7367-5bed-475a-8794-d741de25d327 | exploring-effective-mask-sampling-modeling | 2306.05704 | null | https://arxiv.org/abs/2306.05704v1 | https://arxiv.org/pdf/2306.05704v1.pdf | Exploring Effective Mask Sampling Modeling for Neural Image Compression | Image compression aims to reduce the information redundancy in images. Most existing neural image compression methods rely on side information from hyperprior or context models to eliminate spatial redundancy, but rarely address the channel redundancy. Inspired by the mask sampling modeling in recent self-supervised learning methods for natural language processing and high-level vision, we propose a novel pretraining strategy for neural image compression. Specifically, Cube Mask Sampling Module (CMSM) is proposed to apply both spatial and channel mask sampling modeling to image compression in the pre-training stage. Moreover, to further reduce channel redundancy, we propose the Learnable Channel Mask Module (LCMM) and the Learnable Channel Completion Module (LCCM). Our plug-and-play CMSM, LCMM, LCCM modules can apply to both CNN-based and Transformer-based architectures, significantly reduce the computational cost, and improve the quality of images. Experiments on the public Kodak and Tecnick datasets demonstrate that our method achieves competitive performance with lower computational complexity compared to state-of-the-art image compression methods. | ['Qi Tian', 'Yanfeng Wang', 'Houqiang Li', 'Wengang Zhou', 'Wenlong Lyu', 'Shanxin Yuan', 'Mingming Zhao', 'Lin Liu'] | 2023-06-09 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 7.63250768e-01 -3.82957235e-02 -4.01680917e-01 -2.49612153e-01
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d7b34abf-e51e-487e-b013-00a7bbfbda73 | slice-transformer-and-self-supervised | 2301.08957 | null | https://arxiv.org/abs/2301.08957v1 | https://arxiv.org/pdf/2301.08957v1.pdf | Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps | Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a $360^\circ$ LiDAR scan to leverage its axial properties. Our model, called Slice Transformer, employs multi-head attention while systematically processing the slices. To the best of our knowledge, this is the first instance of leveraging multi-head attention for outdoor point clouds. We additionally introduce the Perth-WA dataset, which provides a large-scale LiDAR map of Perth city in Western Australia, covering $\sim$4km$^2$ area. Localization annotations are provided for Perth-WA. The proposed localization method is thoroughly evaluated on Perth-WA and Appollo-SouthBay datasets. We also establish the efficacy of our self-supervised learning approach for the common downstream task of object classification using ModelNet40 and ScanNN datasets. The code and Perth-WA data will be publicly released. | ['Ajmal Mian', 'Michael Wise', 'Saeed Anwar', 'Naveed Akhtar', 'Muhammad Ibrahim'] | 2023-01-21 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-6.29298389e-02 2.03397814e-02 -1.56311944e-01 -9.01604354e-01
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560d0ff7-5d88-4afa-bfae-136a45030788 | discrepancy-modeling-framework-learning | 2203.05164 | null | https://arxiv.org/abs/2203.05164v1 | https://arxiv.org/pdf/2203.05164v1.pdf | Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects | Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of the system, revealing the approximate nature of the equations and/or the signal-to-noise ratio of the sensor itself. In modern dynamical systems, such discrepancies between model and measurement can lead to poor quantification, often undermining the ability to produce accurate and precise control algorithms. We introduce a discrepancy modeling framework to resolve deterministic model-measurement mismatch with two distinct approaches: (i) by learning a model for the evolution of systematic state-space residual, and (ii) by discovering a model for the missing deterministic physics. Regardless of approach, a common suite of data-driven model discovery methods can be used. Specifically, we use four fundamentally different methods to demonstrate the mathematical implementations of discrepancy modeling: (i) the sparse identification of nonlinear dynamics (SINDy), (ii) dynamic mode decomposition (DMD), (iii) Gaussian process regression (GPR), and (iv) neural networks (NN). The choice of method depends on one's intent for discrepancy modeling, as well as quantity and quality of the sensor measurements. We demonstrate the utility and suitability for both discrepancy modeling approaches using the suite of data-driven modeling methods on three dynamical systems under varying signal-to-noise ratios. We compare reconstruction and forecasting accuracies and provide detailed comparatives, allowing one to select the appropriate approach and method in practice. | ['J. Nathan Kutz', 'Katherine M. Steele', 'Megan R. Ebers'] | 2022-03-10 | null | null | null | null | ['gpr', 'model-discovery', 'gpr'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 3.78449827e-01 -4.18074936e-01 1.75901279e-01 1.81767732e-01
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8ffd3db1-af60-4a50-a8e5-051f0fd7ee50 | lexical-simplification-with-neural-ranking | null | null | https://aclanthology.org/E17-2006 | https://aclanthology.org/E17-2006.pdf | Lexical Simplification with Neural Ranking | We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus - a large set of professionally produced simplifications. We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them using a new neural regression model that learns rankings from annotated data. This strategy leads to the highest Accuracy, Precision and F1 scores to date in standard datasets for the task. | ['Lucia Specia', 'Gustavo Paetzold'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['complex-word-identification'] | ['natural-language-processing'] | [ 3.09099942e-01 4.22828257e-01 -6.69586837e-01 -4.36710328e-01
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8406cd3a-462c-4c9e-bd06-b1e1ac82cc78 | model-reference-gaussian-process-regression-1 | 2303.09828 | null | https://arxiv.org/abs/2303.09828v1 | https://arxiv.org/pdf/2303.09828v1.pdf | Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller | This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller. | ['Hyungbo Shim', 'Hamin Chang', 'Hyuntae Kim'] | 2023-03-17 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.65636474e-01 2.62228489e-01 -3.41810316e-01 4.66923982e-01
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bcf9afe8-ed92-4cb4-a68e-1930989c170c | unified-pose-sequence-modeling | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Foo_Unified_Pose_Sequence_Modeling_CVPR_2023_paper.pdf | Unified Pose Sequence Modeling | We propose a Unified Pose Sequence Modeling approach to unify heterogeneous human behavior understanding tasks based on pose data, e.g., action recognition, 3D pose estimation and 3D early action prediction. A major obstacle is that different pose-based tasks require different output data formats. Specifically, the action recognition and prediction tasks require class predictions as outputs, while 3D pose estimation requires a human pose output, which limits existing methods to leverage task-specific network architectures for each task. Hence, in this paper, we propose a novel Unified Pose Sequence (UPS) model to unify heterogeneous output formats for the aforementioned tasks by considering text-based action labels and coordinate-based human poses as language sequences. Then, by optimizing a single auto-regressive transformer, we can obtain a unified output sequence that can handle all the aforementioned tasks. Moreover, to avoid the interference brought by the heterogeneity between different tasks, a dynamic routing mechanism is also proposed to empower our UPS with the ability to learn which subsets of parameters should be shared among different tasks. To evaluate the efficacy of the proposed UPS, extensive experiments are conducted on four different tasks with four popular behavior understanding benchmarks. | ['Jun Liu', 'Qiuhong Ke', 'Hossein Rahmani', 'Tianjiao Li', 'Lin Geng Foo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-pose-estimation', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 2.57010102e-01 -3.55087787e-01 -3.41772228e-01 -5.23143351e-01
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94743619-f210-4501-9ee6-7993ce6029f9 | erqa-edge-restoration-quality-assessment-for | 2110.09992 | null | https://arxiv.org/abs/2110.09992v2 | https://arxiv.org/pdf/2110.09992v2.pdf | ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution | Despite the growing popularity of video super-resolution (VSR), there is still no good way to assess the quality of the restored details in upscaled frames. Some SR methods may produce the wrong digit or an entirely different face. Whether a method's results are trustworthy depends on how well it restores truthful details. Image super-resolution can use natural distributions to produce a high-resolution image that is only somewhat similar to the real one. VSR enables exploration of additional information in neighboring frames to restore details from the original scene. The ERQA metric, which we propose in this paper, aims to estimate a model's ability to restore real details using VSR. On the assumption that edges are significant for detail and character recognition, we chose edge fidelity as the foundation for this metric. Experimental validation of our work is based on the MSU Video Super-Resolution Benchmark, which includes the most difficult patterns for detail restoration and verifies the fidelity of details from the original frame. Code for the proposed metric is publicly available at https://github.com/msu-video-group/ERQA. | ['Dmitry Vatolin', 'Anastasia Antsiferova', 'Eugene Lyapustin', 'Anastasia Kirillova'] | 2021-10-19 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.22415769e-01 -1.88718751e-01 2.45418884e-02 -3.79135340e-01
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0034ef11-5d9b-47ad-a3a9-762dc1249e1e | dense-graph-convolutional-neural-networks-on | 2106.15778 | null | https://arxiv.org/abs/2106.15778v1 | https://arxiv.org/pdf/2106.15778v1.pdf | Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification | This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh as a graph where each node corresponds to a face. To enhance the descriptive power of the graph, we introduce a 1-ring face neighbourhood structure to derive novel multi-dimensional spatial and structure features to represent the graph nodes. Based on this new graph representation, we then design a densely connected graph convolutional block which aggregates local and regional features as the key construction component to build effective and efficient practical GCN models for 3D object classification and segmentation. We will present experimental results to show that our new technique outperforms state of the art where our models are shown to have the smallest number of parameters and consietently achieve the highest accuracies across a number of benchmark datasets. We will also present ablation studies to demonstrate the soundness of our design principles and the effectiveness of our practical models. | ['Wenming Tang Guoping Qiu'] | 2021-06-30 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-1.91221327e-01 3.94124746e-01 -1.81755185e-01 -3.15081418e-01
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76cdabd0-6dd6-4704-8a39-5301b997f3eb | pico-newton-mechanical-forces-promote-neurite | 1810.08362 | null | http://arxiv.org/abs/1810.08362v1 | http://arxiv.org/pdf/1810.08362v1.pdf | Pico-Newton mechanical forces promote neurite growth | Investigations over half a century have indicated that mechanical forces
induce neurite growth - with neurites elongating at a rate of
0.1-0.3{\mu}mh^{-1} per pico-Newton (pN) of applied force - when mechanical
tension exceeds a threshold, with this being identified as 400-1000 pN for
neurites of PC12 cells. Here we demonstrate that there is no threshold for
neurite elongation of PC12 cells in response to applied mechanical forces.
Instead, this proceeds at the same previously identified rate, on the
application of tensions with intensity below 1pN. This supports the idea of
mechanical tension as an endogenous signal used by neurons for promoting
neurite elongation. | [] | 2018-10-19 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 4.98527110e-01 2.69986957e-01 -3.37429732e-01 2.12454230e-01
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e6f4f089-eeff-46ef-b8b8-24bf2bb7d4c6 | spatial-aggregation-of-holistically-nested | 1606.07830 | null | http://arxiv.org/abs/1606.07830v1 | http://arxiv.org/pdf/1606.07830v1.pdf | Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation | Accurate automatic organ segmentation is an important yet challenging problem
for medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits traditional segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a holistic learning approach
that integrates semantic mid-level cues of deeply-learned organ interior and
boundary maps via robust spatial aggregation using random forest. Our method
generates boundary preserving pixel-wise class labels for pancreas
segmentation. Quantitative evaluation is performed on CT scans of 82 patients
in 4-fold cross-validation. We achieve a (mean $\pm$ std. dev.) Dice Similarity
Coefficient of 78.01% $\pm$ 8.2% in testing which significantly outperforms the
previous state-of-the-art approach of 71.8% $\pm$ 10.7% under the same
evaluation criterion. | ['Andrew Sohn', 'Amal Farag', 'Le Lu', 'Holger R. Roth', 'Ronald M. Summers'] | 2016-06-24 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [ 1.79567456e-01 3.65001440e-01 -2.67535180e-01 -3.26103181e-01
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b14c41a6-ee1a-4f0a-adc2-57955548bbe1 | data-free-knowledge-distillation-via-feature | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Data-Free_Knowledge_Distillation_via_Feature_Exchange_and_Activation_Region_Constraint_CVPR_2023_paper.pdf | Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint | Despite the tremendous progress on data-free knowledge distillation (DFKD) based on synthetic data generation, there are still limitations in diverse and efficient data synthesis. It is naive to expect that a simple combination of generative network-based data synthesis and data augmentation will solve these issues. Therefore, this paper proposes a novel data-free knowledge distillation method (SpaceshipNet) based on channel-wise feature exchange (CFE) and multi-scale spatial activation region consistency (mSARC) constraint. Specifically, CFE allows our generative network to better sample from the feature space and efficiently synthesize diverse images for learning the student network. However, using CFE alone can severely amplify the unwanted noises in the synthesized images, which may result in failure to improve distillation learning and even have negative effects. Therefore, we propose mSARC to assure the student network can imitate not only the logit output but also the spatial activation region of the teacher network in order to alleviate the influence of unwanted noises in diverse synthetic images on distillation learning. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, Imagenette, and ImageNet100 show that our method can work well with different backbone networks, and outperform the state-of-the-art DFKD methods. Code will be available at: https://github.com/skgyu/SpaceshipNet. | ['Shuqiang Jiang', 'Hu Han', 'Jiachen Chen', 'Shikang Yu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-2.00169146e-01 7.60156959e-02 3.90494317e-02 -2.78710306e-01
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dd558bc1-8b0a-422c-a416-2582c962b1fb | deep-offline-reinforcement-learning-for-real | 2302.07549 | null | https://arxiv.org/abs/2302.07549v2 | https://arxiv.org/pdf/2302.07549v2.pdf | Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications | There is increasing interest in data-driven approaches for recommending optimal treatment strategies in many chronic disease management and critical care applications. Reinforcement learning methods are well-suited to this sequential decision-making problem, but must be trained and evaluated exclusively on retrospective medical record datasets as direct online exploration is unsafe and infeasible. Despite this requirement, the vast majority of treatment optimization studies use off-policy RL methods (e.g., Double Deep Q Networks (DDQN) or its variants) that are known to perform poorly in purely offline settings. Recent advances in offline RL, such as Conservative Q-Learning (CQL), offer a suitable alternative. But there remain challenges in adapting these approaches to real-world applications where suboptimal examples dominate the retrospective dataset and strict safety constraints need to be satisfied. In this work, we introduce a practical and theoretically grounded transition sampling approach to address action imbalance during offline RL training. We perform extensive experiments on two real-world tasks for diabetes and sepsis treatment optimization to compare performance of the proposed approach against prominent off-policy and offline RL baselines (DDQN and CQL). Across a range of principled and clinically relevant metrics, we show that our proposed approach enables substantial improvements in expected health outcomes and in accordance with relevant practice and safety guidelines. | ['Pavitra Krishnaswamy', 'Yong Mong Bee', 'Yu En Chan', 'Priscilla Ong', 'Supriyo Ghosh', 'Milashini Nambiar'] | 2023-02-15 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 3.49999636e-01 1.61942735e-01 -8.29854429e-01 -2.62095630e-01
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bb0605f1-33c7-4c17-a15c-c646eec9be92 | optimal-bandwidth-selection-for-denclue | 2307.03206 | null | https://arxiv.org/abs/2307.03206v1 | https://arxiv.org/pdf/2307.03206v1.pdf | Optimal Bandwidth Selection for DENCLUE | In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010. Innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a density-based clustering algorithm named DENCLUE was invented to solve clustering problem for nonlinear data structures. However, its parameter selection problem was largely neglected until 2011. In this paper, we propose a new approach to compute the optimal parameters for the DENCLUE algorithm, and discuss its performance in the experiment section. | ['Hao Wang'] | 2023-07-06 | null | null | null | null | ['clustering'] | ['methodology'] | [-6.62120581e-01 -5.75945079e-01 -2.65227020e-01 -2.94162422e-01
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6aa30bb7-2b5b-4ed6-92c3-6e5e47645176 | building-bilingual-and-code-switched-voice | 2104.10832 | null | https://arxiv.org/abs/2104.10832v1 | https://arxiv.org/pdf/2104.10832v1.pdf | Building Bilingual and Code-Switched Voice Conversion with Limited Training Data Using Embedding Consistency Loss | Building cross-lingual voice conversion (VC) systems for multiple speakers and multiple languages has been a challenging task for a long time. This paper describes a parallel non-autoregressive network to achieve bilingual and code-switched voice conversion for multiple speakers when there are only mono-lingual corpora for each language. We achieve cross-lingual VC between Mandarin speech with multiple speakers and English speech with multiple speakers by applying bilingual bottleneck features. To boost voice cloning performance, we use an adversarial speaker classifier with a gradient reversal layer to reduce the source speaker's information from the output of encoder. Furthermore, in order to improve speaker similarity between reference speech and converted speech, we adopt an embedding consistency loss between the synthesized speech and its natural reference speech in our network. Experimental results show that our proposed method can achieve high quality converted speech with mean opinion score (MOS) around 4. The conversion system performs well in terms of speaker similarity for both in-set speaker conversion and out-set-of one-shot conversion. | ['Ming Li', 'Mingyang Xu', 'Huahua Cui', 'Shanshan Liang', 'Xiaoyi Qin', 'Haozhe Zhang', 'Yaogen Yang'] | 2021-04-22 | null | null | null | null | ['voice-cloning'] | ['speech'] | [-3.27716880e-02 -8.91452376e-03 1.41100250e-02 -4.09712344e-01
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1f2fe447-420e-4709-852a-2b43d5b97ec6 | multi-scale-octave-convolutions-for-robust | 1910.14443 | null | https://arxiv.org/abs/1910.14443v1 | https://arxiv.org/pdf/1910.14443v1.pdf | Multi-scale Octave Convolutions for Robust Speech Recognition | We propose a multi-scale octave convolution layer to learn robust speech representations efficiently. Octave convolutions were introduced by Chen et al [1] in the computer vision field to reduce the spatial redundancy of the feature maps by decomposing the output of a convolutional layer into feature maps at two different spatial resolutions, one octave apart. This approach improved the efficiency as well as the accuracy of the CNN models. The accuracy gain was attributed to the enlargement of the receptive field in the original input space. We argue that octave convolutions likewise improve the robustness of learned representations due to the use of average pooling in the lower resolution group, acting as a low-pass filter. We test this hypothesis by evaluating on two noisy speech corpora - Aurora-4 and AMI. We extend the octave convolution concept to multiple resolution groups and multiple octaves. To evaluate the robustness of the inferred representations, we report the similarity between clean and noisy encodings using an affine projection loss as a proxy robustness measure. The results show that proposed method reduces the WER by up to 6.6% relative for Aurora-4 and 3.6% for AMI, while improving the computational efficiency of the CNN acoustic models. | ['Joanna Rownicka', 'Steve Renals', 'Peter Bell'] | 2019-10-31 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 8.87581483e-02 3.59848589e-01 7.71123409e-01 -3.45645607e-01
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3e8efeb8-75d8-4f75-a430-8dc52e7b7c85 | ixa-pipeline-efficient-and-ready-to-use | null | null | https://aclanthology.org/L14-1605 | https://aclanthology.org/L14-1605.pdf | IXA pipeline: Efficient and Ready to Use Multilingual NLP tools | IXA pipeline is a modular set of Natural Language Processing tools (or pipes) which provide easy access to NLP technology. It offers robust and efficient linguistic annotation to both researchers and non-NLP experts with the aim of lowering the barriers of using NLP technology either for research purposes or for small industrial developers and SMEs. IXA pipeline can be used {``}as is{''} or exploit its modularity to pick and change different components. Given its open-source nature, it can also be modified and extended for it to work with other languages. This paper describes the general data-centric architecture of IXA pipeline and presents competitive results in several NLP annotations for English and Spanish. | ['Rodrigo Agerri', 'Josu Bermudez', 'German Rigau'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-1.59388840e-01 5.29187441e-01 -3.95583838e-01 -3.84699345e-01
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00db50b8-7301-4c93-82e6-586daf5a402e | one-shot-learning-of-stochastic-differential | 2209.12086 | null | https://arxiv.org/abs/2209.12086v3 | https://arxiv.org/pdf/2209.12086v3.pdf | One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels | We consider the problem of learning Stochastic Differential Equations of the form $dX_t = f(X_t)dt+\sigma(X_t)dW_t $ from one sample trajectory. This problem is more challenging than learning deterministic dynamical systems because one sample trajectory only provides indirect information on the unknown functions $f$, $\sigma$, and stochastic process $dW_t$ representing the drift, the diffusion, and the stochastic forcing terms, respectively. We propose a method that combines Computational Graph Completion and data adapted kernels learned via a new variant of cross validation. Our approach can be decomposed as follows: (1) Represent the time-increment map $X_t \rightarrow X_{t+dt}$ as a Computational Graph in which $f$, $\sigma$ and $dW_t$ appear as unknown functions and random variables. (2) Complete the graph (approximate unknown functions and random variables) via Maximum a Posteriori Estimation (given the data) with Gaussian Process (GP) priors on the unknown functions. (3) Learn the covariance functions (kernels) of the GP priors from data with randomized cross-validation. Numerical experiments illustrate the efficacy, robustness, and scope of our method. | ['Peyman Tavallali', 'Houman Owhadi', 'Giulia Livieri', 'Boumediene Hamzi', 'Matthieu Darcy'] | 2022-09-24 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [-3.82544816e-01 -1.02267183e-01 3.96887541e-01 -4.42080162e-02
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31744e15-b930-4c6a-a8f2-1d5b37f91798 | local-contrast-and-global-contextual | 2301.12093 | null | https://arxiv.org/abs/2301.12093v3 | https://arxiv.org/pdf/2301.12093v3.pdf | Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again | Infrared small object detection (ISOS) aims to segment small objects only covered with several pixels from clutter background in infrared images. It's of great challenge due to: 1) small objects lack of sufficient intensity, shape and texture information; 2) small objects are easily lost in the process where detection models, say deep neural networks, obtain high-level semantic features and image-level receptive fields through successive downsampling. This paper proposes a reliable detection model for ISOS, dubbed UCFNet, which can handle well the two issues. It builds upon central difference convolution (CDC) and fast Fourier convolution (FFC). On one hand, CDC can effectively guide the network to learn the contrast information between small objects and the background, as the contrast information is very essential in human visual system dealing with the ISOS task. On the other hand, FFC can gain image-level receptive fields and extract global information while preventing small objects from being overwhelmed.Experiments on several public datasets demonstrate that our method significantly outperforms the state-of-the-art ISOS models, and can provide useful guidelines for designing better ISOS deep models. Code are available at https://github.com/wcyjerry/BasicISOS. | ['Peiwen Pan', 'Huan Wang', 'Chenyi Wang'] | 2023-01-28 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.95810992e-01 -3.01018089e-01 -3.94292586e-02 -1.64064854e-01
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ac72c8d0-7045-452e-9161-1918e16ff112 | a-cnn-based-blind-denoising-method-for-1 | 2003.06986 | null | https://arxiv.org/abs/2003.06986v1 | https://arxiv.org/pdf/2003.06986v1.pdf | A CNN-Based Blind Denoising Method for Endoscopic Images | The quality of images captured by wireless capsule endoscopy (WCE) is key for doctors to diagnose diseases of gastrointestinal (GI) tract. However, there exist many low-quality endoscopic images due to the limited illumination and complex environment in GI tract. After an enhancement process, the severe noise become an unacceptable problem. The noise varies with different cameras, GI tract environments and image enhancement. And the noise model is hard to be obtained. This paper proposes a convolutional blind denoising network for endoscopic images. We apply Deep Image Prior (DIP) method to reconstruct a clean image iteratively using a noisy image without a specific noise model and ground truth. Then we design a blind image quality assessment network based on MobileNet to estimate the quality of the reconstructed images. The estimated quality is used to stop the iterative operation in DIP method. The number of iterations is reduced about 36% by using transfer learning in our DIP process. Experimental results on endoscopic images and real-world noisy images demonstrate the superiority of our proposed method over the state-of-the-art methods in terms of visual quality and quantitative metrics. | ['Xiang Xie', 'Shaofeng Zou', 'Xuyang Wang', 'Zhihua Wang', 'Mingzhu Long', 'Guolin Li'] | 2020-03-16 | a-cnn-based-blind-denoising-method-for | null | null | conference-2019-12 | ['blind-image-quality-assessment'] | ['computer-vision'] | [-1.12457626e-01 -4.30073768e-01 3.41458142e-01 -9.72004086e-02
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c46b56b1-1fcd-416f-b026-3cf5d901987d | shift-reduce-task-oriented-semantic-parsing | 2210.11984 | null | https://arxiv.org/abs/2210.11984v1 | https://arxiv.org/pdf/2210.11984v1.pdf | Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers | Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialog systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. While shift-reduce approaches initially demonstrated to be the best option, recent efforts on sequence-to-sequence systems pushed them to become the highest-performing method for that task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialog. In particular, we implement novel shift-reduce parsers that rely on Stack-Transformers. These allow to adequately model transition systems on the cutting-edge Transformer architecture, notably boosting shift-reduce parsing performance. Additionally, we adapt alternative transition systems from constituency parsing to task-oriented parsing, and empirically prove that the in-order algorithm substantially outperforms the commonly-used top-down strategy. Finally, we extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings. | ['Daniel Fernández-González'] | 2022-10-21 | null | null | null | null | ['constituency-parsing', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.88995016e-01 3.63594353e-01 -8.99212528e-03 -7.38347471e-01
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340d3427-49ed-43f0-8027-01d78fa5b05c | icar-bridging-image-classification-and-image | 2204.10760 | null | https://arxiv.org/abs/2204.10760v1 | https://arxiv.org/pdf/2204.10760v1.pdf | iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition | Image classification, which classifies images by pre-defined categories, has been the dominant approach to visual representation learning over the last decade. Visual learning through image-text alignment, however, has emerged to show promising performance, especially for zero-shot recognition. We believe that these two learning tasks are complementary, and suggest combining them for better visual learning. We propose a deep fusion method with three adaptations that effectively bridge two learning tasks, rather than shallow fusion through naive multi-task learning. First, we modify the previous common practice in image classification, a linear classifier, with a cosine classifier which shows comparable performance. Second, we convert the image classification problem from learning parametric category classifier weights to learning a text encoder as a meta network to generate category classifier weights. The learnt text encoder is shared between image classification and image-text alignment. Third, we enrich each class name with a description to avoid confusion between classes and make the classification method closer to the image-text alignment. We prove that this deep fusion approach performs better on a variety of visual recognition tasks and setups than the individual learning or shallow fusion approach, from zero-shot/few-shot image classification, such as the Kornblith 12-dataset benchmark, to downstream tasks of action recognition, semantic segmentation, and object detection in fine-tuning and open-vocabulary settings. The code will be available at https://github.com/weiyx16/iCAR. | ['Baining Guo', 'Han Hu', 'Zhenda Xie', 'Zhuliang Yao', 'Zheng Zhang', 'Yue Cao', 'Yixuan Wei'] | 2022-04-22 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 7.19644308e-01 -1.37785375e-01 -6.32779002e-01 -5.27337015e-01
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fc02cfc2-378d-4576-a9fb-b057e9a906a9 | color-constancy-convolutional-autoencoder | 1906.01340 | null | https://arxiv.org/abs/1906.01340v1 | https://arxiv.org/pdf/1906.01340v1.pdf | Color Constancy Convolutional Autoencoder | In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models. | ['Alexandros Iosifidis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom'] | 2019-06-04 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.10413039e-01 -4.55847621e-01 2.23301183e-02 -6.51605904e-01
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71649a60-2319-4463-99b6-28c5a3caa348 | thoracic-cartilage-ultrasound-ct-registration | 2307.03800 | null | https://arxiv.org/abs/2307.03800v1 | https://arxiv.org/pdf/2307.03800v1.pdf | Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph | Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications with high acoustic-impedance bone structures under the skin. To address this challenge, a graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface features instead of the skin surface. To this end, the sternum and cartilage branches are segmented using a template matching to assist coarse alignment of US and CT point clouds. Afterward, a directed graph is generated based on the CT template. Then, the self-organizing map using geographical distance is successively performed twice to extract the optimal graph representations for CT and US point clouds, individually. To evaluate the proposed approach, five cartilage point clouds from distinct patients are employed. The results demonstrate that the proposed graph-based registration can effectively map trajectories from CT to the current setup for displaying US views through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean$\pm$SD) is 9.48$\pm$0.27 mm and the path transferring error in terms of Euclidean distance is 2.21$\pm$1.11 mm. | ['Nassir Navab', 'Xuesong Li', 'Chenyang Li', 'Zhongliang Jiang'] | 2023-07-07 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 2.50492841e-01 2.78780192e-01 3.59918952e-01 -8.27172175e-02
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fad0174c-3d64-4f57-9f73-f4d1c2d6e0bd | noise-robust-speech-recognition-with-10 | 2203.15321 | null | https://arxiv.org/abs/2203.15321v1 | https://arxiv.org/pdf/2203.15321v1.pdf | Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data | Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in face of various practical environments. However, such plenty of in-domain data is not always available in the real-life world. In this paper, we propose a generative adversarial network to simulate noisy spectrum from the clean spectrum (Simu-GAN), where only 10 minutes of unparalleled in-domain noisy speech data is required as labels. Furthermore, we also propose a dual-path speech recognition system to improve the robustness of the system under noisy conditions. Experimental results show that the proposed speech recognition system achieves 7.3% absolute improvement with simulated noisy data by Simu-GAN over the best baseline in terms of word error rate (WER). | ['Eng Siong Chng', 'Shashank Shirol', 'Yuchen Hu', 'Nana Hou', 'Chen Chen'] | 2022-03-29 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.36023593e-01 -9.20544565e-02 5.93319952e-01 -4.67379719e-01
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f1c01313-8370-45d6-be9a-0e4310baabd4 | deep-learning-for-laboratory-earthquake | 2203.13313 | null | https://arxiv.org/abs/2203.13313v3 | https://arxiv.org/pdf/2203.13313v3.pdf | Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress | Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory earthquakes. In the lab, frictional stick-slip events provide an analog for earthquakes and the seismic cycle. Labquakes are ideal targets for machine learning (ML) because they can be produced in long sequences under controlled conditions. Recent works show that ML can predict several aspects of labquakes using fault zone acoustic emissions. Here, we generalize these results and explore deep learning (DL) methods for labquake prediction and autoregressive (AR) forecasting. DL improves existing ML methods of labquake prediction. AR methods allow forecasting at future horizons via iterative predictions. We demonstrate that DL models based on Long-Short Term Memory (LSTM) and Convolution Neural Networks predict labquakes under several conditions, and that fault zone stress can be predicted with fidelity, confirming that acoustic energy is a fingerprint of fault zone stress. We predict also time to start of failure (TTsF) and time to the end of Failure (TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all seismic cycles, while the TTsF prediction varies with the amount of preseismic fault creep. We report AR methods to forecast the evolution of fault stress using three sequence modeling frameworks: LSTM, Temporal Convolution Network and Transformer Network. AR forecasting is distinct from existing predictive models, which predict only a target variable at a specific time. The results for forecasting beyond a single seismic cycle are limited but encouraging. Our ML/DL models outperform the state-of-the-art and our autoregressive model represents a novel framework that could enhance current methods of earthquake forecasting. | ['Chris Marone', 'Luca Franco', 'Fabio Galasso', 'Elisa Tinti', 'Laura Laurenti'] | 2022-03-24 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-6.67671934e-02 -3.80465865e-01 2.21923023e-01 -6.94791675e-02
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5676771b-a2e3-4b72-978d-ec1180b553a6 | reactive-perturbation-defocusing-for-textual | 2305.04067 | null | https://arxiv.org/abs/2305.04067v1 | https://arxiv.org/pdf/2305.04067v1.pdf | Reactive Perturbation Defocusing for Textual Adversarial Defense | Recent studies have shown that large pre-trained language models are vulnerable to adversarial attacks. Existing methods attempt to reconstruct the adversarial examples. However, these methods usually have limited performance in defense against adversarial examples, while also negatively impacting the performance on natural examples. To overcome this problem, we propose a method called Reactive Perturbation Defocusing (RPD). RPD uses an adversarial detector to identify adversarial examples and reduce false defenses on natural examples. Instead of reconstructing the adversaries, RPD injects safe perturbations into adversarial examples to distract the objective models from the malicious perturbations. Our experiments on three datasets, two objective models, and various adversarial attacks show that our proposed framework successfully repairs up to approximately 97% of correctly identified adversarial examples with only about a 2% performance decrease on natural examples. We also provide a demo of adversarial detection and repair based on our work. | ['Ke Li', 'Heng Yang'] | 2023-05-06 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.19569936e-01 1.66727811e-01 2.57011622e-01 4.26412709e-02
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450d872d-26e2-4739-88b8-3212643ce97f | musical-prosody-driven-emotion-classification | 2106.02556 | null | https://arxiv.org/abs/2106.02556v2 | https://arxiv.org/pdf/2106.02556v2.pdf | Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning | The task of classifying emotions within a musical track has received widespread attention within the Music Information Retrieval (MIR) community. Music emotion recognition has traditionally relied on the use of acoustic features, verbal features, and metadata-based filtering. The role of musical prosody remains under-explored despite several studies demonstrating a strong connection between prosody and emotion. In this study, we restrict the input of traditional machine learning algorithms to the features of musical prosody. Furthermore, our proposed approach builds upon the prior by classifying emotions under an expanded emotional taxonomy, using the Geneva Wheel of Emotion. We utilize a methodology for individual data collection from vocalists, and personal ground truth labeling by the artist themselves. We found that traditional machine learning algorithms when limited to the features of musical prosody (1) achieve high accuracies for a single singer, (2) maintain high accuracy when the dataset is expanded to multiple singers, and (3) achieve high accuracies when trained on a reduced subset of the total features. | ['Gil Weinberg', 'Richard Savery', 'Brian Model', 'Nicholas Farris'] | 2021-06-04 | null | null | null | null | ['music-emotion-recognition', 'music-information-retrieval'] | ['music', 'music'] | [ 2.49871179e-01 -3.66091698e-01 -1.53642267e-01 -1.69279426e-01
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728da66a-6a0c-4f81-b4df-6c4756e3d8e4 | continual-semantic-segmentation-via-repulsion | 2103.06342 | null | https://arxiv.org/abs/2103.06342v3 | https://arxiv.org/pdf/2103.06342v3.pdf | Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations | Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new categories are made available over time while previous training data is not retained. The proposed continual learning scheme shapes the latent space to reduce forgetting whilst improving the recognition of novel classes. Our framework is driven by three novel components which we also combine on top of existing techniques effortlessly. First, prototypes matching enforces latent space consistency on old classes, constraining the encoder to produce similar latent representation for previously seen classes in the subsequent steps. Second, features sparsification allows to make room in the latent space to accommodate novel classes. Finally, contrastive learning is employed to cluster features according to their semantics while tearing apart those of different classes. Extensive evaluation on the Pascal VOC2012 and ADE20K datasets demonstrates the effectiveness of our approach, significantly outperforming state-of-the-art methods. | ['Pietro Zanuttigh', 'Umberto Michieli'] | 2021-03-10 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.pdf | cvpr-2021-1 | ['overlapped-10-1', 'disjoint-15-5', 'disjoint-10-1', 'disjoint-15-1', 'overlapped-15-5', 'overlapped-15-1', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.84960282e-01 2.30922908e-01 -5.76806553e-02 -4.72476065e-01
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9ad864e0-6956-4a37-9a99-5d930a783db2 | video-segmentation-learning-using-cascade | 2212.10570 | null | https://arxiv.org/abs/2212.10570v1 | https://arxiv.org/pdf/2212.10570v1.pdf | Video Segmentation Learning Using Cascade Residual Convolutional Neural Network | Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$ in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart. | ['João P. Papa', 'Danilo Colombo', 'Rafael G. Pires', 'Daniel F. S. Santos'] | 2022-12-20 | null | null | null | null | ['change-detection', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.73853773e-01 -4.28814471e-01 7.24456906e-02 -1.91614598e-01
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adcf9984-e7df-485f-a75f-0882ea857545 | which-cnns-and-training-settings-to-choose | 2111.08320 | null | https://arxiv.org/abs/2111.08320v1 | https://arxiv.org/pdf/2111.08320v1.pdf | Which CNNs and Training Settings to Choose for Action Unit Detection? A Study Based on a Large-Scale Dataset | In this paper we explore the influence of some frequently used Convolutional Neural Networks (CNNs), training settings, and training set structures, on Action Unit (AU) detection. Specifically, we first compare 10 different shallow and deep CNNs in AU detection. Second, we investigate how the different training settings (i.e. centering/normalizing the inputs, using different augmentation severities, and balancing the data) impact the performance in AU detection. Third, we explore the effect of increasing the number of labelled subjects and frames in the training set on the AU detection performance. These comparisons provide the research community with useful tips about the choice of different CNNs and training settings in AU detection. In our analysis, we use a large-scale naturalistic dataset, consisting of ~55K videos captured in the wild. To the best of our knowledge, there is no work that had investigated the impact of such settings on a large-scale AU dataset. | ['Mohammad Mavadati', 'Mohamed Ashraf', 'Ahmed Ghoneim', 'Mina Bishay'] | 2021-11-16 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.55004436e-01 -1.91964507e-01 -5.10022640e-02 -7.94278607e-02
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8dde0331-5da2-471c-973b-c8246392fb82 | credit-card-fraud-detection-using-enhanced | 2303.06514 | null | https://arxiv.org/abs/2303.06514v1 | https://arxiv.org/pdf/2303.06514v1.pdf | Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data | The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the problem of the imbalanced dataset, the synthetic minority over-sampling technique (SMOTE) was used. Implementing the hyperparameters technique to enhance the performance of the random forest classifier. The results showed that the RF classifier gained an accuracy of 98% and about 98% of F1-score value, which is promising. We also believe that our model is relatively easy to apply and can overcome the issue of imbalanced data for fraud detection applications. | ['Huthaifa I. Ashqar', 'AlsharifHasan Mohamad Aburbeian'] | 2023-03-11 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-1.94885880e-01 -2.42614895e-01 -2.46096551e-01 -4.30873662e-01
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a4f83944-46bf-4a2a-9cba-bc50eec1e31d | semi-supervised-multi-view-concept | 2307.00924 | null | https://arxiv.org/abs/2307.00924v1 | https://arxiv.org/pdf/2307.00924v1.pdf | Semi-supervised multi-view concept decomposition | Concept Factorization (CF), as a novel paradigm of representation learning, has demonstrated superior performance in multi-view clustering tasks. It overcomes limitations such as the non-negativity constraint imposed by traditional matrix factorization methods and leverages kernel methods to learn latent representations that capture the underlying structure of the data, thereby improving data representation. However, existing multi-view concept factorization methods fail to consider the limited labeled information inherent in real-world multi-view data. This often leads to significant performance loss. To overcome these limitations, we propose a novel semi-supervised multi-view concept factorization model, named SMVCF. In the SMVCF model, we first extend the conventional single-view CF to a multi-view version, enabling more effective exploration of complementary information across multiple views. We then integrate multi-view CF, label propagation, and manifold learning into a unified framework to leverage and incorporate valuable information present in the data. Additionally, an adaptive weight vector is introduced to balance the importance of different views in the clustering process. We further develop targeted optimization methods specifically tailored for the SMVCF model. Finally, we conduct extensive experiments on four diverse datasets with varying label ratios to evaluate the performance of SMVCF. The experimental results demonstrate the effectiveness and superiority of our proposed approach in multi-view clustering tasks. | ['Qibin Zhao', 'Guoxu Zhou', 'Qi Jiang'] | 2023-07-03 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.01388210e-01 -5.82115710e-01 -5.18947065e-01 -2.15044677e-01
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744fe29a-96d8-401b-bee3-b9642ccc124d | pointpillars-fast-encoders-for-object | 1812.05784 | null | https://arxiv.org/abs/1812.05784v2 | https://arxiv.org/pdf/1812.05784v2.pdf | PointPillars: Fast Encoders for Object Detection from Point Clouds | Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird's eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 - 4 fold runtime improvement. A faster version of our method matches the state of the art at 105 Hz. These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds. | ['Jiong Yang', 'Alex H. Lang', 'Sourabh Vora', 'Holger Caesar', 'Lubing Zhou', 'Oscar Beijbom'] | 2018-12-14 | pointpillars-fast-encoders-for-object-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['birds-eye-view-object-detection', 'robust-3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.25170495e-02 -1.13208927e-01 4.77544256e-02 -3.25160682e-01
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51ff14b1-294c-4a0a-90fc-7ba378d8aeae | scale-and-context-aware-convolutional-non | 1911.07183 | null | https://arxiv.org/abs/1911.07183v1 | https://arxiv.org/pdf/1911.07183v1.pdf | Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring | Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network, which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context, and we incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods. | ['Jinliang He', 'Yu Zhang', 'Jun Hu', 'Qin Wang', 'Kunjin Chen', 'Hang Fan'] | 2019-11-17 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.95014566e-01 -1.56582251e-01 -1.50498301e-01 -7.58660436e-01
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88cfab5e-dff3-4508-a0c7-f7ee56b44253 | variational-dynamic-mixtures-1 | 2010.10403 | null | https://arxiv.org/abs/2010.10403v2 | https://arxiv.org/pdf/2010.10403v2.pdf | Variational Dynamic Mixtures | Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and cause the generative model to predict mode-averaged dynamics. Modeaveraging is problematic since many real-world sequences are highly multi-modal, and their averaged dynamics are unphysical (e.g., predicted taxi trajectories might run through buildings on the street map). To better capture multi-modality, we develop variational dynamic mixtures (VDM): a new variational family to infer sequential latent variables. The VDM approximate posterior at each time step is a mixture density network, whose parameters come from propagating multiple samples through a recurrent architecture. This results in an expressive multi-modal posterior approximation. In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets from different domains. | ['Maja Rudolph', 'Stephan Mandt', 'Chen Qiu'] | 2020-10-20 | variational-dynamic-mixtures | https://openreview.net/forum?id=aAY23UgDBv0 | https://openreview.net/pdf?id=aAY23UgDBv0 | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.74788892e-01 -2.14139391e-02 -3.50292295e-01 -2.76826590e-01
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6bb3667a-52bf-4fec-8527-56ee849cf77c | photoscene-photorealistic-material-and-1 | 2207.00757 | null | https://arxiv.org/abs/2207.00757v1 | https://arxiv.org/pdf/2207.00757v1.pdf | PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes | Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a photorealistic digital twin with high-quality materials and similar lighting. We model scene materials using procedural material graphs; such graphs represent photorealistic and resolution-independent materials. We optimize the parameters of these graphs and their texture scale and rotation, as well as the scene lighting to best match the input image via a differentiable rendering layer. We evaluate our technique on objects and layout reconstructions from ScanNet, SUN RGB-D and stock photographs, and demonstrate that our method reconstructs high-quality, fully relightable 3D scenes that can be re-rendered under arbitrary viewpoints, zooms and lighting. | ['Manmohan Chandraker', 'Kalyan Sunkavalli', 'Miloš Hašan', 'Zexiang Xu', 'Rui Zhu', 'Yannick Hold-Geoffroy', 'Zhengqin Li', 'Yu-Ying Yeh'] | 2022-07-02 | photoscene-photorealistic-material-and | http://openaccess.thecvf.com//content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 5.35988629e-01 -5.81366122e-02 4.79505122e-01 -3.00358385e-01
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1d65b04a-a384-4c00-86bd-a8ea10cb493b | automated-icd-coding-using-extreme-multi | 2212.05857 | null | https://arxiv.org/abs/2212.05857v2 | https://arxiv.org/pdf/2212.05857v2.pdf | Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models | Background: Encouraged by the success of pretrained Transformer models in many natural language processing tasks, their use for International Classification of Diseases (ICD) coding tasks is now actively being explored. In this study, we investigate three types of Transformer-based models, aiming to address the extreme label set and long text classification challenges that are posed by automated ICD coding tasks. Methods: The Transformer-based model PLM-ICD achieved the current state-of-the-art (SOTA) performance on the ICD coding benchmark dataset MIMIC-III. It was chosen as our baseline model to be further optimised. XR-Transformer, the new SOTA model in the general extreme multi-label text classification domain, and XR-LAT, a novel adaptation of the XR-Transformer model, were also trained on the MIMIC-III dataset. XR-LAT is a recursively trained model chain on a predefined hierarchical code tree with label-wise attention, knowledge transferring and dynamic negative sampling mechanisms. Results: Our optimised PLM-ICD model, which was trained with longer total and chunk sequence lengths, significantly outperformed the current SOTA PLM-ICD model, and achieved the highest micro-F1 score of 60.8%. The XR-Transformer model, although SOTA in the general domain, did not perform well across all metrics. The best XR-LAT based model obtained results that were competitive with the current SOTA PLM-ICD model, including improving the macro-AUC by 2.1%. Conclusion: Our optimised PLM-ICD model is the new SOTA model for automated ICD coding on the MIMIC-III dataset, while our novel XR-LAT model performs competitively with the previous SOTA PLM-ICD model. | ['Louisa Jorm', 'Vicki Bennett', 'Anthony Nguyen', 'Oscar Perez-Concha', 'Leibo Liu'] | 2022-12-12 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.24453616e-01 2.46877596e-01 -3.44205886e-01 -3.80890742e-02
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f1a9a17f-9c88-44b5-8c1c-fdd72ae564d1 | delving-into-robust-object-detection-from | 1908.03856 | null | https://arxiv.org/abs/1908.03856v2 | https://arxiv.org/pdf/1908.03856v2.pdf | Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach | Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when they are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc. Those nuisances constitute a large number of fine-grained domains, across which the detection model has to stay robust. Fortunately, UAVs will record meta-data that depict those varying attributes, which are either freely available along with the UAV images, or can be easily obtained. We propose to utilize those free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances. We demonstrate the effectiveness of our proposed algorithm, by showing state-of-the-art performance (single model) on two existing UAV-based object detection benchmarks. The code is available at https://github.com/TAMU-VITA/UAV-NDFT. | ['Zhen-Yu Wu', 'Heesung Kwon', 'Zhangyang Wang', 'Karthik Suresh', 'Hongyu Xu', 'Priya Narayanan'] | 2019-08-11 | delving-into-robust-object-detection-from-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Delving_Into_Robust_Object_Detection_From_Unmanned_Aerial_Vehicles_A_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wu_Delving_Into_Robust_Object_Detection_From_Unmanned_Aerial_Vehicles_A_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-object-detection'] | ['computer-vision'] | [ 3.06057632e-01 -5.67410767e-01 3.29369865e-02 1.61097690e-01
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b19eb882-b92a-4d43-ba19-9e614bb0db65 | r2cnn-multi-dimensional-attention-based | 1811.07126 | null | https://arxiv.org/abs/1811.07126v4 | https://arxiv.org/pdf/1811.07126v4.pdf | SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects | Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://github.com/DetectionTeamUCAS. | ['Kun fu', 'Tengfei Zhang', 'Zhi Guo', 'Yue Zhang', 'Sun Xian', 'Xue Yang', 'Junchi Yan', 'Jirui Yang'] | 2018-11-17 | scrdet-towards-more-robust-detection-for | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_SCRDet_Towards_More_Robust_Detection_for_Small_Cluttered_and_Rotated_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_SCRDet_Towards_More_Robust_Detection_for_Small_Cluttered_and_Rotated_ICCV_2019_paper.pdf | iccv-2019-10 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.90339768e-01 -2.92637736e-01 6.36234581e-02 -2.89153308e-01
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6ef0fefb-9050-4e90-89d0-43f81e279ec4 | deep-structure-inference-network-for-facial | 1803.05873 | null | http://arxiv.org/abs/1803.05873v2 | http://arxiv.org/pdf/1803.05873v2.pdf | Deep Structure Inference Network for Facial Action Unit Recognition | Facial expressions are combinations of basic components called Action Units
(AU). Recognizing AUs is key for developing general facial expression analysis.
In recent years, most efforts in automatic AU recognition have been dedicated
to learning combinations of local features and to exploiting correlations
between Action Units. In this paper, we propose a deep neural architecture that
tackles both problems by combining learned local and global features in its
initial stages and replicating a message passing algorithm between classes
similar to a graphical model inference approach in later stages. We show that
by training the model end-to-end with increased supervision we improve
state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets,
respectively. | ['Meysam Madadi', 'Ciprian A. Corneanu', 'Sergio Escalera'] | 2018-03-15 | deep-structure-inference-network-for-facial-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Ciprian_Corneanu_Deep_Structure_Inference_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ciprian_Corneanu_Deep_Structure_Inference_ECCV_2018_paper.pdf | eccv-2018-9 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 3.38900149e-01 1.44771367e-01 -2.11234510e-01 -7.32710242e-01
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e439561e-cdc6-4c8d-984f-2605fad0626b | mluke-the-power-of-entity-representations-in | 2110.08151 | null | https://arxiv.org/abs/2110.08151v3 | https://arxiv.org/pdf/2110.08151v3.pdf | mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models | Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages with entity representations and show the model consistently outperforms word-based pretrained models in various cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual knowledge more likely than using only word representations. Our source code and pretrained models are available at https://github.com/studio-ousia/luke. | ['Yoshimasa Tsuruoka', 'Ikuya Yamada', 'Ryokan Ri'] | 2021-10-15 | null | https://aclanthology.org/2022.acl-long.505 | https://aclanthology.org/2022.acl-long.505.pdf | acl-2022-5 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-5.03395319e-01 6.44496605e-02 -4.09761131e-01 -4.74287570e-01
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5fc0d95d-ff51-4e31-8e0b-9d3bf97506f9 | rovist-learning-robust-metrics-for-visual | null | null | https://openreview.net/forum?id=bwIOahh3kHO | https://openreview.net/pdf?id=bwIOahh3kHO | RoViST: Learning Robust Metrics for Visual Storytelling | Visual storytelling (VST) is the task of generating a story paragraph that describes a given image sequence. Most existing storytelling approaches have evaluated their models using traditional natural language generation metrics like BLEU or CIDEr. However, such metrics based on $n$-gram matching tend to have poor correlation with human evaluation scores and do not explicitly consider other criteria necessary for storytelling such as sentence structure or topic coherence. Moreover, a single score is not enough to assess a story as it does not inform us about what specific errors were made by the model. In this paper, we propose 3 evaluation metrics sets that analyses which aspects we would look for in a good story: 1) visual grounding, 2) coherence, and 3) non-redundancy. We measure the reliability of our metric sets by analysing its correlation with human judgement scores on a sample of machine stories obtained from 4 state-of-the-arts models trained on the Visual Storytelling Dataset (VIST). Our metric sets outperforms other metrics on human correlation, and could be served as a learning based evaluation metric set that is complementary to existing rule-based metrics. | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.35055363e-01 3.48009735e-01 3.12672965e-02 -2.41016701e-01
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2c961f10-bd89-4118-ae66-e24aa14fdb8c | rethinking-text-attribute-transfer-a-lexical | 1909.12335 | null | https://arxiv.org/abs/1909.12335v1 | https://arxiv.org/pdf/1909.12335v1.pdf | Rethinking Text Attribute Transfer: A Lexical Analysis | Text attribute transfer is modifying certain linguistic attributes (e.g. sentiment, style, authorship, etc.) of a sentence and transforming them from one type to another. In this paper, we aim to analyze and interpret what is changed during the transfer process. We start from the observation that in many existing models and datasets, certain words within a sentence play important roles in determining the sentence attribute class. These words are referred to as \textit{the Pivot Words}. Based on these pivot words, we propose a lexical analysis framework, \textit{the Pivot Analysis}, to quantitatively analyze the effects of these words in text attribute classification and transfer. We apply this framework to existing datasets and models and show that: (1) the pivot words are strong features for the classification of sentence attributes; (2) to change the attribute of a sentence, many datasets only requires to change certain pivot words; (3) consequently, many transfer models only perform the lexical-level modification, while leaving higher-level sentence structures unchanged. Our work provides an in-depth understanding of linguistic attribute transfer and further identifies the future requirements and challenges of this task\footnote{Our code can be found at https://github.com/FranxYao/pivot_analysis}. | ['Lei LI', 'Yao Fu', 'Hao Zhou', 'Jiaze Chen'] | 2019-09-26 | rethinking-text-attribute-transfer-a-lexical-1 | https://aclanthology.org/W19-8604 | https://aclanthology.org/W19-8604.pdf | ws-2019-10 | ['text-attribute-transfer', 'lexical-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.98687726e-01 1.60117764e-02 -3.60892743e-01 -7.78993368e-01
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53337736-a2eb-4562-aa66-7b8129dcb181 | afrisenti-a-twitter-sentiment-analysis | 2302.08956 | null | https://arxiv.org/abs/2302.08956v4 | https://arxiv.org/pdf/2302.08956v4.pdf | AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages | Africa is home to over 2000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families annotated by native speakers. The data is used in SemEval 2023 Task 12, the first Afro-centric SemEval shared task. We describe the data collection methodology, annotation process, and related challenges when curating each of the datasets. We conduct experiments with different sentiment classification baselines and discuss their usefulness. We hope AfriSenti enables new work on under-represented languages. The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be loaded as a huggingface datasets (https://huggingface.co/datasets/shmuhammad/AfriSenti). | ['Davis David', 'Saif M. Mohammad', 'Steven Arthur', 'Bernard Opoku', 'Hagos Tesfahun Gebremichael', 'Sisay Adugna Chala', 'Hailu Beshada Balcha', 'Wendimu Baye Messelle', 'Tadesse Belay', 'Samuel Rutunda', 'Tajuddeen Gwadabe', 'Falalu Ibrahim', 'Bello Shehu Bello', 'Salomey Osei', 'Felermino Dário Mário António Ali', 'Pavel Brazdil', 'Oumaima Hourrane', 'Sebastian Ruder', 'Meriem Beloucif', "Ibrahim Sa'id Ahmad", 'Seid Muhie Yimam', 'David Ifeoluwa Adelani', 'Nedjma Ousidhoum', 'Abinew Ali Ayele', 'Idris Abdulmumin', 'Shamsuddeen Hassan Muhammad'] | 2023-02-17 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-4.69981849e-01 -1.61472186e-01 -2.72710264e-01 -6.27023995e-01
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3c353805-8809-4c8e-ab37-f5f8a8382c37 | along-the-time-timeline-traced-embedding-for | null | null | https://dl.acm.org/doi/abs/10.1145/3511808.3557233 | https://dl.acm.org/doi/pdf/10.1145/3511808.3557233 | Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion | Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embedding approaches are put forward. While most existing models ignore the independence of semantic and temporal information. We empirically find that current models have difficulty distinguishing representations of the same entity or relation at different timestamps. In this regard, we propose a TimeLine-Traced Knowledge Graph Embedding method (TLT-KGE) for temporal knowledge graph completion. TLT-KGE aims to embed the entities and relations with timestamps as a complex vector or a quaternion vector. Specifically, TLT-KGE models semantic information and temporal information as different axes of complex number space or quaternion space. Meanwhile, two specific components carving the relationship between semantic and temporal information are devised to buoy the modeling. In this way, the proposed method can not only distinguish the independence of the semantic and temporal information, but also establish a connection between them. Experimental results on the link prediction task demonstrate that TLT-KGE achieves substantial improvements over state-of-the-art competitors. The source code will be available on https://github.com/zhangfw123/TLT-KGE. | ['Qing He.', 'Yongjun Xu', 'Fuzhen Zhuang', 'Xiang Ao', 'Zhao Zhang', 'Fuwei Zhang'] | 2022-10-17 | null | null | null | the-31st-acm-international-conference-on | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['graphs', 'graphs', 'graphs', 'knowledge-base', 'knowledge-base'] | [-5.81909239e-01 -1.34362087e-01 -4.82051551e-01 -2.61789560e-01
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7b10f00b-e72a-41ae-b883-65d659fd20cb | polyresponse-a-rank-based-approach-to-task | 1909.01296 | null | https://arxiv.org/abs/1909.01296v1 | https://arxiv.org/pdf/1909.01296v1.pdf | PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking | We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies. The PolyResponse engine is trained on hundreds of millions of examples extracted from real conversations: it learns what responses are appropriate in different conversational contexts. It then ranks a large index of text and visual responses according to their similarity to the given context, and narrows down the list of relevant entities during the multi-turn conversation. We introduce a restaurant search and booking system powered by the PolyResponse engine, currently available in 8 different languages. | ['Pei-Hao Su', 'Nikola Mrkšić', 'Tsung-Hsien Wen', 'Paweł Budzianowski', 'Iñigo Casanueva', 'Ivan Vulić', 'Matthew Henderson', 'Georgios Spithourakis', 'Daniela Gerz', 'Sam Coope'] | 2019-09-03 | polyresponse-a-rank-based-approach-to-task-1 | https://aclanthology.org/D19-3031 | https://aclanthology.org/D19-3031.pdf | ijcnlp-2019-11 | ['conversational-search'] | ['natural-language-processing'] | [-1.05752863e-01 4.25993294e-01 -1.73139483e-01 -4.55612510e-01
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2ba91e82-7d2b-4bf5-9933-7012de01fc76 | cc-3dt-panoramic-3d-object-tracking-via-cross | 2212.01247 | null | https://arxiv.org/abs/2212.01247v1 | https://arxiv.org/pdf/2212.01247v1.pdf | CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion | To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object tracking methods prioritize optimizing the single-camera setup and resort to post-hoc fusion in a multi-camera setup. In this paper, we propose a method for panoramic 3D object tracking, called CC-3DT, that associates and models object trajectories both temporally and across views, and improves the overall tracking consistency. In particular, our method fuses 3D detections from multiple cameras before association, reducing identity switches significantly and improving motion modeling. Our experiments on large-scale driving datasets show that fusion before association leads to a large margin of improvement over post-hoc fusion. We set a new state-of-the-art with 12.6% improvement in average multi-object tracking accuracy (AMOTA) among all camera-based methods on the competitive NuScenes 3D tracking benchmark, outperforming previously published methods by 6.5% in AMOTA with the same 3D detector. | ['Fisher Yu', 'Min Sun', 'Suryansh Kumar', 'Yung-Hsu Yang', 'Tobias Fischer'] | 2022-12-02 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-3.69151115e-01 -5.87218404e-01 -2.71329373e-01 -3.55718993e-02
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9c1588a7-655f-4ba0-973e-b169aa0a2075 | argument-mining-using-bert-and-self-attention | 2302.13906 | null | https://arxiv.org/abs/2302.13906v1 | https://arxiv.org/pdf/2302.13906v1.pdf | Argument Mining using BERT and Self-Attention based Embeddings | Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse. | ['Anurag Goel', 'Pranav Bhatnagar', 'Pranjal Srivastava'] | 2023-02-27 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [-1.50436386e-02 1.02998638e+00 -5.76359570e-01 -1.38456732e-01
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eda2268c-21b0-4a90-b97c-c6808c9edc6e | analyzing-the-use-of-influence-functions-for | 2210.13281 | null | https://arxiv.org/abs/2210.13281v1 | https://arxiv.org/pdf/2210.13281v1.pdf | Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation | Customer feedback can be an important signal for improving commercial machine translation systems. One solution for fixing specific translation errors is to remove the related erroneous training instances followed by re-training of the machine translation system, which we refer to as instance-specific data filtering. Influence functions (IF) have been shown to be effective in finding such relevant training examples for classification tasks such as image classification, toxic speech detection and entailment task. Given a probing instance, IF find influential training examples by measuring the similarity of the probing instance with a set of training examples in gradient space. In this work, we examine the use of influence functions for Neural Machine Translation (NMT). We propose two effective extensions to a state of the art influence function and demonstrate on the sub-problem of copied training examples that IF can be applied more generally than handcrafted regular expressions. | ['Felix Hieber', 'Eva Hasler', 'Tsz Kin Lam'] | 2022-10-24 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 8.82022858e-01 2.35371351e-01 -5.45063257e-01 -6.33394122e-01
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cf9d4bae-cd52-464f-bdee-927150e3d7fa | improving-formality-style-transfer-with | 2106.00210 | null | https://arxiv.org/abs/2106.00210v1 | https://arxiv.org/pdf/2106.00210v1.pdf | Improving Formality Style Transfer with Context-Aware Rule Injection | Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text. Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST). CARI injects multiple rules into an end-to-end BERT-based encoder and decoder model. It learns to select optimal rules based on context. The intrinsic evaluation showed that CARI achieved the new highest performance on the FST benchmark dataset. Our extrinsic evaluation showed that CARI can greatly improve the regular pre-trained models' performance on several tweet sentiment analysis tasks. | ['Hong Yu', 'Zonghai Yao'] | 2021-06-01 | null | https://aclanthology.org/2021.acl-long.124 | https://aclanthology.org/2021.acl-long.124.pdf | acl-2021-5 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 4.20230299e-01 2.97409594e-01 -4.35239375e-01 -6.35591745e-01
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96db94f0-df8a-496e-a98e-149b249ae18b | melanogans-high-resolution-skin-lesion | 1804.04338 | null | http://arxiv.org/abs/1804.04338v1 | http://arxiv.org/pdf/1804.04338v1.pdf | MelanoGANs: High Resolution Skin Lesion Synthesis with GANs | Generative Adversarial Networks (GANs) have been successfully used to
synthesize realistically looking images of faces, scenery and even medical
images. Unfortunately, they usually require large training datasets, which are
often scarce in the medical field, and to the best of our knowledge GANs have
been only applied for medical image synthesis at fairly low resolution.
However, many state-of-the-art machine learning models operate on high
resolution data as such data carries indispensable, valuable information. In
this work, we try to generate realistically looking high resolution images of
skin lesions with GANs, using only a small training dataset of 2000 samples.
The nature of the data allows us to do a direct comparison between the image
statistics of the generated samples and the real dataset. We both
quantitatively and qualitatively compare state-of-the-art GAN architectures
such as DCGAN and LAPGAN against a modification of the latter for the task of
image generation at a resolution of 256x256px. Our investigation shows that we
can approximate the real data distribution with all of the models, but we
notice major differences when visually rating sample realism, diversity and
artifacts. In a set of use-case experiments on skin lesion classification, we
further show that we can successfully tackle the problem of heavy class
imbalance with the help of synthesized high resolution melanoma samples. | ['Shadi Albarqouni', 'Nassir Navab', 'Christoph Baur'] | 2018-04-12 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.22446179e-01 4.30529267e-01 2.33372554e-01 -2.85094995e-02
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16f163cd-5ac0-435e-82d8-bc845a3074cb | bias-in-pruned-vision-models-in-depth | 2304.12622 | null | https://arxiv.org/abs/2304.12622v1 | https://arxiv.org/pdf/2304.12622v1.pdf | Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures | Pruning - that is, setting a significant subset of the parameters of a neural network to zero - is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this phenomenon, the relationship between neural network pruning and induced bias is not well-understood. In this work, we systematically investigate and characterize this phenomenon in Convolutional Neural Networks for computer vision. First, we show that it is in fact possible to obtain highly-sparse models, e.g. with less than 10% remaining weights, which do not decrease in accuracy nor substantially increase in bias when compared to dense models. At the same time, we also find that, at higher sparsities, pruned models exhibit higher uncertainty in their outputs, as well as increased correlations, which we directly link to increased bias. We propose easy-to-use criteria which, based only on the uncompressed model, establish whether bias will increase with pruning, and identify the samples most susceptible to biased predictions post-compression. | ['Dan Alistarh', 'Alexandra Peste', 'Eugenia Iofinova'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023_paper.pdf | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [ 6.77335560e-01 3.92215967e-01 -3.22710425e-01 -4.73704457e-01
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88129cdc-b1ea-4dad-b507-0ea87d5e2c58 | on-the-role-of-parallel-data-in-cross-lingual | 2212.10173 | null | https://arxiv.org/abs/2212.10173v1 | https://arxiv.org/pdf/2212.10173v1.pdf | On the Role of Parallel Data in Cross-lingual Transfer Learning | While prior work has established that the use of parallel data is conducive for cross-lingual learning, it is unclear if the improvements come from the data itself, or if it is the modeling of parallel interactions that matters. Exploring this, we examine the usage of unsupervised machine translation to generate synthetic parallel data, and compare it to supervised machine translation and gold parallel data. We find that even model generated parallel data can be useful for downstream tasks, in both a general setting (continued pretraining) as well as the task-specific setting (translate-train), although our best results are still obtained using real parallel data. Our findings suggest that existing multilingual models do not exploit the full potential of monolingual data, and prompt the community to reconsider the traditional categorization of cross-lingual learning approaches. | ['Mikel Artetxe', 'Machel Reid'] | 2022-12-20 | null | null | null | null | ['unsupervised-machine-translation', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.87594742e-01 7.06841098e-03 -4.06881809e-01 -3.71196687e-01
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7b2c78ba-8590-442a-80c2-10ad631345af | locality-preserving-sentence-encoding | null | null | https://aclanthology.org/2021.findings-emnlp.262 | https://aclanthology.org/2021.findings-emnlp.262.pdf | Locality Preserving Sentence Encoding | Although researches on word embeddings have made great progress in recent years, many tasks in natural language processing are on the sentence level. Thus, it is essential to learn sentence embeddings. Recently, Sentence BERT (SBERT) is proposed to learn embeddings on the sentence level, and it uses the inner product (or, cosine similarity) to compute semantic similarity between sentences. However, this measurement cannot well describe the semantic structures among sentences. The reason is that sentences may lie on a manifold in the ambient space rather than distribute in an Euclidean space. Thus, cosine similarity cannot approximate distances on the manifold. To tackle the severe problem, we propose a novel sentence embedding method called Sentence BERT with Locality Preserving (SBERT-LP), which discovers the sentence submanifold from a high-dimensional space and yields a compact sentence representation subspace by locally preserving geometric structures of sentences. We compare the SBERT-LP with several existing sentence embedding approaches from three perspectives: sentence similarity, sentence classification and sentence clustering. Experimental results and case studies demonstrate that our method encodes sentences better in the sense of semantic structures. | ['Hongfei Lin', 'Bo Xu', 'Liang Yang', 'Yonghe Chu', 'Changrong Min'] | null | null | null | null | findings-emnlp-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [-2.21668798e-02 -2.11090237e-01 1.15875922e-01 -5.48170984e-01
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0b3d0f4f-b85f-4ddd-a311-9babe58bb55c | polar-ducks-and-where-to-find-them-enhancing | 2305.12027 | null | https://arxiv.org/abs/2305.12027v1 | https://arxiv.org/pdf/2305.12027v1.pdf | Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings | Entity linking methods based on dense retrieval are an efficient and widely used solution in large-scale applications, but they fall short of the performance of generative models, as they are sensitive to the structure of the embedding space. In order to address this issue, this paper introduces DUCK, an approach to infusing structural information in the space of entity representations, using prior knowledge of entity types. Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph. Then, porting the concept of box embeddings to spherical polar coordinates, we propose to represent relations as boxes on the hypersphere. We optimize the model to cluster entities of similar type by placing them inside the boxes corresponding to their relations. Our experiments show that our method sets new state-of-the-art results on standard entity-disambiguation benchmarks, it improves the performance of the model by up to 7.9 F1 points, outperforms other type-aware approaches, and matches the results of generative models with 18 times more parameters. | ['Nicola Cancedda', 'Louis Martin', 'Simone Merello', 'Nora Kassner', 'Frédéric A. Dreyer', 'Mikhail Plekhanov', 'Mattia Atzeni'] | 2023-05-19 | null | null | null | null | ['entity-linking', 'entity-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.74948382e-01 2.86391139e-01 -3.14812928e-01 -3.01070988e-01
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ae6a2521-706b-4cbc-a4f9-b491758503c4 | a-joint-3d-unet-graph-neural-network-based | 1908.08588 | null | https://arxiv.org/abs/1908.08588v1 | https://arxiv.org/pdf/1908.08588v1.pdf | A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs | We present an end-to-end deep learning segmentation method by combining a 3D UNet architecture with a graph neural network (GNN) model. In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions. The dense feature maps at this level are transformed into a graph input to the GNN module. The incorporation of graph convolutions in the UNet provides nodes in the graph with information that is based on node connectivity, in addition to the local features learnt through the downsampled paths. This information can help improve segmentation decisions. By stacking several graph convolution layers, the nodes can access higher order neighbourhood information without substantial increase in computational expense. We propose two types of node connectivity in the graph adjacency: i) one predefined and based on a regular node neighbourhood, and ii) one dynamically computed during training and using the nearest neighbour nodes in the feature space. We have applied this method to the task of segmenting the airway tree from chest CT scans. Experiments have been performed on 32 CTs from the Danish Lung Cancer Screening Trial dataset. We evaluate the performance of the UNet-GNN models with two types of graph adjacency and compare it with the baseline UNet. | ['Marleen de Bruijne', 'Antonio Garcia-Uceda Juarez', 'Zaigham Saghir', 'Raghavendra Selvan'] | 2019-08-22 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 3.58109206e-01 7.67589927e-01 6.72381744e-02 -4.34577733e-01
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ab5067f9-e408-4646-bb11-7dbe3b211f58 | lensless-hyperspectral-imaging-by-fourier | 2002.10886 | null | http://arxiv.org/abs/2002.10886v2 | http://arxiv.org/pdf/2002.10886v2.pdf | Lensless hyperspectral imaging by Fourier transform spectroscopy for broadband visible light: phase retrieval technique | A novel phase retrieval algorithm for broadband hyperspectral phase imaging
from noisy intensity observations is proposed. It utilizes advantages of the
Fourier Transform spectroscopy in the self-referencing optical setup and
provides, additionally beyond spectral intensity distribution, reconstruction
of the investigated object's phase. The noise amplification Fellgett's
disadvantage is relaxed by the application of sparse wavefront noise filtering
embedded in the proposed algorithm. The algorithm reliability is proved by
simulation tests and results of physical experiments on transparent objects
which demonstrate precise phase imaging and object depth (profile)
reconstructions. | [] | 2020-03-16 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 8.33750129e-01 -2.11665481e-01 4.57343280e-01 -3.43137234e-01
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065b07d5-21dd-4eea-9383-348a8788ac77 | sparsely-annotated-object-detection-a-region | 2201.04620 | null | https://arxiv.org/abs/2201.04620v1 | https://arxiv.org/pdf/2201.04620v1.pdf | Sparsely Annotated Object Detection: A Region-based Semi-supervised Approach | Research shows a noticeable drop in performance of object detectors when the training data has missing annotations, i.e. sparsely annotated data. Contemporary methods focus on proxies for missing ground-truth annotations either in the form of pseudo-labels or by re-weighing gradients for unlabeled boxes during training. In this work, we revisit the formulation of sparsely annotated object detection. We observe that sparsely annotated object detection can be considered a semi-supervised object detection problem at a region level. Building on this insight, we propose a region-based semi-supervised algorithm, that automatically identifies regions containing unlabeled foreground objects. Our algorithm then processes the labeled and un-labeled foreground regions differently, a common practice in semi-supervised methods. To evaluate the effectiveness of the proposed approach, we conduct exhaustive experiments on five splits commonly used by sparsely annotated approaches on the PASCAL-VOC and COCO datasets and achieve state-of-the-art performance. In addition to this, we show that our approach achieves competitive performance on standard semi-supervised setups demonstrating the strength and broad applicability of our approach. | ['Abhinav Shrivastava', 'Rama Chellappa', 'Saksham Suri', 'Sai Saketh Rambhatla'] | 2022-01-12 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.76340091e-01 2.34795153e-01 -3.14327866e-01 -5.11825085e-01
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2eeda650-5814-4783-aef9-7d5c7a5a673b | weakly-supervised-monocular-3d-object | 2303.08686 | null | https://arxiv.org/abs/2303.08686v1 | https://arxiv.org/pdf/2303.08686v1.pdf | Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency | Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need LiDAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. This inconsistency between the training and inference makes it hard to utilize the large-scale feedback data and increases the data collection expenses. To bridge this gap, we propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images. To be specific, we explore three types of consistency in this task, i.e. the projection, multi-view and direction consistency, and design a weakly-supervised architecture based on these consistencies. Moreover, we propose a new 2D direction labeling method in this task to guide the model for accurate rotation direction prediction. Experiments show that our weakly-supervised method achieves comparable performance with some fully supervised methods. When used as a pre-training method, our model can significantly outperform the corresponding fully-supervised baseline with only 1/3 3D labels. https://github.com/weakmono3d/weakmono3d | ['Jianbing Shen', 'Cheng-Zhong Xu', 'Zhongying Qiu', 'Wencheng Han', 'Runzhou Tao'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tao_Weakly_Supervised_Monocular_3D_Object_Detection_Using_Multi-View_Projection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['monocular-3d-object-detection'] | ['computer-vision'] | [-3.51918727e-01 -1.58346623e-01 -7.02573180e-01 -6.31078541e-01
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e93972f1-ecb7-4947-b843-926795d8cbb8 | optimal-vaccination-policy-to-prevent | 2306.13633 | null | https://arxiv.org/abs/2306.13633v1 | https://arxiv.org/pdf/2306.13633v1.pdf | Optimal Vaccination Policy to Prevent Endemicity: A Stochastic Model | We examine here the effects of recurrent vaccination and waning immunity on the establishment of an endemic equilibrium in a population. An individual-based model that incorporates memory effects for transmission rate during infection and subsequent immunity is introduced, considering stochasticity at the individual level. By letting the population size going to infinity, we derive a set of equations describing the large scale behavior of the epidemic. The analysis of the model's equilibria reveals a criterion for the existence of an endemic equilibrium, which depends on the rate of immunity loss and the distribution of time between booster doses. The outcome of a vaccination policy in this context is influenced by the efficiency of the vaccine in blocking transmissions and the distribution pattern of booster doses within the population. Strategies with evenly spaced booster shots at the individual level prove to be more effective in preventing disease spread compared to irregularly spaced boosters, as longer intervals without vaccination increase susceptibility and facilitate more efficient disease transmission. We provide an expression for the critical fraction of the population required to adhere to the vaccination policy in order to eradicate the disease, that resembles a well-known threshold for preventing an outbreak with an imperfect vaccine. We also investigate the consequences of unequal vaccine access in a population and prove that, under reasonable assumptions, fair vaccine allocation is the optimal strategy to prevent endemicity. | ['Hélène Guérin', 'Arthur Charpentier', 'Félix Foutel-Rodier'] | 2023-06-23 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 1.20595127e-01 2.17136764e-03 -3.69401485e-01 2.05062166e-01
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64da1ad2-3850-4543-9a7b-44f794fa2a63 | physically-explainable-cnn-for-sar-image | 2110.14144 | null | https://arxiv.org/abs/2110.14144v2 | https://arxiv.org/pdf/2110.14144v2.pdf | Physically Explainable CNN for SAR Image Classification | Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL). It comprises three parts: (1) explainable models (XM) to provide prior physics knowledge, (2) physics guided network (PGN) to encode the knowledge into physics-aware features, and (3) physics injected network (PIN) to adaptively introduce the physics-aware features into classification pipeline for label prediction. A hybrid Image-Physics SAR dataset format is proposed for evaluation, with both Sentinel-1 and Gaofen-3 SAR data being experimented. The results show that the proposed PGIL substantially improve the classification performance in case of limited labeled data compared with the counterpart data-driven CNN and other pre-training methods. Additionally, the physics explanations are discussed to indicate the interpretability and the physical consistency preserved in the predictions. We deem the proposed method would promote the development of physically explainable deep learning in SAR image interpretation field. | ['Corneliu Octavian Dumitru', 'Ying Liu', 'Junwei Han', 'Mihai Datcu', 'Xiwen Yao', 'Zhongling Huang'] | 2021-10-27 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.44614705e-01 5.13015032e-01 8.54648724e-02 -9.01628435e-01
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7c6ad6d1-0be5-4f53-8d7a-6864cc4ef0e1 | rolling-unrolling-lstms-for-action | 2005.02190 | null | https://arxiv.org/abs/2005.02190v2 | https://arxiv.org/pdf/2005.02190v2.pdf | Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video | In this paper, we tackle the problem of egocentric action anticipation, i.e., predicting what actions the camera wearer will perform in the near future and which objects they will interact with. Specifically, we contribute Rolling-Unrolling LSTM, a learning architecture to anticipate actions from egocentric videos. The method is based on three components: 1) an architecture comprised of two LSTMs to model the sub-tasks of summarizing the past and inferring the future, 2) a Sequence Completion Pre-Training technique which encourages the LSTMs to focus on the different sub-tasks, and 3) a Modality ATTention (MATT) mechanism to efficiently fuse multi-modal predictions performed by processing RGB frames, optical flow fields and object-based features. The proposed approach is validated on EPIC-Kitchens, EGTEA Gaze+ and ActivityNet. The experiments show that the proposed architecture is state-of-the-art in the domain of egocentric videos, achieving top performances in the 2019 EPIC-Kitchens egocentric action anticipation challenge. The approach also achieves competitive performance on ActivityNet with respect to methods not based on unsupervised pre-training and generalizes to the tasks of early action recognition and action recognition. To encourage research on this challenging topic, we made our code, trained models, and pre-extracted features available at our web page: http://iplab.dmi.unict.it/rulstm. | ['Giovanni Maria Farinella', 'Antonino Furnari'] | 2020-05-04 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.64385188e-01 1.53126568e-01 -1.53064281e-01 -3.82334113e-01
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8470e817-0764-4b8c-ae3a-de5f4b523287 | from-competition-to-collaboration-making-toy | 2211.06212 | null | https://arxiv.org/abs/2211.06212v1 | https://arxiv.org/pdf/2211.06212v1.pdf | From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning | Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease. In real-world clinical use, multiple diseases need to be considered since they can co-exist in the same patient. In this work, we demonstrate how federated learning (FL) can be used to make these toy CXR datasets from Kaggle clinically useful. Specifically, we train a single FL classification model (`global`) using two separate CXR datasets -- one annotated for presence of pneumonia and the other for presence of pneumothorax (two common and life-threatening conditions) -- capable of diagnosing both. We compare the performance of the global FL model with models trained separately on both datasets (`baseline`) for two different model architectures. On a standard, naive 3-layer CNN architecture, the global FL model achieved AUROC of 0.84 and 0.81 for pneumonia and pneumothorax, respectively, compared to 0.85 and 0.82, respectively, for both baseline models (p>0.05). Similarly, on a pretrained DenseNet121 architecture, the global FL model achieved AUROC of 0.88 and 0.91 for pneumonia and pneumothorax, respectively, compared to 0.89 and 0.91, respectively, for both baseline models (p>0.05). Our results suggest that FL can be used to create global `meta` models to make toy datasets from Kaggle clinically useful, a step forward towards bridging the gap from bench to bedside. | ['Vishwa S. Parekh', 'Paul H. Yi', 'Adway Kanhere', 'Pranav Kulkarni'] | 2022-11-11 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [-5.75329969e-03 5.44739738e-02 -7.71058574e-02 -3.29687059e-01
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78ea0c60-de56-4f8d-92fc-0221a633f62e | state-of-the-art-of-user-simulation | 2201.03435 | null | https://arxiv.org/abs/2201.03435v1 | https://arxiv.org/pdf/2201.03435v1.pdf | State of the Art of User Simulation approaches for conversational information retrieval | Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs. In order to optimize these interactions and enhance the user experience, it is necessary to improve IR models by taking into account sequential heterogeneous user-system interactions. Reinforcement learning has emerged as a paradigm particularly suited to optimize sequential decision making in many domains and has recently appeared in IR. However, training these systems by reinforcement learning on users is not feasible. One solution is to train IR systems on user simulations that model the behavior of real users. Our contribution is twofold: 1)reviewing the literature on user modeling and user simulation for information access, and 2) discussing the different research perspectives for user simulations in the context of CIR | ['Ludovic Denoyer', 'Laure Soulier', 'Pierre Erbacher'] | 2022-01-10 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 8.16427171e-02 4.82004315e-01 -1.91920117e-01 -1.91813424e-01
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36c55e2d-98ba-4663-b085-cb9a0e4f138c | camouflaged-object-detection-with-feature-1 | 2307.03943 | null | https://arxiv.org/abs/2307.03943v1 | https://arxiv.org/pdf/2307.03943v1.pdf | Camouflaged Object Detection with Feature Grafting and Distractor Aware | The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Distractor Aware network (FDNet) to handle the COD task. Specifically, we use CNN and Transformer to encode multi-scale images in parallel. In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module. A Distractor Aware Module is designed to explicitly model the two possible distractors in the COD task to refine the coarse camouflage map. We also proposed the largest artificial camouflaged object dataset which contains 2000 images with annotations, named ACOD2K. We conducted extensive experiments on four widely used benchmark datasets and the ACOD2K dataset. The results show that our method significantly outperforms other state-of-the-art methods. The code and the ACOD2K will be available at https://github.com/syxvision/FDNet. | ['Lin Qi', 'Xinyue Li', 'Yuxuan Song'] | 2023-07-08 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 7.96175972e-02 -3.11716676e-01 7.11865872e-02 2.84276102e-02
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cfac9c9e-9202-4de2-b22e-21453216c570 | a-sequence-to-set-network-for-nested-named | 2105.08901 | null | https://arxiv.org/abs/2105.08901v2 | https://arxiv.org/pdf/2105.08901v2.pdf | A Sequence-to-Set Network for Nested Named Entity Recognition | Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods, considering the entity recognition as a span classification task, can deal with nested entities naturally. But they suffer from the huge search space and the lack of interactions between entities. To address these issues, we propose a novel sequence-to-set neural network for nested NER. Instead of specifying candidate spans in advance, we provide a fixed set of learnable vectors to learn the patterns of the valuable spans. We utilize a non-autoregressive decoder to predict the final set of entities in one pass, in which we are able to capture dependencies between entities. Compared with the sequence-to-sequence method, our model is more suitable for such unordered recognition task as it is insensitive to the label order. In addition, we utilize the loss function based on bipartite matching to compute the overall training loss. Experimental results show that our proposed model achieves state-of-the-art on three nested NER corpora: ACE 2004, ACE 2005 and KBP 2017. The code is available at https://github.com/zqtan1024/sequence-to-set. | ['Yueting Zhuang', 'Weiming Lu', 'Shuai Zhang', 'Yongliang Shen', 'Zeqi Tan'] | 2021-05-19 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-6.68154210e-02 -1.31317645e-01 -4.55519138e-03 -4.74609256e-01
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302932ad-18d1-497b-8f1c-7fac3e88d95e | double-retrieval-and-ranking-for-accurate | 2201.05981 | null | https://arxiv.org/abs/2201.05981v1 | https://arxiv.org/pdf/2201.05981v1.pdf | Double Retrieval and Ranking for Accurate Question Answering | Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering. This step is performed by aggregating the embeddings of top $k$ answer candidates to support the verification of a target answer. Although the approach is intuitive and sound still shows two limitations: (i) the supporting candidates are ranked only according to the relevancy with the question and not with the answer, and (ii) the support provided by the other answer candidates is suboptimal as these are retrieved independently of the target answer. In this paper, we address both drawbacks by proposing (i) a double reranking model, which, for each target answer, selects the best support; and (ii) a second neural retrieval stage designed to encode question and answer pair as the query, which finds more specific verification information. The results on three well-known datasets for AS2 show consistent and significant improvement of the state of the art. | ['Alessandro Moschitti', 'Thuy Vu', 'Zeyu Zhang'] | 2022-01-16 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 1.79152563e-01 2.22747609e-01 -2.49138884e-02 -2.25863248e-01
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a9b87f4a-11f3-4a23-ae16-14dc662abc1d | eiffel-tower-a-deep-sea-underwater-dataset | 2305.05301 | null | https://arxiv.org/abs/2305.05301v1 | https://arxiv.org/pdf/2305.05301v1.pdf | Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization | Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or day-night cycles present a major challenge. Under water, the sources of variability are due to other factors such as water conditions or growth of marine organisms. Yet it remains a major obstacle and a much less studied one, partly due to the lack of data. This paper presents a new deep-sea dataset to benchmark underwater long-term visual localization. The dataset is composed of images from four visits to the same hydrothermal vent edifice over the course of five years. Camera poses and a common geometry of the scene were estimated using navigation data and Structure-from-Motion. This serves as a reference when evaluating visual localization techniques. An analysis of the data provides insights about the major changes observed throughout the years. Furthermore, several well-established visual localization methods are evaluated on the dataset, showing there is still room for improvement in underwater long-term visual localization. The data is made publicly available at https://www.seanoe.org/data/00810/92226/. | ['Vincent Hugel', 'Loïc Van Audenhaege', 'Marjolaine Matabos', 'Ricard Marxer', 'Aurélien Arnaubec', 'Maxime Ferrera', 'Claire Dune', 'Clémentin Boittiaux'] | 2023-05-09 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-3.81268144e-01 -3.48390162e-01 4.83954012e-01 -3.86201382e-01
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dfe06575-018b-4b90-9ef1-11bc495a4f71 | vector-space-and-matrix-methods-in-signal-and | 1909.05128 | null | http://arxiv.org/abs/1909.05128v1 | http://arxiv.org/pdf/1909.05128v1.pdf | Vector Space and Matrix Methods in Signal and System Theory | The tools, ideas, and insights from linear algebra, abstract algebra, and
functional analysis can be extremely useful to signal processing and system
theory in various areas of engineering, science, and social science including
approximation, optimization, parameter identification, big data, etc. Indeed,
many important ideas can be developed from the simple operator equation
A x = b
by considering it in a variety of ways. If x and if b are vectors from the
same or, perhaps, different vector spaces and A is an operator, there are three
interesting questions that can be asked which provide a setting for a broad
study. | [] | 2019-09-11 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [-6.34012744e-02 -3.63780826e-01 -6.43018633e-02 -1.42548224e-02
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a8a32c59-0c94-48a9-b245-e3eb98dbb408 | enhancing-document-level-relation-extraction | 2207.11433 | null | https://arxiv.org/abs/2207.11433v1 | https://arxiv.org/pdf/2207.11433v1.pdf | Enhancing Document-level Relation Extraction by Entity Knowledge Injection | Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models. | ['Wei Hu', 'Weijian Sun', 'Zitao Wang', 'Xinyi Wang'] | 2022-07-23 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.15595227e-02 8.79520476e-01 -9.42295969e-01 -9.48147327e-02
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703bd989-bcd5-4bea-8ffe-9ab094bdc300 | ast-audio-spectrogram-transformer | 2104.01778 | null | https://arxiv.org/abs/2104.01778v3 | https://arxiv.org/pdf/2104.01778v3.pdf | AST: Audio Spectrogram Transformer | In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-attention mechanism on top of the CNN, forming a CNN-attention hybrid model. However, it is unclear whether the reliance on a CNN is necessary, and if neural networks purely based on attention are sufficient to obtain good performance in audio classification. In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2. | ['James Glass', 'Yu-An Chung', 'Yuan Gong'] | 2021-04-05 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 1.11060448e-01 -2.56446242e-01 5.96553646e-02 -4.37396228e-01
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09bd61ae-56e7-4be3-9c5d-e10c56009731 | graph-generation-with-destination-driven | 2302.03596 | null | https://arxiv.org/abs/2302.03596v2 | https://arxiv.org/pdf/2302.03596v2.pdf | Graph Generation with Destination-Predicting Diffusion Mixture | Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achieved notable success in graph generation recently, they are ill-suited for modeling the structural information of graphs since learning to denoise the noisy samples does not explicitly capture the graph topology. To tackle this limitation, we propose a novel generative framework that models the topology of graphs by predicting the destination of the diffusion process, which is the original graph that has the correct topology information, as a weighted mean of data. Specifically, we design the generative process as a mixture of diffusion processes conditioned on the endpoint in the data distribution, which drives the process toward the predicted destination, resulting in rapid convergence. We introduce new simulation-free training objectives for predicting the destination, and further discuss the advantages of our framework that can explicitly model the graph topology and exploit the inductive bias of the data. Through extensive experimental validation on general graph and 2D/3D molecule generation tasks, we show that our method outperforms previous generative models, generating graphs with correct topology with both continuous (e.g. 3D coordinates) and discrete (e.g. atom types) features. | ['Sung Ju Hwang', 'DongKi Kim', 'Jaehyeong Jo'] | 2023-02-07 | null | null | null | null | ['3d-molecule-generation'] | ['medical'] | [ 3.37152570e-01 4.60686594e-01 -5.85097373e-02 -2.93727797e-02
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746a2e56-397f-4713-b722-acfd3ef109fd | cmd-self-supervised-3d-action-representation | 2208.12448 | null | https://arxiv.org/abs/2208.12448v3 | https://arxiv.org/pdf/2208.12448v3.pdf | CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation | In 3D action recognition, there exists rich complementary information between skeleton modalities. Nevertheless, how to model and utilize this information remains a challenging problem for self-supervised 3D action representation learning. In this work, we formulate the cross-modal interaction as a bidirectional knowledge distillation problem. Different from classic distillation solutions that transfer the knowledge of a fixed and pre-trained teacher to the student, in this work, the knowledge is continuously updated and bidirectionally distilled between modalities. To this end, we propose a new Cross-modal Mutual Distillation (CMD) framework with the following designs. On the one hand, the neighboring similarity distribution is introduced to model the knowledge learned in each modality, where the relational information is naturally suitable for the contrastive frameworks. On the other hand, asymmetrical configurations are used for teacher and student to stabilize the distillation process and to transfer high-confidence information between modalities. By derivation, we find that the cross-modal positive mining in previous works can be regarded as a degenerated version of our CMD. We perform extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD II datasets. Our approach outperforms existing self-supervised methods and sets a series of new records. The code is available at: https://github.com/maoyunyao/CMD | ['Houqiang Li', 'Jiajun Deng', 'Zhenbo Lu', 'Wengang Zhou', 'Yunyao Mao'] | 2022-08-26 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.69003904e-01 2.76314318e-01 -6.33273304e-01 -2.82982767e-01
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2014ac80-4c89-4c6a-91b0-31b7b6b8e061 | spatiotemporal-enhanced-network-for-click | 2209.09427 | null | https://arxiv.org/abs/2209.09427v1 | https://arxiv.org/pdf/2209.09427v1.pdf | Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services | In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community. | ['Ning Hu', 'Guodong Cao', 'Jia Jia', 'Zisen Sang', 'Hengxu He', 'Taotao Zhou', 'Xiyu Ji', 'Yicong Yu', 'Shaochuan Lin'] | 2022-09-20 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-7.97829553e-02 -7.81920075e-01 -5.80232024e-01 -5.83086371e-01
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60479713-1ddf-47a5-9c9d-89de87b8465b | parallel-reasoning-network-for-human-object | 2301.03510 | null | https://arxiv.org/abs/2301.03510v1 | https://arxiv.org/pdf/2301.03510v1.pdf | Parallel Reasoning Network for Human-Object Interaction Detection | Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-level prediction and relation-level prediction. To solve this problem, we propose a new transformer-based method named Parallel Reasoning Network(PR-Net), which constructs two independent predictors for instance-level localization and relation-level understanding. The former predictor concentrates on instance-level localization by perceiving instances' extremity regions. The latter broadens the scope of relation region to reach a better relation-level semantic understanding. Extensive experiments and analysis on HICO-DET benchmark exhibit that our PR-Net effectively alleviated this problem. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks. | ['Changxin Gao', 'Nong Sang', 'Jing Shao', 'Bin Huang', 'Yangguang Li', 'Fenggang Liu', 'Huan Peng'] | 2023-01-09 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-1.68942120e-02 3.43828231e-01 -2.58553118e-01 -1.33160979e-01
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ed2698f9-a194-4819-b5a4-4b35b7fae021 | discovering-causal-relations-and-equations | 2305.13341 | null | https://arxiv.org/abs/2305.13341v1 | https://arxiv.org/pdf/2305.13341v1.pdf | Discovering Causal Relations and Equations from Data | Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems. | ['Jakob Runge', 'Laure Zanna', 'Emiliano Diaz', 'Ricardo Vinuesa', 'Emili Balaguer-Ballester', 'Georg Martius', 'Gherardo Varando', 'Urmi Ninad', 'Andreas Gerhardus', 'Gustau Camps-Valls'] | 2023-05-21 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 9.29092690e-02 -1.90173060e-01 -4.50134248e-01 -1.25508755e-01
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634ec481-c82e-40c5-aa13-892828acd086 | sparse-subspace-clustering-algorithm-theory | 1203.1005 | null | http://arxiv.org/abs/1203.1005v3 | http://arxiv.org/pdf/1203.1005v3.pdf | Sparse Subspace Clustering: Algorithm, Theory, and Applications | In many real-world problems, we are dealing with collections of
high-dimensional data, such as images, videos, text and web documents, DNA
microarray data, and more. Often, high-dimensional data lie close to
low-dimensional structures corresponding to several classes or categories the
data belongs to. In this paper, we propose and study an algorithm, called
Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of
low-dimensional subspaces. The key idea is that, among infinitely many possible
representations of a data point in terms of other points, a sparse
representation corresponds to selecting a few points from the same subspace.
This motivates solving a sparse optimization program whose solution is used in
a spectral clustering framework to infer the clustering of data into subspaces.
Since solving the sparse optimization program is in general NP-hard, we
consider a convex relaxation and show that, under appropriate conditions on the
arrangement of subspaces and the distribution of data, the proposed
minimization program succeeds in recovering the desired sparse representations.
The proposed algorithm can be solved efficiently and can handle data points
near the intersections of subspaces. Another key advantage of the proposed
algorithm with respect to the state of the art is that it can deal with data
nuisances, such as noise, sparse outlying entries, and missing entries,
directly by incorporating the model of the data into the sparse optimization
program. We demonstrate the effectiveness of the proposed algorithm through
experiments on synthetic data as well as the two real-world problems of motion
segmentation and face clustering. | ['Rene Vidal', 'Ehsan Elhamifar'] | 2012-03-05 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 2.54651159e-01 -2.07835272e-01 -3.00459653e-01 -2.16545478e-01
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289d6984-1363-4f2f-9469-3c0e1c77a7db | cnn-model-tuning-for-global-road-damage | 2103.09512 | null | https://arxiv.org/abs/2103.09512v1 | https://arxiv.org/pdf/2103.09512v1.pdf | CNN Model & Tuning for Global Road Damage Detection | This paper provides a report on our solution including model selection, tuning strategy and results obtained for Global Road Damage Detection Challenge. This Big Data Cup Challenge was held as a part of IEEE International Conference on Big Data 2020. We assess single and multi-stage network architectures for object detection and provide a benchmark using popular state-of-the-art open-source PyTorch frameworks like Detectron2 and Yolov5. Data preparation for provided Road Damage training dataset, captured using smartphone camera from Czech, India and Japan is discussed. We studied the effect of training on a per country basis with respect to a single generalizable model. We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone. Additionally, we compare this to a one-stage Yolov5 model with Cross Stage Partial Network (CSPNet) backbone. We show a mean F1 score of 0.542 on Test2 and 0.536 on Test1 datasets using a multi-stage Faster R-CNN model, with Resnet-50 and Resnet-101 backbones respectively. This shows the generalizability of the Resnet-50 model when compared to its more complex counterparts. Experiments were conducted using Google Colab having K80 and a Linux PC with 1080Ti, NVIDIA consumer grade GPU. A PyTorch based Detectron2 code to preprocess, train, test and submit the Avg F1 score to is made available at https://github.com/vishwakarmarhl/rdd2020 | ['Ravigopal Vennelakanti', 'Rahul Vishwakarma'] | 2021-03-17 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-2.66475618e-01 -8.94651040e-02 -3.72801013e-02 5.61823137e-02
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b0db18ad-ed41-4504-b4bb-078d1e69bba0 | controllable-image-captioning | 2204.13324 | null | https://arxiv.org/abs/2204.13324v4 | https://arxiv.org/pdf/2204.13324v4.pdf | Controllable Image Captioning | State-of-the-art image captioners can generate accurate sentences to describe images in a sequence to sequence manner without considering the controllability and interpretability. This, however, is far from making image captioning widely used as an image can be interpreted in infinite ways depending on the target and the context at hand. Achieving controllability is important especially when the image captioner is used by different people with different way of interpreting the images. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by capturing the co-dependence between Part-Of-Speech tags and semantics. Our model decouples direct dependence between successive variables. In this way, it allows the decoder to exhaustively search through the latent Part-Of-Speech choices, while keeping decoding speed proportional to the size of the POS vocabulary. Given a control signal in the form of a sequence of Part-Of-Speech tags, we propose a method to generate captions through a Transformer network, which predicts words based on the input Part-Of-Speech tag sequences. Experiments on publicly available datasets show that our model significantly outperforms state-of-the-art methods on generating diverse image captions with high qualities. | ['Luka Maxwell'] | 2022-04-28 | null | null | null | null | ['controllable-image-captioning'] | ['computer-vision'] | [ 7.04205573e-01 3.67189735e-01 -1.50693238e-01 -5.70214689e-01
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f851ba29-ee2c-4109-9a50-10bfb0d5ae63 | x-avatar-expressive-human-avatars | 2303.04805 | null | https://arxiv.org/abs/2303.04805v2 | https://arxiv.org/pdf/2303.04805v2.pdf | X-Avatar: Expressive Human Avatars | We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data. To achieve this, we propose a part-aware learned forward skinning module that can be driven by the parameter space of SMPL-X, allowing for expressive animation of X-Avatars. To efficiently learn the neural shape and deformation fields, we propose novel part-aware sampling and initialization strategies. This leads to higher fidelity results, especially for smaller body parts while maintaining efficient training despite increased number of articulated bones. To capture the appearance of the avatar with high-frequency details, we extend the geometry and deformation fields with a texture network that is conditioned on pose, facial expression, geometry and the normals of the deformed surface. We show experimentally that our method outperforms strong baselines in both data domains both quantitatively and qualitatively on the animation task. To facilitate future research on expressive avatars we contribute a new dataset, called X-Humans, containing 233 sequences of high-quality textured scans from 20 participants, totalling 35,500 data frames. | ['Otmar Hilliges', 'Jie Song', 'Julien Valentin', 'Juan Jose Zarate', 'Manuel Kaufmann', 'Chen Guo', 'Kaiyue Shen'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_X-Avatar_Expressive_Human_Avatars_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 6.03631996e-02 4.15292799e-01 2.40714774e-01 -5.02324641e-01
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9ba7ed44-b110-4f42-8c03-e6816d4e5a38 | inductive-relation-prediction-by-bert | 2103.07102 | null | https://arxiv.org/abs/2103.07102v1 | https://arxiv.org/pdf/2103.07102v1.pdf | Inductive Relation Prediction by BERT | Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting. Unfortunately, they are not able to handle inductive learning where unseen entities and relations are present and cannot take advantage of prior knowledge. Furthermore, their inference process is not easily explainable. In this work, we propose an all-in-one solution, called BERTRL (BERT-based Relational Learning), which leverages pre-trained language model and fine-tunes it by taking relation instances and their possible reasoning paths as training samples. BERTRL outperforms the SOTAs in 15 out of 18 cases in both inductive and transductive settings. Meanwhile, it demonstrates strong generalization capability in few-shot learning and is explainable. | ['Xifeng Yan', 'Zhiyu Chen', 'Hanwen Zha'] | 2021-03-12 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [-1.60228699e-01 7.94240355e-01 -7.86929011e-01 -2.29398087e-01
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f29050df-f5e8-4f8c-886b-ae92b39f7854 | deep-continuous-conditional-random-fields | 1806.01183 | null | http://arxiv.org/abs/1806.01183v1 | http://arxiv.org/pdf/1806.01183v1.pdf | Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking | Online Multi-Object Tracking (MOT) is a challenging problem and has many
important applications including intelligence surveillance, robot navigation
and autonomous driving. In existing MOT methods, individual object's movements
and inter-object relations are mostly modeled separately and relations between
them are still manually tuned. In addition, inter-object relations are mostly
modeled in a symmetric way, which we argue is not an optimal setting. To tackle
those difficulties, in this paper, we propose a Deep Continuous Conditional
Random Field (DCCRF) for solving the online MOT problem in a track-by-detection
framework. The DCCRF consists of unary and pairwise terms. The unary terms
estimate tracked objects' displacements across time based on visual appearance
information. They are modeled as deep Convolution Neural Networks, which are
able to learn discriminative visual features for tracklet association. The
asymmetric pairwise terms model inter-object relations in an asymmetric way,
which encourages high-confidence tracklets to help correct errors of
low-confidence tracklets and not to be affected by low-confidence ones much.
The DCCRF is trained in an end-to-end manner for better adapting the influences
of visual information as well as inter-object relations. Extensive experimental
comparisons with state-of-the-arts as well as detailed component analysis of
our proposed DCCRF on two public benchmarks demonstrate the effectiveness of
our proposed MOT framework. | ['Jian Cheng', 'Hui Zhou', 'Hongsheng Li', 'Wanli Ouyang', 'Xiaogang Wang'] | 2018-06-04 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-2.24428892e-01 -2.81723142e-01 -1.99199885e-01 -3.78798753e-01
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180e1e98-7c37-4a85-917f-8476fa3f1982 | identifying-clickbait-a-multi-strategy | 1710.01507 | null | http://arxiv.org/abs/1710.01507v4 | http://arxiv.org/pdf/1710.01507v4.pdf | Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks | Online media outlets, in a bid to expand their reach and subsequently
increase revenue through ad monetisation, have begun adopting clickbait
techniques to lure readers to click on articles. The article fails to fulfill
the promise made by the headline. Traditional methods for clickbait detection
have relied heavily on feature engineering which, in turn, is dependent on the
dataset it is built for. The application of neural networks for this task has
only been explored partially. We propose a novel approach considering all
information found in a social media post. We train a bidirectional LSTM with an
attention mechanism to learn the extent to which a word contributes to the
post's clickbait score in a differential manner. We also employ a Siamese net
to capture the similarity between source and target information. Information
gleaned from images has not been considered in previous approaches. We learn
image embeddings from large amounts of data using Convolutional Neural Networks
to add another layer of complexity to our model. Finally, we concatenate the
outputs from the three separate components, serving it as input to a fully
connected layer. We conduct experiments over a test corpus of 19538 social
media posts, attaining an F1 score of 65.37% on the dataset bettering the
previous state-of-the-art, as well as other proposed approaches, feature
engineering or otherwise. | ['Vasudeva Varma', 'Dhruv Khattar', 'Yash Kumar Lal', 'Siddhartha Gairola', 'Vaibhav Kumar'] | 2017-10-04 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [ 1.07639886e-01 -1.52946889e-01 -2.66012222e-01 -3.94066542e-01
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d1bf2be8-feea-46b6-85cc-e5cadb401f81 | camelparser-a-system-for-arabic-syntactic | null | null | https://aclanthology.org/C16-2048 | https://aclanthology.org/C16-2048.pdf | CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation | In this paper, we present CamelParser, a state-of-the-art system for Arabic syntactic dependency analysis aligned with contextually disambiguated morphological features. CamelParser uses a state-of-the-art morphological disambiguator and improves its results using syntactically driven features. The system offers a number of output formats that include basic dependency with morphological features, two tree visualization modes, and traditional Arabic grammatical analysis. | ['Dima Taji', 'Salam Khalifa', 'Anas Shahrour', 'Nizar Habash'] | 2016-12-01 | camelparser-a-system-for-arabic-syntactic-1 | https://aclanthology.org/C16-2048 | https://aclanthology.org/C16-2048.pdf | coling-2016-12 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-5.55157840e-01 -7.67123103e-02 4.37229797e-02 -6.74616694e-01
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ea5c9ac0-113c-4830-8610-043023cfe842 | adding-knowledge-to-unsupervised-algorithms | 2011.06219 | null | https://arxiv.org/abs/2011.06219v1 | https://arxiv.org/pdf/2011.06219v1.pdf | Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent | Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theory of mind, is our ability to determine whether a perceived behavior or action was performed intentionally or not. In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship. We show how the addition of this basic knowledge leads to a simple, unsupervised algorithm. To test the derived algorithm, we constructed three dedicated datasets from abstract geometric animation to realistic videos of agents performing intentional and non-intentional actions. Experiments on these datasets show that our algorithm can recognize whether an action is intentional or not, even without training data. The performance is comparable to various supervised baselines quantitatively, with sensible intentionality segmentation qualitatively. | ['Aleix Martinez', 'Qianli Feng', 'Stuart Synakowski'] | 2020-11-12 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 4.13759410e-01 2.62785703e-01 -1.36671692e-01 -2.98306733e-01
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ceb7609b-ed76-4fd7-9c2f-64ba6e92ba0a | simcsum-joint-learning-of-simplification-and | 2304.01621 | null | https://arxiv.org/abs/2304.01621v1 | https://arxiv.org/pdf/2304.01621v1.pdf | SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism | Cross-lingual science journalism generates popular science stories of scientific articles different from the source language for a non-expert audience. Hence, a cross-lingual popular summary must contain the salient content of the input document, and the content should be coherent, comprehensible, and in a local language for the targeted audience. We improve these aspects of cross-lingual summary generation by joint training of two high-level NLP tasks, simplification and cross-lingual summarization. The former task reduces linguistic complexity, and the latter focuses on cross-lingual abstractive summarization. We propose a novel multi-task architecture - SimCSum consisting of one shared encoder and two parallel decoders jointly learning simplification and cross-lingual summarization. We empirically investigate the performance of SimCSum by comparing it with several strong baselines over several evaluation metrics and by human evaluation. Overall, SimCSum demonstrates statistically significant improvements over the state-of-the-art on two non-synthetic cross-lingual scientific datasets. Furthermore, we conduct an in-depth investigation into the linguistic properties of generated summaries and an error analysis. | ['Michael Strube', 'Katja Markert', 'Tim Kolber', 'Mehwish Fatima'] | 2023-04-04 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.89976168e-02 3.32581162e-01 -3.05741161e-01 -2.50218511e-01
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065d6b75-ced1-4949-b5e0-2f3a8cb2fbd3 | encoder-decoder-multimodal-speaker-change | 2306.00680 | null | https://arxiv.org/abs/2306.00680v1 | https://arxiv.org/pdf/2306.00680v1.pdf | Encoder-decoder multimodal speaker change detection | The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications. Several studies solved the SCD task using audio inputs only and have shown limited performance. Recently, multimodal SCD (MMSCD) models, which utilise text modality in addition to audio, have shown improved performance. In this study, the proposed model are built upon two main proposals, a novel mechanism for modality fusion and the adoption of a encoder-decoder architecture. Different to previous MMSCD works that extract speaker embeddings from extremely short audio segments, aligned to a single word, we use a speaker embedding extracted from 1.5s. A transformer decoder layer further improves the performance of an encoder-only MMSCD model. The proposed model achieves state-of-the-art results among studies that report SCD performance and is also on par with recent work that combines SCD with automatic speech recognition via human transcription. | ['Bong-Jin Lee', 'Minjae Lee', 'Young-ki Kwon', 'You Jin Kim', 'Geonmin Kim', 'Hee-Soo Heo', 'Soonshin Seo', 'Jee-weon Jung'] | 2023-06-01 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.40033346e-01 3.24041881e-02 1.09592073e-01 -4.30313259e-01
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365abaaf-03ae-44fd-9190-93efb6e57e71 | completed-local-derivative-pattern-for | 1812.04183 | null | http://arxiv.org/abs/1812.04183v1 | http://arxiv.org/pdf/1812.04183v1.pdf | Completed Local Derivative Pattern for Rotation Invariant Texture Classification | In this paper, we propose a new texture descriptor, completed local
derivative pattern (CLDP). In contrast to completed local binary pattern
(CLBP), which involves only local differences at each scale, CLDP encodes the
directional variation of the local differences of two scales as a complementary
component to local patterns in CLBP. The new component in CLDP, with regarded
as the directional derivative pattern, reflects the directional smoothness of
local textures without increasing computation complexity. Experimental results
on the Outex database show that CLDP, as a uni-scale pattern, outperforms
uni-scale state-of-the-art texture descriptors on texture classification and
has comparable performance with multi-scale texture descriptors. | [] | 2018-12-11 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 9.48691368e-02 -7.03496337e-01 -2.28636473e-01 -3.83228660e-01
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7145562b-73a9-4b2a-bc2d-d5af855eeddb | causal-augmentation-for-causal-sentence-1 | null | null | https://openreview.net/forum?id=q985hMM6EvA | https://openreview.net/pdf?id=q985hMM6EvA | Causal Augmentation for Causal Sentence Classification | Scarcity of corpora with annotated causal texts can lead to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we find that these models misclassify on augmented sentences that have been negated or strengthened in terms of their causal meaning. This is worrying because minor linguistic changes in causal sentences can have disparate meanings. To resolve such issues, we propose to generate counterfactual causal sentences by creating contrast sets (Gardner et al., 2020). However, we notice an important finding that simply introducing edits is not sufficient to train models with counterfactuals. We thus introduce heuristics, like sentence shortening or multiplying key causal terms, to emphasize semantically important keywords to the model. We demonstrate these findings on different training setups and across two out-of-domain corpora. Our proposed mixture of augmented edits consistently achieves improved performance compared to baseline across two models and both within and out of corpus' domain, suggesting our proposed augmentation also helps the model generalize. | ['Anonymous'] | 2021-05-16 | null | null | null | acl-arr-may-2021-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.44670641e-01 5.29680729e-01 -3.86394322e-01 -6.93873882e-01
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73405f01-c73c-44db-b44a-45c126490d95 | multimodal-open-vocabulary-video | 2207.07646 | null | https://arxiv.org/abs/2207.07646v1 | https://arxiv.org/pdf/2207.07646v1.pdf | Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models | Utilizing vision and language models (VLMs) pre-trained on large-scale image-text pairs is becoming a promising paradigm for open-vocabulary visual recognition. In this work, we extend this paradigm by leveraging motion and audio that naturally exist in video. We present \textbf{MOV}, a simple yet effective method for \textbf{M}ultimodal \textbf{O}pen-\textbf{V}ocabulary video classification. In MOV, we directly use the vision encoder from pre-trained VLMs with minimal modifications to encode video, optical flow and audio spectrogram. We design a cross-modal fusion mechanism to aggregate complimentary multimodal information. Experiments on Kinetics-700 and VGGSound show that introducing flow or audio modality brings large performance gains over the pre-trained VLM and existing methods. Specifically, MOV greatly improves the accuracy on base classes, while generalizes better on novel classes. MOV achieves state-of-the-art results on UCF and HMDB zero-shot video classification benchmarks, significantly outperforming both traditional zero-shot methods and recent methods based on VLMs. Code and models will be released. | ['Yin Cui', 'Serge Belongie', 'Ming-Hsuan Yang', 'Zheng Xu', 'Yeqing Li', 'Rui Qian'] | 2022-07-15 | null | null | null | null | ['zero-shot-action-recognition', 'video-classification'] | ['computer-vision', 'computer-vision'] | [ 6.23807684e-02 -6.41786456e-01 -5.26116014e-01 -1.81449473e-01
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7.77788103e-01 -9.03059065e-01 -3.68851155e-01 5.72553337e-01] | [10.213282585144043, 0.9673560857772827] |
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