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60a909a7-5c4d-4d88-b253-4eefb8382566 | a-survey-on-stance-detection-for-mis-and | 2103.00242 | null | https://arxiv.org/abs/2103.00242v3 | https://arxiv.org/pdf/2103.00242v3.pdf | A Survey on Stance Detection for Mis- and Disinformation Identification | Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges. | ['Isabelle Augenstein', 'Preslav Nakov', 'Arnav Arora', 'Momchil Hardalov'] | 2021-02-27 | null | https://aclanthology.org/2022.findings-naacl.94 | https://aclanthology.org/2022.findings-naacl.94.pdf | findings-naacl-2022-7 | ['rumour-detection'] | ['natural-language-processing'] | [ 4.52227026e-01 8.91923726e-01 -5.24585843e-01 -2.02974036e-01
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b70a8e2b-fb0b-4360-b94e-693c65768ea6 | communication-efficient-edge-ai-inference | 2004.13351 | null | https://arxiv.org/abs/2004.13351v1 | https://arxiv.org/pdf/2004.13351v1.pdf | Communication-Efficient Edge AI Inference Over Wireless Networks | Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future. As such, the intelligent communication networks will be designed to leverage advanced wireless techniques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we shall present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference. The communication efficiency of edge inference systems is further improved by building up a smart radio propagation environment via intelligent reflecting surface. | ['Zhanpeng Yang', 'Yong Zhou', 'Kai Yang', 'Yuanming Shi'] | 2020-04-28 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 2.71480680e-01 4.11718309e-01 -3.46009552e-01 -8.00363347e-02
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44f3b227-4852-41e6-b5f9-018cff79f9bc | time-series-contrastive-learning-with | 2303.11911 | null | https://arxiv.org/abs/2303.11911v1 | https://arxiv.org/pdf/2303.11911v1.pdf | Time Series Contrastive Learning with Information-Aware Augmentations | Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations. Unlike image and language domains where ``desired'' augmented samples can be generated with the rule of thumb guided by prefabricated human priors, the ad-hoc manual selection of time series augmentations is hindered by their diverse and human-unrecognizable temporal structures. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. In this work, we address the problem by encouraging both high \textit{fidelity} and \textit{variety} based upon information theory. A theoretical analysis leads to the criteria for selecting feasible data augmentations. On top of that, we propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning. Experiments on various datasets show highly competitive performance with up to 12.0\% reduction in MSE on forecasting tasks and up to 3.7\% relative improvement in accuracy on classification tasks over the leading baselines. | ['Xiang Zhang', 'Haifeng Chen', 'Yuncong Chen', 'Yanchi Liu', 'Xuchao Zhang', 'Wenchao Yu', 'Jingchao Ni', 'Dongkuan Xu', 'Yingheng Wang', 'Wei Cheng', 'Dongsheng Luo'] | 2023-03-21 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.38986766e-01 -1.61378190e-01 -4.75004554e-01 -4.24245119e-01
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cd0402c6-48d2-423b-bbb3-285274959935 | aspnet-action-segmentation-with-shared | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.pdf | ASPnet: Action Segmentation With Shared-Private Representation of Multiple Data Sources | Most state-of-the-art methods for action segmentation are based on single input modalities or naive fusion of multiple data sources. However, effective fusion of complementary information can potentially strengthen segmentation models and make them more robust to sensor noise and more accurate with smaller training datasets. In order to improve multimodal representation learning for action segmentation, we propose to disentangle hidden features of a multi-stream segmentation model into modality-shared components, containing common information across data sources, and private components; we then use an attention bottleneck to capture long-range temporal dependencies in the data while preserving disentanglement in consecutive processing layers. Evaluation on 50salads, Breakfast and RARP45 datasets shows that our multimodal approach outperforms different data fusion baselines on both multiview and multimodal data sources, obtaining competitive or better results compared with the state-of-the-art. Our model is also more robust to additive sensor noise and can achieve performance on par with strong video baselines even with less training data. | ['Danail Stoyanov', 'Imanol Luengo', 'Abdolrahim Kadkhodamohammadi', 'Beatrice van Amsterdam'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-segmentation', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 7.16807604e-01 4.22356138e-03 -6.85643435e-01 -4.69002634e-01
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a9a30f97-ccf6-42c1-a796-5d4468815c7c | coherent-false-seizure-prediction-in-epilepsy | 2110.13550 | null | https://arxiv.org/abs/2110.13550v1 | https://arxiv.org/pdf/2110.13550v1.pdf | Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence? | Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data. We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. For 9 out of 12 individuals both methods showed a performance better than chance. For all individuals we observed a positive correlation in predictions. For individuals with strong correlation in false predictions we were able to boost the performance of one method by excluding test samples based on the results of the second method. Substantially different algorithms exhibit a highly consistent performance and a strong coherency in false and missing alarms. Hence, changing the underlying hypothesis of a preictal state of fixed time length prior to each seizure to a proictal state is more helpful than further optimizing classifiers. The outcome is significant for the evaluation of seizure prediction algorithms on continuous data. | ['Ronald Tetzlaff', 'Levin Kuhlmann', 'Ortrud Uckermann', 'Georg Leonhardt', 'Matthias Eberlein', 'Hongliu Yang', 'Jens Müller'] | 2021-10-26 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.08698502e-01 3.80169675e-02 1.01283707e-01 -7.68757105e-01
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ed105142-4cd5-4b2a-a68d-33e42a610cfa | on-the-integration-of-acoustics-and-lidar-a | 2206.03885 | null | https://arxiv.org/abs/2206.03885v1 | https://arxiv.org/pdf/2206.03885v1.pdf | On the Integration of Acoustics and LiDAR: a Multi-Modal Approach to Acoustic Reflector Estimation | Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. In the proposed method, a LiDAR sensor is added to a loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the model mismatch introduced by horizontal reflectors can be resolved by detecting reflectors with the LiDAR sensor to enable elimination of their detrimental influence from the 2D problem in pre-processing. Second, a LiDAR-based method is proposed to compensate for the challenging directions where the directive loudspeaker emits little energy. We show via simulations that this multi-modal approach, i.e., combining microphone and LiDAR sensors, improves the robustness and accuracy of wall detection. | ['Richard C. Hendriks', 'Martin Møller', 'Jorge Martinez', 'Pablo Martínez-Nuevo', 'Ellen Riemens'] | 2022-06-08 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 3.52143526e-01 -2.26157904e-01 8.28411698e-01 7.51209818e-03
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fe7a4894-61bc-4825-a416-22f51dc5d0ce | discodisco-at-the-disrpt2021-shared-task-a | 2109.09777 | null | https://arxiv.org/abs/2109.09777v1 | https://arxiv.org/pdf/2109.09777v1.pdf | DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection | This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pre-trained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification. | ['Amir Zeldes', 'YIlun Zhu', 'Siyao Peng', 'Yang Janet Liu', 'Shabnam Behzad', 'Luke Gessler'] | 2021-09-20 | null | https://aclanthology.org/2021.disrpt-1.6 | https://aclanthology.org/2021.disrpt-1.6.pdf | emnlp-disrpt-2021-11 | ['discourse-segmentation', 'discourse-parsing', 'connective-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.65330088e-01 1.03045893e+00 -5.12940764e-01 -2.43944600e-01
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fda7099e-8b51-487b-8b47-2dd70f94f96e | multi-task-handwritten-document-layout | 1806.08852 | null | http://arxiv.org/abs/1806.08852v3 | http://arxiv.org/pdf/1806.08852v3.pdf | Multi-Task Handwritten Document Layout Analysis | Document Layout Analysis is a fundamental step in Handwritten Text Processing
systems, from the extraction of the text lines to the type of zone it belongs
to. We present a system based on artificial neural networks which is able to
determine not only the baselines of text lines present in the document, but
also performs geometric and logic layout analysis of the document. Experiments
in three different datasets demonstrate the potential of the method and show
competitive results with respect to state-of-the-art methods. | ['Lorenzo Quirós'] | 2018-06-22 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 3.49907845e-01 -4.32343572e-01 2.26468816e-02 -3.57304394e-01
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376da50c-4e51-4d7d-a45b-d3180698b0bd | neural-basis-models-for-interpretability | 2205.14120 | null | https://arxiv.org/abs/2205.14120v4 | https://arxiv.org/pdf/2205.14120v4.pdf | Neural Basis Models for Interpretability | Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations. Generalized Additive Models (GAMs) are an inherently interpretable class of models that address this limitation by learning a non-linear shape function for each feature separately, followed by a linear model on top. However, these models are typically difficult to train, require numerous parameters, and are difficult to scale. We propose an entirely new subfamily of GAMs that utilizes basis decomposition of shape functions. A small number of basis functions are shared among all features, and are learned jointly for a given task, thus making our model scale much better to large-scale data with high-dimensional features, especially when features are sparse. We propose an architecture denoted as the Neural Basis Model (NBM) which uses a single neural network to learn these bases. On a variety of tabular and image datasets, we demonstrate that for interpretable machine learning, NBMs are the state-of-the-art in accuracy, model size, and, throughput and can easily model all higher-order feature interactions. Source code is available at https://github.com/facebookresearch/nbm-spam. | ['Dhruv Mahajan', 'Abhimanyu Dubey', 'Filip Radenovic'] | 2022-05-27 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 9.02302861e-02 2.52476990e-01 -3.46215427e-01 -5.66762805e-01
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892ceaf0-1a9c-4034-9177-82c5a7894d57 | outlier-cluster-formation-in-spectral | 1703.01028 | null | http://arxiv.org/abs/1703.01028v1 | http://arxiv.org/pdf/1703.01028v1.pdf | Outlier Cluster Formation in Spectral Clustering | Outlier detection and cluster number estimation is an important issue for
clustering real data. This paper focuses on spectral clustering, a time-tested
clustering method, and reveals its important properties related to outliers.
The highlights of this paper are the following two mathematical observations:
first, spectral clustering's intrinsic property of an outlier cluster
formation, and second, the singularity of an outlier cluster with a valid
cluster number. Based on these observations, we designed a function that
evaluates clustering and outlier detection results. In experiments, we prepared
two scenarios, face clustering in photo album and person re-identification in a
camera network. We confirmed that the proposed method detects outliers and
estimates the number of clusters properly in both problems. Our method
outperforms state-of-the-art methods in both the 128-dimensional sparse space
for face clustering and the 4,096-dimensional non-sparse space for person
re-identification. | ['Michihiko Minoh', 'Masaaki Iiyama', 'Hidekazu Kasahara', 'Takuro Ina', 'Mikihiko Mori', 'Atsushi Hashimoto'] | 2017-03-03 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-3.54728609e-01 -5.78944802e-01 4.13962334e-01 -1.86836869e-01
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4463c022-47d4-46e4-a767-41e611302713 | meta-sysid-a-meta-learning-approach-for | 2206.00694 | null | https://arxiv.org/abs/2206.00694v1 | https://arxiv.org/pdf/2206.00694v1.pdf | Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction | In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context. Inspired by classical modeling-and-identification approaches, Meta-SysId learns to represent the common law through shared parameters and relies on online optimization to compute system-specific context. Compared to optimization-based meta-learning methods, the separation between class parameters and context variables reduces the computational burden while allowing batch computations and a simple training scheme. We test Meta-SysId on polynomial regression, time-series prediction, model-based control, and real-world traffic prediction domains, empirically finding it outperforms or is competitive with meta-learning baselines. | ['Jinkyoo Park', 'Mykel J. Kochenderfer', 'Arec Jamgochian', 'Federico Berto', 'Junyoung Park'] | 2022-06-01 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.14935809e-01 -3.56127292e-01 -6.34482265e-01 -1.97197855e-01
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96d936b9-ecaf-401d-871d-d0875a66e64b | safeaccess-towards-a-dialogue-enabled-access | 1904.01178 | null | http://arxiv.org/abs/1904.01178v2 | http://arxiv.org/pdf/1904.01178v2.pdf | Person Identification with Visual Summary for a Safe Access to a Smart Home | SafeAccess is an integrated system designed to provide easier and safer
access to a smart home for people with or without disabilities. The system is
designed to enhance safety and promote the independence of people with
disability (i.e., visually impaired). The key functionality of the system
includes the detection and identification of human and generating contextual
visual summary from the real-time video streams obtained from the cameras
placed in strategic locations around the house. In addition, the system
classifies human into groups (i.e. friends/families/caregiver versus
intruders/burglars/unknown). These features allow the user to grant/deny remote
access to the premises or ability to call emergency services. In this paper, we
focus on designing a prototype system for the smart home and building a robust
recognition engine that meets the system criteria and addresses speed,
accuracy, deployment and environmental challenges under a wide variety of
practical and real-life situations. To interact with the system, we implemented
a dialog enabled interface to create a personalized profile using face images
or video of friend/families/caregiver. To improve computational efficiency, we
apply change detection to filter out frames and use Faster-RCNN to detect the
human presence and extract faces using Multitask Cascaded Convolutional
Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person and
groups by matching extracted faces with the profile. SafeAccess sends a visual
summary to the users with an MMS containing a person's name if any match found
or as "Unknown", scene image, facial description, and contextual information.
SafeAccess identifies friends/families/caregiver versus intruders/unknown with
an average F-score 0.97 and generates a visual summary from 10 classes with an
average accuracy of 98.01%. | ['Mohammed Yeasin', 'Shahinur Alam'] | 2019-04-02 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.13413773e-01 -2.04557896e-01 2.02894643e-01 -3.57192457e-01
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534c62a2-bd38-4fb6-96a1-ef503af6ce14 | towards-zero-shot-scale-aware-monocular-depth | 2306.17253 | null | https://arxiv.org/abs/2306.17253v1 | https://arxiv.org/pdf/2306.17253v1.pdf | Towards Zero-Shot Scale-Aware Monocular Depth Estimation | Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions. Even so, the resulting models will be geometry-specific, with learned scales that cannot be directly transferred across domains. Because of that, recent works focus instead on relative depth, eschewing scale in favor of improved up-to-scale zero-shot transfer. In this work we introduce ZeroDepth, a novel monocular depth estimation framework capable of predicting metric scale for arbitrary test images from different domains and camera parameters. This is achieved by (i) the use of input-level geometric embeddings that enable the network to learn a scale prior over objects; and (ii) decoupling the encoder and decoder stages, via a variational latent representation that is conditioned on single frame information. We evaluated ZeroDepth targeting both outdoor (KITTI, DDAD, nuScenes) and indoor (NYUv2) benchmarks, and achieved a new state-of-the-art in both settings using the same pre-trained model, outperforming methods that train on in-domain data and require test-time scaling to produce metric estimates. | ['Adrien Gaidon', 'Rares Ambrus', 'Dian Chen', 'Igor Vasiljevic', 'Vitor Guizilini'] | 2023-06-29 | null | null | null | null | ['depth-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.52869248e-01 2.27169126e-01 -1.92048755e-02 -5.76096654e-01
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1d0ba38e-796c-4af5-a5f5-23c9f51a61f4 | surgical-phase-recognition-of-short-video | 1807.07853 | null | http://arxiv.org/abs/1807.07853v4 | http://arxiv.org/pdf/1807.07853v4.pdf | Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features | Recognizing the phases of a laparoscopic surgery (LS) operation form its
video constitutes a fundamental step for efficient content representation,
indexing and retrieval in surgical video databases. In the literature, most
techniques focus on phase segmentation of the entire LS video using
hand-crafted visual features, instrument usage signals, and recently
convolutional neural networks (CNNs). In this paper we address the problem of
phase recognition of short video shots (10s) of the operation, without
utilizing information about the preceding/forthcoming video frames, their phase
labels or the instruments used. We investigate four state-of-the-art CNN
architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature
extraction via transfer learning. Visual saliency was employed for selecting
the most informative region of the image as input to the CNN. Video shot
representation was based on two temporal pooling mechanisms. Most importantly,
we investigate the role of 'elapsed time' (from the beginning of the
operation), and we show that inclusion of this feature can increase performance
dramatically (69% vs. 75% mean accuracy). Finally, a long short-term memory
(LSTM) network was trained for video shot classification based on the fusion of
CNN features with 'elapsed time', increasing the accuracy to 86%. Our results
highlight the prominent role of visual saliency, long-range temporal recursion
and 'elapsed time' (a feature so far ignored), for surgical phase recognition. | ['Constantinos Loukas'] | 2018-07-20 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 4.84973133e-01 1.45261623e-02 -6.26640797e-01 1.09040655e-01
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4fe9e96d-dbb1-45b0-9893-cb7a8d40fd87 | when-does-clip-generalize-better-than | null | null | https://aclanthology.org/2022.repl4nlp-1.4 | https://aclanthology.org/2022.repl4nlp-1.4.pdf | When does CLIP generalize better than unimodal models? When judging human-centric concepts | CLIP, a vision-language network trained with a multimodal contrastive learning objective on a large dataset of images and captions, has demonstrated impressive zero-shot ability in various tasks. However, recent work showed that in comparison to unimodal (visual) networks, CLIP’s multimodal training does not benefit generalization (e.g. few-shot or transfer learning) for standard visual classification tasks such as object, street numbers or animal recognition. Here, we hypothesize that CLIP’s improved unimodal generalization abilities may be most prominent in domains that involve human-centric concepts (cultural, social, aesthetic, affective...); this is because CLIP’s training dataset is mainly composed of image annotations made by humans for other humans. To evaluate this, we use 3 tasks that require judging human-centric concepts”:" sentiment analysis on tweets, genre classification on books or movies. We introduce and publicly release a new multimodal dataset for movie genre classification. We compare CLIP’s visual stream against two visually trained networks and CLIP’s textual stream against two linguistically trained networks, as well as multimodal combinations of these networks. We show that CLIP generally outperforms other networks, whether using one or two modalities. We conclude that CLIP’s multimodal training is beneficial for both unimodal and multimodal tasks that require classification of human-centric concepts. | ['Rufin VanRullen', 'Tim Van De Cruys', 'Benjamin Devillers', 'Romain Bielawski'] | null | null | null | null | repl4nlp-acl-2022-5 | ['genre-classification'] | ['computer-vision'] | [ 4.02921475e-02 -2.35979885e-01 -3.45411509e-01 -3.16053271e-01
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97df8584-57dc-43b5-b16f-34e416e8399f | denoising-auto-encoder-with-recurrent-skip | 1807.01898 | null | http://arxiv.org/abs/1807.01898v1 | http://arxiv.org/pdf/1807.01898v1.pdf | Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation | Convolutional neural networks with skip connections have shown good
performance in music source separation. In this work, we propose a denoising
Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along
the temporal axis of the time-frequency feature map in all layers of the
fully-convolutional network. The use of 1D convolution makes it possible to
apply recurrent layers to the intermediate outputs of the convolution layers.
In addition, we also propose an enhancement network and a residual regression
method to further improve the separation result. The recurrent skip
connections, the enhancement module, and the residual regression all improve
the separation quality. The ARC model with residual regression achieves 5.74
siganl-to-distoration ratio (SDR) in vocals with MUSDB in SiSEC 2018. We also
evaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016)
and it achieves 5.91 SDR in vocals. | ['Yi-Hsuan Yang', 'Jen-Yu Liu'] | 2018-07-05 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.77471840e-02 -2.81801254e-01 2.93797404e-01 -1.80714607e-01
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3498bfbd-b18c-47b7-a727-e418058063b5 | recurrent-vision-transformers-for-object | 2212.05598 | null | https://arxiv.org/abs/2212.05598v3 | https://arxiv.org/pdf/2212.05598v3.pdf | Recurrent Vision Transformers for Object Detection with Event Cameras | We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: First, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (<12 ms on a T4 GPU) and favorable parameter efficiency (5 times fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. | ['Davide Scaramuzza', 'Mathias Gehrig'] | 2022-12-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.pdf | cvpr-2023-1 | ['event-based-vision'] | ['computer-vision'] | [ 2.38550350e-01 -3.87234747e-01 -4.51443642e-02 -1.56004369e-01
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5b1ade31-3940-48ac-9ec5-0c8b6f4de698 | learning-robust-hash-codes-for-multiple | 1703.05724 | null | http://arxiv.org/abs/1703.05724v1 | http://arxiv.org/pdf/1703.05724v1.pdf | Learning Robust Hash Codes for Multiple Instance Image Retrieval | In this paper, for the first time, we introduce a multiple instance (MI) deep
hashing technique for learning discriminative hash codes with weak bag-level
supervision suited for large-scale retrieval. We learn such hash codes by
aggregating deeply learnt hierarchical representations across bag members
through a dedicated MI pool layer. For better trainability and retrieval
quality, we propose a two-pronged approach that includes robust optimization
and training with an auxiliary single instance hashing arm which is
down-regulated gradually. We pose retrieval for tumor assessment as an MI
problem because tumors often coexist with benign masses and could exhibit
complementary signatures when scanned from different anatomical views.
Experimental validations on benchmark mammography and histology datasets
demonstrate improved retrieval performance over the state-of-the-art methods. | ['Sailesh Conjeti', 'Amin Katouzian', 'Magdalini Paschali', 'Nassir Navab'] | 2017-03-16 | null | null | null | null | ['pose-retrieval'] | ['computer-vision'] | [ 2.70125717e-01 3.89463663e-01 -6.48078442e-01 -4.48317438e-01
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21831710-6dec-4a5f-8c7d-d982ce964aa7 | proper-scoring-rules-for-survival-analysis | 2305.00621 | null | https://arxiv.org/abs/2305.00621v3 | https://arxiv.org/pdf/2305.00621v3.pdf | Proper Scoring Rules for Survival Analysis | Survival analysis is the problem of estimating probability distributions for future event times, which can be seen as a problem in uncertainty quantification. Although there are fundamental theories on strictly proper scoring rules for uncertainty quantification, little is known about those for survival analysis. In this paper, we investigate extensions of four major strictly proper scoring rules for survival analysis and we prove that these extensions are proper under certain conditions, which arise from the discretization of the estimation of probability distributions. We also compare the estimation performances of these extended scoring rules by using real datasets, and the extensions of the logarithmic score and the Brier score performed the best. | ['Hiroki Yanagisawa'] | 2023-05-01 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.23235612e-02 1.41510665e-01 -2.25499630e-01 -4.54963326e-01
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34e1cf91-826f-41d9-87b6-05769b21c7a3 | distilled-reverse-attention-network-for-open | 2303.00404 | null | https://arxiv.org/abs/2303.00404v1 | https://arxiv.org/pdf/2303.00404v1.pdf | Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning | Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world setting degrade severely due to the unconstrained OW test space. While previous works alleviate the issue by pruning compositions according to external knowledge or correlations in seen pairs, they introduce biases that harm the generalization. Some methods thus predict state and object with independently constructed and trained classifiers, ignoring that attributes are highly context-dependent and visually entangled with objects. In this paper, we propose a novel Distilled Reverse Attention Network to address the challenges. We also model attributes and objects separately but with different motivations, capturing contextuality and locality, respectively. We further design a reverse-and-distill strategy that learns disentangled representations of elementary components in training data supervised by reverse attention and knowledge distillation. We conduct experiments on three datasets and consistently achieve state-of-the-art (SOTA) performance. | ['Lina Yao', 'Sally Cripps', 'Saurav Jha', 'Zhe Liu', 'Yun Li'] | 2023-03-01 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 8.61623958e-02 1.98641628e-01 -1.95957258e-01 -2.49823496e-01
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51e472c7-618b-4b11-b5f2-26ba6e5bd642 | ss-shapelets-semi-supervised-clustering-of | 2304.03292 | null | https://arxiv.org/abs/2304.03292v1 | https://arxiv.org/pdf/2304.03292v1.pdf | SS-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets | Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-supervised Clustering of Time Series Using Representative Shapelets (SS-Shapelets) method, which utilizes a small number of labeled and propagated pseudo-labeled time series to help discover representative shapelets, thereby improving the clustering accuracy. In SS-Shapelets, we propose two techniques to discover representative shapelets for the effective clustering of time series. 1) A \textit{salient subsequence chain} ($SSC$) that can extract salient subsequences (as candidate shapelets) of a labeled/pseudo-labeled time series, which helps remove massive uninformative subsequences from the pool. 2) A \textit{linear discriminant selection} ($LDS$) algorithm to identify shapelets that can capture representative local features of time series in different classes, for convenient clustering. Experiments on UCR time series datasets demonstrate that SS-shapelets discovers representative shapelets and achieves higher clustering accuracy than counterpart semi-supervised time series clustering methods. | ['Chi-Hung Chi', 'Yong Xiang', 'Shuiqiao Yang', 'Guangyan Huang', 'Borui Cai'] | 2023-04-06 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-2.16909990e-01 -8.43180120e-01 -4.30572294e-02 -1.61011368e-01
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7fcaafdb-77ab-4cca-9d1c-600ae699add5 | a-pde-approach-to-the-prediction-of-a-binary | 2007.12732 | null | https://arxiv.org/abs/2007.12732v1 | https://arxiv.org/pdf/2007.12732v1.pdf | A PDE Approach to the Prediction of a Binary Sequence with Advice from Two History-Dependent Experts | The prediction of a binary sequence is a classic example of online machine learning. We like to call it the 'stock prediction problem,' viewing the sequence as the price history of a stock that goes up or down one unit at each time step. In this problem, an investor has access to the predictions of two or more 'experts,' and strives to minimize her final-time regret with respect to the best-performing expert. Probability plays no role; rather, the market is assumed to be adversarial. We consider the case when there are two history-dependent experts, whose predictions are determined by the d most recent stock moves. Focusing on an appropriate continuum limit and using methods from optimal control, graph theory, and partial differential equations, we discuss strategies for the investor and the adversarial market, and we determine associated upper and lower bounds for the investor's final-time regret. When d is less than 4 our upper and lower bounds coalesce, so the proposed strategies are asymptotically optimal. Compared to other recent applications of partial differential equations to prediction, ours has a new element: there are two timescales, since the recent history changes at every step whereas regret accumulates more slowly. | ['Nadejda Drenska', 'Robert V. Kohn'] | 2020-07-24 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [ 1.76750913e-01 6.14959121e-01 -2.99851865e-01 2.95020118e-02
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d6837479-5117-4034-8383-21f5590e3302 | hand-guided-high-resolution-feature | 2211.13694 | null | https://arxiv.org/abs/2211.13694v1 | https://arxiv.org/pdf/2211.13694v1.pdf | Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies | Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose vital fine-grained spatial and temporal information required for accurate classification within the manufacturing domain. In order to fully utilise higher resolution video data (often collected within the manufacturing domain) and facilitate real time accurate action segmentation - required for human robot collaboration - we present a novel hand location guided high resolution feature enhanced model. We also propose a simple yet effective method of deploying offline trained action recognition models for real time action segmentation on temporally short fine-grained actions, through the use of surround sampling while training and temporally aware label cleaning at inference. We evaluate our model on a novel action segmentation dataset containing 24 (+background) atomic actions from video data of a real world robotics assembly production line. Showing both high resolution hand features as well as traditional frame wide features improve fine-grained atomic action classification, and that though temporally aware label clearing our model is capable of surpassing similar encoder/decoder methods, while allowing for real time classification. | ['Nicholas Martin', 'Stephen McGough', 'Nick Wright', 'Matthew Kent Myers'] | 2022-11-24 | null | null | null | null | ['action-classification', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.87580335e-01 -1.58501074e-01 -1.21936940e-01 -2.75847048e-01
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d99d1e5d-46f6-4f4e-bde3-ff3038db908f | gpgait-generalized-pose-based-gait | 2303.05234 | null | https://arxiv.org/abs/2303.05234v1 | https://arxiv.org/pdf/2303.05234v1.pdf | GPGait: Generalized Pose-based Gait Recognition | Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a Generalized Pose-based Gait recognition (GPGait) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. The code will be released. | ['Yongzhen Huang', 'Xuecai Hu', 'Saihui Hou', 'Shibei Meng', 'Yang Fu'] | 2023-03-09 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.39225453e-01 -5.21581590e-01 -1.13692895e-01 -2.27809057e-01
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d63e187d-fe8b-40bf-9c07-4e72d09df357 | fully-convolutional-network-with-multi-step | 1811.04323 | null | http://arxiv.org/abs/1811.04323v2 | http://arxiv.org/pdf/1811.04323v2.pdf | Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing | This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep RL for image processing are still limited. Therefore, we
extend deep RL to pixelRL for various image processing applications. In
pixelRL, each pixel has an agent, and the agent changes the pixel value by
taking an action. We also propose an effective learning method for pixelRL that
significantly improves the performance by considering not only the future
states of the own pixel but also those of the neighbor pixels. The proposed
method can be applied to some image processing tasks that require pixel-wise
manipulations, where deep RL has never been applied. We apply the proposed
method to three image processing tasks: image denoising, image restoration, and
local color enhancement. Our experimental results demonstrate that the proposed
method achieves comparable or better performance, compared with the
state-of-the-art methods based on supervised learning. | ['Naoto Inoue', 'Ryosuke Furuta', 'Toshihiko Yamasaki'] | 2018-11-10 | null | null | null | null | ['local-color-enhancement'] | ['computer-vision'] | [ 4.70021337e-01 -8.29039142e-02 -1.58877835e-01 -1.43504411e-01
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56cb7fd1-2c4c-488d-a648-b374da032e31 | convolutional-neural-networks-based-remote | null | null | https://ieeexplore.ieee.org/document/9607791 | https://openaccess.thecvf.com/content/ICCV2021W/LUAI/papers/Sun_Convolutional_Neural_Networks_Based_Remote_Sensing_Scene_Classification_Under_Clear_ICCVW_2021_paper.pdf | Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments | Remote sensing (RS) scene classification has wide ap- plications in the environmental monitoring and geological survey. In the real-world applications, the RS scene images taken by the satellite might have two scenarios: clear and cloudy environments. However, most of existing methods did not consider these two environments simultaneously. In this paper, we assume that the global and local features are discriminative in either clear or cloudy environments. Many existing Convolution Neural Networks (CNN) based models have made excellent achievements in the image classifica- tion, however they somewhat ignored the global and local features in their network structure. In this paper, we pro- pose a new CNN based network (named GLNet) with the Global Encoder and Local Encoder to extract the discrim- inative global and local features for the RS scene classifi- cation, where the constraints for inter-class dispersion and intra-class compactness are embedded in the GLNet train- ing. The experimental results on two publicized RS scene classification datasets show that the proposed GLNet could achieve better performance based on many existing CNN backbones under both clear and cloudy environments. | ['Hongkai Yu1∗', 'Jianwu Fang5', 'Shaoyue Song4', 'Qin Zou3', 'Yuewei Lin2', 'Huiming Sun1'] | 2021-12-30 | null | null | null | iccvw-2021-12 | ['scene-classification'] | ['computer-vision'] | [ 1.06168361e-02 -6.52810156e-01 2.12113634e-01 -8.74409676e-01
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8abd159b-d1a2-4524-855e-e386f032ddbb | improved-target-specific-stance-detection-on | 2211.03061 | null | https://arxiv.org/abs/2211.03061v1 | https://arxiv.org/pdf/2211.03061v1.pdf | Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads | Target-specific stance detection on social media, which aims at classifying a textual data instance such as a post or a comment into a stance class of a target issue, has become an emerging opinion mining paradigm of importance. An example application would be to overcome vaccine hesitancy in combating the coronavirus pandemic. However, existing stance detection strategies rely merely on the individual instances which cannot always capture the expressed stance of a given target. In response, we address a new task called conversational stance detection which is to infer the stance towards a given target (e.g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread. To tackle the task, we first propose a benchmarking conversational stance detection (CSD) dataset with annotations of stances and the structures of conversation threads among the instances based on six major social media platforms in Hong Kong. To infer the desired stances from both data instances and conversation threads, we propose a model called Branch-BERT that incorporates contextual information in conversation threads. Extensive experiments on our CSD dataset show that our proposed model outperforms all the baseline models that do not make use of contextual information. Specifically, it improves the F1 score by 10.3% compared with the state-of-the-art method in the SemEval-2016 Task 6 competition. This shows the potential of incorporating rich contextual information on detecting target-specific stances on social media platforms and implies a more practical way to construct future stance detection tasks. | ['Yunya Song', 'Francis C. M. Lau', 'Shaonan Wang', 'Haorui He', 'Yupeng Li'] | 2022-11-06 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 4.81155992e-01 2.92068124e-01 -4.99359876e-01 -4.64744985e-01
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d88f96a7-5ae1-41fc-b448-c1277d3e1f56 | user-level-membership-inference-attack | 2203.02077 | null | https://arxiv.org/abs/2203.02077v2 | https://arxiv.org/pdf/2203.02077v2.pdf | User-Level Membership Inference Attack against Metric Embedding Learning | Membership inference (MI) determines if a sample was part of a victim model training set. Recent development of MI attacks focus on record-level membership inference which limits their application in many real-world scenarios. For example, in the person re-identification task, the attacker (or investigator) is interested in determining if a user's images have been used during training or not. However, the exact training images might not be accessible to the attacker. In this paper, we develop a user-level MI attack where the goal is to find if any sample from the target user has been used during training even when no exact training sample is available to the attacker. We focus on metric embedding learning due to its dominance in person re-identification, where user-level MI attack is more sensible. We conduct an extensive evaluation on several datasets and show that our approach achieves high accuracy on user-level MI task. | ['Xin Liu', 'Shahbaz Rezaei', 'Guoyao Li'] | 2022-03-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.47991496e-01 -4.81780082e-01 -2.29558647e-01 -4.31490868e-01
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5af30ecc-5835-4d96-b634-f0bfe31e8328 | does-recommend-revise-produce-reliable | 2204.07980 | null | https://arxiv.org/abs/2204.07980v1 | https://arxiv.org/pdf/2204.07980v1.pdf | Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED | DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a \textit{recommend-revise} scheme is adopted to reduce the workload. Within this scheme, annotators are provided with candidate relation instances from distant supervision, and they then manually supplement and remove relational facts based on the recommendations. However, when comparing DocRED with a subset relabeled from scratch, we find that this scheme results in a considerable amount of false negative samples and an obvious bias towards popular entities and relations. Furthermore, we observe that the models trained on DocRED have low recall on our relabeled dataset and inherit the same bias in the training data. Through the analysis of annotators' behaviors, we figure out the underlying reason for the problems above: the scheme actually discourages annotators from supplementing adequate instances in the revision phase. We appeal to future research to take into consideration the issues with the recommend-revise scheme when designing new models and annotation schemes. The relabeled dataset is released at \url{https://github.com/AndrewZhe/Revisit-DocRED}, to serve as a more reliable test set of document RE models. | ['Dongyan Zhao', 'Yansong Feng', 'Shengqi Zhu', 'Yuan Ye', 'Shibo Hao', 'Quzhe Huang'] | 2022-04-17 | null | https://aclanthology.org/2022.acl-long.432 | https://aclanthology.org/2022.acl-long.432.pdf | acl-2022-5 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-9.30906609e-02 7.05859661e-01 -6.57282770e-01 -5.21607280e-01
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39f7cc92-184d-468f-a161-7fdab3ac5469 | enhancing-topic-modeling-for-short-texts-with | null | null | https://openreview.net/forum?id=SklnH9VoeV | https://openreview.net/pdf?id=SklnH9VoeV | Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings | Many applications require semantic understanding of short texts, and inferring discriminative and coherent latent topics is a critical and fundamental task in these applications. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short texts are much more sparse in terms of word co-occurrences. Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic. However, DMM has two main limitations. First, even though it seems reasonable to assume that each short text has only one topic because of its shortness, the definition of “shortness” is subjective and the length of the short texts is dataset dependent. That is, the single-topic assumption may be too strong for some datasets. To address this limitation, we propose to model the topic number as a Poisson distribution, allowing each short text to be associated with a small number of topics (e.g., one to three topics). This model is named PDMM. Second, DMM (and also PDMM) does not have access to background knowledge (e.g., semantic relations between words) when modeling short texts. When a human being interprets a piece of short text, the understanding is not solely based on its content words, but also their semantic relations. Recent advances in word embeddings offer effective learning of word semantic relations from a large corpus. Such auxiliary word embeddings enable us to address the second limitation. To this end, we propose to promote the semantically related words under the same topic during the sampling process, by using the generalized Pólya urn (GPU) model. Through the GPU model, background knowledge about word semantic relations learned from millions of external documents can be easily exploited to improve topic modeling for short texts. By directly extending the PDMM model with the GPU model, we propose two more effective topic models for short texts, named GPU-DMM and GPU-PDMM. Through extensive experiments on two real-world short text collections in two languages, we demonstrate that PDMM achieves better topic representations than state-of-the-art models, measured by topic coherence.
The learned topic representation leads to better accuracy in a text classification task, as an indirect evaluation. Both GPU-DMM and GPU-PDMM further improve topic coherence and text classification accuracy. GPU-PDMM outperforms GPU-DMM at the price of higher computational costs. | ['Zongyang Ma', 'Aixin Sun', 'Zhiqian Zhang', 'Haoran Wang', 'Yu Duan', 'Chenliang Li'] | 2018-12-22 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.01508059e-01 4.41720225e-02 -4.57700640e-01 -3.37906122e-01
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4437c720-1f8e-4958-9d2c-529e3e63c898 | scalable-variational-bayes-methods-for-hawkes | 2212.00293 | null | https://arxiv.org/abs/2212.00293v1 | https://arxiv.org/pdf/2212.00293v1.pdf | Scalable Variational Bayes methods for Hawkes processes | Multivariate Hawkes processes are temporal point processes extensively applied to model event data with dependence on past occurrences and interaction phenomena. In the generalised nonlinear model, positive and negative interactions between the components of the process are allowed, therefore accounting for so-called excitation and inhibition effects. In the nonparametric setting, learning the temporal dependence structure of Hawkes processes is often a computationally expensive task, all the more with Bayesian estimation methods. In general, the posterior distribution in the nonlinear Hawkes model is non-conjugate and doubly intractable. Moreover, existing Monte-Carlo Markov Chain methods are often slow and not scalable to high-dimensional processes in practice. Recently, efficient algorithms targeting a mean-field variational approximation of the posterior distribution have been proposed. In this work, we unify existing variational Bayes inference approaches under a general framework, that we theoretically analyse under easily verifiable conditions on the prior, the variational class, and the model. We notably apply our theory to a novel spike-and-slab variational class, that can induce sparsity through the connectivity graph parameter of the multivariate Hawkes model. Then, in the context of the popular sigmoid Hawkes model, we leverage existing data augmentation technique and design adaptive and sparsity-inducing mean-field variational methods. In particular, we propose a two-step algorithm based on a thresholding heuristic to select the graph parameter. Through an extensive set of numerical simulations, we demonstrate that our approach enjoys several benefits: it is computationally efficient, can reduce the dimensionality of the problem by selecting the graph parameter, and is able to adapt to the smoothness of the underlying parameter. | ['Judith Rousseau', 'Vincent Rivoirard', 'Deborah Sulem'] | 2022-12-01 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 4.71479446e-01 2.17015091e-02 5.22432886e-02 2.01211765e-01
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2b360654-e15d-4302-8dd8-59f8e5b1f617 | mapping-urban-population-growth-from-sentinel | 2303.08511 | null | https://arxiv.org/abs/2303.08511v1 | https://arxiv.org/pdf/2303.08511v1.pdf | Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda | To better understand current trends of urban population growth in Sub-Saharan Africa, high-quality spatiotemporal population estimates are necessary. While the joint use of remote sensing and deep learning has achieved promising results for population distribution estimation, most of the current work focuses on fine-scale spatial predictions derived from single date census, thereby neglecting temporal analyses. In this work, we focus on evaluating how deep learning change detection techniques can unravel temporal population dynamics at short intervals. Since Post-Classification Comparison (PCC) methods for change detection are known to propagate the error of the individual maps, we propose an end-to-end population growth mapping method. Specifically, a ResNet encoder, pretrained on a population mapping task with Sentinel-2 MSI data, was incorporated into a Siamese network. The Siamese network was trained at the census level to accurately predict population change. The effectiveness of the proposed method is demonstrated in Kigali, Rwanda, for the time period 2016-2020, using bi-temporal Sentinel-2 data. Compared to PCC, the Siamese network greatly reduced errors in population change predictions at the census level. These results show promise for future remote sensing-based population growth mapping endeavors. | ['Yifang Ban', 'Theodomir Mugiraneza', 'Stefanos Georganos', 'Sebastian Hafner'] | 2023-03-15 | null | null | null | null | ['change-detection', 'population-mapping'] | ['computer-vision', 'computer-vision'] | [-1.32444128e-01 -1.94447532e-01 -2.47161482e-02 -2.99785256e-01
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221f869f-6fda-4e5d-a9e6-2aa9050ca9d2 | anatomy-x-net-a-semi-supervised-anatomy-aware | 2106.05915 | null | https://arxiv.org/abs/2106.05915v3 | https://arxiv.org/pdf/2106.05915v3.pdf | Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays | Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. Motivated by this, we propose Anatomy-XNet, an anatomy-aware attention-based thoracic disease classification network that prioritizes the spatial features guided by the pre-identified anatomy regions. We adopt a semi-supervised learning method by utilizing available small-scale organ-level annotations to locate the anatomy regions in large-scale datasets where the organ-level annotations are absent. The proposed Anatomy-XNet uses the pre-trained DenseNet-121 as the backbone network with two corresponding structured modules, the Anatomy Aware Attention (A$^3$) and Probabilistic Weighted Average Pooling (PWAP), in a cohesive framework for anatomical attention learning. We experimentally show that our proposed method sets a new state-of-the-art benchmark by achieving an AUC score of 85.78%, 92.07%, and, 84.04% on three publicly available large-scale CXR datasets--NIH, Stanford CheXpert, and MIMIC-CXR, respectively. This not only proves the efficacy of utilizing the anatomy segmentation knowledge to improve the thoracic disease classification but also demonstrates the generalizability of the proposed framework. | ['Taufiq Hasan', 'Nusrat Binta Nizam', 'Mohammad Zunaed', 'Uday Kamal'] | 2021-06-10 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 3.52557264e-02 3.18873614e-01 -3.59315425e-01 -2.58062840e-01
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7d6820f0-e4e3-49dd-91d5-b83ce3f4c2a3 | transfuse-a-unified-transformer-based-image | 2201.07451 | null | https://arxiv.org/abs/2201.07451v1 | https://arxiv.org/pdf/2201.07451v1.pdf | TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning | Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-specific training data and corresponding ground truth, most existing end-to-end image fusion methods easily fall into overfitting or tedious parameter optimization processes. Two-stage methods avoid the need of large amount of task-specific training data by training encoder-decoder network on large natural image datasets and utilizing the extracted features for fusion, but the domain gap between natural images and different fusion tasks results in limited performance. In this study, we design a novel encoder-decoder based image fusion framework and propose a destruction-reconstruction based self-supervised training scheme to encourage the network to learn task-specific features. Specifically, we propose three destruction-reconstruction self-supervised auxiliary tasks for multi-modal image fusion, multi-exposure image fusion and multi-focus image fusion based on pixel intensity non-linear transformation, brightness transformation and noise transformation, respectively. In order to encourage different fusion tasks to promote each other and increase the generalizability of the trained network, we integrate the three self-supervised auxiliary tasks by randomly choosing one of them to destroy a natural image in model training. In addition, we design a new encoder that combines CNN and Transformer for feature extraction, so that the trained model can exploit both local and global information. Extensive experiments on multi-modal image fusion, multi-exposure image fusion and multi-focus image fusion tasks demonstrate that our proposed method achieves the state-of-the-art performance in both subjective and objective evaluations. The code will be publicly available soon. | ['Zhijian Song', 'Qin Qiao', 'Siqi Yin', 'Shiman Li', 'Manning Wang', 'Shaolei Liu', 'Linhao Qu'] | 2022-01-19 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 4.70706671e-01 -3.93150717e-01 -3.80738564e-02 -4.47609842e-01
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eeef857c-23ba-4492-b44c-cdf8a4175c7a | causality-based-neural-network-repair | 2204.09274 | null | https://arxiv.org/abs/2204.09274v2 | https://arxiv.org/pdf/2204.09274v2.pdf | Causality-based Neural Network Repair | Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural network repair technique that 1) performs causality-based fault localization to identify the `guilty' neurons and 2) optimizes the parameters of the identified neurons to reduce the misbehavior. We have empirically evaluated CARE on various tasks such as backdoor removal, neural network repair for fairness and safety properties. Our experiment results show that CARE is able to repair all neural networks efficiently and effectively. For fairness repair tasks, CARE successfully improves fairness by $61.91\%$ on average. For backdoor removal tasks, CARE reduces the attack success rate from over $98\%$ to less than $1\%$. For safety property repair tasks, CARE reduces the property violation rate to less than $1\%$. Results also show that thanks to the causality-based fault localization, CARE's repair focuses on the misbehavior and preserves the accuracy of the neural networks. | ['Jie Shi', 'Hong Long Pham', 'Jun Sun', 'Bing Sun'] | 2022-04-20 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 2.22214699e-01 2.80119956e-01 -2.96819836e-01 -3.71642083e-01
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b538c5e4-8a8e-46bb-9ff4-3e89420a37ad | self-supervised-in-domain-representation | 2302.01793 | null | https://arxiv.org/abs/2302.01793v1 | https://arxiv.org/pdf/2302.01793v1.pdf | Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification | Transferring the ImageNet pre-trained weights to the various remote sensing tasks has produced acceptable results and reduced the need for labeled samples. However, the domain differences between ground imageries and remote sensing images cause the performance of such transfer learning to be limited. Recent research has demonstrated that self-supervised learning methods capture visual features that are more discriminative and transferable than the supervised ImageNet weights. We are motivated by these facts to pre-train the in-domain representations of remote sensing imagery using contrastive self-supervised learning and transfer the learned features to other related remote sensing datasets. Specifically, we used the SimSiam algorithm to pre-train the in-domain knowledge of remote sensing datasets and then transferred the obtained weights to the other scene classification datasets. Thus, we have obtained state-of-the-art results on five land cover classification datasets with varying numbers of classes and spatial resolutions. In addition, By conducting appropriate experiments, including feature pre-training using datasets with different attributes, we have identified the most influential factors that make a dataset a good choice for obtaining in-domain features. We have transferred the features obtained by pre-training SimSiam on remote sensing datasets to various downstream tasks and used them as initial weights for fine-tuning. Moreover, we have linearly evaluated the obtained representations in cases where the number of samples per class is limited. Our experiments have demonstrated that using a higher-resolution dataset during the self-supervised pre-training stage results in learning more discriminative and general representations. | ['Hossein Soleimani', 'Ali Ghanbarzade'] | 2023-02-03 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.38448966e-01 -2.60013819e-01 -2.01414555e-01 -8.48605812e-01
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9b808a1c-a081-473b-be65-a97912bab04b | divide-and-conquer-answering-questions-with | 2303.10482 | null | https://arxiv.org/abs/2303.10482v1 | https://arxiv.org/pdf/2303.10482v1.pdf | Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning | Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions. Inspired by humans' reasoning of the visual world, we tackle the aforementioned challenges from a compositional perspective, and propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space and makes decisions with a compositional reasoning process. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions. In addition to the enhanced generalizability, our framework also provides an interpretable interface for understanding the decision-making process of models. Our code is available at https://github.com/szzexpoi/POEM. | ['Qi Zhao', 'Shi Chen'] | 2023-03-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 8.93592462e-02 2.24499658e-01 -1.64989009e-01 -5.63259184e-01
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b01e91a9-acc4-43f0-9a8c-54525e8f2c47 | metal-conscious-embedding-for-cbct-projection | 2211.16219 | null | https://arxiv.org/abs/2211.16219v1 | https://arxiv.org/pdf/2211.16219v1.pdf | Metal-conscious Embedding for CBCT Projection Inpainting | The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced. | ['Andreas Maier', 'Steffen Kappler', 'Yixing Huang', 'Björn Kreher', 'Marcel Beister', 'Ramyar Biniazan', 'Ludwig Ritschl', 'Yangkong Wang', 'Fuxin Fan'] | 2022-11-29 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 1.9932906e-01 2.1428755e-01 2.6022565e-01 -1.0242654e-01
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6b551a2c-d195-462e-8d01-3600506ff716 | one-shot-federated-learning-for-leo | 2305.12316 | null | https://arxiv.org/abs/2305.12316v1 | https://arxiv.org/pdf/2305.12316v1.pdf | One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes | A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy-friendly. However, FL requires many communication rounds between clients (satellites) and the parameter server (PS), leading to substantial delays of up to several days in LEO constellations. In this paper, we propose a novel one-shot FL approach for LEO satellites, called LEOShot, that needs only a single communication round to complete the entire learning process. LEOShot comprises three processes: (i) synthetic data generation, (ii) knowledge distillation, and (iii) virtual model retraining. We evaluate and benchmark LEOShot against the state of the art and the results show that it drastically expedites FL convergence by more than an order of magnitude. Also surprisingly, despite the one-shot nature, its model accuracy is on par with or even outperforms regular iterative FL schemes by a large margin | ['Tie Luo', 'Mohamed Elmahallawy'] | 2023-05-21 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-3.61150235e-01 -7.13823512e-02 -2.76350468e-01 -5.47150262e-02
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5d23600b-0682-4f7c-bc2d-ae13e9acea56 | r3sgm-real-time-raster-respecting-semi-global | 1810.12988 | null | http://arxiv.org/abs/1810.12988v1 | http://arxiv.org/pdf/1810.12988v1.pdf | R$^3$SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems | Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but
power efficiency is hard to achieve with conventional hardware, making the use
of embedded devices such as FPGAs attractive for low-power applications.
However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so
most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA
context, the accuracy of SGM has been improved by More Global Matching (MGM),
which also helps tackle the streaking artifacts that afflict SGM. In this
paper, we propose a novel, resource-efficient method that is inspired by MGM's
techniques for improving depth quality, but which can be implemented to run in
real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI
and Middlebury), we show that in comparison to other real-time capable stereo
approaches, we can achieve a state-of-the-art balance between accuracy, power
efficiency and speed, making our approach highly desirable for use in real-time
systems with limited power. | ['Simon Walker', 'Tommaso Cavallari', 'Philip H. S. Torr', 'Oscar Rahnama', 'Stuart Golodetz'] | 2018-10-30 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.31441456e-01 -1.56571835e-01 1.63797252e-02 -1.38769925e-01
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e747bf9b-d3df-40d0-bed3-defff32d1215 | domain-adaptive-scene-text-detection-via | 2212.00377 | null | https://arxiv.org/abs/2212.00377v1 | https://arxiv.org/pdf/2212.00377v1.pdf | Domain Adaptive Scene Text Detection via Subcategorization | Most existing scene text detectors require large-scale training data which cannot scale well due to two major factors: 1) scene text images often have domain-specific distributions; 2) collecting large-scale annotated scene text images is laborious. We study domain adaptive scene text detection, a largely neglected yet very meaningful task that aims for optimal transfer of labelled scene text images while handling unlabelled images in various new domains. Specifically, we design SCAST, a subcategory-aware self-training technique that mitigates the network overfitting and noisy pseudo labels in domain adaptive scene text detection effectively. SCAST consists of two novel designs. For labelled source data, it introduces pseudo subcategories for both foreground texts and background stuff which helps train more generalizable source models with multi-class detection objectives. For unlabelled target data, it mitigates the network overfitting by co-regularizing the binary and subcategory classifiers trained in the source domain. Extensive experiments show that SCAST achieves superior detection performance consistently across multiple public benchmarks, and it also generalizes well to other domain adaptive detection tasks such as vehicle detection. | ['Shijian Lu', 'Jingyi Zhang', 'Chuhui Xue', 'Zichen Tian'] | 2022-12-01 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 7.05188811e-01 -2.25719646e-01 -4.23583120e-01 -5.68156660e-01
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e61ae4d7-e96c-4be2-a8a9-c9c044ec0374 | qimera-data-free-quantization-with-synthetic | 2111.02625 | null | https://arxiv.org/abs/2111.02625v1 | https://arxiv.org/pdf/2111.02625v1.pdf | Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples | Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usually requires access to the original training data to maintain the accuracy of the full-precision models, which is often infeasible in real-world scenarios for security and privacy issues. A popular approach to perform quantization without access to the original data is to use synthetically generated samples, based on batch-normalization statistics or adversarial learning. However, the drawback of such approaches is that they primarily rely on random noise input to the generator to attain diversity of the synthetic samples. We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries. To this end, we propose Qimera, a method that uses superposed latent embeddings to generate synthetic boundary supporting samples. For the superposed embeddings to better reflect the original distribution, we also propose using an additional disentanglement mapping layer and extracting information from the full-precision model. The experimental results show that Qimera achieves state-of-the-art performances for various settings on data-free quantization. Code is available at https://github.com/iamkanghyunchoi/qimera. | ['Jinho Lee', 'Youngsok Kim', 'Noseong Park', 'Deokki Hong', 'Kanghyun Choi'] | 2021-11-04 | null | http://proceedings.neurips.cc/paper/2021/hash/7cc234202e98d2722580858573fd0817-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/7cc234202e98d2722580858573fd0817-Paper.pdf | neurips-2021-12 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.14255033e-01 -4.99394797e-02 -5.06791711e-01 -3.07182610e-01
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65a20f02-a105-4951-b18e-9ab6925f2fdf | generating-coherent-drum-accompaniment-with | 2209.00291 | null | https://arxiv.org/abs/2209.00291v1 | https://arxiv.org/pdf/2209.00291v1.pdf | Generating Coherent Drum Accompaniment With Fills And Improvisations | Creating a complex work of art like music necessitates profound creativity. With recent advancements in deep learning and powerful models such as transformers, there has been huge progress in automatic music generation. In an accompaniment generation context, creating a coherent drum pattern with apposite fills and improvisations at proper locations in a song is a challenging task even for an experienced drummer. Drum beats tend to follow a repetitive pattern through stanzas with fills or improvisation at section boundaries. In this work, we tackle the task of drum pattern generation conditioned on the accompanying music played by four melodic instruments: Piano, Guitar, Bass, and Strings. We use the transformer sequence to sequence model to generate a basic drum pattern conditioned on the melodic accompaniment to find that improvisation is largely absent, attributed possibly to its expectedly relatively low representation in the training data. We propose a novelty function to capture the extent of improvisation in a bar relative to its neighbors. We train a model to predict improvisation locations from the melodic accompaniment tracks. Finally, we use a novel BERT-inspired in-filling architecture, to learn the structure of both the drums and melody to in-fill elements of improvised music. | ['Prateek Verma', 'Preeti Rao', 'Vaibhav Talwadker', 'Rishabh Dahale'] | 2022-09-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.98568064e-01 -2.21466824e-01 2.81898111e-01 1.72176138e-01
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6049f7a1-06c6-4bed-8e8c-6b6203c57d81 | using-intermediate-representations-to-solve | null | null | https://aclanthology.org/P18-1039 | https://aclanthology.org/P18-1039.pdf | Using Intermediate Representations to Solve Math Word Problems | To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations. | ['Chin-Yew Lin', 'Jin-Ge Yao', 'Jian Yin', 'Danqing Huang', 'Qingyu Zhou'] | 2018-07-01 | null | null | null | acl-2018-7 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 4.26826477e-01 1.16409771e-01 -3.23787093e-01 -7.61099935e-01
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248a018c-ca0f-4ca1-ba81-6a5f9b942b3d | dialoguecrn-contextual-reasoning-networks-for | 2106.01978 | null | https://arxiv.org/abs/2106.01978v2 | https://arxiv.org/pdf/2106.01978v2.pdf | DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model. | ['Xiaoyong Huai', 'Lingwei Wei', 'Dou Hu'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-long.547 | https://aclanthology.org/2021.acl-long.547.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.32003748e-01 1.28231898e-01 1.43334314e-01 -6.06670856e-01
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ba61c107-626a-450e-96e1-38205f27aec7 | potsdam-semantic-dependency-parsing-by | null | null | https://aclanthology.org/S14-2081 | https://aclanthology.org/S14-2081.pdf | Potsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic Parsing | null | ['er', "{\\v{Z}}eljko Agi{\\'c}", 'Alex Koller'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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69a1faba-bbcb-4510-a6de-62de27163623 | gldqn-explicitly-parameterized-quantile | 2205.15455 | null | https://arxiv.org/abs/2205.15455v2 | https://arxiv.org/pdf/2205.15455v2.pdf | A Simulation Environment and Reinforcement Learning Method for Waste Reduction | In retail (e.g., grocery stores, apparel shops, online retailers), inventory managers have to balance short-term risk (no items to sell) with long-term-risk (over ordering leading to product waste). This balancing task is made especially hard due to the lack of information about future customer purchases. In this paper, we study the problem of restocking a grocery store's inventory with perishable items over time, from a distributional point of view. The objective is to maximize sales while minimizing waste, with uncertainty about the actual consumption by costumers. This problem is of a high relevance today, given the growing demand for food and the impact of food waste on the environment, the economy, and purchasing power. We frame inventory restocking as a new reinforcement learning task that exhibits stochastic behavior conditioned on the agent's actions, making the environment partially observable. We make two main contributions. First, we introduce a new reinforcement learning environment, RetaiL, based on real grocery store data and expert knowledge. This environment is highly stochastic, and presents a unique challenge for reinforcement learning practitioners. We show that uncertainty about the future behavior of the environment is not handled well by classical supply chain algorithms, and that distributional approaches are a good way to account for the uncertainty. Second, we introduce GTDQN, a distributional reinforcement learning algorithm that learns a generalized Tukey Lambda distribution over the reward space. GTDQN provides a strong baseline for our environment. It outperforms other distributional reinforcement learning approaches in this partially observable setting, in both overall reward and reduction of generated waste. | ['Maarten de Rijke', 'Paul Groth', 'Mozhdeh Ariannezhad', 'Sami Jullien'] | 2022-05-30 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.84020305e-01 1.67987540e-01 -5.44961095e-01 -2.67687470e-01
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23e845dd-d618-4441-a44c-a58bf487369f | novel-view-synthesis-from-single-images-via | 2009.08321 | null | https://arxiv.org/abs/2009.08321v2 | https://arxiv.org/pdf/2009.08321v2.pdf | Novel View Synthesis from Single Images via Point Cloud Transformation | In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired. Our method estimates point clouds to capture the geometry of the object, which can be freely rotated into the desired view and then projected into a new image. This image, however, is sparse by nature and hence this coarse view is used as the input of an image completion network to obtain the dense target view. The point cloud is obtained using the predicted pixel-wise depth map, estimated from a single RGB input image,combined with the camera intrinsics. By using forward warping and backward warpingbetween the input view and the target view, the network can be trained end-to-end without supervision on depth. The benefit of using point clouds as an explicit 3D shape for novel view synthesis is experimentally validated on the 3D ShapeNet benchmark. Source code and data will be available at https://lhoangan.github.io/pc4novis/. | ['Theo Gevers', 'Hoang-An Le', 'Thomas Mensink', 'Partha Das'] | 2020-09-17 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 2.52764255e-01 5.23882687e-01 2.17415795e-01 -3.39978486e-01
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e7c1dcf6-c7f8-4419-9cb0-238ae78dae53 | data-centric-ai-approach-to-improve-optic | 2208.03868 | null | https://arxiv.org/abs/2208.03868v1 | https://arxiv.org/pdf/2208.03868v1.pdf | Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images | The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical coherence tomography scans that enables the automated assessment of large amounts of data. Evaluation on manually annotated 2D en face images of the retina demonstrates that training of a standard U-Net can yield improved optic nerve head segmentation and localization performance when the underlying pixel-level binary classification task is spatially relaxed through domain knowledge. | ['Tilman Schmoll', 'Rainer A. Leitgeb', 'Wolfgang Drexler', 'Ursula Schmidt-Erfurth', 'Andreas Pollreisz', 'Michael Niederleithner', 'Heiko Stino', 'Thomas Schlegl'] | 2022-08-08 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 2.71258920e-01 1.43269256e-01 -4.45149727e-02 -4.20170814e-01
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4e244506-1c6b-4062-8591-fd268cbca989 | reading-to-listen-at-the-cocktail-party-multi | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.pdf | Reading To Listen at the Cocktail Party: Multi-Modal Speech Separation | The goal of this paper is speech separation and enhancement in multi-speaker and noisy environments using a combination of different modalities. Previous works have shown good performance when conditioning on temporal or static visual evidence such as synchronised lip movements or face identity. In this paper we present a unified framework for multi-modal speech separation and enhancement based on synchronous or asynchronous cues. To that end we make the following contributions: (i) we design a modern Transformer-based architecture which inputs and outputs raw waveforms and is tailored to fuse different modalities to solve the speech separation task; (ii) we propose conditioning on the text content of a sentence alone or in combination with visual information; (iii) we demonstrate the robustness of our model to audio-visual synchronisation offsets; and, (iv) we obtain state-of-the art performance on the well-established benchmark datasets LRS2 and LRS3. | ['Andrew Zisserman', 'Triantafyllos Afouras', 'Akam Rahimi'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['speech-separation'] | ['speech'] | [ 5.19621670e-01 -2.09084779e-01 1.62250832e-01 -2.04677895e-01
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04a76f3e-151e-422b-910d-a51dc3aafdb0 | multi-sem-fusion-multimodal-semantic-fusion | 2212.05265 | null | https://arxiv.org/abs/2212.05265v2 | https://arxiv.org/pdf/2212.05265v2.pdf | Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection | LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark. | ['Zhi-Xin Yang', 'Sifen Wang', 'Jin Fang', 'Ziying Song', 'Fang Li', 'Shaoqing Xu'] | 2022-12-10 | null | null | null | null | ['scene-parsing', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.33427459e-02 -1.93862960e-01 3.57790403e-02 -4.96509045e-01
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4.09115940e-01 -1.33932090e+00 -3.86226058e-01 -3.90667975e-01] | [7.8376336097717285, -2.5247671604156494] |
85fa50e0-23db-4b7f-b32d-89313f40c76f | bidirectional-lstm-crf-for-clinical-concept-1 | 1610.05858 | null | http://arxiv.org/abs/1610.05858v1 | http://arxiv.org/pdf/1610.05858v1.pdf | Bidirectional LSTM-CRF for Clinical Concept Extraction | Extraction of concepts present in patient clinical records is an essential
step in clinical research. The 2010 i2b2/VA Workshop on Natural Language
Processing Challenges for clinical records presented concept extraction (CE)
task, with aim to identify concepts (such as treatments, tests, problems) and
classify them into predefined categories. State-of-the-art CE approaches
heavily rely on hand crafted features and domain specific resources which are
hard to collect and tune. For this reason, this paper employs bidirectional
LSTM with CRF decoding initialized with general purpose off-the-shelf word
embeddings for CE. The experimental results achieved on 2010 i2b2/VA reference
standard corpora using bidirectional LSTM CRF ranks closely with top ranked
systems. | ['Raghavendra Chalapathy', 'Massimo Piccardi', 'Ehsan Zare Borzeshi'] | 2016-10-19 | bidirectional-lstm-crf-for-clinical-concept-2 | https://aclanthology.org/W16-4202 | https://aclanthology.org/W16-4202.pdf | ws-2016-12 | ['clinical-concept-extraction'] | ['medical'] | [ 2.44157240e-01 1.10028662e-01 -5.70387483e-01 -4.78832006e-01
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abdcd5c1-c5bd-414f-b109-b5c068bfc76a | alternative-visual-units-for-an-optimized | 1909.07147 | null | http://arxiv.org/abs/1909.07147v1 | http://arxiv.org/pdf/1909.07147v1.pdf | Alternative Visual Units for an Optimized Phoneme-Based Lipreading System | Lipreading is understanding speech from observed lip movements. An observed
series of lip motions is an ordered sequence of visual lip gestures. These
gestures are commonly known, but as yet are not formally defined, as `visemes'.
In this article, we describe a structured approach which allows us to create
speaker-dependent visemes with a fixed number of visemes within each set. We
create sets of visemes for sizes two to 45. Each set of visemes is based upon
clustering phonemes, thus each set has a unique phoneme-to-viseme mapping. We
first present an experiment using these maps and the Resource Management
Audio-Visual (RMAV) dataset which shows the effect of changing the viseme map
size in speaker-dependent machine lipreading and demonstrate that word
recognition with phoneme classifiers is possible. Furthermore, we show that
there are intermediate units between visemes and phonemes which are better
still. Second, we present a novel two-pass training scheme for phoneme
classifiers. This approach uses our new intermediary visual units from our
first experiment in the first pass as classifiers; before using the
phoneme-to-viseme maps, we retrain these into phoneme classifiers. This method
significantly improves on previous lipreading results with RMAV speakers. | [] | 2019-09-16 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.28697622e-01 6.86361641e-02 -2.86408246e-01 -1.71177447e-01
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c555b5ae-59ae-423f-956f-1ed51768d8be | learning-to-reduce-information-bottleneck-for | 2204.02033 | null | https://arxiv.org/abs/2204.02033v4 | https://arxiv.org/pdf/2204.02033v4.pdf | Learning to Reduce Information Bottleneck for Object Detection in Aerial Images | Object detection in aerial images is a fundamental research topic in the geoscience and remote sensing domain. However, the advanced approaches on this topic mainly focus on designing the elaborate backbones or head networks but ignore neck networks. In this letter, we first underline the importance of the neck network in object detection from the perspective of information bottleneck. Then, to alleviate the information deficiency problem in the current approaches, we propose a global semantic network (GSNet), which acts as a bridge from the backbone network to the head network in a bidirectional global pattern. Compared to the existing approaches, our model can capture the rich and enhanced image features with less computational costs. Besides, we further propose a feature fusion refinement module (FRM) for different levels of features, which are suffering from the problem of semantic gap in feature fusion. To demonstrate the effectiveness and efficiency of our approach, experiments are carried out on two challenging and representative aerial image datasets (i.e., DOTA and HRSC2016). Experimental results in terms of accuracy and complexity validate the superiority of our method. The code has been open-sourced at GSNet. | ['Zhihao Song', 'Dong Zhang', 'Qiaolin Ye', 'Xuesong Jiang', 'Yuchen Shen'] | 2022-04-05 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.25728118e-01 -2.90088534e-01 1.06847115e-01 -2.04477355e-01
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ce15592d-bb0b-4cc1-83e7-240849835aa2 | hit-scir-at-mrp-2019-a-unified-pipeline-for | null | null | https://aclanthology.org/K19-2007 | https://aclanthology.org/K19-2007.pdf | HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding | This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. We extended the basic transition-based parser with two improvements: a) Efficient Training by realizing Stack LSTM parallel training; b) Effective Encoding via adopting deep contextualized word embeddings BERT. Generally, we proposed a unified pipeline to meaning representation parsing, including framework-specific transition-based parsers, BERT-enhanced word representation, and post-processing. In the final evaluation, our system was ranked first according to ALL-F1 (86.2{\%}) and especially ranked first in UCCA framework (81.67{\%}). | ['Longxu Dou', 'Wanxiang Che', 'Yuxuan Wang', 'Yang Xu', 'Ting Liu', 'Yijia Liu'] | 2019-11-01 | null | null | null | conll-2019-11 | ['ucca-parsing'] | ['natural-language-processing'] | [ 5.78817606e-01 3.98014694e-01 -1.69037893e-01 -6.72503293e-01
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ece315e3-9f80-4238-b611-d8ece48fc704 | frame-flexible-network | 2303.14817 | null | https://arxiv.org/abs/2303.14817v1 | https://arxiv.org/pdf/2303.14817v1.pdf | Frame Flexible Network | Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which are not used in training, we observe the performance will drop significantly (see Fig.1), which is summarized as Temporal Frequency Deviation phenomenon. To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly. Concretely, FFN integrates several sets of training sequences, involves Multi-Frequency Alignment (MFAL) to learn temporal frequency invariant representations, and leverages Multi-Frequency Adaptation (MFAD) to further strengthen the representation abilities. Comprehensive empirical validations using various architectures and popular benchmarks solidly demonstrate the effectiveness and generalization of FFN (e.g., 7.08/5.15/2.17% performance gain at Frame 4/8/16 on Something-Something V1 dataset over Uniformer). Code is available at https://github.com/BeSpontaneous/FFN. | ['Yun Fu', 'Sheng Li', 'Huan Wang', 'Chang Liu', 'Yue Bai', 'Yitian Zhang'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Frame_Flexible_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Frame_Flexible_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition'] | ['computer-vision'] | [ 1.48348674e-01 -6.40930951e-01 -4.57344741e-01 -3.84069443e-01
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d4dcfb81-bb38-46fb-aff2-3d350e199c42 | multilingual-ontology-matching-based-on | 1109.0732 | null | http://arxiv.org/abs/1109.0732v2 | http://arxiv.org/pdf/1109.0732v2.pdf | Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint | Interoperability is a feature required by the Semantic Web. It is provided by
the ontology matching methods and algorithms. But now ontologies are presented
not only in English, but in other languages as well. It is important to use an
automatic translation for obtaining correct matching pairs in multilingual
ontology matching. The translation into many languages could be based on the
Google Translate API, the Wiktionary database, etc. From the point of view of
the balance of presence of many languages, of manually crafted translations, of
a huge size of a dictionary, the most promising resource is the Wiktionary. It
is a collaborative project working on the same principles as the Wikipedia. The
parser of the Wiktionary was developed and the machine-readable dictionary was
designed. The data of the machine-readable Wiktionary are stored in a
relational database, but with the help of D2R server the database is presented
as an RDF store. Thus, it is possible to get lexicographic information
(definitions, translations, synonyms) from web service using SPARQL requests.
In the case study, the problem entity is a task of multilingual ontology
matching based on Wiktionary data accessible via SPARQL endpoint. Ontology
matching results obtained using Wiktionary were compared with results based on
Google Translate API. | ['Andrew Krizhanovsky', 'Feiyu Lin'] | 2011-09-04 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-5.07516682e-01 1.59773812e-01 -1.76292717e-01 -1.48056984e-01
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24d2acd9-99e6-43ba-a3c2-f0510083891f | real-time-mortality-prediction-using-mimic-iv | 2110.08949 | null | https://arxiv.org/abs/2110.08949v3 | https://arxiv.org/pdf/2110.08949v3.pdf | Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards | Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency. One of the most exciting applications of EHR data is in developing a real-time mortality warning system with tools from survival analysis. However, most of the survival analysis methods used recently are based on (semi)parametric models using static covariates. These models do not take advantage of the information conveyed by the time-varying EHR data. In this work, we present an application of a highly scalable survival analysis method, BoXHED 2.0 to develop a real-time in-ICU mortality warning indicator based on the MIMIC IV data set. Importantly, BoXHED can incorporate time-dependent covariates in a fully nonparametric manner and is backed by theory. Our in-ICU mortality model achieves an AUC-PRC of 0.41 and AUC-ROC of 0.83 out of sample, demonstrating the benefit of real-time monitoring. | ['Bobak J. Mortazavi', 'Donald K. K. Lee', 'James Royalty', 'Arash Pakbin', 'Zhale Nowroozilarki'] | 2021-10-17 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-3.39070320e-01 -2.75315821e-01 -1.24599315e-01 -4.40192759e-01
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147ab1a2-81e6-4d97-8be4-853bff32182b | ochadai-kyodai-at-semeval-2021-task-1 | 2105.05535 | null | https://arxiv.org/abs/2105.05535v3 | https://arxiv.org/pdf/2105.05535v3.pdf | OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction | We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWEand outputs its complexity score. Given that a key challenge with this task is the limited size of annotated data, our model relies on pretrained contextual representations from different state-of-the-art transformer-based language models (i.e., BERT and RoBERTa), and on a variety of training methods for further enhancing model generalization and robustness:multi-step fine-tuning and multi-task learning, and adversarial training. Additionally, we propose to enrich contextual representations by adding hand-crafted features during training. Our model achieved competitive results and ranked among the top-10 systems in both sub-tasks. | ['Ichiro Kobayashi', 'Fei Cheng', 'Lis Kanashiro Pereira', 'Yuki Taya'] | 2021-05-12 | null | https://aclanthology.org/2021.semeval-1.2 | https://aclanthology.org/2021.semeval-1.2.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 3.40318412e-01 -1.54673547e-01 -7.86132663e-02 -4.33972269e-01
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a24c16d0-49e1-459f-b91b-9d8f53c284b7 | covid-19-named-entity-recognition-for | 2104.03879 | null | https://arxiv.org/abs/2104.03879v1 | https://arxiv.org/pdf/2104.03879v1.pdf | COVID-19 Named Entity Recognition for Vietnamese | The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other than English. In this paper, we present the first manually-annotated COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is annotated for the named entity recognition (NER) task with newly-defined entity types that can be used in other future epidemics. Our dataset also contains the largest number of entities compared to existing Vietnamese NER datasets. We empirically conduct experiments using strong baselines on our dataset, and find that: automatic Vietnamese word segmentation helps improve the NER results and the highest performances are obtained by fine-tuning pre-trained language models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) produces higher results than the multilingual model XLM-R (Conneau et al., 2020). We publicly release our dataset at: https://github.com/VinAIResearch/PhoNER_COVID19 | ['Dat Quoc Nguyen', 'Mai Hoang Dao', 'Thinh Hung Truong'] | 2021-04-08 | null | https://aclanthology.org/2021.naacl-main.173 | https://aclanthology.org/2021.naacl-main.173.pdf | naacl-2021-4 | ['vietnamese-word-segmentation', 'named-entity-recognition-in-vietnamese'] | ['natural-language-processing', 'natural-language-processing'] | [-5.25173008e-01 -2.39687830e-01 -4.32645887e-01 -1.09845683e-01
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28baedb1-fcb4-45ca-a7e1-2432e6847a88 | domain-adaptive-robotic-gesture-recognition | 2103.04075 | null | https://arxiv.org/abs/2103.04075v2 | https://arxiv.org/pdf/2103.04075v2.pdf | Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment | Automated surgical gesture recognition is of great importance in robot-assisted minimally invasive surgery. However, existing methods assume that training and testing data are from the same domain, which suffers from severe performance degradation when a domain gap exists, such as the simulator and real robot. In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Specifically, we first propose an MDO-K to align kinematics, which exploits temporal continuity to transfer motion directions with smaller gap rather than position values, relieving the adaptation burden. Moreover, we propose a KV-Relation-ATT to transfer the co-occurrence signals of kinematics and vision. Such features attended by correlation similarity are more informative for enhancing domain-invariance of the model. Two feature alignment strategies benefit the model mutually during the end-to-end learning process. We extensively evaluate our method for gesture recognition using DESK dataset with peg transfer procedure. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot. | ['Pheng-Ann Heng', 'Jing Qin', 'Qi Dou', 'Yueming Jin', 'Xueying Shi'] | 2021-03-06 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 0.20661813 0.05886614 -0.34007278 -0.3006639 -0.7829329 -0.56954765
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db1963e4-a7ff-4d4f-ae5f-bd097c7aea30 | neuragen-a-low-resource-neural-network-based | 2203.15253 | null | https://arxiv.org/abs/2203.15253v1 | https://arxiv.org/pdf/2203.15253v1.pdf | NeuraGen-A Low-Resource Neural Network based approach for Gender Classification | Human voice is the source of several important information. This is in the form of features. These Features help in interpreting various features associated with the speaker and speech. The speaker dependent work researchersare targeted towards speaker identification, Speaker verification, speaker biometric, forensics using feature, and cross-modal matching via speech and face images. In such context research, it is a very difficult task to come across clean, and well annotated publicly available speech corpus as data set. Acquiring volunteers to generate such dataset is also very expensive, not to mention the enormous amount of effort and time researchers spend to gather such data. The present paper work, a Neural Network proposal as NeuraGen focused which is a low-resource ANN architecture. The proposed tool used to classify gender of the speaker from the speech recordings. We have used speech recordings collected from the ELSDSR and limited TIMIT datasets, from which we extracted 8 speech features, which were pre-processed and then fed into NeuraGen to identify the gender. NeuraGen has successfully achieved accuracy of 90.7407% and F1 score of 91.227% in train and 20-fold cross validation dataset. | ['Naagamani Molakathaala', 'Chhanda Saha', 'Shankhanil Ghosh'] | 2022-03-29 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-2.10904386e-02 -4.28405777e-02 3.04560483e-01 -8.09273839e-01
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e3a92ad5-1c26-4cc4-870b-b84eba2d1d21 | patching-as-translation-the-data-and-the | 2008.10707 | null | https://arxiv.org/abs/2008.10707v2 | https://arxiv.org/pdf/2008.10707v2.pdf | Patching as Translation: the Data and the Metaphor | Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of this phenomenon: the conceit that "software patching is like language translation". We demonstrate empirically that there are subtle, but critical distinctions between sequence-to-sequence models and translation model: while program repair benefits greatly from the former, general modeling architecture, it actually suffers from design decisions built into the latter, both in terms of translation accuracy and diversity. Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions. Our findings also lend strong support to the recent trend towards synthesizing edits of code conditional on the buggy context, to repair bugs. We implement such models ourselves as "proof-of-concept" tools and empirically confirm that they behave in a fundamentally different, more effective way than the studied translation-based architectures. Overall, our results demonstrate the merit of studying the intricacies of machine learned models in software engineering: not only can this help elucidate potential issues that may be overshadowed by increases in accuracy; it can also help innovate on these models to raise the state-of-the-art further. We will publicly release our replication data and materials at https://github.com/ARiSE-Lab/Patch-as-translation. | ['Vincent J. Hellendoorn', 'Premkumar Devanbu', 'Yangruibo Ding', 'Baishakhi Ray'] | 2020-08-24 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.08028658e-01 3.07094723e-01 -2.72071868e-01 -2.79660702e-01
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4a1dd9d8-da26-4669-8cac-b934d9c92b48 | a-survey-of-point-of-interest-recommendation | 1607.00647 | null | https://arxiv.org/abs/1607.00647v1 | https://arxiv.org/pdf/1607.00647v1.pdf | A Survey of Point-of-interest Recommendation in Location-based Social Networks | Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey. | ['Michael R. Lyu', 'Irwin King', 'Shenglin Zhao'] | 2016-07-03 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.46752343e-01 -1.30618557e-01 -8.81615162e-01 -1.24658339e-01
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249e8d4f-377c-44e3-a1a5-bba4be148dbd | p-fp-extraction-classification-and-prediction | 1711.03656 | null | http://arxiv.org/abs/1711.03656v2 | http://arxiv.org/pdf/1711.03656v2.pdf | p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning | Recent advances in learning Deep Neural Network (DNN) architectures have
received a great deal of attention due to their ability to outperform
state-of-the-art classifiers across a wide range of applications, with little
or no feature engineering. In this paper, we broadly study the applicability of
deep learning to website fingerprinting. We show that unsupervised DNNs can be
used to extract low-dimensional feature vectors that improve the performance of
state-of-the-art website fingerprinting attacks. When used as classifiers, we
show that they can match or exceed performance of existing attacks across a
range of application scenarios, including fingerprinting Tor website traces,
fingerprinting search engine queries over Tor, defeating fingerprinting
defenses, and fingerprinting TLS-encrypted websites. Finally, we show that DNNs
can be used to predict the fingerprintability of a website based on its
contents, achieving 99% accuracy on a data set of 4500 website downloads. | ['Saikrishna Sunkam', 'Nicholas Hopper', 'Se Eun Oh'] | 2017-11-10 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [-9.32549965e-03 -4.85237449e-01 -7.50740707e-01 -5.03987849e-01
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8826e706-4b28-4a26-af21-792bba90d31a | times-series-averaging-and-denoising-from-a | 1611.09194 | null | http://arxiv.org/abs/1611.09194v4 | http://arxiv.org/pdf/1611.09194v4.pdf | Times series averaging and denoising from a probabilistic perspective on time-elastic kernels | In the light of regularized dynamic time warping kernels, this paper
re-considers the concept of time elastic centroid for a setof time series. We
derive a new algorithm based on a probabilistic interpretation of kernel
alignment matrices. This algorithm expressesthe averaging process in terms of a
stochastic alignment automata. It uses an iterative agglomerative heuristic
method for averagingthe aligned samples, while also averaging the times of
occurrence of the aligned samples. By comparing classification accuracies for45
heterogeneous time series datasets obtained by first nearest centroid/medoid
classifiers we show that: i) centroid-basedapproaches significantly outperform
medoid-based approaches, ii) for the considered datasets, our algorithm that
combines averagingin the sample space and along the time axes, emerges as the
most significantly robust model for time-elastic averaging with apromising
noise reduction capability. We also demonstrate its benefit in an isolated
gesture recognition experiment and its ability tosignificantly reduce the size
of training instance sets. Finally we highlight its denoising capability using
demonstrative synthetic data:we show that it is possible to retrieve, from few
noisy instances, a signal whose components are scattered in a wide spectral
band. | ['Pierre-François Marteau'] | 2016-11-28 | null | null | null | null | ['time-series-denoising'] | ['time-series'] | [ 3.42776090e-01 -3.97755086e-01 1.71152011e-01 2.14112084e-02
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1edb171b-1a6c-4116-8f47-14dc5676beff | on-the-strength-of-character-language-models | 1809.05157 | null | http://arxiv.org/abs/1809.05157v2 | http://arxiv.org/pdf/1809.05157v2.pdf | On the Strength of Character Language Models for Multilingual Named Entity Recognition | Character-level patterns have been widely used as features in English Named
Entity Recognition (NER) systems. However, to date there has been no direct
investigation of the inherent differences between name and non-name tokens in
text, nor whether this property holds across multiple languages. This paper
analyzes the capabilities of corpus-agnostic Character-level Language Models
(CLMs) in the binary task of distinguishing name tokens from non-name tokens.
We demonstrate that CLMs provide a simple and powerful model for capturing
these differences, identifying named entity tokens in a diverse set of
languages at close to the performance of full NER systems. Moreover, by adding
very simple CLM-based features we can significantly improve the performance of
an off-the-shelf NER system for multiple languages. | ['Xiaodong Yu', 'Mark Sammons', 'Dan Roth', 'Stephen Mayhew'] | 2018-09-13 | on-the-strength-of-character-language-models-1 | https://aclanthology.org/D18-1345 | https://aclanthology.org/D18-1345.pdf | emnlp-2018-10 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.40566069e-01 -4.53792512e-01 -2.41060525e-01 -3.92645031e-01
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48e9f393-6b04-466f-beba-20e8b82b8f59 | high-probability-bounds-for-stochastic-1 | 2302.00999 | null | https://arxiv.org/abs/2302.00999v1 | https://arxiv.org/pdf/2302.00999v1.pdf | High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance | During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as the boundedness of the gradient noise variance or of the objective's gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded central $\alpha$-th moment for $\alpha \in (1,2]$ in the following setups: (i) smooth non-convex / Polyak-Lojasiewicz / convex / strongly convex / quasi-strongly convex minimization problems, (ii) Lipschitz / star-cocoercive and monotone / quasi-strongly monotone variational inequalities. These results justify the usage of the considered methods for solving problems that do not fit standard functional classes studied in stochastic optimization. | ['Peter Richtárik', 'Alexander Gasnikov', 'Pavel Dvurechensky', 'Gauthier Gidel', 'Samuel Horváth', 'Eduard Gorbunov', 'Marina Danilova', 'Abdurakhmon Sadiev'] | 2023-02-02 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.62857508e-02 6.72459006e-02 -2.36410392e-03 -1.26464441e-01
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e1c927bb-d094-41d5-bcc5-4ae891a6c5a7 | xtrimoabfold-improving-antibody-structure | 2212.00735 | null | https://arxiv.org/abs/2212.00735v3 | https://arxiv.org/pdf/2212.00735v3.pdf | xTrimoABFold: De novo Antibody Structure Prediction without MSA | In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2, a breakthrough in the field of structural biology, provides a solution to predict protein structure based on protein sequences and computationally expensive coevolutionary multiple sequence alignments (MSAs). However, the computational efficiency and undesirable prediction accuracy of antibodies, especially on the complementarity-determining regions (CDRs) of antibodies limit their applications in the industrially high-throughput drug design. To learn an informative representation of antibodies, we employed a deep antibody language model (ALM) on curated sequences from the observed antibody space database via a transformer model. We also developed a novel model named xTrimoABFold to predict antibody structure from antibody sequence based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss. xTrimoABFold outperforms AlphaFold2 and other protein language model based SOTAs, e.g., OmegaFold, HelixFold-Single, and IgFold with a large significant margin (30+\% improvement on RMSD) while performing 151 times faster than AlphaFold2. To the best of our knowledge, xTrimoABFold achieved state-of-the-art antibody structure prediction. Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory. | ['Le Song', 'Cheng Yang', 'Yangang Wang', 'Hui Li', 'Chuan Shi', 'YiWu Sun', 'Bing Yang', 'Shaochuan Li', 'Xumeng Gong', 'Yining Wang'] | 2022-11-30 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 2.07505658e-01 -2.29989290e-01 -9.49307457e-02 -3.89855206e-01
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ad6796b1-3652-4dc8-bb50-b855950a14a5 | deep-hough-transform-for-semantic-line | 2003.04676 | null | https://arxiv.org/abs/2003.04676v4 | https://arxiv.org/pdf/2003.04676v4.pdf | Deep Hough Transform for Semantic Line Detection | We focus on a fundamental task of detecting meaningful line structures, a.k.a. semantic line, in natural scenes. Many previous methods regard this problem as a special case of object detection and adjust existing object detectors for semantic line detection. However, these methods neglect the inherent characteristics of lines, leading to sub-optimal performance. Lines enjoy much simpler geometric property than complex objects and thus can be compactly parameterized by a few arguments. To better exploit the property of lines, in this paper, we incorporate the classical Hough transform technique into deeply learned representations and propose a one-shot end-to-end learning framework for line detection. By parameterizing lines with slopes and biases, we perform Hough transform to translate deep representations into the parametric domain, in which we perform line detection. Specifically, we aggregate features along candidate lines on the feature map plane and then assign the aggregated features to corresponding locations in the parametric domain. Consequently, the problem of detecting semantic lines in the spatial domain is transformed into spotting individual points in the parametric domain, making the post-processing steps, i.e. non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features eg features along lines close to a specific line, that are critical for accurate line detection. In addition to the proposed method, we design an evaluation metric to assess the quality of line detection and construct a large scale dataset for the line detection task. Experimental results on our proposed dataset and another public dataset demonstrate the advantages of our method over previous state-of-the-art alternatives. | ['Chang-Bin Zhang', 'Ming-Ming Cheng', 'Qi Han', 'Jun Xu', 'Kai Zhao'] | 2020-03-10 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/779_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540239.pdf | eccv-2020-8 | ['line-detection'] | ['computer-vision'] | [ 7.70619214e-02 -2.18314171e-01 -8.40276480e-02 -3.19449902e-01
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2fa442f2-7957-4135-85ff-fe1cd615413f | video-compressive-sensing-for-spatial | 1503.02727 | null | http://arxiv.org/abs/1503.02727v2 | http://arxiv.org/pdf/1503.02727v2.pdf | Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models | Spatial multiplexing cameras (SMCs) acquire a (typically static) scene
through a series of coded projections using a spatial light modulator (e.g., a
digital micro-mirror device) and a few optical sensors. This approach finds use
in imaging applications where full-frame sensors are either too expensive
(e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC
systems reconstruct static scenes using techniques from compressive sensing
(CS). For videos, however, existing acquisition and recovery methods deliver
poor quality. In this paper, we propose the CS multi-scale video (CS-MUVI)
sensing and recovery framework for high-quality video acquisition and recovery
using SMCs. Our framework features novel sensing matrices that enable the
efficient computation of a low-resolution video preview, while enabling
high-resolution video recovery using convex optimization. To further improve
the quality of the reconstructed videos, we extract optical-flow estimates from
the low-resolution previews and impose them as constraints in the recovery
procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of
synthetic and real measured SMC video data, and we show that high-quality
videos can be recovered at roughly $60\times$ compression. | ['Aswin C. Sankaranarayanan', 'Yun Li', 'Kevin Kelly', 'Richard G. Baraniuk', 'Lina Xu', 'Christoph Studer'] | 2015-03-09 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 9.80557561e-01 -4.36177939e-01 -5.37290871e-02 6.14557900e-02
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5922c692-fab6-42e7-baf6-c1ab020a7461 | a-data-driven-methodology-for-considering | 2204.08094 | null | https://arxiv.org/abs/2204.08094v1 | https://arxiv.org/pdf/2204.08094v1.pdf | A Data-Driven Methodology for Considering Feasibility and Pairwise Likelihood in Deep Learning Based Guitar Tablature Transcription Systems | Guitar tablature transcription is an important but understudied problem within the field of music information retrieval. Traditional signal processing approaches offer only limited performance on the task, and there is little acoustic data with transcription labels for training machine learning models. However, guitar transcription labels alone are more widely available in the form of tablature, which is commonly shared among guitarists online. In this work, a collection of symbolic tablature is leveraged to estimate the pairwise likelihood of notes on the guitar. The output layer of a baseline tablature transcription model is reformulated, such that an inhibition loss can be incorporated to discourage the co-activation of unlikely note pairs. This naturally enforces playability constraints for guitar, and yields tablature which is more consistent with the symbolic data used to estimate pairwise likelihoods. With this methodology, we show that symbolic tablature can be used to shape the distribution of a tablature transcription model's predictions, even when little acoustic data is available. | ['Zhiyao Duan', 'Jonathan Driedger', 'Frank Cwitkowitz'] | 2022-04-17 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.74896252e-01 -8.25461447e-02 -1.55747116e-01 -3.94075811e-01
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d53d0456-26ad-45ca-a0c6-fbfb3f3fc810 | a-unifying-bayesian-approach-for-preterm | 1809.07102 | null | http://arxiv.org/abs/1809.07102v1 | http://arxiv.org/pdf/1809.07102v1.pdf | A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age | Preterm newborns undergo various stresses that may materialize as learning
problems at school-age. Sleep staging of the Electroencephalogram (EEG),
followed by prediction of their brain-age from these sleep states can quantify
deviations from normal brain development early (when compared to the known
age). Current automation of this approach relies on explicit sleep state
classification, optimizing algorithms using clinician visually labelled sleep
stages, which remains a subjective gold-standard. Such models fail to perform
consistently over a wide age range and impacts the subsequent brain-age
estimates that could prevent identification of subtler developmental
deviations. We introduce a Bayesian Network utilizing multiple Gaussian Mixture
Models, as a novel, unified approach for directly estimating brain-age,
simultaneously modelling for both age and sleep dependencies on the EEG, to
improve the accuracy of prediction over a wider age range. | ['Kirubin Pillay', 'Maarten De Vos'] | 2018-09-19 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 1.45018533e-01 -3.10810027e-03 4.85840067e-03 -4.78072643e-01
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64b439cd-3ad0-41db-8424-6074cb85ba54 | hdformer-a-higher-dimensional-transformer-for | 2303.11340 | null | https://arxiv.org/abs/2303.11340v1 | https://arxiv.org/pdf/2303.11340v1.pdf | HDformer: A Higher Dimensional Transformer for Diabetes Detection Utilizing Long Range Vascular Signals | Diabetes mellitus is a worldwide concern, and early detection can help to prevent serious complications. Low-cost, non-invasive detection methods, which take cardiovascular signals into deep learning models, have emerged. However, limited accuracy constrains their clinical usage. In this paper, we present a new Transformer-based architecture, Higher Dimensional Transformer (HDformer), which takes long-range photoplethysmography (PPG) signals to detect diabetes. The long-range PPG contains broader and deeper signal contextual information compared to the less-than-one-minute PPG signals commonly utilized in existing research. To increase the capability and efficiency of processing the long range data, we propose a new attention module Time Square Attention (TSA), reducing the volume of the tokens by more than 10x, while retaining the local/global dependencies. It converts the 1-dimensional inputs into 2-dimensional representations and groups adjacent points into a single 2D token, using the 2D Transformer models as the backbone of the encoder. It generates the dynamic patch sizes into a gated mixture-of-experts (MoE) network as decoder, which optimizes the learning on different attention areas. Extensive experimentations show that HDformer results in the state-of-the-art performance (sensitivity 98.4, accuracy 97.3, specificity 92.8, and AUC 0.929) on the standard MIMIC-III dataset, surpassing existing studies. This work is the first time to take long-range, non-invasive PPG signals via Transformer for diabetes detection, achieving a more scalable and convenient solution compared to traditional invasive approaches. The proposed HDformer can also be scaled to analyze general long-range biomedical waveforms. A wearable prototype finger-ring is designed as a proof of concept. | ['Ella Lan'] | 2023-03-17 | null | null | null | null | ['photoplethysmography-ppg', 'specificity'] | ['medical', 'natural-language-processing'] | [ 1.99023753e-01 -8.82742107e-02 -1.14213891e-01 -4.93583202e-01
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31b7d243-b879-47eb-8f12-dc7f48583594 | two-sides-of-the-same-coin-heterophily-and | 2102.06462 | null | https://arxiv.org/abs/2102.06462v8 | https://arxiv.org/pdf/2102.06462v8.pdf | Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks | In node classification tasks, graph convolutional neural networks (GCNs) have demonstrated competitive performance over traditional methods on diverse graph data. However, it is known that the performance of GCNs degrades with increasing number of layers (oversmoothing problem) and recent studies have also shown that GCNs may perform worse in heterophilous graphs, where neighboring nodes tend to belong to different classes (heterophily problem). These two problems are usually viewed as unrelated, and thus are studied independently, often at the graph filter level from a spectral perspective. We are the first to take a unified perspective to jointly explain the oversmoothing and heterophily problems at the node level. Specifically, we profile the nodes via two quantitative metrics: the relative degree of a node (compared to its neighbors) and the node-level heterophily. Our theory shows that the interplay of these two profiling metrics defines three cases of node behaviors, which explain the oversmoothing and heterophily problems jointly and can predict the performance of GCNs. Based on insights from our theory, we show theoretically and empirically the effectiveness of two strategies: structure-based edge correction, which learns corrected edge weights from structural properties (i.e., degrees), and feature-based edge correction, which learns signed edge weights from node features. Compared to other approaches, which tend to handle well either heterophily or oversmoothing, we show that {our model, GGCN}, which incorporates the two strategies performs well in both problems. | ['Danai Koutra', 'Yaoqing Yang', 'Kevin Swersky', 'Milad Hashemi', 'Yujun Yan'] | 2021-02-12 | two-sides-of-the-same-coin-heterophily-and-1 | https://openreview.net/forum?id=R2aCiGQ9Qc | https://openreview.net/pdf?id=R2aCiGQ9Qc | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-5.16286939e-02 3.67263705e-01 -2.11209163e-01 -1.13737278e-01
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13799acf-987a-4313-adaa-9402c5f4526c | scientific-computing-algorithms-to-learn | 2304.00338 | null | https://arxiv.org/abs/2304.00338v1 | https://arxiv.org/pdf/2304.00338v1.pdf | Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics | Data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. Among them, graph neural networks (GNNs) that operate on mesh-based data are desirable because they possess inductive biases that promote physical faithfulness, but hardware limitations have precluded their application to large computational domains. We show that it is \textit{possible} to train a class of GNN surrogates on 3D meshes. We scale MeshGraphNets (MGN), a subclass of GNNs for mesh-based physics modeling, via our domain decomposition approach to facilitate training that is mathematically equivalent to training on the whole domain under certain conditions. With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations. Furthermore, we show how to enhance MGN via higher-order numerical integration, which can reduce MGN's error and training time. We validated our methods on an accompanying dataset of 3D $\text{CO}_2$-capture CFD simulations on a 3.1M-node mesh. This work presents a practical path to scaling MGN for real-world applications. | ['Phan Nguyen', 'Brenda Ng', 'Zhijie Xu', 'Jie Bao', 'Yucheng Fu', 'Jose Cadena', 'Amar Saini', 'Yeping Hu', 'Brian R. Bartoldson'] | 2023-04-01 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-9.45566967e-03 2.93730140e-01 1.80286214e-01 -7.48427510e-02
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c37af03f-1950-483e-9a51-24bbfa362f46 | upsampling-artifacts-in-neural-audio | 2010.14356 | null | https://arxiv.org/abs/2010.14356v2 | https://arxiv.org/pdf/2010.14356v2.pdf | Upsampling artifacts in neural audio synthesis | A number of recent advances in neural audio synthesis rely on upsampling layers, which can introduce undesired artifacts. In computer vision, upsampling artifacts have been studied and are known as checkerboard artifacts (due to their characteristic visual pattern). However, their effect has been overlooked so far in audio processing. Here, we address this gap by studying this problem from the audio signal processing perspective. We first show that the main sources of upsampling artifacts are: (i) the tonal and filtering artifacts introduced by problematic upsampling operators, and (ii) the spectral replicas that emerge while upsampling. We then compare different upsampling layers, showing that nearest neighbor upsamplers can be an alternative to the problematic (but state-of-the-art) transposed and subpixel convolutions which are prone to introduce tonal artifacts. | ['Joan Serrà', 'Giulio Cengarle', 'Santiago Pascual', 'Jordi Pons'] | 2020-10-27 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 6.27933204e-01 -2.35923052e-01 4.45171356e-01 2.37478137e-01
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aa168ee9-4d55-4d84-a7fc-a19711f775a9 | developing-a-curated-topic-model-for-covid-19 | null | null | https://aclanthology.org/2020.nlpcovid19-2.30 | https://aclanthology.org/2020.nlpcovid19-2.30.pdf | Developing a Curated Topic Model for COVID-19 Medical Research Literature | Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature. | ['Mike Moran', 'Katherine E. Goodman', 'Philip Resnik'] | null | null | null | null | emnlp-nlp-covid19-2020-12 | ['topic-models'] | ['natural-language-processing'] | [-1.49618149e-01 3.82336915e-01 -7.16487288e-01 -2.31935740e-01
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967c1261-b859-414a-bb7c-3f30dbfba200 | interactive-learning-from-activity | 2102.07024 | null | https://arxiv.org/abs/2102.07024v2 | https://arxiv.org/pdf/2102.07024v2.pdf | Interactive Learning from Activity Description | We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities. Unlike imitation learning (IL), our protocol allows the teaching agent to provide feedback in a language that is most appropriate for them. Compared with reward in reinforcement learning (RL), the description feedback is richer and allows for improved sample complexity. We develop a probabilistic framework and an algorithm that practically implements our protocol. Empirical results in two challenging request-fulfilling problems demonstrate the strengths of our approach: compared with RL baselines, it is more sample-efficient; compared with IL baselines, it achieves competitive success rates without requiring the teaching agent to be able to demonstrate the desired behavior using the learning agent's actions. Apart from empirical evaluation, we also provide theoretical guarantees for our algorithm under certain assumptions about the teacher and the environment. | ['Patrick Shafto', 'Miro Dudík', 'Robert Schapire', 'Dipendra Misra', 'Khanh Nguyen'] | 2021-02-13 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.08709084e-02 5.73869824e-01 -4.68085498e-01 -1.85714379e-01
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1a29e24a-6317-4a5e-badc-ef5bb6bd1d4f | stabiliser-states-are-efficiently-pac | 1705.00345 | null | http://arxiv.org/abs/1705.00345v2 | http://arxiv.org/pdf/1705.00345v2.pdf | Stabiliser states are efficiently PAC-learnable | The exponential scaling of the wave function is a fundamental property of
quantum systems with far reaching implications in our ability to process
quantum information. A problem where these are particularly relevant is quantum
state tomography. State tomography, whose objective is to obtain a full
description of a quantum system, can be analysed in the framework of
computational learning theory. In this model, quantum states have been shown to
be Probably Approximately Correct (PAC)-learnable with sample complexity linear
in the number of qubits. However, it is conjectured that in general quantum
states require an exponential amount of computation to be learned. Here, using
results from the literature on the efficient classical simulation of quantum
systems, we show that stabiliser states are efficiently PAC-learnable. Our
results solve an open problem formulated by Aaronson [Proc. R. Soc. A, 2088,
(2007)] and propose learning theory as a tool for exploring the power of
quantum computation. | ['Andrea Rocchetto'] | 2017-04-30 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.02725601e-01 2.29447961e-01 8.01529214e-02 -1.37043938e-01
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f4420124-9235-4402-b533-7ba96c890e62 | 3d-human-pose-regression-using-graph | 2105.10379 | null | https://arxiv.org/abs/2105.10379v2 | https://arxiv.org/pdf/2105.10379v2.pdf | 3D Human Pose Regression using Graph Convolutional Network | 3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of the human skeleton in the form of an adjacency matrix, which is beneficial for better pose prediction. We propose one such graph convolutional network named PoseGraphNet for 3D human pose regression from 2D poses. Our network uses an adaptive adjacency matrix and kernels specific to neighbor groups. We evaluate our model on the Human3.6M dataset which is a standard dataset for 3D pose estimation. Our model's performance is close to the state-of-the-art, but with much fewer parameters. The model learns interesting adjacency relations between joints that have no physical connections, but are behaviorally similar. | ['Alois Knoll', 'Alejandro Mendoza Gracia', 'Soubarna Banik'] | 2021-05-21 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-2.82690465e-01 3.72123480e-01 -3.80414844e-01 -2.68223077e-01
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c0158f9d-47cc-4d77-90a9-ac68c8531674 | masked-and-adaptive-transformer-for-exemplar | 2303.17123 | null | https://arxiv.org/abs/2303.17123v1 | https://arxiv.org/pdf/2303.17123v1.pdf | Masked and Adaptive Transformer for Exemplar Based Image Translation | We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at \url{https://github.com/AiArt-HDU/MATEBIT}. | ['Gang Xu', 'Nannan Wang', 'YuHao Lin', 'Biao Ma', 'Fei Gao', 'Chang Jiang'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semantic-correspondence'] | ['computer-vision'] | [ 5.80573082e-01 -3.24803621e-01 -1.21060364e-01 -2.42450118e-01
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56f5afc1-8d06-4bb8-b6f8-6ec44a1cee9c | multiframe-based-adaptive-despeckling | 1912.00815 | null | https://arxiv.org/abs/1912.00815v4 | https://arxiv.org/pdf/1912.00815v4.pdf | Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture | Removing speckle noise from medical ultrasound images while preserving image features without introducing artifact and distortion is a major challenge in ultrasound image restoration. In this paper, we propose a multiframe-based adaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-mode image from raw radio-frequency (RF) data that is based on a multiple input single output (MISO) model. As a prior step to despeckling, the speckle pattern in each frame is estimated using a novel multiframe-based adaptive approach for ultrasonic speckle noise estimation (MSNE) based on a single input multiple output (SIMO) modeling of consecutive deconvolved ultrasound image frames. The elegance of the proposed despeckling algorithm is that it addresses the despeckling problem by completely following the signal generation model unlike conventional ad-hoc smoothening or filtering based approaches, and therefore, it is likely to maximally preserve the image features. As deconvolution is a necessary pre-processing step to despeckling, we describe here a 2-D extension of the SIMO model-based 1-D deconvolution method. Finally, a complete framework for the generation of high-resolution ultrasound B-mode image has been also established in this paper. The results show 8.55-15.91 dB, 8.24-14.94 dB improvement in terms of SNR and PSNR, respectively, for simulation data and 2.22-3.17, 13.24-32.85 improvement in terms of NIQE and BRISQUE, respectively, for in-vivo data compared to the traditional despeckling algorithms. Visual comparison shows superior texture, resolution, details of B-mode images offered by our method compared to those by a commercial scanner, and hence, it may significantly improve the diagnostic quality of ultrasound images. | ['Md. Kamrul Hasan', 'Jayanta Dey'] | 2019-12-02 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 7.12670743e-01 -5.27346544e-02 8.48710120e-01 -2.34925061e-01
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c0dd862d-a012-41c3-9c4e-eb3f86fc0256 | synbody-synthetic-dataset-with-layered-human | 2303.17368 | null | https://arxiv.org/abs/2303.17368v1 | https://arxiv.org/pdf/2303.17368v1.pdf | SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling | Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, Synbody, with three appealing features: 1) a clothed parametric human model that can generate a diverse range of subjects; 2) the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks; 3) a scalable system for producing realistic data to facilitate real-world tasks. The dataset comprises 1.7M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1000 actions, and various viewpoints. The dataset includes two subsets for human mesh recovery as well as human neural rendering. Extensive experiments on SynBody indicate that it substantially enhances both SMPL and SMPL-X estimation. Furthermore, the incorporation of layered annotations offers a valuable training resource for investigating the Human Neural Radiance Fields (NeRF). | ['Lei Yang', 'Ziwei Liu', 'Dahua Lin', 'Chen Qian', 'Wayne Wu', 'Bo Dai', 'Chen Wei', 'Zhongfei Qing', 'Yukun Wei', 'Weiye Xiao', 'Zhaoxi Chen', 'Shuai Liu', 'Haiyi Mei', 'Zhongang Cai', 'Zhitao Yang'] | 2023-03-30 | null | null | null | null | ['neural-rendering', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 1.92009434e-01 1.19325504e-01 -6.00060299e-02 -4.61647034e-01
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d0936875-5eb3-4376-939f-1480ea4d1131 | spectral-clustering-under-the-degree | 2105.00987 | null | https://arxiv.org/abs/2105.00987v2 | https://arxiv.org/pdf/2105.00987v2.pdf | Spectral clustering under degree heterogeneity: a case for the random walk Laplacian | This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree. Under a generalised random dot product graph, the embedding provides uniformly consistent estimates of degree-corrected latent positions, with asymptotically Gaussian error. In the special case of a degree-corrected stochastic block model, the embedding concentrates about K distinct points, representing communities. These can be recovered perfectly, asymptotically, through a subsequent clustering step, without spherical projection, as commonly required by algorithms based on the adjacency or normalised, symmetric Laplacian matrices. While the estimand does not depend on degree, the asymptotic variance of its estimate does -- higher degree nodes are embedded more accurately than lower degree nodes. Our central limit theorem therefore suggests fitting a weighted Gaussian mixture model as the subsequent clustering step, for which we provide an expectation-maximisation algorithm. | ['Patrick Rubin-Delanchy', 'Alexander Modell'] | 2021-05-03 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.67458773e-01 6.85687840e-01 -1.32384583e-01 8.43568146e-02
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d6ff63c3-cdae-42b3-912f-fc84ad52f2de | model-based-offline-meta-reinforcement-1 | 2202.02929 | null | https://arxiv.org/abs/2202.02929v2 | https://arxiv.org/pdf/2202.02929v2.pdf | Model-Based Offline Meta-Reinforcement Learning with Regularization | Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline Meta-RL is emerging as a promising approach to address these challenges, aiming to learn an informative meta-policy from a collection of tasks. Nevertheless, as shown in our empirical studies, offline Meta-RL could be outperformed by offline single-task RL methods on tasks with good quality of datasets, indicating that a right balance has to be delicately calibrated between "exploring" the out-of-distribution state-actions by following the meta-policy and "exploiting" the offline dataset by staying close to the behavior policy. Motivated by such empirical analysis, we explore model-based offline Meta-RL with regularized Policy Optimization (MerPO), which learns a meta-model for efficient task structure inference and an informative meta-policy for safe exploration of out-of-distribution state-actions. In particular, we devise a new meta-Regularized model-based Actor-Critic (RAC) method for within-task policy optimization, as a key building block of MerPO, using conservative policy evaluation and regularized policy improvement; and the intrinsic tradeoff therein is achieved via striking the right balance between two regularizers, one based on the behavior policy and the other on the meta-policy. We theoretically show that the learnt policy offers guaranteed improvement over both the behavior policy and the meta-policy, thus ensuring the performance improvement on new tasks via offline Meta-RL. Experiments corroborate the superior performance of MerPO over existing offline Meta-RL methods. | ['Junshan Zhang', 'Yingbin Liang', 'Tengyu Xu', 'Jialin Wan', 'Sen Lin'] | 2022-02-07 | model-based-offline-meta-reinforcement | https://openreview.net/forum?id=EBn0uInJZWh | https://openreview.net/pdf?id=EBn0uInJZWh | iclr-2022-4 | ['safe-exploration'] | ['robots'] | [-1.40735731e-01 2.29691252e-01 -6.30041659e-01 9.34170838e-03
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21b37e78-fe7a-4a5b-ae2a-6ebb57067686 | interpretable-scientific-discovery-with | 2211.10873 | null | https://arxiv.org/abs/2211.10873v2 | https://arxiv.org/pdf/2211.10873v2.pdf | Interpretable Scientific Discovery with Symbolic Regression: A Review | Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations. | ['Sanjay Chawla', 'Nour Makke'] | 2022-11-20 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 3.73732775e-01 1.77360132e-01 -8.80170822e-01 -6.34990275e-01
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ab6318ea-1f45-4466-93c1-f0a309a5b2de | vocabulary-informed-zero-shot-and-open-set | 2301.00998 | null | https://arxiv.org/abs/2301.00998v2 | https://arxiv.org/pdf/2301.00998v2.pdf | Vocabulary-informed Zero-shot and Open-set Learning | Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets. | ['Leonid Sigal', 'xiangyang xue', 'Meng Wang', 'Yu-Gang Jiang', 'Hanze Dong', 'Xiaomei Wang', 'Yanwei Fu'] | 2023-01-03 | null | null | null | null | ['object-categorization', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 3.92330378e-01 2.53427297e-01 -5.62625110e-01 -7.65906751e-01
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c234bb80-83d3-4106-8bd6-20a3eaa0f76e | supervised-deep-learning-for-content-aware | 2306.07383 | null | https://arxiv.org/abs/2306.07383v1 | https://arxiv.org/pdf/2306.07383v1.pdf | Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions | Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, regular convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model's ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach. | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'Mohammadreza Naderi', 'MohammadHossein Givkashi'] | 2023-06-12 | null | null | null | null | ['image-quality-assessment', 'image-retargeting'] | ['computer-vision', 'computer-vision'] | [ 6.45331681e-01 4.66300160e-01 7.29415864e-02 -2.71475077e-01
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b36a69f4-59f2-4315-85d6-9c8d4c2eba1f | generalised-gillespie-algorithms-for | 2210.09511 | null | https://arxiv.org/abs/2210.09511v2 | https://arxiv.org/pdf/2210.09511v2.pdf | Generalised Gillespie Algorithms for Simulations in a Rule-Based Epidemiological Model Framework | Rule-based models have been successfully used to represent different aspects of the COVID-19 pandemic, including age, testing, hospitalisation, lockdowns, immunity, infectivity, behaviour, mobility and vaccination of individuals. These rule-based approaches are motivated by chemical reaction rules which are traditionally solved numerically with the standard Gillespie algorithm proposed in the context of molecular dynamics. When applying reaction system type of approaches to epidemiology, generalisations of the Gillespie algorithm are required due to the time-dependency of the problems. In this article, we present different generalisations of the standard Gillespie algorithm which address discrete subtypes (e.g., incorporating the age structure of the population), time-discrete updates (e.g., incorporating daily imposed change of rates for lockdowns) and deterministic delays (e.g., given waiting time until a specific change in types such as release from isolation occurs). These algorithms are complemented by relevant examples in the context of the COVID-19 pandemic and numerical results. | ['Lisa Maria Kreusser', 'Luca Sbano', 'Markus Kirkilionis', 'Steffen Bauer', 'David Alonso'] | 2022-10-17 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 2.37128779e-01 -2.31935069e-01 3.26815575e-01 1.10331729e-01
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a2565197-7935-42a7-947f-49d0e74ced29 | multilingual-protest-news-detection-shared | null | null | https://aclanthology.org/2021.case-1.11 | https://aclanthology.org/2021.case-1.11.pdf | Multilingual Protest News Detection - Shared Task 1, CASE 2021 | Benchmarking state-of-the-art text classification and information extraction systems in multilingual, cross-lingual, few-shot, and zero-shot settings for socio-political event information collection is achieved in the scope of the shared task Socio-political and Crisis Events Detection at the workshop CASE @ ACL-IJCNLP 2021. Socio-political event data is utilized for national and international policy- and decision-making. Therefore, the reliability and validity of these datasets are of the utmost importance. We split the shared task into three parts to address the three aspects of data collection (Task 1), fine-grained semantic classification (Task 2), and evaluation (Task 3). Task 1, which is the focus of this report, is on multilingual protest news detection and comprises four subtasks that are document classification (subtask 1), sentence classification (subtask 2), event sentence coreference identification (subtask 3), and event extraction (subtask 4). All subtasks had English, Portuguese, and Spanish for both training and evaluation data. Data in Hindi language was available only for the evaluation of subtask 1. The majority of the submissions, which are 238 in total, are created using multi- and cross-lingual approaches. Best scores are above 77.27 F1-macro for subtask 1, above 85.32 F1-macro for subtask 2, above 84.23 CoNLL 2012 average score for subtask 3, and above 66.20 F1-macro for subtask 4 in all evaluation settings. The performance of the best system for subtask 4 is above 66.20 F1 for all available languages. Although there is still a significant room for improvement in cross-lingual and zero-shot settings, the best submissions for each evaluation scenario yield remarkable results. Monolingual models outperformed the multilingual models in a few evaluation scenarios. | ['Shyam Ratan', 'Ritesh Kumar', 'Farhana Ferdousi Liza', 'Erdem Yörük', 'Osman Mutlu', 'Ali Hürriyetoğlu'] | null | null | null | null | acl-case-2021-8 | ['sentence-classification'] | ['natural-language-processing'] | [-1.18035838e-01 -8.18330571e-02 -1.81725904e-01 -3.54180872e-01
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e4625baa-3e18-4861-a20d-835f4b60d1f6 | scotch-and-soda-a-transformer-video-shadow | 2211.06885 | null | https://arxiv.org/abs/2211.06885v2 | https://arxiv.org/pdf/2211.06885v2.pdf | SCOTCH and SODA: A Transformer Video Shadow Detection Framework | Shadows in videos are difficult to detect because of the large shadow deformation between frames. In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method. To this end, we introduce the shadow deformation attention trajectory (SODA), a new type of video self-attention module, specially designed to handle the large shadow deformations in videos. Moreover, we present a new shadow contrastive learning mechanism (SCOTCH) which aims at guiding the network to learn a unified shadow representation from massive positive shadow pairs across different videos. We demonstrate empirically the effectiveness of our two contributions in an ablation study. Furthermore, we show that SCOTCH and SODA significantly outperforms existing techniques for video shadow detection. Code is available at the project page: https://lihaoliu-cambridge.github.io/scotch_and_soda/ | ['Angelica I Aviles-Rivero', 'Carola-Bibiane Schönlieb', 'Pietro Liò', 'Nicolas Papadakis', 'Lei Zhu', 'Jean Prost', 'Lihao Liu'] | 2022-11-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.pdf | cvpr-2023-1 | ['shadow-detection'] | ['computer-vision'] | [ 1.35648787e-01 8.15558508e-02 6.10495694e-02 -3.61833163e-02
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23ef5a8e-54ea-4965-bd49-a57c79cb20c6 | fast-optimization-of-wildfire-suppression | 1703.09391 | null | http://arxiv.org/abs/1703.09391v1 | http://arxiv.org/pdf/1703.09391v1.pdf | Fast Optimization of Wildfire Suppression Policies with SMAC | Managers of US National Forests must decide what policy to apply for dealing
with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber
companies, home owners, and wildlife biologists) have often led to spirited
political debates and even violent eco-terrorism. One way to transform these
conflicts into multi-stakeholder negotiations is to provide a high-fidelity
simulation environment in which stakeholders can explore the space of
alternative policies and understand the tradeoffs therein. Such an environment
needs to support fast optimization of MDP policies so that users can adjust
reward functions and analyze the resulting optimal policies. This paper
assesses the suitability of SMAC---a black-box empirical function optimization
algorithm---for rapid optimization of MDP policies. The paper describes five
reward function components and four stakeholder constituencies. It then
introduces a parameterized class of policies that can be easily understood by
the stakeholders. SMAC is applied to find the optimal policy in this class for
the reward functions of each of the stakeholder constituencies. The results
confirm that SMAC is able to rapidly find good policies that make sense from
the domain perspective. Because the full-fidelity forest fire simulator is far
too expensive to support interactive optimization, SMAC is applied to a
surrogate model constructed from a modest number of runs of the full-fidelity
simulator. To check the quality of the SMAC-optimized policies, the policies
are evaluated on the full-fidelity simulator. The results confirm that the
surrogate values estimates are valid. This is the first successful optimization
of wildfire management policies using a full-fidelity simulation. The same
methodology should be applicable to other contentious natural resource
management problems where high-fidelity simulation is extremely expensive. | ['Claire Montgomery', 'Rachel Houtman', 'Sean McGregor', 'Thomas G. Dietterich', 'Ronald Metoyer'] | 2017-03-28 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.08220771e-01 -1.70338079e-01 -1.63752630e-01 -1.54021472e-01
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01c8a61b-4487-4534-a86c-c9670014b0ca | agnostic-multi-group-active-learning | 2306.01922 | null | https://arxiv.org/abs/2306.01922v1 | https://arxiv.org/pdf/2306.01922v1.pdf | Agnostic Multi-Group Active Learning | Inspired by the problem of improving classification accuracy on rare or hard subsets of a population, there has been recent interest in models of learning where the goal is to generalize to a collection of distributions, each representing a ``group''. We consider a variant of this problem from the perspective of active learning, where the learner is endowed with the power to decide which examples are labeled from each distribution in the collection, and the goal is to minimize the number of label queries while maintaining PAC-learning guarantees. Our main challenge is that standard active learning techniques such as disagreement-based active learning do not directly apply to the multi-group learning objective. We modify existing algorithms to provide a consistent active learning algorithm for an agnostic formulation of multi-group learning, which given a collection of $G$ distributions and a hypothesis class $\mathcal{H}$ with VC-dimension $d$, outputs an $\epsilon$-optimal hypothesis using $\tilde{O}\left( (\nu^2/\epsilon^2+1) G d \theta_{\mathcal{G}}^2 \log^2(1/\epsilon) + G\log(1/\epsilon)/\epsilon^2 \right)$ label queries, where $\theta_{\mathcal{G}}$ is the worst-case disagreement coefficient over the collection. Roughly speaking, this guarantee improves upon the label complexity of standard multi-group learning in regimes where disagreement-based active learning algorithms may be expected to succeed, and the number of groups is not too large. We also consider the special case where each distribution in the collection is individually realizable with respect to $\mathcal{H}$, and demonstrate $\tilde{O}\left( G d \theta_{\mathcal{G}} \log(1/\epsilon) \right)$ label queries are sufficient for learning in this case. We further give an approximation result for the full agnostic case inspired by the group realizable strategy. | ['Kamalika Chaudhuri', 'Nick Rittler'] | 2023-06-02 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 2.32524604e-01 7.07524478e-01 -3.96271944e-01 -3.04219246e-01
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5c9e2d01-47be-41ab-9fc8-734955333d71 | adaptive-channel-estimation-based-on-deep | null | null | https://ieeexplore.ieee.org/document/9348501 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9348501 | Adaptive Channel Estimation based on Deep Learning | Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error. | ['Gerhard Fettweis', 'Ahmad Nimr', 'Marwa Chafii', 'Abdul Karim Gizzini'] | 2020-11-18 | null | null | null | ieee-92nd-vehicular-technology-conference | ['indoor-localization'] | ['computer-vision'] | [ 1.51444137e-01 -4.74884780e-03 1.68654453e-02 -7.15957731e-02
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764c8351-679c-4586-ade9-fbe2cd8cca75 | the-replica-dataset-a-digital-replica-of | 1906.05797 | null | https://arxiv.org/abs/1906.05797v1 | https://arxiv.org/pdf/1906.05797v1.pdf | The Replica Dataset: A Digital Replica of Indoor Spaces | We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents. | ['Steven Lovegrove', 'Michael Goesele', 'Renzo De Nardi', 'Luis Pesqueira', 'Kimberly Leon', 'Shobhit Verma', 'Raul Mur-Artal', 'Lingni Ma', 'June Yon', 'Hauke M. Strasdat', 'Erik Wijmans', 'Carl Ren', 'Richard Newcombe', 'Nigel Carter', 'Manolis Savva', 'Jesus Briales', 'Brian Budge', 'Yufan Chen', 'Simon Green', 'Jakob J. Engel', 'Dhruv Batra', 'Anton Clarkson', 'Yuyang Zou', 'Yajie Yan', 'Xiaqing Pan', 'Tyler Gillingham', 'Thomas Whelan', 'Mingfei Yan', 'Julian Straub', 'Elias Mueggler'] | 2019-06-13 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 1.00093037e-01 2.06905901e-01 5.33752978e-01 -3.20476711e-01
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c55a3d6a-4b0f-4549-8d1f-1f9cb82e6b12 | marine-iot-systems-with-space-air-sea | 2301.03815 | null | https://arxiv.org/abs/2301.03815v1 | https://arxiv.org/pdf/2301.03815v1.pdf | Marine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing | Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging to efficiently and reliably collect and compute a huge amount of maritime data. In this paper, we propose a hybrid low-Earth orbit (LEO) and unmanned aerial vehicle (UAV) edge computing method in space-air-sea integrated networks for marine IoT systems. Specifically, two types of edge servers mounted on UAVs and LEO satellites are endowed with computational capabilities for the real-time utilization of a sizable data collected from ocean IoT sensors. Our system aims at minimizing the total energy consumption of the battery-constrained UAV by jointly optimizing the bit allocation of communication and computation along with the UAV path planning under latency, energy budget and operational constraints. For availability and practicality, the proposed methods were developed for three different cases according to the accessibility of the LEO satellite, ``Always On," ``Always Off" and ``Intermediate Disconnected", by leveraging successive convex approximation (SCA) strategies. Via numerical results, we verify that significant energy savings can be accrued for all cases of LEO accessibility by means of joint optimization of bit allocation and UAV path planning compared to partial optimization schemes that design for only the bit allocation or trajectory of the UAV. | ['Joonhyuk Kang', 'Jinkyu Kang', 'Seongah Jeong', 'Sooyeob Jung'] | 2023-01-10 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.57931000e-01 -5.20282723e-02 6.42139539e-02 2.61398643e-01
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66a63003-fdf7-4066-a289-3ba9081e6121 | pointconv-deep-convolutional-networks-on-3d | 1811.07246 | null | https://arxiv.org/abs/1811.07246v3 | https://arxiv.org/pdf/1811.07246v3.pdf | PointConv: Deep Convolutional Networks on 3D Point Clouds | Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build deep convolutional networks. We treat convolution kernels as nonlinear functions of the local coordinates of 3D points comprised of weight and density functions. With respect to a given point, the weight functions are learned with multi-layer perceptron networks and density functions through kernel density estimation. The most important contribution of this work is a novel reformulation proposed for efficiently computing the weight functions, which allowed us to dramatically scale up the network and significantly improve its performance. The learned convolution kernel can be used to compute translation-invariant and permutation-invariant convolution on any point set in the 3D space. Besides, PointConv can also be used as deconvolution operators to propagate features from a subsampled point cloud back to its original resolution. Experiments on ModelNet40, ShapeNet, and ScanNet show that deep convolutional neural networks built on PointConv are able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds. Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure. | ['Li Fuxin', 'Zhongang Qi', 'Wenxuan Wu'] | 2018-11-17 | pointconv-deep-convolutional-networks-on-3d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-4.05495971e-01 -3.69784772e-01 2.10481763e-01 -4.60699201e-01
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b0ed060e-1f99-44cf-9a8a-716f20744706 | nlfiit-at-semeval-2020-task-11-neural-network | null | null | https://aclanthology.org/2020.semeval-1.232 | https://aclanthology.org/2020.semeval-1.232.pdf | NLFIIT at SemEval-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles | Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection. In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: {``}Detection of Propaganda Techniques in News Articles{''}. We participated in both subtasks, propaganda span identification and propaganda technique classification. Our model utilizes recurrent Bi-LSTM layers with pre-trained word representations and also takes advantage of self-attention mechanism. Our model managed to achieve score 0.405 F1 for subtask 1 and 0.553 F1 for subtask 2 on test set resulting in 17th and 16th place in subtask 1 and subtask 2, respectively. | ['Marian Simko', 'Samuel Pecar', 'Matej Martinkovic'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-span-identification'] | ['natural-language-processing'] | [ 1.29352123e-01 2.58483082e-01 -2.70310313e-01 -6.02778532e-02
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26ad996d-4f2d-4b23-846b-9d6cb841b745 | pretraining-de-biased-language-model-with | 2302.13498 | null | https://arxiv.org/abs/2302.13498v1 | https://arxiv.org/pdf/2302.13498v1.pdf | Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking | Pre-trained language models have achieved great success in various large-scale information retrieval tasks. However, most of pretraining tasks are based on counterfeit retrieval data where the query produced by the tailored rule is assumed as the user's issued query on the given document or passage. Therefore, we explore to use large-scale click logs to pretrain a language model instead of replying on the simulated queries. Specifically, we propose to use user behavior features to pretrain a debiased language model for document ranking. Extensive experiments on Baidu desensitization click logs validate the effectiveness of our method. Our team on WSDM Cup 2023 Pre-training for Web Search won the 1st place with a Discounted Cumulative Gain @ 10 (DCG@10) score of 12.16525 on the final leaderboard. | ['Zhanhui Kang', 'Zeqian Huang', 'Lei Jiang', 'Bin Hu', 'Kunliang Wei', 'Xiaoshu Chen', 'Xiangsheng Li'] | 2023-02-27 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-2.41658807e-01 -3.49822164e-01 -5.09729147e-01 -3.57981831e-01
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2.65252292e-01 -3.25533658e-01 -7.95960367e-01 -3.36579382e-01] | [11.558622360229492, 7.66481876373291] |
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