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5f674d43-2bd2-49eb-b24d-fcc5e19241de | who-would-be-interested-in-services-an-entity | 2305.18780 | null | https://arxiv.org/abs/2305.18780v1 | https://arxiv.org/pdf/2305.18780v1.pdf | Who Would be Interested in Services? An Entity Graph Learning System for User Targeting | With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System. | ['Guannan Zhang', 'Jinjie Gu', 'Zhiqiang Zhang', 'Yue Shen', 'Xiaoyan Yang', 'Binbin Hu', 'Dan Yang'] | 2023-05-30 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-1.83865815e-01 -2.23498307e-02 -7.70115077e-01 -2.34047994e-01
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fbfd6f3d-bfd5-4a23-a753-31e3ac750af0 | spatially-and-color-consistent-environment | 2108.07903 | null | https://arxiv.org/abs/2108.07903v1 | https://arxiv.org/pdf/2108.07903v1.pdf | Spatially and color consistent environment lighting estimation using deep neural networks for mixed reality | The representation of consistent mixed reality (XR) environments requires adequate real and virtual illumination composition in real-time. Estimating the lighting of a real scenario is still a challenge. Due to the ill-posed nature of the problem, classical inverse-rendering techniques tackle the problem for simple lighting setups. However, those assumptions do not satisfy the current state-of-art in computer graphics and XR applications. While many recent works solve the problem using machine learning techniques to estimate the environment light and scene's materials, most of them are limited to geometry or previous knowledge. This paper presents a CNN-based model to estimate complex lighting for mixed reality environments with no previous information about the scene. We model the environment illumination using a set of spherical harmonics (SH) environment lighting, capable of efficiently represent area lighting. We propose a new CNN architecture that inputs an RGB image and recognizes, in real-time, the environment lighting. Unlike previous CNN-based lighting estimation methods, we propose using a highly optimized deep neural network architecture, with a reduced number of parameters, that can learn high complex lighting scenarios from real-world high-dynamic-range (HDR) environment images. We show in the experiments that the CNN architecture can predict the environment lighting with an average mean squared error (MSE) of \num{7.85e-04} when comparing SH lighting coefficients. We validate our model in a variety of mixed reality scenarios. Furthermore, we present qualitative results comparing relights of real-world scenes. | ['Cristina Nader Vasconcelos', 'Anselmo Antunes Montenegro', 'Esteban Walter Gonzalez Clua', 'Bruno Augusto Dorta Marques'] | 2021-08-17 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.45105162e-01 -2.36105546e-01 7.49434650e-01 -6.42904997e-01
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8b08521e-d7a0-4675-9627-11d14a283724 | delta-degradation-free-fully-test-time | 2301.13018 | null | https://arxiv.org/abs/2301.13018v1 | https://arxiv.org/pdf/2301.13018v1.pdf | DELTA: degradation-free fully test-time adaptation | Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution. Several efforts have been devoted to improving adaptation performance. However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes. In addition to the extensively studied test stream with independent and class-balanced samples, we further observe that the defects can be exacerbated in more complicated test environments, such as (time) dependent or class-imbalanced data. We observe that previous approaches work well in certain scenarios while show performance degradation in others due to their faults. In this paper, we provide a plug-in solution called DELTA for Degradation-freE fuLly Test-time Adaptation, which consists of two components: (i) Test-time Batch Renormalization (TBR), introduced to improve the estimated normalization statistics. (ii) Dynamic Online re-weighTing (DOT), designed to address the class bias within optimization. We investigate various test-time adaptation methods on three commonly used datasets with four scenarios, and a newly introduced real-world dataset. DELTA can help them deal with all scenarios simultaneously, leading to SOTA performance. | ['Shu-Tao Xia', 'Chen Chen', 'Bowen Zhao'] | 2023-01-30 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.37831223e-01 -5.01371980e-01 -3.35565835e-01 -5.60467303e-01
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e5d32496-2c71-4fd5-aeb6-1ec7cfdcc872 | uavm-a-unified-model-for-audio-visual | 2208.00061 | null | https://arxiv.org/abs/2208.00061v2 | https://arxiv.org/pdf/2208.00061v2.pdf | UAVM: Towards Unifying Audio and Visual Models | Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). The UAVM achieves a new state-of-the-art audio-visual event classification accuracy of 65.8% on VGGSound. More interestingly, we also find a few intriguing properties of UAVM that the modality-independent counterparts do not have. | ['James Glass', 'Andrew Rouditchenko', 'Alexander H. Liu', 'Yuan Gong'] | 2022-07-29 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [-1.80351764e-01 -2.76919007e-01 -2.15081856e-01 -3.06036007e-02
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f3609091-a7d7-46fa-b2bf-aad3b02e4f34 | a-neural-layered-model-for-nested-named | null | null | https://aclanthology.org/N18-1131 | https://aclanthology.org/N18-1131.pdf | A Neural Layered Model for Nested Named Entity Recognition | Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7{\%} and 72.2{\%} on GENIA and ACE2005 datasets, respectively, in terms of F-score. | ['Meizhi Ju', 'Sophia Ananiadou', 'Makoto Miwa'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-3.57790649e-01 5.41542292e-01 1.03399217e-01 -4.30310726e-01
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688e48a1-da4d-4d36-b3d7-4162b26dc5ad | robust-multilingual-named-entity-recognition | 1701.09123 | null | http://arxiv.org/abs/1701.09123v1 | http://arxiv.org/pdf/1701.09123v1.pdf | Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features | We present a multilingual Named Entity Recognition approach based on a robust
and general set of features across languages and datasets. Our system combines
shallow local information with clustering semi-supervised features induced on
large amounts of unlabeled text. Understanding via empirical experimentation
how to effectively combine various types of clustering features allows us to
seamlessly export our system to other datasets and languages. The result is a
simple but highly competitive system which obtains state of the art results
across five languages and twelve datasets. The results are reported on standard
shared task evaluation data such as CoNLL for English, Spanish and Dutch.
Furthermore, and despite the lack of linguistically motivated features, we also
report best results for languages such as Basque and German. In addition, we
demonstrate that our method also obtains very competitive results even when the
amount of supervised data is cut by half, alleviating the dependency on
manually annotated data. Finally, the results show that our emphasis on
clustering features is crucial to develop robust out-of-domain models. The
system and models are freely available to facilitate its use and guarantee the
reproducibility of results. | ['Rodrigo Agerri', 'German Rigau'] | 2017-01-31 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.59398454e-01 -2.31781021e-01 -2.07093686e-01 -7.77367353e-01
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2172fecd-b9f8-4bc5-8832-fd98bb378b1b | trader-company-method-a-metaheuristic-for | 2012.10215 | null | https://arxiv.org/abs/2012.10215v1 | https://arxiv.org/pdf/2012.10215v1.pdf | Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction | Investors try to predict returns of financial assets to make successful investment. Many quantitative analysts have used machine learning-based methods to find unknown profitable market rules from large amounts of market data. However, there are several challenges in financial markets hindering practical applications of machine learning-based models. First, in financial markets, there is no single model that can consistently make accurate prediction because traders in markets quickly adapt to newly available information. Instead, there are a number of ephemeral and partially correct models called "alpha factors". Second, since financial markets are highly uncertain, ensuring interpretability of prediction models is quite important to make reliable trading strategies. To overcome these challenges, we propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders belonging to it. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders. A Trader holds a collection of simple mathematical formulae, each of which represents a candidate of an alpha factor and would be interpretable for real-world investors. The aggregation algorithm, called a Company, maintains multiple Traders. By randomly generating new Traders and retraining them, Companies can efficiently find financially meaningful formulae whilst avoiding overfitting to a transient state of the market. We show the effectiveness of our method by conducting experiments on real market data. | ['Kei Nakagawa', 'Kentaro Imajo', 'Kentaro Minami', 'Katsuya Ito'] | 2020-12-18 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-3.85042220e-01 1.42775318e-02 -2.83961296e-01 -2.13506356e-01
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1fe431de-646e-4d30-b1f2-be6f5219c509 | object-region-video-transformers-1 | 2110.06915 | null | https://arxiv.org/abs/2110.06915v3 | https://arxiv.org/pdf/2110.06915v3.pdf | Object-Region Video Transformers | Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, although objects can be essential for recognizing actions. In this work, we present Object-Region Video Transformers (ORViT), an \emph{object-centric} approach that extends video transformer layers with a block that directly incorporates object representations. The key idea is to fuse object-centric representations starting from early layers and propagate them into the transformer-layers, thus affecting the spatio-temporal representations throughout the network. Our ORViT block consists of two object-level streams: appearance and dynamics. In the appearance stream, an "Object-Region Attention" module applies self-attention over the patches and \emph{object regions}. In this way, visual object regions interact with uniform patch tokens and enrich them with contextualized object information. We further model object dynamics via a separate "Object-Dynamics Module", which captures trajectory interactions, and show how to integrate the two streams. We evaluate our model on four tasks and five datasets: compositional and few-shot action recognition on SomethingElse, spatio-temporal action detection on AVA, and standard action recognition on Something-Something V2, Diving48 and Epic-Kitchen100. We show strong performance improvement across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture. For code and pretrained models, visit the project page at \url{https://roeiherz.github.io/ORViT/} | ['Amir Globerson', 'Trevor Darrell', 'Anna Rohrbach', 'Gal Chechik', 'Amir Bar', 'Karttikeya Mangalam', 'Elad Ben-Avraham', 'Roei Herzig'] | 2021-10-13 | object-region-video-transformers | http://openaccess.thecvf.com//content/CVPR2022/html/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 1.47555918e-01 -2.20138580e-01 -2.68305331e-01 -1.22217223e-01
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0fa9f063-9095-4451-9a72-25596d43b385 | insta-beeer-explicit-error-estimation-and | 2306.16132 | null | https://arxiv.org/abs/2306.16132v1 | https://arxiv.org/pdf/2306.16132v1.pdf | INSTA-BEEER: Explicit Error Estimation and Refinement for Fast and Accurate Unseen Object Instance Segmentation | Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over- or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we propose INSTAnce Boundary Explicit Error Estimation and Refinement (INSTA-BEEER), a novel refinement model that allows for adding and deleting instances and sharpening boundaries. Leveraging an error-estimation-then-refinement scheme, the model first estimates the pixel-wise boundary explicit errors: true positive, true negative, false positive, and false negative pixels of the instance boundary in the initial segmentation. It then refines the initial segmentation using these error estimates as guidance. Experiments show that the proposed model significantly enhances segmentation, achieving state-of-the-art performance. Furthermore, with a fast runtime (less than 0.1 s), the model consistently improves performance across various initial segmentation methods, making it highly suitable for practical robotic applications. | ['Kyoobin Lee', 'Jaemo Maeng', 'Sungho Shin', 'Joosoon Lee', 'KangMin Kim', 'Sangbeom Lee', 'Seunghyeok Back'] | 2023-06-28 | null | null | null | null | ['unseen-object-instance-segmentation', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.63837004e-01 3.27765435e-01 -5.34192473e-02 -9.40782726e-02
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fa79eb1f-1edc-436c-9576-ac0e622ad71c | a-comparative-study-for-time-to-event | null | null | http://jeeemi.org/index.php/jeeemi/article/view/225 | http://jeeemi.org/index.php/jeeemi/article/view/225/94 | A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques | Heart Failure, an ailment in which the heart isn’t functioning as effectively as it should, causing in an insufficient cardiac output. The effectual functioning of the human body is dependent on how well the heart is able to pump oxygenated, and nutrient rich blood to the tissues and cells. Heart failure falls into the category of cardiovascular diseases - the disorders of the heart and blood vessels. One of the leading causes of global deaths resulting in an estimated 17.9 million deaths globally every year. The condition of heart failure results out of structural changes to the cardiac muscles majorly in the left ventricle. The weakened muscles cause the ventricle to lose its ability to contract completely. Since the left ventricle generates the required pressure for blood circulation, any kind of a failure condition results in the reduction of cardiac power output. This study aims to conduct a thorough survival analysis and survival prediction on the data of 299 patients classified into the class III/IV of heart failure and diagnosed with left ventricular systolic dysfunction. Survival analysis involves the study of the effect of a mediation assessed by measuring the number of subjects survived after that mediation over a period of time. The time starting from a distinct point to the occurrence of a certain event, for example death is known as survival time and the corresponding analysis is known as survival analysis. The analysis was performed using the methods of Kaplan-Meier (KM) estimates and Cox Potential Hazard regression. KM plots showed the survival estimates as a function of each clinical feature and how each feature at various levels affect survival over the period of time. Cox regression modelled the hazard of death event around the clinical features used for the study. As a result of the analysis, ejection fraction, serum creatinine, time and age were identified as highly significant and major risk factors in the advanced stages of heart failure. Age and rise in level of serum creatinine have a deleterious effect on the survival chances. Ejection Fraction has a beneficial effect on survival and with a unit increase in the in the EF level the probability of death event decreases by ~5.2%. Higher rate of mortality is observed during the initial days post diagnosis and the hazard gradually decreases if patients have lived for a certain number of days. Hypertension and anemic condition also seem to be high risk factors. Machine learning classification models for survival prediction were built using the most significant variables found from survival analysis. SVM, decision tree, random forest, XGBoost, and LightGBM algorithm were implemented, and all the models seem to perform well enough. However, the availability of more data will make the models more stable and robust. Smart solutions, like this can reduce the risk of heart failure condition by providing accurate prognosis, survival projections, and risk predictions. Technology and data can combine together to address any disparities in treatment, design better care plan, and improve patient health outcomes. Smart health AI solutions would enhance healthcare policies, enable physicians to look beyond the conventional practices, and increase the patient satisfaction levels not only in case of heart failure conditions but healthcare in general. | ['Saurav Mishra'] | 2022-07-25 | null | null | null | journal-of-electronics-electromedical-1 | ['survival-analysis'] | ['miscellaneous'] | [-1.67117849e-01 -9.68321413e-02 -2.33862251e-01 -1.06246345e-01
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8cc84344-acd0-4f89-84ba-519052edf2d5 | evolutionary-multi-objective-reinforcement | 2202.12028 | null | https://arxiv.org/abs/2202.12028v1 | https://arxiv.org/pdf/2202.12028v1.pdf | Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing | This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV has two roles to play: MEC server or wireless relay. The UAV makes task offloading decisions online, in which the collected tasks can be executed locally on the UAV or offloaded to the BS for remote processing. The TCTO problem involves multi-objective optimization as its objectives are to minimize the task delay and the UAV's energy consumption, and maximize the number of tasks collected by the UAV, simultaneously. This problem is challenging because the three objectives conflict with each other. The existing reinforcement learning (RL) algorithms, either single-objective RLs or single-policy multi-objective RLs, cannot well address the problem since they cannot output multiple policies for various preferences (i.e. weights) across objectives in a single run. This paper adapts the evolutionary multi-objective RL (EMORL), a multi-policy multi-objective RL, to the TCTO problem. This algorithm can output multiple optimal policies in just one run, each optimizing a certain preference. The simulation results demonstrate that the proposed algorithm can obtain more excellent nondominated policies by striking a balance between the three objectives regarding policy quality, compared with two evolutionary and two multi-policy RL algorithms. | ['Bowen Zhao', 'Zhiwen Xiao', 'Penglin Dai', 'Shouxi Luo', 'Xinhan Wang', 'Huanlai Xing', 'Fuhong Song'] | 2022-02-24 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 4.36156429e-02 -1.70778275e-01 -5.60717881e-01 2.42846578e-01
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37e6cad4-a25d-42a7-898b-7a8d0bb9c9a8 | semi-supervised-sparse-representation-based | 1609.03279 | null | http://arxiv.org/abs/1609.03279v2 | http://arxiv.org/pdf/1609.03279v2.pdf | Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples | This paper addresses the problem of face recognition when there is only few,
or even only a single, labeled examples of the face that we wish to recognize.
Moreover, these examples are typically corrupted by nuisance variables, both
linear (i.e., additive nuisance variables such as bad lighting, wearing of
glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such
as expression changes). The small number of labeled examples means that it is
hard to remove these nuisance variables between the training and testing faces
to obtain good recognition performance. To address the problem we propose a
method called Semi-Supervised Sparse Representation based Classification
(S$^3$RC). This is based on recent work on sparsity where faces are represented
in terms of two dictionaries: a gallery dictionary consisting of one or more
examples of each person, and a variation dictionary representing linear
nuisance variables (e.g., different lighting conditions, different glasses).
The main idea is that (i) we use the variation dictionary to characterize the
linear nuisance variables via the sparsity framework, then (ii) prototype face
images are estimated as a gallery dictionary via a Gaussian Mixture Model
(GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to
deal with the non-linear nuisance variations between labeled and unlabeled
samples. We have done experiments with insufficient labeled samples, even when
there is only a single labeled sample per person. Our results on the AR,
Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is
able to deliver significantly improved performance over existing methods. | ['Yuan Gao', 'Jiayi Ma', 'Alan L. Yuille'] | 2016-09-12 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.10553899e-01 -3.11766297e-01 -1.67826012e-01 -6.22790396e-01
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1217ac5b-6d77-447a-9c45-ea2d716ac2cc | salt-subspace-alignment-as-an-auxiliary | 1906.04338 | null | https://arxiv.org/abs/1906.04338v2 | https://arxiv.org/pdf/1906.04338v2.pdf | SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation | Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adaptation include: (a) maximizing performance on the target, and (b) aligning the source and target domains. Traditionally, these tasks have either been considered as separate, or assumed to be implicitly addressed together with high-capacity feature extractors. When considered separately, alignment is usually viewed as a problem of aligning data distributions, either through geometric approaches such as subspace alignment or through distributional alignment such as optimal transport. This paper represents a hybrid approach, where we assume simplified data geometry in the form of subspaces, and consider alignment as an auxiliary task to the primary task of maximizing performance on the source. The alignment is made rather simple by leveraging tractable data geometry in the form of subspaces. We synergistically allow certain parameters derived from the closed-form auxiliary solution, to be affected by gradients from the primary task. The proposed approach represents a unique fusion of geometric and model-based alignment with gradients from a data-driven primary task. Our approach termed SALT, is a simple framework that achieves comparable or sometimes outperforms state-of-the-art on multiple standard benchmarks. | ['Pavan Turaga', 'Kowshik Thopalli', 'Jayaraman J. Thiagarajan', 'Rushil Anirudh'] | 2019-06-11 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.86383808e-01 4.23404723e-02 -2.43092448e-01 -5.83789825e-01
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57ee11c3-4c0d-4d1c-b80d-0a4ae9e0ab9b | expanding-synthetic-real-world-degradations | 2305.02660 | null | https://arxiv.org/abs/2305.02660v1 | https://arxiv.org/pdf/2305.02660v1.pdf | Expanding Synthetic Real-World Degradations for Blind Video Super Resolution | Video super-resolution (VSR) techniques, especially deep-learning-based algorithms, have drastically improved over the last few years and shown impressive performance on synthetic data. However, their performance on real-world video data suffers because of the complexity of real-world degradations and misaligned video frames. Since obtaining a synthetic dataset consisting of low-resolution (LR) and high-resolution (HR) frames are easier than obtaining real-world LR and HR images, in this paper, we propose synthesizing real-world degradations on synthetic training datasets. The proposed synthetic real-world degradations (SRWD) include a combination of the blur, noise, downsampling, pixel binning, and image and video compression artifacts. We then propose using a random shuffling-based strategy to simulate these degradations on the training datasets and train a single end-to-end deep neural network (DNN) on the proposed larger variation of realistic synthesized training data. Our quantitative and qualitative comparative analysis shows that the proposed training strategy using diverse realistic degradations improves the performance by 7.1 % in terms of NRQM compared to RealBasicVSR and by 3.34 % compared to BSRGAN on the VideoLQ dataset. We also introduce a new dataset that contains high-resolution real-world videos that can serve as a common ground for bench-marking. | ['Sunil Jaiswal', 'Philipp Slusallek', 'Klaus Illgner-Fehns', 'Noshaba Cheema', 'Sadbhawna', 'Mehran Jeelani'] | 2023-05-04 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 4.41477686e-01 -5.30654192e-01 2.33827040e-01 -1.88027188e-01
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8ebb9647-873c-4921-a505-bc69e80ebfa4 | uncertain-machine-ethical-decisions-using | 2305.01424 | null | https://arxiv.org/abs/2305.01424v1 | https://arxiv.org/pdf/2305.01424v1.pdf | Uncertain Machine Ethical Decisions Using Hypothetical Retrospection | We propose the use of the hypothetical retrospection argumentation procedure, developed by Sven Hansson, to improve existing approaches to machine ethical reasoning by accounting for probability and uncertainty from a position of Philosophy that resonates with humans. Actions are represented with a branching set of potential outcomes, each with a state, utility, and either a numeric or poetic probability estimate. Actions are chosen based on comparisons between sets of arguments favouring actions from the perspective of their branches, even those branches that led to an undesirable outcome. This use of arguments allows a variety of philosophical theories for ethical reasoning to be used, potentially in flexible combination with each other. We implement the procedure, applying consequentialist and deontological ethical theories, independently and concurrently, to an autonomous library system use case. We introduce a a preliminary framework that seems to meet the varied requirements of a machine ethics system: versatility under multiple theories and a resonance with humans that enables transparency and explainability. | ['Mengwei Xu', 'Ramon Fraga Pereira', 'Louise Dennis', 'Simon Kolker'] | 2023-05-02 | null | null | null | null | ['ethics', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [ 2.35608891e-01 1.30091345e+00 -1.65291697e-01 -6.12660289e-01
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5da67698-7370-4dc3-9285-e2474e1bd21b | monocular-3d-object-detection-with-bounding | 2304.01289 | null | https://arxiv.org/abs/2304.01289v1 | https://arxiv.org/pdf/2304.01289v1.pdf | Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver | The main challenge of monocular 3D object detection is the accurate localization of 3D center. Motivated by a new and strong observation that this challenge can be remedied by a 3D-space local-grid search scheme in an ideal case, we propose a stage-wise approach, which combines the information flow from 2D-to-3D (3D bounding box proposal generation with a single 2D image) and 3D-to-2D (proposal verification by denoising with 3D-to-2D contexts) in a top-down manner. Specifically, we first obtain initial proposals from off-the-shelf backbone monocular 3D detectors. Then, we generate a 3D anchor space by local-grid sampling from the initial proposals. Finally, we perform 3D bounding box denoising at the 3D-to-2D proposal verification stage. To effectively learn discriminative features for denoising highly overlapped proposals, this paper presents a method of using the Perceiver I/O model to fuse the 3D-to-2D geometric information and the 2D appearance information. With the encoded latent representation of a proposal, the verification head is implemented with a self-attention module. Our method, named as MonoXiver, is generic and can be easily adapted to any backbone monocular 3D detectors. Experimental results on the well-established KITTI dataset and the challenging large-scale Waymo dataset show that MonoXiver consistently achieves improvement with limited computation overhead. | ['Tianfu Wu', 'Guo-Jun Qi', 'Nan Xue', 'Kelvin Cheng', 'Ce Zheng', 'Xianpeng Liu'] | 2023-04-03 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-1.81745544e-01 1.76614989e-02 -6.16817735e-02 -3.43075901e-01
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47bda9d8-d3e0-4e34-a137-719816b4d220 | a-corpus-with-multi-level-annotations-of | 1806.04185 | null | http://arxiv.org/abs/1806.04185v1 | http://arxiv.org/pdf/1806.04185v1.pdf | A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature | We present a corpus of 5,000 richly annotated abstracts of medical articles
describing clinical randomized controlled trials. Annotations include
demarcations of text spans that describe the Patient population enrolled, the
Interventions studied and to what they were Compared, and the Outcomes measured
(the `PICO' elements). These spans are further annotated at a more granular
level, e.g., individual interventions within them are marked and mapped onto a
structured medical vocabulary. We acquired annotations from a diverse set of
workers with varying levels of expertise and cost. We describe our data
collection process and the corpus itself in detail. We then outline a set of
challenging NLP tasks that would aid searching of the medical literature and
the practice of evidence-based medicine. | ['Byron C. Wallace', 'Yinfei Yang', 'Roma Patel', 'Ani Nenkova', 'Junyi Jessy Li', 'Iain J. Marshall', 'Benjamin Nye'] | 2018-06-11 | a-corpus-with-multi-level-annotations-of-1 | https://aclanthology.org/P18-1019 | https://aclanthology.org/P18-1019.pdf | acl-2018-7 | ['participant-intervention-comparison-outcome', 'pico'] | ['medical', 'natural-language-processing'] | [ 6.28396928e-01 4.08797920e-01 -9.32650685e-01 -1.73811987e-01
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959339c9-b00e-4742-bb86-3b656e594d26 | learning-physical-spatio-temporal-features | 2303.09370 | null | https://arxiv.org/abs/2303.09370v1 | https://arxiv.org/pdf/2303.09370v1.pdf | Learning Physical-Spatio-Temporal Features for Video Shadow Removal | Shadow removal in a single image has received increasing attention in recent years. However, removing shadows over dynamic scenes remains largely under-explored. In this paper, we propose the first data-driven video shadow removal model, termed PSTNet, by exploiting three essential characteristics of video shadows, i.e., physical property, spatio relation, and temporal coherence. Specifically, a dedicated physical branch was established to conduct local illumination estimation, which is more applicable for scenes with complex lighting and textures, and then enhance the physical features via a mask-guided attention strategy. Then, we develop a progressive aggregation module to enhance the spatio and temporal characteristics of features maps, and effectively integrate the three kinds of features. Furthermore, to tackle the lack of datasets of paired shadow videos, we synthesize a dataset (SVSRD-85) with aid of the popular game GTAV by controlling the switch of the shadow renderer. Experiments against 9 state-of-the-art models, including image shadow removers and image/video restoration methods, show that our method improves the best SOTA in terms of RMSE error for the shadow area by 14.7. In addition, we develop a lightweight model adaptation strategy to make our synthetic-driven model effective in real world scenes. The visual comparison on the public SBU-TimeLapse dataset verifies the generalization ability of our model in real scenes. | ['Huazhu Fu', 'Lei Zhu', 'Yefan Xiao', 'Liang Wan', 'Zhihao Chen'] | 2023-03-16 | null | null | null | null | ['shadow-removal', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 5.10693789e-01 -4.38828856e-01 3.04145336e-01 -1.46441385e-01
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bec6042f-8bba-412f-ac54-3be938e4017c | a-generalist-neural-algorithmic-learner | 2209.11142 | null | https://arxiv.org/abs/2209.11142v2 | https://arxiv.org/pdf/2209.11142v2.pdf | A Generalist Neural Algorithmic Learner | The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused on building specialist models. Specialist models are capable of learning to neurally execute either only one algorithm or a collection of algorithms with identical control-flow backbone. Here, instead, we focus on constructing a generalist neural algorithmic learner -- a single graph neural network processor capable of learning to execute a wide range of algorithms, such as sorting, searching, dynamic programming, path-finding and geometry. We leverage the CLRS benchmark to empirically show that, much like recent successes in the domain of perception, generalist algorithmic learners can be built by "incorporating" knowledge. That is, it is possible to effectively learn algorithms in a multi-task manner, so long as we can learn to execute them well in a single-task regime. Motivated by this, we present a series of improvements to the input representation, training regime and processor architecture over CLRS, improving average single-task performance by over 20% from prior art. We then conduct a thorough ablation of multi-task learners leveraging these improvements. Our results demonstrate a generalist learner that effectively incorporates knowledge captured by specialist models. | ['Petar Veličković', 'Charles Blundell', 'Yaroslav Ganin', 'Beatrice Bevilacqua', 'Andreea Deac', 'Yulia Rubanova', 'Alex Vitvitskyi', 'Matko Bošnjak', 'Andrew Dudzik', 'Róbert Csordás', 'Mehdi Bennani', 'Kyriacos Nikiforou', 'George Papamakarios', 'Vitaly Kurin', 'Borja Ibarz'] | 2022-09-22 | null | null | null | null | ['learning-to-execute'] | ['computer-code'] | [ 5.91158390e-01 1.70484841e-01 -3.13879224e-03 -1.58997253e-01
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891dbcd0-cd61-4f9d-b372-56b41baef927 | learning-stiff-chemical-kinetics-using | 2302.12645 | null | https://arxiv.org/abs/2302.12645v1 | https://arxiv.org/pdf/2302.12645v1.pdf | Learning stiff chemical kinetics using extended deep neural operators | We utilize neural operators to learn the solution propagator for the challenging chemical kinetics equation. Specifically, we apply the deep operator network (DeepONet) along with its extensions, such as the autoencoder-based DeepONet and the newly proposed Partition-of-Unity (PoU-) DeepONet to study a range of examples, including the ROBERS problem with three species, the POLLU problem with 25 species, pure kinetics of the syngas skeletal model for $CO/H_2$ burning, which contains 11 species and 21 reactions and finally, a temporally developing planar $CO/H_2$ jet flame (turbulent flame) using the same syngas mechanism. We have demonstrated the advantages of the proposed approach through these numerical examples. Specifically, to train the DeepONet for the syngas model, we solve the skeletal kinetic model for different initial conditions. In the first case, we parametrize the initial conditions based on equivalence ratios and initial temperature values. In the second case, we perform a direct numerical simulation of a two-dimensional temporally developing $CO/H_2$ jet flame. Then, we initialize the kinetic model by the thermochemical states visited by a subset of grid points at different time snapshots. Stiff problems are computationally expensive to solve with traditional stiff solvers. Thus, this work aims to develop a neural operator-based surrogate model to solve stiff chemical kinetics. The operator, once trained offline, can accurately integrate the thermochemical state for arbitrarily large time advancements, leading to significant computational gains compared to stiff integration schemes. | ['George Em Karniadakis', 'Bryan T. Susi', 'Hessam Babaee', 'Ameya D. Jagtap', 'Somdatta Goswami'] | 2023-02-23 | null | null | null | null | ['unity'] | ['computer-vision'] | [-3.34095120e-01 -2.86606938e-01 4.42419022e-01 4.65267032e-01
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2b90afd9-1f90-4512-a6c7-97c32088f7f1 | scene-understanding-for-autonomous | 1903.09761 | null | http://arxiv.org/abs/1903.09761v1 | http://arxiv.org/pdf/1903.09761v1.pdf | Scene Understanding for Autonomous Manipulation with Deep Learning | Over the past few years, deep learning techniques have achieved tremendous
success in many visual understanding tasks such as object detection, image
segmentation, and caption generation. Despite this thriving in computer vision
and natural language processing, deep learning has not yet shown significant
impact in robotics. Due to the gap between theory and application, there are
many challenges when applying the results of deep learning to the real robotic
systems. In this study, our long-term goal is to bridge the gap between
computer vision and robotics by developing visual methods that can be used in
real robots. In particular, this work tackles two fundamental visual problems
for autonomous robotic manipulation: affordance detection and fine-grained
action understanding. Theoretically, we propose different deep architectures to
further improves the state of the art in each problem. Empirically, we show
that the outcomes of our proposed methods can be applied in real robots and
allow them to perform useful manipulation tasks. | ['Anh Nguyen'] | 2019-03-23 | null | null | null | null | ['action-understanding', 'affordance-detection'] | ['computer-vision', 'computer-vision'] | [ 2.28154510e-01 1.59593537e-01 -1.78696096e-01 -1.45455554e-01
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430dcbb9-81d3-43f0-b752-f998421eb6de | ergo-event-relational-graph-transformer-for | 2204.07434 | null | https://arxiv.org/abs/2204.07434v1 | https://arxiv.org/pdf/2204.07434v1.pdf | ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification | Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI, which improves existing state-of-the-art (SOTA) methods upon two aspects. First, we formulate DECI as a node classification problem by constructing an event relational graph, without the needs of prior knowledge or tools. Second, ERGO seamlessly integrates event-pair relation classification and global inference, which leverages a Relational Graph Transformer (RGT) to capture the potential causal chain. Besides, we introduce edge-building strategies and adaptive focal loss to deal with the massive false positives caused by common spurious correlation. Extensive experiments on two benchmark datasets show that ERGO significantly outperforms previous SOTA methods (13.1% F1 gains on average). We have conducted extensive quantitative analysis and case studies to provide insights for future research directions (Section 4.8). | ['Yan Zhang', 'Jing Shao', 'Kun Wang', 'Mukai Li', 'Kunquan Deng', 'Yixin Cao', 'Meiqi Chen'] | 2022-04-15 | null | https://aclanthology.org/2022.coling-1.185 | https://aclanthology.org/2022.coling-1.185.pdf | coling-2022-10 | ['relation-classification', 'event-causality-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.56831163e-01 9.82584432e-02 -5.19665182e-01 -1.94191635e-01
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86a2be65-0433-428a-b8a7-9e4aca628474 | towards-sampling-from-nondirected | 1905.00159 | null | http://arxiv.org/abs/1905.00159v1 | http://arxiv.org/pdf/1905.00159v1.pdf | Towards Sampling from Nondirected Probabilistic Graphical models using a D-Wave Quantum Annealer | A D-Wave quantum annealer (QA) having a 2048 qubit lattice, with no missing
qubits and couplings, allowed embedding of a complete graph of a Restricted
Boltzmann Machine (RBM). A handwritten digit OptDigits data set having 8x7
pixels of visible units was used to train the RBM using a classical Contrastive
Divergence. Embedding of the classically-trained RBM into the D-Wave lattice
was used to demonstrate that the QA offers a high-efficiency alternative to the
classical Markov Chain Monte Carlo (MCMC) for reconstructing missing labels of
the test images as well as a generative model. At any training iteration, the
D-Wave-based classification had classification error more than two times lower
than MCMC. The main goal of this study was to investigate the quality of the
sample from the RBM model distribution and its comparison to a classical MCMC
sample. For the OptDigits dataset, the states in the D-Wave sample belonged to
about two times more local valleys compared to the MCMC sample. All the
lowest-energy (the highest joint probability) local minima in the MCMC sample
were also found by the D-Wave. The D-Wave missed many of the higher-energy
local valleys, while finding many "new" local valleys consistently missed by
the MCMC. It was established that the "new" local valleys that the D-Wave finds
are important for the model distribution in terms of the energy of the
corresponding local minima, the width of the local valleys, and the height of
the escape barrier. | ['M. A. Novotny', 'Yaroslav Koshka'] | 2019-05-01 | null | null | null | null | ['2048'] | ['playing-games'] | [ 3.26543897e-01 3.48231941e-01 1.82668626e-01 -1.70439541e-01
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fee8e433-7d82-424d-9278-ced3978b5990 | deep-neural-network-solution-of-the-1 | null | null | https://www.nature.com/articles/s41557-020-0544-y | https://pub.hrmnn.net/4a/bc8d5cac/preprint.pdf | Deep-neural-network solution of the electronic Schrödinger equation | The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient. | ['Frank Noé', 'Zeno Schätzle', 'Jan Hermann'] | 2020-09-23 | null | null | null | nature-chemistry-2020-9 | ['total-energy'] | ['miscellaneous'] | [-2.21599579e-01 -2.11211175e-01 -9.93043035e-02 -7.32861385e-02
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14fddbcf-6571-4f90-89e5-46b8e349d630 | combining-fully-convolutional-and-recurrent | 1609.01006 | null | http://arxiv.org/abs/1609.01006v2 | http://arxiv.org/pdf/1609.01006v2.pdf | Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation | Segmentation of 3D images is a fundamental problem in biomedical image
analysis. Deep learning (DL) approaches have achieved state-of-the-art
segmentation perfor- mance. To exploit the 3D contexts using neural networks,
known DL segmentation methods, including 3D convolution, 2D convolution on
planes orthogonal to 2D image slices, and LSTM in multiple directions, all
suffer incompatibility with the highly anisotropic dimensions in common 3D
biomedical images. In this paper, we propose a new DL framework for 3D image
segmentation, based on a com- bination of a fully convolutional network (FCN)
and a recurrent neural network (RNN), which are responsible for exploiting the
intra-slice and inter-slice contexts, respectively. To our best knowledge, this
is the first DL framework for 3D image segmentation that explicitly leverages
3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal
Structure Segmentation Challenge and in-house image stacks for 3D fungus
segmentation, our approach achieves promising results comparing to the known
DL-based 3D segmentation approaches. | ['Danny Z. Chen', 'Yizhe Zhang', 'Mark Alber', 'Lin Yang', 'Jianxu Chen'] | 2016-09-05 | combining-fully-convolutional-and-recurrent-1 | http://papers.nips.cc/paper/6448-combining-fully-convolutional-and-recurrent-neural-networks-for-3d-biomedical-image-segmentation | http://papers.nips.cc/paper/6448-combining-fully-convolutional-and-recurrent-neural-networks-for-3d-biomedical-image-segmentation.pdf | neurips-2016-12 | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 3.62376988e-01 2.26927876e-01 4.99570370e-02 -4.47035968e-01
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f60cceb2-b311-4a0b-8e16-c4849b1a278f | translation-invariant-word-embeddings | null | null | https://aclanthology.org/D15-1127 | https://aclanthology.org/D15-1127.pdf | Translation Invariant Word Embeddings | null | ['Partha P. Talukdar', 'Xiao Fu', 'Christos Faloutsos', 'Tom Mitchell', 'Matt Gardner', 'Kejun Huang', 'Nikos Sidiropoulos', 'Evangelos Papalexakis'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['multilingual-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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3ba65346-ce06-44b8-a9fe-6947967c9634 | barkour-benchmarking-animal-level-agility | 2305.14654 | null | https://arxiv.org/abs/2305.14654v1 | https://arxiv.org/pdf/2305.14654v1.pdf | Barkour: Benchmarking Animal-level Agility with Quadruped Robots | Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biological counterparts and show various agile skills to navigate complex environments quickly. Despite the interest, the field lacks systematic benchmarks to measure the performance of control policies and hardware in agility. We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. This encourages researchers to develop controllers that not only move fast, but do so in a controllable and versatile way. To set strong baselines, we present two methods for tackling the benchmark. In the first approach, we train specialist locomotion skills using on-policy reinforcement learning methods and combine them with a high-level navigation controller. In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states. Using a custom-built quadruped robot, we demonstrate that our method can complete the course at half the speed of a dog. We hope that our work represents a step towards creating controllers that enable robots to reach animal-level agility. | ['Jie Tan', 'Vincent Vanhoucke', 'Vikas Sindhwani', 'Carolina Parada', 'Jeff Seto', 'Francesco Nori', 'Nicolas Heess', 'Raia Hadsell', 'Michael Neunert', 'Daniel Zheng', 'Baruch Tabanpour', 'Ron Sloat', 'Feresteh Sadeghi', 'Francesco Romano', 'Diego Reyes', 'Jason Powell', 'Ken Oslund', 'Ofir Nachum', 'Linda Luu', 'Edward Lee', 'Yuheng Kuang', 'Bauyrjan Jyenis', 'Deepali Jain', 'Roland Hafner', 'Erik Frey', 'Alejandro Escontrela', 'Gabriel Dulac-Arnold', 'Adil Dostmohamed', 'Erwin Coumans', 'Omar Cortes', 'Jose Enrique Chen', 'Federico Casarini', 'Steven Bohez', 'Nathan Batchelor', 'Vincent Zhuang', 'Stefano Saliceti', 'Lisa Lee', 'Kuang-Huei Lee', 'Daniel Freeman', 'Tingnan Zhang', 'Wenhao Yu', 'J. Chase Kew', 'Atil Iscen', 'Ken Caluwaerts'] | 2023-05-24 | null | null | null | null | ['navigate'] | ['reasoning'] | [-2.86021799e-01 8.86375681e-02 -4.80725132e-02 1.40432976e-02
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6c370c0a-f1f9-4b54-86c5-2f6529c5276c | learning-from-mistakes-self-regularizing | 2301.11145 | null | https://arxiv.org/abs/2301.11145v1 | https://arxiv.org/pdf/2301.11145v1.pdf | Learning from Mistakes: Self-Regularizing Hierarchical Semantic Representations in Point Cloud Segmentation | Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accomplish fine-grained scene understanding by acting directly on raw content provided by sensors. Recent solutions showed how different learning techniques can be used to improve the performance of the model, without any architectural or dataset change. Following this trend, we present a coarse-to-fine setup that LEArns from classification mistaKes (LEAK) derived from a standard model. First, classes are clustered into macro groups according to mutual prediction errors; then, the learning process is regularized by: (1) aligning class-conditional prototypical feature representation for both fine and coarse classes, (2) weighting instances with a per-class fairness index. Our LEAK approach is very general and can be seamlessly applied on top of any segmentation architecture; indeed, experimental results showed that it enables state-of-the-art performances on different architectures, datasets and tasks, while ensuring more balanced class-wise results and faster convergence. | ['Simone Milani', 'Umberto Michieli', 'Elena Camuffo'] | 2023-01-26 | null | null | null | null | ['lidar-semantic-segmentation', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.73347515e-01 1.36293799e-01 -2.79041857e-01 -7.26130784e-01
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d66cc120-b836-47d7-a564-7d1c326fb625 | unsupervised-energy-based-adversarial-domain | null | null | https://aclanthology.org/2021.findings-acl.103 | https://aclanthology.org/2021.findings-acl.103.pdf | Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification | null | ['Xiaojian Wu', 'Jianfei Yang', 'Han Zou'] | null | null | null | null | findings-acl-2021-8 | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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fa9a88e5-0bcf-4b24-b15f-4f8e20e91649 | relieving-triplet-ambiguity-consensus-network | 2306.02092 | null | https://arxiv.org/abs/2306.02092v1 | https://arxiv.org/pdf/2306.02092v1.pdf | Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image Retrieval | Language-guided image retrieval enables users to search for images and interact with the retrieval system more naturally and expressively by using a reference image and a relative caption as a query. Most existing studies mainly focus on designing image-text composition architecture to extract discriminative visual-linguistic relations. Despite great success, we identify an inherent problem that obstructs the extraction of discriminative features and considerably compromises model training: \textbf{triplet ambiguity}. This problem stems from the annotation process wherein annotators view only one triplet at a time. As a result, they often describe simple attributes, such as color, while neglecting fine-grained details like location and style. This leads to multiple false-negative candidates matching the same modification text. We propose a novel Consensus Network (Css-Net) that self-adaptively learns from noisy triplets to minimize the negative effects of triplet ambiguity. Inspired by the psychological finding that groups perform better than individuals, Css-Net comprises 1) a consensus module featuring four distinct compositors that generate diverse fused image-text embeddings and 2) a Kullback-Leibler divergence loss, which fosters learning among the compositors, enabling them to reduce biases learned from noisy triplets and reach a consensus. The decisions from four compositors are weighted during evaluation to further achieve consensus. Comprehensive experiments on three datasets demonstrate that Css-Net can alleviate triplet ambiguity, achieving competitive performance on benchmarks, such as $+2.77\%$ R@10 and $+6.67\%$ R@50 on FashionIQ. | ['Yi Yang', 'Xiaohan Wang', 'Zhedong Zheng', 'Xu Zhang'] | 2023-06-03 | null | null | null | null | ['multi-modal'] | ['miscellaneous'] | [ 2.53254116e-01 -2.98020273e-01 -2.35153392e-01 -6.79710627e-01
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c4b13cf7-f74b-4e65-9fbd-7c71928776e6 | a-methodology-to-identify-cognition-gaps-in | 2110.02080 | null | https://arxiv.org/abs/2110.02080v1 | https://arxiv.org/pdf/2110.02080v1.pdf | A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks | Developing consistently well performing visual recognition applications based on convolutional neural networks, e.g. for autonomous driving, is very challenging. One of the obstacles during the development is the opaqueness of their cognitive behaviour. A considerable amount of literature has been published which describes irrational behaviour of trained CNNs showcasing gaps in their cognition. In this paper, a methodology is presented that creates worstcase images using image augmentation techniques. If the CNN's cognitive performance on such images is weak while the augmentation techniques are supposedly harmless, a potential gap in the cognition has been found. The presented worst-case image generator is using adversarial search approaches to efficiently identify the most challenging image. This is evaluated with the well-known AlexNet CNN using images depicting a typical driving scenario. | ['Michael Weyrich', 'Nasser Jazdi', 'Andreas Löcklin', 'Tristan Rauch', 'Hannes Vietz'] | 2021-10-05 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.24596727e-01 9.15699124e-01 4.17899936e-01 -2.52504736e-01
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1d3acf15-8610-4697-bae9-e3944e1e7a13 | posecontrast-class-agnostic-object-viewpoint | 2105.05643 | null | https://arxiv.org/abs/2105.05643v2 | https://arxiv.org/pdf/2105.05643v2.pdf | PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning | Motivated by the need for estimating the 3D pose of arbitrary objects, we consider the challenging problem of class-agnostic object viewpoint estimation from images only, without CAD model knowledge. The idea is to leverage features learned on seen classes to estimate the pose for classes that are unseen, yet that share similar geometries and canonical frames with seen classes. We train a direct pose estimator in a class-agnostic way by sharing weights across all object classes, and we introduce a contrastive learning method that has three main ingredients: (i) the use of pre-trained, self-supervised, contrast-based features; (ii) pose-aware data augmentations; (iii) a pose-aware contrastive loss. We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset fashion, with both seen and unseen classes. We report state-of-the-art results, including against methods that additionally use CAD models as input. | ['Renaud Marlet', 'Yuming Du', 'Yang Xiao'] | 2021-05-12 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 8.83458927e-02 1.44486532e-01 1.80291757e-02 -6.34434819e-01
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1bb6c117-5525-4795-a575-052b04ac7247 | 190910278 | 1909.10278 | null | https://arxiv.org/abs/1909.10278v1 | https://arxiv.org/pdf/1909.10278v1.pdf | Detection of Classifier Inconsistencies in Image Steganalysis | In this paper, a methodology to detect inconsistencies in classification-based image steganalysis is presented. The proposed approach uses two classifiers: the usual one, trained with a set formed by cover and stego images, and a second classifier trained with the set obtained after embedding additional random messages into the original training set. When the decisions of these two classifiers are not consistent, we know that the prediction is not reliable. The number of inconsistencies in the predictions of a testing set may indicate that the classifier is not performing correctly in the testing scenario. This occurs, for example, in case of cover source mismatch, or when we are trying to detect a steganographic method that the classifier is no capable of modelling accurately. We also show how the number of inconsistencies can be used to predict the reliability of the classifier (classification errors). | ['David Megías', 'Daniel Lerch-Hostalot'] | 2019-09-23 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 8.83884072e-01 4.00655478e-01 1.78635791e-01 -4.93145064e-02
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0b0a3efc-f485-466b-bd3a-e1258ba50939 | traffsign-multilingual-traffic-signboard-text | null | null | https://link.springer.com/chapter/10.1007/978-3-031-06555-2_50 | https://link.springer.com/chapter/10.1007/978-3-031-06555-2_50 | TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and English | Scene-text detection and recognition methods have demonstrated remarkable performance on standard benchmark datasets. These methods can be utilized in human-driven/self-driving cars to perform navigation assistance through traffic signboard text detection and recognition. Existing datasets include scripts in numerous languages like English, Chinese, French, Arabic, German, etc. However, traffic navigation signboards in Pakistan and many states of India are written in Urdu along with the English translation to guide human drivers. To this end, we present Deep Learning Laboratory’s Traffic Signboards Dataset (DLL-TraffSiD) to develop multi-lingual text detection and recognition methods for traffic signboards. In addition, we present a pipeline for multi-lingual text detection and recognition for an outdoor road environment. The results show that our presented system signified better applicability in text-detection and text recognition, and achieved 89% and 92.18% accuracy on the proposed dataset (The proposed dataset along with implementation is available at https://github.com/aatiibutt/TraffSign/). | ['and Faisal Shafait', 'Adnan Ul-Hasan', 'Muhammad Atif Butt'] | 2022-05-18 | null | null | null | document-analysis-systems-2022-5 | ['scene-text-detection'] | ['computer-vision'] | [ 1.22300431e-01 -6.64125323e-01 -2.03433573e-01 -5.41616619e-01
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a0935a9e-03d2-4a5d-b3f9-bf96a7b69086 | forecasting-loss-of-signal-in-optical | 2201.07089 | null | https://arxiv.org/abs/2201.07089v2 | https://arxiv.org/pdf/2201.07089v2.pdf | Forecasting Loss of Signal in Optical Networks with Machine Learning | Loss of Signal (LOS) represents a significant cost for operators of optical networks. By studying large sets of real-world Performance Monitoring (PM) data collected from six international optical networks, we find that it is possible to forecast LOS events with good precision 1-7 days before they occur, albeit at relatively low recall, with supervised machine learning (ML). Our study covers twelve facility types, including 100G lines and ETH10G clients. We show that the precision for a given network improves when training on multiple networks simultaneously relative to training on an individual network. Furthermore, we show that it is possible to forecast LOS from all facility types and all networks with a single model, whereas fine-tuning for a particular facility or network only brings modest improvements. Hence our ML models remain effective for optical networks previously unknown to the model, which makes them usable for commercial applications. | ['David Cote', 'Yan Liu', 'Chris Barber', 'Wenjie Du'] | 2022-01-08 | null | null | null | null | ['classification-on-time-series-with-missing'] | ['time-series'] | [-1.50672689e-01 -4.54509519e-02 -5.71020067e-01 -4.37438488e-01
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9b9220bc-a427-44d2-a48a-67ecfbe6bf97 | ji-yu-kuang-jia-yu-yi-ying-she-he-lei-xing | null | null | https://aclanthology.org/2022.ccl-1.22 | https://aclanthology.org/2022.ccl-1.22.pdf | 基于框架语义映射和类型感知的篇章事件抽取(Document-Level Event Extraction Based on Frame Semantic Mapping and Type Awareness) | “篇章事件抽取是从给定的文本中识别其事件类型和事件论元。目前篇章事件普遍存在数据稀疏和多值论元耦合的问题。基于此,本文将汉语框架网(CFN)与中文篇章事件建立映射,同时引入滑窗机制和触发词释义改善了事件检测的数据稀疏问题;使用基于类型感知标签的多事件分离策略缓解了论元耦合问题。为了提升模型的鲁棒性,进一步引入对抗训练。本文提出的方法在DuEE-Fin和CCKS2021数据集上实验结果显著优于现有方法。” | ['Jiaxing Chen', 'Zhichao Yan', 'Xuefeng Su', 'Ru Li', 'Jiang Lu'] | null | null | null | null | ccl-2022-10 | ['event-extraction', 'document-level-event-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-6.74544752e-01 -1.17104435e+00 6.30947709e-01 5.67468703e-01
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8233e89d-e3e0-49f4-bfcc-21ba7c88ef2a | chasing-the-tail-in-monocular-3d-human | 2012.14739 | null | https://arxiv.org/abs/2012.14739v1 | https://arxiv.org/pdf/2012.14739v1.pdf | Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory | Deep neural networks have achieved great progress in single-image 3D human reconstruction. However, existing methods still fall short in predicting rare poses. The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses. In this work, we 1) identify and analyze this learning obstacle and 2) propose a prototype memory-augmented network, PM-Net, that effectively improves performances of predicting rare poses. The core of our framework is a memory module that learns and stores a set of 3D human prototypes capturing local distributions for either common poses or rare poses. With this formulation, the regression starts from a better initialization, which is relatively easier to converge. Extensive experiments on several widely employed datasets demonstrate the proposed framework's effectiveness compared to other state-of-the-art methods. Notably, our approach significantly improves the models' performances on rare poses while generating comparable results on other samples. | ['Chen Change Loy', 'Ziwei Liu', 'Yu Rong'] | 2020-12-29 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-2.28644431e-01 -1.66860312e-01 -4.45247442e-01 -1.77383497e-01
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e9fb1552-6191-4988-b1a3-1d437ed6e9c8 | fast-graspnext-a-fast-self-attention-neural | 2304.11196 | null | https://arxiv.org/abs/2304.11196v1 | https://arxiv.org/pdf/2304.11196v1.pdf | Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge | Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multi-task learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5x smaller), 259 GFLOPs (as much as >5x lower) and as much as >3.15x faster on a NVIDIA Jetson TX2 embedded processor. | ['Mohammad Javad Shafiee', 'Yuhao Chen', 'Saeejith Nair', 'Saad Abbasi', 'Yifan Wu', 'Alexander Wong'] | 2023-04-21 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.01105593e-01 1.87197141e-02 -1.38118878e-01 -3.18758219e-01
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e8261423-f6d7-4241-b554-fed0e1e8597a | topic-driven-and-knowledge-aware-transformer | 2106.01071 | null | https://arxiv.org/abs/2106.01071v1 | https://arxiv.org/pdf/2106.01071v1.pdf | Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection | Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories. | ['Yulan He', 'Deyu Zhou', 'Lin Gui', 'Gabriele Pergola', 'Lixing Zhu'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-long.125 | https://aclanthology.org/2021.acl-long.125.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.06608886e-01 2.66474217e-01 -1.23321332e-01 -6.54209912e-01
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36042faf-a76d-4d30-8942-3f5871858d8e | deformable-vistr-spatio-temporal-deformable | 2203.06318 | null | https://arxiv.org/abs/2203.06318v1 | https://arxiv.org/pdf/2203.06318v1.pdf | Deformable VisTR: Spatio temporal deformable attention for video instance segmentation | Video instance segmentation (VIS) task requires classifying, segmenting, and tracking object instances over all frames in a video clip. Recently, VisTR has been proposed as end-to-end transformer-based VIS framework, while demonstrating state-of-the-art performance. However, VisTR is slow to converge during training, requiring around 1000 GPU hours due to the high computational cost of its transformer attention module. To improve the training efficiency, we propose Deformable VisTR, leveraging spatio-temporal deformable attention module that only attends to a small fixed set of key spatio-temporal sampling points around a reference point. This enables Deformable VisTR to achieve linear computation in the size of spatio-temporal feature maps. Moreover, it can achieve on par performance as the original VisTR with 10$\times$ less GPU training hours. We validate the effectiveness of our method on the Youtube-VIS benchmark. Code is available at https://github.com/skrya/DefVIS. | ['Junsong Yuan', 'Yi Xu', 'Pan Ji', 'Jialian Wu', 'Sudhir Yarram'] | 2022-03-12 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-8.31966773e-02 -1.80730030e-01 -1.45101488e-01 -1.61525756e-01
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24bf8efc-da07-46af-9618-a18462314007 | tactile-image-to-image-disentanglement-of | 2109.03615 | null | https://arxiv.org/abs/2109.03615v1 | https://arxiv.org/pdf/2109.03615v1.pdf | Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear | Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the geometry of the stimulus. In this work, we propose a supervised convolutional deep neural network model that learns to disentangle, in the latent space, the components of sensor deformations caused by contact geometry from those due to sliding-induced shear. The approach is validated by reconstructing unsheared tactile images from sheared images and showing they match unsheared tactile images collected with no sliding motion. In addition, the unsheared tactile images give a faithful reconstruction of the contact geometry that is not possible from the sheared data, and robust estimation of the contact pose that can be used for servo control sliding around various 2D shapes. Finally, the contact geometry reconstruction in conjunction with servo control sliding were used for faithful full object reconstruction of various 2D shapes. The methods have broad applicability to deep learning models for robots with a shear-sensitive sense of touch. | ['Nathan F. Lepora', 'Laurence Aitchison', 'Anupam K. Gupta'] | 2021-09-08 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 5.66839576e-01 8.03738087e-02 1.32187620e-01 -1.99187398e-01
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207741f1-0ad0-4798-b0a2-c1a6703b8103 | are-you-robert-or-roberta-deceiving-online | 2203.09813 | null | https://arxiv.org/abs/2203.09813v1 | https://arxiv.org/pdf/2203.09813v1.pdf | Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators | Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, there have been many studies focused on online authorship attribution (AA). That is, the use of models to identify the authors of online texts. Given the power of natural language models in generating convincing texts, this paper examines the degree to which these language models can generate texts capable of deceiving online AA models. Experimenting with both blog and Twitter data, we utilise GPT-2 language models to generate texts using the existing posts of online users. We then examine whether these AI-based text generators are capable of mimicking authorial style to such a degree that they can deceive typical AA models. From this, we find that current AI-based text generators are able to successfully mimic authorship, showing capabilities towards this on both datasets. Our findings, in turn, highlight the current capacity of powerful natural language models to generate original online posts capable of mimicking authorial style sufficiently to deceive popular AA methods; a key finding given the proposed role of AA in real world applications such as spam-detection and forensic investigation. | ['Shujun Li', 'Jason R. C. Nurse', 'Keenan Jones'] | 2022-03-18 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.66399106e-01 7.64874518e-01 2.18471721e-01 1.67921588e-01
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618f57f0-528f-46a0-9bd0-f30c75fe01d4 | self-supervised-high-fidelity-and-re | 2111.08282 | null | https://arxiv.org/abs/2111.08282v2 | https://arxiv.org/pdf/2111.08282v2.pdf | Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image | Reconstructing high-fidelity 3D facial texture from a single image is a quite challenging task due to the lack of complete face information and the domain gap between the 3D face and 2D image. Further, obtaining re-renderable 3D faces has become a strongly desired property in many applications, where the term 're-renderable' demands the facial texture to be spatially complete and disentangled with environmental illumination. In this paper, we propose a new self-supervised deep learning framework for reconstructing high-quality and re-renderable facial albedos from single-view images in-the-wild. Our main idea is to first utilize a prior generation module based on the 3DMM proxy model to produce an unwrapped texture and a globally parameterized prior albedo. Then we apply a detail refinement module to synthesize the final texture with both high-frequency details and completeness. To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation which is reconstructed with the detailed albedo together. We also design several novel regularization losses on both the albedo and illumination maps to facilitate the disentanglement of these two factors. Finally, by leveraging a differentiable renderer, each face attribute can be jointly trained in a self-supervised manner without requiring ground-truth facial reflectance. Extensive comparisons and ablation studies on challenging datasets demonstrate that our framework outperforms state-of-the-art approaches. | ['Dong-Ming Yan', 'Xiaopeng Zhang', 'Zhanglin Cheng', 'Jianwei Guo', 'Mingxin Yang'] | 2021-11-16 | null | null | null | null | ['3d-face-reconstruction'] | ['computer-vision'] | [ 3.58472407e-01 5.45413941e-02 2.12749720e-01 -6.52281880e-01
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50406879-b7e2-4cd0-9495-84e76e0ea0ea | improved-churn-causal-analysis-through-1 | 2304.11503 | null | https://arxiv.org/abs/2304.11503v1 | https://arxiv.org/pdf/2304.11503v1.pdf | Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions | Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds. | ['Guandong Xu', 'Huan Huo', 'David Hason Rudd'] | 2023-04-23 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-2.44559839e-01 5.10050617e-02 -5.38051009e-01 -5.34113050e-01
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210d3e00-745b-4077-ad77-fdc51284082d | covost-2-a-massively-multilingual-speech-to | 2007.10310 | null | https://arxiv.org/abs/2007.10310v3 | https://arxiv.org/pdf/2007.10310v3.pdf | CoVoST 2 and Massively Multilingual Speech-to-Text Translation | Speech translation has recently become an increasingly popular topic of research, partly due to the development of benchmark datasets. Nevertheless, current datasets cover a limited number of languages. With the aim to foster research in massive multilingual speech translation and speech translation for low resource language pairs, we release CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. This represents the largest open dataset available to date from total volume and language coverage perspective. Data sanity checks provide evidence about the quality of the data, which is released under CC0 license. We also provide extensive speech recognition, bilingual and multilingual machine translation and speech translation baselines with open-source implementation. | ['Anne Wu', 'Juan Pino', 'Changhan Wang'] | 2020-07-20 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [-1.46331098e-02 -3.23464796e-02 -8.00700903e-01 -2.32710123e-01
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69a51256-ae47-4166-bc87-9040d89aeb60 | the-second-place-solution-for-eccv-2022 | 2211.13509 | null | https://arxiv.org/abs/2211.13509v2 | https://arxiv.org/pdf/2211.13509v2.pdf | The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge | This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. Our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. After using our C-BIoU for online tracking, we applied the offline refinement introduced by ReMOTS. | ['Shan Jiang', 'Shoichi Masui', 'Shigeyuki Odashima', 'Fan Yang'] | 2022-11-24 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-1.93543106e-01 -2.49560997e-01 6.36787042e-02 2.54765183e-01
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23572c1c-610a-4fae-bb0c-d6c855dda4b5 | real-time-action-recognition-for-fine-grained | 2210.07400 | null | https://arxiv.org/abs/2210.07400v1 | https://arxiv.org/pdf/2210.07400v1.pdf | Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset | In this paper we present a three-stream algorithm for real-time action recognition and a new dataset of handwash videos, with the intent of aligning action recognition with real-world constraints to yield effective conclusions. A three-stream fusion algorithm is proposed, which runs both accurately and efficiently, in real-time even on low-powered systems such as a Raspberry Pi. The cornerstone of the proposed algorithm is the incorporation of both spatial and temporal information, as well as the information of the objects in a video while using an efficient architecture, and Optical Flow computation to achieve commendable results in real-time. The results achieved by this algorithm are benchmarked on the UCF-101 as well as the HMDB-51 datasets, achieving an accuracy of 92.7% and 64.9% respectively. An important point to note is that the algorithm is novel in the aspect that it is also able to learn the intricate differences between extremely similar actions, which would be difficult even for the human eye. Additionally, noticing a dearth in the number of datasets for the recognition of very similar or fine-grained actions, this paper also introduces a new dataset that is made publicly available, the Hand Wash Dataset with the intent of introducing a new benchmark for fine-grained action recognition tasks in the future. | ['Gowri Srinivasa', 'Chetna Sureka', 'Mukund Sood', 'Akash Nagaraj'] | 2022-10-13 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.55842352e-01 -4.87188578e-01 2.15092003e-02 -1.17399424e-01
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5e5f2105-afac-4e69-a675-6701f37f929f | informed-machine-learning-centrality-cnn | 2303.14475 | null | https://arxiv.org/abs/2303.14475v1 | https://arxiv.org/pdf/2303.14475v1.pdf | Informed Machine Learning, Centrality, CNN, Relevant Document Detection, Repatriation of Indigenous Human Remains | Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora. | ['Cressida Fforde', 'Paul Turnbull', 'Gareth Knapman', 'Richi Nayak', 'Md Abul Bashar'] | 2023-03-25 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.79236609e-01 2.15466261e-01 -3.11564982e-01 -1.33070216e-01
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6398ff67-5bb5-4566-b877-75bba48763ad | boba-byzantine-robust-federated-learning-with | 2208.12932 | null | https://arxiv.org/abs/2208.12932v1 | https://arxiv.org/pdf/2208.12932v1.pdf | BOBA: Byzantine-Robust Federated Learning with Label Skewness | In federated learning, most existing techniques for robust aggregation against Byzantine attacks are designed for the IID setting, i.e., the data distributions for clients are independent and identically distributed. In this paper, we address label skewness, a more realistic and challenging non-IID setting, where each client only has access to a few classes of data. In this setting, state-of-the-art techniques suffer from selection bias, leading to significant performance drop for particular classes; they are also more vulnerable to Byzantine attacks due to the increased deviation among gradients of honest clients. To address these limitations, we propose an efficient two-stage method named BOBA. Theoretically, we prove the convergence of BOBA with an error of optimal order. Empirically, we verify the superior unbiasedness and robustness of BOBA across a wide range of models and data sets against various baselines. | ['Jingrui He', 'Wenxuan Bao'] | 2022-08-27 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-2.20199004e-01 -3.13828707e-01 -2.96392202e-01 -3.16843659e-01
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639f6105-2b34-4f9d-a493-457877092bad | sts-surround-view-temporal-stereo-for-multi | 2208.10145 | null | https://arxiv.org/abs/2208.10145v1 | https://arxiv.org/pdf/2208.10145v1.pdf | STS: Surround-view Temporal Stereo for Multi-view 3D Detection | Learning accurate depth is essential to multi-view 3D object detection. Recent approaches mainly learn depth from monocular images, which confront inherent difficulties due to the ill-posed nature of monocular depth learning. Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning. Specifically, we regard the field of views from all cameras around the ego vehicle as a unified view, namely surroundview, and conduct temporal stereo matching on it. The resulting geometrical correspondence between different frames from STS is utilized and combined with the monocular depth to yield final depth prediction. Comprehensive experiments on nuScenes show that STS greatly boosts 3D detection ability, notably for medium and long distance objects. On BEVDepth with ResNet-50 backbone, STS improves mAP and NDS by 2.6% and 1.4%, respectively. Consistent improvements are observed when using a larger backbone and a larger image resolution, demonstrating its effectiveness | ['Di Huang', 'Hongyu Yang', 'Zeming Li', 'Yinhao Li', 'Zheng Ge', 'Chen Min', 'Zengran Wang'] | 2022-08-22 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-6.55172691e-02 -3.20011020e-01 -2.67861009e-01 -2.77497709e-01
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79ab9bc0-54aa-4188-90c4-df3ae7ef6ffe | masked-bayesian-neural-networks-theoretical | 2305.14765 | null | https://arxiv.org/abs/2305.14765v1 | https://arxiv.org/pdf/2305.14765v1.pdf | Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference | Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of having better generalization ability as well as better uncertainty quantification. For the success of BNN, search an appropriate architecture of the neural networks is an important task, and various algorithms to find good sparse neural networks have been proposed. In this paper, we propose a new node-sparse BNN model which has good theoretical properties and is computationally feasible. We prove that the posterior concentration rate to the true model is near minimax optimal and adaptive to the smoothness of the true model. In particular the adaptiveness is the first of its kind for node-sparse BNNs. In addition, we develop a novel MCMC algorithm which makes the Bayesian inference of the node-sparse BNN model feasible in practice. | ['Yongdai Kim', 'Gyuseung Baek', 'Ilsang Ohn', 'Jongjin Lee', 'Dongyoon Yang', 'Insung Kong'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.53574552e-02 6.49137050e-02 -2.26725072e-01 -5.91114998e-01
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b72bf202-131a-4e85-9d50-f559d2293398 | self-supervised-document-clustering-based-on | 2011.08523 | null | https://arxiv.org/abs/2011.08523v3 | https://arxiv.org/pdf/2011.08523v3.pdf | Self-supervised Document Clustering Based on BERT with Data Augment | Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers, we propose self-supervised contrastive learning (SCL) as well as few-shot contrastive learning (FCL) with unsupervised data augmentation (UDA) for text clustering. SCL outperforms state-of-the-art unsupervised clustering approaches for short texts and those for long texts in terms of several clustering evaluation measures. FCL achieves performance close to supervised learning, and FCL with UDA further improves the performance for short texts. | ['Cen Wang', 'Haoxiang Shi'] | 2020-11-17 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-1.28422186e-01 -2.62450185e-02 -8.64849091e-02 -4.82233256e-01
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d9c08670-e88d-4f02-a791-4bc2290985c0 | deep-1d-convnet-for-accurate-parkinson | 1910.11509 | null | https://arxiv.org/abs/1910.11509v4 | https://arxiv.org/pdf/1910.11509v4.pdf | Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait | Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians. In this context, the use of an intelligent gait analysis algorithm may assist physicians in order to facilitate the diagnosis process. This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information. We used 1D convolutional neural network (1D-Convnet) to build a Deep Neural Network (DNN) classifier. The proposed model processes 18 1D-signals coming from foot sensors measuring the vertical ground reaction force (VGRF). The first part of the network consists of 18 parallel 1D-Convnet corresponding to system inputs. The second part is a fully connected network that connects the concatenated outputs of the 1D-Convnets to obtain a final classification. We tested our algorithm in Parkinson's detection and in the prediction of the severity of the disease with the Unified Parkinson's Disease Rating Scale (UPDRS). Our experiments demonstrate the high efficiency of the proposed method in the detection of Parkinson disease based on gait data. The proposed algorithm achieved an accuracy of 98.7 %. To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition. Furthermore, we achieved an accuracy of 85.3 % in Parkinson's severity prediction. To the best of our knowledge, this is the first algorithm to perform a severity prediction based on the UPDRS. Our results show that the model is able to learn intrinsic characteristics from gait data and to generalize to unseen subjects, which could be helpful in a clinical diagnosis. | ['Guillaume-Alexandre Bilodeau', 'Imanne El Maachi', 'Wassim Bouachir'] | 2019-10-25 | deep-1d-convnet-for-accurate-parkinson-1 | null | null | expert-systems-with-applications-2020-4 | ['severity-prediction'] | ['computer-vision'] | [-9.49036479e-02 -8.30944330e-02 -6.52295724e-02 -1.87159047e-01
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f081a4b1-a650-4b2a-b9f5-c58874678609 | august-an-automatic-generation-understudy-for | 2306.09631 | null | https://arxiv.org/abs/2306.09631v1 | https://arxiv.org/pdf/2306.09631v1.pdf | AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets | High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existing works contribute heavy human efforts to manually labeling or designing and extending recommender dialogue templates. However, they suffer from (i) the limited number of human annotators results in that datasets can hardly capture rich and large-scale cases in the real world, (ii) the limited experience and knowledge of annotators account for the uninformative corpus and inappropriate recommendations. In this paper, we propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues through a data2text generation process, where unstructured recommendation conversations are generated from structured graphs based on user-item information from the real world. In doing so, we comprehensively exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets. Extensive experiments validate the benefit brought by the automatically synthesized data under low-resource scenarios and demonstrate the promising potential to facilitate the development of a more effective conversational recommendation system. | ['Xiaodong He', 'Shuguang Cui', 'Youzheng Wu', 'Xiaoguang Han', 'Zichen Ma', 'Junwei Bao', 'Yu Lu'] | 2023-06-16 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [ 3.54599692e-02 4.93093669e-01 -1.65795967e-01 -3.14939290e-01
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676204f9-8979-43d1-813c-3820a1ee1ac1 | robust-product-classification-with-instance-1 | 2209.06946 | null | https://arxiv.org/abs/2209.06946v1 | https://arxiv.org/pdf/2209.06946v1.pdf | Robust Product Classification with Instance-Dependent Noise | Noisy labels in large E-commerce product data (i.e., product items are placed into incorrect categories) are a critical issue for product categorization task because they are unavoidable, non-trivial to remove and degrade prediction performance significantly. Training a product title classification model which is robust to noisy labels in the data is very important to make product classification applications more practical. In this paper, we study the impact of instance-dependent noise to performance of product title classification by comparing our data denoising algorithm and different noise-resistance training algorithms which were designed to prevent a classifier model from over-fitting to noise. We develop a simple yet effective Deep Neural Network for product title classification to use as a base classifier. Along with recent methods of stimulating instance-dependent noise, we propose a novel noise stimulation algorithm based on product title similarity. Our experiments cover multiple datasets, various noise methods and different training solutions. Results uncover the limit of classification task when noise rate is not negligible and data distribution is highly skewed. | ['Devashish Khatwani', 'Huy Nguyen'] | 2022-09-14 | robust-product-classification-with-instance | https://aclanthology.org/2022.ecnlp-1.20 | https://aclanthology.org/2022.ecnlp-1.20.pdf | ecnlp-acl-2022-5 | ['product-categorization'] | ['miscellaneous'] | [ 4.86821383e-01 -4.24868762e-01 -7.75444461e-03 -6.42059505e-01
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e90ebf6a-94c4-41fa-80aa-5795c8aec251 | exploring-multimodal-approaches-for-alzheimer | 2307.02514 | null | https://arxiv.org/abs/2307.02514v1 | https://arxiv.org/pdf/2307.02514v1.pdf | Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data | Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease. This study investigates various methods for detecting AD using patients' speech and transcripts data from the DementiaBank Pitt database. The proposed approach involves pre-trained language models and Graph Neural Network (GNN) that constructs a graph from the speech transcript, and extracts features using GNN for AD detection. Data augmentation techniques, including synonym replacement, GPT-based augmenter, and so on, were used to address the small dataset size. Audio data was also introduced, and WavLM model was used to extract audio features. These features were then fused with text features using various methods. Finally, a contrastive learning approach was attempted by converting speech transcripts back to audio and using it for contrastive learning with the original audio. We conducted intensive experiments and analysis on the above methods. Our findings shed light on the challenges and potential solutions in AD detection using speech and audio data. | ['Xiang Li', 'Tianming Liu', 'Quanzheng Li', 'Hui Ren', 'Dajiang Zhu', 'Zihao Wu', 'Haixing Dai', 'Wenxiong Liao', 'Zhengliang Liu', 'Xiaoke Huang', 'Hongmin Cai'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'alzheimer-s-disease-detection', 'contrastive-learning'] | ['computer-vision', 'medical', 'methodology'] | [ 4.75320667e-01 4.34296191e-01 1.16680376e-01 -4.64208037e-01
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c854b019-3eee-4787-88e0-299183584eeb | layered-depth-refinement-with-mask-guidance-1 | 2206.03048 | null | https://arxiv.org/abs/2206.03048v1 | https://arxiv.org/pdf/2206.03048v1.pdf | Layered Depth Refinement with Mask Guidance | Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtain, using commercial auto-masking tools or off-the-shelf methods of segmentation and matting or even by manual editing. Hence, in this paper, we formulate a novel problem of mask-guided depth refinement that utilizes a generic mask to refine the depth prediction of SIDE models. Our framework performs layered refinement and inpainting/outpainting, decomposing the depth map into two separate layers signified by the mask and the inverse mask. As datasets with both depth and mask annotations are scarce, we propose a self-supervised learning scheme that uses arbitrary masks and RGB-D datasets. We empirically show that our method is robust to different types of masks and initial depth predictions, accurately refining depth values in inner and outer mask boundary regions. We further analyze our model with an ablation study and demonstrate results on real applications. More information can be found at https://sooyekim.github.io/MaskDepth/ . | ['Munchurl Kim', 'Zhe Lin', 'Simon Chen', 'Yifei Fan', 'Simon Niklaus', 'Jianming Zhang', 'Soo Ye Kim'] | 2022-06-07 | layered-depth-refinement-with-mask-guidance | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-matting'] | ['computer-vision'] | [ 7.79950023e-01 4.02863383e-01 -8.70717019e-02 -3.20140779e-01
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44736dd2-0566-47ff-97be-dbfe36a534d2 | hiring-now-a-skill-aware-multi-attention | null | null | https://aclanthology.org/2020.acl-main.281 | https://aclanthology.org/2020.acl-main.281.pdf | Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation | Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per the company information and job description. To this end, we propose a novel task of Job Posting Generation (JPG) which is cast as a conditional text generation problem to generate job requirements according to the job descriptions. To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA. Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels. At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results. The proposed approach is evaluated on real-world job posting data. Experimental results clearly demonstrate the effectiveness of the proposed method. | ['YaLou Huang', 'Ziming Chi', 'Jie Liu', 'Wenzheng Zhang', 'Wenxuan Shi', 'Liting Liu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 7.66121209e-01 3.22324127e-01 4.42296006e-02 -4.98096198e-01
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6dffe8f4-95e2-417d-b05f-af2319bbfb0f | rotational-subgroup-voting-and-pose | 1709.02142 | null | http://arxiv.org/abs/1709.02142v1 | http://arxiv.org/pdf/1709.02142v1.pdf | Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition | It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to handle noisy and incomplete data. However, this correspondence set
is usually contaminated by outliers in practical scenarios, which has led to
many past contributions based on robust detectors such as the Hough transform
or RANSAC. The key insight of our work is that a single correspondence between
oriented points on the two models is constrained to cast votes in a 1 DoF
rotational subgroup of the full group of poses, SE(3). Kernel density
estimation allows combining the set of votes efficiently to determine a full 6
DoF candidate pose between the models. This modal pose with the highest density
is stable under challenging conditions, such as noise, clutter, and occlusions,
and provides the output estimate of our method.
We first analyze the robustness of our method in relation to noise and show
that it handles high outlier rates much better than RANSAC for the task of 6
DoF pose estimation. We then apply our method to four state of the art data
sets for 3D object recognition that contain occluded and cluttered scenes. Our
method achieves perfect recall on two LIDAR data sets and outperforms competing
methods on two RGB-D data sets, thus setting a new standard for general 3D
object recognition using point cloud data. | ['Dirk Kraft', 'Anders Glent Buch', 'Lilita Kiforenko'] | 2017-09-07 | rotational-subgroup-voting-and-pose-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Buch_Rotational_Subgroup_Voting_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Buch_Rotational_Subgroup_Voting_ICCV_2017_paper.pdf | iccv-2017-10 | ['3d-object-recognition'] | ['computer-vision'] | [-1.55906649e-02 -2.77097523e-01 -1.41875565e-01 -1.91430494e-01
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c5d86a25-1d48-44d3-af89-f0fdfed5ee40 | apes-audiovisual-person-search-in-untrimmed | 2106.01667 | null | https://arxiv.org/abs/2106.01667v1 | https://arxiv.org/pdf/2106.01667v1.pdf | APES: Audiovisual Person Search in Untrimmed Video | Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person reidentification and retrieval domains, few works have developed audiovisual search strategies. In this paper, we present the Audiovisual Person Search dataset (APES), a new dataset composed of untrimmed videos whose audio (voices) and visual (faces) streams are densely annotated. APES contains over 1.9K identities labeled along 36 hours of video, making it the largest dataset available for untrimmed audiovisual person search. A key property of APES is that it includes dense temporal annotations that link faces to speech segments of the same identity. To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval. Our study shows that modeling audiovisual cues benefits the recognition of people's identities. To enable reproducibility and promote future research, the dataset annotations and baseline code are available at: https://github.com/fuankarion/audiovisual-person-search | ['Fabian Caba Heilbron', 'Bernard Ghanem', 'Pablo Arbelaez', 'Joon-Young Lee', 'Federico Perazzi', 'Long Mai', 'Juan Leon Alcazar'] | 2021-06-03 | null | null | null | null | ['person-retrieval', 'person-search'] | ['computer-vision', 'computer-vision'] | [-0.03824044 -0.32337388 -0.20153074 -0.33802474 -0.890442 -0.80429494
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3ec24ea5-b07c-4638-90a5-ed127d3cd22b | scene-relighting-with-illumination-estimation | 2006.02333 | null | https://arxiv.org/abs/2006.02333v1 | https://arxiv.org/pdf/2006.02333v1.pdf | Scene relighting with illumination estimation in the latent space on an encoder-decoder scheme | The image relighting task of transferring illumination conditions between two images offers an interesting and difficult challenge with potential applications in photography, cinematography and computer graphics. In this report we present methods that we tried to achieve that goal. Our models are trained on a rendered dataset of artificial locations with varied scene content, light source location and color temperature. With this dataset, we used a network with illumination estimation component aiming to infer and replace light conditions in the latent space representation of the concerned scenes. | ['Jakub Jan Gwizdała', 'Martin Nicolas Everaert', 'Alexandre Pierre Dherse'] | 2020-06-03 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 5.48424125e-01 -1.59539610e-01 3.69829714e-01 -5.68665504e-01
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811edacc-87b5-4eaf-8f08-aa87d61cc905 | a-meta-probabilistic-programming-language-for | 2203.15970 | null | https://arxiv.org/abs/2203.15970v3 | https://arxiv.org/pdf/2203.15970v3.pdf | A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems | We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded. We are motivated here by the desire to allow an AGI to learn not only relevant knowledge (programs/proofs), but also appropriate ways of reasoning (logics/type systems). We draw on the frameworks of cubical type theory and dependent typed metagraphs to formalize our approach. In doing so, we show that specific constructions within the meta-language can be related via bisimulation (implying path equivalence) to the type systems they correspond. This allows our approach to provide a convenient means of deriving synthetic denotational semantics for various type systems. Particularly, we derive bisimulations for pure type systems (PTS), and probabilistic dependent type systems (PDTS). We discuss further the relationship of PTS to non-well-founded set theory, and demonstrate the feasibility of our approach with an implementation of a bisimulation proof in a Guarded Cubical Type Theory type checker. | ['Ben Goertzel', 'Adam Vandervorst', 'Alexey Potapov', 'Jonathan Warrell'] | 2022-03-30 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 3.91175151e-02 4.48343396e-01 -8.51327479e-02 -2.50614762e-01
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3a42a94f-aab0-4f11-ad6a-69a5f8dceba9 | two-stream-network-for-sign-language | 2211.01367 | null | https://arxiv.org/abs/2211.01367v2 | https://arxiv.org/pdf/2211.01367v2.pdf | Two-Stream Network for Sign Language Recognition and Translation | Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existing approaches directly encode RGB videos into hidden representations. RGB videos, however, are raw signals with substantial visual redundancy, leading the encoder to overlook the key information for sign language understanding. To mitigate this problem and better incorporate domain knowledge, such as handshape and body movement, we introduce a dual visual encoder containing two separate streams to model both the raw videos and the keypoint sequences generated by an off-the-shelf keypoint estimator. To make the two streams interact with each other, we explore a variety of techniques, including bidirectional lateral connection, sign pyramid network with auxiliary supervision, and frame-level self-distillation. The resulting model is called TwoStream-SLR, which is competent for sign language recognition (SLR). TwoStream-SLR is extended to a sign language translation (SLT) model, TwoStream-SLT, by simply attaching an extra translation network. Experimentally, our TwoStream-SLR and TwoStream-SLT achieve state-of-the-art performance on SLR and SLT tasks across a series of datasets including Phoenix-2014, Phoenix-2014T, and CSL-Daily. Code and models are available at: https://github.com/FangyunWei/SLRT. | ['Brian Mak', 'Shujie Liu', 'Yu Wu', 'Fangyun Wei', 'Ronglai Zuo', 'Yutong Chen'] | 2022-11-02 | null | null | null | null | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 1.12448208e-01 -1.57819420e-01 -4.23223794e-01 -4.16407585e-01
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fceb7e3b-cffb-43a8-b82d-2980947eab39 | andi-at-semeval-2021-task-1-predicting | null | null | https://aclanthology.org/2021.semeval-1.84 | https://aclanthology.org/2021.semeval-1.84.pdf | ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources | In this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context. Our approach relies on a combination of distributional models, both context-dependent and context-independent, together with behavioural norms and lexical resources. | ['Armand Rotaru'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-3.14397961e-01 -2.08735704e-01 -1.82910711e-01 -6.41974807e-01
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659331cf-5cc0-43e2-8ccd-52ab6380ff9c | fusion-of-detected-objects-in-text-for-visual | 1908.05054 | null | https://arxiv.org/abs/1908.05054v2 | https://arxiv.org/pdf/1908.05054v2.pdf | Fusion of Detected Objects in Text for Visual Question Answering | To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The "Bounding Boxes in Text Transformer" (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark (https://visualcommonsense.com), achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided (https://github.com/google-research/language/tree/master/language/question_answering/b2t2). | ['Michael Collins', 'Chris Alberti', 'Jeffrey Ling', 'David Reitter'] | 2019-08-14 | fusion-of-detected-objects-in-text-for-visual-1 | https://aclanthology.org/D19-1219 | https://aclanthology.org/D19-1219.pdf | ijcnlp-2019-11 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 1.11505133e-03 1.24009758e-01 -7.58841038e-02 -3.88833582e-01
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9e4ca1a8-a43a-41c5-8163-e422acee888f | twitter-topic-summarization-by-ranking-tweets | null | null | https://aclanthology.org/C12-1047 | https://aclanthology.org/C12-1047.pdf | Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality | null | ['Heung-Yeung Shum', 'Furu Wei', 'Yajuan Duan', 'Ming Zhou', 'Zhumin Chen'] | 2012-12-01 | twitter-topic-summarization-by-ranking-tweets-1 | https://aclanthology.org/C12-1047 | https://aclanthology.org/C12-1047.pdf | coling-2012-12 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d4fba237-0b5e-4013-83bf-484bbacdfffc | scalable-spectral-clustering-using-random | 1805.11048 | null | https://arxiv.org/abs/1805.11048v3 | https://arxiv.org/pdf/1805.11048v3.pdf | Scalable Spectral Clustering Using Random Binning Features | Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods have been proposed to accelerate spectral clustering, most of them compromise considerable information loss in the original data for reducing computational bottlenecks. In this paper, we present a novel scalable spectral clustering method using Random Binning features (RB) to simultaneously accelerate both similarity graph construction and the eigendecomposition. Specifically, we implicitly approximate the graph similarity (kernel) matrix by the inner product of a large sparse feature matrix generated by RB. Then we introduce a state-of-the-art SVD solver to effectively compute eigenvectors of this large matrix for spectral clustering. Using these two building blocks, we reduce the computational cost from quadratic to linear in the number of data points while achieving similar accuracy. Our theoretical analysis shows that spectral clustering via RB converges faster to the exact spectral clustering than the standard Random Feature approximation. Extensive experiments on 8 benchmarks show that the proposed method either outperforms or matches the state-of-the-art methods in both accuracy and runtime. Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features. | ['Ian En-Hsu Yen', 'Pin-Yu Chen', 'Lingfei Wu', 'Charu Aggarwal', 'Fangli Xu', 'Yinglong Xia'] | 2018-05-25 | null | null | null | null | ['imagedocument-clustering', 'graph-similarity'] | ['computer-vision', 'graphs'] | [-3.00314724e-01 -4.91502345e-01 4.19921987e-02 -1.26264364e-01
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e2c33079-b1e3-4606-b7b8-fa64e458d9a4 | generative-adversarial-network-for-future | 2203.11305 | null | https://arxiv.org/abs/2203.11305v2 | https://arxiv.org/pdf/2203.11305v2.pdf | Generative Adversarial Network for Future Hand Segmentation from Egocentric Video | We introduce the novel problem of anticipating a time series of future hand masks from egocentric video. A key challenge is to model the stochasticity of future head motions, which globally impact the head-worn camera video analysis. To this end, we propose a novel deep generative model -- EgoGAN, which uses a 3D Fully Convolutional Network to learn a spatio-temporal video representation for pixel-wise visual anticipation, generates future head motion using Generative Adversarial Network (GAN), and then predicts the future hand masks based on the video representation and the generated future head motion. We evaluate our method on both the EPIC-Kitchens and the EGTEA Gaze+ datasets. We conduct detailed ablation studies to validate the design choices of our approach. Furthermore, we compare our method with previous state-of-the-art methods on future image segmentation and show that our method can more accurately predict future hand masks. | ['James M. Rehg', 'Miao Liu', 'Wenqi Jia'] | 2022-03-21 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 1.72676325e-01 3.29762697e-01 2.32219383e-01 -5.73834777e-01
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0cd2e63d-0990-4551-bf7c-3708ad86e495 | learning-to-push-the-limits-of-efficient-fft | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Kruse_Learning_to_Push_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Kruse_Learning_to_Push_ICCV_2017_paper.pdf | Learning to Push the Limits of Efficient FFT-Based Image Deconvolution | This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize existing discriminative approaches by using more powerful regularization, based on convolutional neural networks. Additionally, we propose a simple, yet effective, boundary adjustment method that alleviates the problematic circular convolution assumption, which is necessary for FFT-based deconvolution. We evaluate our approach on two common non-blind deconvolution benchmarks and achieve state-of-the-art results even when including methods which are computationally considerably more expensive.
| ['Uwe Schmidt', 'Jakob Kruse', 'Carsten Rother'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['image-deconvolution'] | ['computer-vision'] | [ 2.58634925e-01 -3.53983551e-01 2.00459555e-01 -1.47254646e-01
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31e24640-e74f-49f2-9a67-59e75c228862 | learning-word-embeddings-without-context | null | null | https://aclanthology.org/W19-4329 | https://aclanthology.org/W19-4329.pdf | Learning Word Embeddings without Context Vectors | Most word embedding algorithms such as word2vec or fastText construct two sort of vectors: for words and for contexts. Naive use of vectors of only one sort leads to poor results. We suggest using indefinite inner product in skip-gram negative sampling algorithm. This allows us to use only one sort of vectors without loss of quality. Our {``}context-free{''} cf algorithm performs on par with SGNS on word similarity datasets | ['Evgenia Elistratova', 'Alexey Zobnin'] | 2019-08-01 | null | null | null | ws-2019-8 | ['learning-word-embeddings'] | ['methodology'] | [-4.92075179e-03 -1.88945960e-02 -2.83940226e-01 -3.64893466e-01
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16a050d2-d5f0-4e42-a0f9-a3a5a849a865 | pushing-the-boundaries-of-audiovisual-word | 1811.01194 | null | http://arxiv.org/abs/1811.01194v1 | http://arxiv.org/pdf/1811.01194v1.pdf | Pushing the boundaries of audiovisual word recognition using Residual Networks and LSTMs | Visual and audiovisual speech recognition are witnessing a renaissance which
is largely due to the advent of deep learning methods. In this paper, we
present a deep learning architecture for lipreading and audiovisual word
recognition, which combines Residual Networks equipped with spatiotemporal
input layers and Bidirectional LSTMs. The lipreading architecture attains
11.92% misclassification rate on the challenging Lipreading-In-The-Wild
database, which is composed of excerpts from BBC-TV, each containing one of the
500 target words. Audiovisual experiments are performed using both intermediate
and late integration, as well as several types and levels of environmental
noise, and notable improvements over the audio-only network are reported, even
in the case of clean speech. A further analysis on the utility of target word
boundaries is provided, as well as on the capacity of the network in modeling
the linguistic context of the target word. Finally, we examine difficult word
pairs and discuss how visual information helps towards attaining higher
recognition accuracy. | ['Georgios Tzimiropoulos', 'Themos Stafylakis', 'Muhammad Haris Khan'] | 2018-11-03 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.45685115e-01 6.06833212e-03 -1.98578879e-01 7.25364238e-02
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68320548-0b44-4542-999e-4ca128e2239c | can-self-supervised-neural-networks-pre | 2305.14035 | null | https://arxiv.org/abs/2305.14035v3 | https://arxiv.org/pdf/2305.14035v3.pdf | Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers? | Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is not limited to modeling human speech alone. Building on this understanding, this paper explores the cross-transferability of SSL neural representations learned from human speech to analyze bio-acoustic signals. We conduct a caller discrimination analysis and a caller detection study on Marmoset vocalizations using eleven SSL models pre-trained with various pretext tasks. The results show that the embedding spaces carry meaningful caller information and can successfully distinguish the individual identities of Marmoset callers without fine-tuning. This demonstrates that representations pre-trained on human speech can be effectively applied to the bio-acoustics domain, providing valuable insights for future investigations in this field. | ['Mathew Magimai. -Doss', 'Eklavya Sarkar'] | 2023-05-23 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 4.77740556e-01 1.53219223e-01 4.07450408e-01 -5.76110184e-01
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2f285ca2-8ad8-4d72-9b18-3c0c0d40ffd8 | all-are-worth-words-a-vit-backbone-for-score | 2209.12152 | null | https://arxiv.org/abs/2209.12152v4 | https://arxiv.org/pdf/2209.12152v4.pdf | All are Worth Words: A ViT Backbone for Diffusion Models | Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion models. We design a simple and general ViT-based architecture (named U-ViT) for image generation with diffusion models. U-ViT is characterized by treating all inputs including the time, condition and noisy image patches as tokens and employing long skip connections between shallow and deep layers. We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size. In particular, latent diffusion models with U-ViT achieve record-breaking FID scores of 2.29 in class-conditional image generation on ImageNet 256x256, and 5.48 in text-to-image generation on MS-COCO, among methods without accessing large external datasets during the training of generative models. Our results suggest that, for diffusion-based image modeling, the long skip connection is crucial while the down-sampling and up-sampling operators in CNN-based U-Net are not always necessary. We believe that U-ViT can provide insights for future research on backbones in diffusion models and benefit generative modeling on large scale cross-modality datasets. | ['Jun Zhu', 'Hang Su', 'Yue Cao', 'Kaiwen Xue', 'Shen Nie', 'Chongxuan Li', 'Fan Bao'] | 2022-09-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 2.76914567e-01 3.01171243e-01 -2.40416657e-02 -1.61489695e-01
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ae269813-ab62-4101-9ebb-aa6f76d351ed | beta-embeddings-for-multi-hop-logical | 2010.11465 | null | https://arxiv.org/abs/2010.11465v1 | https://arxiv.org/pdf/2010.11465v1.pdf | Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs | One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop logical reasoning over the facts captured by a knowledge graph (KG). This problem is challenging, because KGs can be massive and incomplete. Recent approaches embed KG entities in a low dimensional space and then use these embeddings to find the answer entities. However, it has been an outstanding challenge of how to handle arbitrary first-order logic (FOL) queries as present methods are limited to only a subset of FOL operators. In particular, the negation operator is not supported. An additional limitation of present methods is also that they cannot naturally model uncertainty. Here, we present BetaE, a probabilistic embedding framework for answering arbitrary FOL queries over KGs. BetaE is the first method that can handle a complete set of first-order logical operations: conjunction ($\wedge$), disjunction ($\vee$), and negation ($\neg$). A key insight of BetaE is to use probabilistic distributions with bounded support, specifically the Beta distribution, and embed queries/entities as distributions, which as a consequence allows us to also faithfully model uncertainty. Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings. We demonstrate the performance of BetaE on answering arbitrary FOL queries on three large, incomplete KGs. While being more general, BetaE also increases relative performance by up to 25.4% over the current state-of-the-art KG reasoning methods that can only handle conjunctive queries without negation. | ['Jure Leskovec', 'Hongyu Ren'] | 2020-10-22 | null | http://proceedings.neurips.cc/paper/2020/hash/e43739bba7cdb577e9e3e4e42447f5a5-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e43739bba7cdb577e9e3e4e42447f5a5-Paper.pdf | neurips-2020-12 | ['complex-query-answering'] | ['knowledge-base'] | [-3.58958185e-01 5.17234504e-01 -2.63885140e-01 -4.94419336e-01
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a6075b3e-cdf6-428d-b6e3-bd30f860c5c1 | 3d-solid-spherical-bispectrum-cnns-for | 2004.13371 | null | https://arxiv.org/abs/2004.13371v2 | https://arxiv.org/pdf/2004.13371v2.pdf | 3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis | Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can be obtained by computing the responses to the discrete rotation of local descriptors, such as Local Binary Patterns (LBP) or the Scale Invariant Feature Transform (SIFT). Other strategies achieve this invariance using Laplacian of Gaussian or steerable wavelets for instance, preventing the introduction of sampling errors during the discretization of the rotations. In this work, we obtain LRI operators via the local projection of the image on the spherical harmonics basis, followed by the computation of the bispectrum, which shares and extends the invariance properties of the spectrum. We investigate the benefits of using the bispectrum over the spectrum in the design of a LRI layer embedded in a shallow Convolutional Neural Network (CNN) for 3D image analysis. The performance of each design is evaluated on two datasets and compared against a standard 3D CNN. The first dataset is made of 3D volumes composed of synthetically generated rotated patterns, while the second contains malignant and benign pulmonary nodules in Computed Tomography (CT) images. The results indicate that bispectrum CNNs allows for a significantly better characterization of 3D textures than both the spectral and standard CNN. In addition, it can efficiently learn with fewer training examples and trainable parameters when compared to a standard convolutional layer. | ['Julien Fageot', 'Valentin Oreiller', 'Adrien Depeursinge', 'Vincent Andrearczyk', 'John O. Prior'] | 2020-04-28 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 4.33514267e-01 -2.35001385e-01 -5.73835224e-02 -2.19966352e-01
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69dca4ed-3828-4b71-9244-d0d45b13cd40 | neumesh-learning-disentangled-neural-mesh | 2207.11911 | null | https://arxiv.org/abs/2207.11911v1 | https://arxiv.org/pdf/2207.11911v1.pdf | NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editing | Very recently neural implicit rendering techniques have been rapidly evolved and shown great advantages in novel view synthesis and 3D scene reconstruction. However, existing neural rendering methods for editing purposes offer limited functionality, e.g., rigid transformation, or not applicable for fine-grained editing for general objects from daily lives. In this paper, we present a novel mesh-based representation by encoding the neural implicit field with disentangled geometry and texture codes on mesh vertices, which facilitates a set of editing functionalities, including mesh-guided geometry editing, designated texture editing with texture swapping, filling and painting operations. To this end, we develop several techniques including learnable sign indicators to magnify spatial distinguishability of mesh-based representation, distillation and fine-tuning mechanism to make a steady convergence, and the spatial-aware optimization strategy to realize precise texture editing. Extensive experiments and editing examples on both real and synthetic data demonstrate the superiority of our method on representation quality and editing ability. Code is available on the project webpage: https://zju3dv.github.io/neumesh/. | ['Guofeng Zhang', 'Zhaopeng Cui', 'yinda zhang', 'Hujun Bao', 'Junyi Zeng', 'Chong Bao', 'Bangbang Yang'] | 2022-07-25 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.12382078e-01 -1.02857001e-01 6.84316531e-02 -4.30472076e-01
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3c18ad70-eb41-458d-987d-43b818191440 | covered-collaborative-robot-environment | 2302.12656 | null | https://arxiv.org/abs/2302.12656v2 | https://arxiv.org/pdf/2302.12656v2.pdf | COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentation | Safe human-robot collaboration (HRC) has recently gained a lot of interest with the emerging Industry 5.0 paradigm. Conventional robots are being replaced with more intelligent and flexible collaborative robots (cobots). Safe and efficient collaboration between cobots and humans largely relies on the cobot's comprehensive semantic understanding of the dynamic surrounding of industrial environments. Despite the importance of semantic understanding for such applications, 3D semantic segmentation of collaborative robot workspaces lacks sufficient research and dedicated datasets. The performance limitation caused by insufficient datasets is called 'data hunger' problem. To overcome this current limitation, this work develops a new dataset specifically designed for this use case, named "COVERED", which includes point-wise annotated point clouds of a robotic cell. Lastly, we also provide a benchmark of current state-of-the-art (SOTA) algorithm performance on the dataset and demonstrate a real-time semantic segmentation of a collaborative robot workspace using a multi-LiDAR system. The promising results from using the trained Deep Networks on a real-time dynamically changing situation shows that we are on the right track. Our perception pipeline achieves 20Hz throughput with a prediction point accuracy of $>$96\% and $>$92\% mean intersection over union (mIOU) while maintaining an 8Hz throughput. | ['Hans Wernher van de Venn', 'Davide Scaramuzza', 'Fatemeh Mohammadi Amin', 'Charith Munasinghe'] | 2023-02-24 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-5.92543706e-02 2.47250885e-01 4.35450137e-01 -4.14377600e-01
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b71d04cc-1bd4-448a-9fb1-d7af8044b672 | deep-neural-models-for-color-discrimination | 2012.14402 | null | https://arxiv.org/abs/2012.14402v1 | https://arxiv.org/pdf/2012.14402v1.pdf | Deep Neural Models for color discrimination and color constancy | Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance with varying cues. Inputs to the networks consisted of the cone excitations in 3D-rendered images of 2115 different 3D-shapes, with spectral reflectances of 1600 different Munsell chips, illuminated under 278 different natural illuminations. The models were trained to classify the reflectance of the objects. One network, Deep65, was trained under a fixed daylight D65 illumination, while DeepCC was trained under varying illuminations. Testing was done with 4 new illuminations with equally spaced CIEL*a*b* chromaticities, 2 along the daylight locus and 2 orthogonal to it. We found a high degree of color constancy for DeepCC, and constancy was higher along the daylight locus. When gradually removing cues from the scene, constancy decreased. High levels of color constancy were achieved with different DNN architectures. Both ResNets and classical ConvNets of varying degrees of complexity performed well. However, DeepCC, a convolutional network, represented colors along the 3 color dimensions of human color vision, while ResNets showed a more complex representation. | ['Karl R. Gegenfurtner', 'Felix A. Wichmann', 'Roland W. Fleming', 'Heiko H. Schütt', 'Arash Akbarinia', 'Alban Flachot'] | 2020-12-28 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 3.80473733e-02 -5.68661809e-01 3.75737309e-01 -2.72638530e-01
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6f393da3-61c4-4ff4-b9b6-e5798f60cd0b | pointhop-a-lightweight-learning-model-on | 2002.03281 | null | https://arxiv.org/abs/2002.03281v2 | https://arxiv.org/pdf/2002.03281v2.pdf | PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification | The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods. | ['C. -C. Jay Kuo', 'Yifan Wang', 'Shan Liu', 'Pranav Kadam', 'Min Zhang'] | 2020-02-09 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 7.74917798e-03 -2.36311436e-01 -2.33337834e-01 -3.67585152e-01
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c4eda250-e0ed-4739-ba83-044f458d32fc | divide-and-contrast-self-supervised-learning | 2105.08054 | null | https://arxiv.org/abs/2105.08054v1 | https://arxiv.org/pdf/2105.08054v1.pdf | Divide and Contrast: Self-supervised Learning from Uncurated Data | Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data such as ImageNet. We explore the effects of contrastive learning from larger, less-curated image datasets such as YFCC, and find there is indeed a large difference in the resulting representation quality. We hypothesize that this curation gap is due to a shift in the distribution of image classes -- which is more diverse and heavy-tailed -- resulting in less relevant negative samples to learn from. We test this hypothesis with a new approach, Divide and Contrast (DnC), which alternates between contrastive learning and clustering-based hard negative mining. When pretrained on less curated datasets, DnC greatly improves the performance of self-supervised learning on downstream tasks, while remaining competitive with the current state-of-the-art on curated datasets. | ['Aaron van den Oord', 'Olivier J. Henaff', 'Yonglong Tian'] | 2021-05-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Tian_Divide_and_Contrast_Self-Supervised_Learning_From_Uncurated_Data_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Tian_Divide_and_Contrast_Self-Supervised_Learning_From_Uncurated_Data_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.41304445e-01 3.32719207e-01 -3.96157742e-01 -5.15207171e-01
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2bb15a97-91ed-4079-b48a-9005f3bd5f2a | on-the-influence-of-masking-policies-in | 2104.08840 | null | https://arxiv.org/abs/2104.08840v2 | https://arxiv.org/pdf/2104.08840v2.pdf | On the Influence of Masking Policies in Intermediate Pre-training | Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline. Prior work has shown that inserting an intermediate pre-training stage, using heuristic masking policies for masked language modeling (MLM), can significantly improve final performance. However, it is still unclear (1) in what cases such intermediate pre-training is helpful, (2) whether hand-crafted heuristic objectives are optimal for a given task, and (3) whether a masking policy designed for one task is generalizable beyond that task. In this paper, we perform a large-scale empirical study to investigate the effect of various masking policies in intermediate pre-training with nine selected tasks across three categories. Crucially, we introduce methods to automate the discovery of optimal masking policies via direct supervision or meta-learning. We conclude that the success of intermediate pre-training is dependent on appropriate pre-train corpus, selection of output format (i.e., masked spans or full sentence), and clear understanding of the role that MLM plays for the downstream task. In addition, we find our learned masking policies outperform the heuristic of masking named entities on TriviaQA, and policies learned from one task can positively transfer to other tasks in certain cases, inviting future research in this direction. | ['Madian Khabsa', 'Xiang Ren', 'Wen-tau Yih', 'Hao Ma', 'Benjamin Bolte', 'Sinong Wang', 'Belinda Z. Li', 'Qinyuan Ye'] | 2021-04-18 | null | https://aclanthology.org/2021.emnlp-main.573 | https://aclanthology.org/2021.emnlp-main.573.pdf | emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [ 4.39933538e-01 2.47877195e-01 -2.93342829e-01 -5.41827261e-01
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44af1e59-7c61-4818-a031-8b17081b60ad | self-adaptive-matrix-completion-for-heart | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Tulyakov_Self-Adaptive_Matrix_Completion_CVPR_2016_paper.pdf | Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions | Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state-of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of-the-art HR estimation methods in naturalistic conditions. | ['Xavier Alameda-Pineda', 'Jeffrey F. Cohn', 'Elisa Ricci', 'Sergey Tulyakov', 'Lijun Yin', 'Nicu Sebe'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['heart-rate-estimation'] | ['medical'] | [ 2.90607154e-01 1.26426771e-01 -1.45498544e-01 -3.98014635e-01
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1a59a1b2-b958-43fb-b4f9-a159fdc9ca93 | hard-net-hardness-aware-discrimination | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1360_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560409.pdf | HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction | Predicting the class label from the partially observed activity sequence is a very hard task, as the observed early segments of different activities can be very similar. In this paper, we propose a novel Hardness-AwaRe Discrimination Network (HARD-Net) to specifically investigate the relationships between the similar activity pairs that are hard to be discriminated. Specifically, a Hard Instance-Interference Class (HI-IC) bank is designed, which dynamically records the hard similar pairs. Based on the HI-IC bank, a novel adversarial learning scheme is proposed to train our HARD-Net, which thus grants our network with the strong capability in mining subtle discrimination information for 3D early activity prediction. We evaluate our proposed HARD-Net on two public activity datasets and achieve state-of-the-art performance. | ['Ling-Yu Duan', 'Wei zhang', 'Tianjiao Li', 'Jun Liu'] | null | null | null | null | eccv-2020-8 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 6.21209741e-01 -1.45469718e-02 -5.39814413e-01 -3.82130891e-01
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3bfeb63f-c294-4400-b025-11abf1189281 | compositional-zero-shot-learning-via-fine | null | null | http://proceedings.neurips.cc/paper/2020/hash/e58cc5ca94270acaceed13bc82dfedf7-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e58cc5ca94270acaceed13bc82dfedf7-Paper.pdf | Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition | We develop a novel generative model for zero-shot learning to recognize fine-grained unseen classes without training samples. Our observation is that generating holistic features of unseen classes fails to capture every attribute needed to distinguish small differences among classes. We propose a feature composition framework that learns to extract attribute-based features from training samples and combines them to construct fine-grained features for unseen classes. Feature composition allows us to not only selectively compose features of unseen classes from only relevant training samples, but also obtain diversity among composed features via changing samples used for composition. In addition, instead of building a global feature of an unseen class, we use all attribute-based features to form a dense representation consisting of fine-grained attribute details. To recognize unseen classes, we propose a novel training scheme that uses a discriminative model to construct features that are subsequently used to train itself. Therefore, we directly train the discriminative model on composed features without learning separate generative models. We conduct experiments on four popular datasets of DeepFashion, AWA2, CUB, and SUN, showing that our method significantly improves the state of the art. | ['Ehsan Elhamifar', 'Dat Huynh'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.66348749e-01 -3.14846665e-01 2.34100297e-02 -7.38745332e-01
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6a89000f-605e-4efc-b751-2c2e73a85bfc | copent-estimating-copula-entropy-in-r | 2005.14025 | null | https://arxiv.org/abs/2005.14025v3 | https://arxiv.org/pdf/2005.14025v3.pdf | copent: Estimating Copula Entropy and Transfer Entropy in R | Statistical independence and conditional independence are two fundamental concepts in statistics and machine learning. Copula Entropy is a mathematical concept defined by Ma and Sun for multivariate statistical independence measuring and testing, and also proved to be closely related to conditional independence (or transfer entropy). As the unified framework for measuring both independence and causality, CE has been applied to solve several related statistical or machine learning problems, including association discovery, structure learning, variable selection, and causal discovery. The nonparametric methods for estimating copula entropy and transfer entropy were also proposed previously. This paper introduces copent, the R package which implements these proposed methods for estimating copula entropy and transfer entropy. The implementation detail of the package is introduced. Three examples with simulated data and real-world data on variable selection and causal discovery are also presented to demonstrate the usage of this package. The examples on variable selection and causal discovery show the strong ability of copent on testing (conditional) independence compared with the related packages. The copent package is available on the Comprehensive R Archive Network (CRAN) and also on GitHub at https://github.com/majianthu/copent. | ['Jian Ma'] | 2020-05-27 | null | null | null | null | ['mutual-information-estimation', 'statistical-independence-testing'] | ['methodology', 'methodology'] | [-4.50339288e-01 -2.41041169e-01 -4.29850638e-01 -4.37578261e-01
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01d2b5c0-1221-4536-a68e-7a876a522ed8 | corruption-is-not-all-bad-incorporating | 2010.06137 | null | https://arxiv.org/abs/2010.06137v1 | https://arxiv.org/pdf/2010.06137v1.pdf | Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring | Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this paper, we propose an unsupervised pre-training approach to capture discourse structure of essays in terms of coherence and cohesion that does not require any discourse parser or annotation. We introduce several types of token, sentence and paragraph-level corruption techniques for our proposed pre-training approach and augment masked language modeling pre-training with our pre-training method to leverage both contextualized and discourse information. Our proposed unsupervised approach achieves new state-of-the-art result on essay Organization scoring task. | ['Kentaro Inui', 'Hiroki Ouchi', 'Paul Reisert', 'Naoya Inoue', 'Farjana Sultana Mim'] | 2020-10-13 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 2.36991003e-01 4.74685580e-01 -4.42545980e-01 -5.03160000e-01
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e566690f-ea44-447c-8c4b-1d6109d084a5 | the-diabetic-buddy-a-diet-regulator | 2101.03203 | null | https://arxiv.org/abs/2101.03203v1 | https://arxiv.org/pdf/2101.03203v1.pdf | The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics | The prevalence of Diabetes mellitus (DM) in the Middle East is exceptionally high as compared to the rest of the world. In fact, the prevalence of diabetes in the Middle East is 17-20%, which is well above the global average of 8-9%. Research has shown that food intake has strong connections with the blood glucose levels of a patient. In this regard, there is a need to build automatic tools to monitor the blood glucose levels of diabetics and their daily food intake. This paper presents an automatic way of tracking continuous glucose and food intake of diabetics using off-the-shelf sensors and machine learning, respectively. Our system not only helps diabetics to track their daily food intake but also assists doctors to analyze the impact of the food in-take on blood glucose in real-time. For food recognition, we collected a large-scale Middle-Eastern food dataset and proposed a fusion-based framework incorporating several existing pre-trained deep models for Middle-Eastern food recognition. | ['Marwa Qaraqe', 'Amir Sohail', 'Kashif Ahmad', 'Muhammad Usman'] | 2021-01-08 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-2.86436945e-01 -4.04640645e-01 -6.45407796e-01 -8.04891706e-01
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d6ce39a4-f081-4d84-937c-81b7bd9f151f | icdar-2021-competition-on-integrated-circuit | 2107.05279 | null | https://arxiv.org/abs/2107.05279v1 | https://arxiv.org/pdf/2107.05279v1.pdf | ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment | With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of realistic text on chips datasets to act as a strong foundation. Hence, a text on chips dataset, ICText is used as the main target for the proposed Robust Reading Challenge on Integrated Circuit Text Spotting and Aesthetic Assessment (RRC-ICText) 2021 to encourage the research on this problem. Throughout the entire competition, we have received a total of 233 submissions from 10 unique teams/individuals. Details of the competition and submission results are presented in this report. | ['Lixin Fan', 'Yipeng Sun', 'Lianwen Jin', 'Chee Seng Chan', 'Yuliang Liu', 'Xinyu Wang', 'Yeong Khang Lee', 'Akmalul Khairi Bin Nazaruddin', 'Chun Chet Ng'] | 2021-07-12 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.43045121e-01 1.04381107e-01 2.12554246e-01 -3.36457103e-01
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ff3b4c20-ee56-4525-9a60-97586a82fdf5 | quantum-data-center-theories-and-applications | 2207.14336 | null | https://arxiv.org/abs/2207.14336v2 | https://arxiv.org/pdf/2207.14336v2.pdf | Quantum Data Center: Theories and Applications | In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for $T$-gate resources, QDC for multi-party private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers. | ['Liang Jiang', 'Connor T. Hann', 'Junyu Liu'] | 2022-07-28 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 8.58120471e-02 2.96557527e-02 -2.33749539e-01 -3.23997378e-01
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5cb74183-bb78-41bd-9fd6-dea19b359960 | a-system-for-real-time-interactive-analysis | 2001.01215 | null | https://arxiv.org/abs/2001.01215v2 | https://arxiv.org/pdf/2001.01215v2.pdf | A System for Real-Time Interactive Analysis of Deep Learning Training | Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before the training process starts. Each time a new information is desired, a cycle of stop-change-restart is required in the training process. These limitations make interactive exploration and diagnosis tasks difficult, imposing long tedious iterations during the model development. We present a new system that enables users to perform interactive queries on live processes generating real-time information that can be rendered in multiple formats on multiple surfaces in the form of several desired visualizations simultaneously. To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar. Our design achieves generality and extensibility by defining composable primitives which is a fundamentally different approach than is used by currently available systems. The open source implementation of our system is available as TensorWatch project at https://github.com/microsoft/tensorwatch. | ['Steven Drucker', 'Shital Shah', 'Roland Fernandez'] | 2020-01-05 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.67765240e-02 1.97967160e-02 4.05583590e-01 -4.89900529e-01
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86f72dff-b6ba-4a78-bc71-16479cb621d1 | from-motor-control-to-team-play-in-simulated | 2105.12196 | null | https://arxiv.org/abs/2105.12196v1 | https://arxiv.org/pdf/2105.12196v1.pdf | From Motor Control to Team Play in Simulated Humanoid Football | Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals defined on much longer timescales, and in terms of relations that extend far beyond the body itself, ultimately involving coordination with other agents. Recent research in artificial intelligence has shown the promise of learning-based approaches to the respective problems of complex movement, longer-term planning and multi-agent coordination. However, there is limited research aimed at their integration. We study this problem by training teams of physically simulated humanoid avatars to play football in a realistic virtual environment. We develop a method that combines imitation learning, single- and multi-agent reinforcement learning and population-based training, and makes use of transferable representations of behaviour for decision making at different levels of abstraction. In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds. We investigate the emergence of behaviours at different levels of abstraction, as well as the representations that underlie these behaviours using several analysis techniques, including statistics from real-world sports analytics. Our work constitutes a complete demonstration of integrated decision-making at multiple scales in a physically embodied multi-agent setting. See project video at https://youtu.be/KHMwq9pv7mg. | ['Nicolas Heess', 'Thore Graepel', 'Karl Tuyls', 'Brendan D. Tracey', 'Tuomas Haarnoja', 'Paul Muller', 'Markus Wulfmeier', 'H. Francis Song', 'Saran Tunyasuvunakool', 'Luke Marris', 'Leonard Hasenclever', 'Noah Y. Siegel', 'Abbas Abdolmaleki', 'Shayegan Omidshafiei', 'Yuval Tassa', 'Wojciech M. Czarnecki', 'Daniel Hennes', 'S. M. Ali Eslami', 'Josh Merel', 'Zhe Wang', 'Guy Lever', 'SiQi Liu'] | 2021-05-25 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 3.88673320e-02 9.28151682e-02 -1.22926950e-01 2.82936215e-01
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151fc439-8298-4179-88f8-1988b4c194c2 | loc-vae-learning-structurally-localized | 2210.00506 | null | https://arxiv.org/abs/2210.00506v1 | https://arxiv.org/pdf/2210.00506v1.pdf | Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval | Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the resulting low-dimensional representation must be associated with a neurologically interpretable region. We propose a localized variational autoencoder (Loc-VAE) that provides neuroanatomically interpretable low-dimensional representation from 3D brain MR images for clinical CBIR. Loc-VAE is based on $\beta$-VAE with the additional constraint that each dimension of the low-dimensional representation corresponds to a local region of the brain. The proposed Loc-VAE is capable of acquiring representation that preserves disease features and is highly localized, even under high-dimensional compression ratios (4096:1). The low-dimensional representation obtained by Loc-VAE improved the locality measure of each dimension by 4.61 points compared to naive $\beta$-VAE, while maintaining comparable brain reconstruction capability and information about the diagnosis of Alzheimer's disease. | ['Kenichi Oishi', 'Hitoshi Iyatomi', 'Yuto Onga', 'Kumpei Ikuta', 'Kei Nishimaki'] | 2022-10-02 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-1.09513260e-01 2.51829196e-02 -2.53128279e-02 -2.47799993e-01
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b672616f-5879-4d96-8888-d59cf740ef70 | a-joint-model-for-semantic-sequences-frames | null | null | https://aclanthology.org/K17-1019 | https://aclanthology.org/K17-1019.pdf | A Joint Model for Semantic Sequences: Frames, Entities, Sentiments | Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task. These events typically carry multiple aspects of semantics including actions, entities and emotions. Not only does each individual aspect contribute to the meaning of the story, so does the interaction among these aspects. Building on this intuition, we propose to jointly model important aspects of semantic knowledge {--} frames, entities and sentiments {--} via a semantic language model. We achieve this by first representing these aspects{'} semantic units at an appropriate level of abstraction and then using the resulting vector representations for each semantic aspect to learn a joint representation via a neural language model. We show that the joint semantic language model is of high quality and can generate better semantic sequences than models that operate on the word level. We further demonstrate that our joint model can be applied to story cloze test and shallow discourse parsing tasks with improved performance and that each semantic aspect contributes to the model. | ['Haoruo Peng', 'Snigdha Chaturvedi', 'Dan Roth'] | 2017-08-01 | null | null | null | conll-2017-8 | ['cloze-test'] | ['natural-language-processing'] | [ 4.18846428e-01 4.93365467e-01 -3.31042588e-01 -7.32692838e-01
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6f69ddb9-aacf-403e-b513-5de78511d389 | covfefe-a-computer-vision-approach-for | 1809.09293 | null | http://arxiv.org/abs/1809.09293v1 | http://arxiv.org/pdf/1809.09293v1.pdf | Covfefe: A Computer Vision Approach For Estimating Force Exertion | Cumulative exposure to repetitive and forceful activities may lead to
musculoskeletal injuries which not only reduce workers' efficiency and
productivity, but also affect their quality of life. Thus, widely accessible
techniques for reliable detection of unsafe muscle force exertion levels for
human activity is necessary for their well-being. However, measurement of force
exertion levels is challenging and the existing techniques pose a great
challenge as they are either intrusive, interfere with human-machine interface,
and/or subjective in the nature, thus are not scalable for all workers. In this
work, we use face videos and the photoplethysmography (PPG) signals to classify
force exertion levels of 0\%, 50\%, and 100\% (representing rest, moderate
effort, and high effort), thus providing a non-intrusive and scalable approach.
Efficient feature extraction approaches have been investigated, including
standard deviation of the movement of different landmarks of the face,
distances between peaks and troughs in the PPG signals. We note that the PPG
signals can be obtained from the face videos, thus giving an efficient
classification algorithm for the force exertion levels using face videos. Based
on the data collected from 20 subjects, features extracted from the face videos
give 90\% accuracy in classification among the 100\% and the combination of 0\%
and 50\% datasets. Further combining the PPG signals provide 81.7\% accuracy.
The approach is also shown to be robust to the correctly identify force level
when the person is talking, even though such datasets are not included in the
training. | ['Denny Yu', 'Mayank Gupta', 'Vaneet Aggarwal', 'Jae Joong Lee', 'Hamed Asadi'] | 2018-09-25 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 4.58483309e-01 -3.54128741e-02 -1.03331305e-01 1.36822656e-01
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1056fc67-5654-4139-88b0-6509a08f33f9 | deer-a-data-efficient-language-model-for | 2012.15283 | null | https://arxiv.org/abs/2012.15283v3 | https://arxiv.org/pdf/2012.15283v3.pdf | ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning | While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. We design self-supervised learning objectives to recover masked-out event and temporal indicators and to discriminate sentences from their corrupted counterparts (where event or temporal indicators got replaced). By further pre-training a PTLM with these objectives jointly, we reinforce its attention to event and temporal information, yielding enhanced capability on event temporal reasoning. This effective continual pre-training framework for event temporal reasoning (ECONET) improves the PTLMs' fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art performances in most of our downstream tasks. | ['Nanyun Peng', 'Xiang Ren', 'Rujun Han'] | 2020-12-30 | null | https://aclanthology.org/2021.emnlp-main.436 | https://aclanthology.org/2021.emnlp-main.436.pdf | emnlp-2021-11 | ['continual-pretraining'] | ['methodology'] | [ 3.04261059e-01 4.72468466e-01 -2.43325949e-01 -5.39505005e-01
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b4b0e855-0abd-4e03-8eff-1753ae197f3e | transducer-based-language-embedding-for | 2204.03888 | null | https://arxiv.org/abs/2204.03888v2 | https://arxiv.org/pdf/2204.03888v2.pdf | Transducer-based language embedding for spoken language identification | The acoustic and linguistic features are important cues for the spoken language identification (LID) task. Recent advanced LID systems mainly use acoustic features that lack the usage of explicit linguistic feature encoding. In this paper, we propose a novel transducer-based language embedding approach for LID tasks by integrating an RNN transducer model into a language embedding framework. Benefiting from the advantages of the RNN transducer's linguistic representation capability, the proposed method can exploit both phonetically-aware acoustic features and explicit linguistic features for LID tasks. Experiments were carried out on the large-scale multilingual LibriSpeech and VoxLingua107 datasets. Experimental results showed the proposed method significantly improves the performance on LID tasks with 12% to 59% and 16% to 24% relative improvement on in-domain and cross-domain datasets, respectively. | ['Hisashi Kawai', 'Xugang Lu', 'Peng Shen'] | 2022-04-08 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-0.45604488 -0.14864185 -0.05721155 -0.35952166 -1.3895006 -0.32705575
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43c4127b-b696-4caa-88e7-3feab53f5b62 | assume-augment-and-learn-unsupervised-few | 1902.09884 | null | http://arxiv.org/abs/1902.09884v3 | http://arxiv.org/pdf/1902.09884v3.pdf | Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation | The field of few-shot learning has been laboriously explored in the
supervised setting, where per-class labels are available. On the other hand,
the unsupervised few-shot learning setting, where no labels of any kind are
required, has seen little investigation. We propose a method, named Assume,
Augment and Learn or AAL, for generating few-shot tasks using unlabeled data.
We randomly label a random subset of images from an unlabeled dataset to
generate a support set. Then by applying data augmentation on the support set's
images, and reusing the support set's labels, we obtain a target set. The
resulting few-shot tasks can be used to train any standard meta-learning
framework. Once trained, such a model, can be directly applied on small
real-labeled datasets without any changes or fine-tuning required. In our
experiments, the learned models achieve good generalization performance in a
variety of established few-shot learning tasks on Omniglot and Mini-Imagenet. | ['Amos Storkey', 'Antreas Antoniou'] | 2019-02-26 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.60765982e-01 1.25847116e-01 -5.07860184e-01 -5.54179788e-01
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b01e6e14-07ae-4484-8d6d-0f47b9d67ee2 | 190807721 | 1908.07721 | null | https://arxiv.org/abs/1908.07721v2 | https://arxiv.org/pdf/1908.07721v2.pdf | Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text | Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natural language processing tasks. In this paper, we present a focused attention model for the joint entity and relation extraction task. Our model integrates well-known BERT language model into joint learning through dynamic range attention mechanism, thus improving the feature representation ability of shared parameter layer. Experimental results on coronary angiography texts collected from Shuguang Hospital show that the F1-score of named entity recognition and relation classification tasks reach 96.89% and 88.51%, which are better than state-of-the-art methods 1.65% and 1.22%, respectively. | ['Tong Ruan', 'Yangming Zhou', 'Kui Xue', 'Huanhuan Zhang', 'Zhiyuan Ma', 'Ping He'] | 2019-08-21 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.15772046e-01 2.05549583e-01 -5.83626390e-01 -4.05027032e-01
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6c356133-9a01-4da3-9ce8-b33d3284a535 | understanding-the-world-to-solve-social | 2305.11358 | null | https://arxiv.org/abs/2305.11358v1 | https://arxiv.org/pdf/2305.11358v1.pdf | Understanding the World to Solve Social Dilemmas Using Multi-Agent Reinforcement Learning | Social dilemmas are situations where groups of individuals can benefit from mutual cooperation but conflicting interests impede them from doing so. This type of situations resembles many of humanity's most critical challenges, and discovering mechanisms that facilitate the emergence of cooperative behaviors is still an open problem. In this paper, we study the behavior of self-interested rational agents that learn world models in a multi-agent reinforcement learning (RL) setting and that coexist in environments where social dilemmas can arise. Our simulation results show that groups of agents endowed with world models outperform all the other tested ones when dealing with scenarios where social dilemmas can arise. We exploit the world model architecture to qualitatively assess the learnt dynamics and confirm that each agent's world model is capable to encode information of the behavior of the changing environment and the other agent's actions. This is the first work that shows that world models facilitate the emergence of complex coordinated behaviors that enable interacting agents to ``understand'' both environmental and social dynamics. | ['Luis Felipe Giraldo', 'Nicanor Quijano', 'Manuel Rios'] | 2023-05-19 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.48906752e-01 4.63093042e-01 3.12247217e-01 1.59028634e-01
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fcd921c8-9105-43c7-a20c-fe74da85950f | sosr-source-free-image-super-resolution-with | 2303.17783 | null | https://arxiv.org/abs/2303.17783v2 | https://arxiv.org/pdf/2303.17783v2.pdf | SOSR: Source-Free Image Super-Resolution with Wavelet Augmentation Transformer | Real-world images taken by different cameras with different degradation kernels often result in a cross-device domain gap in image super-resolution. A prevalent attempt to this issue is unsupervised domain adaptation (UDA) that needs to access source data. Considering privacy policies or transmission restrictions of data in many practical applications, we propose a SOurce-free image Super-Resolution framework (SOSR) to address this issue, i.e., adapt a model pre-trained on labeled source data to a target domain with only unlabeled target data. SOSR leverages the source model to generate refined pseudo-labels for teacher-student learning. To better utilize the pseudo-labels, this paper proposes a novel wavelet-based augmentation method, named Wavelet Augmentation Transformer (WAT), which can be flexibly incorporated with existing networks, to implicitly produce useful augmented data. WAT learns low-frequency information of varying levels across diverse samples, which is aggregated efficiently via deformable attention. Furthermore, an uncertainty-aware self-training mechanism is proposed to improve the accuracy of pseudo-labels, with inaccurate predictions being rectified by uncertainty estimation. To acquire better SR results and avoid overfitting pseudo-labels, several regularization losses are proposed to constrain the frequency information between target LR and SR images. Experiments show that without accessing source data, SOSR achieves superior results to the state-of-the-art UDA methods. | ['Ran He', 'Lei Zhang', 'Huaibo Huang', 'Xiaoqiang Zhou', 'Yuang Ai'] | 2023-03-31 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 7.70311415e-01 2.00795442e-01 -4.82511520e-01 -5.48421681e-01
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5.35074294e-01 -6.85013354e-01 -3.30741286e-01 4.36067909e-01] | [11.04179573059082, -2.070509195327759] |
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