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6c98bc4f-fd08-4a0e-a436-28eef7f4248e | cross-stream-contrastive-learning-for-self | 2305.02324 | null | https://arxiv.org/abs/2305.02324v1 | https://arxiv.org/pdf/2305.02324v1.pdf | Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition | Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the complicated movement patterns. In this paper, we advocate that the defect of single-stream contrast and the lack of necessary feature transformation are responsible for easy positives, and therefore propose a Cross-Stream Contrastive Learning framework for skeleton-based action Representation learning (CSCLR). Specifically, the proposed CSCLR not only utilizes intra-stream contrast pairs, but introduces inter-stream contrast pairs as hard samples to formulate a better representation learning. Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets NTU-RGB+D 60, NTU-RGB+D 120 and PKU-MMD. Experimental results show that our proposed CSCLR exceeds the state-of-the-art methods on a diverse range of evaluation protocols. | ['Wensheng Zhang', 'Zhizhong Zhang', 'Yongqiang Tang', 'Ding Li'] | 2023-05-03 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 5.29561937e-01 -4.81479675e-01 -5.60592949e-01 -1.19474038e-01
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7fb69470-0412-4481-996c-65f1be0f01e6 | on-imitation-in-mean-field-games | 2306.14799 | null | https://arxiv.org/abs/2306.14799v1 | https://arxiv.org/pdf/2306.14799v1.pdf | On Imitation in Mean-field Games | We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash equilibrium policy according to some unknown payoff function. IL in MFGs presents new challenges compared to single-agent IL, particularly when both the reward function and the transition kernel depend on the population distribution. In this paper, departing from the existing literature on IL for MFGs, we introduce a new solution concept called the Nash imitation gap. Then we show that when only the reward depends on the population distribution, IL in MFGs can be reduced to single-agent IL with similar guarantees. However, when the dynamics is population-dependent, we provide a novel upper-bound that suggests IL is harder in this setting. To address this issue, we propose a new adversarial formulation where the reinforcement learning problem is replaced by a mean-field control (MFC) problem, suggesting progress in IL within MFGs may have to build upon MFC. | ['Matthieu Geist', 'Mathieu Laurière', 'Niao He', 'Olivier Pietquin', 'Pavel Kolev', 'Giorgia Ramponi'] | 2023-06-26 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 2.06220984e-01 4.91335332e-01 4.04543690e-02 6.72985196e-01
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5f17853b-3b64-4bce-9ebd-afb68127837b | dynamical-variational-autoencoders-a | 2008.12595 | null | https://arxiv.org/abs/2008.12595v4 | https://arxiv.org/pdf/2008.12595v4.pdf | Dynamical Variational Autoencoders: A Comprehensive Review | Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, the input data vectors are processed independently. Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models. In this paper, we perform a literature review of these models. We introduce and discuss a general class of models, called dynamical variational autoencoders (DVAEs), which encompasses a large subset of these temporal VAE extensions. Then, we present in detail seven recently proposed DVAE models, with an aim to homogenize the notations and presentation lines, as well as to relate these models with existing classical temporal models. We have reimplemented those seven DVAE models and present the results of an experimental benchmark conducted on the speech analysis-resynthesis task (the PyTorch code is made publicly available). The paper concludes with a discussion on important issues concerning the DVAE class of models and future research guidelines. | ['Xavier Alameda-Pineda', 'Julien Diard', 'Xiaoyu Bie', 'Simon Leglaive', 'Thomas Hueber', 'Laurent Girin'] | 2020-08-28 | null | null | null | null | ['3d-human-dynamics'] | ['computer-vision'] | [-1.73195571e-01 -9.20927687e-06 6.89745992e-02 -3.35203740e-03
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f1e699bb-71a5-4443-b217-0c094fb5d275 | learning-under-selective-labels-with | 2306.07566 | null | https://arxiv.org/abs/2306.07566v2 | https://arxiv.org/pdf/2306.07566v2.pdf | Learning under Selective Labels with Data from Heterogeneous Decision-makers: An Instrumental Variable Approach | We study the problem of learning with selectively labeled data, which arises when outcomes are only partially labeled due to historical decision-making. The labeled data distribution may substantially differ from the full population, especially when the historical decisions and the target outcome can be simultaneously affected by some unobserved factors. Consequently, learning with only the labeled data may lead to severely biased results when deployed to the full population. Our paper tackles this challenge by exploiting the fact that in many applications the historical decisions were made by a set of heterogeneous decision-makers. In particular, we analyze this setup in a principled instrumental variable (IV) framework. We establish conditions for the full-population risk of any given prediction rule to be point-identified from the observed data and provide sharp risk bounds when the point identification fails. We further propose a weighted learning approach that learns prediction rules robust to the label selection bias in both identification settings. Finally, we apply our proposed approach to a semi-synthetic financial dataset and demonstrate its superior performance in the presence of selection bias. | ['Xiaojie Mao', 'Zhehao Li', 'Jian Chen'] | 2023-06-13 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.80898082e-01 3.58238339e-01 -8.96201909e-01 -3.41895223e-01
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432ffe44-a943-43a6-8f4e-1a19c55c244f | applying-standards-to-advance-upstream | 2306.03503 | null | https://arxiv.org/abs/2306.03503v2 | https://arxiv.org/pdf/2306.03503v2.pdf | Applying Standards to Advance Upstream & Downstream Ethics in Large Language Models | This paper explores how AI-owners can develop safeguards for AI-generated content by drawing from established codes of conduct and ethical standards in other content-creation industries. It delves into the current state of ethical awareness on Large Language Models (LLMs). By dissecting the mechanism of content generation by LLMs, four key areas (upstream/downstream and at user prompt/answer), where safeguards could be effectively applied, are identified. A comparative analysis of these four areas follows and includes an evaluation of the existing ethical safeguards in terms of cost, effectiveness, and alignment with established industry practices. The paper's key argument is that existing IT-related ethical codes, while adequate for traditional IT engineering, are inadequate for the challenges posed by LLM-based content generation. Drawing from established practices within journalism, we propose potential standards for businesses involved in distributing and selling LLM-generated content. Finally, potential conflicts of interest between dataset curation at upstream and ethical benchmarking downstream are highlighted to underscore the need for a broader evaluation beyond mere output. This study prompts a nuanced conversation around ethical implications in this rapidly evolving field of content generation. | ['Marybeth Sandell', 'Jose Berengueres'] | 2023-06-06 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 4.70721126e-01 1.01539683e+00 -1.79342583e-01 7.04308599e-02
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f1bd682f-410c-4d75-963b-ff0b07fa57ff | multi-microgrid-collaborative-optimization | 2304.01223 | null | https://arxiv.org/abs/2304.01223v1 | https://arxiv.org/pdf/2304.01223v1.pdf | Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm | The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of a MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes a MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates en-ergy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities, and reduces the MMG system operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency. | ['Haibo Wu', 'Bin Wang', 'Yang Li', 'Jiankai Gao'] | 2023-04-01 | null | null | null | null | ['automl', 'energy-management'] | ['methodology', 'time-series'] | [-1.09725654e+00 9.78907943e-02 -1.95465177e-01 2.12610543e-01
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22609bd4-9144-452d-9fef-3263b0c77b2d | hardware-agnostic-computation-for-large-scale | 2207.08544 | null | https://arxiv.org/abs/2207.08544v1 | https://arxiv.org/pdf/2207.08544v1.pdf | Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings | Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in research related applications. Yet, these frameworks do not fulfill many requirements of real-world applications. As the size of the knowledge graph grows, moving computation from a commodity computer to a cluster of computers in these frameworks becomes more challenging. Finding suitable hyperparameter settings w.r.t. time and computational budgets are left to practitioners. In addition, the continual learning aspect in knowledge graph embedding frameworks is often ignored, although continual learning plays an important role in many real-world (deep) learning-driven applications. Arguably, these limitations explain the lack of publicly available knowledge graph embedding models for large knowledge graphs. We developed a framework based on the frameworks DASK, Pytorch Lightning and Hugging Face to compute embeddings for large-scale knowledge graphs in a hardware-agnostic manner, which is able to address real-world challenges pertaining to the scale of real application. We provide an open-source version of our framework along with a hub of pre-trained models having more than 11.4 B parameters. | ['Axel-Cyrille Ngonga Ngomo', 'Caglar Demir'] | 2022-07-18 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.81150603e-01 1.36475056e-01 -4.97020245e-01 4.46500853e-02
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a9556ad6-7f88-4e53-8fd4-ae8951ac2032 | cluster-analysis-of-online-mental-health | null | null | https://aclanthology.org/2021.louhi-1.10 | https://aclanthology.org/2021.louhi-1.10.pdf | Cluster Analysis of Online Mental Health Discourse using Topic-Infused Deep Contextualized Representations | With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models. The research focusing on the identification of discussion clusters in online mental health communities has been relatively limited. Moreover, as the underlying methodologies used in these studies mainly conform to the traditional machine learning models and statistical methods, the scope for introducing contextualized word representations for topic and theme extraction from online mental health communities remains open. Thus, in this research, we propose topic-infused deep contextualized representations, a novel data representation technique that uses autoencoders to combine deep contextual embeddings with topical information, generating robust representations for text clustering. Investigating the Reddit discourse on Post-Traumatic Stress Disorder (PTSD) and Complex Post-Traumatic Stress Disorder (C-PTSD), we elicit the thematic clusters representing the latent topics and themes discussed in the r/ptsd and r/CPTSD subreddits. Furthermore, we also present a qualitative analysis and characterization of each cluster, unraveling the prevalent discourse themes. | ['Manisha Marathe', 'Pradnya Kulkarni', 'Amey Hengle', 'Atharva Kulkarni'] | null | null | null | null | eacl-louhi-2021-4 | ['text-clustering'] | ['natural-language-processing'] | [ 1.60124257e-01 7.99790502e-01 -5.16586065e-01 -1.26134068e-01
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5f0da607-7529-4742-ae02-fe5d87ecd2d5 | diffusion-model-based-posterior-sampling-for | 2211.12343 | null | https://arxiv.org/abs/2211.12343v2 | https://arxiv.org/pdf/2211.12343v2.pdf | Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems | We consider the ubiquitous linear inverse problems with additive Gaussian noise and propose an unsupervised sampling approach called diffusion model based posterior sampling (DMPS) to reconstruct the unknown signal from noisy linear measurements. Specifically, using one diffusion model (DM) as an implicit prior, the fundamental difficulty in performing posterior sampling is that the noise-perturbed likelihood score, i.e., gradient of an annealed likelihood function, is intractable. To circumvent this problem, we introduce a simple yet effective closed-form approximation of it using an uninformative prior assumption. Extensive experiments are conducted on a variety of noisy linear inverse problems such as noisy super-resolution, denoising, deblurring, and colorization. In all tasks, the proposed DMPS demonstrates highly competitive or even better performances on various tasks while being 3 times faster than the state-of-the-art competitor diffusion posterior sampling (DPS). The code to reproduce the results is available at https://github.com/mengxiangming/dmps. | ['Yoshiyuki Kabashima', 'Xiangming Meng'] | 2022-11-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.84694129e-01 -1.17663488e-01 2.47093171e-01 -2.19735518e-01
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0261d714-ac5c-467e-a9e5-2f00a9724a3b | policy-regularization-with-dataset-constraint | 2306.06569 | null | https://arxiv.org/abs/2306.06569v1 | https://arxiv.org/pdf/2306.06569v1.pdf | Policy Regularization with Dataset Constraint for Offline Reinforcement Learning | We consider the problem of learning the best possible policy from a fixed dataset, known as offline Reinforcement Learning (RL). A common taxonomy of existing offline RL works is policy regularization, which typically constrains the learned policy by distribution or support of the behavior policy. However, distribution and support constraints are overly conservative since they both force the policy to choose similar actions as the behavior policy when considering particular states. It will limit the learned policy's performance, especially when the behavior policy is sub-optimal. In this paper, we find that regularizing the policy towards the nearest state-action pair can be more effective and thus propose Policy Regularization with Dataset Constraint (PRDC). When updating the policy in a given state, PRDC searches the entire dataset for the nearest state-action sample and then restricts the policy with the action of this sample. Unlike previous works, PRDC can guide the policy with proper behaviors from the dataset, allowing it to choose actions that do not appear in the dataset along with the given state. It is a softer constraint but still keeps enough conservatism from out-of-distribution actions. Empirical evidence and theoretical analysis show that PRDC can alleviate offline RL's fundamentally challenging value overestimation issue with a bounded performance gap. Moreover, on a set of locomotion and navigation tasks, PRDC achieves state-of-the-art performance compared with existing methods. Code is available at https://github.com/LAMDA-RL/PRDC | ['Yang Yu', 'Zongzhang Zhang', 'Fuxiang Zhang', 'Yi-Chen Li', 'Yuhang Ran'] | 2023-06-11 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.36436069e-01 7.67949373e-02 -1.06301999e+00 -4.98431697e-02
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2fee2e94-3d2f-4865-af03-4f0c40d65bd9 | techniques-to-improve-neural-math-word | 2302.03145 | null | https://arxiv.org/abs/2302.03145v1 | https://arxiv.org/pdf/2302.03145v1.pdf | Techniques to Improve Neural Math Word Problem Solvers | Developing automatic Math Word Problem (MWP) solvers is a challenging task that demands the ability of understanding and mathematical reasoning over the natural language. Recent neural-based approaches mainly encode the problem text using a language model and decode a mathematical expression over quantities and operators iteratively. Note the problem text of a MWP consists of a context part and a question part, a recent work finds these neural solvers may only perform shallow pattern matching between the context text and the golden expression, where question text is not well used. Meanwhile, existing decoding processes fail to enforce the mathematical laws into the design, where the representations for mathematical equivalent expressions are different. To address these two issues, we propose a new encoder-decoder architecture that fully leverages the question text and preserves step-wise commutative law. Besides generating quantity embeddings, our encoder further encodes the question text and uses it to guide the decoding process. At each step, our decoder uses Deep Sets to compute expression representations so that these embeddings are invariant under any permutation of quantities. Experiments on four established benchmarks demonstrate that our framework outperforms state-of-the-art neural MWP solvers, showing the effectiveness of our techniques. We also conduct a detailed analysis of the results to show the limitations of our approach and further discuss the potential future work. Code is available at https://github.com/sophistz/Question-Aware-Deductive-MWP. | ['Youyuan Zhang'] | 2023-02-06 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 7.29949027e-02 9.69119277e-03 -2.15173244e-01 -4.90798622e-01
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477cd1e9-8dab-4f86-a74f-d20723c204eb | autofits-automatic-feature-engineering-for | 2112.14806 | null | https://arxiv.org/abs/2112.14806v1 | https://arxiv.org/pdf/2112.14806v1.pdf | AutoFITS: Automatic Feature Engineering for Irregular Time Series | A time series represents a set of observations collected over time. Typically, these observations are captured with a uniform sampling frequency (e.g. daily). When data points are observed in uneven time intervals the time series is referred to as irregular or intermittent. In such scenarios, the most common solution is to reconstruct the time series to make it regular, thus removing its intermittency. We hypothesise that, in irregular time series, the time at which each observation is collected may be helpful to summarise the dynamics of the data and improve forecasting performance. We study this idea by developing a novel automatic feature engineering framework, which focuses on extracting information from this point of view, i.e., when each instance is collected. We study how valuable this information is by integrating it in a time series forecasting workflow and investigate how it compares to or complements state-of-the-art methods for regular time series forecasting. In the end, we contribute by providing a novel framework that tackles feature engineering for time series from an angle previously vastly ignored. We show that our approach has the potential to further extract more information about time series that significantly improves forecasting performance. | ['João Vinagre', 'Vitor Cerqueira', 'Pedro Costa'] | 2021-12-29 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 2.75752187e-01 -1.67248741e-01 1.08174756e-01 -1.46750599e-01
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b19d2234-2dd3-4e2c-b5ff-1e3f8cdd862f | eva-exploring-the-limits-of-masked-visual | 2211.07636 | null | https://arxiv.org/abs/2211.07636v2 | https://arxiv.org/pdf/2211.07636v2.pdf | EVA: Exploring the Limits of Masked Visual Representation Learning at Scale | We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation and semantic segmentation without heavy supervised training. Moreover, we observe quantitative changes in scaling EVA result in qualitative changes in transfer learning performance that are not present in other models. For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVISv1.0 dataset with over a thousand categories and COCO dataset with only eighty categories. Beyond a pure vision encoder, EVA can also serve as a vision-centric, multi-modal pivot to connect images and text. We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models. To facilitate future research, we release all the code and models at https://github.com/baaivision/EVA. | ['Yue Cao', 'Xinlong Wang', 'Tiejun Huang', 'Xinggang Wang', 'Ledell Wu', 'Quan Sun', 'Binhui Xie', 'Wen Wang', 'Yuxin Fang'] | 2022-11-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-supervised-image-classification', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 1.28148749e-01 8.47141668e-02 -2.62374967e-01 -2.42308363e-01
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be62439c-d7a1-4035-a0d4-3b735210317b | transformer-based-source-free-domain | 2105.14138 | null | https://arxiv.org/abs/2105.14138v1 | https://arxiv.org/pdf/2105.14138v1.pdf | Transformer-Based Source-Free Domain Adaptation | In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. Previous works on SFDA mainly focus on aligning the cross-domain distributions. However, they ignore the generalization ability of the pretrained source model, which largely influences the initial target outputs that are vital to the target adaptation stage. To address this, we make the interesting observation that the model accuracy is highly correlated with whether or not attention is focused on the objects in an image. To this end, we propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA. Specifically, we apply the Transformer as the attention module and inject it into a convolutional network. By doing so, the model is encouraged to turn attention towards the object regions, which can effectively improve the model's generalization ability on the target domains. Moreover, a novel self-supervised knowledge distillation approach is proposed to adapt the Transformer with target pseudo-labels, thus further encouraging the network to focus on the object regions. Experiments on three domain adaptation tasks, including closed-set, partial-set, and open-set adaption, demonstrate that TransDA can greatly improve the adaptation accuracy and produce state-of-the-art results. The source code and trained models are available at https://github.com/ygjwd12345/TransDA. | ['Elisa Ricci', 'Nicu Sebe', 'Ling Shao', 'Mingli Ding', 'Zhun Zhong', 'Hao Tang', 'Guanglei Yang'] | 2021-05-28 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-8.51676147e-03 -1.80344671e-01 -2.64727652e-01 -5.22336304e-01
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26d5b7a5-df10-43e0-b25f-09177d41b3d5 | clustering-friendly-representation-learning-1 | 2106.00131 | null | https://arxiv.org/abs/2106.00131v1 | https://arxiv.org/pdf/2106.00131v1.pdf | Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation | Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we propose a clustering-friendly representation learning method using instance discrimination and feature decorrelation. Our deep-learning-based representation learning method is motivated by the properties of classical spectral clustering. Instance discrimination learns similarities among data and feature decorrelation removes redundant correlation among features. We utilize an instance discrimination method in which learning individual instance classes leads to learning similarity among instances. Through detailed experiments and examination, we show that the approach can be adapted to learning a latent space for clustering. We design novel softmax-formulated decorrelation constraints for learning. In evaluations of image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy of 81.5% and 95.4%, respectively. We also show that the softmax-formulated constraints are compatible with various neural networks. | ['Kouta Nakata', 'Kentaro Takagi', 'Yaling Tao'] | 2021-05-31 | clustering-friendly-representation-learning | https://openreview.net/forum?id=e12NDM7wkEY | https://openreview.net/pdf?id=e12NDM7wkEY | iclr-2021-1 | ['image-clustering'] | ['computer-vision'] | [ 1.65758103e-01 -2.09109202e-01 -1.06121980e-01 -7.78420329e-01
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00acb101-278c-465a-840b-502be5bc7987 | utnlp-at-semeval-2021-task-5-a-comparative | 2104.04770 | null | https://arxiv.org/abs/2104.04770v1 | https://arxiv.org/pdf/2104.04770v1.pdf | UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models | Detecting which parts of a sentence contribute to that sentence's toxicity -- rather than providing a sentence-level verdict of hatefulness -- would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team's, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection. We test multiple models and contextual embeddings and report the best setting out of all. The experiments start with keyword-based models and are followed by attention-based, named entity-based, transformers-based, and ensemble models. Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition's evaluation phase. | ['Azadeh Shakery', 'Behnam Bahrak', 'Emad Kebriaei', 'Nazanin Sabri', 'Alireza Salemi'] | 2021-04-10 | null | https://aclanthology.org/2021.semeval-1.136 | https://aclanthology.org/2021.semeval-1.136.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-6.04791008e-02 1.83241487e-01 -8.74322578e-02 -3.22537124e-01
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28b27bdd-50c8-458a-80a6-7ade1cfc8d0c | document-grounded-goal-oriented-dialogue | null | null | https://aclanthology.org/2021.dialdoc-1.12 | https://aclanthology.org/2021.dialdoc-1.12.pdf | Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation | Document-grounded goal-oriented dialog system understands users’ utterances, and generates proper responses by using information obtained from documents. The Dialdoc21 shared task consists of two subtasks; subtask1, finding text spans associated with users’ utterances from documents, and subtask2, generating responses based on information obtained from subtask1. In this paper, we propose two models (i.e., a knowledge span prediction model and a response generation model) for the subtask1 and the subtask2. In the subtask1, dialogue act losses are used with RoBERTa, and title embeddings are added to input representation of RoBERTa. In the subtask2, various special tokens and embeddings are added to input representation of BART’s encoder. Then, we propose a method to assign different difficulty scores to leverage curriculum learning. In the subtask1, our span prediction model achieved F1-scores of 74.81 (ranked at top 7) and 73.41 (ranked at top 5) in test-dev phase and test phase, respectively. In the subtask2, our response generation model achieved sacreBLEUs of 37.50 (ranked at top 3) and 41.06 (ranked at top 1) in in test-dev phase and test phase, respectively. | ['Harksoo Kim', 'Oh-Woog Kwon', 'Jin-Xia Huang', 'Yejin Lee', 'Sihyung Kim', 'Dohaeng Lee', 'Boeun Kim'] | null | null | null | null | acl-dialdoc-2021-8 | ['goal-oriented-dialog', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.83127132e-02 4.52748954e-01 1.13552772e-01 -5.29964685e-01
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8acc148b-9547-4235-9d83-7b12f036b4a5 | rainbow-combining-improvements-in-deep | 1710.02298 | null | http://arxiv.org/abs/1710.02298v1 | http://arxiv.org/pdf/1710.02298v1.pdf | Rainbow: Combining Improvements in Deep Reinforcement Learning | The deep reinforcement learning community has made several independent
improvements to the DQN algorithm. However, it is unclear which of these
extensions are complementary and can be fruitfully combined. This paper
examines six extensions to the DQN algorithm and empirically studies their
combination. Our experiments show that the combination provides
state-of-the-art performance on the Atari 2600 benchmark, both in terms of data
efficiency and final performance. We also provide results from a detailed
ablation study that shows the contribution of each component to overall
performance. | ['Dan Horgan', 'Joseph Modayil', 'Hado van Hasselt', 'Matteo Hessel', 'David Silver', 'Will Dabney', 'Tom Schaul', 'Mohammad Azar', 'Georg Ostrovski', 'Bilal Piot'] | 2017-10-06 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-4.12856042e-01 -2.98887193e-01 -5.10828674e-01 -1.30848423e-01
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2dba3319-93c4-44f0-8390-ba4809775fb8 | graph-toolformer-to-empower-llms-with-graph | null | null | https://github.com/jwzhanggy/Graph_Toolformer/blob/main/figures/README.md | http://www.ifmlab.org/files/paper/graph_toolformer.pdf | Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT | In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing multi-step logic reasoning, precise mathematical calculation and perception about the spatial and temporal factors.
To address such challenges, in this paper, we will investigate the principles, methodologies and algorithms to empower existing LLMs with graph reasoning ability, which will have tremendous impacts on the current research of both LLMs and graph learning. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools. Specifically, we will investigate to teach Graph-ToolFormer to handle various graph data reasoning tasks in this paper, including both (1) very basic graph data loading and graph property reasoning tasks, ranging from simple graph order and size to the graph diameter and periphery, and (2) more advanced reasoning tasks on real-world graph data, such as bibliographic networks, protein molecules, sequential recommender systems, social networks and knowledge graphs.
Technically, to build Graph-ToolFormer, we propose to handcraft both the instruction and a small-sized of prompt templates for each of the graph reasoning tasks, respectively. Via in-context learning, based on such instructions and prompt template examples, we adopt ChatGPT to annotate and augment a larger graph reasoning statement dataset with the most appropriate calls of external API functions. Such augmented prompt datasets will be post-processed with selective filtering and used for fine-tuning existing pre-trained causal LLMs, such as the GPT-J, to teach them how to use graph reasoning tools in the output generation. To demonstrate the effectiveness of Graph-ToolFormer, we conduct some preliminary experimental studies on various graph reasoning datasets and tasks, and will launch a LLM demo online with various graph reasoning abilities. | ['Jiawei Zhang'] | 2023-04-10 | null | null | null | preprint-2023-4 | ['graph-classification', 'community-detection'] | ['graphs', 'graphs'] | [-7.19883293e-02 4.89944518e-01 -2.02843212e-02 -2.76498795e-01
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00399539-9331-499d-8f01-10013f08a383 | unifying-topic-sentiment-preference-in-an-hdp | 1812.07805 | null | http://arxiv.org/abs/1812.07805v1 | http://arxiv.org/pdf/1812.07805v1.pdf | Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews | This paper proposes a new HDP based online review rating regression model
named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines
topics (i.e. product aspects), word sentiment and user preference as regression
factors, and is able to perform topic clustering, review rating prediction,
sentiment analysis and what we invent as "critical aspect" analysis altogether
in one framework. TSPRA extends sentiment approaches by integrating the key
concept "user preference" in collaborative filtering (CF) models into
consideration, while it is distinct from current CF models by decoupling "user
preference" and "sentiment" as independent factors. Our experiments conducted
on 22 Amazon datasets show overwhelming better performance in rating
predication against a state-of-art model FLAME (2015) in terms of error,
Pearson's Correlation and number of inverted pairs. For sentiment analysis, we
compare the derived word sentiments against a public sentiment resource
SenticNet3 and our sentiment estimations clearly make more sense in the context
of online reviews. Last, as a result of the de-correlation of "user preference"
from "sentiment", TSPRA is able to evaluate a new concept "critical aspects",
defined as the product aspects seriously concerned by users but negatively
commented in reviews. Improvement to such "critical aspects" could be most
effective to enhance user experience. | ['Yue Shang', 'Yong Zhang', 'Xiaohua Hu', 'Zheng Chen'] | 2018-12-19 | null | null | null | null | ['online-review-rating'] | ['miscellaneous'] | [-3.74637604e-01 2.37745587e-02 -5.05818665e-01 -5.02610445e-01
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8f5655bb-84b8-4586-8508-f731d98efa8c | real-time-user-guided-image-colorization-with | 1705.02999 | null | http://arxiv.org/abs/1705.02999v1 | http://arxiv.org/pdf/1705.02999v1.pdf | Real-Time User-Guided Image Colorization with Learned Deep Priors | We propose a deep learning approach for user-guided image colorization. The
system directly maps a grayscale image, along with sparse, local user "hints"
to an output colorization with a Convolutional Neural Network (CNN). Rather
than using hand-defined rules, the network propagates user edits by fusing
low-level cues along with high-level semantic information, learned from
large-scale data. We train on a million images, with simulated user inputs. To
guide the user towards efficient input selection, the system recommends likely
colors based on the input image and current user inputs. The colorization is
performed in a single feed-forward pass, enabling real-time use. Even with
randomly simulated user inputs, we show that the proposed system helps novice
users quickly create realistic colorizations, and offers large improvements in
colorization quality with just a minute of use. In addition, we demonstrate
that the framework can incorporate other user "hints" to the desired
colorization, showing an application to color histogram transfer. Our code and
models are available at https://richzhang.github.io/ideepcolor. | ['Xinyang Geng', 'Jun-Yan Zhu', 'Alexei A. Efros', 'Tianhe Yu', 'Richard Zhang', 'Angela S. Lin', 'Phillip Isola'] | 2017-05-08 | null | null | null | null | ['point-interactive-image-colorization'] | ['computer-vision'] | [ 2.09096938e-01 -1.69619784e-01 -2.15380676e-02 -5.68196058e-01
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1650a676-e30a-415e-822f-e438d9577421 | latent-structure-blockmodels-for-bayesian | 2107.01734 | null | https://arxiv.org/abs/2107.01734v2 | https://arxiv.org/pdf/2107.01734v2.pdf | Latent structure blockmodels for Bayesian spectral graph clustering | Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden substructure is expected to arise when the graph is generated from a latent position model. Furthermore, the presence of communities within the network might generate community-specific submanifold structures in the embedding, but this is not explicitly accounted for in most statistical models for networks. In this article, a class of models called latent structure block models (LSBM) is proposed to address such scenarios, allowing for graph clustering when community-specific one dimensional manifold structure is present. LSBMs focus on a specific class of latent space model, the random dot product graph (RDPG), and assign a latent submanifold to the latent positions of each community. A Bayesian model for the embeddings arising from LSBMs is discussed, and shown to have a good performance on simulated and real world network data. The model is able to correctly recover the underlying communities living in a one-dimensional manifold, even when the parametric form of the underlying curves is unknown, achieving remarkable results on a variety of real data. | ['Nicholas A. Heard', 'Francesco Sanna Passino'] | 2021-07-04 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 5.86167350e-02 5.89754105e-01 -6.64734915e-02 7.75935724e-02
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542a18e9-247b-4e86-80e5-6009e4eb1732 | ibbt-informed-batch-belief-trees-for-motion | 2304.10984 | null | https://arxiv.org/abs/2304.10984v1 | https://arxiv.org/pdf/2304.10984v1.pdf | IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty | In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties. The original stochastic motion planning problem is divided into a deterministic motion planning problem and a graph search problem. We solve the deterministic planning problem using sampling-based methods such as PRM or RRG to construct a graph of nominal trajectories. Then, an informed cost-to-go heuristic for the original problem is computed based on the nominal trajectory graph. Finally, we grow a belief tree by searching over the graph using the proposed heuristic. IBBT interleaves between batch state sampling, nominal trajectory graph construction, heuristic computing, and search over the graph to find belief space motion plans. IBBT is an anytime, incremental algorithm. With an increasing number of batches of samples added to the graph, the algorithm finds motion plans that converge to the optimal one. IBBT is efficient by reusing results between sequential iterations. The belief tree searching is an ordered search guided by an informed heuristic. We test IBBT in different planning environments. Our numerical investigation confirms that IBBT finds non-trivial motion plans and is faster compared with previous similar methods. | ['Panagiotis Tsiotras', 'Dongliang Zheng'] | 2023-04-21 | null | null | null | null | ['graph-construction', 'motion-planning'] | ['graphs', 'robots'] | [ 1.80791214e-01 4.99648601e-01 -2.62833089e-01 4.11753282e-02
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a85dac7f-155d-4d99-9763-d69f62bc524a | blulab-temporal-information-extraction-for | null | null | https://aclanthology.org/S15-2137 | https://aclanthology.org/S15-2137.pdf | BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge | null | ['Danielle L. Mowery', 'Samir AbdelRahman', 'Sumithra Velupillai', 'Lee Christensen', 'Wendy Chapman'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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fdffe57d-792d-4fd9-af13-bb76a038a92c | sar-image-despeckling-by-deep-neural-networks | 2006.15559 | null | https://arxiv.org/abs/2006.15559v4 | https://arxiv.org/pdf/2006.15559v4.pdf | SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy | Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free / speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers. | ['Loïc Denis', 'Florence Tupin', 'Xiangli Yang', 'Wen Yang', 'Emanuele Dalsasso'] | 2020-06-28 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 5.62550068e-01 -3.17431897e-01 6.75299823e-01 -3.02437067e-01
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c5da8f16-c7e5-4555-9240-6de62a117e1c | unifying-count-based-exploration-and | 1606.01868 | null | http://arxiv.org/abs/1606.01868v2 | http://arxiv.org/pdf/1606.01868v2.pdf | Unifying Count-Based Exploration and Intrinsic Motivation | We consider an agent's uncertainty about its environment and the problem of
generalizing this uncertainty across observations. Specifically, we focus on
the problem of exploration in non-tabular reinforcement learning. Drawing
inspiration from the intrinsic motivation literature, we use density models to
measure uncertainty, and propose a novel algorithm for deriving a pseudo-count
from an arbitrary density model. This technique enables us to generalize
count-based exploration algorithms to the non-tabular case. We apply our ideas
to Atari 2600 games, providing sensible pseudo-counts from raw pixels. We
transform these pseudo-counts into intrinsic rewards and obtain significantly
improved exploration in a number of hard games, including the infamously
difficult Montezuma's Revenge. | ['David Saxton', 'Sriram Srinivasan', 'Tom Schaul', 'Remi Munos', 'Marc G. Bellemare', 'Georg Ostrovski'] | 2016-06-06 | unifying-count-based-exploration-and-1 | http://papers.nips.cc/paper/6383-unifying-count-based-exploration-and-intrinsic-motivation | http://papers.nips.cc/paper/6383-unifying-count-based-exploration-and-intrinsic-motivation.pdf | neurips-2016-12 | ['montezumas-revenge'] | ['playing-games'] | [ 6.99571669e-02 2.48778626e-01 -3.31196517e-01 -2.54476607e-01
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7095fdab-cde5-4a8c-950b-7428ab3873f7 | physics-informed-neural-networks-for-solving | 2002.08235 | null | https://arxiv.org/abs/2002.08235v1 | https://arxiv.org/pdf/2002.08235v1.pdf | Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations | This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. Specifically, we investigate how to extend the methodology of physics-informed neural networks to solve both the forward and inverse problems in relation to the nonlinear diffusivity and Biot's equations. We explore the accuracy of the physics-informed neural networks with different training example sizes and choices of hyperparameters. The impacts of the stochastic variations between various training realizations are also investigated. In the inverse case, we also study the effects of noisy measurements. Furthermore, we address the challenge of selecting the hyperparameters of the inverse model and illustrate how this challenge is linked to the hyperparameters selection performed for the forward one. | ['Teeratorn Kadeethum', 'Hamidreza M Nick', 'Thomas M Jorgensen'] | 2020-02-19 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [ 3.56088579e-01 5.44428006e-02 3.13381612e-01 1.08549967e-01
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bf92ae91-3340-429c-a022-8cfc9004c030 | human-detection-of-machine-manipulated-media | 1907.05276 | null | https://arxiv.org/abs/1907.05276v2 | https://arxiv.org/pdf/1907.05276v2.pdf | Human detection of machine manipulated media | Recent advances in neural networks for content generation enable artificial intelligence (AI) models to generate high-quality media manipulations. Here we report on a randomized experiment designed to study the effect of exposure to media manipulations on over 15,000 individuals' ability to discern machine-manipulated media. We engineer a neural network to plausibly and automatically remove objects from images, and we deploy this neural network online with a randomized experiment where participants can guess which image out of a pair of images has been manipulated. The system provides participants feedback on the accuracy of each guess. In the experiment, we randomize the order in which images are presented, allowing causal identification of the learning curve surrounding participants' ability to detect fake content. We find sizable and robust evidence that individuals learn to detect fake content through exposure to manipulated media when provided iterative feedback on their detection attempts. Over a succession of only ten images, participants increase their rating accuracy by over ten percentage points. Our study provides initial evidence that human ability to detect fake, machine-generated content may increase alongside the prevalence of such media online. | ['Nick Obradovich', 'Ziv Epstein', 'Matthew Groh', 'Manuel Cebrian', 'Iyad Rahwan'] | 2019-07-06 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 4.52673167e-01 1.56132162e-01 4.94501814e-02 -6.42258376e-02
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8f7566cb-a270-4a66-b311-624b37b68073 | prior-enhanced-temporal-action-localization | 2211.05299 | null | https://arxiv.org/abs/2211.05299v1 | https://arxiv.org/pdf/2211.05299v1.pdf | Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention | Temporal action localization (TAL) aims to detect the boundary and identify the class of each action instance in a long untrimmed video. Current approaches treat video frames homogeneously, and tend to give background and key objects excessive attention. This limits their sensitivity to localize action boundaries. To this end, we propose a prior-enhanced temporal action localization method (PETAL), which only takes in RGB input and incorporates action subjects as priors. This proposal leverages action subjects' information with a plug-and-play subject-aware spatial attention module (SA-SAM) to generate an aggregated and subject-prioritized representation. Experimental results on THUMOS-14 and ActivityNet-1.3 datasets demonstrate that the proposed PETAL achieves competitive performance using only RGB features, e.g., boosting mAP by 2.41% or 0.25% over the state-of-the-art approach that uses RGB features or with additional optical flow features on the THUMOS-14 dataset. | ['Haoqian Wang', 'Ruei-Sung Lin', 'Ning Zhang', 'YouBao Tang', 'Yifan Liu'] | 2022-11-10 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 3.30187738e-01 -1.34370431e-01 -4.39289212e-01 -1.70434892e-01
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fe6ac83c-333d-4e28-930c-de65608a87b5 | enhancing-medical-named-entity-recognition | null | null | https://aclanthology.org/E14-3003 | https://aclanthology.org/E14-3003.pdf | Enhancing Medical Named Entity Recognition with Features Derived from Unsupervised Methods | null | ['Maria Skeppstedt'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c1a6beeb-61c8-4ec8-bc4a-5e388a635792 | trajectory-oriented-optimization-of | 2305.03926 | null | https://arxiv.org/abs/2305.03926v1 | https://arxiv.org/pdf/2305.03926v1.pdf | Trajectory-oriented optimization of stochastic epidemiological models | Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is generally described via an ensemble or a distribution. Each member of the ensemble is usually mapped to a random number seed (explicitly or implicitly). With the goal of finding not only the input parameter settings but also the random seeds that are consistent with the ground truth, we propose a class of Gaussian process (GP) surrogates along with an optimization strategy based on Thompson sampling. This Trajectory Oriented Optimization (TOO) approach produces actual trajectories close to the empirical observations instead of a set of parameter settings where only the mean simulation behavior matches with the ground truth. | ['Jonathan Ozik', 'Kok Ben Toh', 'Abby Stevens', 'Nicholson Collier', 'Mickael Binois', 'Arindam Fadikar'] | 2023-05-06 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.08825934e-01 2.41393015e-01 -2.03788191e-01 -3.38510305e-01
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ab929e59-30b5-49e7-a78f-5e1f2fffa136 | character-time-series-matching-for-robust | null | null | https://ieeexplore.ieee.org/document/9924897 | https://ieeexplore.ieee.org/document/9924897 | Character Time-series Matching For Robust License Plate Recognition | Automatic License Plate Recognition (ALPR) is becoming a popular study area and is applied in many fields such as transportation or smart city. However, there are still several limitations when applying many current methods to practical problems due to the variation in real-world situations such as light changes, unclear License Plate (LP) characters, and image quality. Almost recent ALPR algorithms process on a single frame, which reduces accuracy in case of worse image quality. This paper presents methods to improve license plate recognition accuracy by tracking the license plate in multiple frames. First, the Adaptive License Plate Rotation algorithm is applied to correctly align the detected license plate. Second, we propose a method called Character Time-series Matching to recognize license plate characters from many consequence frames. The proposed method archives high performance in the UFPR-ALPR dataset which is 96.7% accuracy in real-time on RTX A5000 GPU card. We also deploy the algorithm for the Vietnamese ALPR system. The accuracy for license plate detection and character recognition are 0.881 and 0.979 mAP test @.5 respectively. The source code is available at https: //github.com/chequanghuy/Character-Time-series-Matching.git | ['Cuong Truong Van', 'Tung Do Thanh', 'Huy Che Quang'] | 2022-10-25 | null | null | null | international-conference-on-multimedia | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [ 6.36856705e-02 -1.27892351e+00 9.51326080e-03 -2.03676745e-02
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d20a7226-77e4-4333-b203-875afe992329 | reading-and-writing-discriminative-and | 2207.00193 | null | https://arxiv.org/abs/2207.00193v2 | https://arxiv.org/pdf/2207.00193v2.pdf | Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition | Existing text recognition methods usually need large-scale training data. Most of them rely on synthetic training data due to the lack of annotated real images. However, there is a domain gap between the synthetic data and real data, which limits the performance of the text recognition models. Recent self-supervised text recognition methods attempted to utilize unlabeled real images by introducing contrastive learning, which mainly learns the discrimination of the text images. Inspired by the observation that humans learn to recognize the texts through both reading and writing, we propose to learn discrimination and generation by integrating contrastive learning and masked image modeling in our self-supervised method. The contrastive learning branch is adopted to learn the discrimination of text images, which imitates the reading behavior of humans. Meanwhile, masked image modeling is firstly introduced for text recognition to learn the context generation of the text images, which is similar to the writing behavior. The experimental results show that our method outperforms previous self-supervised text recognition methods by 10.2%-20.2% on irregular scene text recognition datasets. Moreover, our proposed text recognizer exceeds previous state-of-the-art text recognition methods by averagely 5.3% on 11 benchmarks, with similar model size. We also demonstrate that our pre-trained model can be easily applied to other text-related tasks with obvious performance gain. The code is available at https://github.com/ayumiymk/DiG. | ['Xiang Bai', 'Qi Tian', 'Hualin Luo', 'Shenggao Zhu', 'Jing Wang', 'Pu Lu', 'Minghui Liao', 'Mingkun Yang'] | 2022-07-01 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.64074481e-01 -4.92435217e-01 -1.44546986e-01 -5.68685830e-01
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99bd6a4f-87cd-4864-8ab7-f93513276bf1 | michelangelo-conditional-3d-shape-generation | 2306.17115 | null | https://arxiv.org/abs/2306.17115v2 | https://arxiv.org/pdf/2306.17115v2.pdf | Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation | We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional generative model from images or texts to 3D shapes is prone to producing inconsistent results with the conditions because 3D shapes have an additional dimension whose distribution significantly differs from that of 2D images and texts. To bridge the domain gap among the three modalities and facilitate multi-modal-conditioned 3D shape generation, we explore representing 3D shapes in a shape-image-text-aligned space. Our framework comprises two models: a Shape-Image-Text-Aligned Variational Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model (ASLDM). The former model encodes the 3D shapes into the shape latent space aligned to the image and text and reconstructs the fine-grained 3D neural fields corresponding to given shape embeddings via the transformer-based decoder. The latter model learns a probabilistic mapping function from the image or text space to the latent shape space. Our extensive experiments demonstrate that our proposed approach can generate higher-quality and more diverse 3D shapes that better semantically conform to the visual or textural conditional inputs, validating the effectiveness of the shape-image-text-aligned space for cross-modality 3D shape generation. | ['Shenghua Gao', 'Gang Yu', 'Tao Chen', 'Bin Fu', 'Pei Cheng', 'Rui Wang', 'Xianfang Zeng', 'Xin Chen', 'Wen Liu', 'Zibo Zhao'] | 2023-06-29 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 4.1989794e-01 3.2050121e-01 7.7689812e-02 -3.6888969e-01
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7394e9ab-1900-4790-8bcd-018eb4c33d2b | an-empirical-study-on-google-research | 2305.09458 | null | https://arxiv.org/abs/2305.09458v1 | https://arxiv.org/pdf/2305.09458v1.pdf | An Empirical Study on Google Research Football Multi-agent Scenarios | Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this work, we fill the gap by providing a population-based MARL training pipeline and hyperparameter settings on multi-agent football scenario that outperforms the bot with difficulty 1.0 from scratch within 2 million steps. Our experiments serve as a reference for the expected performance of Independent Proximal Policy Optimization (IPPO), a state-of-the-art multi-agent reinforcement learning algorithm where each agent tries to maximize its own policy independently across various training configurations. Meanwhile, we open-source our training framework Light-MALib which extends the MALib codebase by distributed and asynchronized implementation with additional analytical tools for football games. Finally, we provide guidance for building strong football AI with population-based training and release diverse pretrained policies for benchmarking. The goal is to provide the community with a head start for whoever experiment their works on GRF and a simple-to-use population-based training framework for further improving their agents through self-play. The implementation is available at https://github.com/Shanghai-Digital-Brain-Laboratory/DB-Football. | ['Jun Wang', 'Weinan Zhang', 'Zonghong Dai', 'Jiangcheng Zhu', 'Yingping Zhang', 'Haifeng Zhang', 'Zheng Tian', 'He Jiang', 'Yan Song'] | 2023-05-16 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-7.11255968e-01 -3.75477880e-01 -5.02217293e-01 1.90842673e-01
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68283045-7ea0-44ea-9689-b5d7ff0b9d23 | deep-multi-modality-soft-decoding-of-very-low | 2008.01652 | null | https://arxiv.org/abs/2008.01652v1 | https://arxiv.org/pdf/2008.01652v1.pdf | Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos | We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads. Such video contents are very common in social media, teleconferencing, distance education, tele-medicine, etc., and often need to be transmitted with limited bandwidth. The proposed CNN method exploits the correlations among three modalities, video, audio and emotion state of the speaker, to remove the video compression artifacts caused by spatial down sampling and quantization. The deep learning approach turns out to be ideally suited for the video restoration task, as the complex non-linear cross-modality correlations are very difficult to model analytically and explicitly. The new method is a video post processor that can significantly boost the perceptual quality of aggressively compressed talking head videos, while being fully compatible with all existing video compression standards. | ['Yanhui Guo', 'Xi Zhang', 'Xiaolin Wu'] | 2020-08-02 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 2.47251287e-01 -8.85324478e-02 -7.04205260e-02 -1.34432733e-01
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80bcdf4c-bc12-490e-b400-39c75b98a84a | automatic-discourse-segmentation-review-and | 2005.00468 | null | https://arxiv.org/abs/2005.00468v1 | https://arxiv.org/pdf/2005.00468v1.pdf | Automatic Discourse Segmentation: Review and Perspectives | Multilingual discourse parsing is a very prominent research topic. The first stage for discourse parsing is discourse segmentation. The study reported in this article addresses a review of two on-line available discourse segmenters (for English and Portuguese). We evaluate the possibility of developing similar discourse segmenters for Spanish, French and African languages. | ['Juan-Manuel Torres-Moreno', 'Iria da Cunha'] | 2020-05-01 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 1.43196568e-01 9.12714183e-01 -5.37476301e-01 -8.98712277e-02
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4d538d24-fb59-4acf-b80a-39e3148343aa | sdfusion-multimodal-3d-shape-completion | 2212.04493 | null | https://arxiv.org/abs/2212.04493v2 | https://arxiv.org/pdf/2212.04493v2.pdf | SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation | In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing to adjust the strength of each input. At the core of our approach is an encoder-decoder, compressing 3D shapes into a compact latent representation, upon which a diffusion model is learned. To enable a variety of multi-modal inputs, we employ task-specific encoders with dropout followed by a cross-attention mechanism. Due to its flexibility, our model naturally supports a variety of tasks, outperforming prior works on shape completion, image-based 3D reconstruction, and text-to-3D. Most interestingly, our model can combine all these tasks into one swiss-army-knife tool, enabling the user to perform shape generation using incomplete shapes, images, and textual descriptions at the same time, providing the relative weights for each input and facilitating interactivity. Despite our approach being shape-only, we further show an efficient method to texture the generated shape using large-scale text-to-image models. | ['LiangYan Gui', 'Alexander Schwing', 'Sergey Tulyakov', 'Hsin-Ying Lee', 'Yen-Chi Cheng'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-generation', 'text-to-shape-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.79289097e-01 4.91978496e-01 2.34142005e-01 -1.48723960e-01
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0754f922-98dc-4c1e-a181-8b3620e95f00 | web-photo-source-identification-based-on | 2302.09228 | null | https://arxiv.org/abs/2302.09228v1 | https://arxiv.org/pdf/2302.09228v1.pdf | Web Photo Source Identification based on Neural Enhanced Camera Fingerprint | With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf. | ['Lei Yang', 'Xiaobo Zhang', 'Huanyu Ma', 'Honghao Huang', 'Sifeng He', 'Feng Qian'] | 2023-02-18 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 4.86593574e-01 -6.96444511e-01 -7.58020461e-01 -4.89332438e-01
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bfe5c0d6-941b-4f97-9e6d-6e05aba33df7 | partnet-a-recursive-part-decomposition | 1903.00709 | null | https://arxiv.org/abs/1903.00709v5 | https://arxiv.org/pdf/1903.00709v5.pdf | PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation | Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks. Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where the decomposition network at all nodes in the hierarchy share weights. At each node, a node classifier is trained to determine the type (adjacency or symmetry) and stopping criteria of its decomposition. The features extracted in higher level nodes are recursively propagated to lower level ones. Thus, the meaningful decompositions in higher levels provide strong contextual cues constraining the segmentations in lower levels. Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part. Our method segments a 3D shape in point cloud into an unfixed number of parts, depending on the shape complexity, showing strong generality and flexibility. It achieves the state-of-the-art performance, both for fine-grained and semantic segmentation, on the public benchmark and a new benchmark of fine-grained segmentation proposed in this work. We also demonstrate its application for fine-grained part refinements in image-to-shape reconstruction. | ['Kun Liu', 'Fenggen Yu', 'Yan Zhang', 'Kai Xu', 'Chenyang Zhu'] | 2019-03-02 | partnet-a-recursive-part-decomposition-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_PartNet_A_Recursive_Part_Decomposition_Network_for_Fine-Grained_and_Hierarchical_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_PartNet_A_Recursive_Part_Decomposition_Network_for_Fine-Grained_and_Hierarchical_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-instance-segmentation-1', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.24566779e-01 3.19852710e-01 -2.98246175e-01 -3.60335916e-01
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02c10805-1f82-4841-a9ab-7ba59c520bb5 | structural-adapters-in-pretrained-language | 2103.09120 | null | https://arxiv.org/abs/2103.09120v2 | https://arxiv.org/pdf/2103.09120v2.pdf | Structural Adapters in Pretrained Language Models for AMR-to-text Generation | Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. Contrary to prior work, StructAdapt effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters. In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph. We empirically show the benefits of explicitly encoding graph structure into PLMs using StructAdapt, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters. | ['Iryna Gurevych', 'Yue Zhang', 'Leonardo F. R. Ribeiro'] | 2021-03-16 | null | https://aclanthology.org/2021.emnlp-main.351 | https://aclanthology.org/2021.emnlp-main.351.pdf | emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.60728651e-01 7.22549260e-01 -7.96752274e-02 -1.80587053e-01
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1ceefccd-9017-4a88-8780-379a148af1b9 | medical-image-deidentification-cleaning-and | 2304.12322 | null | https://arxiv.org/abs/2304.12322v5 | https://arxiv.org/pdf/2304.12322v5.pdf | Medical Image Deidentification, Cleaning and Compression Using Pylogik | Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. Facilitating data sharing and harmonization for multi-center collaborations are particularly difficult when protected health information (PHI) is contained or embedded in image meta-data. We propose a novel library in the Python framework, called PyLogik, to help alleviate this issue for ultrasound images, which are particularly challenging because of the frequent inclusion of PHI directly on the images. PyLogik processes the image volumes through a series of text detection/extraction, filtering, thresholding, morphological and contour comparisons. This methodology de-identifies the images, reduces file sizes, and prepares image volumes for applications in deep learning and data sharing. To evaluate its effectiveness in processing ultrasound data, a random sample of 50 cardiac ultrasounds (echocardiograms) were processed through PyLogik, and the outputs were compared with the manual segmentations by an expert user. The Dice coefficient of the two approaches achieved an average value of 0.976. Next, an investigation was conducted to ascertain the degree of information compression achieved using the algorithm. Resultant data was found to be on average ~72% smaller after processing by PyLogik. Our results suggest that PyLogik is a viable methodology for data cleaning and de-identification, determining ROI, and file compression which will facilitate efficient storage, use, and dissemination of ultrasound data. Variants of the pipeline have also been created for use with other medical imaging data types. | ['Sanjiv Shah', 'Yuan Luo', 'Vinesh Appadurai', 'Adrienne Kline'] | 2023-04-20 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 1.57227457e-01 -1.02514267e-01 3.77400100e-01 -3.66246998e-01
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152b3138-34ba-4197-9f9f-d3c59a4e3753 | incremental-speech-synthesis-for-speech-to | 2110.08214 | null | https://arxiv.org/abs/2110.08214v3 | https://arxiv.org/pdf/2110.08214v3.pdf | From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation | Speech-to-speech translation (S2ST) converts input speech to speech in another language. A challenge of delivering S2ST in real time is the accumulated delay between the translation and speech synthesis modules. While recently incremental text-to-speech (iTTS) models have shown large quality improvements, they typically require additional future text inputs to reach optimal performance. In this work, we minimize the initial waiting time of iTTS by adapting the upstream speech translator to generate high-quality pseudo lookahead for the speech synthesizer. After mitigating the initial delay, we demonstrate that the duration of synthesized speech also plays a crucial role on latency. We formalize this as a latency metric and then present a simple yet effective duration-scaling approach for latency reduction. Our approaches consistently reduce latency by 0.2-0.5 second without sacrificing speech translation quality. | ['Juan Pino', 'Yun Tang', 'Xutai Ma', 'Hongyu Gong', 'Changhan Wang', 'Danni Liu'] | 2021-10-15 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.40961570e-01 7.13964775e-02 -3.57057810e-01 -2.92026639e-01
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7d39c310-25a6-4a44-929c-eaa2e1cb1287 | unsupervised-discontinuous-constituency | 2212.09140 | null | https://arxiv.org/abs/2212.09140v2 | https://arxiv.org/pdf/2212.09140v2.pdf | Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars | We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computational complexity of both parsing and parameter estimation, we restrict the grammar formalism to LCFRS-2 (i.e., binary LCFRS with fan-out two) and further discard rules that require O(n^6) time to parse, reducing inference to O(n^5). We find that using a large number of nonterminals is beneficial and thus make use of tensor decomposition-based rank-space dynamic programming with an embedding-based parameterization of rule probabilities to scale up the number of nonterminals. Experiments on German and Dutch show that our approach is able to induce linguistically meaningful trees with continuous and discontinuous structures | ['Yoon Kim', 'Roger P. Levy', 'Songlin Yang'] | 2022-12-18 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.97197241e-01 3.68746907e-01 -4.98535819e-02 -4.76899892e-01
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626b1d45-e784-4b4f-b896-3d5ffaea7eb4 | dyernie-dynamic-evolution-of-riemannian | 2011.03984 | null | https://arxiv.org/abs/2011.03984v2 | https://arxiv.org/pdf/2011.03984v2.pdf | DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion | There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose Dy- ERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds. | ['Volker Tresp', 'Yunpu Ma', 'Peng Chen', 'Zhen Han'] | 2020-11-08 | null | https://aclanthology.org/2020.emnlp-main.593 | https://aclanthology.org/2020.emnlp-main.593.pdf | emnlp-2020-11 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-6.61999881e-01 -8.22141021e-02 9.23843384e-02 -2.08449394e-01
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00e4c461-99ec-4d2d-879a-ddbcb7687325 | a-robust-and-efficient-framework-for-sports | null | null | https://assets.amazon.science/2c/75/0f3bbae44ac7aed02c700bed0694/a-robust-and-efficient-framework-for-sports-field-registration.pdf | https://assets.amazon.science/2c/75/0f3bbae44ac7aed02c700bed0694/a-robust-and-efficient-framework-for-sports-field-registration.pdf | A Robust and Efficient Framework for Sports-Field Registration | We propose a novel framework to register sports-fields as
they appear in broadcast sports videos. Unlike previous
approaches, we particularly address the challenge of fieldregistration when: (a) there are not enough distinguishable
features on the field, and (b) no prior knowledge is available about the camera. To this end, we detect a grid of keypoints distributed uniformly on the entire field instead of using only sparse local corners and line intersections, thereby
extending the keypoint coverage to the texture-less parts of
the field as well. To further improve keypoint based homography estimate, we differentialbly warp and align it with a
set of dense field-features defined as normalized distancemap of pixels to their nearest lines and key-regions. We predict the keypoints and dense field-features simultaneously
using a multi-task deep network to achieve computational
efficiency. To have a comprehensive evaluation, we have
compiled a new dataset called SportsFields which is collected from 192 video-clips from 5 different sports covering
large environmental and camera variations. We empirically
demonstrate that our algorithm not only achieves state of
the art field-registration accuracy but also runs in real-time
for HD resolution videos using commodity hardware. | ['and Raffay Hamid', 'Shixing Chen', 'Xiaohan Nie'] | 2021-01-01 | null | null | null | ieee-winter-conference-on-applications-of-7 | ['homography-estimation'] | ['computer-vision'] | [ 1.00186907e-01 -5.04313648e-01 -2.68132448e-01 -2.46817306e-01
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f8ab82ca-2cc9-4760-a8ec-be09afb2c39c | using-natural-language-for-reward-shaping-in | 1903.02020 | null | https://arxiv.org/abs/1903.02020v2 | https://arxiv.org/pdf/1903.02020v2.pdf | Using Natural Language for Reward Shaping in Reinforcement Learning | Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal. However, designing appropriate shaping rewards is known to be difficult as well as time-consuming. In this work, we address this problem by using natural language instructions to perform reward shaping. We propose the LanguagE-Action Reward Network (LEARN), a framework that maps free-form natural language instructions to intermediate rewards based on actions taken by the agent. These intermediate language-based rewards can seamlessly be integrated into any standard reinforcement learning algorithm. We experiment with Montezuma's Revenge from the Atari Learning Environment, a popular benchmark in RL. Our experiments on a diverse set of 15 tasks demonstrate that, for the same number of interactions with the environment, language-based rewards lead to successful completion of the task 60% more often on average, compared to learning without language. | ['Scott Niekum', 'Prasoon Goyal', 'Raymond J. Mooney'] | 2019-03-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.65256667e-02 1.02921855e-02 -2.36659288e-01 -1.27274022e-01
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70ebeea9-5e35-4ba2-aadd-b61c257f0462 | unsupervised-pansharpening-based-on-self | 2006.09303 | null | https://arxiv.org/abs/2006.09303v3 | https://arxiv.org/pdf/2006.09303v3.pdf | Unsupervised Pansharpening Based on Self-Attention Mechanism | Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high-spatial-resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the up-sampled MSI. Recent deep learning endeavors are mostly supervised assuming the HR MSI is available, which is unrealistic especially for satellite images. Nonetheless, these methods could not fully exploit the rich spectral characteristics in the MSI. Due to the wide existence of mixed pixels in satellite images where each pixel tends to cover more than one constituent material, pansharpening at the subpixel level becomes essential. In this paper, we propose an unsupervised pansharpening (UP) method in a deep-learning framework to address the above challenges based on the self-attention mechanism (SAM), referred to as UP-SAM. The contribution of this paper is three-fold. First, the self-attention mechanism is proposed where the spatial varying detail extraction and injection functions are estimated according to the attention representations indicating spectral characteristics of the MSI with sub-pixel accuracy. Second, such attention representations are derived from mixed pixels with the proposed stacked attention network powered with a stick-breaking structure to meet the physical constraints of mixed pixel formulations. Third, the detail extraction and injection functions are spatial varying based on the attention representations, which largely improves the reconstruction accuracy. Extensive experimental results demonstrate that the proposed approach is able to reconstruct sharper MSI of different types, with more details and less spectral distortion as compared to the state-of-the-art. | ['Razieh Kaviani Baghbaderani', 'Ying Qu', 'Hairong Qi', 'Chiman Kwan'] | 2020-06-16 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 6.47656262e-01 -1.59114957e-01 -8.30061641e-03 -4.76448946e-02
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59a22247-45d6-4dfb-90ed-29cc938130aa | guarded-policy-optimization-with-imperfect | 2303.01728 | null | https://arxiv.org/abs/2303.01728v2 | https://arxiv.org/pdf/2303.01728v2.pdf | Guarded Policy Optimization with Imperfect Online Demonstrations | The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrations. Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, providing safety guarantee and exploration guidance. Nevertheless, in many real-world settings it is expensive or even impossible to obtain a well-performing teacher policy. In this work, we relax the assumption of a well-performing teacher and develop a new method that can incorporate arbitrary teacher policies with modest or inferior performance. We instantiate an Off-Policy Reinforcement Learning algorithm, termed Teacher-Student Shared Control (TS2C), which incorporates teacher intervention based on trajectory-based value estimation. Theoretical analysis validates that the proposed TS2C algorithm attains efficient exploration and substantial safety guarantee without being affected by the teacher's own performance. Experiments on various continuous control tasks show that our method can exploit teacher policies at different performance levels while maintaining a low training cost. Moreover, the student policy surpasses the imperfect teacher policy in terms of higher accumulated reward in held-out testing environments. Code is available at https://metadriverse.github.io/TS2C. | ['Bolei Zhou', 'Zhihan Liu', 'Quanyi Li', 'Zhenghao Peng', 'Zhenghai Xue'] | 2023-03-03 | null | null | null | null | ['efficient-exploration', 'continuous-control'] | ['methodology', 'playing-games'] | [-4.53796536e-02 3.94780487e-01 -4.48147744e-01 7.48269334e-02
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fb209fac-041c-4184-a337-5933bfcc3a98 | thompson-sampling-for-robust-transfer-in | 2206.08556 | null | https://arxiv.org/abs/2206.08556v1 | https://arxiv.org/pdf/2206.08556v1.pdf | Thompson Sampling for Robust Transfer in Multi-Task Bandits | We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across multiple related tasks through robust transfer of knowledge. While an upper confidence bound (UCB)-based algorithm has recently been shown to achieve nearly-optimal performance guarantees in a setting where all tasks are solved concurrently, it remains unclear whether Thompson sampling (TS) algorithms, which have superior empirical performance in general, share similar theoretical properties. In this work, we present a TS-type algorithm for a more general online multi-task learning protocol, which extends the concurrent setting. We provide its frequentist analysis and prove that it is also nearly-optimal using a novel concentration inequality for multi-task data aggregation at random stopping times. Finally, we evaluate the algorithm on synthetic data and show that the TS-type algorithm enjoys superior empirical performance in comparison with the UCB-based algorithm and a baseline algorithm that performs TS for each individual task without transfer. | ['Kamalika Chaudhuri', 'Chicheng Zhang', 'Zhi Wang'] | 2022-06-17 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.25297838e-01 -6.59368336e-02 -4.50056702e-01 -1.17774233e-01
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1a41cba4-0e57-47d6-855a-0c2ccccb64df | using-vaes-and-normalizing-flows-for-one-shot | 1911.12760 | null | https://arxiv.org/abs/1911.12760v2 | https://arxiv.org/pdf/1911.12760v2.pdf | Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech | We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Householder Flow. The proposed system provides a 22% KL-divergence reduction while jointly improving perceptual metrics over state-of-the-art. At synthesis time we use one example of expressive style as a reference input to the encoder for generating any text in the desired style. Perceptual MUSHRA evaluations show that we can create a voice with a 9% relative naturalness improvement over standard Neural Text-to-Speech, while also improving the perceived emotional intensity (59 compared to the 55 of neutral speech). | ['Roberto Barra-Chicote', 'Jaime Lorenzo-Trueba', 'Marius Cotescu', 'Nishant Prateek', 'Vatsal Aggarwal'] | 2019-11-28 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.97479147e-01 4.48904812e-01 2.20273614e-01 -2.86696762e-01
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03dd9691-9204-4514-9dbf-9b03a6b3f52b | gem-2-next-generation-molecular-property | 2208.05863 | null | https://arxiv.org/abs/2208.05863v4 | https://arxiv.org/pdf/2208.05863v4.pdf | GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions | Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which is a quantum many-body system and can be exactly described by the Schr"odinger equation. Full-range many-body interactions between electrons have been proven effective in obtaining an accurate solution of the Schr"odinger equation by classical computational chemistry methods, although modeling such interactions consumes an expensive computational cost. Meanwhile, deep learning methods have also demonstrated their competence in molecular property prediction tasks. Inspired by the classical computational chemistry methods, we design a novel method, namely GEM-2, which comprehensively considers full-range many-body interactions in molecules. Multiple tracks are utilized to model the full-range interactions between the many-bodies with different orders, and a novel axial attention mechanism is designed to approximate the full-range interaction modeling with much lower computational cost. Extensive experiments demonstrate the overwhelming superiority of GEM-2 over multiple baseline methods in quantum chemistry and drug discovery tasks. The ablation studies also verify the effectiveness of the full-range many-body interactions. | ['Hua Wu', 'Jingzhou He', 'Fan Wang', 'Shanzhuo Zhang', 'Xiaomin Fang', 'Donglong He', 'Lihang Liu'] | 2022-08-11 | null | null | null | null | ['graph-regression', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [-1.43638372e-01 -4.15127933e-01 -5.15820682e-01 -3.86831045e-01
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39e4922b-691c-4a33-a4db-73906c56756b | hindi-visual-genome-a-dataset-for-multimodal | 1907.08948 | null | https://arxiv.org/abs/1907.08948v1 | https://arxiv.org/pdf/1907.08948v1.pdf | Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation | Visual Genome is a dataset connecting structured image information with English language. We present ``Hindi Visual Genome'', a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing which took the associated images into account. We prepared a set of 31525 segments, accompanied by a challenge test set of 1400 segments. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. Our dataset is the first for multimodal English-Hindi machine translation, freely available for non-commercial research purposes. Our Hindi version of Visual Genome also allows to create Hindi image labelers or other practical tools. Hindi Visual Genome also serves in Workshop on Asian Translation (WAT) 2019 Multi-Modal Translation Task. | ['Satya Ranjan Dash', 'Ondřej Bojar', 'Shantipriya Parida'] | 2019-07-21 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 6.25821173e-01 1.84530169e-01 -2.68576536e-02 -4.90024388e-01
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e5da3fdb-0314-4028-9388-955128c3692d | dilated-deep-residual-network-for-image | 1708.05473 | null | http://arxiv.org/abs/1708.05473v3 | http://arxiv.org/pdf/1708.05473v3.pdf | Dilated Deep Residual Network for Image Denoising | Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoising have many layers. In such cases, the models involve a large
amount of parameters and are computationally expensive to train. In this paper,
we develop a dilated residual CNN for Gaussian image denoising. Compared with
the recently proposed residual denoiser, our method can achieve comparable
performance with less computational cost. Specifically, we enlarge receptive
field by adopting dilated convolution in residual network, and the dilation
factor is set to a certain value. We utilize appropriate zero padding to make
the dimension of the output the same as the input. It has been proven that the
expansion of receptive field can boost the CNN performance in image
classification, and we further demonstrate that it can also lead to competitive
performance for denoising problem. Moreover, we present a formula to calculate
receptive field size when dilated convolution is incorporated. Thus, the change
of receptive field can be interpreted mathematically. To validate the efficacy
of our approach, we conduct extensive experiments for both gray and color image
denoising with specific or randomized noise levels. Both of the quantitative
measurements and the visual results of denoising are promising comparing with
state-of-the-art baselines. | ['Mingxuan Sun', 'Kaoning Hu', 'Tianyang Wang'] | 2017-08-18 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 2.37282485e-01 -3.39273334e-01 4.68650162e-01 -5.43356180e-01
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73e28940-1f72-4f7e-a433-50ee5f5378bb | penalized-deep-partially-linear-cox-models | 2303.05341 | null | https://arxiv.org/abs/2303.05341v1 | https://arxiv.org/pdf/2303.05341v1.pdf | Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients | Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) was a nationwide study aimed at investigating risk factors for lung cancer. The study employed computed tomography texture analysis (CTTA), which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models are becoming a popular tool for modeling survival outcomes, as they effectively handle both established risk factors (such as age and other clinical factors) and new risk factors (such as image features) in a single framework. The challenge in identifying the texture features that impact cancer survival is due to their sensitivity to factors such as scanner type, segmentation, and organ motion. To overcome this challenge, we propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select significant texture features and employs a deep neural network to estimate the nonparametric component of the model accurately. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes. | ['Yi Li', 'David C. Christiani', 'Chi-Fu Jeffrey Yang', 'Alexandra L. Potter', 'Nicholas R. Mayne', 'Chinmay Haridas', 'Jian Kang', 'Yuming Sun'] | 2023-03-09 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-6.26117215e-02 -5.03636301e-01 -8.93055558e-01 -2.13639185e-01
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9b6fecf3-a301-48dc-a56d-5d4f56cd2eaf | freebaseqa-a-new-factoid-qa-data-set-matching | null | null | https://aclanthology.org/N19-1028 | https://aclanthology.org/N19-1028.pdf | FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase | In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove any false positive in these matched triples. Using this method, we are able to efficiently generate over 54K matches from about 28K unique questions with minimal cost. Our analysis shows that this data set is suitable for model training in factoid QA tasks beyond simpler questions since FreebaseQA provides more linguistically sophisticated questions than other existing data sets. | ['Hui Jiang', 'Dekun Wu', 'Kelvin Jiang'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['set-matching'] | ['computer-vision'] | [-3.58449399e-01 8.77916098e-01 -7.27144629e-02 -4.62307811e-01
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0e657964-70fd-4d69-9cbf-00c826cc0ac5 | coresets-for-relational-data-and-the | 2210.04249 | null | https://arxiv.org/abs/2210.04249v1 | https://arxiv.org/pdf/2210.04249v1.pdf | Coresets for Relational Data and The Applications | A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM. | ['Hu Ding', 'Ruomin Huang', 'Qingyuan Yang', 'Jiaxiang Chen'] | 2022-10-09 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 5.16400672e-02 2.14032918e-01 -1.98315606e-01 -3.44386339e-01
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4eb50db7-0eea-49a7-85d3-ce60483003b3 | f-vlm-open-vocabulary-object-detection-upon | 2209.15639 | null | https://arxiv.org/abs/2209.15639v2 | https://arxiv.org/pdf/2209.15639v2.pdf | F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models | We present F-VLM, a simple open-vocabulary object detection method built upon Frozen Vision and Language Models. F-VLM simplifies the current multi-stage training pipeline by eliminating the need for knowledge distillation or detection-tailored pretraining. Surprisingly, we observe that a frozen VLM: 1) retains the locality-sensitive features necessary for detection, and 2) is a strong region classifier. We finetune only the detector head and combine the detector and VLM outputs for each region at inference time. F-VLM shows compelling scaling behavior and achieves +6.5 mask AP improvement over the previous state of the art on novel categories of LVIS open-vocabulary detection benchmark. In addition, we demonstrate very competitive results on COCO open-vocabulary detection benchmark and cross-dataset transfer detection, in addition to significant training speed-up and compute savings. Code will be released at the https://sites.google.com/view/f-vlm/home | ['Anelia Angelova', 'AJ Piergiovanni', 'Xiuye Gu', 'Yin Cui', 'Weicheng Kuo'] | 2022-09-30 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [-2.02801749e-01 -1.30558133e-01 -3.24218839e-01 -2.03790531e-01
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528b1555-eacf-4b33-af42-0af08ad7540b | lda2net-digging-under-the-surface-of-covid-19 | 2112.01181 | null | https://arxiv.org/abs/2112.01181v2 | https://arxiv.org/pdf/2112.01181v2.pdf | LDA2Net: Digging under the surface of COVID-19 topics in scientific literature | During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically, both in terms of the number of publications and of its impact on people's life. This literature encompasses a varied set of sensible topics, ranging from vaccination, to protective equipment efficacy, to lockdown policy evaluation. Up to now, hundreds of thousands of papers have been uploaded on online repositories and published in scientific journals. As a result, the development of digital methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis to investigate topics under their surface. Specifically, LDA2Net exploits the frequencies of pairs of consecutive words to reconstruct the network structure of topics discussed in the Cord-19 corpus. The results suggest that the effectiveness of topic models can be magnified by enriching them with word network representations, and by using the latter to display, analyse, and explore COVID-related topics at different levels of granularity. | ['Massimo Warglien', 'Carlo R. M. A. Santagiustina', 'Giorgia Minello'] | 2021-12-02 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-8.33511204e-02 -1.04770578e-01 -3.61663699e-01 1.62871480e-01
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0ebb5123-8c49-4c95-bbc0-4d696cccb5e2 | molecule-property-prediction-based-on-spatial | null | null | https://doi.org/10.1021/acs.jcim.9b00410 | https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00410?rand=oin4mnup | Molecule Property Prediction Based on Spatial Graph Embedding | Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced to retain the spatial connection information on molecules. And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning. In order to enhance the robustness of the network, molecular fingerprints are also combined with C-SGEN to build a composite model for predicting molecular properties. Our comparative experiments have shown that our method is accurate and achieves the best results on some open benchmark datasets. | ['Xiao-Feng Wang', 'Zhiqiang Wei', 'Shugang Zhang', 'Shuang Wang', 'Mingjian Jiang', 'Zhen Li'] | 2019-08-22 | null | null | null | journal-of-chemical-information-and-modeling | ['graph-regression'] | ['graphs'] | [ 3.02646589e-02 -3.10264915e-01 -3.13556284e-01 -2.73290575e-01
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a725eb37-26da-4666-9542-7fe82dd6425e | retcl-a-selection-based-approach-for-1 | 2105.00795 | null | https://arxiv.org/abs/2105.00795v2 | https://arxiv.org/pdf/2105.00795v2.pdf | RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning | Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, we design an efficient reactant selection framework, named RetCL (retrosynthesis via contrastive learning), for enumerating all of the candidate molecules based on selection scores computed by graph neural networks. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining. Extensive experiments demonstrate the benefits of the proposed selection-based approach. For example, when all 671k reactants in the USPTO {database} are given as candidates, our RetCL achieves top-1 exact match accuracy of $71.3\%$ for the USPTO-50k benchmark, while a recent transformer-based approach achieves $59.6\%$. We also demonstrate that RetCL generalizes well to unseen templates in various settings in contrast to template-based approaches. | ['Jinwoo Shin', 'Eunho Yang', 'Sung-Ju Hwang', 'You Young Song', 'Seung-Woo Seo', 'Sungsoo Ahn', 'Hankook Lee'] | 2021-05-03 | retcl-a-selection-based-approach-for | https://openreview.net/forum?id=3u3ny6UYmjy | https://openreview.net/pdf?id=3u3ny6UYmjy | null | ['retrosynthesis'] | ['medical'] | [ 3.87747496e-01 -2.72991657e-01 -5.65787971e-01 -1.34521589e-01
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ddc8182d-3c61-4ccf-9071-dff18f9f66c5 | multi-label-music-genre-classification-from | 1707.04916 | null | http://arxiv.org/abs/1707.04916v1 | http://arxiv.org/pdf/1707.04916v1.pdf | Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features | Music genres allow to categorize musical items that share common
characteristics. Although these categories are not mutually exclusive, most
related research is traditionally focused on classifying tracks into a single
class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for
certain applications. In this work we aim to expand this task by categorizing
musical items into multiple and fine-grained labels, using three different data
modalities: audio, text, and images. To this end we present MuMu, a new dataset
of more than 31k albums classified into 250 genre classes. For every album we
have collected the cover image, text reviews, and audio tracks. Additionally,
we propose an approach for multi-label genre classification based on the
combination of feature embeddings learned with state-of-the-art deep learning
methodologies. Experiments show major differences between modalities, which not
only introduce new baselines for multi-label genre classification, but also
suggest that combining them yields improved results. | ['Serra Xavier', 'Barbieri Francesco', 'Nieto Oriol', 'Oramas Sergio'] | 2017-07-16 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.88307703e-01 -5.27959406e-01 -3.00434083e-01 -1.20269164e-01
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ef987f72-d42b-4667-b1db-bfd00e21cf25 | adaptive-graph-contrastive-learning-for | 2305.10837 | null | https://arxiv.org/abs/2305.10837v2 | https://arxiv.org/pdf/2305.10837v2.pdf | Adaptive Graph Contrastive Learning for Recommendation | Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quality training data. However, user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution. To address these issues, some recommendation approaches, such as SGL, leverage self-supervised learning to improve user representations. These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators to better empower the CF paradigm. Specifically, we use two trainable view generators - a graph generative model and a graph denoising model - to create adaptive contrastive views. With two adaptive contrastive views, AdaGCL introduces additional high-quality training signals into the CF paradigm, helping to alleviate data sparsity and noise issues. Extensive experiments on three real-world datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Our model implementation codes are available at the link https://github.com/HKUDS/AdaGCL. | ['Lianghao Xia', 'Chao Huang', 'Yangqin Jiang'] | 2023-05-18 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-9.58201289e-02 -1.43917486e-01 -3.45563203e-01 -3.93702716e-01
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69db42f7-1563-4846-a2ba-b819248d32de | enhancing-targeted-minority-class-prediction | null | null | https://www.mdpi.com/1424-8220/22/13/4911 | https://www.mdpi.com/1424-8220/22/13/4911/pdf?version=1656502842 | Enhancing Targeted Minority Class Prediction in Sentence-Level Relation Extraction | Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i.e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label. Due to those challenges, i.e., label noise and low source availability, most of the models fail to learn MC and get zero or very low F1 scores on MCs. Previous studies, however, have rather focused on micro F1 scores and MCs have not been addressed adequately. To tackle high mis-classification errors for MCs, we introduce (1) a minority class attention module (MCAM), and (2) effective augmentation methods specialized in RE. MCAM calculates the confidence scores on MC instances to select reliable ones for augmentation, and aggregates MCs information in the process of training a model. Our experiments show that our methods achieve a state-of-the-art F1 scores on TACRED as well as enhancing minority class F1 score dramatically. | ['Yong-Suk Choi', 'Hyeong-Ryeol Baek'] | 2022-06-29 | null | null | null | sensors-2022 | ['relation-classification'] | ['natural-language-processing'] | [ 2.88235664e-01 5.56324899e-01 -7.85225868e-01 -4.63149458e-01
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6278100f-84da-4b60-97eb-b0e2ffe0d870 | autotrigger-named-entity-recognition-with-1 | null | null | https://openreview.net/forum?id=8LQwB0FkR0X | https://openreview.net/pdf?id=8LQwB0FkR0X | AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction | Deep neural models for low-resource named entity recognition (NER) have shown impressive results by leveraging distant super-vision or other meta-level information (e.g. explanation). However, the costs of acquiring such additional information are generally prohibitive, especially in domains where existing resources (e.g. databases to be used for distant supervision) may not exist. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging "entity triggers" which are essentially human-readable clues in the text that can help guide the model to make better decisions. Thus, the framework is able to both create and leverage auxiliary supervision by itself. Through experiments on three well-studied NER datasets, we show that our automatically extracted triggers are well-matched to human triggers, and AutoTriggER improves performance over a RoBERTa-CRFarchitecture by nearly 0.5 F1 points on average and much more in a low resource setting. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.62776604e-01 3.92206967e-01 -2.11520776e-01 -6.26025259e-01
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85da096b-4900-4875-bc07-b9e7f0a3a476 | cdn-medal-two-stage-density-and-difference | 2106.03776 | null | https://arxiv.org/abs/2106.03776v4 | https://arxiv.org/pdf/2106.03776v4.pdf | CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis | Background modeling and subtraction is a promising research area with a variety of applications for video surveillance. Recent years have witnessed a proliferation of effective learning-based deep neural networks in this area. However, the techniques have only provided limited descriptions of scenes' properties while requiring heavy computations, as their single-valued mapping functions are learned to approximate the temporal conditional averages of observed target backgrounds and foregrounds. On the other hand, statistical learning in imagery domains has been a prevalent approach with high adaptation to dynamic context transformation, notably using Gaussian Mixture Models (GMM) with its generalization capabilities. By leveraging both, we propose a novel method called CDN-MEDAL-net for background modeling and subtraction with two convolutional neural networks. The first architecture, CDN-GM, is grounded on an unsupervised GMM statistical learning strategy to describe observed scenes' salient features. The second one, MEDAL-net, implements a light-weighted pipeline of online video background subtraction. Our two-stage architecture is small, but it is very effective with rapid convergence to representations of intricate motion patterns. Our experiments show that the proposed approach is not only capable of effectively extracting regions of moving objects in unseen cases, but it is also very efficient. | ['Cuong Tien Nguyen', 'Phuong Hoai Ha', 'Hung Ngoc Phan', 'Nhat Minh Chung', 'Synh Viet-Uyen Ha'] | 2021-06-07 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 4.76501167e-01 -4.17806566e-01 1.10909477e-01 -2.01057047e-01
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d651fd73-9494-4218-8f80-533f72af3fef | large-scale-weakly-supervised-pre-training | 1905.00561 | null | http://arxiv.org/abs/1905.00561v1 | http://arxiv.org/pdf/1905.00561v1.pdf | Large-scale weakly-supervised pre-training for video action recognition | Current fully-supervised video datasets consist of only a few hundred
thousand videos and fewer than a thousand domain-specific labels. This hinders
the progress towards advanced video architectures. This paper presents an
in-depth study of using large volumes of web videos for pre-training video
models for the task of action recognition. Our primary empirical finding is
that pre-training at a very large scale (over 65 million videos), despite on
noisy social-media videos and hashtags, substantially improves the
state-of-the-art on three challenging public action recognition datasets.
Further, we examine three questions in the construction of weakly-supervised
video action datasets. First, given that actions involve interactions with
objects, how should one construct a verb-object pre-training label space to
benefit transfer learning the most? Second, frame-based models perform quite
well on action recognition; is pre-training for good image features sufficient
or is pre-training for spatio-temporal features valuable for optimal transfer
learning? Finally, actions are generally less well-localized in long videos vs.
short videos; since action labels are provided at a video level, how should one
choose video clips for best performance, given some fixed budget of number or
minutes of videos? | ['Xueting Yan', 'Heng Wang', 'Du Tran', 'Dhruv Mahajan', 'Matt Feiszli', 'Deepti Ghadiyaram'] | 2019-05-02 | large-scale-weakly-supervised-pre-training-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ghadiyaram_Large-Scale_Weakly-Supervised_Pre-Training_for_Video_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ghadiyaram_Large-Scale_Weakly-Supervised_Pre-Training_for_Video_Action_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['activity-recognition-in-videos', 'egocentric-activity-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.56637949e-01 3.91514599e-02 -5.80047369e-01 -4.73559022e-01
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31e4b1fe-e504-4ef1-817e-e0f242b18766 | gapartnet-cross-category-domain-generalizable | 2211.05272 | null | https://arxiv.org/abs/2211.05272v2 | https://arxiv.org/pdf/2211.05272v2.pdf | GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts | For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world. Our dataset, code, and demos are available on our project page. | ['He Wang', 'Siyuan Huang', 'Li Yi', 'Chao Xu', 'Chengyang Zhao', 'Helin Xu', 'Haoran Geng'] | 2022-11-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.43573955e-01 2.20248699e-01 -5.55108450e-02 -3.10677707e-01
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7d0812b8-1c89-4d34-ac1c-572c999edf33 | speech-driven-facial-reenactment-using | 1803.07461 | null | http://arxiv.org/abs/1803.07461v1 | http://arxiv.org/pdf/1803.07461v1.pdf | Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks | We present a novel approach to generating photo-realistic images of a face
with accurate lip sync, given an audio input. By using a recurrent neural
network, we achieved mouth landmarks based on audio features. We exploited the
power of conditional generative adversarial networks to produce
highly-realistic face conditioned on a set of landmarks. These two networks
together are capable of producing a sequence of natural faces in sync with an
input audio track. | ['Hamid Aghajan', 'Hosein Hasani', 'Seyed Ali Jalalifar'] | 2018-03-20 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 5.91537297e-01 6.79419458e-01 3.03145260e-01 -2.74504542e-01
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8e2eec36-2e5c-44fc-8a70-2b22b77b82d2 | estimating-the-probabilities-of-causation-via | 2109.01904 | null | https://arxiv.org/abs/2109.01904v6 | https://arxiv.org/pdf/2109.01904v6.pdf | Estimating Categorical Counterfactuals via Deep Twin Networks | Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but the case of categorical variables remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference -- an alternative to the abduction, action, & prediction method. We empirically test our approach on diverse real-world and semi-synthetic data from medicine, epidemiology, and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced. | ['Ciaran M. Gilligan-Lee', 'Bernhard Kainz', 'Athanasios Vlontzos'] | 2021-09-04 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.03071129e-01 5.59103549e-01 -7.64414728e-01 -1.97224557e-01
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0784daca-be8c-4fa1-80e4-d41a6619bda6 | improving-heterogeneous-model-reuse-by | 2305.13871 | null | https://arxiv.org/abs/2305.13871v1 | https://arxiv.org/pdf/2305.13871v1.pdf | Improving Heterogeneous Model Reuse by Density Estimation | This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method. | ['DaCheng Tao', 'Yixin Chen', 'Bo Du', 'Kehua Su', 'Fengxiang He', 'Han Hu', 'Yong Luo', 'Anke Tang'] | 2023-05-23 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-7.06238002e-02 5.51937111e-02 -5.55637896e-01 -5.61432779e-01
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85796fd3-33ab-46c9-b215-9476bd596675 | online-human-action-detection-using-joint | 1604.05633 | null | http://arxiv.org/abs/1604.05633v2 | http://arxiv.org/pdf/1604.05633v2.pdf | Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks | Human action recognition from well-segmented 3D skeleton data has been
intensively studied and has been attracting an increasing attention. Online
action detection goes one step further and is more challenging, which
identifies the action type and localizes the action positions on the fly from
the untrimmed stream data. In this paper, we study the problem of online action
detection from streaming skeleton data. We propose a multi-task end-to-end
Joint Classification-Regression Recurrent Neural Network to better explore the
action type and temporal localization information. By employing a joint
classification and regression optimization objective, this network is capable
of automatically localizing the start and end points of actions more
accurately. Specifically, by leveraging the merits of the deep Long Short-Term
Memory (LSTM) subnetwork, the proposed model automatically captures the complex
long-range temporal dynamics, which naturally avoids the typical sliding window
design and thus ensures high computational efficiency. Furthermore, the subtask
of regression optimization provides the ability to forecast the action prior to
its occurrence. To evaluate our proposed model, we build a large streaming
video dataset with annotations. Experimental results on our dataset and the
public G3D dataset both demonstrate very promising performance of our scheme. | ['Wen-Jun Zeng', 'Junliang Xing', 'Jiaying Liu', 'Yanghao Li', 'Chunfeng Yuan', 'Cuiling Lan'] | 2016-04-19 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.95346189e-01 -2.02855185e-01 -5.07035434e-01 -2.06903607e-01
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82cba57a-3f2c-42a5-ae9a-6fb35d2773f7 | bi-sampling-approach-to-classify-music-mood | 2203.06583 | null | https://arxiv.org/abs/2203.06583v1 | https://arxiv.org/pdf/2203.06583v1.pdf | Bi-Sampling Approach to Classify Music Mood leveraging Raga-Rasa Association in Indian Classical Music | The impact of Music on the mood or emotion of the listener is a well-researched area in human psychology and behavioral science. In Indian classical music, ragas are the melodic structure that defines the various styles and forms of the music. Each raga has been found to evoke a specific emotion in the listener. With the advent of advanced capabilities of audio signal processing and the application of machine learning, the demand for intelligent music classifiers and recommenders has received increased attention, especially in the 'Music as a service' cloud applications. This paper explores a novel framework to leverage the raga-rasa association in Indian classical Music to build an intelligent classifier and its application in music recommendation system based on user's current mood and the mood they aspire to be in. | ['Narayana Darapaneni', 'Sudha G', 'Pathi Mohan Rao', 'Ullas M S', 'Gayathri Ramesh K K', 'Sushmitha M N', 'Harsha M N', 'Vinayak Arkachaari', 'Mohan Rao B C'] | 2022-03-13 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 7.81554654e-02 -6.92374706e-01 -4.19939160e-01 -1.86919525e-01
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5429593b-2689-4246-9194-fd6d8db0aaf0 | global-local-face-upsampling-network | 1603.07235 | null | http://arxiv.org/abs/1603.07235v2 | http://arxiv.org/pdf/1603.07235v2.pdf | Global-Local Face Upsampling Network | Face hallucination, which is the task of generating a high-resolution face
image from a low-resolution input image, is a well-studied problem that is
useful in widespread application areas. Face hallucination is particularly
challenging when the input face resolution is very low (e.g., 10 x 12 pixels)
and/or the image is captured in an uncontrolled setting with large pose and
illumination variations. In this paper, we revisit the algorithm introduced in
[1] and present a deep interpretation of this framework that achieves
state-of-the-art under such challenging scenarios. In our deep network
architecture the global and local constraints that define a face can be
efficiently modeled and learned end-to-end using training data. Conceptually
our network design can be partitioned into two sub-networks: the first one
implements the holistic face reconstruction according to global constraints,
and the second one enhances face-specific details and enforces local patch
statistics. We optimize the deep network using a new loss function for
super-resolution that combines reconstruction error with a learned face quality
measure in adversarial setting, producing improved visual results. We conduct
extensive experiments in both controlled and uncontrolled setups and show that
our algorithm improves the state of the art both numerically and visually. | ['Oncel Tuzel', 'John R. Hershey', 'Yuichi Taguchi'] | 2016-03-23 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 4.53109056e-01 2.65119761e-01 1.35956302e-01 -3.66073847e-01
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74a31fdf-fd88-4163-bcd9-885aa77acf1e | semantic-table-retrieval-using-keyword-and | 2105.06365 | null | https://arxiv.org/abs/2105.06365v1 | https://arxiv.org/pdf/2105.06365v1.pdf | Semantic Table Retrieval using Keyword and Table Queries | Tables on the Web contain a vast amount of knowledge in a structured form. To tap into this valuable resource, we address the problem of table retrieval: answering an information need with a ranked list of tables. We investigate this problem in two different variants, based on how the information need is expressed: as a keyword query or as an existing table ("query-by-table"). The main novel contribution of this work is a semantic table retrieval framework for matching information needs (keyword or table queries) against tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using two purpose-built test collections based on Wikipedia tables, we demonstrate significant and substantial improvements over state-of-the-art baselines. | ['Krisztian Balog', 'Shuo Zhang'] | 2021-05-13 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 2.22996905e-01 1.79466203e-01 -5.13928592e-01 -4.95981783e-01
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571c0261-bccc-454a-8842-29913cb4dfae | segloc-learning-segmentation-based | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.pdf | SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization | Inspired by properties of semantic segmentation, in this paper we investigate how to leverage robust image segmentation in the context of privacy-preserving visual localization. We propose a new localization framework, SegLoc, that leverages image segmentation to create robust, compact, and privacy-preserving scene representations, i.e., 3D maps. We build upon the correspondence-supervised, fine-grained segmentation approach from Larsson et al (ICCV'19), making it more robust by learning a set of cluster labels with discriminative clustering, additional consistency regularization terms and we jointly learn a global image representation along with a dense local representation. In our localization pipeline, the former will be used for retrieving the most similar images, the latter to refine the retrieved poses by minimizing the label inconsistency between the 3D points of the map and their projection onto the query image. In various experiments, we show that our proposed representation allows to achieve (close-to) state-of-the-art pose estimation results while only using a compact 3D map that does not contain enough information about the original images for an attacker to reconstruct personal information. | ['Gabriela Csurka', 'Torsten Sattler', 'Martin Humenberger', 'Maxime Pietrantoni'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [ 2.55021900e-01 3.01654518e-01 -2.19775572e-01 -4.50432986e-01
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ca9b9fd4-34b3-4a18-a73b-3ed20a9686e9 | revisiting-resnets-improved-training-and | 2103.07579 | null | https://arxiv.org/abs/2103.07579v1 | https://arxiv.org/pdf/2103.07579v1.pdf | Revisiting ResNets: Improved Training and Scaling Strategies | Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al., 2015) and studies these three aspects in an effort to disentangle them. Perhaps surprisingly, we find that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan & Le, 2019). Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1.7x - 2.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. In a large-scale semi-supervised learning setup, ResNet-RS achieves 86.2% top-1 ImageNet accuracy, while being 4.7x faster than EfficientNet NoisyStudent. The training techniques improve transfer performance on a suite of downstream tasks (rivaling state-of-the-art self-supervised algorithms) and extend to video classification on Kinetics-400. We recommend practitioners use these simple revised ResNets as baselines for future research. | ['Barret Zoph', 'Jonathon Shlens', 'Tsung-Yi Lin', 'Aravind Srinivas', 'Ekin D. Cubuk', 'Xianzhi Du', 'William Fedus', 'Irwan Bello'] | 2021-03-13 | null | http://proceedings.neurips.cc/paper/2021/hash/bef4d169d8bddd17d68303877a3ea945-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/bef4d169d8bddd17d68303877a3ea945-Paper.pdf | neurips-2021-12 | ['document-image-classification'] | ['computer-vision'] | [ 1.96856156e-01 -4.51506302e-02 -2.47733891e-01 -1.74229339e-01
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62202db6-a9ca-4167-8f8c-c247902a6485 | exploring-deep-spiking-neural-networks-for | 1903.02080 | null | http://arxiv.org/abs/1903.02080v1 | http://arxiv.org/pdf/1903.02080v1.pdf | Exploring Deep Spiking Neural Networks for Automated Driving Applications | Neural networks have become the standard model for various computer vision
tasks in automated driving including semantic segmentation, moving object
detection, depth estimation, visual odometry, etc. The main flavors of neural
networks which are used commonly are convolutional (CNN) and recurrent (RNN).
In spite of rapid progress in embedded processors, power consumption and cost
is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing
to achieve low-power event-driven hardware architecture which has a potential
for high efficiency. In this paper, we explore the role of deep spiking neural
networks (SNN) for automated driving applications. We provide an overview of
progress on SNN and argue how it can be a good fit for automated driving
applications. | ['Sambit Mohapatra', 'Senthil Yogamani', 'Raoul Zollner', 'Stefan Milz', 'Heinrich Gotzig'] | 2019-01-11 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 2.29158774e-01 -2.59999305e-01 -5.97899631e-02 -1.91786617e-01
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8350b92b-0b1c-4ffb-b1c2-8ff14ffd2dc4 | distributive-pre-training-of-generative | 2306.14787 | null | https://arxiv.org/abs/2306.14787v1 | https://arxiv.org/pdf/2306.14787v1.pdf | Distributive Pre-Training of Generative Modeling Using Matrix-Product States | Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning. The most common approaches for training these models are gradient descent methods. In this work, we consider an alternative training scheme utilizing basic tensor network operations, e.g., summation and compression. The training algorithm is based on compressing the superposition state constructed from all the training data in product state representation. The algorithm could be parallelized easily and only iterates through the dataset once. Hence, it serves as a pre-training algorithm. We benchmark the algorithm on the MNIST dataset and show reasonable results for generating new images and classification tasks. Furthermore, we provide an interpretation of the algorithm as a compressed quantum kernel density estimation for the probability amplitude of input data. | ['Frank Pollmann', 'Sebastian Peterhansl', 'Olivier Kuijpers', 'Sheng-Hsuan Lin'] | 2023-06-26 | null | null | null | null | ['tensor-networks', 'density-estimation'] | ['methodology', 'methodology'] | [ 5.91823578e-01 -4.01095673e-02 -2.53292710e-01 -4.45629090e-01
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71ba565d-8eb1-4c3e-8b26-25766fc1e6c2 | pca-semi-supervised-segmentation-with-patch | 2207.11683 | null | https://arxiv.org/abs/2207.11683v1 | https://arxiv.org/pdf/2207.11683v1.pdf | PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training | Deep learning based semi-supervised learning (SSL) methods have achieved strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training based methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preference. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. Rather than single scalar classification results or pixel-level confidence maps, our proposed discriminator creates patch confidence maps and classifies them at the scale of the patches. The prediction of unlabeled data learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state and improves semi-supervised segmentation performance. Furthermore, at the discriminator's input, we supplement semantic information constraints on images, making it simpler for unlabeled data to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. | ['Thomas Lukasiewicz', 'Shuo Zhang', 'Zhenghua Xu', 'Zihang Xu'] | 2022-07-24 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 5.63095748e-01 7.06591845e-01 -4.76797402e-01 -5.46814322e-01
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6256ddd7-4374-4c84-83df-a9ed0f022f00 | aesthetics-of-neural-network-art | 1903.05696 | null | http://arxiv.org/abs/1903.05696v2 | http://arxiv.org/pdf/1903.05696v2.pdf | Aesthetics of Neural Network Art | This paper proposes a way to understand neural network artworks as
juxtapositions of natural image cues. It is hypothesized that images with
unusual combinations of realistic visual cues are interesting, and, neural
models trained to model natural images are well-suited to creating interesting
images. Art using neural models produces new images similar to those of natural
images, but with weird and intriguing variations. This analysis is applied to
neural art based on Generative Adversarial Networks, image stylization, Deep
Dreams, and Perception Engines. | ['Aaron Hertzmann'] | 2019-03-13 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 4.24644798e-01 6.84302866e-01 -2.02176534e-02 -1.70078173e-01
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697ad28b-9578-49d3-84d1-d7978ca771c3 | exploiting-relationship-for-complex-scene | 2104.00356 | null | https://arxiv.org/abs/2104.00356v1 | https://arxiv.org/pdf/2104.00356v1.pdf | Exploiting Relationship for Complex-scene Image Generation | The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generation (with various interactions among multiple objects) still suffers from messy layouts and object distortions, due to diverse configurations in layouts and appearances. Prior methods are mostly object-driven and ignore their inter-relations that play a significant role in complex-scene images. This work explores relationship-aware complex-scene image generation, where multiple objects are inter-related as a scene graph. With the help of relationships, we propose three major updates in the generation framework. First, reasonable spatial layouts are inferred by jointly considering the semantics and relationships among objects. Compared to standard location regression, we show relative scales and distances serve a more reliable target. Second, since the relations between objects significantly influence an object's appearance, we design a relation-guided generator to generate objects reflecting their relationships. Third, a novel scene graph discriminator is proposed to guarantee the consistency between the generated image and the input scene graph. Our method tends to synthesize plausible layouts and objects, respecting the interplay of multiple objects in an image. Experimental results on Visual Genome and HICO-DET datasets show that our proposed method significantly outperforms prior arts in terms of IS and FID metrics. Based on our user study and visual inspection, our method is more effective in generating logical layout and appearance for complex-scenes. | ['Tao Mei', 'Xiao-Ping Zhang', 'Wei zhang', 'Yalong Bai', 'Hongdong Zheng', 'Tianyu Hua'] | 2021-04-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.19645548e-01 -5.42575307e-02 2.41114721e-01 -3.90405476e-01
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6eee81c4-844c-46e7-a95f-7da56d6198b1 | on-the-limits-of-learning-to-actively-learn | 1910.02228 | null | https://arxiv.org/abs/1910.02228v1 | https://arxiv.org/pdf/1910.02228v1.pdf | On the Limits of Learning to Actively Learn Semantic Representations | One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively-learn (LTAL) is a recent paradigm for reducing the amount of labeled data by learning a policy that selects which samples should be labeled. In this work, we examine LTAL for learning semantic representations, such as QA-SRL. We show that even an oracle policy that is allowed to pick examples that maximize performance on the test set (and constitutes an upper bound on the potential of LTAL), does not substantially improve performance compared to a random policy. We investigate factors that could explain this finding and show that a distinguishing characteristic of successful applications of LTAL is the interaction between optimization and the oracle policy selection process. In successful applications of LTAL, the examples selected by the oracle policy do not substantially depend on the optimization procedure, while in our setup the stochastic nature of optimization strongly affects the examples selected by the oracle. We conclude that the current applicability of LTAL for improving data efficiency in learning semantic meaning representations is limited. | ['Jonathan Berant', 'Gabriel Stanovsky', 'Yichu Zhou', 'Vivek Srikumar', 'Omri Koshorek'] | 2019-10-05 | on-the-limits-of-learning-to-actively-learn-1 | https://aclanthology.org/K19-1042 | https://aclanthology.org/K19-1042.pdf | conll-2019-11 | ['learning-semantic-representations'] | ['methodology'] | [ 5.68651140e-01 5.46838701e-01 -4.05797809e-01 -6.98311806e-01
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4975adda-81d9-4277-943b-c0b68e2377e5 | graph-laplacians-on-shared-nearest-neighbor | 2302.12399 | null | https://arxiv.org/abs/2302.12399v2 | https://arxiv.org/pdf/2302.12399v2.pdf | Graph Laplacians on Shared Nearest Neighbor graphs and graph Laplacians on $k$-Nearest Neighbor graphs having the same limit | A Shared Nearest Neighbor (SNN) graph is a type of graph construction using shared nearest neighbor information, which is a secondary similarity measure based on the rankings induced by a primary $k$-nearest neighbor ($k$-NN) measure. SNN measures have been touted as being less prone to the curse of dimensionality than conventional distance measures, and thus methods using SNN graphs have been widely used in applications, particularly in clustering high-dimensional data sets and in finding outliers in subspaces of high dimensional data. Despite this, the theoretical study of SNN graphs and graph Laplacians remains unexplored. In this pioneering work, we make the first contribution in this direction. We show that large scale asymptotics of an SNN graph Laplacian reach a consistent continuum limit; this limit is the same as that of a $k$-NN graph Laplacian. Moreover, we show that the pointwise convergence rate of the graph Laplacian is linear with respect to $(k/n)^{1/m}$ with high probability. | ['A. Martina Neuman'] | 2023-02-24 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-4.99552898e-02 1.62937999e-01 -7.73943067e-02 -3.34329307e-01
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7066c99a-31af-4593-b351-bab691124fae | hdr-video-reconstruction-with-tri-exposure | 2103.10982 | null | https://arxiv.org/abs/2103.10982v1 | https://arxiv.org/pdf/2103.10982v1.pdf | HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors | We propose a novel high dynamic range (HDR) video reconstruction method with new tri-exposure quad-bayer sensors. Thanks to the larger number of exposure sets and their spatially uniform deployment over a frame, they are more robust to noise and spatial artifacts than previous spatially varying exposure (SVE) HDR video methods. Nonetheless, the motion blur from longer exposures, the noise from short exposures, and inherent spatial artifacts of the SVE methods remain huge obstacles. Additionally, temporal coherence must be taken into account for the stability of video reconstruction. To tackle these challenges, we introduce a novel network architecture that divides-and-conquers these problems. In order to better adapt the network to the large dynamic range, we also propose LDR-reconstruction loss that takes equal contributions from both the highlighted and the shaded pixels of HDR frames. Through a series of comparisons and ablation studies, we show that the tri-exposure quad-bayer with our solution is more optimal to capture than previous reconstruction methods, particularly for the scenes with larger dynamic range and objects with motion. | ['Jinwei Gu', 'Jun Jiang', 'Inchang Choi', 'Yitong Jiang'] | 2021-03-19 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 3.81157458e-01 -6.20957255e-01 3.19109827e-01 -1.02406338e-01
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a20abccf-4ab8-4ba7-a796-66ebd87225fe | contextual-sentence-classification-detecting | 2110.03727 | null | https://arxiv.org/abs/2110.03727v2 | https://arxiv.org/pdf/2110.03727v2.pdf | Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports | We introduce the novel task of detecting sustainability initiatives in company reports. Given a full report, the aim is to automatically identify mentions of practical activities that a company has performed in order to tackle specific societal issues. New methods for identifying continuous sentence spans need to be developed for capturing the multi-sentence structure of individual sustainability initiatives. We release a new dataset of company reports in which the text has been manually annotated with sustainability initiatives. We also evaluate different models for initiative detection, introducing a novel aggregation and evaluation methodology. Our proposed architecture uses sequences of consecutive sentences to account for contextual information when making classification decisions at the individual sentence level. | ['Marek Rei', 'Maurizio Zollo', 'Christopher Bryant', 'Dan Hirlea'] | 2021-10-07 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 6.64556563e-01 1.95460349e-01 -3.35873216e-01 -2.01197192e-01
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1b76019a-1b92-416b-889b-39032fe54af0 | carma-context-aware-runtime-reconfiguration | 2306.15748 | null | https://arxiv.org/abs/2306.15748v1 | https://arxiv.org/pdf/2306.15748v1.pdf | CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion | Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learning models, and powerful hardware platforms to perceive and safely operate in real-time. However, in many contexts, some sensing modalities negatively impact perception while increasing the system's overall energy consumption. Since AS are often energy-constrained edge devices, energy-efficient sensor fusion methods have been proposed. However, existing methods either fail to adapt to changing scenario conditions or to optimize energy efficiency system-wide. We propose CARMA: a context-aware sensor fusion approach that uses context to dynamically reconfigure the computation flow on a Field-Programmable Gate Array (FPGA) at runtime. By clock-gating unused sensors and model sub-components, CARMA significantly reduces the energy used by a multi-sensory object detector without compromising performance. We use a Deep-learning Processor Unit (DPU) based reconfiguration approach to minimize the latency of model reconfiguration. We evaluate multiple context-identification strategies, propose a novel system-wide energy-performance joint optimization, and evaluate scenario-specific perception performance. Across challenging real-world sensing contexts, CARMA outperforms state-of-the-art methods with up to 1.3x speedup and 73% lower energy consumption. | ['Sitao Huang', 'Mohammad Abdullah Al Faruque', 'DongHwan Seong', 'Yuhui Li', 'Xiaofang Zhang', 'Arnav Vaibhav Malawade', 'Yifan Zhang'] | 2023-06-27 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 5.33916771e-01 -3.83692682e-01 -1.81353778e-01 -2.28559658e-01
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bf8f2082-1cb1-411e-b22f-bb9103317470 | language-guided-face-animation-by-recurrent | 2208.05617 | null | https://arxiv.org/abs/2208.05617v1 | https://arxiv.org/pdf/2208.05617v1.pdf | Language-Guided Face Animation by Recurrent StyleGAN-based Generator | Recent works on language-guided image manipulation have shown great power of language in providing rich semantics, especially for face images. However, the other natural information, motions, in language is less explored. In this paper, we leverage the motion information and study a novel task, language-guided face animation, that aims to animate a static face image with the help of languages. To better utilize both semantics and motions from languages, we propose a simple yet effective framework. Specifically, we propose a recurrent motion generator to extract a series of semantic and motion information from the language and feed it along with visual information to a pre-trained StyleGAN to generate high-quality frames. To optimize the proposed framework, three carefully designed loss functions are proposed including a regularization loss to keep the face identity, a path length regularization loss to ensure motion smoothness, and a contrastive loss to enable video synthesis with various language guidance in one single model. Extensive experiments with both qualitative and quantitative evaluations on diverse domains (\textit{e.g.,} human face, anime face, and dog face) demonstrate the superiority of our model in generating high-quality and realistic videos from one still image with the guidance of language. Code will be available at https://github.com/TiankaiHang/language-guided-animation.git. | ['Baining Guo', 'Xin Geng', 'Jianlong Fu', 'Bei Liu', 'Huan Yang', 'Tiankai Hang'] | 2022-08-11 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 1.28193021e-01 7.49178091e-03 -1.90795034e-01 -4.25326526e-01
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3f6ebe8b-e524-4bb6-a70d-3652c4fbfe34 | integrated-parameter-efficient-tuning-for | 2211.02227 | null | https://arxiv.org/abs/2211.02227v2 | https://arxiv.org/pdf/2211.02227v2.pdf | Integrated Parameter-Efficient Tuning for General-Purpose Audio Models | The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for downstream tasks. Nevertheless, re-training all the parameters of these massive models entails an enormous amount of time and cost, along with a huge carbon footprint. To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain. We also propose an integrated parameter-efficient tuning (IPET) framework by aggregating the embedding prompt (a prompt-based learning approach), and the adapter (an effective transfer learning method). We demonstrate the efficacy of the proposed framework using two backbone pre-trained audio models with different characteristics: the audio spectrogram transformer and wav2vec 2.0. The proposed IPET framework exhibits remarkable performance compared to fine-tuning method with fewer trainable parameters in four downstream tasks: sound event classification, music genre classification, keyword spotting, and speaker verification. Furthermore, the authors identify and analyze the shortcomings of the IPET framework, providing lessons and research directions for parameter efficient tuning in the audio domain. | ['Ha-Jin Yu', 'Chan-yeong Lim', 'Hyun-seo Shin', 'Jungwoo Heo', 'Ju-ho Kim'] | 2022-11-04 | null | null | null | null | ['genre-classification', 'keyword-spotting', 'speaker-verification'] | ['computer-vision', 'speech', 'speech'] | [ 3.16117108e-01 -3.55304867e-01 1.41017541e-01 -2.76466191e-01
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060bc08c-b5df-4d28-b34b-5f13afda3e03 | enabling-country-scale-land-cover-mapping | 2209.00727 | null | https://arxiv.org/abs/2209.00727v2 | https://arxiv.org/pdf/2209.00727v2.pdf | Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery | High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries, few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 square kilometers, the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution. | ['Xiao Xiang Zhu', 'Gui-Song Xia', 'Xin-Yi Tong'] | 2022-09-01 | null | null | null | null | ['segmentation-of-remote-sensing-imagery', 'the-semantic-segmentation-of-remote-sensing'] | ['miscellaneous', 'miscellaneous'] | [ 1.65641397e-01 -2.76155442e-01 -4.82699275e-01 -4.75697845e-01
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4ce3a00f-5eb7-45c5-be1e-4f466ac35197 | signed-network-embedding-with-application-to | 2207.09324 | null | https://arxiv.org/abs/2207.09324v2 | https://arxiv.org/pdf/2207.09324v2.pdf | Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies | Signed networks are frequently observed in real life with additional sign information associated with each edge, yet such information has been largely ignored in existing network models. This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downstream analysis, including community detection, anomaly detection, and network inference. The proposed model captures both balance structure and anomaly effect through a low rank plus sparse matrix decomposition, which are jointly estimated via a regularized formulation. Its theoretical guarantees are established in terms of asymptotic consistency and finite-sample probability bounds for network embedding, community detection and anomaly detection. The advantage of the proposed embedding model is also demonstrated through extensive numerical experiments on both synthetic networks and an international relation network. | ['Junhui Wang', 'Haoran Zhang'] | 2022-07-08 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 5.90545572e-02 1.02360100e-01 -4.34429288e-01 -9.58589464e-02
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6660ded1-9ad5-4236-a449-4603bf1f123d | gamesh-guided-and-augmented-meshing-for-deep | 2010.09774 | null | https://arxiv.org/abs/2010.09774v1 | https://arxiv.org/pdf/2010.09774v1.pdf | GAMesh: Guided and Augmented Meshing for Deep Point Networks | We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network. By projecting the output points onto this prior and simplifying the resulting mesh, GAMesh ensures a surface with the same topology as the mesh prior but whose geometric fidelity is controlled by the point network. This makes GAMesh independent of both the density and distribution of the output points, a common artifact in traditional surface reconstruction algorithms. We show that such a separation of geometry from topology can have several advantages especially in single-view shape prediction, fair evaluation of point networks and reconstructing surfaces for networks which output sparse point clouds. We further show that by training point networks with GAMesh, we can directly optimize the vertex positions to generate adaptive meshes with arbitrary topologies. | ['M Gopi', 'Nitin Agarwal'] | 2020-10-19 | null | null | null | null | ['3d-surface-generation', 'single-view-3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.24921221e-01 8.66747916e-01 2.77373433e-01 -1.45236298e-01
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a04a8cf6-b5b2-4514-97bd-631815f6e837 | increasing-the-scope-as-you-learn-adaptive | 2304.11468 | null | https://arxiv.org/abs/2304.11468v1 | https://arxiv.org/pdf/2304.11468v1.pdf | Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces | Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-the-art methods for high-dimensional Bayesian optimization (HDBO) suffer from degrading performance as the number of dimensions increases or even risk failure if certain unverifiable assumptions are not met. This paper proposes BAxUS that leverages a novel family of nested random subspaces to adapt the space it optimizes over to the problem. This ensures high performance while removing the risk of failure, which we assert via theoretical guarantees. A comprehensive evaluation demonstrates that BAxUS achieves better results than the state-of-the-art methods for a broad set of applications. | ['Matthias Poloczek', 'Luigi Nardi', 'Leonard Papenmeier'] | 2023-04-22 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.56652063e-01 -9.27507356e-02 -1.95751652e-01 -1.88598335e-01
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2f573719-3bb9-446c-a875-847f7cbb654f | contrastive-embedding-distribution-refinement | 2201.11388 | null | https://arxiv.org/abs/2201.11388v1 | https://arxiv.org/pdf/2201.11388v1.pdf | Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification | Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision. Different from images where RGB pixels are stored in the regular grid, for point clouds, the underlying semantic and structural information of point clouds is the spatial layout of the points. Moreover, the properties of challenging in-context and background noise pose more challenges to point cloud analysis. One assumption is that the poor performance of the classification model can be attributed to the indistinguishable embedding feature that impedes the search for the optimal classifier. This work offers a new strategy for learning powerful representations via a contrastive learning approach that can be embedded into any point cloud classification network. First, we propose a supervised contrastive classification method to implement embedding feature distribution refinement by improving the intra-class compactness and inter-class separability. Second, to solve the confusion problem caused by small inter-class compactness and inter-class separability. Second, to solve the confusion problem caused by small inter-class variations between some similar-looking categories, we propose a confusion-prone class mining strategy to alleviate the confusion effect. Finally, considering that outliers of the sample clusters in the embedding space may cause performance degradation, we design an entropy-aware attention module with information entropy theory to identify the outlier cases and the unstable samples by measuring the uncertainty of predicted probability. The results of extensive experiments demonstrate that our method outperforms the state-of-the-art approaches by achieving 82.9% accuracy on the real-world ScanObjectNN dataset and substantial performance gains up to 2.9% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet. | ['Weigong Zhang', 'Xuanpeng Li', 'Shuai Jin', 'Qifan Xue', 'Yichao Cao', 'Feng Yang'] | 2022-01-27 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 9.79845971e-03 -2.24265829e-02 -2.19879858e-02 -3.45483094e-01
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d3da0e45-fbbe-4e1c-b128-b135d04cdf8d | a-3d-model-based-approach-for-fitting-masks | 2103.00803 | null | https://arxiv.org/abs/2103.00803v2 | https://arxiv.org/pdf/2103.00803v2.pdf | A 3D model-based approach for fitting masks to faces in the wild | Face recognition now requires a large number of labelled masked face images in the era of this unprecedented COVID-19 pandemic. Unfortunately, the rapid spread of the virus has left us little time to prepare for such dataset in the wild. To circumvent this issue, we present a 3D model-based approach called WearMask3D for augmenting face images of various poses to the masked face counterparts. Our method proceeds by first fitting a 3D morphable model on the input image, second overlaying the mask surface onto the face model and warping the respective mask texture, and last projecting the 3D mask back to 2D. The mask texture is adapted based on the brightness and resolution of the input image. By working in 3D, our method can produce more natural masked faces of diverse poses from a single mask texture. To compare precisely between different augmentation approaches, we have constructed a dataset comprising masked and unmasked faces with labels called MFW-mini. Experimental results demonstrate WearMask3D produces more realistic masked faces, and utilizing these images for training leads to state-of-the-art recognition accuracy for masked faces. | ['Ig-Jae Kim', 'Hyeong-Seok Ko', 'Junghyun Cho', 'Gi Pyo Nam', 'Minsoo Kim', 'Hanjo Kim', 'Je Hyeong Hong'] | 2021-03-01 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 6.58073723e-01 4.44978446e-01 2.67389715e-01 -5.36540985e-01
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fe5fb1b6-87a3-4cae-97dd-85b9f8c9b85a | learning-from-synthetic-data-facial | 2207.10025 | null | https://arxiv.org/abs/2207.10025v2 | https://arxiv.org/pdf/2207.10025v2.pdf | Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks | Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic in the facial expression recognition task. In this paper, we propose a multi-task learning-based facial expression recognition approach which consists of emotion and appearance learning branches that can share all face information, and present preliminary results for the LSD challenge introduced in the 4th affective behavior analysis in-the-wild (ABAW) competition. Our method achieved the mean F1 score of 0.71. | ['Yuchul Jung', 'Jin-Woo Jeong', 'Sumin Hong', 'JiYeon Oh', 'Yeong-Gi Hong', 'Jae-Yeop Jeong'] | 2022-07-20 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-9.54078417e-03 -2.93357909e-01 1.17046304e-01 -9.81216133e-01
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6ecedcae-977c-4c4c-8c34-55c74878e946 | towards-metamerism-via-foveated-style | 1705.10041 | null | http://arxiv.org/abs/1705.10041v3 | http://arxiv.org/pdf/1705.10041v3.pdf | Towards Metamerism via Foveated Style Transfer | The problem of $\textit{visual metamerism}$ is defined as finding a family of
perceptually indistinguishable, yet physically different images. In this paper,
we propose our NeuroFovea metamer model, a foveated generative model that is
based on a mixture of peripheral representations and style transfer
forward-pass algorithms. Our gradient-descent free model is parametrized by a
foveated VGG19 encoder-decoder which allows us to encode images in high
dimensional space and interpolate between the content and texture information
with adaptive instance normalization anywhere in the visual field. Our
contributions include: 1) A framework for computing metamers that resembles a
noisy communication system via a foveated feed-forward encoder-decoder network
-- We observe that metamerism arises as a byproduct of noisy perturbations that
partially lie in the perceptual null space; 2) A perceptual optimization scheme
as a solution to the hyperparametric nature of our metamer model that requires
tuning of the image-texture tradeoff coefficients everywhere in the visual
field which are a consequence of internal noise; 3) An ABX psychophysical
evaluation of our metamers where we also find that the rate of growth of the
receptive fields in our model match V1 for reference metamers and V2 between
synthesized samples. Our model also renders metamers at roughly a second,
presenting a $\times1000$ speed-up compared to the previous work, which allows
for tractable data-driven metamer experiments. | ['Miguel Eckstein', 'Aditya Jonnalagadda', 'Arturo Deza'] | 2017-05-29 | towards-metamerism-via-foveated-style-1 | https://openreview.net/forum?id=BJzbG20cFQ | https://openreview.net/pdf?id=BJzbG20cFQ | iclr-2019-5 | ['metamerism'] | ['computer-vision'] | [ 4.02605146e-01 2.57555485e-01 6.68764710e-01 -2.18586624e-01
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ce67cd3f-4ddc-4321-936b-98e30773283e | efficient-semantic-scene-completion-network-1 | 1907.05091 | null | https://arxiv.org/abs/1907.05091v1 | https://arxiv.org/pdf/1907.05091v1.pdf | Efficient Semantic Scene Completion Network with Spatial Group Convolution | We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimensions rather than feature channel dimension. It divides input voxels into different groups, then conducts 3D sparse convolution on these separated groups. As only valid voxels are considered when performing convolution, computation can be significantly reduced with a slight loss of accuracy. The proposed operations are validated on semantic scene completion task, which aims to predict a complete 3D volume with semantic labels from a single depth image. With SGC, we further present an efficient 3D sparse convolutional network, which harnesses a multiscale architecture and a coarse-to-fine prediction strategy. Evaluations are conducted on the SUNCG dataset, achieving state-of-the-art performance and fast speed. Code is available at https://github.com/zjhthu/SGC-Release.git | ['Yurong Chen', 'Li Zhang', 'Anbang Yao', 'Hongen Liao', 'Hao Zhao', 'Jiahui Zhang'] | 2019-07-11 | efficient-semantic-scene-completion-network | http://openaccess.thecvf.com/content_ECCV_2018/html/Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 7.03955665e-02 7.20365494e-02 1.51083723e-01 -4.88589227e-01
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66cc39d7-a347-4419-ab3f-5106bde12c30 | cooperative-perception-for-safe-control-of | 2302.07341 | null | https://arxiv.org/abs/2302.07341v1 | https://arxiv.org/pdf/2302.07341v1.pdf | Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks | Autonomous vehicles rely on LiDAR sensors to detect obstacles such as pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks have been demonstrated that either create erroneous obstacles or prevent detection of real obstacles, resulting in unsafe driving behaviors. In this paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by leveraging LiDAR scan data from other neighboring vehicles. This approach exploits the fact that spoofing attacks can typically only be mounted on one vehicle at a time, and introduce additional points into the victim's scan that can be readily detected by comparison from other, non-modified scans. We develop a Fault Detection, Identification, and Isolation procedure that identifies non-existing obstacle, physical removal, and adversarial object attacks, while also estimating the actual locations of obstacles. We propose a control algorithm that guarantees that these estimated object locations are avoided. We validate our framework using a CARLA simulation study, in which we verify that our FDII algorithm correctly detects each attack pattern. | ['Andrew Clark', 'Shiyu Cheng', 'Zhouchi Li', 'Hongchao Zhang'] | 2023-02-14 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.73485124e-01 9.72065888e-03 -4.97315787e-02 3.07966005e-02
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b9516821-e196-411f-86c0-21aa659176da | scalable-low-rank-autoregressive-tensor | 2008.03194 | null | https://arxiv.org/abs/2008.03194v3 | https://arxiv.org/pdf/2008.03194v3.pdf | Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation | Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large data tensors. In this paper, we focus on addressing the missing data imputation problem for large-scale spatiotemporal traffic data. To achieve both high accuracy and efficiency, we develop a scalable tensor learning model -- Low-Tubal-Rank Smoothing Tensor Completion (LSTC-Tubal) -- based on the existing framework of Low-Rank Tensor Completion, which is well-suited for spatiotemporal traffic data that is characterized by multidimensional structure of location$\times$ time of day $\times$ day. In particular, the proposed LSTC-Tubal model involves a scalable tensor nuclear norm minimization scheme by integrating linear unitary transformation. Therefore, tensor nuclear norm minimization can be solved by singular value thresholding on the transformed matrix of each day while the day-to-day correlation can be effectively preserved by the unitary transform matrix. We compare LSTC-Tubal with state-of-the-art baseline models, and find that LSTC-Tubal can achieve competitive accuracy with a significantly lower computational cost. In addition, the LSTC-Tubal will also benefit other tasks in modeling large-scale spatiotemporal traffic data, such as network-level traffic forecasting. | ['Nicolas Saunier', 'Xinyu Chen', 'Lijun Sun', 'Yixian Chen'] | 2020-08-07 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-2.06115380e-01 -7.51294017e-01 -3.02877814e-01 -2.72595644e-01
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-6.38861299e-01 8.32146704e-01 -4.55248624e-01 -5.74164698e-03
8.02143157e-01 -2.12742880e-01 -5.06087244e-01 -2.30128944e-01
-7.52778232e-01 4.17003959e-01 8.92200619e-02 6.38027966e-01
6.30380988e-01 -1.95690048e+00 -1.07108152e+00 1.66289136e-01
2.48207048e-01 -3.58463824e-01 9.84721422e-01 1.58682048e+00
7.50366002e-02 6.18227839e-01 9.76017937e-02 -8.23686659e-01
-8.34636688e-01 6.39049888e-01 -1.81929290e-01 -3.40385646e-01
-4.01261389e-01 9.74566191e-02 -1.00340456e-01 -8.42236400e-01
-1.00273408e-01 -3.97417247e-01 1.33271918e-01 1.91365555e-01
3.17366689e-01 8.90318155e-01 3.06394517e-01 -1.37684274e+00
-2.59314209e-01 8.54826033e-01 2.24563017e-01 1.11101136e-01
1.44400299e+00 -3.42806756e-01 -5.19949198e-01 4.00456667e-01
1.43489611e+00 -9.61280800e-03 -1.13294137e+00 -5.74585676e-01
-3.96691449e-03 -7.99158514e-01 9.87161398e-02 -2.51086116e-01
-1.33956707e+00 8.74137759e-01 3.49259913e-01 2.79448926e-02
7.45905876e-01 -4.27670032e-01 1.15099680e+00 4.76034969e-01
5.63927233e-01 -1.00225055e+00 4.23062444e-02 7.82245517e-01
9.01802957e-01 -1.34501147e+00 1.38599306e-01 -5.37143826e-01
-7.53850520e-01 7.15032458e-01 3.29254359e-01 -2.56539490e-02
9.38442826e-01 -1.14900097e-01 -2.05839902e-01 -3.61498475e-01
-9.07061398e-01 -4.80459511e-01 5.56652665e-01 5.46059668e-01
3.27651262e-01 3.76278192e-01 -1.11668505e-01 1.44250765e-01
2.41300706e-02 -7.76280090e-02 3.98720175e-01 6.41562521e-01
1.16704486e-01 -1.28685498e+00 -7.12779939e-01 5.75681865e-01
-2.17399210e-01 -4.60567214e-02 1.04414843e-01 4.52315599e-01
2.00207412e-01 1.35842168e+00 -1.14907302e-01 -7.97768354e-01
5.14691234e-01 -1.90404549e-01 -1.34014100e-01 -1.27923131e-01
-3.21470559e-01 1.90945670e-01 3.20486069e-01 -9.16542947e-01
-4.86206740e-01 -1.06112039e+00 -6.86942995e-01 -8.87751818e-01
4.54245657e-02 2.19791129e-01 6.98922396e-01 8.23611319e-01
8.02629054e-01 2.31877431e-01 9.06671405e-01 -7.58792758e-01
-3.36290598e-01 -9.04336512e-01 -7.20261157e-01 8.03571165e-01
4.52793866e-01 -9.41497207e-01 -3.42755198e-01 -1.37349501e-01] | [6.588647842407227, 2.1368134021759033] |
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