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a9e12292-36b5-459d-93e0-7e9a1551ae8b | towards-cross-language-prosody-transfer-for | 2307.04123 | null | https://arxiv.org/abs/2307.04123v1 | https://arxiv.org/pdf/2307.04123v1.pdf | Towards cross-language prosody transfer for dialog | Speech-to-speech translation systems today do not adequately support use for dialog purposes. In particular, nuances of speaker intent and stance can be lost due to improper prosody transfer. We present an exploration of what needs to be done to overcome this. First, we developed a data collection protocol in which bilingual speakers re-enact utterances from an earlier conversation in their other language, and used this to collect an English-Spanish corpus, so far comprising 1871 matched utterance pairs. Second, we developed a simple prosodic dissimilarity metric based on Euclidean distance over a broad set of prosodic features. We then used these to investigate cross-language prosodic differences, measure the likely utility of three simple baseline models, and identify phenomena which will require more powerful modeling. Our findings should inform future research on cross-language prosody and the design of speech-to-speech translation systems capable of effective prosody transfer. | ['Nigel G. Ward', 'Jonathan E. Avila'] | 2023-07-09 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 2.04925790e-01 2.49728486e-01 -2.57600754e-01 -7.37151444e-01
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8676fed3-4ec6-45a3-a1eb-c6c5acfb2919 | how-modular-should-neural-module-networks-be | 2106.08170 | null | https://arxiv.org/abs/2106.08170v2 | https://arxiv.org/pdf/2106.08170v2.pdf | How Modular Should Neural Module Networks Be for Systematic Generalization? | Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generalization, i.e., overcoming biasing factors in the training distribution. However, the aspects of NMNs that facilitate systematic generalization are not fully understood. In this paper, we demonstrate that the degree of modularity of the NMN have large influence on systematic generalization. In a series of experiments on three VQA datasets (VQA-MNIST, SQOOP, and CLEVR-CoGenT), our results reveal that tuning the degree of modularity, especially at the image encoder stage, reaches substantially higher systematic generalization. These findings lead to new NMN architectures that outperform previous ones in terms of systematic generalization. | ['Xavier Boix', 'Tomotake Sasaki', "Vanessa D'Amario"] | 2021-06-15 | null | http://proceedings.neurips.cc/paper/2021/hash/c467978aaae44a0e8054e174bc0da4bb-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c467978aaae44a0e8054e174bc0da4bb-Paper.pdf | neurips-2021-12 | ['systematic-generalization'] | ['reasoning'] | [-1.90770272e-02 1.95278093e-01 4.54490911e-03 -2.83296824e-01
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52aa2ccd-93d7-409b-96e3-59885293a102 | fuzzy-logic-for-vagueness-management-in | null | null | https://aclanthology.org/2020.intellang-1.8 | https://aclanthology.org/2020.intellang-1.8.pdf | Fuzzy Logic for Vagueness Management in Referring Expression Generation | null | ['Daniel Sánchez', 'Gustavo Rivas-Gervilla', 'Nicolás Marín'] | null | null | null | null | intellang-2020-9-1 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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86565ac7-d78b-49d1-b47c-9696cb98325e | robust-brain-magnetic-resonance-image | 2001.03857 | null | https://arxiv.org/abs/2001.03857v1 | https://arxiv.org/pdf/2001.03857v1.pdf | Robust Brain Magnetic Resonance Image Segmentation for Hydrocephalus Patients: Hard and Soft Attention | Brain magnetic resonance (MR) segmentation for hydrocephalus patients is considered as a challenging work. Encoding the variation of the brain anatomical structures from different individuals cannot be easily achieved. The task becomes even more difficult especially when the image data from hydrocephalus patients are considered, which often have large deformations and differ significantly from the normal subjects. Here, we propose a novel strategy with hard and soft attention modules to solve the segmentation problems for hydrocephalus MR images. Our main contributions are three-fold: 1) the hard-attention module generates coarse segmentation map using multi-atlas-based method and the VoxelMorph tool, which guides subsequent segmentation process and improves its robustness; 2) the soft-attention module incorporates position attention to capture precise context information, which further improves the segmentation accuracy; 3) we validate our method by segmenting insula, thalamus and many other regions-of-interests (ROIs) that are critical to quantify brain MR images of hydrocephalus patients in real clinical scenario. The proposed method achieves much improved robustness and accuracy when segmenting all 17 consciousness-related ROIs with high variations for different subjects. To the best of our knowledge, this is the first work to employ deep learning for solving the brain segmentation problems of hydrocephalus patients. | ['Lichi Zhang', 'Kai Xuan', 'Xuhua Ren', 'Qian Wang', 'Jiayu Huo', 'Dongming Wei'] | 2020-01-12 | null | null | null | null | ['hard-attention'] | ['methodology'] | [-1.05060562e-01 2.21868232e-01 5.03695607e-01 -4.80189025e-01
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9508b1fe-2d4e-4242-9fdd-d98f747aff9c | x-former-in-memory-acceleration-of | 2303.07470 | null | https://arxiv.org/abs/2303.07470v1 | https://arxiv.org/pdf/2303.07470v1.pdf | X-Former: In-Memory Acceleration of Transformers | Transformers have achieved great success in a wide variety of natural language processing (NLP) tasks due to the attention mechanism, which assigns an importance score for every word relative to other words in a sequence. However, these models are very large, often reaching hundreds of billions of parameters, and therefore require a large number of DRAM accesses. Hence, traditional deep neural network (DNN) accelerators such as GPUs and TPUs face limitations in processing Transformers efficiently. In-memory accelerators based on non-volatile memory promise to be an effective solution to this challenge, since they provide high storage density while performing massively parallel matrix vector multiplications within memory arrays. However, attention score computations, which are frequently used in Transformers (unlike CNNs and RNNs), require matrix vector multiplications (MVM) where both operands change dynamically for each input. As a result, conventional NVM-based accelerators incur high write latency and write energy when used for Transformers, and further suffer from the low endurance of most NVM technologies. To address these challenges, we present X-Former, a hybrid in-memory hardware accelerator that consists of both NVM and CMOS processing elements to execute transformer workloads efficiently. To improve the hardware utilization of X-Former, we also propose a sequence blocking dataflow, which overlaps the computations of the two processing elements and reduces execution time. Across several benchmarks, we show that X-Former achieves upto 85x and 7.5x improvements in latency and energy over a NVIDIA GeForce GTX 1060 GPU and upto 10.7x and 4.6x improvements in latency and energy over a state-of-the-art in-memory NVM accelerator. | ['Anand Raghunathan', 'Kaushik Roy', 'Jacob R. Stevens', 'Shrihari Sridharan'] | 2023-03-13 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-5.10990545e-02 -5.85467696e-01 -1.93433344e-01 -2.10205063e-01
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7a4541c7-9f4f-4c37-a697-04bb5e30d6bb | static-and-dynamic-speaker-modeling-based-on | null | null | https://aclanthology.org/2022.naacl-srw.31 | https://aclanthology.org/2022.naacl-srw.31.pdf | Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation | Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the internal state of the speaker (personality) as Static and Dynamic speaker state, where the Dynamic speaker state is modeled with a graph neural network based encoder. Experiments on benchmark dataset shows the effectiveness of our model. Our model outperforms baseline and other graph-based methods. Analysis of results also show the importance of explicit speaker modeling. | ['Sadao Kurohashi', 'Yin Jou Huang', 'Prakhar Saxena'] | null | null | null | null | naacl-acl-2022-7 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.18726805e-01 3.71756911e-01 -6.62963167e-02 -8.44702661e-01
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e20e4294-84f3-4a0f-9dbe-1712e743a626 | aiomics-exploring-more-of-the-proteome-using | 2305.09513 | null | https://arxiv.org/abs/2305.09513v1 | https://arxiv.org/pdf/2305.09513v1.pdf | AIomics: exploring more of the proteome using mass spectral libraries extended by AI | The unbounded permutations of biological molecules, including proteins and their constituent peptides, presents a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides. | ['Stephen E. Stein', 'Tytus D. Mak', 'Douglas J. Slotta', 'Joel Lapin', 'Lewis Y. Geer'] | 2023-05-16 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 1.02226579e+00 -1.89173386e-01 -2.98313290e-01 -3.97063673e-01
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0c72a78f-8449-4e07-9cdc-5d6a94daefb0 | convolutional-neural-network-based-on-sparse | 2305.17898 | null | https://arxiv.org/abs/2305.17898v1 | https://arxiv.org/pdf/2305.17898v1.pdf | Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution | Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, existing deep learning-based SR methods predominantly rely on convolutional neural networks (CNNs), which inherently limit the expressive capabilities of these models and therefore make it challenging to discover potential relationships between different image features. To overcome this limitation, we propose an A-network that utilizes multiple convolution operator feature extraction modules (MCO) for extracting image features using multiple convolution operators. These extracted features are passed through multiple sets of cross-feature extraction modules (MSC) to highlight key features through inter-channel feature interactions, enabling subsequent feature learning. An attention-based sparse graph neural network module is incorporated to establish relationships between pixel features, learning which adjacent pixels have the greatest impact on determining the features to be filled. To evaluate our model's effectiveness, we conducted experiments using different models on data generated from multiple datasets with different degradation multiples, and the experimental results show that our method is a significant improvement over the current state-of-the-art methods. | ['Jixin Maa', 'Hongjian Yu', 'Zhijiang Du', 'Xin Hua'] | 2023-05-29 | null | null | null | null | ['image-super-resolution', 'graph-attention'] | ['computer-vision', 'graphs'] | [ 7.17177689e-01 -1.74487561e-01 -1.11539505e-01 -3.49138170e-01
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54af5097-4fb6-4138-823a-e431550f1855 | the-online-behaviour-of-the-algerian-abusers | 2203.10369 | null | https://arxiv.org/abs/2203.10369v1 | https://arxiv.org/pdf/2203.10369v1.pdf | The Online Behaviour of the Algerian Abusers in Social Media Networks | Connecting to social media networks becomes a daily task for the majority of people around the world, and the amount of shared information is growing exponentially. Thus, controlling the way in which people communicate is necessary, in order to protect them from disorientation, conflicts, aggressions, etc. In this paper, we conduct a statistical study on the cyber-bullying and the abusive content in social media (i.e. Facebook), where we try to spot the online behaviour of the abusers in the Algerian community. More specifically, we have involved 200 Facebook users from different regions among 600 to carry out this study. The aim of this investigation is to aid automatic systems of abuse detection to take decision by incorporating the online activity. Abuse detection systems require a large amount of data to perform better on such kind of texts (i.e. unstructured and informal texts), and this is due to the lack of standard orthography, where there are various Algerian dialects and languages spoken. | ['Kheireddine Abainia'] | 2022-03-19 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-5.20263910e-01 -1.14180960e-01 8.24423209e-02 -1.90005690e-01
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048e0ac9-eeb4-407a-88df-e069c3a8a753 | mixformer-end-to-end-tracking-with-iterative-1 | 2203.11082 | null | https://arxiv.org/abs/2203.11082v2 | https://arxiv.org/pdf/2203.11082v2.pdf | MixFormer: End-to-End Tracking with Iterative Mixed Attention | Tracking often uses a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer tracking framework simply by stacking multiple MAMs with progressive patch embedding and placing a localization head on top. In addition, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer sets a new state-of-the-art performance on five tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on VOT2020. We also perform in-depth ablation studies to demonstrate the effectiveness of simultaneous feature extraction and information integration. Code and trained models are publicly available at https://github.com/MCG-NJU/MixFormer. | ['Cheng Jiang', 'Gangshan Wu', 'LiMin Wang', 'Yutao Cui'] | 2022-03-21 | mixformer-end-to-end-tracking-with-iterative | http://openaccess.thecvf.com//content/CVPR2022/html/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-object-tracking'] | ['computer-vision'] | [-3.43601733e-01 -3.95321965e-01 -4.18718904e-01 -9.92989913e-02
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f25ae47c-e03c-4fc6-ae16-b5b2106eefba | dorothie-spoken-dialogue-for-handling | 2210.12511 | null | https://arxiv.org/abs/2210.12511v1 | https://arxiv.org/pdf/2210.12511v1.pdf | DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents | In the real world, autonomous driving agents navigate in highly dynamic environments full of unexpected situations where pre-trained models are unreliable. In these situations, what is immediately available to vehicles is often only human operators. Empowering autonomous driving agents with the ability to navigate in a continuous and dynamic environment and to communicate with humans through sensorimotor-grounded dialogue becomes critical. To this end, we introduce Dialogue On the ROad To Handle Irregular Events (DOROTHIE), a novel interactive simulation platform that enables the creation of unexpected situations on the fly to support empirical studies on situated communication with autonomous driving agents. Based on this platform, we created the Situated Dialogue Navigation (SDN), a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed audio. SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions. We further developed a transformer-based baseline model for these SDN tasks. Our empirical results indicate that language guided-navigation in a highly dynamic environment is an extremely difficult task for end-to-end models. These results will provide insight towards future work on robust autonomous driving agents. The DOROTHIE platform, SDN benchmark, and code for the baseline model are available at https://github.com/sled-group/DOROTHIE. | ['Joyce Chai', 'Matthew Marge', 'Felix Gervits', 'Eui-In Kim', 'Huang Yidong', 'Cristian-Paul Bara', 'Ben VanDerPloeg', 'Ziqiao Ma'] | 2022-10-22 | null | null | null | null | ['dialogue-act-classification', 'vision-and-language-navigation'] | ['natural-language-processing', 'robots'] | [-4.22444865e-02 3.76928926e-01 3.24399501e-01 -6.72766447e-01
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a9ed62e7-f1b2-4897-8071-7f679f51826b | semeval-2016-task-12-clinical-tempeval | null | null | https://aclanthology.org/s16-1165 | https://aclanthology.org/s16-1165.pdf | SemEval-2016 Task 12: Clinical TempEval | null | ['Wei-Te Chen', 'Marc Verhagen', 'Leon Derczynski', 'Guergana Savova', 'Steven Bethard', 'James Pustejovsky'] | 2016-06-01 | semeval-2016-task-12-clinical-tempeval-1 | https://aclanthology.org/S16-1165 | https://aclanthology.org/S16-1165.pdf | semeval-2016-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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9c6237f7-abc1-42af-92e9-dd586c774d23 | direct-simultaneous-speech-to-text | 2106.06636 | null | https://arxiv.org/abs/2106.06636v1 | https://arxiv.org/pdf/2106.06636v1.pdf | Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR | Simultaneous speech-to-text translation is widely useful in many scenarios. The conventional cascaded approach uses a pipeline of streaming ASR followed by simultaneous MT, but suffers from error propagation and extra latency. To alleviate these issues, recent efforts attempt to directly translate the source speech into target text simultaneously, but this is much harder due to the combination of two separate tasks. We instead propose a new paradigm with the advantages of both cascaded and end-to-end approaches. The key idea is to use two separate, but synchronized, decoders on streaming ASR and direct speech-to-text translation (ST), respectively, and the intermediate results of ASR guide the decoding policy of (but is not fed as input to) ST. During training time, we use multitask learning to jointly learn these two tasks with a shared encoder. En-to-De and En-to-Es experiments on the MuSTC dataset demonstrate that our proposed technique achieves substantially better translation quality at similar levels of latency. | ['Liang Huang', 'Renjie Zheng', 'Mingbo Ma', 'Junkun Chen'] | 2021-06-11 | null | https://aclanthology.org/2021.findings-acl.406 | https://aclanthology.org/2021.findings-acl.406.pdf | findings-acl-2021-8 | ['speech-to-text-translation', 'simultaneous-speech-to-text-translation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.87594700e-01 -7.56156966e-02 -1.07685238e-01 -3.97215039e-01
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ddd82dac-66e5-4146-8ccd-563c9947edf2 | surface-realisation-from-knowledge-bases | null | null | https://aclanthology.org/P14-1040 | https://aclanthology.org/P14-1040.pdf | Surface Realisation from Knowledge-Bases | null | ['Claire Gardent', 'Bikash Gyawali'] | 2014-06-01 | null | null | null | acl-2014-6 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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0bb35921-e845-4b6c-abdb-ae639f632197 | fully-neuromorphic-vision-and-control-for | 2303.08778 | null | https://arxiv.org/abs/2303.08778v1 | https://arxiv.org/pdf/2303.08778v1.pdf | Fully neuromorphic vision and control for autonomous drone flight | Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions due to the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present the first fully neuromorphic vision-to-control pipeline for controlling a freely flying drone. Specifically, we train a spiking neural network that accepts high-dimensional raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28.8k neurons, maps incoming raw events to ego-motion estimates and is trained with self-supervised learning on real event data. The control part consists of a single decoding layer and is learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone can accurately follow different ego-motion setpoints, allowing for hovering, landing, and maneuvering sideways$\unicode{x2014}$even while yawing at the same time. The neuromorphic pipeline runs on board on Intel's Loihi neuromorphic processor with an execution frequency of 200 Hz, spending only 27 $\unicode{x00b5}$J per inference. These results illustrate the potential of neuromorphic sensing and processing for enabling smaller, more intelligent robots. | ['Guido de Croon', 'Yingfu Xu', 'Stein Stroobants', 'Julien Dupeyroux', 'Jesse Hagenaars', 'Federico Paredes-Vallés'] | 2023-03-15 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.52161163e-01 -1.92373425e-01 5.56458950e-01 -1.64830223e-01
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d6db6019-97b4-4b54-b5b2-0d3c5584336a | a-multilayer-perceptron-based-ensemble | null | null | https://aclanthology.org/D17-1057 | https://aclanthology.org/D17-1057.pdf | A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis | In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These are trained on top of pre-trained, autoencoder-based, financial word embeddings and lexicon features. An ensemble is constructed by combining these deep learning models and a classical supervised model based on Support Vector Regression (SVR). We evaluate our proposed technique on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The propose model shows impressive results on two datasets, i.e. microblogs and news headlines datasets. Comparisons show that our proposed model performs better than the existing state-of-the-art systems for the above two datasets by 2.0 and 4.1 cosine points, respectively. | ['Md. Shad Akhtar', 'Asif Ekbal', 'Deepanway Ghosal', 'Pushpak Bhattacharyya', 'Abhishek Kumar'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['stock-prediction'] | ['time-series'] | [-4.67869163e-01 -3.33749831e-01 -1.29193559e-01 -7.42709696e-01
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ea1a6fb8-a560-4600-955f-d9a899976512 | integrating-multiple-sources-of-ordinal | 2211.00420 | null | https://arxiv.org/abs/2211.00420v2 | https://arxiv.org/pdf/2211.00420v2.pdf | Integrating multiple sources of ordinal information in portfolio optimization | Active portfolio management tries to incorporate any source of meaningful information into the asset selection process. In this contribution we consider qualitative views specified as total orders of the expected asset returns and discuss two different approaches for incorporating this input in a mean-variance portfolio optimization model. In the robust optimization approach we first compute a posterior expectation of asset returns for every given total order by an extension of the Black-Litterman (BL) framework. Then these expected asset returns are considered as possible input scenarios for robust optimization variants of the mean-variance portfolio model (max-min robustness, min regret robustness and soft robustness). In the order aggregation approach rules from social choice theory (Borda, Footrule, Copeland, Best-of-k and MC4) are used to aggregate the total order in a single ``consensus total order''. Then expected asset returns are computed for this ``consensus total order'' by the extended BL framework mentioned above. Finally, these expectations are used as an input of the classical mean-variance optimization. Using data from EUROSTOXX 50 and S&P 100 we empirically compare the success of the two approaches in the context of portfolio performance analysis and observe that in general aggregating orders by social choice methods outperforms robust optimization based methods for both data sets. | ['Ulrich Pferschy', 'Roland Mestel', 'Stephan Hafner', 'Eranda Çela'] | 2022-11-01 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.00150481e-01 4.19648200e-01 -4.72861156e-02 -4.61649179e-01
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c2f8fc14-5494-4619-a856-4a3561716c16 | folding-and-stabilization-of-native-sequence | 1606.05373 | null | http://arxiv.org/abs/1606.05373v1 | http://arxiv.org/pdf/1606.05373v1.pdf | Folding and Stabilization of Native-Sequence-Reversed Proteins | Though the problem of sequence-reversed protein folding is largely
unexplored, one might speculate that reversed native protein sequences should
be significantly more foldable than purely random heteropolymer sequences. In
this article, we investigate how the reverse-sequences of native proteins might
fold by examining a series of small proteins of increasing structural
complexity ({\alpha}-helix, \b{eta}-hairpin, {\alpha}-helix bundle, and
{\alpha}/\b{eta}-protein). Employing a tandem protein structure prediction
algorithmic and molecular dynamics simulation approach, we find that the
ability of reverse sequences to adopt native-like folds is strongly in
influenced by protein size and the flexibility of the native hydrophobic core.
For \b{eta}-hairpins with reverse-sequences that fail to fold, we employ a
simple mutational strategy for guiding stable hairpin formation that involves
the insertion of amino acids into the \b{eta}-turn region. This systematic look
at reverse sequence duality sheds new light on the problem of protein
sequence-structure mapping and may serve to inspire new protein design and
protein structure prediction protocols. | [] | 2016-06-16 | null | null | null | null | ['protein-design'] | ['medical'] | [ 6.78139389e-01 3.95025671e-01 2.72813868e-02 -5.23725927e-01
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d6e5567a-7f3d-44b3-b408-fa24e1d6af32 | multi-label-cloud-segmentation-using-a-deep | 1903.06562 | null | http://arxiv.org/abs/1903.06562v1 | http://arxiv.org/pdf/1903.06562v1.pdf | Multi-label Cloud Segmentation Using a Deep Network | Different empirical models have been developed for cloud detection. There is
a growing interest in using the ground-based sky/cloud images for this purpose.
Several methods exist that perform binary segmentation of clouds. In this
paper, we propose to use a deep learning architecture (U-Net) to perform
multi-label sky/cloud image segmentation. The proposed approach outperforms
recent literature by a large margin. | ['Yee Hui Lee', 'Soumyabrata Dev', 'Stefan Winkler', 'Shilpa Manandhar'] | 2019-03-15 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-6.22198097e-02 -7.17554867e-01 -1.63014278e-01 -7.31797576e-01
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81d405f2-a32b-4db8-99c1-683b818bb921 | learning-to-reason-over-visual-objects | 2303.02260 | null | https://arxiv.org/abs/2303.02260v1 | https://arxiv.org/pdf/2303.02260v1.pdf | Learning to reason over visual objects | A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM). Motivated by the goal of designing AI systems with this capacity, recent work has focused on evaluating whether neural networks can learn to solve RPM-like problems. Previous work has generally found that strong performance on these problems requires the incorporation of inductive biases that are specific to the RPM problem format, raising the question of whether such models might be more broadly useful. Here, we investigated the extent to which a general-purpose mechanism for processing visual scenes in terms of objects might help promote abstract visual reasoning. We found that a simple model, consisting only of an object-centric encoder and a transformer reasoning module, achieved state-of-the-art results on both of two challenging RPM-like benchmarks (PGM and I-RAVEN), as well as a novel benchmark with greater visual complexity (CLEVR-Matrices). These results suggest that an inductive bias for object-centric processing may be a key component of abstract visual reasoning, obviating the need for problem-specific inductive biases. | ['Jonathan D. Cohen', 'Taylor Webb', 'Shanka Subhra Mondal'] | 2023-03-03 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.22077832e-01 1.72973067e-01 1.70125499e-01 -2.98711479e-01
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a1479f80-090e-4ca2-ad30-e2d152d00d7f | evolutionary-generalized-zero-shot-learning | 2211.13174 | null | https://arxiv.org/abs/2211.13174v1 | https://arxiv.org/pdf/2211.13174v1.pdf | Evolutionary Generalized Zero-Shot Learning | An open problem on the path to artificial intelligence is generalization from the known to the unknown, which is instantiated as Generalized Zero-Shot Learning (GZSL) task. In this work, we propose a novel Evolutionary Generalized Zero-Shot Learning setting, which (i) avoids the domain shift problem in inductive GZSL, and (ii) is more in line with the needs of real-world deployments than transductive GZSL. In the proposed setting, a zero-shot model with poor initial performance is able to achieve online evolution during application. We elaborate on three challenges of this special task, i.e., catastrophic forgetting, initial prediction bias, and evolutionary data class bias. Moreover, we propose targeted solutions for each challenge, resulting in a generic method capable of continuing to evolve on a given initial IGZSL model. Experiments on three popular GZSL benchmark datasets show that our model can learn from the test data stream while other baselines fail. | ['Ling Shao', 'Yang Long', 'Yuming Shen', 'Haofeng Zhang', 'Dubing Chen'] | 2022-11-23 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 3.90755594e-01 4.99052584e-01 -7.79354945e-02 -4.22834083e-02
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fcce4247-5a3b-4c6a-a4cf-a7147959465f | simultaneously-learning-neighborship-and | 1709.02896 | null | http://arxiv.org/abs/1709.02896v1 | http://arxiv.org/pdf/1709.02896v1.pdf | Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction | Explicitly or implicitly, most of dimensionality reduction methods need to
determine which samples are neighbors and the similarity between the neighbors
in the original highdimensional space. The projection matrix is then learned on
the assumption that the neighborhood information (e.g., the similarity) is
known and fixed prior to learning. However, it is difficult to precisely
measure the intrinsic similarity of samples in high-dimensional space because
of the curse of dimensionality. Consequently, the neighbors selected according
to such similarity might and the projection matrix obtained according to such
similarity and neighbors are not optimal in the sense of classification and
generalization. To overcome the drawbacks, in this paper we propose to let the
similarity and neighbors be variables and model them in low-dimensional space.
Both the optimal similarity and projection matrix are obtained by minimizing a
unified objective function. Nonnegative and sum-to-one constraints on the
similarity are adopted. Instead of empirically setting the regularization
parameter, we treat it as a variable to be optimized. It is interesting that
the optimal regularization parameter is adaptive to the neighbors in
low-dimensional space and has intuitive meaning. Experimental results on the
YALE B, COIL-100, and MNIST datasets demonstrate the effectiveness of the
proposed method. | ['Feiping Nie', 'Yanwei Pang', 'Bo Zhou'] | 2017-09-09 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.17403039e-01 -2.33238250e-01 -2.90193230e-01 -5.82338870e-01
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8e87d540-3a59-4dc4-9fe6-575547c9e3cd | sensitivity-and-robustness-of-large-language | 2305.08714 | null | https://arxiv.org/abs/2305.08714v2 | https://arxiv.org/pdf/2305.08714v2.pdf | Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks | Prompt engineering relevance research has seen a notable surge in recent years, primarily driven by advancements in pre-trained language models and large language models. However, a critical issue has been identified within this domain: the inadequate of sensitivity and robustness of these models towards Prompt Templates, particularly in lesser-studied languages such as Japanese. This paper explores this issue through a comprehensive evaluation of several representative Large Language Models (LLMs) and a widely-utilized pre-trained model(PLM). These models are scrutinized using a benchmark dataset in Japanese, with the aim to assess and analyze the performance of the current multilingual models in this context. Our experimental results reveal startling discrepancies. A simple modification in the sentence structure of the Prompt Template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44. This observation underscores the fact that even the highly performance GPT-4 model encounters significant stability issues when dealing with diverse Japanese prompt templates, rendering the consistency of the model's output results questionable. In light of these findings, we conclude by proposing potential research trajectories to further enhance the development and performance of Large Language Models in their current stage. | ['Tatsunori Mori', 'Chengguang Gan'] | 2023-05-15 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 1.26407981e-01 1.73660189e-01 -1.92999300e-02 -2.38662437e-01
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9c3cd049-063d-4b17-ac47-56da0fcef674 | non-homogeneous-haze-removal-via-artificial | 2104.01888 | null | https://arxiv.org/abs/2104.01888v2 | https://arxiv.org/pdf/2104.01888v2.pdf | Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning | Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose haze degrees are different and enrich the underlying scene prior. Secondly, beyond utilizing the local neighboring relationship, we build a bidimensional graph reasoning module to conduct non-local filtering in the spatial and channel dimensions of feature maps, which models their long-range dependency and propagates the natural scene prior between the well-preserved nodes and the nodes contaminated by haze. To the best of our knowledge, this is the first exploration to remove non-homogeneous haze via the graph reasoning based framework. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks. The source code of the proposed NHRN is available on https://github.com/whrws/NHRNet. | ['Linfeng Xu', 'Fanman Meng', 'Hongliang Li', 'King Ngi Ngan', 'Hui Li', 'Qingbo Wu', 'Haoran Wei'] | 2021-04-05 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.12437010e-01 -1.10277615e-01 5.41057944e-01 -7.41694793e-02
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7a1ba392-ea69-42df-b3c7-0e56a93b1946 | temporal-action-segmentation-an-analysis-of | 2210.10352 | null | https://arxiv.org/abs/2210.10352v3 | https://arxiv.org/pdf/2210.10352v3.pdf | Temporal Action Segmentation: An Analysis of Modern Techniques | Temporal action segmentation (TAS) from videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. In this survey, we analyze and summarize the most significant contributions and trends to this endeavor. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, i.e., frame representation, and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps. In addition, we have curated a list of TAS resources, which is available at https://github.com/atlas-eccv22/awesome-temporal-action-segmentation. | ['Angela Yao', 'Fadime Sener', 'Guodong Ding'] | 2022-10-19 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 4.33892071e-01 -2.34770000e-01 -9.00801361e-01 -2.52404243e-01
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39b3147d-647b-41ec-89aa-f5b10e1d3e88 | nu-gan-high-resolution-neural-upsampling-with | 2010.11362 | null | https://arxiv.org/abs/2010.11362v1 | https://arxiv.org/pdf/2010.11362v1.pdf | NU-GAN: High resolution neural upsampling with GAN | In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods are equipped to handle a maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio upsampling as a separate component in the text-to-speech (TTS) pipeline by leveraging techniques for audio generation using GANs. ABX preference tests indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz audio that is distinguishable from original audio only 7.4% higher than random chance for single speaker dataset, and 10.8% higher than chance for multi-speaker dataset. | ['Aaron Courville', 'Yoshua Bengio', 'Vicki Anand', 'Kundan Kumar', 'Rithesh Kumar'] | 2020-10-22 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.47460115e-01 6.50476873e-01 6.48812056e-02 -1.02381207e-01
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79373d0c-e32a-4da9-aa1a-0688286dd570 | when-do-transformers-shine-in-rl-decoupling | 2307.03864 | null | https://arxiv.org/abs/2307.03864v1 | https://arxiv.org/pdf/2307.03864v1.pdf | When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment | Reinforcement learning (RL) algorithms face two distinct challenges: learning effective representations of past and present observations, and determining how actions influence future returns. Both challenges involve modeling long-term dependencies. The transformer architecture has been very successful to solve problems that involve long-term dependencies, including in the RL domain. However, the underlying reason for the strong performance of Transformer-based RL methods remains unclear: is it because they learn effective memory, or because they perform effective credit assignment? After introducing formal definitions of memory length and credit assignment length, we design simple configurable tasks to measure these distinct quantities. Our empirical results reveal that Transformers can enhance the memory capacity of RL algorithms, scaling up to tasks that require memorizing observations $1500$ steps ago. However, Transformers do not improve long-term credit assignment. In summary, our results provide an explanation for the success of Transformers in RL, while also highlighting an important area for future research and benchmark design. | ['Pierre-Luc Bacon', 'Benjamin Eysenbach', 'Michel Ma', 'Tianwei Ni'] | 2023-07-07 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-1.08475223e-01 -5.82348481e-02 -6.56690300e-01 -3.50489497e-01
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e3f90abc-9de6-40d4-af69-256617ddf31e | robot-design-with-neural-networks-milp | 2010.09842 | null | https://arxiv.org/abs/2010.09842v4 | https://arxiv.org/pdf/2010.09842v4.pdf | Robot Design With Neural Networks, MILP Solvers and Active Learning | Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem. This paper presents CNMA, a new method of solving this problem that is conservative in the number of potentially expensive blackbox function evaluations; allows specifying complex, even recursive constraints directly rather than as hard-to-design penalty or barrier functions; and is resilient to the non-termination of function evaluations. CNMA leverages the ability of neural networks to approximate any continuous function, their transformation into equivalent mixed integer linear programs (MILPs) and their optimization subject to constraints with industrial strength MILP solvers. A new learning-from-failure step guides the learning to be relevant to solving the constrained optimization problem. Thus, the amount of learning is orders of magnitude smaller than that needed to learn functions over their entire domains. CNMA is illustrated with the design of several robotic systems: wave-energy propelled boat, lunar lander, hexapod, cartpole, acrobot and parallel parking. These range from 6 real-valued dimensions to 36. We show that CNMA surpasses the Nelder-Mead, Gaussian and Random Search optimization methods against the metric of number of function evaluations. | ['Karthik Narayan', 'Niraj K. Jha', 'Brendan Englot', 'Kishore Pochiraju', 'Jeremy Cohen', 'Todd Huster', 'Dana Chee', 'Emily Mak', 'Sanjai Narain'] | 2020-10-19 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-7.75556117e-02 4.24759835e-01 -5.95240414e-01 -1.73633978e-01
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3c1e3de2-98db-4750-94fc-5e5f37ddf2ec | traditional-saliency-reloaded-a-good-old | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Frintrop_Traditional_Saliency_Reloaded_2015_CVPR_paper.pdf | Traditional Saliency Reloaded: A Good Old Model in New Shape | In this paper, we show that the seminal, biologically-inspired saliency model by Itti et al. is still competitive with current state-of-the-art methods for salient object segmentation if some important adaptions are made. We show which changes are necessary to achieve high performance, with special emphasis on the scale-space: we introduce a twin pyramid for computing Difference-of-Gaussians, which enables a flexible center-surround ratio. The resulting system, called VOCUS2, is elegant and coherent in structure, fast, and computes saliency at the pixel level. It is not only suitable for images with few objects, but also for complex scenes as captured by mobile devices. Furthermore, we integrate the saliency system into an object proposal generation framework to obtain segment-based saliency maps and boost the results for salient object segmentation. We show that our system achieves state-of-the-art performance on a large collection of benchmark data. | ['German Martin Garcia', 'Thomas Werner', 'Simone Frintrop'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['object-proposal-generation'] | ['computer-vision'] | [ 4.36985850e-01 -5.22280149e-02 -1.78340271e-01 -1.78306535e-01
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e1732392-2bb2-4b95-8e02-484f2b1f00c6 | panoptic-animal-pose-estimators-are-zero-shot | 2203.07436 | null | https://arxiv.org/abs/2203.07436v2 | https://arxiv.org/pdf/2203.07436v2.pdf | SuperAnimal models pretrained for plug-and-play analysis of animal behavior | Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present a series of technical innovations that enable a new method, collectively called SuperAnimal, to develop and deploy deep learning models that require zero additional human labels and model training. SuperAnimal allows video inference on over 45 species with only two global classes of animal pose models. If the models need fine-tuning, we show SuperAnimal models are 10$\times$ more data efficient and outperform prior transfer learning approaches. Moreover, we provide a new video-adaptation method to perform unsupervised refinement of videos, and we illustrate the utility of our model in behavioral classification. Collectively, this presents a data-efficient, plug-and-play solution for behavioral analysis. | ['Alexander Mathis', 'Tian Qiu', 'Steffen Schneider', 'Maxime Vidal', 'Jessy Lauer', 'Anastasiia Filippova', 'Mackenzie Weygandt Mathis', 'Shaokai Ye'] | 2022-03-14 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 7.70959929e-02 -2.76132017e-01 -1.89179018e-01 -4.95067239e-01
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c44a98b3-6729-4be4-aac8-a233cc8f5434 | stereopose-category-level-6d-transparent | 2211.01644 | null | https://arxiv.org/abs/2211.01644v1 | https://arxiv.org/pdf/2211.01644v1.pdf | StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS | Most existing methods for category-level pose estimation rely on object point clouds. However, when considering transparent objects, depth cameras are usually not able to capture meaningful data, resulting in point clouds with severe artifacts. Without a high-quality point cloud, existing methods are not applicable to challenging transparent objects. To tackle this problem, we present StereoPose, a novel stereo image framework for category-level object pose estimation, ideally suited for transparent objects. For a robust estimation from pure stereo images, we develop a pipeline that decouples category-level pose estimation into object size estimation, initial pose estimation, and pose refinement. StereoPose then estimates object pose based on representation in the normalized object coordinate space~(NOCS). To address the issue of image content aliasing, we further define a back-view NOCS map for the transparent object. The back-view NOCS aims to reduce the network learning ambiguity caused by content aliasing, and leverage informative cues on the back of the transparent object for more accurate pose estimation. To further improve the performance of the stereo framework, StereoPose is equipped with a parallax attention module for stereo feature fusion and an epipolar loss for improving the stereo-view consistency of network predictions. Extensive experiments on the public TOD dataset demonstrate the superiority of the proposed StereoPose framework for category-level 6D transparent object pose estimation. | ['Qi Dou', 'Pieter Abbeel', 'Yun-hui Liu', 'Congying Sui', 'Stephen James', 'Kai Chen'] | 2022-11-03 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 5.27311042e-02 1.91943836e-03 1.93809494e-01 -3.16477090e-01
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7e254a3d-d326-4f23-8a1a-34733997c5ba | incomplete-multi-view-clustering-via-cross | 2112.00739 | null | https://arxiv.org/abs/2112.00739v1 | https://arxiv.org/pdf/2112.00739v1.pdf | Incomplete Multi-view Clustering via Cross-view Relation Transfer | In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and recovers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method. | ['Yao Zhao', 'Zhiqiang Fu', 'Dongxia Chang', 'Yiming Wang'] | 2021-12-01 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-7.82166421e-02 -2.70915143e-02 -1.83304399e-01 -4.71046567e-01
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203a1743-38ed-4c86-8d25-95a20d5045e8 | alphapose-whole-body-regional-multi-person | 2211.03375 | null | https://arxiv.org/abs/2211.03375v1 | https://arxiv.org/pdf/2211.03375v1.pdf | AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time | Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose. | ['Cewu Lu', 'Yong-Lu Li', 'Yuliang Xiu', 'Haoyi Zhu', 'Chao Xu', 'Hongyang Tang', 'Jiefeng Li', 'Hao-Shu Fang'] | 2022-11-07 | null | null | null | null | ['multi-person-pose-estimation', 'multi-person-pose-estimation-and-tracking'] | ['computer-vision', 'computer-vision'] | [-2.51369834e-01 -2.03827709e-01 -7.46472329e-02 8.30937456e-03
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543edd35-ece2-4b71-9fb4-c80816583c75 | medical-image-analysis-using-deep-relational | 2303.16099 | null | https://arxiv.org/abs/2303.16099v1 | https://arxiv.org/pdf/2303.16099v1.pdf | Medical Image Analysis using Deep Relational Learning | In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames. | ['Zhihua Liu'] | 2023-03-28 | null | null | null | null | ['tumor-segmentation', 'homography-estimation', 'brain-tumor-segmentation', 'relational-reasoning'] | ['computer-vision', 'computer-vision', 'medical', 'natural-language-processing'] | [ 3.30675006e-01 1.43935546e-01 -2.39811204e-02 -3.79106075e-01
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8ac6cc22-d533-4b8d-ac6b-fdb34506682d | cure-tsr-challenging-unreal-and-real | 1712.02463 | null | http://arxiv.org/abs/1712.02463v2 | http://arxiv.org/pdf/1712.02463v2.pdf | CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition | In this paper, we investigate the robustness of traffic sign recognition
algorithms under challenging conditions. Existing datasets are limited in terms
of their size and challenging condition coverage, which motivated us to
generate the Challenging Unreal and Real Environments for Traffic Sign
Recognition (CURE-TSR) dataset. It includes more than two million traffic sign
images that are based on real-world and simulator data. We benchmark the
performance of existing solutions in real-world scenarios and analyze the
performance variation with respect to challenging conditions. We show that
challenging conditions can decrease the performance of baseline methods
significantly, especially if these challenging conditions result in loss or
misplacement of spatial information. We also investigate the effect of data
augmentation and show that utilization of simulator data along with real-world
data enhance the average recognition performance in real-world scenarios. The
dataset is publicly available at https://ghassanalregib.com/cure-tsr/. | ['Gukyeong Kwon', 'Ghassan AlRegib', 'Dogancan Temel', 'Mohit Prabhushankar'] | 2017-12-07 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.96861792e-02 -7.91894317e-01 -8.18242356e-02 -3.69668305e-01
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3ae08a3f-c8e1-4861-b925-716a117f187c | researchers-eye-view-of-sarcasm-detection-in | 2304.08582 | null | https://arxiv.org/abs/2304.08582v1 | https://arxiv.org/pdf/2304.08582v1.pdf | Researchers eye-view of sarcasm detection in social media textual content | The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, special symbols. So, that will be a challenging task for machines to differentiate sarcastic sentences from non-sarcastic sentences. There are no exact rules based on which model will accurately detect sarcasm from many text corpus in the current situation. So, one needs to focus on optimistic and forthcoming approaches in the sarcasm detection domain. This paper discusses various sarcasm detection techniques and concludes with some approaches, related datasets with optimal features, and the researcher's challenges. | ['Vaibhav Khatavkar', 'Swapnil Mane'] | 2023-04-17 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-1.80364758e-01 2.85962541e-02 -2.37252474e-01 -5.05248368e-01
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2aa6f089-1f96-4028-9e27-8733931d2e97 | optimal-thermal-management-and-charging-of | 2210.03393 | null | https://arxiv.org/abs/2210.03393v1 | https://arxiv.org/pdf/2210.03393v1.pdf | Optimal Thermal Management and Charging of Battery Electric Vehicles over Long Trips | This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips. The focus is on the potential benefits of including a heat pump in the thermal management system for waste heat recovery, and charging point planning, in a way to achieve optimality in time, energy, or their trade-off. An optimal control problem is formulated, in which the objective function includes the energy delivered by the charger(s), and the total charging time including the actual charging time and the detour time to and from the charging stop. To reduce the computational complexity, the formulated problem is then transformed into a hybrid dynamical system, where charging dynamics are modelled in the domain of normalized charging time. Driving dynamics can be modelled in either of the trip time or travel distance domains, as the vehicle speed is assumed to be known a priori, and the vehicle is only stopping at charging locations. Within the hybrid dynamical system, a binary variable is introduced for each charging location, in order to decide to use or skip a charger. This problem is solved numerically, and simulations are performed to evaluate the performance in terms of energy efficiency and time. The simulation results indicate that the time required for charging and total energy consumption are reduced up to 30.6% and 19.4%, respectively, by applying the proposed algorithm. | ['Jonas Fredriksson', 'Viktor Larsson', 'Mitra Pourabdollah', 'Jimmy Forsman', 'Nikolce Murgovski', 'Jiaming Zhao', 'Victor Hanson', 'Ahad Hamednia'] | 2022-10-07 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.16812252e-01 6.43695965e-02 -2.37086222e-01 -1.60603523e-01
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7e902d44-1fc9-4e5d-87dd-243f06a3c610 | overview-of-the-third-workshop-on-scholarly | null | null | https://aclanthology.org/2022.sdp-1.1 | https://aclanthology.org/2022.sdp-1.1.pdf | Overview of the Third Workshop on Scholarly Document Processing | With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 3rd Workshop on Scholarly Document Processing (SDP) at COLING as a hybrid event (https://sdproc.org/2022/). The SDP workshop consisted of a research track, three invited talks and five Shared Tasks: 1) MSLR22: Multi-Document Summarization for Literature Reviews, 2) DAGPap22: Detecting automatically generated scientific papers, 3) SV-Ident 2022: Survey Variable Identification in Social Science Publications, 4) SKGG: Scholarly Knowledge Graph Generation, 5) MuP 2022: Multi Perspective Scientific Document Summarization. The program was geared towards NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges. | ['Lucy Lu Wang', 'Anita de Waard', 'Michal Shmueli-Scheuer', 'Philipp Mayr', 'Kyle Lo', 'Petr Knoth', 'Drahomira Herrmannova', 'Tirthankar Ghosal', 'Dayne Freitag', 'Guy Feigenblat', 'Arman Cohan'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.91777587e-01 1.52786538e-01 -4.30863589e-01 1.83940321e-01
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66a4a7ed-2211-4b2a-9ffd-3683d7f2d740 | mixed-signals-sign-language-production-via-a | 2107.11317 | null | https://arxiv.org/abs/2107.11317v2 | https://arxiv.org/pdf/2107.11317v2.pdf | Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives | It is common practice to represent spoken languages at their phonetic level. However, for sign languages, this implies breaking motion into its constituent motion primitives. Avatar based Sign Language Production (SLP) has traditionally done just this, building up animation from sequences of hand motions, shapes and facial expressions. However, more recent deep learning based solutions to SLP have tackled the problem using a single network that estimates the full skeletal structure. We propose splitting the SLP task into two distinct jointly-trained sub-tasks. The first translation sub-task translates from spoken language to a latent sign language representation, with gloss supervision. Subsequently, the animation sub-task aims to produce expressive sign language sequences that closely resemble the learnt spatio-temporal representation. Using a progressive transformer for the translation sub-task, we propose a novel Mixture of Motion Primitives (MoMP) architecture for sign language animation. A set of distinct motion primitives are learnt during training, that can be temporally combined at inference to animate continuous sign language sequences. We evaluate on the challenging RWTH-PHOENIX-Weather-2014T(PHOENIX14T) dataset, presenting extensive ablation studies and showing that MoMP outperforms baselines in user evaluations. We achieve state-of-the-art back translation performance with an 11% improvement over competing results. Importantly, and for the first time, we showcase stronger performance for a full translation pipeline going from spoken language to sign, than from gloss to sign. | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2021-07-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Saunders_Mixed_SIGNals_Sign_Language_Production_via_a_Mixture_of_Motion_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Saunders_Mixed_SIGNals_Sign_Language_Production_via_a_Mixture_of_Motion_ICCV_2021_paper.pdf | iccv-2021-1 | ['sign-language-production'] | ['natural-language-processing'] | [ 2.84379870e-01 2.60554217e-02 -1.95481628e-01 -3.28936309e-01
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cd1eaf63-c6aa-4797-9b12-be3d294c4902 | frozen-in-time-a-joint-video-and-image | 2104.00650 | null | https://arxiv.org/abs/2104.00650v2 | https://arxiv.org/pdf/2104.00650v2.pdf | Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval | Our objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design of the visual architecture and the nature of the training data, in that the available large scale video-text training datasets, such as HowTo100M, are noisy and hence competitive performance is achieved only at scale through large amounts of compute. We address both these challenges in this paper. We propose an end-to-end trainable model that is designed to take advantage of both large-scale image and video captioning datasets. Our model is an adaptation and extension of the recent ViT and Timesformer architectures, and consists of attention in both space and time. The model is flexible and can be trained on both image and video text datasets, either independently or in conjunction. It is trained with a curriculum learning schedule that begins by treating images as 'frozen' snapshots of video, and then gradually learns to attend to increasing temporal context when trained on video datasets. We also provide a new video-text pretraining dataset WebVid-2M, comprised of over two million videos with weak captions scraped from the internet. Despite training on datasets that are an order of magnitude smaller, we show that this approach yields state-of-the-art results on standard downstream video-retrieval benchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC. | ['Andrew Zisserman', 'Gül Varol', 'Arsha Nagrani', 'Max Bain'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bain_Frozen_in_Time_A_Joint_Video_and_Image_Encoder_for_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bain_Frozen_in_Time_A_Joint_Video_and_Image_Encoder_for_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 3.41364563e-01 -3.92416745e-01 -4.57382083e-01 -2.23211870e-01
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0efe45f3-c161-45e5-a162-18655c56909c | instant-nvr-instant-neural-volumetric | 2304.03184 | null | https://arxiv.org/abs/2304.03184v1 | https://arxiv.org/pdf/2304.03184v1.pdf | Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream | Convenient 4D modeling of human-object interactions is essential for numerous applications. However, monocular tracking and rendering of complex interaction scenarios remain challenging. In this paper, we propose Instant-NVR, a neural approach for instant volumetric human-object tracking and rendering using a single RGBD camera. It bridges traditional non-rigid tracking with recent instant radiance field techniques via a multi-thread tracking-rendering mechanism. In the tracking front-end, we adopt a robust human-object capture scheme to provide sufficient motion priors. We further introduce a separated instant neural representation with a novel hybrid deformation module for the interacting scene. We also provide an on-the-fly reconstruction scheme of the dynamic/static radiance fields via efficient motion-prior searching. Moreover, we introduce an online key frame selection scheme and a rendering-aware refinement strategy to significantly improve the appearance details for online novel-view synthesis. Extensive experiments demonstrate the effectiveness and efficiency of our approach for the instant generation of human-object radiance fields on the fly, notably achieving real-time photo-realistic novel view synthesis under complex human-object interactions. | ['Lan Xu', 'Haimin Luo', 'Zhehao Shen', 'Zhuo Su', 'Kaixin Yao', 'Yuheng Jiang'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Instant-NVR_Instant_Neural_Volumetric_Rendering_for_Human-Object_Interactions_From_Monocular_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 0.32014057 -0.36197728 0.32523346 -0.1569523 -0.52584136 -0.5498301
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fc474c79-506c-444c-9aa6-2eef4d17e49e | semantic-helm-an-interpretable-memory-for | 2306.09312 | null | https://arxiv.org/abs/2306.09312v1 | https://arxiv.org/pdf/2306.09312v1.pdf | Semantic HELM: An Interpretable Memory for Reinforcement Learning | Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive success stories in mastering partially observable environments, mostly in the realm of computer games like Dota 2, StarCraft II, or MineCraft. However, none of these methods are interpretable in the sense that it is not comprehensible for humans how the agent decides which actions to take based on its inputs. Yet, human understanding is necessary in order to deploy such methods in high-stake domains like autonomous driving or medical applications. We propose a novel memory mechanism that operates on human language to illuminate the decision-making process. First, we use CLIP to associate visual inputs with language tokens. Then we feed these tokens to a pretrained language model that serves the agent as memory and provides it with a coherent and interpretable representation of the past. Our memory mechanism achieves state-of-the-art performance in environments where memorizing the past is crucial to solve tasks. Further, we present situations where our memory component excels or fails to demonstrate strengths and weaknesses of our new approach. | ['Sepp Hochreiter', 'Markus Hofmarcher', 'Thomas Adler', 'Fabian Paischer'] | 2023-06-15 | null | null | null | null | ['starcraft-ii', 'dota-2', 'starcraft'] | ['playing-games', 'playing-games', 'playing-games'] | [ 1.78289130e-01 1.83889568e-01 -5.63694760e-02 -2.02926412e-01
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0e35e183-051c-4008-8dae-253b76c19f82 | ledeepchef-deep-reinforcement-learning-agent | 1909.01646 | null | https://arxiv.org/abs/1909.01646v1 | https://arxiv.org/pdf/1909.01646v1.pdf | LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games | While Reinforcement Learning (RL) approaches lead to significant achievements in a variety of areas in recent history, natural language tasks remained mostly unaffected, due to the compositional and combinatorial nature that makes them notoriously hard to optimize. With the emerging field of Text-Based Games (TBGs), researchers try to bridge this gap. Inspired by the success of RL algorithms on Atari games, the idea is to develop new methods in a restricted game world and then gradually move to more complex environments. Previous work in the area of TBGs has mainly focused on solving individual games. We, however, consider the task of designing an agent that not just succeeds in a single game, but performs well across a whole family of games, sharing the same theme. In this work, we present our deep RL agent--LeDeepChef--that shows generalization capabilities to never-before-seen games of the same family with different environments and task descriptions. The agent participated in Microsoft Research's "First TextWorld Problems: A Language and Reinforcement Learning Challenge" and outperformed all but one competitor on the final test set. The games from the challenge all share the same theme, namely cooking in a modern house environment, but differ significantly in the arrangement of the rooms, the presented objects, and the specific goal (recipe to cook). To build an agent that achieves high scores across a whole family of games, we use an actor-critic framework and prune the action-space by using ideas from hierarchical reinforcement learning and a specialized module trained on a recipe database. | ['Leonard Adolphs', 'Thomas Hofmann'] | 2019-09-04 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-4.27096225e-02 3.70547771e-02 2.45494008e-01 -3.33860330e-02
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a485468a-c1b3-4a84-8826-3d21236054a3 | reasoning-with-transformer-based-models-deep | null | null | https://openreview.net/forum?id=Ozp1WrgtF5_ | https://openreview.net/pdf?id=Ozp1WrgtF5_ | Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning | Recent years have seen impressive performance of transformer-based models on different natural language processing tasks. However, it is not clear to what degree the transformers can reason on natural language. To shed light on this question, this survey paper discusses the performance of transformers on different reasoning tasks, including mathematical reasoning, commonsense reasoning, and logical reasoning. We point out successes and limitations, of both empirical and theoretical nature.
| ['Fabian M. Suchanek', 'Chloé Clavel', 'Chadi Helwe'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 8.18592906e-02 4.84955460e-01 -2.13206522e-02 -4.47303951e-01
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0bc75116-0893-4416-84e4-47a7ce43eb5b | a-quantum-neural-network-with-efficient | 2211.05793 | null | https://arxiv.org/abs/2211.05793v2 | https://arxiv.org/pdf/2211.05793v2.pdf | A fermion neural network with efficient optimization and quantum applicability | Classical artificial neural networks have witnessed widespread successes in machine-learning applications. Here, we propose fermion neural networks (FNNs) whose physical properties, such as local density of states or conditional conductance, serve as outputs, once the inputs are incorporated as an initial layer. Comparable to back-propagation, we establish an efficient optimization, which entitles FNNs to competitive performance on challenging machine-learning benchmarks. FNNs also directly apply to quantum systems, including hard ones with interactions, and offer in-situ analysis without preprocessing or presumption. Following machine learning, FNNs precisely determine topological phases and emergent charge orders. Their quantum nature also brings various advantages: quantum correlation entitles more general network connectivity and insight into the vanishing gradient problem, quantum entanglement opens up novel avenues for interpretable machine learning, etc. | ['Yi Zhang', 'Jia-Bao Wang', 'Pei-Lin Zheng'] | 2022-11-10 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.84929085e-01 -1.55257778e-02 -3.70581597e-01 -2.40682423e-01
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722a90ee-b8fa-4e57-aeab-46a689d0d5e8 | a-novel-approach-for-predicting | 2307.01157 | null | https://arxiv.org/abs/2307.01157v1 | https://arxiv.org/pdf/2307.01157v1.pdf | A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation | This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-based population density maps and Air Quality data. We implement an ensemble of Convolutional Neural Networks (CNN) models using various data sources and fusion methodology to build robust predictions and simulate several dynamic parameters that could improve the decision-making process for policymakers. Additionally, we used data assimilation to estimate the state of our system from fused CNN predictions. The combination of meteorological signals and social media-based population density maps improved the performance and flexibility of our prediction of the COVID-19 outbreak in London. While the proposed approach outperforms standard models, such as compartmental models traditionally used in disease forecasting (SEIR), generating robust and consistent predictions allows us to increase the stability of our model while increasing its accuracy. | ['Ovidiu Şerban', 'Rossella Arcucci', 'César Quilodrán Casas', 'Romain Molinas'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-1.87769845e-01 -1.19374886e-01 3.97190034e-01 -1.35007307e-01
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7768c5b9-aa64-4dcb-8a2b-dae1c9b1593d | tallyqa-answering-complex-counting-questions | 1810.12440 | null | http://arxiv.org/abs/1810.12440v2 | http://arxiv.org/pdf/1810.12440v2.pdf | TallyQA: Answering Complex Counting Questions | Most counting questions in visual question answering (VQA) datasets are
simple and require no more than object detection. Here, we study algorithms for
complex counting questions that involve relationships between objects,
attribute identification, reasoning, and more. To do this, we created TallyQA,
the world's largest dataset for open-ended counting. We propose a new algorithm
for counting that uses relation networks with region proposals. Our method lets
relation networks be efficiently used with high-resolution imagery. It yields
state-of-the-art results compared to baseline and recent systems on both
TallyQA and the HowMany-QA benchmark. | ['Manoj Acharya', 'Kushal Kafle', 'Christopher Kanan'] | 2018-10-29 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-2.54944891e-01 -1.75187752e-01 2.06853189e-02 -4.10669029e-01
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95c0bea3-5524-43fd-9ab9-e3d720ea43e9 | optimal-operating-mr-contrast-for-brain | 2304.02056 | null | https://arxiv.org/abs/2304.02056v1 | https://arxiv.org/pdf/2304.02056v1.pdf | Optimal operating MR contrast for brain ventricle parcellation | Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy. In this paper, we use image harmonization to explore the impact of different T1-w MR contrasts on a state-of-the-art ventricle parcellation algorithm VParNet. We identify an optimal operating contrast (OOC) for ventricle parcellation; by showing that the performance of a pretrained VParNet can be boosted by adjusting contrast to the OOC. | ['Jerry L. Prince', 'Aaron Carass', 'Mark G. Luciano', 'Yuli Wang', 'Lianrui Zuo', 'Savannah P. Hays'] | 2023-04-04 | null | null | null | null | ['image-harmonization', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 2.22037420e-01 3.16337883e-01 -2.38259099e-02 -2.63016403e-01
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61a9a184-1906-46ac-b53f-a0490cc93339 | on-the-detection-to-track-association-for | 2107.00500 | null | https://arxiv.org/abs/2107.00500v1 | https://arxiv.org/pdf/2107.00500v1.pdf | On the detection-to-track association for online multi-object tracking | Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that appearance information plays an essential role in the detection-to-track association, which lies at the core of the tracking-by-detection paradigm. While most existing works consider the appearance distances between the detections and the tracks, they ignore the statistical information implied by the historical appearance distance records in the tracks, which can be particularly useful when a detection has similar distances with two or more tracks. In this work, we propose a hybrid track association (HTA) algorithm that models the historical appearance distances of a track with an incremental Gaussian mixture model (IGMM) and incorporates the derived statistical information into the calculation of the detection-to-track association cost. Experimental results on three MOT benchmarks confirm that HTA effectively improves the target identification performance with a small compromise to the tracking speed. Additionally, compared to many state-of-the-art trackers, the DeepSORT tracker equipped with HTA achieves better or comparable performance in terms of the balance of tracking quality and speed. | ['Carsten Maple', 'Victor Sanchez', 'Chang-Tsun Li', 'Xufeng Lin'] | 2021-07-01 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-2.64073104e-01 -6.31306171e-01 -1.25891492e-01 -1.23651534e-01
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969190fa-d369-4817-8a56-59ffd7c3b10e | vax-culture-a-dataset-for-studying-vaccine | 2304.06858 | null | https://arxiv.org/abs/2304.06858v3 | https://arxiv.org/pdf/2304.06858v3.pdf | Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter | Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic. As this hesitancy undermines vaccine campaigns, many researchers have sought to identify its root causes, finding that the increasing volume of anti-vaccine misinformation on social media platforms is a key element of this problem. We explored Twitter as a source of misleading content with the goal of extracting overlapping cultural and political beliefs that motivate the spread of vaccine misinformation. To do this, we have collected a data set of vaccine-related Tweets and annotated them with the help of a team of annotators with a background in communications and journalism. Ultimately we hope this can lead to effective and targeted public health communication strategies for reaching individuals with anti-vaccine beliefs. Moreover, this information helps with developing Machine Learning models to automatically detect vaccine misinformation posts and combat their negative impacts. In this paper, we present Vax-Culture, a novel Twitter COVID-19 dataset consisting of 6373 vaccine-related tweets accompanied by an extensive set of human-provided annotations including vaccine-hesitancy stance, indication of any misinformation in tweets, the entities criticized and supported in each tweet and the communicated message of each tweet. Moreover, we define five baseline tasks including four classification and one sequence generation tasks, and report the results of a set of recent transformer-based models for them. The dataset and code are publicly available at https://github.com/mrzarei5/Vax-Culture. | ['Majid Komeili', 'Sarah Everts', 'Michael Christensen', 'Mohammad Reza Zarei'] | 2023-04-13 | null | null | null | null | ['misinformation', 'culture'] | ['miscellaneous', 'speech'] | [ 3.12084049e-01 2.21869245e-01 -5.64252853e-01 -1.05566375e-01
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fb5557ec-c066-43d1-9f38-01bc33425e94 | spatial-state-action-features-for-general | 2201.06401 | null | https://arxiv.org/abs/2201.06401v2 | https://arxiv.org/pdf/2201.06401v2.pdf | Spatial State-Action Features for General Games | In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. This approach is defined for a highly general and abstract description of the problem -- phrased as optimising the order in which propositions of formulas in disjunctive normal form are evaluated -- and may therefore also be of interest to other types of problems than board games. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees, and demonstrates that the additional efficiency significantly improves the playing strength of agents using the features to guide search. | ['Cameron Browne', 'Matthew Stephenson', 'Éric Piette', 'Dennis J. N. J. Soemers'] | 2022-01-17 | null | null | null | null | ['game-of-go', 'board-games'] | ['playing-games', 'playing-games'] | [ 6.66468367e-02 1.10630549e-01 -1.10066339e-01 5.06125838e-02
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547f99f9-66d4-4f6c-b790-c6fe86869734 | monocular-camera-localization-in-prior-lidar | 2004.00740 | null | https://arxiv.org/abs/2004.00740v2 | https://arxiv.org/pdf/2004.00740v2.pdf | Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences | Light-weight camera localization in existing maps is essential for vision-based navigation. Currently, visual and visual-inertial odometry (VO\&VIO) techniques are well-developed for state estimation but with inevitable accumulated drifts and pose jumps upon loop closure. To overcome these problems, we propose an efficient monocular camera localization method in prior LiDAR maps using direct 2D-3D line correspondences. To handle the appearance differences and modality gaps between LiDAR point clouds and images, geometric 3D lines are extracted offline from LiDAR maps while robust 2D lines are extracted online from video sequences. With the pose prediction from VIO, we can efficiently obtain coarse 2D-3D line correspondences. Then the camera poses and 2D-3D correspondences are iteratively optimized by minimizing the projection error of correspondences and rejecting outliers. Experimental results on the EurocMav dataset and our collected dataset demonstrate that the proposed method can efficiently estimate camera poses without accumulated drifts or pose jumps in structured environments. | ['Ji Zhang', 'Huai Yu', 'Weikun Zhen', 'Sebastian Scherer', 'Wen Yang'] | 2020-04-01 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.71669239e-01 -5.02475083e-01 -2.45765045e-01 -4.59081560e-01
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5a8a6de7-3cfb-4ea7-8966-f90bafe959d3 | the-effect-of-emission-lines-on-the | 2101.11368 | null | https://arxiv.org/abs/2101.11368v1 | https://arxiv.org/pdf/2101.11368v1.pdf | The effect of emission lines on the performance of photometric redshift estimation algorithms | We investigate the effect of strong emission line galaxies on the performance of empirical photometric redshift estimation methods. In order to artificially control the contribution of photometric error and emission lines to total flux, we develop a PCA-based stochastic mock catalogue generation technique that allows for generating infinite signal-to-noise ratio model spectra with realistic emission lines on top of theoretical stellar continua. Instead of running the computationally expensive stellar population synthesis and nebular emission codes, our algorithm generates realistic spectra with a statistical approach, and - as an alternative to attempting to constrain the priors on input model parameters - works by matching output observational parameters. Hence, it can be used to match the luminosity, colour, emission line and photometric error distribution of any photometric sample with sufficient flux-calibrated spectroscopic follow-up. We test three simple empirical photometric estimation methods and compare the results with and without photometric noise and strong emission lines. While photometric noise clearly dominates the uncertainty of photometric redshift estimates, the key findings are that emission lines play a significant role in resolving colour space degeneracies and good spectroscopic coverage of the entire colour space is necessary to achieve good results with empirical photo-z methods. Template fitting methods, on the other hand, must use a template set with sufficient variation in emission line strengths and ratios, or even better, first estimate the redshift empirically and fit the colours with templates at the best-fit redshift to calculate the K-correction and various physical parameters. | ['István Csabai', 'László Dobos', 'Géza Csörnyei'] | 2021-01-27 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [ 4.73636150e-01 -2.61671424e-01 4.21338350e-01 -1.65485397e-01
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b3deeb03-df7a-4114-b33d-23dbbdc193ff | complete-3d-human-reconstruction-from-a | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.pdf | Complete 3D Human Reconstruction From a Single Incomplete Image | This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no pixel to reconstruct where many existing single-view human reconstruction methods are not designed to handle such invisible parts, leading to missing data in 3D. To address this challenge, we introduce a novel coarse-to-fine human reconstruction framework. For coarse reconstruction, explicit volumetric features are learned to generate a complete human geometry with 3D convolutional neural networks conditioned by a 3D body model and the style features from visible parts. An implicit network combines the learned 3D features with the high-quality surface normals enhanced from multiview to produce fine local details, e.g., high-frequency wrinkles. Finally, we perform progressive texture inpainting to reconstruct a complete appearance of the person in a view-consistent way, which is not possible without the reconstruction of a complete geometry. In experiments, we demonstrate that our method can reconstruct high-quality 3D humans, which is robust to occlusion. | ['Ulrich Neumann', 'Krishna Kumar Singh', 'Tuanfeng Y. Wang', 'Jae Shin Yoon', 'Junying Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 1.95400968e-01 3.47221911e-01 2.90978312e-01 -2.01144174e-01
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8a151229-b44a-40cd-a3fb-06d36b593abf | deep-implicit-moving-least-squares-functions | 2103.12266 | null | https://arxiv.org/abs/2103.12266v2 | https://arxiv.org/pdf/2103.12266v2.pdf | Deep Implicit Moving Least-Squares Functions for 3D Reconstruction | Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducing the well-known implicit moving least-squares (IMLS) surface formulation, which naturally defines locally implicit functions on point sets. We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces. Our IMLSNet predicts an octree structure as a scaffold for generating MLS points where needed and characterizes shape geometry with learned local priors. Furthermore, our implicit function evaluation is independent of the neural network once the MLS points are predicted, thus enabling fast runtime evaluation. Our experiments on 3D object reconstruction demonstrate that IMLSNets outperform state-of-the-art learning-based methods in terms of reconstruction quality and computational efficiency. Extensive ablation tests also validate our network design and loss functions. | ['Yang Liu', 'Xin Tong', 'Peng-Shuai Wang', 'Hao Pan', 'Hao-Xiang Guo', 'Shi-Lin Liu'] | 2021-03-23 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-object-reconstruction'] | ['computer-vision'] | [-7.33149573e-02 2.89576083e-01 -1.49518475e-02 -3.45792651e-01
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a08d77e9-84ca-4b2a-a4d8-c1f74d38f2f7 | using-3d-convolutional-neural-networks-to | 1907.11454 | null | https://arxiv.org/abs/1907.11454v1 | https://arxiv.org/pdf/1907.11454v1.pdf | Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video | Automatically recognizing surgical gestures is a crucial step towards a thorough understanding of surgical skill. Possible areas of application include automatic skill assessment, intra-operative monitoring of critical surgical steps, and semi-automation of surgical tasks. Solutions that rely only on the laparoscopic video and do not require additional sensor hardware are especially attractive as they can be implemented at low cost in many scenarios. However, surgical gesture recognition based only on video is a challenging problem that requires effective means to extract both visual and temporal information from the video. Previous approaches mainly rely on frame-wise feature extractors, either handcrafted or learned, which fail to capture the dynamics in surgical video. To address this issue, we propose to use a 3D Convolutional Neural Network (CNN) to learn spatiotemporal features from consecutive video frames. We evaluate our approach on recordings of robot-assisted suturing on a bench-top model, which are taken from the publicly available JIGSAWS dataset. Our approach achieves high frame-wise surgical gesture recognition accuracies of more than 84%, outperforming comparable models that either extract only spatial features or model spatial and low-level temporal information separately. For the first time, these results demonstrate the benefit of spatiotemporal CNNs for video-based surgical gesture recognition. | ['Stefanie Speidel', 'Jürgen Weitz', 'Felix von Bechtolsheim', 'Sebastian Bodenstedt', 'Isabel Funke', 'Florian Oehme'] | 2019-07-26 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 2.66985387e-01 -1.48227021e-01 -2.49510676e-01 -2.44859606e-01
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bbee0fc0-edde-456c-b758-039351dc05f4 | maas-multi-modal-assignation-for-active | 2101.03682 | null | https://arxiv.org/abs/2101.03682v2 | https://arxiv.org/pdf/2101.03682v2.pdf | MAAS: Multi-modal Assignation for Active Speaker Detection | Active speaker detection requires a solid integration of multi-modal cues. While individual modalities can approximate a solution, accurate predictions can only be achieved by explicitly fusing the audio and visual features and modeling their temporal progression. Despite its inherent muti-modal nature, current methods still focus on modeling and fusing short-term audiovisual features for individual speakers, often at frame level. In this paper we present a novel approach to active speaker detection that directly addresses the multi-modal nature of the problem, and provides a straightforward strategy where independent visual features from potential speakers in the scene are assigned to a previously detected speech event. Our experiments show that, an small graph data structure built from a single frame, allows to approximate an instantaneous audio-visual assignment problem. Moreover, the temporal extension of this initial graph achieves a new state-of-the-art on the AVA-ActiveSpeaker dataset with a mAP of 88.8\%. | ['Bernard Ghanem', 'Ali Thabet', 'Fabian Caba Heilbron', 'Juan León-Alcázar'] | 2021-01-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Alcazar_MAAS_Multi-Modal_Assignation_for_Active_Speaker_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Alcazar_MAAS_Multi-Modal_Assignation_for_Active_Speaker_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 2.29746148e-01 8.05538669e-02 -3.56382281e-02 -3.78032148e-01
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d5617d84-153a-4b64-b252-078e6ed1a37d | detection-of-adversarial-attacks-and | 1910.12084 | null | https://arxiv.org/abs/1910.12084v1 | https://arxiv.org/pdf/1910.12084v1.pdf | Detection of Adversarial Attacks and Characterization of Adversarial Subspace | Adversarial attacks have always been a serious threat for any data-driven model. In this paper, we explore subspaces of adversarial examples in unitary vector domain, and we propose a novel detector for defending our models trained for environmental sound classification. We measure chordal distance between legitimate and malicious representation of sounds in unitary space of generalized Schur decomposition and show that their manifolds lie far from each other. Our front-end detector is a regularized logistic regression which discriminates eigenvalues of legitimate and adversarial spectrograms. The experimental results on three benchmarking datasets of environmental sounds represented by spectrograms reveal high detection rate of the proposed detector for eight types of adversarial attacks and outperforms other detection approaches. | ['Alessandro Lameiras Koerich', 'Patrick Cardinal', 'Mohammad Esmaeilpour'] | 2019-10-26 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.35938632e-01 -1.89927235e-01 7.04067945e-01 2.20453367e-01
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a8f42f7e-0517-4403-b1c2-0f0c06b5d00a | natural-policy-gradients-in-reinforcement | 2209.01820 | null | https://arxiv.org/abs/2209.01820v1 | https://arxiv.org/pdf/2209.01820v1.pdf | Natural Policy Gradients In Reinforcement Learning Explained | Traditional policy gradient methods are fundamentally flawed. Natural gradients converge quicker and better, forming the foundation of contemporary Reinforcement Learning such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). This lecture note aims to clarify the intuition behind natural policy gradients, focusing on the thought process and the key mathematical constructs. | ['W. J. A. van Heeswijk'] | 2022-09-05 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-6.38775945e-01 -1.10878222e-01 -7.90427566e-01 -1.50941953e-01
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425b3a76-2834-4946-ba49-c1f237fc92ae | multimodal-driven-talking-face-generation | 2305.02594 | null | https://arxiv.org/abs/2305.02594v2 | https://arxiv.org/pdf/2305.02594v2.pdf | Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator | Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio. However, existing methods ignore the potential of text modal, and their generators mainly follow the source-oriented feature rearrange paradigm coupled with unstable GAN frameworks. In this work, we first represent the emotion in the text prompt, which could inherit rich semantics from the CLIP, allowing flexible and generalized emotion control. We further reorganize these tasks as the target-oriented texture transfer and adopt the Diffusion Models. More specifically, given a textured face as the source and the rendered face projected from the desired 3DMM coefficients as the target, our proposed Texture-Geometry-aware Diffusion Model decomposes the complex transfer problem into multi-conditional denoising process, where a Texture Attention-based module accurately models the correspondences between appearance and geometry cues contained in source and target conditions, and incorporate extra implicit information for high-fidelity talking face generation. Additionally, TGDM can be gracefully tailored for face swapping. We derive a novel paradigm free of unstable seesaw-style optimization, resulting in simple, stable, and effective training and inference schemes. Extensive experiments demonstrate the superiority of our method. | ['Yong liu', 'Ying Tai', 'Jiangning Zhang', 'Tianxin Huang', 'Junwei Zhu', 'Shaoting Zhu', 'Chao Xu'] | 2023-05-04 | null | null | null | null | ['face-swapping', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.80914569e-01 1.49990901e-01 3.17826718e-01 -6.17274046e-01
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ee36b828-2161-46d6-8a33-e618bbf41ae6 | autonomous-driving-with-deep-reinforcement | 2306.11217 | null | https://arxiv.org/abs/2306.11217v1 | https://arxiv.org/pdf/2306.11217v1.pdf | Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation | Nowadays, autonomous vehicles are gaining traction due to their numerous potential applications in resolving a variety of other real-world challenges. However, developing autonomous vehicles need huge amount of training and testing before deploying it to real world. While the field of reinforcement learning (RL) has evolved into a powerful learning framework to the development of deep representation learning, and it is now capable of learning complicated policies in high-dimensional environments like in autonomous vehicles. In this regard, we make an effort, using Deep Q-Learning, to discover a method by which an autonomous car may maintain its lane at top speed while avoiding other vehicles. After that, we used CARLA simulation environment to test and verify our newly acquired policy based on the problem formulation. | ['Jumman Hossain'] | 2023-06-20 | null | null | null | null | ['autonomous-vehicles', 'q-learning'] | ['computer-vision', 'methodology'] | [-3.65551859e-01 5.91462106e-02 -4.28705633e-01 -3.94342750e-01
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89fc73a7-501c-4cce-a3c9-959ec6e78648 | fusecap-leveraging-large-language-models-to | 2305.17718 | null | https://arxiv.org/abs/2305.17718v1 | https://arxiv.org/pdf/2305.17718v1.pdf | FuseCap: Leveraging Large Language Models to Fuse Visual Data into Enriched Image Captions | Image captioning is a central task in computer vision which has experienced substantial progress following the advent of vision-language pre-training techniques. In this paper, we highlight a frequently overlooked limitation of captioning models that often fail to capture semantically significant elements. This drawback can be traced back to the text-image datasets; while their captions typically offer a general depiction of image content, they frequently omit salient details. To mitigate this limitation, we propose FuseCap - a novel method for enriching captions with additional visual information, obtained from vision experts, such as object detectors, attribute recognizers, and Optical Character Recognizers (OCR). Our approach fuses the outputs of such vision experts with the original caption using a large language model (LLM), yielding enriched captions that present a comprehensive image description. We validate the effectiveness of the proposed caption enrichment method through both quantitative and qualitative analysis. Our method is then used to curate the training set of a captioning model based BLIP which surpasses current state-of-the-art approaches in generating accurate and detailed captions while using significantly fewer parameters and training data. As additional contributions, we provide a dataset comprising of 12M image-enriched caption pairs and show that the proposed method largely improves image-text retrieval. | ['Ron Kimmel', 'Roy Ganz', 'Shaked Brody', 'David Bensaid', 'Noam Rotstein'] | 2023-05-28 | null | null | null | null | ['optical-character-recognition', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 6.71775162e-01 3.29429120e-01 -1.02681935e-01 -2.41875410e-01
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c2782780-2858-4d4e-bca4-2901042fa40f | trajectory-aware-body-interaction-transformer | 2303.05095 | null | https://arxiv.org/abs/2303.05095v2 | https://arxiv.org/pdf/2303.05095v2.pdf | Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting | Multi-person pose forecasting remains a challenging problem, especially in modeling fine-grained human body interaction in complex crowd scenarios. Existing methods typically represent the whole pose sequence as a temporal series, yet overlook interactive influences among people based on skeletal body parts. In this paper, we propose a novel Trajectory-Aware Body Interaction Transformer (TBIFormer) for multi-person pose forecasting via effectively modeling body part interactions. Specifically, we construct a Temporal Body Partition Module that transforms all the pose sequences into a Multi-Person Body-Part sequence to retain spatial and temporal information based on body semantics. Then, we devise a Social Body Interaction Self-Attention (SBI-MSA) module, utilizing the transformed sequence to learn body part dynamics for inter- and intra-individual interactions. Furthermore, different from prior Euclidean distance-based spatial encodings, we present a novel and efficient Trajectory-Aware Relative Position Encoding for SBI-MSA to offer discriminative spatial information and additional interactive clues. On both short- and long-term horizons, we empirically evaluate our framework on CMU-Mocap, MuPoTS-3D as well as synthesized datasets (6 ~ 10 persons), and demonstrate that our method greatly outperforms the state-of-the-art methods. Code will be made publicly available upon acceptance. | ['Zizhao Wu', 'Siyuan Mao', 'Xiaogang Peng'] | 2023-03-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Peng_Trajectory-Aware_Body_Interaction_Transformer_for_Multi-Person_Pose_Forecasting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-person-pose-forecasting'] | ['computer-vision'] | [-1.12294503e-01 -1.03294201e-01 1.71508357e-01 -3.56866032e-01
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047680ea-92f3-4a26-82c7-87785dd3d36a | localized-traffic-sign-detection-with-multi | 1804.10428 | null | http://arxiv.org/abs/1804.10428v2 | http://arxiv.org/pdf/1804.10428v2.pdf | Localized Traffic Sign Detection with Multi-scale Deconvolution Networks | Autonomous driving is becoming a future practical lifestyle greatly driven by
deep learning. Specifically, an effective traffic sign detection by deep
learning plays a critical role for it. However, different countries have
different sets of traffic signs, making localized traffic sign recognition
model training a tedious and daunting task. To address the issues of taking
amount of time to compute complicate algorithm and low ratio of detecting
blurred and sub-pixel images of localized traffic signs, we propose Multi-Scale
Deconvolution Networks (MDN), which flexibly combines multi-scale convolutional
neural network with deconvolution sub-network, leading to efficient and
reliable localized traffic sign recognition model training. It is demonstrated
that the proposed MDN is effective compared with classical algorithms on the
benchmarks of the localized traffic sign, such as Chinese Traffic Sign Dataset
(CTSD), and the German Traffic Sign Benchmarks (GTSRB). | ['Yanfei Ji', 'Fuwu Tang', 'Zhong Ning', 'Songwen Pei', 'Jing Fan'] | 2018-04-27 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [-1.40571445e-01 -8.16303194e-01 -7.64180496e-02 -4.11911190e-01
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b0911aef-df32-495d-b9b0-98e095df8d73 | self-supervised-visual-feature-learning-with | 1902.06162 | null | http://arxiv.org/abs/1902.06162v1 | http://arxiv.org/pdf/1902.06162v1.pdf | Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey | Large-scale labeled data are generally required to train deep neural networks
in order to obtain better performance in visual feature learning from images or
videos for computer vision applications. To avoid extensive cost of collecting
and annotating large-scale datasets, as a subset of unsupervised learning
methods, self-supervised learning methods are proposed to learn general image
and video features from large-scale unlabeled data without using any
human-annotated labels. This paper provides an extensive review of deep
learning-based self-supervised general visual feature learning methods from
images or videos. First, the motivation, general pipeline, and terminologies of
this field are described. Then the common deep neural network architectures
that used for self-supervised learning are summarized. Next, the main
components and evaluation metrics of self-supervised learning methods are
reviewed followed by the commonly used image and video datasets and the
existing self-supervised visual feature learning methods. Finally, quantitative
performance comparisons of the reviewed methods on benchmark datasets are
summarized and discussed for both image and video feature learning. At last,
this paper is concluded and lists a set of promising future directions for
self-supervised visual feature learning. | ['YingLi Tian', 'Longlong Jing'] | 2019-02-16 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [-9.03824344e-02 -2.30992094e-01 -5.21550059e-01 -9.27456439e-01
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ebbeb2ad-170d-44e2-a896-11534060fce3 | scientific-computing-with-diffractive-optical | 2302.10905 | null | https://arxiv.org/abs/2302.10905v1 | https://arxiv.org/pdf/2302.10905v1.pdf | Scientific Computing with Diffractive Optical Neural Networks | Diffractive optical neural networks (DONNs) have been emerging as a high-throughput and energy-efficient hardware platform to perform all-optical machine learning (ML) in machine vision systems. However, the current demonstrated applications of DONNs are largely straightforward image classification tasks, which undermines the prospect of developing and utilizing such hardware for other ML applications. Here, we numerically and experimentally demonstrate the deployment of an all-optical reconfigurable DONNs system for scientific computing, including guiding two-dimensional quantum material synthesis, predicting the properties of nanomaterials and small molecular cancer drugs, predicting the device response of nanopatterned integrated photonic power splitters, and the dynamic stabilization of an inverted pendulum with reinforcement learning. Despite a large variety of input data structures, we develop a universal feature engineering approach to convert categorical input features to the images that can be processed in the DONNs system. Our results open up new opportunities of employing DONNs systems for a broad range of ML applications. | ['Weilu Gao', 'Jianzhu Ma', 'Yingheng Tang', 'Ruiyang Chen'] | 2023-02-12 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 6.34648800e-01 -8.31088647e-02 -3.74936573e-02 2.34377142e-02
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362fd8fa-19cd-4d53-884a-6bafec39b92c | deep-neural-networks-based-modrec-some | 1811.06103 | null | http://arxiv.org/abs/1811.06103v1 | http://arxiv.org/pdf/1811.06103v1.pdf | Deep Neural Networks based Modrec: Some Results with Inter-Symbol Interference and Adversarial Examples | Recent successes and advances in Deep Neural Networks (DNN) in machine vision
and Natural Language Processing (NLP) have motivated their use in traditional
signal processing and communications systems. In this paper, we present results
of such applications to the problem of automatic modulation recognition.
Variations in wireless communication channels are represented by statistical
channel models and their parameterization will increase with the advent of 5G.
In this paper, we report effect of simple two path channel model on our naive
deep neural network based implementation. We also report impact of adversarial
perturbation to the input signal. | ['S. Asim Ahmed', 'Michael Newhouse', 'Subhashish Chakravarty'] | 2018-11-14 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 3.95047665e-01 -6.76498413e-02 -1.28425270e-01 -3.54918182e-01
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ecf80ad0-71ba-4a8f-af40-ba68db95e56c | meloform-generating-melody-with-musical-form | 2208.14345 | null | https://arxiv.org/abs/2208.14345v1 | https://arxiv.org/pdf/2208.14345v1.pdf | MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks | Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks. Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models. Both subjective and objective experimental evaluations demonstrate that MeloForm generates melodies with precise musical form control with 97.79% accuracy, and outperforms baseline systems in terms of subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure, thematic, richness and overall quality, without any labelled musical form data. Besides, MeloForm can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc. | ['Tie-Yan Liu', 'Sheng Zhao', 'Tao Qin', 'Botao Yu', 'Xu Tan', 'Peiling Lu'] | 2022-08-30 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-9.89542808e-03 -3.15255016e-01 5.94988354e-02 1.38467118e-01
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064c528b-e0f2-4fbb-902c-2945cc2a5073 | demystifying-ten-big-ideas-and-rules-every | 2111.13756 | null | https://arxiv.org/abs/2111.13756v1 | https://arxiv.org/pdf/2111.13756v1.pdf | Demystifying Ten Big Ideas and Rules Every Fire Scientist & Engineer Should Know About Blackbox, Whitebox & Causal Artificial Intelligence | Artificial intelligence (AI) is paving the way towards the fourth industrial revolution with the fire domain (Fire 4.0). As a matter of fact, the next few years will be elemental to how this technology will shape our academia, practice, and entrepreneurship. Despite the growing interest between fire research groups, AI remains absent of our curriculum, and we continue to lack a methodical framework to adopt, apply and create AI solutions suitable for our problems. The above is also true for parallel engineering domains (i.e., civil/mechanical engineering), and in order to negate the notion of history repeats itself (e.g., look at the continued debate with regard to modernizing standardized fire testing, etc.), it is the motivation behind this letter to the Editor to demystify some of the big ideas behind AI to jump-start prolific and strategic discussions on the front of AI & Fire. In addition, this letter intends to explain some of the most fundamental concepts and clear common misconceptions specific to the adoption of AI in fire engineering. This short letter is a companion to the Smart Systems in Fire Engineering special issue sponsored by Fire Technology. An in-depth review of AI algorithms [1] and success stories to the proper implementations of such algorithms can be found in the aforenoted special issue and collection of papers. This letter comprises two sections. The first section outlines big ideas pertaining to AI, and answers some of the burning questions with regard to the merit of adopting AI in our domain. The second section presents a set of rules or technical recommendations an AI user may deem helpful to practice whenever AI is used as an investigation methodology. The presented set of rules are complementary to the big ideas. | ['M. Z. Naser'] | 2021-11-23 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 4.30135936e-01 1.39408261e-01 -8.05237442e-02 1.85691535e-01
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c6e0f396-7dec-47e4-bb38-0e6e49233d39 | uatta-ens-uncertainty-aware-test-time | 2211.03148 | null | https://arxiv.org/abs/2211.03148v2 | https://arxiv.org/pdf/2211.03148v2.pdf | UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection | Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions. | ['Akshat Bhandari', 'Saurabh Kumar Mishra', 'Ananya Gupta', 'Adil Khan', 'Pratinav Seth'] | 2022-11-06 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 1.66775912e-01 8.39565620e-02 1.00378007e-01 -9.44549143e-01
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014b363b-559b-4c3f-8967-1904186672af | schema-aware-reference-as-prompt-improves | 2210.10709 | null | https://arxiv.org/abs/2210.10709v4 | https://arxiv.org/pdf/2210.10709v4.pdf | Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction | With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance. However, existing prompt-based learning methods for knowledge graph construction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structured knowledge with pre-defined schema, which means model cannot fully exploit semantic knowledge with the constrained templates; (ii) representation learning with locally individual instances limits the performance given the insufficient features, which are unable to unleash the potential analogical capability of pre-trained language models. Motivated by these observations, we propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP), for data-efficient knowledge graph construction. It can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample, which is model-agnostic and can be plugged into widespread existing approaches. Experimental results demonstrate that previous methods integrated with RAP can achieve impressive performance gains in low-resource settings on five datasets of relational triple extraction and event extraction for knowledge graph construction. Code is available in https://github.com/zjunlp/RAP. | ['Huajun Chen', 'Xi Chen', 'Xiang Chen', 'Shumin Deng', 'Ningyu Zhang', 'Shengyu Mao', 'Yunzhi Yao'] | 2022-10-19 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-4.20804881e-02 4.85562414e-01 -9.15267885e-01 -2.18501270e-01
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20819599-375f-4b2c-82ed-c2845cd3e656 | self-supervised-semantic-segmentation | 2203.13868 | null | https://arxiv.org/abs/2203.13868v2 | https://arxiv.org/pdf/2203.13868v2.pdf | Self-supervised Semantic Segmentation Grounded in Visual Concepts | Unsupervised semantic segmentation requires assigning a label to every pixel without any human annotations. Despite recent advances in self-supervised representation learning for individual images, unsupervised semantic segmentation with pixel-level representations is still a challenging task and remains underexplored. In this work, we propose a self-supervised pixel representation learning method for semantic segmentation by using visual concepts (i.e., groups of pixels with semantic meanings, such as parts, objects, and scenes) extracted from images. To guide self-supervised learning, we leverage three types of relationships between pixels and concepts, including the relationships between pixels and local concepts, local and global concepts, as well as the co-occurrence of concepts. We evaluate the learned pixel embeddings and visual concepts on three datasets, including PASCAL VOC 2012, COCO 2017, and DAVIS 2017. Our results show that the proposed method gains consistent and substantial improvements over recent unsupervised semantic segmentation approaches, and also demonstrate that visual concepts can reveal insights into image datasets. | ['Liu Ren', 'Liang Gou', 'Arvind Kumar Shekar', 'William Surmeier', 'Wenbin He'] | 2022-03-25 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.79621851e-01 2.29224101e-01 -4.76230770e-01 -7.30157375e-01
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67afd259-b877-4d3f-8d7d-c37c6210728b | 4d-association-graph-for-realtime-multi | 2002.12625 | null | https://arxiv.org/abs/2002.12625v1 | https://arxiv.org/pdf/2002.12625v1.pdf | 4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras | This paper contributes a novel realtime multi-person motion capture algorithm using multiview video inputs. Due to the heavy occlusions in each view, joint optimization on the multiview images and multiple temporal frames is indispensable, which brings up the essential challenge of realtime efficiency. To this end, for the first time, we unify per-view parsing, cross-view matching, and temporal tracking into a single optimization framework, i.e., a 4D association graph that each dimension (image space, viewpoint and time) can be treated equally and simultaneously. To solve the 4D association graph efficiently, we further contribute the idea of 4D limb bundle parsing based on heuristic searching, followed with limb bundle assembling by proposing a bundle Kruskal's algorithm. Our method enables a realtime online motion capture system running at 30fps using 5 cameras on a 5-person scene. Benefiting from the unified parsing, matching and tracking constraints, our method is robust to noisy detection, and achieves high-quality online pose reconstruction quality. The proposed method outperforms the state-of-the-art method quantitatively without using high-level appearance information. We also contribute a multiview video dataset synchronized with a marker-based motion capture system for scientific evaluation. | ['Tao Yu', 'Liang An', 'Yuxiang Zhang', 'Xiu Li', 'Kun Li', 'Yebin Liu'] | 2020-02-28 | 4d-association-graph-for-realtime-multi-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_4D_Association_Graph_for_Realtime_Multi-Person_Motion_Capture_Using_Multiple_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-1.50420383e-01 -4.87236768e-01 -1.46788448e-01 -1.16483048e-02
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59084802-4952-4227-a7b2-b14796bfbb58 | folding-home-achievements-from-over-twenty | 2303.08993 | null | https://arxiv.org/abs/2303.08993v1 | https://arxiv.org/pdf/2303.08993v1.pdf | Folding@home: achievements from over twenty years of citizen science herald the exascale era | Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach to biomolecular simulation, harnessing the resources of citizen scientists across the globe. Here, we summarize the scientific and technical advances this perspective has enabled. As the project's name implies, the early years of Folding@home focused on driving advances in our understanding of protein folding by developing statistical methods for capturing long-timescale processes and facilitating insight into complex dynamical processes. Success laid a foundation for broadening the scope of Folding@home to address other functionally relevant conformational changes, such as receptor signaling, enzyme dynamics, and ligand binding. Continued algorithmic advances, hardware developments such as GPU-based computing, and the growing scale of Folding@home have enabled the project to focus on new areas where massively parallel sampling can be impactful. While previous work sought to expand toward larger proteins with slower conformational changes, new work focuses on large-scale comparative studies of different protein sequences and chemical compounds to better understand biology and inform the development of small molecule drugs. Progress on these fronts enabled the community to pivot quickly in response to the COVID-19 pandemic, expanding to become the world's first exascale computer and deploying this massive resource to provide insight into the inner workings of the SARS-CoV-2 virus and aid the development of new antivirals. This success provides a glimpse of what's to come as exascale supercomputers come online, and Folding@home continues its work. | ['Gregory R. Bowman', 'Vijay S. Pande', 'Vincent A. Voelz'] | 2023-03-15 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [-4.30892557e-02 -5.85566938e-01 -8.20334405e-02 -3.06937009e-01
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00cf2719-3edb-4a20-b392-11cff6a45bc3 | exploiting-completeness-and-uncertainty-of | 2212.04090 | null | https://arxiv.org/abs/2212.04090v1 | https://arxiv.org/pdf/2212.04090v1.pdf | Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection | Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial role, we propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training. Specifically, we first design a multi-head classification module (each head serves as a classifier) with a diversity loss to maximize the distribution differences of predicted pseudo labels across heads. This encourages the generated pseudo labels to cover as many abnormal events as possible. We then devise an iterative uncertainty pseudo label refinement strategy, which improves not only the initial pseudo labels but also the updated ones obtained by the desired classifier in the second stage. Extensive experimental results demonstrate the proposed method performs favorably against state-of-the-art approaches on the UCF-Crime, TAD, and XD-Violence benchmark datasets. | ['Ming-Hsuan Yang', 'Qingming Huang', 'Laiyun Qing', 'Shuhui Wang', 'Yuankai Qi', 'Guorong Li', 'Chen Zhang'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [ 2.50379533e-01 1.97240323e-01 -2.67477959e-01 -6.16985857e-01
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d45860ed-7ea0-455a-a454-1eb0acbfe4b0 | economic-and-energetic-assessment-of-a-hybrid | 2301.02535 | null | https://arxiv.org/abs/2301.02535v1 | https://arxiv.org/pdf/2301.02535v1.pdf | Economic and Energetic Assessment of a Hybrid Vanadium Redox Flow and Lithium-ion batteries considering different Energy Management Strategies | Hybrid energy storage systems (HESS) combine different energy storage technologies aiming at overall system performance and lifetime improvement compared to a single technology system. In this work, control combinations for a vanadium redox flow battery (VRFB, 5/60 kW/kWh) and a lithium-ion battery (LIB, 3.3/9.8 kW/kWh) are investigated for the design of a HESS. Four real-time energy management/power allocation scenarios are considered for the operation of the hybrid storage solution through a 15-year economic and energetic analysis. The results obtained for each scenario are compared with a single technology battery performance. In the definition of the scenarios, real electricity generation is considered from two solar photovoltaic installations (3.2 kWp and 6.7 kWp) and an estimated representative load of a services building. HESS performance is evaluated through specific energy and economic key performance indicators. The results obtained indicate that the use of customized energy management strategies (EMSs) renders the VRFB and LIB characteristics complementary, besides enhancing the competitiveness of VRFB, as a single technology. Moreover, the HESS management impacts the seasonality factor, contributing to the overall electric system smart management. | ['Manuel Collares-Pereira', 'Pedro Horta', 'Luís Fialho', 'Ana Foles'] | 2023-01-06 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.93273938e-01 -2.18277231e-01 -8.26552138e-02 3.19713622e-01
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0c5fb90f-7bd7-4503-9509-6b36a5fb9bbc | benchmarking-state-of-the-art-gradient | 2305.17094 | null | https://arxiv.org/abs/2305.17094v1 | https://arxiv.org/pdf/2305.17094v1.pdf | Benchmarking state-of-the-art gradient boosting algorithms for classification | This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world datasets of sufficient diversity. In the study, special emphasis was placed on hyperparameter optimization, specifically comparing two tuning strategies, i.e. randomized search and Bayesian optimization using the Tree-stuctured Parzen Estimator. The performance of considered methods was investigated in terms of common classification accuracy metrics as well as runtime and tuning time. Additionally, obtained results have been validated using appropriate statistical testing. An attempt was made to indicate a gradient boosting variant showing the right balance between effectiveness, reliability and ease of use. | ['Adam Zagdański', 'Piotr Florek'] | 2023-05-26 | null | null | null | null | ['hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-3.07172567e-01 -3.45477819e-01 -4.27180648e-01 -8.17380786e-01
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1bdefdd8-1717-44a3-9f8b-fc37025afe30 | user-constrained-thumbnail-generation-using | 1810.13054 | null | http://arxiv.org/abs/1810.13054v3 | http://arxiv.org/pdf/1810.13054v3.pdf | User Constrained Thumbnail Generation using Adaptive Convolutions | Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails
in real time. GCA is used to selectively attend and aggregate the global
context information from the entire image while the RPN is used to predict
candidate bounding boxes for the thumbnail image. Adaptive convolution
eliminates the problem of generating thumbnails of various aspect ratios by
using filter weights dynamically generated from the aspect ratio information.
The experimental results indicate the superior performance of the proposed
model over existing state-of-the-art techniques. | ['Ayan Kumar Bhunia', 'Perla Sai Raj Kishore', 'Shuvozit Ghose', 'Partha Pratim Roy'] | 2018-10-31 | null | null | null | null | ['user-constrained-thumbnail-generation'] | ['computer-vision'] | [ 2.96734631e-01 -3.27248663e-01 4.17466551e-01 -2.03989550e-01
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60ffdea5-a199-4957-a2bb-72746aadfd66 | bipartite-mixed-membership-distribution-free | 2211.00912 | null | https://arxiv.org/abs/2211.00912v2 | https://arxiv.org/pdf/2211.00912v2.pdf | Bipartite Mixed Membership Distribution-Free Model. A novel model for community detection in overlapping bipartite weighted networks | Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model. Our model allows an adjacency matrix to follow any distribution as long as its expectation has a block structure related to node membership. In particular, BiMMDF can model overlapping bipartite signed networks and it is an extension of many previous models, including the popular mixed membership stochastic blcokmodels. An efficient algorithm with a theoretical guarantee of consistent estimation is applied to fit BiMMDF. We then obtain the separation conditions of BiMMDF for different distributions. Furthermore, we also consider missing edges for sparse networks. The advantage of BiMMDF is demonstrated in extensive synthetic networks and eight real-world networks. | ['Jingli Wang', 'Huan Qing'] | 2022-11-02 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.45370424e-01 2.16492981e-01 -2.91523069e-01 -2.54203469e-01
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8eee0f7d-24c6-453d-8cae-1f878de7805a | cuisinenet-food-attributes-classification | 1805.12081 | null | http://arxiv.org/abs/1805.12081v2 | http://arxiv.org/pdf/1805.12081v2.pdf | CuisineNet: Food Attributes Classification using Multi-scale Convolution Network | Diversity of food and its attributes represents the culinary habits of
peoples from different countries. Thus, this paper addresses the problem of
identifying food culture of people around the world and its flavor by
classifying two main food attributes, cuisine and flavor. A deep learning model
based on multi-scale convotuional networks is proposed for extracting more
accurate features from input images. The aggregation of multi-scale convolution
layers with different kernel size is also used for weighting the features
results from different scales. In addition, a joint loss function based on
Negative Log Likelihood (NLL) is used to fit the model probability to multi
labeled classes for multi-modal classification task. Furthermore, this work
provides a new dataset for food attributes, so-called Yummly48K, extracted from
the popular food website, Yummly. Our model is assessed on the constructed
Yummly48K dataset. The experimental results show that our proposed method
yields 65% and 62% average F1 score on validation and test set which
outperforming the state-of-the-art models. | ['Md. Mostafa Kamal Sarker', 'Petia Radeva', 'Mohammed Jabreel', 'Domenec Puig', 'Antonio Moreno', 'Syeda Furruka Banu', 'Hatem A. Rashwan'] | 2018-05-30 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [-5.05860984e-01 -5.67379594e-01 -1.99734017e-01 -6.69479847e-01
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c8c1ba88-b016-46f2-9ec3-d8005e7b416f | small-changes-make-big-differences-improving | 2111.10154 | null | https://arxiv.org/abs/2111.10154v2 | https://arxiv.org/pdf/2111.10154v2.pdf | Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning | Retrieve-based dialogue response selection aims to find a proper response from a candidate set given a multi-turn context. Pre-trained language models (PLMs) based methods have yielded significant improvements on this task. The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response. However, we observe that different context-response pairs sharing the same context always have a greater similarity in the sequence representations calculated by PLMs, which makes it hard to distinguish positive responses from negative ones. Motivated by this, we propose a novel \textbf{F}ine-\textbf{G}rained \textbf{C}ontrastive (FGC) learning method for the response selection task based on PLMs. This FGC learning strategy helps PLMs to generate more distinguishable matching representations of each dialogue at fine grains, and further make better predictions on choosing positive responses. Empirical studies on two benchmark datasets demonstrate that the proposed FGC learning method can generally and significantly improve the model performance of existing PLM-based matching models. | ['Daxin Jiang', 'Yan Zhang', 'Lei Sha', 'Huang Hu', 'Can Xu', 'Yuntao Li'] | 2021-11-19 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 6.45356476e-01 -1.85688585e-01 -4.58941191e-01 -7.94994235e-01
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972c3efe-82d9-4a63-b7b7-069a808738e7 | debiasing-neural-retrieval-via-in-batch | 2205.09240 | null | https://arxiv.org/abs/2205.09240v1 | https://arxiv.org/pdf/2205.09240v1.pdf | Debiasing Neural Retrieval via In-batch Balancing Regularization | People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provide a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven't been intuitive objective functions that depend on the click probability and user engagement to directly optimize towards this. In this work, we propose the In-Batch Balancing Regularization (IBBR) to mitigate the ranking disparity among subgroups. In particular, we develop a differentiable \textit{normed Pairwise Ranking Fairness} (nPRF) and leverage the T-statistics on top of nPRF over subgroups as a regularization to improve fairness. Empirical results with the BERT-based neural rankers on the MS MARCO Passage Retrieval dataset with the human-annotated non-gendered queries benchmark \citep{rekabsaz2020neural} show that our IBBR method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline. | ['Andrew Arnold', 'Xiaofei Ma', 'Parminder Bhatia', 'Shen Wang', 'Zijian Wang', 'Xiaokai Wei', 'Yuantong Li'] | 2022-05-18 | null | https://aclanthology.org/2022.gebnlp-1.5 | https://aclanthology.org/2022.gebnlp-1.5.pdf | naacl-gebnlp-2022-7 | ['passage-retrieval'] | ['natural-language-processing'] | [-7.45200813e-02 1.26019612e-01 -2.50205636e-01 -1.15228927e+00
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81ec148e-efff-408d-9936-334beadaf655 | probing-multilingual-cognate-prediction | null | null | https://aclanthology.org/2022.findings-acl.299 | https://aclanthology.org/2022.findings-acl.299.pdf | Probing Multilingual Cognate Prediction Models | Character-based neural machine translation models have become the reference models for cognate prediction, a historical linguistics task. So far, all linguistic interpretations about latent information captured by such models have been based on external analysis (accuracy, raw results, errors). In this paper, we investigate what probing can tell us about both models and previous interpretations, and learn that though our models store linguistic and diachronic information, they do not achieve it in previously assumed ways. | ['Benoît Sagot', 'Clémentine Fourrier'] | null | null | null | null | findings-acl-2022-5 | ['cognate-prediction'] | ['natural-language-processing'] | [ 1.54019818e-01 5.00446558e-01 -7.53495753e-01 -5.90055287e-01
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5b0a91ab-a87b-4a54-b015-49835bc3db9a | detection-and-recognition-of-malaysian | 1504.06921 | null | http://arxiv.org/abs/1504.06921v1 | http://arxiv.org/pdf/1504.06921v1.pdf | Detection and Recognition of Malaysian Special License Plate Based On SIFT Features | Automated car license plate recognition systems are developed and applied for
purpose of facilitating the surveillance, law enforcement, access control and
intelligent transportation monitoring with least human intervention. In this
paper, an algorithm based on SIFT feature points clustering and matching is
proposed to address the issue of recognizing Malaysian special plates. These
special plates do not follow the format of standard car plates as they may
contain italic, cursive, connected and small letters. The algorithm is tested
with 150 Malaysian special plate images under different environment and the
promising experimental results demonstrate that the proposed algorithm is
relatively robust. | ['Yong Haur Tay', 'Hock Woon Hon', 'Kim Meng Liang', 'Hamam Mokayed', 'Hooi Sin Ng'] | 2015-04-27 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-3.81093137e-02 -8.49095345e-01 1.22400135e-01 -7.31710047e-02
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3ef13580-b35c-44aa-b50f-eaa5e18ed77e | ntuaails-at-semeval-2020-task-11-propaganda | null | null | https://aclanthology.org/2020.semeval-1.195 | https://aclanthology.org/2020.semeval-1.195.pdf | NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo | This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC). The goal for the SI sub-task is to identify specific fragments, in a given plain text, containing at least one propaganda technique. The TC sub-task aims to identify the applied propaganda technique in a given text fragment. A different model was trained for each sub-task. Our best performing system for the SI task consists of pre-trained ELMo word embeddings followed by residual bidirectional LSTM network. For the TC sub-task pre-trained word embeddings from GloVe fed to a bidirectional LSTM neural network. The models achieved rank 28 among 36 teams with F1 score of 0.335 and rank 25 among 31 teams with 0.463 F1 score for SI and TC sub-tasks respectively. Our results indicate that the proposed deep learning models, although relatively simple in architecture and fast to train, achieve satisfactory results in the tasks on hand. | ['Georgios Siolas', 'Anastasios Arsenos'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.52950229e-02 -2.23061949e-01 -2.03757942e-01 3.72516364e-02
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f2b9821b-5ffa-4a17-93db-80d008f6e96e | tunbert-pretrained-contextualized-text | 2111.13138 | null | https://arxiv.org/abs/2111.13138v1 | https://arxiv.org/pdf/2111.13138v1.pdf | TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect | Pretrained contextualized text representation models learn an effective representation of a natural language to make it machine understandable. After the breakthrough of the attention mechanism, a new generation of pretrained models have been proposed achieving good performances since the introduction of the Transformer. Bidirectional Encoder Representations from Transformers (BERT) has become the state-of-the-art model for language understanding. Despite their success, most of the available models have been trained on Indo-European languages however similar research for under-represented languages and dialects remains sparse. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for under represented languages, with a specific focus on the Tunisian dialect. We evaluate our language model on sentiment analysis task, dialect identification task and reading comprehension question-answering task. We show that the use of noisy web crawled data instead of structured data (Wikipedia, articles, etc.) is more convenient for such non-standardized language. Moreover, results indicate that a relatively small web crawled dataset leads to performances that are as good as those obtained using larger datasets. Finally, our best performing TunBERT model reaches or improves the state-of-the-art in all three downstream tasks. We release the TunBERT pretrained model and the datasets used for fine-tuning. | ['Amine Kerkeni', 'Faten Ghriss', 'Malek Naski', 'Abir Korched', 'Moez BenHajhmida', 'Nourchene Ferchichi', 'Hatem Haddad', 'Ahmed Cheikhrouhou', 'Abir Messaoudi'] | 2021-11-25 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-3.28382961e-02 2.33977303e-01 -3.46076861e-02 -5.52472413e-01
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cfd420f3-cec4-447f-86fc-9b7b19a67415 | braitenberg-vehicles-as-developmental | 2003.07689 | null | https://arxiv.org/abs/2003.07689v3 | https://arxiv.org/pdf/2003.07689v3.pdf | Braitenberg Vehicles as Developmental Neurosimulation | Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental BVs (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach. | ['Bradly Alicea', 'Jesse Parent', 'Ankit Gupta', 'Ziyi Gong', 'Stefan Dvoretskii'] | 2020-02-28 | null | null | null | null | ['developmental-learning'] | ['robots'] | [-3.09789497e-02 4.24286127e-01 4.40434545e-01 1.74300849e-01
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346c9a15-534f-4ea7-ad47-04c7145d66d7 | on-the-robustness-of-language-encoders | 2005.05683 | null | https://arxiv.org/abs/2005.05683v1 | https://arxiv.org/pdf/2005.05683v1.pdf | On the Robustness of Language Encoders against Grammatical Errors | We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. We use this approach to facilitate debugging models on downstream applications. Results confirm that the performance of all tested models is affected but the degree of impact varies. To interpret model behaviors, we further design a linguistic acceptability task to reveal their abilities in identifying ungrammatical sentences and the position of errors. We find that fixed contextual encoders with a simple classifier trained on the prediction of sentence correctness are able to locate error positions. We also design a cloze test for BERT and discover that BERT captures the interaction between errors and specific tokens in context. Our results shed light on understanding the robustness and behaviors of language encoders against grammatical errors. | ['Kai-Wei Chang', 'Tao Meng', 'Quanyu Long', 'Fan Yin'] | 2020-05-12 | on-the-robustness-of-language-encoders-1 | https://aclanthology.org/2020.acl-main.310 | https://aclanthology.org/2020.acl-main.310.pdf | acl-2020-6 | ['cloze-test', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.51380026e-02 3.58697891e-01 3.10221046e-01 -4.44840580e-01
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835f6018-94e8-437e-9ade-00526a85968c | videoofa-two-stage-pre-training-for-video-to | 2305.03204 | null | https://arxiv.org/abs/2305.03204v1 | https://arxiv.org/pdf/2305.03204v1.pdf | VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation | We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an intermediate video-text pre-training stage to learn video-specific skills such as spatio-temporal reasoning. As a result, our VideoOFA model achieves new state-of-the-art performance on four Video Captioning benchmarks, beating prior art by an average of 9.7 points in CIDEr score. It also outperforms existing models on two open-ended Video Question Answering datasets, showcasing its generalization capability as a universal video-to-text model. | ['Wen-tau Yih', 'Yashar Mehdad', 'Barlas Oğuz', 'Wenhan Xiong', 'Lili Yu', 'Xilun Chen'] | 2023-05-04 | null | null | null | null | ['video-captioning', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [ 4.10766333e-01 2.51947075e-01 -2.06672907e-01 -4.21948373e-01
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48722433-9fb8-447d-9548-d7f77e81b90f | probing-the-need-for-visual-context-in | 1903.08678 | null | https://arxiv.org/abs/1903.08678v2 | https://arxiv.org/pdf/1903.08678v2.pdf | Probing the Need for Visual Context in Multimodal Machine Translation | Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence of the very simple, short and repetitive sentences used in the only available dataset for the task (Multi30K), rendering the source text sufficient as context. In the general case, however, we believe that it is possible to combine visual and textual information in order to ground translations. In this paper we probe the contribution of the visual modality to state-of-the-art MMT models by conducting a systematic analysis where we partially deprive the models from source-side textual context. Our results show that under limited textual context, models are capable of leveraging the visual input to generate better translations. This contradicts the current belief that MMT models disregard the visual modality because of either the quality of the image features or the way they are integrated into the model. | ['Loïc Barrault', 'Ozan Caglayan', 'Lucia Specia', 'Pranava Madhyastha'] | 2019-03-20 | probing-the-need-for-visual-context-in-1 | https://aclanthology.org/N19-1422 | https://aclanthology.org/N19-1422.pdf | naacl-2019-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.02517229e-01 1.41079098e-01 -9.28496718e-02 -9.58541632e-02
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cfd9e0ce-e908-43b7-9130-614b17d027f5 | reinforced-cross-modal-matching-and-self | 1811.10092 | null | http://arxiv.org/abs/1811.10092v2 | http://arxiv.org/pdf/1811.10092v2.pdf | Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation | Vision-language navigation (VLN) is the task of navigating an embodied agent
to carry out natural language instructions inside real 3D environments. In this
paper, we study how to address three critical challenges for this task: the
cross-modal grounding, the ill-posed feedback, and the generalization problems.
First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that
enforces cross-modal grounding both locally and globally via reinforcement
learning (RL). Particularly, a matching critic is used to provide an intrinsic
reward to encourage global matching between instructions and trajectories, and
a reasoning navigator is employed to perform cross-modal grounding in the local
visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model
significantly outperforms previous methods by 10% on SPL and achieves the new
state-of-the-art performance. To improve the generalizability of the learned
policy, we further introduce a Self-Supervised Imitation Learning (SIL) method
to explore unseen environments by imitating its own past, good decisions. We
demonstrate that SIL can approximate a better and more efficient policy, which
tremendously minimizes the success rate performance gap between seen and unseen
environments (from 30.7% to 11.7%). | ['Yuan-Fang Wang', 'Jianfeng Gao', 'William Yang Wang', 'Qiuyuan Huang', 'Asli Celikyilmaz', 'Lei Zhang', 'Xin Wang', 'Dinghan Shen'] | 2018-11-25 | reinforced-cross-modal-matching-and-self-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Reinforced_Cross-Modal_Matching_and_Self-Supervised_Imitation_Learning_for_Vision-Language_Navigation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Reinforced_Cross-Modal_Matching_and_Self-Supervised_Imitation_Learning_for_Vision-Language_Navigation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vision-language-navigation'] | ['computer-vision'] | [ 4.33640182e-02 5.99616393e-02 -5.07544130e-02 -1.36032194e-01
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d8892b10-58c1-4829-9e6c-033b9e2c0a82 | how-to-find-a-unicorn-a-novel-model-free | 2004.11468 | null | https://arxiv.org/abs/2004.11468v3 | https://arxiv.org/pdf/2004.11468v3.pdf | How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series | Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called "unicorn" or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord algorithms even in recognizing traditional outliers and it also recognized unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully recognized unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years. | ['Zoltán Somogyvári', 'Tamás Bábel', 'Zsigmond Benkő'] | 2020-04-23 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [-1.55110300e-01 -2.90750086e-01 2.65110075e-01 -1.70765430e-01
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b5c7ccae-a95e-4c20-abae-d78022044d04 | sgcn-exploiting-compressed-sparse-features-in | 2301.10388 | null | https://arxiv.org/abs/2301.10388v1 | https://arxiv.org/pdf/2301.10388v1.pdf | SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators | Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome the limited applicability of prior neural networks. A GCN takes as input an arbitrarily structured graph and executes a series of layers which exploit the graph's structure to calculate their output features. One recent trend in GCNs is the use of deep network architectures. As opposed to the traditional GCNs which only span around two to five layers deep, modern GCNs now incorporate tens to hundreds of layers with the help of residual connections. From such deep GCNs, we find an important characteristic that they exhibit very high intermediate feature sparsity. We observe that with deep layers and residual connections, the number of zeros in the intermediate features sharply increases. This reveals a new opportunity for accelerators to exploit in GCN executions that was previously not present. In this paper, we propose SGCN, a fast and energy-efficient GCN accelerator which fully exploits the sparse intermediate features of modern GCNs. SGCN suggests several techniques to achieve significantly higher performance and energy efficiency than the existing accelerators. First, SGCN employs a GCN-friendly feature compression format. We focus on reducing the off-chip memory traffic, which often is the bottleneck for GCN executions. Second, we propose microarchitectures for seamlessly handling the compressed feature format. Third, to better handle locality in the existence of the varying sparsity, SGCN employs sparsity-aware cooperation. Sparsity-aware cooperation creates a pattern that exhibits multiple reuse windows, such that the cache can capture diverse sizes of working sets and therefore adapt to the varying level of sparsity. We show that SGCN achieves 1.71x speedup and 43.9% higher energy efficiency compared to the existing accelerators. | ['Jinho Lee', 'Youngsok Kim', 'Namhyung Kim', 'Jounghoo Lee', 'Jaeyong Song', 'Mingi Yoo'] | 2023-01-25 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-1.54327706e-01 -1.21586822e-01 -4.74237621e-01 -1.26571342e-01
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f6eff013-9f2e-4646-a69e-6edc5e8bdd66 | kartalol-transfer-learning-using-deep-neural | 2112.05236 | null | https://arxiv.org/abs/2112.05236v1 | https://arxiv.org/pdf/2112.05236v1.pdf | KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation | Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KartalOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301. | ['Morteza Noshad', 'Yasin Amini', 'Rana Pourmohamad', 'Seyed Naeim Moafinejad', 'Farhang Jaryani', 'Samaneh Salehi Nasab', 'Jalil Nourmohammadi Khiarak'] | 2021-12-09 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-9.11200568e-02 -4.33440864e-01 -3.19018871e-01 -3.70644540e-01
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48a9fb80-2a2d-4e6c-8ebf-97e3831efd8a | botnet-detection-using-recurrent-variational | 2004.00234 | null | https://arxiv.org/abs/2004.00234v1 | https://arxiv.org/pdf/2004.00234v1.pdf | Botnet Detection Using Recurrent Variational Autoencoder | Botnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users. To address this growing danger, we propose to study methods to detect botnets, especially those that are hard to capture with the commonly used methods, such as the signature based ones and the existing anomaly-based ones. More specifically, we propose a novel machine learning based method, named Recurrent Variational Autoencoder (RVAE), for detecting botnets through sequential characteristics of network traffic flow data including attacks by botnets. We validate robustness of our method with the CTU-13 dataset, where we have chosen the testing dataset to have different types of botnets than those of training dataset. Tests show that RVAE is able to detect botnets with the same accuracy as the best known results published in literature. In addition, we propose an approach to assign anomaly score based on probability distributions, which allows us to detect botnets in streaming mode as the new networking statistics becomes available. This on-line detection capability would enable real-time detection of unknown botnets. | ['Jinoh Kim', 'Kesheng Wu', 'Jeeyung Kim', 'Alex Sim'] | 2020-04-01 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-1.88620195e-01 -3.32087755e-01 2.14476436e-01 2.86626101e-01
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d72f6caf-1326-4e21-aced-f6badaa0623a | iterative-linear-quadratic-optimization-for | 2207.06362 | null | https://arxiv.org/abs/2207.06362v1 | https://arxiv.org/pdf/2207.06362v1.pdf | Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates | We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint. We present a gradient descent, a Gauss-Newton method, a Newton method, differential dynamic programming approaches with linear quadratic or quadratic approximations, various line-search strategies, and regularized variants of these algorithms. We derive the computational complexities of all algorithms in a differentiable programming framework and present sufficient optimality conditions. We compare the algorithms on several benchmarks, such as autonomous car racing using a bicycle model of a car. The algorithms are coded in a differentiable programming language in a publicly available package. | ['Zaid Harchaoui', 'Maryam Fazel', 'Siddhartha Srinivasa', 'Vincent Roulet'] | 2022-07-13 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-4.53702271e-01 1.00937881e-01 -4.01792109e-01 -3.56408656e-01
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f4044a9e-2279-4db6-8e6d-00fc9b2ef218 | evading-deepfake-detectors-via-adversarial | 2304.11670 | null | https://arxiv.org/abs/2304.11670v1 | https://arxiv.org/pdf/2304.11670v1.pdf | Evading DeepFake Detectors via Adversarial Statistical Consistency | In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds,more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFakegenerated images in both spatial and frequency domains. In this work, we propose to explicitly minimize the statistical differences to evade state-of-the-art DeepFake detectors. To this end, we propose a statistical consistency attack (StatAttack) against DeepFake detectors, which contains two main parts. First, we select several statistical-sensitive natural degradations (i.e., exposure, blur, and noise) and add them to the fake images in an adversarial way. Second, we find that the statistical differences between natural and DeepFake images are positively associated with the distribution shifting between the two kinds of images, and we propose to use a distribution-aware loss to guide the optimization of different degradations. As a result, the feature distributions of generated adversarial examples is close to the natural images.Furthermore, we extend the StatAttack to a more powerful version, MStatAttack, where we extend the single-layer degradation to multi-layer degradations sequentially and use the loss to tune the combination weights jointly. Comprehensive experimental results on four spatial-based detectors and two frequency-based detectors with four datasets demonstrate the effectiveness of our proposed attack method in both white-box and black-box settings. | ['Jianjun Zhao', 'Lei Ma', 'Xiaofei Xie', 'Yihao Huang', 'Qing Guo', 'Yang Hou'] | 2023-04-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hou_Evading_DeepFake_Detectors_via_Adversarial_Statistical_Consistency_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [-1.58448964e-02 -3.02478939e-01 6.71160892e-02 -3.08715582e-01
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8ff45a89-6382-49a3-9206-6e5abc1addd0 | an-overview-of-indian-spoken-language | 2212.03812 | null | https://arxiv.org/abs/2212.03812v1 | https://arxiv.org/pdf/2212.03812v1.pdf | An Overview of Indian Spoken Language Recognition from Machine Learning Perspective | Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields. | ['Goutam Saha', 'Md Sahidullah', 'Spandan Dey'] | 2022-11-30 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-2.24311858e-01 -2.14723945e-01 -1.19841047e-01 -2.58907616e-01
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24e622eb-d15d-42d8-a489-8d48caaadbef | attentive-statistics-pooling-for-deep-speaker | 1803.10963 | null | http://arxiv.org/abs/1803.10963v2 | http://arxiv.org/pdf/1803.10963v2.pdf | Attentive Statistics Pooling for Deep Speaker Embedding | This paper proposes attentive statistics pooling for deep speaker embedding
in text-independent speaker verification. In conventional speaker embedding,
frame-level features are averaged over all the frames of a single utterance to
form an utterance-level feature. Our method utilizes an attention mechanism to
give different weights to different frames and generates not only weighted
means but also weighted standard deviations. In this way, it can capture
long-term variations in speaker characteristics more effectively. An evaluation
on the NIST SRE 2012 and the VoxCeleb data sets shows that it reduces equal
error rates (EERs) from the conventional method by 7.5% and 8.1%, respectively. | [] | 2019-02-25 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 5.35268039e-02 -2.51179010e-01 1.56905204e-02 -9.06664729e-01
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c1fbb23f-10b8-460f-a02b-472a2ce4b9d8 | a-next-basket-recommendation-reality-check | 2109.14233 | null | https://arxiv.org/abs/2109.14233v2 | https://arxiv.org/pdf/2109.14233v2.pdf | A Next Basket Recommendation Reality Check | The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. Recently, a number of methods with complex modules have been proposed that claim state-of-the-art performance. They rarely look into the predicted basket and just provide intuitive reasons for the observed improvements, e.g., better representation, capturing intentions or relations, etc. We provide a novel angle on the evaluation of next basket recommendation methods, centered on the distinction between repetition and exploration: the next basket is typically composed of previously consumed items (i.e., repeat items) and new items (i.e, explore items). We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models. Using these new metrics, we analyze state-of-the-art NBR models. The results of our analysis help to clarify the extent of the actual progress achieved by existing NBR methods as well as the underlying reasons for the improvements. Overall, our work sheds light on the evaluation problem of NBR and provides useful insights into the model design for this task. | ['Maarten de Rijke', 'Mozhdeh Ariannezhad', 'Sami Jullien', 'Ming Li'] | 2021-09-29 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [-4.48653251e-02 -2.16525882e-01 -8.00708771e-01 -2.68128335e-01
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