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08ad854c-2ab2-4956-8c96-b18400c5a0d5 | magicbrush-a-manually-annotated-dataset-for | 2306.10012 | null | https://arxiv.org/abs/2306.10012v1 | https://arxiv.org/pdf/2306.10012v1.pdf | MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing | Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to produce desirable outcomes in practice. To address this issue, we introduce MagicBrush (https://osu-nlp-group.github.io/MagicBrush/), the first large-scale, manually annotated dataset for instruction-guided real image editing that covers diverse scenarios: single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises over 10K manually annotated triples (source image, instruction, target image), which supports trainining large-scale text-guided image editing models. We fine-tune InstructPix2Pix on MagicBrush and show that the new model can produce much better images according to human evaluation. We further conduct extensive experiments to evaluate current image editing baselines from multiple dimensions including quantitative, qualitative, and human evaluations. The results reveal the challenging nature of our dataset and the gap between current baselines and real-world editing needs. | ['Yu Su', 'Huan Sun', 'Wenhu Chen', 'Lingbo Mo', 'Kai Zhang'] | 2023-06-16 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 3.93654197e-01 -1.89446300e-01 -3.47210467e-01 -5.00488997e-01
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8fd4ece3-1a51-470f-9020-ee0b6543cda5 | tensor-networks-and-efficient-descriptions-of | 2103.06872 | null | https://arxiv.org/abs/2103.06872v1 | https://arxiv.org/pdf/2103.06872v1.pdf | Tensor networks and efficient descriptions of classical data | We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size $L$, similarly to how it is done in quantum many-body physics. We find that for text, the mutual information scales as a power law $L^\nu$ with a close to volume law exponent, indicating that text cannot be efficiently described by 1D tensor networks. For images, the scaling is close to an area law, hinting at 2D tensor networks such as PEPS could have an adequate expressibility. For the numerical analysis, we introduce a mutual information estimator based on autoregressive networks, and we also use convolutional neural networks in a neural estimator method. | ['J. Ignacio Cirac', 'Ivan Kukuljan', 'Márton Kanász-Nagy', 'Sirui Lu'] | 2021-03-11 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-3.22857350e-01 1.84068486e-01 4.55700792e-02 -1.86608940e-01
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bffecd18-f5b1-4aa0-a7fd-c117cba0dfa7 | continuous-indeterminate-probability-neural | 2303.12964 | null | https://arxiv.org/abs/2303.12964v1 | https://arxiv.org/pdf/2303.12964v1.pdf | Continuous Indeterminate Probability Neural Network | This paper introduces a general model called CIPNN - Continuous Indeterminate Probability Neural Network, and this model is based on IPNN, which is used for discrete latent random variables. Currently, posterior of continuous latent variables is regarded as intractable, with the new theory proposed by IPNN this problem can be solved. Our contributions are Four-fold. First, we derive the analytical solution of the posterior calculation of continuous latent random variables and propose a general classification model (CIPNN). Second, we propose a general auto-encoder called CIPAE - Continuous Indeterminate Probability Auto-Encoder, the decoder part is not a neural network and uses a fully probabilistic inference model for the first time. Third, we propose a new method to visualize the latent random variables, we use one of N dimensional latent variables as a decoder to reconstruct the input image, which can work even for classification tasks, in this way, we can see what each latent variable has learned. Fourth, IPNN has shown great classification capability, CIPNN has pushed this classification capability to infinity. Theoretical advantages are reflected in experimental results. | ['Tao Yang'] | 2023-03-23 | null | null | null | null | ['classification'] | ['methodology'] | [ 4.27543595e-02 3.97926360e-01 -1.36117712e-01 -1.04877926e-01
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78974fe5-60d4-47ef-a0c4-84dc2d25223d | paying-u-attention-to-textures-multi-stage | 2202.11703 | null | https://arxiv.org/abs/2202.11703v2 | https://arxiv.org/pdf/2202.11703v2.pdf | U-Attention to Textures: Hierarchical Hourglass Vision Transformer for Universal Texture Synthesis | We present a novel U-Attention vision Transformer for universal texture synthesis. We exploit the natural long-range dependencies enabled by the attention mechanism to allow our approach to synthesize diverse textures while preserving their structures in a single inference. We propose a hierarchical hourglass backbone that attends to the global structure and performs patch mapping at varying scales in a coarse-to-fine-to-coarse stream. Completed by skip connection and convolution designs that propagate and fuse information at different scales, our hierarchical U-Attention architecture unifies attention to features from macro structures to micro details, and progressively refines synthesis results at successive stages. Our method achieves stronger 2$\times$ synthesis than previous work on both stochastic and structured textures while generalizing to unseen textures without fine-tuning. Ablation studies demonstrate the effectiveness of each component of our architecture. | ['Arthur Roullier', 'Douglas Noll', 'Valentin Deschaintre', 'Shouchang Guo'] | 2022-02-23 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 3.48593950e-01 2.71545351e-01 -8.21519047e-02 -2.91433007e-01
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7881a5e3-efed-4f4c-91a0-dfd62e52a0f7 | enhanced-prediction-accuracy-with-uncertainty | 2212.04567 | null | https://arxiv.org/abs/2212.04567v1 | https://arxiv.org/pdf/2212.04567v1.pdf | Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks | Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural network via pinball loss. Here, we incorporate this technique into seismic inversion for purposes of CO2 monitoring. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also propose a novel data-augmentation method by sampling the uncertainty to further improve prediction accuracy. The developed methodology is tested on synthetic Kimberlina data, which are created by the Department of Energy and based on a CO2 capture and sequestration (CCS) project in California. The results prove that the proposed network can estimate the subsurface velocity rapidly and with sufficient resolution. Furthermore, the computed uncertainty quantifies the prediction accuracy. The method remains robust even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error. | ['Youzuo Lin', 'Ilya Tsvankin', 'Xitong Zhang', 'Yanhua Liu'] | 2022-12-08 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 2.41804108e-01 -6.93342537e-02 1.15763734e-03 -1.32945672e-01
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6627aaf9-d23c-41ae-8655-929740a9845a | active-continual-learning-labelling-queries | 2305.03923 | null | https://arxiv.org/abs/2305.03923v1 | https://arxiv.org/pdf/2305.03923v1.pdf | Active Continual Learning: Labelling Queries in a Sequence of Tasks | Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL). While tasks arrive sequentially, the training data are often prepared and annotated independently, leading to CL of incoming supervised learning tasks. This paper considers the under-explored problem of active continual learning (ACL) for a sequence of active learning (AL) tasks, where each incoming task includes a pool of unlabelled data and an annotation budget. We investigate the effectiveness and interplay between several AL and CL algorithms in the domain, class and task-incremental scenarios. Our experiments reveal the trade-off between two contrasting goals of not forgetting the old knowledge and the ability to quickly learn in CL and AL. While conditioning the query strategy on the annotations collected for the previous tasks leads to improved task performance on the domain and task incremental learning, our proposed forgetting-learning profile suggests a gap in balancing the effect of AL and CL for the class-incremental scenario. | ['Gholamreza Haffari', 'Dinh Phung', 'Shahram Khadivi', 'Thuy-Trang Vu'] | 2023-05-06 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 6.84562862e-01 4.87363428e-01 -2.66106278e-01 -3.00498337e-01
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99437f0d-c83c-46db-80f5-59906550eb13 | dictionary-learning-for-adaptive-gpr-target | 1806.04599 | null | https://arxiv.org/abs/1806.04599v2 | https://arxiv.org/pdf/1806.04599v2.pdf | Dictionary Learning for Adaptive GPR Landmine Classification | Ground penetrating radar (GPR) target detection and classification is a challenging task. Here, we consider online dictionary learning (DL) methods to obtain sparse representations (SR) of the GPR data to enhance feature extraction for target classification via support vector machines. Online methods are preferred because traditional batch DL like K-SVD is not scalable to high-dimensional training sets and infeasible for real-time operation. We also develop Drop-Off MINi-batch Online Dictionary Learning (DOMINODL) which exploits the fact that a lot of the training data may be correlated. The DOMINODL algorithm iteratively considers elements of the training set in small batches and drops off samples which become less relevant. For the case of abandoned anti-personnel landmines classification, we compare the performance of K-SVD with three online algorithms: classical Online Dictionary Learning, its correlation-based variant, and DOMINODL. Our experiments with real data from L-band GPR show that online DL methods reduce learning time by 36-93% and increase mine detection by 4-28% over K-SVD. Our DOMINODL is the fastest and retains similar classification performance as the other two online DL approaches. We use a Kolmogorov-Smirnoff test distance and the Dvoretzky-Kiefer-Wolfowitz inequality for the selection of DL input parameters leading to enhanced classification results. To further compare with state-of-the-art classification approaches, we evaluate a convolutional neural network (CNN) classifier which performs worse than the proposed approach. Moreover, when the acquired samples are randomly reduced by 25%, 50% and 75%, sparse decomposition based classification with DL remains robust while the CNN accuracy is drastically compromised. | ['Yonina C. Eldar', 'Maria Antonia Gonzalez-Huici', 'Fabio Giovanneschi', 'Joachim H. G. Ender', 'Kumar Vijay Mishra'] | 2018-05-24 | null | null | null | null | ['landmine'] | ['computer-vision'] | [ 1.98915035e-01 -1.95186108e-01 -8.61728266e-02 -1.70766413e-01
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37812f12-0833-414a-a6ef-b6d62b391173 | fatezero-fusing-attentions-for-zero-shot-text | 2303.09535 | null | https://arxiv.org/abs/2303.09535v2 | https://arxiv.org/pdf/2303.09535v2.pdf | FateZero: Fusing Attentions for Zero-shot Text-based Video Editing | The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models for real-world visual content editing, especially in videos. In this paper, we propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. To edit videos consistently, we propose several techniques based on the pre-trained models. Firstly, in contrast to the straightforward DDIM inversion technique, our approach captures intermediate attention maps during inversion, which effectively retain both structural and motion information. These maps are directly fused in the editing process rather than generated during denoising. To further minimize semantic leakage of the source video, we then fuse self-attentions with a blending mask obtained by cross-attention features from the source prompt. Furthermore, we have implemented a reform of the self-attention mechanism in denoising UNet by introducing spatial-temporal attention to ensure frame consistency. Yet succinct, our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model. We also have a better zero-shot shape-aware editing ability based on the text-to-video model. Extensive experiments demonstrate our superior temporal consistency and editing capability than previous works. | ['Qifeng Chen', 'Ying Shan', 'Xintao Wang', 'Chenyang Lei', 'Yong Zhang', 'Xiaodong Cun', 'Chenyang Qi'] | 2023-03-16 | null | null | null | null | ['video-style-transfer', 'text-to-video-editing'] | ['computer-vision', 'computer-vision'] | [ 4.86020118e-01 -3.51824835e-02 1.48020864e-01 -2.00708240e-01
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b056e1cf-f6c5-4f89-b176-f557e5311e8c | automated-software-vulnerability-detection | 1803.04497 | null | http://arxiv.org/abs/1803.04497v2 | http://arxiv.org/pdf/1803.04497v2.pdf | Automated software vulnerability detection with machine learning | Thousands of security vulnerabilities are discovered in production software
each year, either reported publicly to the Common Vulnerabilities and Exposures
database or discovered internally in proprietary code. Vulnerabilities often
manifest themselves in subtle ways that are not obvious to code reviewers or
the developers themselves. With the wealth of open source code available for
analysis, there is an opportunity to learn the patterns of bugs that can lead
to security vulnerabilities directly from data. In this paper, we present a
data-driven approach to vulnerability detection using machine learning,
specifically applied to C and C++ programs. We first compile a large dataset of
hundreds of thousands of open-source functions labeled with the outputs of a
static analyzer. We then compare methods applied directly to source code with
methods applied to artifacts extracted from the build process, finding that
source-based models perform better. We also compare the application of deep
neural network models with more traditional models such as random forests and
find the best performance comes from combining features learned by deep models
with tree-based models. Ultimately, our highest performing model achieves an
area under the precision-recall curve of 0.49 and an area under the ROC curve
of 0.87. | ['Jeffrey M. Opper', 'Rebecca L. Russell', 'Peter Chin', 'Marc W. McConley', 'Erik Antelman', 'Paul M. Ellingwood', 'Tomo Lazovich', 'Onur Ozdemir', 'Louis Y. Kim', 'Jacob A. Harer', 'Leonard R. Kosta', 'Lei H. Hamilton', 'Jonathan R. Key', 'Gabriel I. Centeno', 'Alan Mackay', 'Akshay Rangamani'] | 2018-02-14 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.19550833e-01 -2.79336963e-02 -3.84320945e-01 -1.77330852e-01
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a6a1e8c2-6403-4d5b-90fe-4fd937ab90bc | minscie-citation-centered-open-information | null | null | https://ub-madoc.bib.uni-mannheim.de/49216/1/_JCDL19Demo__MinScIE%20%284%29.pdf | https://ub-madoc.bib.uni-mannheim.de/49216/1/_JCDL19Demo__MinScIE%20%284%29.pdf | MinScIE: Citation-centered Open Information Extraction | Acknowledging the importance of citations in scientific literature,
in this work we present MinScIE, an Open Information Extraction
system which provides structured knowledge enriched with semantic information about citations. By comparing our system to it’s
original core, MinIE, we show that our approach improves extraction precision by 3 percentage points. | ['Anne Lauscher', 'Yide Song', 'Kiril Gashteovski'] | 2019-06-01 | null | null | null | joint-conference-on-digital-libraries-jcdl | ['open-information-extraction'] | ['natural-language-processing'] | [-3.12513411e-01 6.38053060e-01 -7.43799806e-01 4.96843874e-01
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c5f0df01-5461-4c7d-a2d2-75e3e280478e | automatic-comment-generation-via-multi-pass | 2209.06634 | null | https://arxiv.org/abs/2209.06634v1 | https://arxiv.org/pdf/2209.06634v1.pdf | Automatic Comment Generation via Multi-Pass Deliberation | Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comment generation. DECOM consists of multiple Deliberation Models and one Evaluation Model. Given a code snippet, we first extract keywords from the code and retrieve a similar code fragment from a pre-defined corpus. Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process. At each deliberation, the deliberation model polishes the draft and generates a new comment. The evaluation model measures the quality of the newly generated comment to determine whether to end the iterative process or not. When the iterative process is terminated, the best-generated comment will be selected as the target comment. Our approach is evaluated on two real-world datasets in Java (87K) and Python (108K), and experiment results show that our approach outperforms the state-of-the-art baselines. A human evaluation study also confirms the comments generated by DECOM tend to be more readable, informative, and useful. | ['Qing Wang', 'Song Wang', 'Lin Shi', 'Xiao Chen', 'Fangwen Mu'] | 2022-09-14 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 3.87043297e-01 1.86319038e-01 -1.63889870e-01 -1.37188032e-01
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bf0c0cad-b14d-44b0-ae0f-60741f63d949 | thank-you-bart-rewarding-pre-trained-models | 2105.06947 | null | https://arxiv.org/abs/2105.06947v2 | https://arxiv.org/pdf/2105.06947v2.pdf | Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer | Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content -- the two core aspects of the task -- we achieve a new state-of-the-art. | ['Malvina Nissim', 'Antonio Toral', 'Huiyuan Lai'] | 2021-05-14 | null | https://aclanthology.org/2021.acl-short.62 | https://aclanthology.org/2021.acl-short.62.pdf | acl-2021-5 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 3.50456953e-01 3.91823128e-02 -3.32210124e-01 -2.09531665e-01
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ac5a7063-d96c-4d45-b4b1-b97fdf389fce | fault-detection-for-non-condensing-boilers | 2205.08418 | null | https://arxiv.org/abs/2205.08418v2 | https://arxiv.org/pdf/2205.08418v2.pdf | Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data | Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing sensor networks and IoT devices has the potential to minimize this waste by continually identifying system degradation and re-tuning control strategies to adapt to real building performance. Due to its significant contribution to greenhouse gas emissions, the performance of gas boiler systems for building heating is critical. A review of boiler performance studies has been used to develop a set of common faults and degraded performance conditions, which have been integrated into a MATLAB/Simulink emulator. This resulted in a labeled dataset with approximately 10,000 simulations of steady-state performance for each of 14 non-condensing boilers. The collected data is used for training and testing fault classification using K-nearest neighbour, Decision tree, Random Forest, and Support Vector Machines. The results show that the Support Vector Machines method gave the best prediction accuracy, consistently exceeding 90%, and generalization across multiple boilers is not possible due to low classification accuracy. | ['J. J. McArthur', 'Y. Wang', 'Mohamed Kandil', 'Rony Shohet'] | 2022-05-13 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 8.18204284e-02 -1.83468223e-01 9.10523757e-02 -5.85662909e-02
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c7ad5155-c341-424f-ae5c-6bd44d413796 | patch-wise-contrastive-style-learning-for | 2204.07486 | null | https://arxiv.org/abs/2204.07486v1 | https://arxiv.org/pdf/2204.07486v1.pdf | Patch-wise Contrastive Style Learning for Instagram Filter Removal | Image-level corruptions and perturbations degrade the performance of CNNs on different downstream vision tasks. Social media filters are one of the most common resources of various corruptions and perturbations for real-world visual analysis applications. The negative effects of these distractive factors can be alleviated by recovering the original images with their pure style for the inference of the downstream vision tasks. Assuming these filters substantially inject a piece of additional style information to the social media images, we can formulate the problem of recovering the original versions as a reverse style transfer problem. We introduce Contrastive Instagram Filter Removal Network (CIFR), which enhances this idea for Instagram filter removal by employing a novel multi-layer patch-wise contrastive style learning mechanism. Experiments show our proposed strategy produces better qualitative and quantitative results than the previous studies. Moreover, we present the results of our additional experiments for proposed architecture within different settings. Finally, we present the inference outputs and quantitative comparison of filtered and recovered images on localization and segmentation tasks to encourage the main motivation for this problem. | ['Furkan Kıraç', 'Barış Özcan', 'Furkan Kınlı'] | 2022-04-15 | null | null | null | null | ['reverse-style-transfer'] | ['computer-vision'] | [ 2.79579461e-01 -1.46323696e-01 2.49833897e-01 -1.79034919e-01
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33768d70-5ad8-48b1-b79f-1b3215a8bf4c | smart-learning-to-find-dumb-contracts | 2304.10726 | null | https://arxiv.org/abs/2304.10726v2 | https://arxiv.org/pdf/2304.10726v2.pdf | Smart Learning to Find Dumb Contracts (Extended Version) | We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it overcame a 1.25% error rate mislabeled contracts, and--the student surpassing the teacher--found vulnerable contracts that Slither mislabeled. DLVA is much faster than other smart contract vulnerability detectors: DLVA checks contracts for 29 vulnerabilities in 0.2 seconds, a 10-1,000x speedup. DLVA has three key components. First, Smart Contract to Vector (SC2V) uses neural networks to map smart contract bytecode to a high-dimensional floating-point vector. We benchmark SC2V against 4 state-of-the-art graph neural networks and show that it improves model differentiation by 2.2%. Second, Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55.7% of the contracts in our test set, it has a Slither-predictive accuracy of 97.4% with a false positive rate of only 0.1%. Third, Core Classifier (CC) uses neural networks to infer vulnerable contracts regardless of vector distance. We benchmark DLVA's CC with 10 ML techniques and show that the CC improves accuracy by 11.3%. Overall, DLVA predicts Slither's labels with an overall accuracy of 92.7% and associated false positive rate of 7.2%. Lastly, we benchmark DLVA against nine well-known smart contract analysis tools. Despite using much less analysis time, DLVA completed every query, leading the pack with an average accuracy of 99.7%, pleasingly balancing high true positive rates with low false positive rates. | ['Aquinas Hobor', 'Tamer Abdelaziz'] | 2023-04-21 | null | null | null | null | ['feature-engineering', 'vulnerability-detection'] | ['methodology', 'miscellaneous'] | [-1.82074711e-01 3.25066298e-01 -6.52541518e-01 -2.81040460e-01
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b39b0b48-1866-42c9-8b0e-5170f2f3fa42 | 3d-human-pose-estimation-using-spatio-1 | 2004.11822 | null | https://arxiv.org/abs/2004.11822v1 | https://arxiv.org/pdf/2004.11822v1.pdf | 3D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Training | Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in recent years. Generally, the performance of existing methods drops when the target person is too small/large, or the motion is too fast/slow relative to the scale and speed of the training data. Moreover, to our knowledge, many of these methods are not designed or trained under severe occlusion explicitly, making their performance on handling occlusion compromised. Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation. As humans in videos may appear in different scales and have various motion speeds, we apply multi-scale spatial features for 2D joints or keypoints prediction in each individual frame, and multi-stride temporal convolutional net-works (TCNs) to estimate 3D joints or keypoints. Furthermore, we design a spatio-temporal discriminator based on body structures as well as limb motions to assess whether the predicted pose forms a valid pose and a valid movement. During training, we explicitly mask out some keypoints to simulate various occlusion cases, from minor to severe occlusion, so that our network can learn better and becomes robust to various degrees of occlusion. As there are limited 3D ground-truth data, we further utilize 2D video data to inject a semi-supervised learning capability to our network. Experiments on public datasets validate the effectiveness of our method, and our ablation studies show the strengths of our network\'s individual submodules. | ['Bo wang', 'Yu Cheng', 'Bo Yang', 'Robby T. Tan'] | 2020-04-07 | 3d-human-pose-estimation-using-spatio | https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/02/AAAI20-ProgramWeb.pdf | https://www.dropbox.com/s/tm8q3agp4chtlnz/3DPoseEstimation_AAAI20.pdf?dl=0 | aaai-conference-on-artificial-intelligence-1 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.51192659e-01 -3.73840600e-01 -4.81125623e-01 -1.13618508e-01
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5c108cd8-1584-43b5-8c58-29af957989fc | ambipun-generating-humorous-puns-with-1 | 2205.01825 | null | https://arxiv.org/abs/2205.01825v1 | https://arxiv.org/pdf/2205.01825v1.pdf | AmbiPun: Generating Humorous Puns with Ambiguous Context | In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52\% of the time, outperforming well-crafted baselines and the state-of-the-art models by a large margin. | ['Nanyun Peng', 'Yufei Tian', 'Anirudh Mittal'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.77 | https://aclanthology.org/2022.naacl-main.77.pdf | naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 3.01369458e-01 -7.73051307e-02 1.35074677e-02 7.20536560e-02
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4fcb8b16-6d15-4c1c-bf90-bd2fe0c413cf | exploiting-symmetry-and-heuristic | 2304.06055 | null | https://arxiv.org/abs/2304.06055v1 | https://arxiv.org/pdf/2304.06055v1.pdf | Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation | Reinforcement learning demonstrates significant potential in automatically building control policies in numerous domains, but shows low efficiency when applied to robot manipulation tasks due to the curse of dimensionality. To facilitate the learning of such tasks, prior knowledge or heuristics that incorporate inherent simplification can effectively improve the learning performance. This paper aims to define and incorporate the natural symmetry present in physical robotic environments. Then, sample-efficient policies are trained by exploiting the expert demonstrations in symmetrical environments through an amalgamation of reinforcement and behavior cloning, which gives the off-policy learning process a diverse yet compact initiation. Furthermore, it presents a rigorous framework for a recent concept and explores its scope for robot manipulation tasks. The proposed method is validated via two point-to-point reaching tasks of an industrial arm, with and without an obstacle, in a simulation experiment study. A PID controller, which tracks the linear joint-space trajectories with hard-coded temporal logic to produce interim midpoints, is used to generate demonstrations in the study. The results of the study present the effect of the number of demonstrations and quantify the magnitude of behavior cloning to exemplify the possible improvement of model-free reinforcement learning in common manipulation tasks. A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications. | ['Homayoun Najjaran', 'Kashish Gupta', 'Zengjie Zhang', 'Amir M. Soufi Enayati'] | 2023-04-12 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.78453767e-01 4.38729286e-01 -3.33311796e-01 1.34872705e-01
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a4e9546b-ffc7-4f79-8b65-49cf174d3b70 | a-privacy-preserving-image-retrieval-scheme-1 | 2202.00382 | null | https://arxiv.org/abs/2202.00382v1 | https://arxiv.org/pdf/2202.00382v1.pdf | A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images | In this paper, we propose a novel content-based image-retrieval scheme that allows us to use a mixture of plain images and compressible encrypted ones called "encryption-then-compression (EtC) images." In the proposed scheme, extended SIMPLE descriptors are extracted from EtC images as well as from plain ones, so the mixed use of plain and encrypted images is available for image retrieval. In an experiment, the proposed scheme was demonstrated to have almost the same retrieval performance as that for plain images, even with a mixture of plain and encrypted images. | ['Hitoshi Kiya', 'Kenta Iida'] | 2022-02-01 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 4.05322522e-01 -6.48735344e-01 -6.81772754e-02 -2.72962004e-01
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95ad4280-c309-4df0-aeb4-294ce8f9f37b | partially-shared-semi-supervised-deep-matrix | 2012.00993 | null | https://arxiv.org/abs/2012.00993v1 | https://arxiv.org/pdf/2012.00993v1.pdf | Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data | Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically based on matrix factorization models. Recently, it is extended to the deep structure to exploit the hierarchical information of multi-view data, but the view-specific features and the label information are seldom considered. To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF). By integrating the partially shared deep decomposition structure, graph regularization and the semi-supervised regression model, PSDMF can learn a compact and discriminative representation through eliminating the effects of uncorrelated information. In addition, we develop an efficient iterative updating algorithm for PSDMF. Extensive experiments on five benchmark datasets demonstrate that PSDMF can achieve better performance than the state-of-the-art multi-view learning approaches. The MATLAB source code is available at https://github.com/libertyhhn/PartiallySharedDMF. | ['Weijun Sun', 'Zuyuan Yang', 'Wei Yan', 'Naiyao Liang', 'Haonan Huang'] | 2020-12-02 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.33990896e-01 -2.62890756e-01 -4.16039258e-01 -4.83196110e-01
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389b6900-9e77-45bc-9ec9-4bb299de6b76 | naver-at-activitynet-challenge-2019-task-b | 1906.10555 | null | https://arxiv.org/abs/1906.10555v1 | https://arxiv.org/pdf/1906.10555v1.pdf | Naver at ActivityNet Challenge 2019 -- Task B Active Speaker Detection (AVA) | This report describes our submission to the ActivityNet Challenge at CVPR 2019. We use a 3D convolutional neural network (CNN) based front-end and an ensemble of temporal convolution and LSTM classifiers to predict whether a visible person is speaking or not. Our results show significant improvements over the baseline on the AVA-ActiveSpeaker dataset. | ['Joon Son Chung'] | 2019-06-25 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.00068435e-01 1.06620222e-01 -5.60606271e-02 -4.80521947e-01
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63f89871-6a72-41d4-ae9d-d55b992c0da6 | disc-learning-from-noisy-labels-via-dynamic | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_DISC_Learning_From_Noisy_Labels_via_Dynamic_Instance-Specific_Selection_and_CVPR_2023_paper.pdf | DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and Correction | Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we propose a Dynamic Instance-specific Selection and Correction method (DISC) for learning from noisy labels (LNL). We first use a two-view-based backbone for image classification, obtaining confidence for each image from two views. Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data. Benefiting from the dynamic threshold strategy and two-view learning, we can effectively group each instance into one of the three subsets (i.e., clean, hard, and purified) based on the prediction consistency and discrepancy by two views at each epoch. Finally, we employ different regularization strategies to conquer subsets with different degrees of label noise, improving the whole network's robustness. Comprehensive evaluations on three controllable and four real-world LNL benchmarks show that our method outperforms the state-of-the-art (SOTA) methods to leverage useful information in noisy data while alleviating the pollution of label noise. | ['Xilin Chen', 'Shiguang Shan', 'Hu Han', 'YiFan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['learning-with-noisy-labels', 'memorization', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 7.47475699e-02 -2.11237296e-01 -1.27424330e-01 -6.74062073e-01
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a04861c9-fe16-43a7-bf47-966f88cd3179 | investigation-of-feature-processing-modules | null | null | https://aclanthology.org/2022.rocling-1.11 | https://aclanthology.org/2022.rocling-1.11.pdf | Investigation of feature processing modules and attention mechanisms in speaker verification system | In this paper, we use several combinations of feature front-end modules and attention mechanisms to improve the performance of our speaker verification system. An updated version of ECAPA-TDNN is chosen as a baseline. We replace and integrate different feature front-end and attention mechanism modules to compare and find the most effective model design, and this model would be our final system. We use VoxCeleb 2 dataset as our training set, and test the performance of our models on several test sets. With our final proposed model, we improved performance by 16% over baseline on VoxSRC2022 valudation set, achieving better results for our speaker verification system. | ['Wei-Yu Chen', 'Hsiang-Feng Chuang', 'Yu-Han Cheng', 'Bo-Cheng Chan', 'Chung-Li Lu', 'Chia-Ping Chen', 'Wei-Ting Lin', 'Ting-Wei Chen'] | null | null | null | null | rocling-2022-11 | ['speaker-verification'] | ['speech'] | [-5.23693800e-01 -9.02868062e-02 -4.64004874e-02 -7.69669116e-01
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056618fb-53f3-4e93-af5b-bef6dee4c0f9 | scalable-quantum-neural-networks-for | 2208.07719 | null | https://arxiv.org/abs/2208.07719v1 | https://arxiv.org/pdf/2208.07719v1.pdf | Scalable Quantum Neural Networks for Classification | Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is frequently utilized to build a quantum neural network (QNN), which is a counterpart to the conventional neural network. Due to hardware limitations, however, current quantum devices only allow one to use few qubits to represent data and perform simple quantum computations. The limited quantum resource on a single quantum device degrades the data usage and limits the scale of the quantum circuits, preventing quantum advantage to some extent. To alleviate this constraint, we propose an approach to implementing a scalable quantum neural network (SQNN) by utilizing the quantum resource of multiple small-size quantum devices cooperatively. In an SQNN system, several quantum devices are used as quantum feature extractors, extracting local features from an input instance in parallel, and a quantum device works as a quantum predictor, performing prediction over the local features collected through classical communication channels. The quantum feature extractors in the SQNN system are independent of each other, so one can flexibly use quantum devices of varying sizes, with larger quantum devices extracting more local features. Especially, the SQNN can be performed on a single quantum device in a modular fashion. Our work is exploratory and carried out on a quantum system simulator using the TensorFlow Quantum library. The evaluation conducts a binary classification on the MNIST dataset. It shows that the SQNN model achieves a comparable classification accuracy to a regular QNN model of the same scale. Furthermore, it demonstrates that the SQNN model with more quantum resources can significantly improve classification accuracy. | ['Qun Li', 'Zeyi Tao', 'Jindi Wu'] | 2022-08-04 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.03126007e-01 -1.51363492e-01 -2.68962055e-01 -1.61387876e-01
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9001f0d0-4412-422c-9d75-22e2bcd509aa | towards-better-citation-intent-classification | null | null | https://openreview.net/forum?id=iyuluxX9K9B | https://openreview.net/pdf?id=iyuluxX9K9B | Towards Better Citation Intent Classification | Accurate classification of citation intents in a scientific article provides deeper contextual understanding of and better quantifies the contributions of cited articles. This improves scientific literature platform capabilities such as search relevance, ranking and more. To our knowledge, we present the most comprehensive survey of Transformer-based language models performance on the citation intent classification task using SciCite dataset. Here, we make three recommendations. Firstly, we propose to report model performance as a distribution in contrast to a single averaged performance value. This arises from our observation that model performance is sensitive to the random seed choice resulting in wide performance variations from multiple finetuning runs. Secondly, this provides practical insights for model selection, showing the model's best possible performance. Thus, we propose that practitioners perform multiple finetuning runs before selecting the best performing model. Thirdly, we propose a simple data augmentation to improve the distribution of model performance overall. Moving forward, we suggest exploring improvements to the finetuning and model selection process as promising future directions. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['citation-intent-classification'] | ['natural-language-processing'] | [ 5.36710247e-02 -1.33530676e-01 -7.94169247e-01 -1.58274412e-01
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a7748bc0-9185-4272-949c-f14a8409aa0a | compressing-sentence-representation-with | 2304.12674 | null | https://arxiv.org/abs/2304.12674v1 | https://arxiv.org/pdf/2304.12674v1.pdf | Compressing Sentence Representation with maximum Coding Rate Reduction | In most natural language inference problems, sentence representation is needed for semantic retrieval tasks. In recent years, pre-trained large language models have been quite effective for computing such representations. These models produce high-dimensional sentence embeddings. An evident performance gap between large and small models exists in practice. Hence, due to space and time hardware limitations, there is a need to attain comparable results when using the smaller model, which is usually a distilled version of the large language model. In this paper, we assess the model distillation of the sentence representation model Sentence-BERT by augmenting the pre-trained distilled model with a projection layer additionally learned on the Maximum Coding Rate Reduction (MCR2)objective, a novel approach developed for general-purpose manifold clustering. We demonstrate that the new language model with reduced complexity and sentence embedding size can achieve comparable results on semantic retrieval benchmarks. | ['Domagoj Matijević', 'Jurica Maltar', 'Luka Borozan', 'Antonio Jovanović', 'Tomislav Prusina', 'Domagoj Ševerdija'] | 2023-04-25 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-retrieval'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.75922143e-01 2.77931243e-01 -2.08737664e-02 -1.76758662e-01
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a371242b-549f-4c2c-bc2b-90c729d8cbec | enhancing-personalized-dialogue-generation | 2305.11482 | null | https://arxiv.org/abs/2305.11482v1 | https://arxiv.org/pdf/2305.11482v1.pdf | Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona | The personalized dialogue explores the consistent relationship between dialogue generation and personality. Existing personalized dialogue agents model persona profiles from three resources: sparse or dense persona descriptions and dialogue histories. However, sparse structured persona attributes are explicit but uninformative, dense persona texts contain rich persona descriptions with much noise, and dialogue history query is both noisy and uninformative for persona modeling. In this work, we combine the advantages of the three resources to obtain a richer and more accurate persona. We design a Contrastive Latent Variable-based model (CLV) that clusters the dense persona descriptions into sparse categories, which are combined with the history query to generate personalized responses. Experimental results on Chinese and English datasets demonstrate our model's superiority in personalization. | ['Yuexian Hou', 'Ruifang He', 'Kun Huang', 'Dongming Zhao', 'Miao Fang', 'Bo wang', 'Yihong Tang'] | 2023-05-19 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-4.48862016e-01 4.51088190e-01 -3.35385114e-01 -8.23274076e-01
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3e32a35b-ad10-4c12-a7ec-ce71c425efac | an-automated-theorem-proving-framework-for | 2101.12370 | null | https://arxiv.org/abs/2101.12370v4 | https://arxiv.org/pdf/2101.12370v4.pdf | An Automated Theorem Proving Framework for Information-Theoretic Results | We present a versatile automated theorem proving framework capable of automated discovery, simplification and proofs of inner and outer bounds in network information theory, deduction of properties of information-theoretic quantities (e.g. Wyner and G\'acs-K\"orner common information), and discovery of non-Shannon-type inequalities, under a unified framework. Our implementation successfully generated proofs for 32 out of 56 theorems in Chapters 1-14 of the book Network Information Theory by El Gamal and Kim. Our framework is based on the concept of existential information inequalities, which provides an axiomatic framework for a wide range of problems in information theory. | ['Cheuk Ting Li'] | 2021-01-29 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.48887068e-01 3.27874094e-01 -5.85210323e-01 2.75967214e-02
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189a74e8-96d8-4421-8242-c65140b7598d | ovanet-one-vs-all-network-for-universal | 2104.03344 | null | https://arxiv.org/abs/2104.03344v4 | https://arxiv.org/pdf/2104.03344v4.pdf | OVANet: One-vs-All Network for Universal Domain Adaptation | Universal Domain Adaptation (UNDA) aims to handle both domain-shift and category-shift between two datasets, where the main challenge is to transfer knowledge while rejecting unknown classes which are absent in the labeled source data but present in the unlabeled target data. Existing methods manually set a threshold to reject unknown samples based on validation or a pre-defined ratio of unknown samples, but this strategy is not practical. In this paper, we propose a method to learn the threshold using source samples and to adapt it to the target domain. Our idea is that a minimum inter-class distance in the source domain should be a good threshold to decide between known or unknown in the target. To learn the inter-and intra-class distance, we propose to train a one-vs-all classifier for each class using labeled source data. Then, we adapt the open-set classifier to the target domain by minimizing class entropy. The resulting framework is the simplest of all baselines of UNDA and is insensitive to the value of a hyper-parameter yet outperforms baselines with a large margin. | ['Kate Saenko', 'Kuniaki Saito'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Saito_OVANet_One-vs-All_Network_for_Universal_Domain_Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Saito_OVANet_One-vs-All_Network_for_Universal_Domain_Adaptation_ICCV_2021_paper.pdf | iccv-2021-1 | ['universal-domain-adaptation'] | ['computer-vision'] | [ 5.78046679e-01 7.00154603e-02 -6.29788995e-01 -7.77721643e-01
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134c35e9-7f43-4c76-babd-7808c84d0531 | a-dual-cycled-cross-view-transformer-network | 2209.08844 | null | https://arxiv.org/abs/2209.08844v1 | https://arxiv.org/pdf/2209.08844v1.pdf | A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View | The bird's-eye-view (BEV) representation allows robust learning of multiple tasks for autonomous driving including road layout estimation and 3D object detection. However, contemporary methods for unified road layout estimation and 3D object detection rarely handle the class imbalance of the training dataset and multi-class learning to reduce the total number of networks required. To overcome these limitations, we propose a unified model for road layout estimation and 3D object detection inspired by the transformer architecture and the CycleGAN learning framework. The proposed model deals with the performance degradation due to the class imbalance of the dataset utilizing the focal loss and the proposed dual cycle loss. Moreover, we set up extensive learning scenarios to study the effect of multi-class learning for road layout estimation in various situations. To verify the effectiveness of the proposed model and the learning scheme, we conduct a thorough ablation study and a comparative study. The experiment results attest the effectiveness of our model; we achieve state-of-the-art performance in both the road layout estimation and 3D object detection tasks. | ['Ue-Hwan Kim', 'Curie Kim'] | 2022-09-19 | null | null | null | null | ['monocular-cross-view-road-scene-parsing', 'monocular-cross-view-road-scene-parsing-road'] | ['computer-vision', 'computer-vision'] | [ 1.86964255e-02 -2.88371183e-02 -4.33575958e-01 -3.73196006e-01
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f43b021f-7209-4ca9-a845-70c576ed25fa | debiasing-pipeline-improves-deep-learning | 2201.09563 | null | https://arxiv.org/abs/2201.09563v1 | https://arxiv.org/pdf/2201.09563v1.pdf | Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection | Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled approach that combines mobility, low cost, speed, accuracy, and privacy. We can resolve these issues by combining the chest X-ray imaging mode with a federated deep-learning approach, provided that the federated model is trained on homogenous data to ensure that no single data source can adversely bias the model at any point in time. In this study we show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization, paving the way for a low-cost and accessible deep learning-based clinical system for lung cancer screening. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is performed using all combinations of lung field segmentation, close cropping, and rib suppression operators. We show that this pre-processing pipeline results in deep learning models that successfully generalize an independent lung nodule dataset using ablation studies to assess the contribution of each operator in this pipeline. In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data. | ['U. Rajendra Arharya', 'Prabal Datta Barua', 'Hui Wen Loh', 'Jing Zhu', 'Manoranjan Paul', 'Biswajeet Pradhan', 'Subrata Chakraborty', 'Michael Horry'] | 2022-01-24 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 5.24271667e-01 2.64779210e-01 -2.61313349e-01 -2.36766145e-01
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f6b3bd45-ea26-4a2b-b351-293fbac4f14a | stablenet-semi-online-multi-scale-deep-video | 1907.10283 | null | https://arxiv.org/abs/1907.10283v1 | https://arxiv.org/pdf/1907.10283v1.pdf | StableNet: Semi-Online, Multi-Scale Deep Video Stabilization | Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions. In this paper we propose a data-driven online video stabilization method along with a paired dataset for deep learning. The network processes each unsteady frame progressively in a multi-scale manner, from low resolution to high resolution, and then outputs an affine transformation to stabilize the frame. Different from conventional methods which require explicit feature tracking or optical flow estimation, the underlying stabilization process is learned implicitly from the training data, and the stabilization process can be done online. Since there are limited public video stabilization datasets available, we synthesized unstable videos with different extent of shake that simulate real-life camera movement. Experiments show that our method is able to outperform other stabilization methods in several unstable samples while remaining comparable in general. Also, our method is tested on complex contents and found robust enough to dampen these samples to some extent even it was not explicitly trained in the contents. | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Chia-Hung Huang', 'Hang Yin'] | 2019-07-24 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-4.16791625e-02 -1.47350952e-01 -2.00299606e-01 1.79471910e-01
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28e1733a-05ab-4d0e-abc2-5c543364bcdc | signing-outside-the-studio-benchmarking | 2211.00448 | null | https://arxiv.org/abs/2211.00448v1 | https://arxiv.org/pdf/2211.00448v1.pdf | Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition | The goal of this work is background-robust continuous sign language recognition. Most existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed backgrounds and are filmed in studios with a static monochromatic background. However, signing is not limited only to studios in the real world. In order to analyze the robustness of CSLR models under background shifts, we first evaluate existing state-of-the-art CSLR models on diverse backgrounds. To synthesize the sign videos with a variety of backgrounds, we propose a pipeline to automatically generate a benchmark dataset utilizing existing CSLR benchmarks. Our newly constructed benchmark dataset consists of diverse scenes to simulate a real-world environment. We observe even the most recent CSLR method cannot recognize glosses well on our new dataset with changed backgrounds. In this regard, we also propose a simple yet effective training scheme including (1) background randomization and (2) feature disentanglement for CSLR models. The experimental results on our dataset demonstrate that our method generalizes well to other unseen background data with minimal additional training images. | ['In So Kweon', 'Joon Son Chung', 'Dong-Jin Kim', 'Jae Won Cho', 'Youngtaek Oh', 'Youngjoon Jang'] | 2022-11-01 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 6.38217092e-01 -7.48267114e-01 5.98482192e-02 -2.33492568e-01
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5ff3e4b3-c9d2-4619-b2a5-91b603ff4746 | tsi-gan-unsupervised-time-series-anomaly | 2303.12952 | null | https://arxiv.org/abs/2303.12952v1 | https://arxiv.org/pdf/2303.12952v1.pdf | TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks | Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. This paper proposes TSI-GAN, an unsupervised anomaly detection model for time-series that can learn complex temporal patterns automatically and generalize well, i.e., no need for choosing dataset-specific parameters, making statistical assumptions about underlying data, or changing model architectures. To achieve these goals, we convert each input time-series into a sequence of 2D images using two encoding techniques with the intent of capturing temporal patterns and various types of deviance. Moreover, we design a reconstructive GAN that uses convolutional layers in an encoder-decoder network and employs cycle-consistency loss during training to ensure that inverse mappings are accurate as well. In addition, we also instrument a Hodrick-Prescott filter in post-processing to mitigate false positives. We evaluate TSI-GAN using 250 well-curated and harder-than-usual datasets and compare with 8 state-of-the-art baseline methods. The results demonstrate the superiority of TSI-GAN to all the baselines, offering an overall performance improvement of 13% and 31% over the second-best performer MERLIN and the third-best performer LSTM-AE, respectively. | ['Mao Van Ngo', 'Tie Luo', 'Shyam Sundar Saravanan'] | 2023-03-22 | null | null | null | null | ['medical-diagnosis', 'network-intrusion-detection', 'time-series-anomaly-detection'] | ['medical', 'miscellaneous', 'time-series'] | [ 2.20454529e-01 -2.58560598e-01 1.33566648e-01 -4.30766940e-01
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0b7dbfd5-37d6-4f07-a35c-8366379b228e | image-reconstruction-for-accelerated-mr-scan | 2306.02886 | null | https://arxiv.org/abs/2306.02886v1 | https://arxiv.org/pdf/2306.02886v1.pdf | Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks | Partial scan is a common approach to accelerate Magnetic Resonance Imaging (MRI) data acquisition in both 2D and 3D settings. However, accurately reconstructing images from partial scan data (i.e., incomplete k-space matrices) remains challenging due to lack of an effectively global receptive field in both spatial and k-space domains. To address this problem, we propose the following: (1) a novel convolutional operator called Faster Fourier Convolution (FasterFC) to replace the two consecutive convolution operations typically used in convolutional neural networks (e.g., U-Net, ResNet). Based on the spectral convolution theorem in Fourier theory, FasterFC employs alternating kernels of size 1 in 3D case) in different domains to extend the dual-domain receptive field to the global and achieves faster calculation speed than traditional Fast Fourier Convolution (FFC). (2) A 2D accelerated MRI method, FasterFC-End-to-End-VarNet, which uses FasterFC to improve the sensitivity maps and reconstruction quality. (3) A multi-stage 3D accelerated MRI method called FasterFC-based Single-to-group Network (FAS-Net) that utilizes a single-to-group algorithm to guide k-space domain reconstruction, followed by FasterFC-based cascaded convolutional neural networks to expand the effective receptive field in the dual-domain. Experimental results on the fastMRI and Stanford MRI Data datasets demonstrate that FasterFC improves the quality of both 2D and 3D reconstruction. Moreover, FAS-Net, as a 3D high-resolution multi-coil (eight) accelerated MRI method, achieves superior reconstruction performance in both qualitative and quantitative results compared with state-of-the-art 2D and 3D methods. | ['Xuelong Li', 'ZhenChang Wang', 'Yonghong Hou', 'Yiming Liu', 'Xuebin Sun', 'Yanwei Pang', 'Xiaohan Liu'] | 2023-06-05 | null | null | null | null | ['image-reconstruction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.46354714e-01 -1.43478051e-01 1.03809617e-01 -5.05378366e-01
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823a99b3-082e-4aa4-84dd-40d858e00743 | neurorehab-an-interface-for-rehabilitation | 2301.10957 | null | https://arxiv.org/abs/2301.10957v1 | https://arxiv.org/pdf/2301.10957v1.pdf | Neurorehab: An Interface for Rehabilitation | About 15% of the world population is affected by a disability in some form, amongst whom only 31% perform the recommended exercises without intervention. We are working on developing a motivating and effective way to encourage people. In our work, we leverage the fact that repetitive exercises can help people with motor disabilities due to the robust plasticity of the pre-frontal cognitive control system in the brain. We investigate the role of repetitive activities for neurorehabilitation with the help of a brain computer interface, formulated using immersive game design with Kinect v2.0 and Unity 3D. We also introduce a game design paradigm for adaptive learning for the patients. | ['Roopeswar Kommalapati', 'Adam Fendler', 'Adeboye A. Adejare Jr', 'Atul Dhingra'] | 2023-01-26 | null | null | null | null | ['unity'] | ['computer-vision'] | [-2.03167424e-01 3.56596410e-01 -1.59395933e-01 2.77900040e-01
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41c4c44a-010e-43d0-a0f4-60ce97f7275a | 190513147 | 1905.13147 | null | https://arxiv.org/abs/1905.13147v1 | https://arxiv.org/pdf/1905.13147v1.pdf | Anomaly Detection in Images | Visual defect assessment is a form of anomaly detection. This is very relevant in finding faults such as cracks and markings in various surface inspection tasks like pavement and automotive parts. The task involves detection of deviation/divergence of anomalous samples from the normal ones. Two of the major challenges in supervised anomaly detection are the lack of labelled training data and the low availability of anomaly instances. Semi-supervised methods which learn the underlying distribution of the normal samples and then measure the deviation/divergence from the estimated model as the anomaly score have limitations in their overall ability to detect anomalies. This paper proposes the application of network-based deep transfer learning using convolutional neural networks (CNNs) for the task of anomaly detection. Single class SVMs have been used in the past with some success, however we hypothesize that deeper networks for single class classification should perform better. Results obtained on established anomaly detection benchmarks as well as on a real-world dataset, show that the proposed method clearly outperforms the existing state-of-the-art methods, by achieving a staggering average area under the receiver operating characteristic curve value of 0.99 for the tested data-sets which is an average improvement of 41% on the CIFAR10, 20% on MNIST and 16% on Cement Crack data-sets. | ['Manpreet Singh Minhas', 'John Zelek'] | 2019-05-09 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.50677919e-01 1.77448139e-01 3.04979175e-01 -3.72510761e-01
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a355f3bc-3532-4835-95d7-9ed645376d0a | improving-panoptic-segmentation-for-nighttime | 2306.13725 | null | https://arxiv.org/abs/2306.13725v1 | https://arxiv.org/pdf/2306.13725v1.pdf | Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving Scenes | Autonomous vehicles and driving systems use scene parsing as an essential tool to understand the surrounding environment. Panoptic segmentation is a state-of-the-art technique which proves to be pivotal in this use case. Deep learning-based architectures have been utilized for effective and efficient Panoptic Segmentation in recent times. However, when it comes to adverse conditions like dark scenes with poor illumination or nighttime images, existing methods perform poorly in comparison to daytime images. One of the main factors for poor results is the lack of sufficient and accurately annotated nighttime images for urban driving scenes. In this work, we propose two new methods, first to improve the performance, and second to improve the robustness of panoptic segmentation in nighttime or poor illumination urban driving scenes using a domain translation approach. The proposed approach makes use of CycleGAN (Zhu et al., 2017) to translate daytime images with existing panoptic annotations into nighttime images, which are then utilized to retrain a Panoptic segmentation model to improve performance and robustness under poor illumination and nighttime conditions. In our experiments, Approach-1 demonstrates a significant improvement in the Panoptic segmentation performance on the converted Cityscapes dataset with more than +10% PQ, +12% RQ, +2% SQ, +14% mIoU and +10% AP50 absolute gain. Approach-2 demonstrates improved robustness to varied nighttime driving environments. Both the approaches are supported via comprehensive quantitative and qualitative analysis. | ['Ankur Chrungoo'] | 2023-06-23 | null | null | null | null | ['scene-parsing', 'panoptic-segmentation', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.45681411e-01 -2.58438855e-01 5.33955768e-02 -4.97065127e-01
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01d6b76d-0696-4dec-9ade-d923097d5fc0 | vilt-vision-and-language-transformer-without | 2102.03334 | null | https://arxiv.org/abs/2102.03334v2 | https://arxiv.org/pdf/2102.03334v2.pdf | ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision | Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt. | ['Ildoo Kim', 'Bokyung Son', 'Wonjae Kim'] | 2021-02-05 | null | null | null | null | ['multimodal-intent-recognition', 'zero-shot-cross-modal-retrieval'] | ['miscellaneous', 'miscellaneous'] | [ 1.53186753e-01 1.34650990e-01 2.23408267e-02 -2.74932086e-01
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7a2b6196-5505-4f5e-8e79-0e7fd0f7bf4b | sparsett-visual-tracking-with-sparse | 2205.03776 | null | https://arxiv.org/abs/2205.03776v1 | https://arxiv.org/pdf/2205.03776v1.pdf | SparseTT: Visual Tracking with Sparse Transformers | Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT. | ['Yunhong Wang', 'Wenrui Cai', 'Qingjie Liu', 'Zehua Fu', 'Zhihong Fu'] | 2022-05-08 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-4.37915891e-01 -4.23238069e-01 -2.73906499e-01 -4.43322994e-02
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a9011504-c928-48c4-a5fa-3ad0d9337ab4 | twitter-data-analysis-izmir-earthquake-case | 2212.01453 | null | https://arxiv.org/abs/2212.01453v1 | https://arxiv.org/pdf/2212.01453v1.pdf | Twitter Data Analysis: Izmir Earthquake Case | T\"urkiye is located on a fault line; earthquakes often occur on a large and small scale. There is a need for effective solutions for gathering current information during disasters. We can use social media to get insight into public opinion. This insight can be used in public relations and disaster management. In this study, Twitter posts on Izmir Earthquake that took place on October 2020 are analyzed. We question if this analysis can be used to make social inferences on time. Data mining and natural language processing (NLP) methods are used for this analysis. NLP is used for sentiment analysis and topic modelling. The latent Dirichlet Allocation (LDA) algorithm is used for topic modelling. We used the Bidirectional Encoder Representations from Transformers (BERT) model working with Transformers architecture for sentiment analysis. It is shown that the users shared their goodwill wishes and aimed to contribute to the initiated aid activities after the earthquake. The users desired to make their voices heard by competent institutions and organizations. The proposed methods work effectively. Future studies are also discussed. | ['Enis Karaarslan', 'Hakan Sökün', 'Özgür Agrali'] | 2022-12-02 | null | null | null | null | ['public-relations'] | ['miscellaneous'] | [-2.87920535e-01 3.92311096e-01 -1.97766237e-02 -3.70156407e-01
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2b375532-f5ed-4834-8a9f-559f5b034815 | sig-vc-a-speaker-information-guided-zero-shot | 2111.03811 | null | https://arxiv.org/abs/2111.03811v3 | https://arxiv.org/pdf/2111.03811v3.pdf | SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines | Nowadays, as more and more systems achieve good performance in traditional voice conversion (VC) tasks, people's attention gradually turns to VC tasks under extreme conditions. In this paper, we propose a novel method for zero-shot voice conversion. We aim to obtain intermediate representations for speaker-content disentanglement of speech to better remove speaker information and get pure content information. Accordingly, our proposed framework contains a module that removes the speaker information from the acoustic feature of the source speaker. Moreover, speaker information control is added to our system to maintain the voice cloning performance. The proposed system is evaluated by subjective and objective metrics. Results show that our proposed system significantly reduces the trade-off problem in zero-shot voice conversion, while it also manages to have high spoofing power to the speaker verification system. | ['Ming Li', 'Xiaoyi Qin', 'Zexin Cai', 'Haozhe Zhang'] | 2021-11-06 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 1.11930393e-01 -2.00206354e-01 -8.33856091e-02 -1.05347276e-01
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53270e25-bcd5-4f8e-8f24-c46f01377f2a | vpn-verification-of-poisoning-in-neural | 2205.03894 | null | https://arxiv.org/abs/2205.03894v1 | https://arxiv.org/pdf/2205.03894v1.pdf | VPN: Verification of Poisoning in Neural Networks | Neural networks are successfully used in a variety of applications, many of them having safety and security concerns. As a result researchers have proposed formal verification techniques for verifying neural network properties. While previous efforts have mainly focused on checking local robustness in neural networks, we instead study another neural network security issue, namely data poisoning. In this case an attacker inserts a trigger into a subset of the training data, in such a way that at test time, this trigger in an input causes the trained model to misclassify to some target class. We show how to formulate the check for data poisoning as a property that can be checked with off-the-shelf verification tools, such as Marabou and nneum, where counterexamples of failed checks constitute the triggers. We further show that the discovered triggers are `transferable' from a small model to a larger, better-trained model, allowing us to analyze state-of-the art performant models trained for image classification tasks. | ['Corina S. Păsăreanu', 'Divya Gopinath', 'Muhammad Usman', 'Youcheng Sun'] | 2022-05-08 | null | null | null | null | ['neural-network-security'] | ['miscellaneous'] | [ 6.78081989e-01 4.56532866e-01 -4.18612957e-01 -2.89971679e-01
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f9751d9f-a558-471c-9049-29bab0a407cb | region-wise-attentive-multi-view | 2307.03212 | null | https://arxiv.org/abs/2307.03212v1 | https://arxiv.org/pdf/2307.03212v1.pdf | Region-Wise Attentive Multi-View Representation Learning for Urban Region Embeddings | Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focus on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17\% improvement. | ['Qianqian Ren', 'Weiliang Chan'] | 2023-07-06 | null | null | null | null | ['graph-attention', 'representation-learning'] | ['graphs', 'methodology'] | [-2.15907469e-01 -1.05004638e-01 -5.52773893e-01 -3.15839559e-01
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c6bf8488-f7cf-4a47-b029-8e3fcd91e848 | learning-skeletal-graph-neural-networks-for | 2108.07181 | null | https://arxiv.org/abs/2108.07181v2 | https://arxiv.org/pdf/2108.07181v2.pdf | Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation | Various deep learning techniques have been proposed to solve the single-view 2D-to-3D pose estimation problem. While the average prediction accuracy has been improved significantly over the years, the performance on hard poses with depth ambiguity, self-occlusion, and complex or rare poses is still far from satisfactory. In this work, we target these hard poses and present a novel skeletal GNN learning solution. To be specific, we propose a hop-aware hierarchical channel-squeezing fusion layer to effectively extract relevant information from neighboring nodes while suppressing undesired noises in GNN learning. In addition, we propose a temporal-aware dynamic graph construction procedure that is robust and effective for 3D pose estimation. Experimental results on the Human3.6M dataset show that our solution achieves 10.3\% average prediction accuracy improvement and greatly improves on hard poses over state-of-the-art techniques. We further apply the proposed technique on the skeleton-based action recognition task and also achieve state-of-the-art performance. Our code is available at https://github.com/ailingzengzzz/Skeletal-GNN. | ['Qiang Xu', 'Minhao Liu', 'Nanxuan Zhao', 'Lei Yang', 'Xiao Sun', 'Ailing Zeng'] | 2021-08-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zeng_Learning_Skeletal_Graph_Neural_Networks_for_Hard_3D_Pose_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zeng_Learning_Skeletal_Graph_Neural_Networks_for_Hard_3D_Pose_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.68965095e-01 1.43498793e-01 -3.38470668e-01 -3.15687448e-01
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1716ab0f-5ef4-4aad-9fa5-3ebf995f1279 | unsupervised-learning-for-color-constancy | 1712.00436 | null | http://arxiv.org/abs/1712.00436v4 | http://arxiv.org/pdf/1712.00436v4.pdf | Unsupervised Learning for Color Constancy | Most digital camera pipelines use color constancy methods to reduce the
influence of illumination and camera sensor on the colors of scene objects. The
highest accuracy of color correction is obtained with learning-based color
constancy methods, but they require a significant amount of calibrated training
images with known ground-truth illumination. Such calibration is time
consuming, preferably done for each sensor individually, and therefore a major
bottleneck in acquiring high color constancy accuracy. Statistics-based methods
do not require calibrated training images, but they are less accurate. In this
paper an unsupervised learning-based method is proposed that learns its
parameter values after approximating the unknown ground-truth illumination of
the training images, thus avoiding calibration. In terms of accuracy the
proposed method outperforms all statistics-based and many learning-based
methods. An extension of the method is also proposed, which learns the needed
parameters from non-calibrated images taken with one sensor and which can then
be successfully applied to images taken with another sensor. This effectively
enables inter-camera unsupervised learning for color constancy. Additionally, a
new high quality color constancy benchmark dataset with 1707 calibrated images
is created, used for testing, and made publicly available. The results are
presented and discussed. The source code and the dataset are available at
http://www.fer.unizg.hr/ipg/resources/color_constancy/. | ['Sven Lončarić', 'Karlo Koščević', 'Nikola Banić'] | 2017-12-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.55147576e-01 -5.70675135e-01 2.74532158e-02 -4.74295080e-01
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2aea91bf-c662-480f-aaa7-cd363df9424b | apb2facev2-real-time-audio-guided-multi-face | 2010.13017 | null | https://arxiv.org/abs/2010.13017v1 | https://arxiv.org/pdf/2010.13017v1.pdf | APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment | Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. However, current methods can only reenact a special person once the model is trained or need extra operations such as 3D rendering and image post-fusion on the premise of generating vivid faces. To solve the above challenge, we propose a novel \emph{R}eal-time \emph{A}udio-guided \emph{M}ulti-face reenactment approach named \emph{APB2FaceV2}, which can reenact different target faces among multiple persons with corresponding reference face and drive audio signal as inputs. Enabling the model to be trained end-to-end and have a faster speed, we design a novel module named Adaptive Convolution (AdaConv) to infuse audio information into the network, as well as adopt a lightweight network as our backbone so that the network can run in real time on CPU and GPU. Comparison experiments prove the superiority of our approach than existing state-of-the-art methods, and further experiments demonstrate that our method is efficient and flexible for practical applications https://github.com/zhangzjn/APB2FaceV2 | ['Yunliang Jiang', 'Yong liu', 'Jun Chen', 'Chao Xu', 'Xianfang Zeng', 'Jiangning Zhang'] | 2020-10-25 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 2.08071411e-01 2.55880952e-02 6.54172361e-01 -5.03410041e-01
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eb3cd1f4-57ab-4f0f-a0ae-ab0a43cb5fb7 | chatgpt-for-plc-dcs-control-logic-generation | 2305.15809 | null | https://arxiv.org/abs/2305.15809v1 | https://arxiv.org/pdf/2305.15809v1.pdf | ChatGPT for PLC/DCS Control Logic Generation | Large language models (LLMs) providing generative AI have become popular to support software engineers in creating, summarizing, optimizing, and documenting source code. It is still unknown how LLMs can support control engineers using typical control programming languages in programming tasks. Researchers have explored GitHub CoPilot or DeepMind AlphaCode for source code generation but did not yet tackle control logic programming. The contribution of this paper is an exploratory study, for which we created 100 LLM prompts in 10 representative categories to analyze control logic generation for of PLCs and DCS from natural language. We tested the prompts by generating answers with ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3 Structured Text code in many cases and demonstrated useful reasoning skills that could boost control engineer productivity. Our prompt collection is the basis for a more formal LLM benchmark to test and compare such models for control logic generation. | ['Virendra Ashiwal', 'Sten Gruener', 'Heiko Koziolek'] | 2023-05-25 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-6.39319643e-02 7.58482456e-01 -9.15904790e-02 -4.47372675e-01
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74f01432-835c-4b12-8bae-c821afe3a8ee | topic-adaptation-and-prototype-encoding-for | 2008.04504 | null | https://arxiv.org/abs/2008.04504v1 | https://arxiv.org/pdf/2008.04504v1.pdf | Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling | Visual Storytelling~(VIST) is a task to tell a narrative story about a certain topic according to the given photo stream. The existing studies focus on designing complex models, which rely on a huge amount of human-annotated data. However, the annotation of VIST is extremely costly and many topics cannot be covered in the training dataset due to the long-tail topic distribution. In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting. Inspired by the way humans tell a story, we propose a topic adaptive storyteller to model the ability of inter-topic generalization. In practice, we apply the gradient-based meta-learning algorithm on multi-modal seq2seq models to endow the model the ability to adapt quickly from topic to topic. Besides, We further propose a prototype encoding structure to model the ability of intra-topic derivation. Specifically, we encode and restore the few training story text to serve as a reference to guide the generation at inference time. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model on BLEU and METEOR metric. The further case study shows that the stories generated after few-shot adaptation are more relative and expressive. | ['ShiLiang Pu', 'Siliang Tang', 'Juncheng Li', 'Jiacheng Li', 'Yueting Zhuang', 'Jun Xiao', 'Fei Wu'] | 2020-08-11 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.43965849e-01 2.17525885e-01 -2.25433022e-01 -3.95799667e-01
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03040daa-5868-4750-a9d8-cc51a9d16ed4 | unsupervised-opinion-summarization-using | 2209.07496 | null | https://arxiv.org/abs/2209.07496v2 | https://arxiv.org/pdf/2209.07496v2.pdf | Unsupervised Opinion Summarization Using Approximate Geodesics | Opinion summarization is the task of creating summaries capturing popular opinions from user reviews. In this paper, we introduce Geodesic Summarizer (GeoSumm), a novel system to perform unsupervised extractive opinion summarization. GeoSumm involves an encoder-decoder based representation learning model, that generates representations of text as a distribution over latent semantic units. GeoSumm generates these representations by performing dictionary learning over pre-trained text representations at multiple decoder layers. We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism. We use the relevance scores to identify popular opinions in order to compose general and aspect-specific summaries. Our proposed model, GeoSumm, achieves state-of-the-art performance on three opinion summarization datasets. We perform additional experiments to analyze the functioning of our model and showcase the generalization ability of {\X} across different domains. | ['Snigdha Chaturvedi', 'Amr Ahmed', 'Avinava Dubey', 'Nicholas Monath', 'Somnath Basu Roy Chowdhury'] | 2022-09-15 | null | null | null | null | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 3.11840594e-01 4.51652825e-01 -2.12765589e-01 -5.29128611e-01
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811860df-6cc7-4b17-8e3f-3469899623b4 | fashion-focus-multi-modal-retrieval-system | 2102.04727 | null | https://arxiv.org/abs/2102.04727v1 | https://arxiv.org/pdf/2102.04727v1.pdf | Fashion Focus: Multi-modal Retrieval System for Video Commodity Localization in E-commerce | Nowadays, live-stream and short video shopping in E-commerce have grown exponentially. However, the sellers are required to manually match images of the selling products to the timestamp of exhibition in the untrimmed video, resulting in a complicated process. To solve the problem, we present an innovative demonstration of multi-modal retrieval system called "Fashion Focus", which enables to exactly localize the product images in the online video as the focuses. Different modality contributes to the community localization, including visual content, linguistic features and interaction context are jointly investigated via presented multi-modal learning. Our system employs two procedures for analysis, including video content structuring and multi-modal retrieval, to automatically achieve accurate video-to-shop matching. Fashion Focus presents a unified framework that can orientate the consumers towards relevant product exhibitions during watching videos and help the sellers to effectively deliver the products over search and recommendation. | ['Yinghui Xu', 'Siyang Sun', 'Cheng Da', 'Yun Zheng', 'Pan Pan', 'Qiang Wang', 'Yanhao Zhang'] | 2021-02-09 | null | null | null | null | ['video-to-shop'] | ['computer-vision'] | [ 3.26986648e-02 -7.81832397e-01 -4.13677186e-01 -3.40134859e-01
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a32ed6eb-6809-49fb-8fa0-fc6a93572d9b | optimal-boxes-boosting-end-to-end-scene-text | 2207.11934 | null | https://arxiv.org/abs/2207.11934v2 | https://arxiv.org/pdf/2207.11934v2.pdf | Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning | Text detection and recognition are essential components of a modern OCR system. Most OCR approaches attempt to obtain accurate bounding boxes of text at the detection stage, which is used as the input of the text recognition stage. We observe that when using tight text bounding boxes as input, a text recognizer frequently fails to achieve optimal performance due to the inconsistency between bounding boxes and deep representations of text recognition. In this paper, we propose Box Adjuster, a reinforcement learning-based method for adjusting the shape of each text bounding box to make it more compatible with text recognition models. Additionally, when dealing with cross-domain problems such as synthetic-to-real, the proposed method significantly reduces mismatches in domain distribution between the source and target domains. Experiments demonstrate that the performance of end-to-end text recognition systems can be improved when using the adjusted bounding boxes as the ground truths for training. Specifically, on several benchmark datasets for scene text understanding, the proposed method outperforms state-of-the-art text spotters by an average of 2.0% F-Score on end-to-end text recognition tasks and 4.6% F-Score on domain adaptation tasks. | ['Xiang Bai', 'Lan Li', 'Xiena Dong', 'Luchuan Song', 'Wenming Qian', 'Jingqun Tang'] | 2022-07-25 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.05992419e-01 -3.40313852e-01 -6.81254789e-02 -4.66686606e-01
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827c83cf-7cac-45a3-b21b-12b1409f339f | diffusion-based-3d-human-pose-estimation-with | 2303.11579 | null | https://arxiv.org/abs/2303.11579v1 | https://arxiv.org/pdf/2303.11579v1.pdf | Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation | In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses the ground truth 3D poses to a random distribution, and learns a denoiser conditioned on 2D keypoints to recover the uncontaminated 3D poses. The proposed D3DP is compatible with existing 3D pose estimators and supports users to balance efficiency and accuracy during inference through two customizable parameters. On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use. It reprojects 3D pose hypotheses to the 2D camera plane, selects the best hypothesis joint-by-joint based on the reprojection errors, and combines the selected joints into the final pose. The proposed JPMA conducts aggregation at the joint level and makes use of the 2D prior information, both of which have been overlooked by previous approaches. Extensive experiments on Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the state-of-the-art deterministic and probabilistic approaches by 1.5% and 8.9%, respectively. Code is available at https://github.com/paTRICK-swk/D3DP. | ['Wen Gao', 'Siwei Ma', 'Shanshe Wang', 'Kai Han', 'Zhao Wang', 'Xinfeng Zhang', 'Zhenhua Liu', 'Wenkang Shan'] | 2023-03-21 | null | null | null | null | ['multi-hypotheses-3d-human-pose-estimation', '3d-pose-estimation', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-4.50598955e-01 6.03872836e-02 -4.65645902e-02 -1.19135231e-01
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f6d2c7ca-9d66-46d1-89b1-0f521b5ca375 | deepem-deep-3d-convnets-with-em-for-weakly | 1805.05373 | null | http://arxiv.org/abs/1805.05373v3 | http://arxiv.org/pdf/1805.05373v3.pdf | DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection | Recently deep learning has been witnessing widespread adoption in various
medical image applications. However, training complex deep neural nets requires
large-scale datasets labeled with ground truth, which are often unavailable in
many medical image domains. For instance, to train a deep neural net to detect
pulmonary nodules in lung computed tomography (CT) images, current practice is
to manually label nodule locations and sizes in many CT images to construct a
sufficiently large training dataset, which is costly and difficult to scale. On
the other hand, electronic medical records (EMR) contain plenty of partial
information on the content of each medical image. In this work, we explore how
to tap this vast, but currently unexplored data source to improve pulmonary
nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework
augmented with expectation-maximization (EM), to mine weakly supervised labels
in EMRs for pulmonary nodule detection. Experimental results show that DeepEM
can lead to 1.5\% and 3.9\% average improvement in free-response receiver
operating characteristic (FROC) scores on LUNA16 and Tianchi datasets,
respectively, demonstrating the utility of incomplete information in EMRs for
improving deep learning
algorithms.\footnote{https://github.com/uci-cbcl/DeepEM-for-Weakly-Supervised-Detection.git} | ['Yeeleng S. Vang', 'Yufang Huang', 'Wentao Zhu', 'Xiaohui Xie'] | 2018-05-14 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 2.11631030e-01 3.49668860e-01 -5.08314967e-01 -3.44515711e-01
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ea567e2f-8b97-45e6-b7ca-c3fa430b94b7 | atem-a-topic-evolution-model-for-the | 2306.02221 | null | https://arxiv.org/abs/2306.02221v1 | https://arxiv.org/pdf/2306.02221v1.pdf | ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives | This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles. | ['Bernd Amann', 'Camelia Constantin', 'Hubert Naacke', 'Hamed Rahimi'] | 2023-06-04 | null | null | null | null | ['graph-embedding', 'dynamic-graph-embedding', 'dynamic-topic-modeling'] | ['graphs', 'graphs', 'natural-language-processing'] | [-8.16941381e-01 2.24357992e-01 -5.70873320e-01 4.90884990e-01
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c5a937e8-f74f-4f74-a472-000c99a8a7ad | exploiting-reasoning-chains-for-multi-hop | 2109.02905 | null | https://arxiv.org/abs/2109.02905v1 | https://arxiv.org/pdf/2109.02905v1.pdf | Exploiting Reasoning Chains for Multi-hop Science Question Answering | We propose a novel Chain Guided Retriever-reader ({\tt CGR}) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A \textit{Chain-aware loss}, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability. | ['Wai Lam', 'Deng Cai', 'Huihui Zhang', 'Yang Deng', 'Weiwen Xu'] | 2021-09-07 | null | https://aclanthology.org/2021.findings-emnlp.99 | https://aclanthology.org/2021.findings-emnlp.99.pdf | findings-emnlp-2021-11 | ['science-question-answering'] | ['miscellaneous'] | [ 1.58392325e-01 8.20127189e-01 -2.75657803e-01 -5.44840157e-01
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73efc940-f23b-4a11-9547-83337bb8769d | multi-tenant-optimization-for-few-shot-task | 2301.10517 | null | https://arxiv.org/abs/2301.10517v1 | https://arxiv.org/pdf/2301.10517v1.pdf | Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval | Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis-\`a-vis community based FAQs. Each FAQ question represents an intent which is usually an umbrella term for many related user queries. We evaluate performance for such Business FAQs both with standard FAQ retrieval techniques using query-Question (q-Q) similarity and few-shot intent detection techniques. Implementing a real world solution for FAQ retrieval in order to support multiple tenants (FAQ sets) entails optimizing speed, accuracy and cost. We propose a novel approach to scale multi-tenant FAQ applications in real-world context by contrastive fine-tuning of the last layer in sentence Bi-Encoders along with tenant-specific weight switching. | ['Chandra Shekhar Kandpal', 'Gautham Vadakkekara Suresh', 'Rajeev Unnikrishnan Warrier', 'Asha Vishwanathan'] | 2023-01-25 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [-1.33597016e-01 -2.89699614e-01 -1.79002270e-01 -5.95639706e-01
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06163e39-1781-43f1-9abb-ae0e9a78247e | prediction-of-slam-ate-using-an-ensemble | 2303.00616 | null | https://arxiv.org/abs/2303.00616v1 | https://arxiv.org/pdf/2303.00616v1.pdf | Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization | Robustness and resilience of simultaneous localization and mapping (SLAM) are critical requirements for modern autonomous robotic systems. One of the essential steps to achieve robustness and resilience is the ability of SLAM to have an integrity measure for its localization estimates, and thus, have internal fault tolerance mechanisms to deal with performance degradation. In this work, we introduce a novel method for predicting SLAM localization error based on the characterization of raw sensor inputs. The proposed method relies on using a random forest regression model trained on 1-D global pooled features that are generated from characterized raw sensor data. The model is validated by using it to predict the performance of ORB-SLAM3 on three different datasets running on four different operating modes, resulting in an average prediction accuracy of up to 94.7\%. The paper also studies the impact of 12 different 1-D global pooling functions on regression quality, and the superiority of 1-D global averaging is quantitatively proven. Finally, the paper studies the quality of prediction with limited training data, and proves that we are able to maintain proper prediction quality when only 20 \% of the training examples are used for training, which highlights how the proposed model can optimize the evaluation footprint of SLAM systems. | ['Hong Zhang', 'Wan', 'Bingqing', 'Islam Ali'] | 2023-03-01 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.32829621e-01 -1.91203520e-01 -1.49447501e-01 -5.51570654e-01
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745b369b-a32e-4bb7-9321-0dfc2351cc9b | dawn-dual-augmented-memory-network-for | 1908.00777 | null | https://arxiv.org/abs/1908.00777v2 | https://arxiv.org/pdf/1908.00777v2.pdf | DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking | Psychological studies have found that human visual tracking system involves learning, memory, and planning. Despite recent successes, not many works have focused on memory and planning in deep learning based tracking. We are thus interested in memory augmented network, where an external memory remembers the evolving appearance of the target (foreground) object without backpropagation for updating weights. Our Dual Augmented Memory Network (DAWN) is unique in remembering both target and background, and using an improved attention LSTM memory to guide the focus on memorized features. DAWN is effective in unsupervised tracking in handling total occlusion, severe motion blur, abrupt changes in target appearance, multiple object instances, and similar foreground and background features. We present extensive quantitative and qualitative experimental comparison with state-of-the-art methods including top contenders in recent VOT challenges. Notably, despite the straightforward implementation, DAWN is ranked third in both VOT2016 and VOT2017 challenges with excellent success rate among all VOT fast trackers running at fps > 10 in unsupervised tracking in both challenges. We propose DAWN-RPN, where we simply augment our memory and attention LSTM modules to the state-of-the-art SiamRPN, and report immediate performance gain, thus demonstrating DAWN can work well with and directly benefit other models to handle difficult cases as well. | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Zhenmei Shi', 'Haoyang Fang'] | 2019-08-02 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.45752013e-01 -2.70801753e-01 -3.91764671e-01 8.91152173e-02
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f868ba36-6337-4e3b-bc99-a382ad47a4c8 | angular-triplet-center-loss-for-multi-view-3d | 1811.08622 | null | http://arxiv.org/abs/1811.08622v3 | http://arxiv.org/pdf/1811.08622v3.pdf | Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval | How to obtain the desirable representation of a 3D shape, which is
discriminative across categories and polymerized within classes, is a
significant challenge in 3D shape retrieval. Most existing 3D shape retrieval
methods focus on capturing strong discriminative shape representation with
softmax loss for the classification task, while the shape feature learning with
metric loss is neglected for 3D shape retrieval. In this paper, we address this
problem based on the intuition that the cosine distance of shape embeddings
should be close enough within the same class and far away across categories.
Since most of 3D shape retrieval tasks use cosine distance of shape features
for measuring shape similarity, we propose a novel metric loss named angular
triplet-center loss, which directly optimizes the cosine distances between the
features. It inherits the triplet-center loss property to achieve larger
inter-class distance and smaller intra-class distance simultaneously. Unlike
previous metric loss utilized in 3D shape retrieval methods, where Euclidean
distance is adopted and the margin design is difficult, the proposed method is
more convenient to train feature embeddings and more suitable for 3D shape
retrieval. Moreover, the angle margin is adopted to replace the cosine margin
in order to provide more explicit discriminative constraints on an embedding
space. Extensive experimental results on two popular 3D object retrieval
benchmarks, ModelNet40 and ShapeNetCore 55, demonstrate the effectiveness of
our proposed loss, and our method has achieved state-of-the-art results on
various 3D shape datasets. | ['Cheng Xu', 'Biao Leng', 'Zhaoqun Li'] | 2018-11-21 | null | null | null | null | ['3d-shape-retrieval', 'multi-view-3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.18279260e-01 -4.49087411e-01 -4.44643527e-01 -5.56398451e-01
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4ab2b437-4a44-408c-8a40-5080992621c0 | multilingual-multimodal-pretraining-for-zero | null | null | https://openreview.net/forum?id=XC-PgiyCwj3 | https://openreview.net/pdf?id=XC-PgiyCwj3 | Multilingual Multimodal Pretraining for Zero-Shot Cross-Lingual Transfer of Vision-Language Models | This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pretraining strategy, and collect a new multilingual instructional video dataset (Multi-HowTo100M) for pretraining. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M will be made available. | ['Anonymous'] | 2020-12-07 | null | null | null | null | ['text-to-video-search'] | ['natural-language-processing'] | [-2.35043898e-01 -5.83433747e-01 -6.65297806e-01 -1.71605587e-01
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41ccf886-12ea-4c88-8b98-23977e5ee69a | findings-of-the-vardial-evaluation-campaign-1 | null | null | https://aclanthology.org/2021.vardial-1.1 | https://aclanthology.org/2021.vardial-1.1.pdf | Findings of the VarDial Evaluation Campaign 2021 | This paper describes the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2021. The campaign was part of the eighth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2021. Four separate shared tasks were included this year: Dravidian Language Identification (DLI), Romanian Dialect Identification (RDI), Social Media Variety Geolocation (SMG), and Uralic Language Identification (ULI). DLI was organized for the first time and the other three continued a series of tasks from previous evaluation campaigns. | ['Marcos Zampieri', 'Yves Scherrer', 'Eswari Rajagopal', 'Christoph Purschke', 'Ruba Priyadharshini', 'Niko Partanen', 'Nikola Ljubešić', 'Krister Lindén', 'Tommi Jauhiainen', 'Heidi Jauhiainen', 'Radu Tudor Ionescu', 'Gaman Mihaela', 'Bharathi Raja Chakravarthi'] | null | null | null | null | eacl-vardial-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-5.05995750e-01 -2.54030675e-01 -1.86807960e-01 -4.79945153e-01
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f44f11bb-e7d8-4966-b06e-c4c86d84fe9f | weakly-supervised-action-localization-by | 1712.05080 | null | http://arxiv.org/abs/1712.05080v2 | http://arxiv.org/pdf/1712.05080v2.pdf | Weakly Supervised Action Localization by Sparse Temporal Pooling Network | We propose a weakly supervised temporal action localization algorithm on
untrimmed videos using convolutional neural networks. Our algorithm learns from
video-level class labels and predicts temporal intervals of human actions with
no requirement of temporal localization annotations. We design our network to
identify a sparse subset of key segments associated with target actions in a
video using an attention module and fuse the key segments through adaptive
temporal pooling. Our loss function is comprised of two terms that minimize the
video-level action classification error and enforce the sparsity of the segment
selection. At inference time, we extract and score temporal proposals using
temporal class activations and class-agnostic attentions to estimate the time
intervals that correspond to target actions. The proposed algorithm attains
state-of-the-art results on the THUMOS14 dataset and outstanding performance on
ActivityNet1.3 even with its weak supervision. | ['Bohyung Han', 'Phuc Nguyen', 'Ting Liu', 'Gautam Prasad'] | 2017-12-14 | weakly-supervised-action-localization-by-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Nguyen_Weakly_Supervised_Action_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Nguyen_Weakly_Supervised_Action_CVPR_2018_paper.pdf | cvpr-2018-6 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 5.34899712e-01 -1.08642168e-01 -1.08645713e+00 -4.35746402e-01
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53e53f4b-18df-49c3-ac12-b05b7df629c6 | utterance-level-dialogue-understanding-an | 2009.13902 | null | https://arxiv.org/abs/2009.13902v5 | https://arxiv.org/pdf/2009.13902v5.pdf | Utterance-level Dialogue Understanding: An Empirical Study | The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances. In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines. Specifically, we employ various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental contextual controlling factors of different aspects of a dialogue. Such insights can inspire more effective dialogue understanding models, and provide support for future text generation approaches. The implementation pertaining to this work is available at https://github.com/declare-lab/dialogue-understanding. | ['Soujanya Poria', 'Navonil Majumder', 'Deepanway Ghosal', 'Rada Mihalcea'] | 2020-09-29 | null | null | null | null | ['dialogue-understanding', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.52588272e-01 4.65632081e-01 -3.88236977e-02 -8.84766161e-01
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0fa75d11-6eff-4f53-adcf-8e8d35cf9319 | dydx-liquidity-providers-incentive-programme | 2307.03935 | null | https://arxiv.org/abs/2307.03935v1 | https://arxiv.org/pdf/2307.03935v1.pdf | dYdX: Liquidity Providers' Incentive Programme Review | Liquidity providers are currently incentivised to provide liquidity through the LP Incentives Programme on dYdX. Based on the various parameters - makerVolume, depths and spreads, they are rewarded accordingly based on their activities. Given the maturity of the BTC and ETH markets, alongside other altcoins which enjoy a consistent amount of liquidity, this paper aims to update the formula to encourage more active and efficient liquidity, improving the overall trading experience. In this research, I begin by providing a basic understanding of spread management, before introducing the methodology with the various metrics and conditions. This includes gathering orderbooks on a minute interval and reconstructing the depths based on historical trades to establish an upper bound. I end off by providing recommendations to update the maxSpread parameter and alternative mechanisms/solutions to improve the existing market structures. | ['Colin Chan'] | 2023-07-08 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.03706837e+00 1.15665898e-01 -5.93509316e-01 -1.78970546e-01
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bb7d7eaf-5c95-48c9-852f-6b14efafe67a | deep-subdomain-adaptation-network-for-image | 2106.09388 | null | https://arxiv.org/abs/2106.09388v1 | https://arxiv.org/pdf/2106.09388v1.pdf | Deep Subdomain Adaptation Network for Image Classification | For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a global domain shift, i.e., align the global source and target distributions without considering the relationships between two subdomains within the same category of different domains, leading to unsatisfying transfer learning performance without capturing the fine-grained information. Recently, more and more researchers pay attention to Subdomain Adaptation which focuses on accurately aligning the distributions of the relevant subdomains. However, most of them are adversarial methods which contain several loss functions and converge slowly. Based on this, we present Deep Subdomain Adaptation Network (DSAN) which learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD). Our DSAN is very simple but effective which does not need adversarial training and converges fast. The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation. Experiments demonstrate that DSAN can achieve remarkable results on both object recognition tasks and digit classification tasks. Our code will be available at: https://github.com/easezyc/deep-transfer-learning | ['Qing He', 'Hui Xiong', 'Jiang Bian', 'Jingwu Chen', 'Guolin Ke', 'Jindong Wang', 'Fuzhen Zhuang', 'Yongchun Zhu'] | 2021-06-17 | null | null | null | null | ['subdomain-adaptation'] | ['methodology'] | [-4.14450504e-02 -8.19619447e-02 -3.20822179e-01 -6.39159918e-01
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fd58312d-b9d6-4950-b815-39162644f2f0 | intimate-partner-violence-and-injury | 2009.09084 | null | https://arxiv.org/abs/2009.09084v2 | https://arxiv.org/pdf/2009.09084v2.pdf | Intimate Partner Violence and Injury Prediction From Radiology Reports | Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provided by emergency radiology fellowship-trained physicians. Our dataset includes 34,642 radiology reports and 1479 patients of IPV victims and control patients. Our best model predicts IPV a median of 3.08 years before violence prevention program entry with a sensitivity of 64% and a specificity of 95%. We conduct error analysis to determine for which patients our model has especially high or low performance and discuss next steps for a deployed clinical risk model. | ['Bharti Khurana', 'Rahul Gujrathi', 'Babina Gosangi', 'Richard Thomas', 'Hyesun Park', 'Emily Alsentzer', 'Irene Y. Chen'] | 2020-08-28 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [ 1.51542068e-01 4.38623786e-01 -6.48191273e-01 -4.07638639e-01
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efe573f9-c993-4ec1-b62c-36c64ec65c8a | automatic-classification-of-irregularly | 1605.05142 | null | http://arxiv.org/abs/1605.05142v1 | http://arxiv.org/pdf/1605.05142v1.pdf | Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate | A patient's estimated glomerular filtration rate (eGFR) can provide important
information about disease progression and kidney function. Traditionally, an
eGFR time series is interpreted by a human expert labelling it as stable or
unstable. While this approach works for individual patients, the time consuming
nature of it precludes the quick evaluation of risk in large numbers of
patients. However, automating this process poses significant challenges as eGFR
measurements are usually recorded at irregular intervals and the series of
measurements differs in length between patients. Here we present a two-tier
system to automatically classify an eGFR trend. First, we model the time series
using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed
size vector of fifty time-dependent observations. Second, we classify the
resampled eGFR time series using a K-NN/SVM classifier, and evaluate its
performance via 5-fold cross validation. Using this approach we achieved an
F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst
themselves. | ['Simon Bull', 'Santosh Tirunagari', 'Norman Poh'] | 2016-05-17 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 3.02135676e-01 -7.29920641e-02 -2.12246254e-01 -6.03809476e-01
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dfc207fa-943b-4286-9bc3-3fe515eef98c | multi-level-feature-abstraction-from | 1807.01332 | null | http://arxiv.org/abs/1807.01332v1 | http://arxiv.org/pdf/1807.01332v1.pdf | Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification | In this paper, we propose a deep multimodal fusion network to fuse multiple
modalities (face, iris, and fingerprint) for person identification. The
proposed deep multimodal fusion algorithm consists of multiple streams of
modality-specific Convolutional Neural Networks (CNNs), which are jointly
optimized at multiple feature abstraction levels. Multiple features are
extracted at several different convolutional layers from each modality-specific
CNN for joint feature fusion, optimization, and classification. Features
extracted at different convolutional layers of a modality-specific CNN
represent the input at several different levels of abstract representations. We
demonstrate that an efficient multimodal classification can be accomplished
with a significant reduction in the number of network parameters by exploiting
these multi-level abstract representations extracted from all the
modality-specific CNNs. We demonstrate an increase in multimodal person
identification performance by utilizing the proposed multi-level feature
abstract representations in our multimodal fusion, rather than using only the
features from the last layer of each modality-specific CNNs. We show that our
deep multi-modal CNNs with multimodal fusion at several different feature level
abstraction can significantly outperform the unimodal representation accuracy.
We also demonstrate that the joint optimization of all the modality-specific
CNNs excels the score and decision level fusions of independently optimized
CNNs. | ['Sobhan Soleymani', 'Nasser M. Nasrabadi', 'Hadi Kazemi', 'Jeremy Dawson', 'Ali Dabouei'] | 2018-07-03 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 5.52169867e-02 -4.36719894e-01 2.57401802e-02 -6.03298545e-01
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4ef3c7d4-09f3-435a-8fbb-caa60a78dabc | rvl-bert-visual-relationship-detection-with | 2009.04965 | null | https://arxiv.org/abs/2009.04965v3 | https://arxiv.org/pdf/2009.04965v3.pdf | Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations | Visual relationship detection aims to reason over relationships among salient objects in images, which has drawn increasing attention over the past few years. Inspired by human reasoning mechanisms, it is believed that external visual commonsense knowledge is beneficial for reasoning visual relationships of objects in images, which is however rarely considered in existing methods. In this paper, we propose a novel approach named Relational Visual-Linguistic Bidirectional Encoder Representations from Transformers (RVL-BERT), which performs relational reasoning with both visual and language commonsense knowledge learned via self-supervised pre-training with multimodal representations. RVL-BERT also uses an effective spatial module and a novel mask attention module to explicitly capture spatial information among the objects. Moreover, our model decouples object detection from visual relationship recognition by taking in object names directly, enabling it to be used on top of any object detection system. We show through quantitative and qualitative experiments that, with the transferred knowledge and novel modules, RVL-BERT achieves competitive results on two challenging visual relationship detection datasets. The source code is available at https://github.com/coldmanck/RVL-BERT. | ['Roger Zimmermann', 'Meng-Jiun Chiou', 'Jiashi Feng'] | 2020-09-10 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.03332838e-02 3.73974860e-01 -1.83639348e-01 -3.93132299e-01
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b578d1d5-5d63-4871-8e11-8adc1267725d | improving-video-retrieval-by-adaptive-margin | 2303.05093 | null | https://arxiv.org/abs/2303.05093v1 | https://arxiv.org/pdf/2303.05093v1.pdf | Improving Video Retrieval by Adaptive Margin | Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representations by pushing the distance between the similarity of positive pairs and that of negative pairs apart from a fixed margin. However, negative pairs used for training are sampled randomly, which indicates that the semantics between negative pairs may be related or even equivalent, while most methods still enforce dissimilar representations to decrease their similarity. This phenomenon leads to inaccurate supervision and poor performance in learning video-text representations. While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue. First, we design the calculation framework of the adaptive margin, including the method of distance measurement and the function between the distance and the margin. Then, we explore a novel implementation called "Cross-Modal Generalized Self-Distillation" (CMGSD), which can be built on the top of most video retrieval models with few modifications. Notably, CMGSD adds few computational overheads at train time and adds no computational overhead at test time. Experimental results on three widely used datasets demonstrate that the proposed method can yield significantly better performance than the corresponding backbone model, and it outperforms state-of-the-art methods by a large margin. | ['Xiao Tan', 'Yong Zhu', 'Yajuan Lv', 'Wenbin Jiang', 'Zhifan Feng', 'Qi Wang', 'Feng He'] | 2023-03-09 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-7.17094447e-03 -5.05638957e-01 -5.30868769e-01 -2.92708784e-01
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1f46b8d4-6720-44ac-aa60-3e8feb65255b | face-emotion-recognization-using-dataset | 2210.12689 | null | https://arxiv.org/abs/2210.12689v2 | https://arxiv.org/pdf/2210.12689v2.pdf | Face Emotion Recognization Using Dataset Augmentation Based on Neural Network | Facial expression is one of the most external indications of a person's feelings and emotions. In daily conversation, according to the psychologist, only 7% and 38% of information is communicated through words and sounds respective, while up to 55% is through facial expression. It plays an important role in coordinating interpersonal relationships. Ekman and Friesen recognized six essential emotions in the nineteenth century depending on a cross-cultural study, which indicated that people feel each basic emotion in the same fashion despite culture. As a branch of the field of analyzing sentiment, facial expression recognition offers broad application prospects in a variety of domains, including the interaction between humans and computers, healthcare, and behavior monitoring. Therefore, many researchers have devoted themselves to facial expression recognition. In this paper, an effective hybrid data augmentation method is used. This approach is operated on two public datasets, and four benchmark models see some remarkable results. | ['Ruyi Bao', 'Liangshun Dong', 'Mengyu Rao'] | 2022-10-23 | null | null | null | null | ['facial-expression-recognition', 'culture'] | ['computer-vision', 'speech'] | [-9.20659378e-02 -1.50232241e-01 -4.84018356e-01 -5.76363206e-01
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1d8e0570-672c-4de6-be36-301e66629bfc | xnet-a-convolutional-neural-network-cnn | 1812.00548 | null | http://arxiv.org/abs/1812.00548v2 | http://arxiv.org/pdf/1812.00548v2.pdf | XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets | X-Ray image enhancement, along with many other medical image processing
applications, requires the segmentation of images into bone, soft tissue, and
open beam regions. We apply a machine learning approach to this problem,
presenting an end-to-end solution which results in robust and efficient
inference. Since medical institutions frequently do not have the resources to
process and label the large quantity of X-Ray images usually needed for neural
network training, we design an end-to-end solution for small datasets, while
achieving state-of-the-art results. Our implementation produces an overall
accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical
image processing techniques, such as clustering and entropy based methods,
while improving upon the output of existing neural networks used for
segmentation in non-medical contexts. The code used for this project is
available online. | ['Arnau Quera-Bofarull', 'Carolina Cuesta-Lazaro', 'Joseph Bullock'] | 2018-12-03 | null | null | null | null | ['medical-x-ray-image-segmentation'] | ['medical'] | [ 5.24405301e-01 5.05424500e-01 -6.94008023e-02 -6.77922904e-01
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c7e9099e-2ab3-4f73-bad2-2f13d87578a3 | post-processing-temporal-action-detection | 2211.14924 | null | https://arxiv.org/abs/2211.14924v2 | https://arxiv.org/pdf/2211.14924v2.pdf | Post-Processing Temporal Action Detection | Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequence, before temporal boundary estimation and action classification. This pre-processing step would temporally downsample the video, reducing the inference resolution and hampering the detection performance in the original temporal resolution. In essence, this is due to a temporal quantization error introduced during the resolution downsampling and recovery. This could negatively impact the TAD performance, but is largely ignored by existing methods. To address this problem, in this work we introduce a novel model-agnostic post-processing method without model redesign and retraining. Specifically, we model the start and end points of action instances with a Gaussian distribution for enabling temporal boundary inference at a sub-snippet level. We further introduce an efficient Taylor-expansion based approximation, dubbed as Gaussian Approximated Post-processing (GAP). Extensive experiments demonstrate that our GAP can consistently improve a wide variety of pre-trained off-the-shelf TAD models on the challenging ActivityNet (+0.2% -0.7% in average mAP) and THUMOS (+0.2% -0.5% in average mAP) benchmarks. Such performance gains are already significant and highly comparable to those achieved by novel model designs. Also, GAP can be integrated with model training for further performance gain. Importantly, GAP enables lower temporal resolutions for more efficient inference, facilitating low-resource applications. The code will be available in https://github.com/sauradip/GAP | ['Tao Xiang', 'Yi-Zhe Song', 'Xiatian Zhu', 'Sauradip Nag'] | 2022-11-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nag_Post-Processing_Temporal_Action_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification'] | ['computer-vision'] | [ 4.87132043e-01 -1.65834785e-01 -3.79663706e-01 -5.53362258e-02
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4b641310-aadc-4911-8648-e291ec6311b5 | self-supervised-unseen-object-instance | 2302.03793 | null | https://arxiv.org/abs/2302.03793v1 | https://arxiv.org/pdf/2302.03793v1.pdf | Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction | We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. Instead, our system defers the decision on segmenting objects after a sequence of robot pushing actions. By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way. These include images where objects are very close to each other, and segmentation errors usually occur on these images for existing object segmentation networks. We demonstrate the usefulness of our system by fine-tuning segmentation networks trained on synthetic data with real-world data collected by our system. We show that, after fine-tuning, the segmentation accuracy of the networks is significantly improved both in the same domain and across different domains. In addition, we verify that the fine-tuned networks improve top-down robotic grasping of unseen objects in the real world. | ['Yu Xiang', 'Nicholas Ruozzi', 'Yunhui Guo', 'Kaiyu Hang', 'Kamalesh Palanisamy', 'Charles Averill', 'Zesheng Xu', 'Ninad Khargonkar', 'Yangxiao Lu'] | 2023-02-07 | null | null | null | null | ['unseen-object-instance-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'robots'] | [ 4.48835462e-01 1.74512759e-01 9.06900037e-03 -4.34731185e-01
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1cc45aad-9bff-4f8a-b349-758526627e16 | nl-linknet-toward-lighter-but-more-accurate | 1908.08223 | null | https://arxiv.org/abs/1908.08223v3 | https://arxiv.org/pdf/1908.08223v3.pdf | NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations | Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing. In this paper, we propose an efficient Non-Local LinkNet with non-local blocks that can grasp relations between global features. This enables each spatial feature point to refer to all other contextual information and results in more accurate road segmentation. In detail, our single model without any post-processing like CRF refinement, performed better than any other published state-of-the-art ensemble model in the official DeepGlobe Challenge. Moreover, our NL-LinkNet beat the D-LinkNet, the winner of the DeepGlobe challenge, with 43 \% less parameters, less giga floating-point operations per seconds (GFLOPs) and shorter training convergence time. We also present empirical analyses on the proper usages of non-local blocks for the baseline model. | ['Yooseung Wang', 'Junghoon Seo', 'Taegyun Jeon'] | 2019-08-22 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-1.10981070e-01 1.32687557e-02 6.89157611e-03 -4.96646613e-01
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e8b068d9-cdd4-484f-87f9-7bc32a92933f | carle-s-game-an-open-ended-challenge-in | 2107.05786 | null | https://arxiv.org/abs/2107.05786v1 | https://arxiv.org/pdf/2107.05786v1.pdf | Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity | This paper is both an introduction and an invitation. It is an introduction to CARLE, a Life-like cellular automata simulator and reinforcement learning environment. It is also an invitation to Carle's Game, a challenge in open-ended machine exploration and creativity. Inducing machine agents to excel at creating interesting patterns across multiple cellular automata universes is a substantial challenge, and approaching this challenge is likely to require contributions from the fields of artificial life, AI, machine learning, and complexity, at multiple levels of interest. Carle's Game is based on machine agent interaction with CARLE, a Cellular Automata Reinforcement Learning Environment. CARLE is flexible, capable of simulating any of the 262,144 different rules defining Life-like cellular automaton universes. CARLE is also fast and can simulate automata universes at a rate of tens of thousands of steps per second through a combination of vectorization and GPU acceleration. Finally, CARLE is simple. Compared to high-fidelity physics simulators and video games designed for human players, CARLE's two-dimensional grid world offers a discrete, deterministic, and atomic universal playground, despite its complexity. In combination with CARLE, Carle's Game offers an initial set of agent policies, learning and meta-learning algorithms, and reward wrappers that can be tailored to encourage exploration or specific tasks. | ['Q. Tyrell Davis'] | 2021-07-13 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-3.32440466e-01 -5.83918840e-02 2.52988309e-01 6.38674974e-01
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e14e5869-f9be-4bbf-8e4d-ed48e6c6f3de | cross-database-and-cross-channel-ecg | 2306.04433 | null | https://arxiv.org/abs/2306.04433v1 | https://arxiv.org/pdf/2306.04433v1.pdf | Cross-Database and Cross-Channel ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation | The classification of electrocardiogram (ECG) plays a crucial role in the development of an automatic cardiovascular diagnostic system. However, considerable variances in ECG signals between individuals is a significant challenge. Changes in data distribution limit cross-domain utilization of a model. In this study, we propose a solution to classify ECG in an unlabeled dataset by leveraging knowledge obtained from labeled source domain. We present a domain-adaptive deep network based on cross-domain feature discrepancy optimization. Our method comprises three stages: pre-training, cluster-centroid computing, and adaptation. In pre-training, we employ a Distributionally Robust Optimization (DRO) technique to deal with the vanishing worst-case training loss. To enhance the richness of the features, we concatenate three temporal features with the deep learning features. The cluster computing stage involves computing centroids of distinctly separable clusters for the source using true labels, and for the target using confident predictions. We propose a novel technique to select confident predictions in the target domain. In the adaptation stage, we minimize compacting loss within the same cluster, separating loss across different clusters, inter-domain cluster discrepancy loss, and running combined loss to produce a domain-robust model. Experiments conducted in both cross-domain and cross-channel paradigms show the efficacy of the proposed method. Our method achieves superior performance compared to other state-of-the-art approaches in detecting ventricular ectopic beats (V), supraventricular ectopic beats (S), and fusion beats (F). Our method achieves an average improvement of 11.78% in overall accuracy over the non-domain-adaptive baseline method on the three test datasets. | ['Naimul Khan', 'Md Niaz Imtiaz'] | 2023-06-07 | null | null | null | null | ['heartbeat-classification', 'unsupervised-domain-adaptation'] | ['medical', 'methodology'] | [ 2.18466088e-01 -3.31973612e-01 5.82328364e-02 -7.51803517e-01
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fc966893-dd9d-4e35-a005-3d0c58a6e130 | cluster-based-contrastive-disentangling-for | 2203.02648 | null | https://arxiv.org/abs/2203.02648v1 | https://arxiv.org/pdf/2203.02648v1.pdf | Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning | Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class. In this paper, we propose a Cluster-based Contrastive Disentangling (CCD) method to improve GZSL by alleviating the semantic gap and domain shift problems. Specifically, we first cluster the batch data to form several sets containing similar classes. Then, we disentangle the visual features into semantic-unspecific and semantic-matched variables, and further disentangle the semantic-matched variables into class-shared and class-unique variables according to the clustering results. The disentangled learning module with random swapping and semantic-visual alignment bridges the semantic gap. Moreover, we introduce contrastive learning on semantic-matched and class-unique variables to learn high intra-set and intra-class similarity, as well as inter-set and inter-class discriminability. Then, the generated visual features conform to the underlying characteristics of general images and have strong discriminative information, which alleviates the domain shift problem well. We evaluate our proposed method on four datasets and achieve state-of-the-art results in both conventional and generalized settings. | ['Jiancheng Lv', 'Chenwei Tang', 'Yi Gao'] | 2022-03-05 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.51339278e-01 -1.48104772e-01 -2.15235397e-01 -5.62329233e-01
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51fe95b0-a673-47c8-bb06-da278cd64b66 | crack-detection-as-a-weakly-supervised | 2011.02208 | null | https://arxiv.org/abs/2011.02208v1 | https://arxiv.org/pdf/2011.02208v1.pdf | Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors | Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly improved the detection accuracy. However, the methods often heavily rely on costly annotation processes. In addition, to handle a wide variety of target domains, new batches of annotations are usually required for each new environment. This makes the data annotation cost a significant bottleneck when deploying crack detection systems in real life. To resolve this issue, we formulate the crack detection problem as a weakly-supervised problem and propose a two-branched framework. By combining predictions of a supervised model trained on low quality annotations with predictions based on pixel brightness, our framework is less affected by the annotation quality. Experimental results show that the proposed framework retains high detection accuracy even when provided with low quality annotations. Implementation of the proposed framework is publicly available at https://github.com/hitachi-rd-cv/weakly-sup-crackdet. | ['Hiroto Nagayoshi', 'Yuki Inoue'] | 2020-11-04 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.74169850e-01 -1.74647495e-02 8.67079645e-02 -3.40322196e-01
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e9daf767-3dd9-4e6d-b7cf-a123125e1234 | a-masked-image-reconstruction-network-for | 2204.09851 | null | https://arxiv.org/abs/2204.09851v2 | https://arxiv.org/pdf/2204.09851v2.pdf | A Masked Image Reconstruction Network for Document-level Relation Extraction | Document-level relation extraction aims to extract relations among entities within a document. Compared with its sentence-level counterpart, Document-level relation extraction requires inference over multiple sentences to extract complex relational triples. Previous research normally complete reasoning through information propagation on the mention-level or entity-level document-graphs, regardless of the correlations between the relationships. In this paper, we propose a novel Document-level Relation Extraction model based on a Masked Image Reconstruction network (DRE-MIR), which models inference as a masked image reconstruction problem to capture the correlations between relationships. Specifically, we first leverage an encoder module to get the features of entities and construct the entity-pair matrix based on the features. After that, we look on the entity-pair matrix as an image and then randomly mask it and restore it through an inference module to capture the correlations between the relationships. We evaluate our model on three public document-level relation extraction datasets, i.e. DocRED, CDR, and GDA. Experimental results demonstrate that our model achieves state-of-the-art performance on these three datasets and has excellent robustness against the noises during the inference process. | ['Yidong Cheng', 'Liang Zhang'] | 2022-04-21 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 3.68734300e-01 4.64859903e-01 -4.14987534e-01 -2.70511568e-01
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53338326-798b-4de8-a8c6-8b7aff98ada9 | generalization-of-change-point-detection-in | 2001.06386 | null | https://arxiv.org/abs/2001.06386v1 | https://arxiv.org/pdf/2001.06386v1.pdf | Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation | The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of the change-point detection are based on direct density ratio estimation. In this work we show how existing algorithms can be generalized using various binary classification and regression models. In particular, we show that the Gradient Boosting over Decision Trees and Neural Networks can be used for this purpose. The algorithms are tested on several synthetic and real-world datasets. The results show that the proposed methods outperform classical RuLSIF algorithm. Discussion of cases where the proposed algorithms have advantages over existing methods are also provided. | ['Mikhail Hushchyn', 'Andrey Ustyuzhanin'] | 2020-01-17 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 3.42257395e-02 -7.14834332e-01 -3.83363932e-01 -2.28119090e-01
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1c339b3e-a305-410c-8f29-79c77f5ee6a5 | towards-alleviating-the-modeling-ambiguity-of | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Towards_Alleviating_the_Modeling_Ambiguity_of_Unsupervised_Monocular_3D_Human_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Towards_Alleviating_the_Modeling_Ambiguity_of_Unsupervised_Monocular_3D_Human_ICCV_2021_paper.pdf | Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation | In this work, we study the ambiguity problem in the task of unsupervised 3D human pose estimation from 2D counterpart. On one hand, without explicit annotation, the scale of 3D pose is difficult to be accurately captured (scale ambiguity). On the other hand, one 2D pose might correspond to multiple 3D gestures, where the lifting procedure is inherently ambiguous (pose ambiguity). Previous methods generally use temporal constraints (e.g., constant bone length and motion smoothness) to alleviate the above issues. However, these methods commonly enforce the outputs to fulfill multiple training objectives simultaneously, which often lead to sub-optimal results. In contrast to the majority of previous works, we propose to split the whole problem into two sub-tasks, i.e., optimizing 2D input poses via a scale estimation module and then mapping optimized 2D pose to 3D counterpart via a pose lifting module. Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively. These two modules are optimized via a iterative training scheme with corresponding temporal constraints, which effectively reduce the learning difficulty and lead to better performance. Results on the Human3.6M dataset demonstrate that our approach improves upon the prior art by 23.1% and also outperforms several weakly supervised approaches that rely on 3D annotations. Our project is available at https://sites.google.com/view/ambiguity-aware-hpe. | ['Wenjun Zhang', 'Chenglong Zhao', 'Junjie Wang', 'Jingwei Xu', 'Bingbing Ni', 'Zhenbo Yu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['monocular-3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.44029611e-01 7.48455599e-02 -1.96213499e-01 -3.72112364e-01
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d7f4b12a-72ff-4146-a881-243f7ec18895 | computationally-assisted-quality-control-for | 2306.16914 | null | https://arxiv.org/abs/2306.16914v1 | https://arxiv.org/pdf/2306.16914v1.pdf | Computationally Assisted Quality Control for Public Health Data Streams | Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of daily-updated public health data streams could assist an expert reviewer in identifying these irregularities. However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams. Accordingly, we developed FlaSH (Flagging Streams in public Health), a practical outlier detection framework for public health data users that uses simple, scalable models to capture these statistical properties explicitly. In an experiment where human experts evaluate FlaSH and existing methods (including deep learning approaches), FlaSH scales to the data volume of this task, matches or exceeds these other methods in mean accuracy, and identifies the outlier points that users empirically rate as more helpful. Based on these results, FlaSH has been deployed on data streams used by public health stakeholders. | ['Bryan Wilder', 'Roni Rosenfeld', 'Kathryn Mazaitis', 'Ananya Joshi'] | 2023-06-29 | null | null | null | null | ['outlier-detection', 'decision-making'] | ['methodology', 'reasoning'] | [-3.40138942e-01 -1.39951542e-01 -2.99756557e-01 -1.95538774e-01
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5ff037a0-53e1-4449-aa21-1c45cb27a5b7 | self-configuration-in-machine-learning | 1809.06463 | null | http://arxiv.org/abs/1809.06463v1 | http://arxiv.org/pdf/1809.06463v1.pdf | Self Configuration in Machine Learning | In this paper we first present a class of algorithms for training multi-level
neural networks with a quadratic cost function one layer at a time starting
from the input layer. The algorithm is based on the fact that for any layer to
be trained, the effect of a direct connection to an optimized linear output
layer can be computed without the connection being made. Thus, starting from
the input layer, we can train each layer in succession in isolation from the
other layers. Once trained, the weights are kept fixed and the outputs of the
trained layer then serve as the inputs to the next layer to be trained. The
result is a very fast algorithm. The simplicity of this training arrangement
allows the activation function and step size in weight adjustment to be
adaptive and self-adjusting. Furthermore, the stability of the training process
allows relatively large steps to be taken and thereby achieving in even greater
speeds. Finally, in our context configuring the network means determining the
number of outputs for each layer. By decomposing the overall cost function into
separate components related to approximation and estimation, we obtain an
optimization formula for determining the number of outputs for each layer. With
the ability to self-configure and set parameters, we now have more than a fast
training algorithm, but the ability to build automatically a fully trained deep
neural network starting with nothing more than data. | ['Eugene Wong'] | 2018-09-17 | null | null | null | null | ['self-organized-clustering'] | ['miscellaneous'] | [ 3.57284397e-02 2.46416166e-01 9.17731002e-02 -4.77416515e-01
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2dc95c78-5b17-4df8-abe4-abc005c8accc | local-implicit-grid-representations-for-3d-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Local_Implicit_Grid_Representations_for_3D_Scenes_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Local_Implicit_Grid_Representations_for_3D_Scenes_CVPR_2020_paper.pdf | Local Implicit Grid Representations for 3D Scenes | Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D shape representation designed for scalability and generality. The motivating idea is that most 3D surfaces share geometric details at some scale -- i.e., at a scale smaller than an entire object and larger than a small patch. We train an autoencoder to learn an embedding of local crops of 3D shapes at that size. Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation. We demonstrate the value of this proposed approach for 3D surface reconstruction from sparse point observations, showing significantly better results than alternative approaches.
| [' Thomas Funkhouser', ' Matthias Niessner', ' Jingwei Huang', ' Ameesh Makadia', ' Avneesh Sud', 'Chiyu "Max" Jiang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-shape-representation'] | ['computer-vision'] | [ 1.26719266e-01 3.40461761e-01 1.80276394e-01 -2.77920216e-01
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c1154178-1515-40c6-b6d8-7e4373d02f25 | neural-speech-enhancement-with-very-low | 2304.08707 | null | https://arxiv.org/abs/2304.08707v1 | https://arxiv.org/pdf/2304.08707v1.pdf | Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling | We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in the short-time Fourier transform (STFT) domain. The model maintains an information highway to flow an over-complete input representation through multiple FSB-LSTM modules. Each FSB-LSTM module consists of a full-band block to model spectro-temporal patterns at all frequencies and a sub-band block to model patterns within each sub-band, where each of the two blocks takes a down-sampled representation as input and returns an up-sampled discriminative representation to be added to the block input via a residual connection. The model is designed to have a low algorithmic complexity, a small run-time buffer and a very low algorithmic latency, at the same time producing a strong enhancement performance on a noisy-reverberant speech enhancement task even if the hop size is as low as $2$ ms. | ['Shinji Watanabe', 'Byeong-Yeol Kim', 'Younglo Lee', 'Shukjae Choi', 'Samuele Cornell', 'Zhong-Qiu Wang'] | 2023-04-18 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 3.72653186e-01 2.15142965e-02 7.02352226e-02 -4.83421147e-01
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59f66d18-8908-4fcd-b37b-f4cfac05a0fe | latenthuman-shape-and-pose-disentangled | 2111.15113 | null | https://arxiv.org/abs/2111.15113v1 | https://arxiv.org/pdf/2111.15113v1.pdf | LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies | 3D representation and reconstruction of human bodies have been studied for a long time in computer vision. Traditional methods rely mostly on parametric statistical linear models, limiting the space of possible bodies to linear combinations. It is only recently that some approaches try to leverage neural implicit representations for human body modeling, and while demonstrating impressive results, they are either limited by representation capability or not physically meaningful and controllable. In this work, we propose a novel neural implicit representation for the human body, which is fully differentiable and optimizable with disentangled shape and pose latent spaces. Contrary to prior work, our representation is designed based on the kinematic model, which makes the representation controllable for tasks like pose animation, while simultaneously allowing the optimization of shape and pose for tasks like 3D fitting and pose tracking. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses. Experiments demonstrate the improved 3D reconstruction performance over SoTA approaches and show the applicability of our method to shape interpolation, model fitting, pose tracking, and motion retargeting. | ['Zhaopeng Cui', 'Marc Pollefeys', 'Guofeng Zhang', 'Hujun Bao', 'Tianxing Fan', 'Bangbang Yang', 'Sandro Lombardi'] | 2021-11-30 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 2.73625981e-02 6.07583344e-01 -2.68579960e-01 -1.91069528e-01
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97c418b3-5900-4722-8a82-5d212b9b0851 | parameter-free-channel-attention-for-image | 2303.11055 | null | https://arxiv.org/abs/2303.11055v1 | https://arxiv.org/pdf/2303.11055v1.pdf | Parameter-Free Channel Attention for Image Classification and Super-Resolution | The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution. It is usually designed as a parameterized sub-network and embedded into the convolutional layers of the network to learn more powerful feature representations. However, current channel attention induces more parameters and therefore leads to higher computational costs. To deal with this issue, in this work, we propose a Parameter-Free Channel Attention (PFCA) module to boost the performance of popular image classification and image super-resolution networks, but completely sweep out the parameter growth of channel attention. Experiments on CIFAR-100, ImageNet, and DIV2K validate that our PFCA module improves the performance of ResNet on image classification and improves the performance of MSRResNet on image super-resolution tasks, respectively, while bringing little growth of parameters and FLOPs. | ['Liuqing Wang', 'XianTong Zhen', 'Wangpeng An', 'Lingxiao Yang', 'Yuxuan Shi'] | 2023-03-20 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 1.11955091e-01 -9.42420959e-02 -1.63737446e-01 -3.52658033e-01
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d74136ce-b1ba-4edf-81af-c8bcc71b1819 | bidirectional-multiscale-feature-aggregation | 2104.00230 | null | https://arxiv.org/abs/2104.00230v1 | https://arxiv.org/pdf/2104.00230v1.pdf | Bidirectional Multiscale Feature Aggregation for Speaker Verification | In this paper, we propose a novel bidirectional multiscale feature aggregation (BMFA) network with attentional fusion modules for text-independent speaker verification. The feature maps from different stages of the backbone network are iteratively combined and refined in both a bottom-up and top-down manner. Furthermore, instead of simple concatenation or element-wise addition of feature maps from different stages, an attentional fusion module is designed to compute the fusion weights. Experiments are conducted on the NIST SRE16 and VoxCeleb1 datasets. The experimental results demonstrate the effectiveness of the bidirectional aggregation strategy and show that the proposed attentional fusion module can further improve the performance. | ['Bin Gu', 'Wu Guo', 'Jiajun Qi'] | 2021-04-01 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.54908255e-01 -4.07441735e-01 2.07160875e-01 -7.97383964e-01
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926c18a3-a0a5-49a5-afe7-24afc50f8174 | co-clustering-based-exploratory-analysis-of | 2212.11728 | null | https://arxiv.org/abs/2212.11728v1 | https://arxiv.org/pdf/2212.11728v1.pdf | Co-clustering based exploratory analysis of mixed-type data tables | Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering method based on a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by equal frequency discretization in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a co-clustering to the instances and the binary variables, leading to groups of instances and groups of variable parts. We apply this methodology on several data sets and compare with the results of a Multiple Correspondence Analysis applied to the same data. | ['Fabrice Rossi', 'Fabrice Clérot', 'Marc Boullé', 'Aichetou Bouchareb'] | 2022-12-22 | null | null | null | null | ['type'] | ['speech'] | [ 7.35007674e-02 -6.61242232e-02 -1.83197856e-01 -4.36980873e-01
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e54ad32f-cd80-49c4-9bfe-0a0a8569fea5 | predicting-discourse-trees-from-transformer | 2104.07058 | null | https://arxiv.org/abs/2104.07058v1 | https://arxiv.org/pdf/2104.07058v1.pdf | Predicting Discourse Trees from Transformer-based Neural Summarizers | Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain. | ['Giuseppe Carenini', 'Patrick Huber', 'Wen Xiao'] | 2021-04-14 | null | https://aclanthology.org/2021.naacl-main.326 | https://aclanthology.org/2021.naacl-main.326.pdf | naacl-2021-4 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.73980194e-01 1.09270954e+00 -6.55967295e-01 -6.19859219e-01
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16dbcec5-6f05-44f4-8d8d-784611f9a4af | robust-mode-connectivity-oriented-adversarial | 2303.10225 | null | https://arxiv.org/abs/2303.10225v1 | https://arxiv.org/pdf/2303.10225v1.pdf | Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks | Adversarial robustness is a key concept in measuring the ability of neural networks to defend against adversarial attacks during the inference phase. Recent studies have shown that despite the success of improving adversarial robustness against a single type of attack using robust training techniques, models are still vulnerable to diversified $\ell_p$ attacks. To achieve diversified $\ell_p$ robustness, we propose a novel robust mode connectivity (RMC)-oriented adversarial defense that contains two population-based learning phases. The first phase, RMC, is able to search the model parameter space between two pre-trained models and find a path containing points with high robustness against diversified $\ell_p$ attacks. In light of the effectiveness of RMC, we develop a second phase, RMC-based optimization, with RMC serving as the basic unit for further enhancement of neural network diversified $\ell_p$ robustness. To increase computational efficiency, we incorporate learning with a self-robust mode connectivity (SRMC) module that enables the fast proliferation of the population used for endpoints of RMC. Furthermore, we draw parallels between SRMC and the human immune system. Experimental results on various datasets and model architectures demonstrate that the proposed defense methods can achieve high diversified $\ell_p$ robustness against $\ell_\infty$, $\ell_2$, $\ell_1$, and hybrid attacks. Codes are available at \url{https://github.com/wangren09/MCGR}. | ['Sijia Liu', 'YuXuan Li', 'Ren Wang'] | 2023-03-17 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.72871262e-01 -1.54846475e-01 -1.43437192e-01 2.52925128e-01
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0925264a-fbbf-41e3-8450-959690cdccab | formal-development-of-safe-automated-driving | 2204.06873 | null | https://arxiv.org/abs/2204.06873v1 | https://arxiv.org/pdf/2204.06873v1.pdf | Formal Development of Safe Automated Driving using Differential Dynamic Logic | The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee correctness in all possible scenarios. Formal methods is an approach to provide mathematical proofs of correctness, using a model of a system, that could be used to give the necessary arguments. This paper investigates the use of differential dynamic logic and the deductive verification tool KeYmaera X in the development of an AD feature. Specifically, formal models and safety proofs of different design variants of a Decision & Control module for an in-lane AD feature are presented. In doing so, the assumptions and invariant conditions necessary to guarantee safety are identified, and the paper shows how such an analysis helps during the development process in requirement refinement and formulation of the operational design domain. Furthermore, it is shown how the performance of the different models is formally analyzed exhaustively, in all their allowed behaviors. | ['Martin Fabian', 'Wolfgang Ahrendt', 'Yuvaraj Selvaraj'] | 2022-04-14 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 1.67627573e-01 6.09509110e-01 1.07293399e-02 -2.22989842e-01
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5ed4a934-27df-45f0-8ce5-32af1b517218 | online-updated-high-order-collaborative | 2202.06568 | null | https://arxiv.org/abs/2202.06568v1 | https://arxiv.org/pdf/2202.06568v1.pdf | Online-updated High-order Collaborative Networks for Single Image Deraining | Single image deraining is an important and challenging task for some downstream artificial intelligence applications such as video surveillance and self-driving systems. Most of the existing deep-learning-based methods constrain the network to generate derained images but few of them explore features from intermediate layers, different levels, and different modules which are beneficial for rain streaks removal. In this paper, we propose a high-order collaborative network with multi-scale compact constraints and a bidirectional scale-content similarity mining module to exploit features from deep networks externally and internally for rain streaks removal. Externally, we design a deraining framework with three sub-networks trained in a collaborative manner, where the bottom network transmits intermediate features to the middle network which also receives shallower rainy features from the top network and sends back features to the bottom network. Internally, we enforce multi-scale compact constraints on the intermediate layers of deep networks to learn useful features via a Laplacian pyramid. Further, we develop a bidirectional scale-content similarity mining module to explore features at different scales in a down-to-up and up-to-down manner. To improve the model performance on real-world images, we propose an online-update learning approach, which uses real-world rainy images to fine-tune the network and update the deraining results in a self-supervised manner. Extensive experiments demonstrate that our proposed method performs favorably against eleven state-of-the-art methods on five public synthetic datasets and one real-world dataset. The source code will be available at \url{https://supercong94.wixsite.com/supercong94}. | ['Xiao-Ming Wu', 'Jinshan Pan', 'Cong Wang'] | 2022-02-14 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.18552120e-02 -1.95080563e-01 3.05485219e-01 -6.16179883e-01
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9bae905c-945a-4375-b1ba-045a728698f4 | knowledge-aware-bayesian-co-attention-for | 2302.09856 | null | https://arxiv.org/abs/2302.09856v3 | https://arxiv.org/pdf/2302.09856v3.pdf | Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition | Multimodal emotion recognition is a challenging research area that aims to fuse different modalities to predict human emotion. However, most existing models that are based on attention mechanisms have difficulty in learning emotionally relevant parts on their own. To solve this problem, we propose to incorporate external emotion-related knowledge in the co-attention based fusion of pre-trained models. To effectively incorporate this knowledge, we enhance the co-attention model with a Bayesian attention module (BAM) where a prior distribution is estimated using the emotion-related knowledge. Experimental results on the IEMOCAP dataset show that the proposed approach can outperform several state-of-the-art approaches by at least 0.7% unweighted accuracy (UA). | ['Yanfeng Wang', 'Yu Wang', 'Zihan Zhao'] | 2023-02-20 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.74647674e-01 -1.03921425e-02 -4.05706428e-02 -6.39461517e-01
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ecf5e65b-a1cd-4965-b410-ceb564ff2a93 | tlmote-a-topic-based-language-modelling | null | null | https://journals.flvc.org/FLAIRS/article/view/130676 | https://journals.flvc.org/FLAIRS/article/view/130676/133877 | TLMOTE: A Topic-based Language Modelling Approach for Text Oversampling | Training machine learning and deep learning models on unbalanced datasets can lead to a bias portrayed by the models towards the majority classes. To tackle the problem of bias towards majority classes, researchers have presented various techniques to oversample the minority class data points. Most of the available state-of-the-art oversampling techniques generate artificial data points which cannot be comprehensibly understood by the reader, despite the synthetic data points generated being similar to the original minority class data points. In this work, we present Topic-based Language Modelling Approach for Text Oversampling (TLMOTE), a novel text oversampling technique for supervised learning from unbalanced datasets. TLMOTE improves upon previous approaches by generating data points which can be intelligibly understood by the reader, can relate to the main topics of the minority class, and introduces more variations to the synthetic data generated. We evaluate the efficacy of our approach on various tasks like Suggestion Mining SemEval 2019 Task 9 Subtasks A and B, SMS Spam Detection, and Sentiment Analysis. Experimental results verify that oversampling unbalanced datasets using TLMOTE yields a higher macro F1 score than with other oversampling techniques. | ['Anubhav Sharma', 'Anmol Bansal', 'Seba Susan', 'Arjun Choudhry'] | 2022-05-04 | null | null | null | the-35th-international-florida-artificial | ['spam-detection', 'suggestion-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.47641891e-01 6.95434451e-01 -2.60557353e-01 -6.91356778e-01
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fe3ddabc-f324-4bd7-bbef-eff4290e7e0a | calibrating-constitutive-models-with-full | 2203.16577 | null | https://arxiv.org/abs/2203.16577v1 | https://arxiv.org/pdf/2203.16577v1.pdf | Calibrating constitutive models with full-field data via physics informed neural networks | The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation. In this paper, we propose a physics-informed deep-learning framework for the discovery of constitutive model parameterizations given full-field displacement data and global force-displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic materials models suitable for different material classes is considered including the Neo-Hookean, Gent, and Blatz-Ko constitutive models as exemplars for general hyperelastic behavior, polymer behavior with lock-up, and compressible foam behavior respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full-field experimental data is utilized to calibrate constitutive models under finite deformations. | ['Sharlotte L. B. Kramer', 'Kevin N. Long', 'Craig M. Hamel'] | 2022-03-30 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.42930725e-01 -3.51438597e-02 -1.01656385e-01 -3.36251259e-01
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6fe987ff-8bc7-44bb-bd18-499acf540a4d | build-a-bot-teaching-conversational-ai-using | 2212.07542 | null | https://arxiv.org/abs/2212.07542v1 | https://arxiv.org/pdf/2212.07542v1.pdf | Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture | As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-a-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material, but also learn the fundamentals of AI through the model creation process. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the majority of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for students at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations. | ['Cynthia Breazeal', 'Sharifa Alghowinem', 'Kate Pearce'] | 2022-12-14 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.00094357e-02 5.33595860e-01 2.67016646e-02 -4.37228560e-01
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61cca0d1-819b-4447-8611-b2cb0ecd5081 | styleavatar-real-time-photo-realistic | 2305.00942 | null | https://arxiv.org/abs/2305.00942v1 | https://arxiv.org/pdf/2305.00942v1.pdf | StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video | Face reenactment methods attempt to restore and re-animate portrait videos as realistically as possible. Existing methods face a dilemma in quality versus controllability: 2D GAN-based methods achieve higher image quality but suffer in fine-grained control of facial attributes compared with 3D counterparts. In this work, we propose StyleAvatar, a real-time photo-realistic portrait avatar reconstruction method using StyleGAN-based networks, which can generate high-fidelity portrait avatars with faithful expression control. We expand the capabilities of StyleGAN by introducing a compositional representation and a sliding window augmentation method, which enable faster convergence and improve translation generalization. Specifically, we divide the portrait scenes into three parts for adaptive adjustments: facial region, non-facial foreground region, and the background. Besides, our network leverages the best of UNet, StyleGAN and time coding for video learning, which enables high-quality video generation. Furthermore, a sliding window augmentation method together with a pre-training strategy are proposed to improve translation generalization and training performance, respectively. The proposed network can converge within two hours while ensuring high image quality and a forward rendering time of only 20 milliseconds. Furthermore, we propose a real-time live system, which further pushes research into applications. Results and experiments demonstrate the superiority of our method in terms of image quality, full portrait video generation, and real-time re-animation compared to existing facial reenactment methods. Training and inference code for this paper are at https://github.com/LizhenWangT/StyleAvatar. | ['Yebin Liu', 'Tao Yu', 'Hongwen Zhang', 'Yuxiang Zhang', 'Jingxiang Sun', 'Xiaochen Zhao', 'Lizhen Wang'] | 2023-05-01 | null | null | null | null | ['video-generation', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [ 3.08168054e-01 1.49561197e-01 -1.16552845e-01 -1.38019040e-01
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-1.61933506e+00 5.97614050e-01 4.83378112e-01 -2.54667193e-01
1.61185420e+00 -3.28330904e-01 1.39211142e+00 -2.64704883e-01
2.03262895e-01 -5.88509500e-01 4.78012621e-01 -4.77444082e-02
9.92394090e-01 -9.14933085e-01 -4.51772884e-02 -5.20219922e-01
-7.20118284e-01 1.09542096e+00 7.53343523e-01 -1.89660639e-01
2.26377815e-01 4.51046884e-01 8.38948414e-02 1.64254963e-01
-8.32567394e-01 3.34655344e-01 1.49026081e-01 6.03498459e-01
2.29783654e-01 -3.52893695e-02 1.33892879e-01 1.98483720e-01
-4.02590543e-01 2.57022083e-02 5.38020313e-01 5.46010256e-01
8.96922201e-02 -1.10577559e+00 -3.01310927e-01 -5.33383638e-02
-2.56880105e-01 -4.19989899e-02 -1.40432313e-01 9.30772960e-01
2.12001041e-01 7.18799233e-01 1.42942563e-01 -5.04848063e-01
2.14316025e-01 -4.09243405e-01 8.14908862e-01 -1.63559943e-01
-5.01771033e-01 2.49640703e-01 -6.81765750e-02 -8.26585770e-01
-3.57277334e-01 -3.46479625e-01 -1.03416359e+00 -1.11863351e+00
-1.20731972e-01 -1.03653453e-01 4.20452356e-01 5.09409666e-01
7.50747979e-01 5.10846138e-01 9.94332314e-01 -1.06277871e+00
-7.27850199e-02 -5.41473389e-01 -3.72843474e-01 2.55927026e-01
3.19288641e-01 -2.55928069e-01 -1.40942559e-01 3.95101994e-01] | [12.530317306518555, -0.39858102798461914] |
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