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5263c809-7c17-4695-a311-1bfa71fbb3e6 | xlcost-a-benchmark-dataset-for-cross-lingual | 2206.08474 | null | https://arxiv.org/abs/2206.08474v1 | https://arxiv.org/pdf/2206.08474v1.pdf | XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence | Recent advances in machine learning have significantly improved the understanding of source code data and achieved good performance on a number of downstream tasks. Open source repositories like GitHub enable this process with rich unlabeled code data. However, the lack of high quality labeled data has largely hindered the progress of several code related tasks, such as program translation, summarization, synthesis, and code search. This paper introduces XLCoST, Cross-Lingual Code SnippeT dataset, a new benchmark dataset for cross-lingual code intelligence. Our dataset contains fine-grained parallel data from 8 languages (7 commonly used programming languages and English), and supports 10 cross-lingual code tasks. To the best of our knowledge, it is the largest parallel dataset for source code both in terms of size and the number of languages. We also provide the performance of several state-of-the-art baseline models for each task. We believe this new dataset can be a valuable asset for the research community and facilitate the development and validation of new methods for cross-lingual code intelligence. | ['Chandan K. Reddy', 'Sindhu Tipirneni', 'Roshan Ravindran', 'Karthik Suresh', 'Aneesh Jain', 'Ming Zhu'] | 2022-06-16 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.33644503e-01 -3.18452388e-01 -1.07462907e+00 -4.31942314e-01
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b02e711f-5aad-4ef1-8708-8c1551bd3f5d | regularized-submodular-maximization-at-scale | 2002.03503 | null | https://arxiv.org/abs/2002.03503v1 | https://arxiv.org/pdf/2002.03503v1.pdf | Regularized Submodular Maximization at Scale | In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$. Indeed, submodularity is inherently related to the notions of diversity, coverage, and representativeness. In particular, finding the mode of many popular probabilistic models of diversity, such as determinantal point processes, submodular probabilistic models, and strongly log-concave distributions, involves maximization of (regularized) submodular functions. Since a regularized function $f$ can potentially take on negative values, the classic theory of submodular maximization, which heavily relies on the non-negativity assumption of submodular functions, may not be applicable. To circumvent this challenge, we develop the first one-pass streaming algorithm for maximizing a regularized submodular function subject to a $k$-cardinality constraint. It returns a solution $S$ with the guarantee that $f(S)\geq(\phi^{-2}-\epsilon) \cdot g(OPT)-\ell (OPT)$, where $\phi$ is the golden ratio. Furthermore, we develop the first distributed algorithm that returns a solution $S$ with the guarantee that $\mathbb{E}[f(S)] \geq (1-\epsilon) [(1-e^{-1}) \cdot g(OPT)-\ell(OPT)]$ in $O(1/ \epsilon)$ rounds of MapReduce computation, without keeping multiple copies of the entire dataset in each round (as it is usually done). We should highlight that our result, even for the unregularized case where the modular term $\ell$ is zero, improves the memory and communication complexity of the existing work by a factor of $O(1/ \epsilon)$ while arguably provides a simpler distributed algorithm and a unifying analysis. We also empirically study the performance of our scalable methods on a set of real-life applications, including finding the mode of distributions, data summarization, and product recommendation. | ['Shervin Minaee', 'Amin Karbasi', 'Moran Feldman', 'Ehsan Kazemi'] | 2020-02-10 | null | null | null | null | ['product-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [-1.08168773e-01 1.87779084e-01 -2.38587737e-01 -1.80569008e-01
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2bbf48a4-e801-433c-aa88-6f5f0227915a | lifelong-learning-crf-for-supervised-aspect | 1705.00251 | null | http://arxiv.org/abs/1705.00251v1 | http://arxiv.org/pdf/1705.00251v1.pdf | Lifelong Learning CRF for Supervised Aspect Extraction | This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications. | ['Bing Liu', 'Hu Xu', 'Lei Shu'] | 2017-04-29 | lifelong-learning-crf-for-supervised-aspect-1 | https://aclanthology.org/P17-2023 | https://aclanthology.org/P17-2023.pdf | acl-2017-7 | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.80498995e-03 7.09971607e-01 -9.45724845e-01 -4.39330846e-01
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115a558e-4544-4387-8421-4e0216657865 | affordance-grounding-from-demonstration-video-1 | 2303.14644 | null | https://arxiv.org/abs/2303.14644v1 | https://arxiv.org/pdf/2303.14644v1.pdf | Affordance Grounding from Demonstration Video to Target Image | Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass view. The video-to-image affordance grounding task is challenging due to (1) the need to predict fine-grained affordances, and (2) the limited training data, which inadequately covers video-image discrepancies and negatively impacts grounding. To tackle them, we propose Affordance Transformer (Afformer), which has a fine-grained transformer-based decoder that gradually refines affordance grounding. Moreover, we introduce Mask Affordance Hand (MaskAHand), a self-supervised pre-training technique for synthesizing video-image data and simulating context changes, enhancing affordance grounding across video-image discrepancies. Afformer with MaskAHand pre-training achieves state-of-the-art performance on multiple benchmarks, including a substantial 37% improvement on the OPRA dataset. Code is made available at https://github.com/showlab/afformer. | ['Mike Zheng Shou', 'Kevin Qinghong Lin', 'Difei Gao', 'Joya Chen'] | 2023-03-26 | affordance-grounding-from-demonstration-video | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-to-image-affordance-grounding'] | ['computer-vision'] | [ 2.40883946e-01 7.56019577e-02 -6.69402629e-02 -2.65957475e-01
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1e2c64e3-9956-4db2-a38b-3de0f4e2d695 | integrating-parametric-and-non-parametric | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Shuai_Integrating_Parametric_and_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shuai_Integrating_Parametric_and_2015_CVPR_paper.pdf | Integrating Parametric and Non-Parametric Models For Scene Labeling | We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by putting a global scene constraint. We estimate the global potential in a non-parametric framework. Furthermore, a large margin based CNN metric learning method is proposed for better global potential estimation. The final pixel class prediction is performed by integrating local and global beliefs. Even without any post-processing, we achieve state-of-the-art on SiftFlow and competitive results on Stanford Background benchmark. | ['Lifan Zhao', 'Gang Wang', 'Bing Shuai', 'Zhen Zuo', 'Bing Wang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-labeling'] | ['computer-vision'] | [-7.43422434e-02 -2.32765168e-01 -7.15442359e-01 -8.09541464e-01
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63a93922-d498-4430-9bae-872058b943c4 | sparsifying-transformer-models-with | 2009.05169 | null | https://arxiv.org/abs/2009.05169v4 | https://arxiv.org/pdf/2009.05169v4.pdf | Sparsifying Transformer Models with Trainable Representation Pooling | We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-$k$ operator. Our experiments on a challenging long document summarization task show that even our simple baseline performs comparably to the current SOTA, and with trainable pooling, we can retain its top quality, while being $1.8\times$ faster during training, $4.5\times$ faster during inference, and up to $13\times$ more computationally efficient in the decoder. | ['Łukasz Garncarek', 'Łukasz Borchmann', 'Michał Pietruszka'] | 2020-09-10 | null | https://aclanthology.org/2022.acl-long.590 | https://aclanthology.org/2022.acl-long.590.pdf | acl-2022-5 | ['summarization'] | ['natural-language-processing'] | [ 3.28330457e-01 5.96202791e-01 -1.88318953e-01 -2.24406168e-01
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5a10c6ae-39e6-4204-ac0a-0098c329dbd3 | generative-moment-matching-network-based | 1902.03389 | null | http://arxiv.org/abs/1902.03389v1 | http://arxiv.org/pdf/1902.03389v1.pdf | Generative Moment Matching Network-based Random Modulation Post-filter for DNN-based Singing Voice Synthesis and Neural Double-tracking | This paper proposes a generative moment matching network (GMMN)-based
post-filter that provides inter-utterance pitch variation for deep neural
network (DNN)-based singing voice synthesis. The natural pitch variation of a
human singing voice leads to a richer musical experience and is used in
double-tracking, a recording method in which two performances of the same
phrase are recorded and mixed to create a richer, layered sound. However,
singing voices synthesized using conventional DNN-based methods never vary
because the synthesis process is deterministic and only one waveform is
synthesized from one musical score. To address this problem, we use a GMMN to
model the variation of the modulation spectrum of the pitch contour of natural
singing voices and add a randomized inter-utterance variation to the pitch
contour generated by conventional DNN-based singing voice synthesis.
Experimental evaluations suggest that 1) our approach can provide perceptible
inter-utterance pitch variation while preserving speech quality. We extend our
approach to double-tracking, and the evaluation demonstrates that 2) GMMN-based
neural double-tracking is perceptually closer to natural double-tracking than
conventional signal processing-based artificial double-tracking is. | ['Hiroki Tamaru', 'Yuki Saito', 'Tomoki Koriyama', 'Shinnosuke Takamichi', 'Hiroshi Saruwatari'] | 2019-02-09 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-5.76188304e-02 -1.80309072e-01 1.04650900e-01 1.17004313e-01
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819d1183-3a2c-4760-ad03-64ba5b8f0be3 | hybrid-transformer-and-cnn-attention-network | 2305.05177 | null | https://arxiv.org/abs/2305.05177v1 | https://arxiv.org/pdf/2305.05177v1.pdf | Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution | Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, they have not yet shown significant advantages over CNN-based methods in stereo super-resolution tasks. This can be attributed to two key factors: first, current single-image super-resolution transformers are unable to leverage the complementary stereo information during the process; second, the performance of transformers is typically reliant on sufficient data, which is absent in common stereo-image super-resolution algorithms. To address these issues, we propose a Hybrid Transformer and CNN Attention Network (HTCAN), which utilizes a transformer-based network for single-image enhancement and a CNN-based network for stereo information fusion. Furthermore, we employ a multi-patch training strategy and larger window sizes to activate more input pixels for super-resolution. We also revisit other advanced techniques, such as data augmentation, data ensemble, and model ensemble to reduce overfitting and data bias. Finally, our approach achieved a score of 23.90dB and emerged as the winner in Track 1 of the NTIRE 2023 Stereo Image Super-Resolution Challenge. | ['Li Zhang', 'Junlin Li', 'Shijie Zhao', 'Xuhan Sheng', 'Zhenyu Zhang', 'Weiqi Li', 'Xiaopeng Sun', 'Qiufang Ma', 'Haoyu Ma', 'Ming Cheng'] | 2023-05-09 | null | null | null | null | ['image-super-resolution', 'image-enhancement', 'stereo-image-super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.44585013e-01 -6.25900328e-02 7.80579001e-02 -3.62497717e-01
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6e009372-bade-419d-b5a3-b7dc381ac0a3 | an-experience-based-direct-generation | 2212.14561 | null | https://arxiv.org/abs/2212.14561v1 | https://arxiv.org/pdf/2212.14561v1.pdf | An Experience-based Direct Generation approach to Automatic Image Cropping | Automatic Image Cropping is a challenging task with many practical downstream applications. The task is often divided into sub-problems - generating cropping candidates, finding the visually important regions, and determining aesthetics to select the most appealing candidate. Prior approaches model one or more of these sub-problems separately, and often combine them sequentially. We propose a novel convolutional neural network (CNN) based method to crop images directly, without explicitly modeling image aesthetics, evaluating multiple crop candidates, or detecting visually salient regions. Our model is trained on a large dataset of images cropped by experienced editors and can simultaneously predict bounding boxes for multiple fixed aspect ratios. We consider the aspect ratio of the cropped image to be a critical factor that influences aesthetics. Prior approaches for automatic image cropping, did not enforce the aspect ratio of the outputs, likely due to a lack of datasets for this task. We, therefore, benchmark our method on public datasets for two related tasks - first, aesthetic image cropping without regard to aspect ratio, and second, thumbnail generation that requires fixed aspect ratio outputs, but where aesthetics are not crucial. We show that our strategy is competitive with or performs better than existing methods in both these tasks. Furthermore, our one-stage model is easier to train and significantly faster than existing two-stage or end-to-end methods for inference. We present a qualitative evaluation study, and find that our model is able to generalize to diverse images from unseen datasets and often retains compositional properties of the original images after cropping. Our results demonstrate that explicitly modeling image aesthetics or visual attention regions is not necessarily required to build a competitive image cropping algorithm. | ['Aneesh Vartakavi', 'Casper Christensen'] | 2022-12-30 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 6.51001334e-01 7.16488361e-02 1.05915908e-02 -1.42131880e-01
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ea2088aa-c3d6-4bbd-9bf4-8109e1800c7c | gift-a-real-time-and-scalable-3d-shape-search | 1604.01879 | null | http://arxiv.org/abs/1604.01879v2 | http://arxiv.org/pdf/1604.01879v2.pdf | GIFT: A Real-time and Scalable 3D Shape Search Engine | Projective analysis is an important solution for 3D shape retrieval, since
human visual perceptions of 3D shapes rely on various 2D observations from
different view points. Although multiple informative and discriminative views
are utilized, most projection-based retrieval systems suffer from heavy
computational cost, thus cannot satisfy the basic requirement of scalability
for search engines. In this paper, we present a real-time 3D shape search
engine based on the projective images of 3D shapes. The real-time property of
our search engine results from the following aspects: (1) efficient projection
and view feature extraction using GPU acceleration; (2) the first inverted
file, referred as F-IF, is utilized to speed up the procedure of multi-view
matching; (3) the second inverted file (S-IF), which captures a local
distribution of 3D shapes in the feature manifold, is adopted for efficient
context-based re-ranking. As a result, for each query the retrieval task can be
finished within one second despite the necessary cost of IO overhead. We name
the proposed 3D shape search engine, which combines GPU acceleration and
Inverted File Twice, as GIFT. Besides its high efficiency, GIFT also
outperforms the state-of-the-art methods significantly in retrieval accuracy on
various shape benchmarks and competitions. | ['Zhichao Zhou', 'Song Bai', 'Longin Jan Latecki', 'Zhaoxiang Zhang', 'Xiang Bai'] | 2016-04-07 | gift-a-real-time-and-scalable-3d-shape-search-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Bai_GIFT_A_Real-Time_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Bai_GIFT_A_Real-Time_CVPR_2016_paper.pdf | cvpr-2016-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [-1.34257793e-01 -1.04849744e+00 2.08557211e-02 -1.52837336e-01
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dd768c54-a164-4ab6-a2ca-5932514adb17 | controllable-person-image-synthesis-with-1 | 2105.14739 | null | https://arxiv.org/abs/2105.14739v3 | https://arxiv.org/pdf/2105.14739v3.pdf | Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization | Controllable person image generation aims to produce realistic human images with desirable attributes such as a given pose, cloth textures, or hairstyles. However, the large spatial misalignment between source and target images makes the standard image-to-image translation architectures unsuitable for this task. Most state-of-the-art methods focus on alignment for global pose-transfer tasks. However, they fail to deal with region-specific texture-transfer tasks, especially for person images with complex textures. To solve this problem, we propose a novel Spatially-Adaptive Warped Normalization (SAWN) which integrates a learned flow-field to warp modulation parameters. It allows us to efficiently align person spatially-adaptive styles with pose features. Moreover, we propose a novel Self-Training Part Replacement (STPR) strategy to refine the model for the texture-transfer task, which improves the quality of the generated clothes and the preservation ability of non-target regions. Our experimental results on the widely used DeepFashion dataset demonstrate a significant improvement of the proposed method over the state-of-the-art methods on pose-transfer and texture-transfer tasks. The code is available at https://github.com/zhangqianhui/Sawn. | ['Humphrey Sh', 'Wei Wang', 'Nicu Sebe', 'Enver Sangineto', 'Hao Tang', 'Aliaksandr Siarohin', 'Jichao Zhang'] | 2021-05-31 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 4.79060948e-01 -3.20481896e-01 2.45947037e-02 -4.09289122e-01
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f2f920cb-c131-45d2-b8f8-cfbee84740e3 | video-action-understanding-a-tutorial | 2010.06647 | null | https://arxiv.org/abs/2010.06647v2 | https://arxiv.org/pdf/2010.06647v2.pdf | Video Action Understanding | Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, due to the scale and temporal nature of video, the span of video understanding problems and the set of proposed deep learning solutions is arguably wider and more diverse than those of their 2D image siblings. Finding, identifying, and predicting actions are a few of the most salient tasks in this emerging and rapidly evolving field. With a pedagogical emphasis, this tutorial introduces and systematizes fundamental topics, basic concepts, and notable examples in supervised video action understanding. Specifically, we clarify a taxonomy of action problems, catalog and highlight video datasets, describe common video data preparation methods, present the building blocks of state-of-the art deep learning model architectures, and formalize domain-specific metrics to baseline proposed solutions. This tutorial is intended to be accessible to a general computer science audience and assumes a conceptual understanding of supervised learning. | ['Vijay Gadepally', 'Matthew Hutchinson'] | 2020-10-13 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 3.05645049e-01 -1.01857428e-02 -5.60920119e-01 -3.77298146e-01
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4b374758-d27f-4c2a-92e7-4b08805b571d | a-survey-on-uncertainty-quantification | 2302.13425 | null | https://arxiv.org/abs/2302.13425v2 | https://arxiv.org/pdf/2302.13425v2.pdf | A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty Source Perspective | Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make unexpected, incorrect, but overconfident predictions. This can cause serious consequences in high-stake applications, such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) aims to estimate the confidence of DNN predictions beyond prediction accuracy. In recent years, many UQ methods have been developed for DNNs. It is of great practical value to systematically categorize these UQ methods and compare their advantages and disadvantages. However, existing surveys mostly focus on categorizing UQ methodologies from a neural network architecture perspective or a Bayesian perspective and ignore the source of uncertainty that each methodology can incorporate, making it difficult to select an appropriate UQ method in practice. To fill the gap, this paper presents a systematic taxonomy of UQ methods for DNNs based on the types of uncertainty sources (data uncertainty versus model uncertainty). We summarize the advantages and disadvantages of methods in each category. We show how our taxonomy of UQ methodologies can potentially help guide the choice of UQ method in different machine learning problems (e.g., active learning, robustness, and reinforcement learning). We also identify current research gaps and propose several future research directions. | ['Zhe Jiang', 'Wenchong He'] | 2023-02-26 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-1.29110932e-01 4.57437605e-01 -3.75042170e-01 -6.80856049e-01
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0c1fc147-ab12-4b47-bba3-826c490414bc | geometry-aware-learning-of-maps-for-camera | 1712.03342 | null | http://arxiv.org/abs/1712.03342v3 | http://arxiv.org/pdf/1712.03342v3.pdf | Geometry-Aware Learning of Maps for Camera Localization | Maps are a key component in image-based camera localization and visual SLAM
systems: they are used to establish geometric constraints between images,
correct drift in relative pose estimation, and relocalize cameras after lost
tracking. The exact definitions of maps, however, are often
application-specific and hand-crafted for different scenarios (e.g. 3D
landmarks, lines, planes, bags of visual words). We propose to represent maps
as a deep neural net called MapNet, which enables learning a data-driven map
representation. Unlike prior work on learning maps, MapNet exploits cheap and
ubiquitous sensory inputs like visual odometry and GPS in addition to images
and fuses them together for camera localization. Geometric constraints
expressed by these inputs, which have traditionally been used in bundle
adjustment or pose-graph optimization, are formulated as loss terms in MapNet
training and also used during inference. In addition to directly improving
localization accuracy, this allows us to update the MapNet (i.e., maps) in a
self-supervised manner using additional unlabeled video sequences from the
scene. We also propose a novel parameterization for camera rotation which is
better suited for deep-learning based camera pose regression. Experimental
results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar
dataset show significant performance improvement over prior work. The MapNet
project webpage is https://goo.gl/mRB3Au. | ['Samarth Brahmbhatt', 'Jan Kautz', 'James Hays', 'Kihwan Kim', 'Jinwei Gu'] | 2017-12-09 | geometry-aware-learning-of-maps-for-camera-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Brahmbhatt_Geometry-Aware_Learning_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Brahmbhatt_Geometry-Aware_Learning_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['camera-localization'] | ['computer-vision'] | [-8.39836746e-02 -1.63651571e-01 -3.08337748e-01 -5.59554338e-01
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58413aa5-c753-4f7b-8fd4-4b397c52cace | context-aware-domain-adaptation-for-time | 2304.07453 | null | https://arxiv.org/abs/2304.07453v1 | https://arxiv.org/pdf/2304.07453v1.pdf | Context-aware Domain Adaptation for Time Series Anomaly Detection | Time series anomaly detection is a challenging task with a wide range of real-world applications. Due to label sparsity, training a deep anomaly detector often relies on unsupervised approaches. Recent efforts have been devoted to time series domain adaptation to leverage knowledge from similar domains. However, existing solutions may suffer from negative knowledge transfer on anomalies due to their diversity and sparsity. Motivated by the empirical study of context alignment between two domains, we aim to transfer knowledge between two domains via adaptively sampling context information for two domains. This is challenging because it requires simultaneously modeling the complex in-domain temporal dependencies and cross-domain correlations while exploiting label information from the source domain. To this end, we propose a framework that combines context sampling and anomaly detection into a joint learning procedure. We formulate context sampling into the Markov decision process and exploit deep reinforcement learning to optimize the time series domain adaptation process via context sampling and design a tailored reward function to generate domain-invariant features that better align two domains for anomaly detection. Experiments on three public datasets show promise for knowledge transfer between two similar domains and two entirely different domains. | ['Xia Hu', 'Hao Yang', 'Fei Wang', 'Kaixiong Zhou', 'Huiyuan Chen', 'Lan Wang', 'Kwei-Herng Lai'] | 2023-04-15 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 4.69402045e-01 -4.00837213e-01 -6.12479784e-02 -6.66714728e-01
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61f8b28e-d7e5-4f9c-b4b4-a5124f23a586 | bengali-handwritten-digit-recognition-using | 2212.12146 | null | https://arxiv.org/abs/2212.12146v1 | https://arxiv.org/pdf/2212.12146v1.pdf | Bengali Handwritten Digit Recognition using CNN with Explainable AI | Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily understand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image. | ['Md. Golam Rabiul Alam', 'Raihan Tanvir', 'MD Tanvir Rouf Shawon'] | 2022-12-23 | null | null | null | null | ['optical-character-recognition', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [-1.29060075e-01 -4.65165585e-01 -1.09672859e-01 -3.16556454e-01
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b2d76d43-e70a-44de-ab43-4372a6cc42cd | empirical-study-of-drone-sound-detection-in | 1701.05779 | null | http://arxiv.org/abs/1701.05779v1 | http://arxiv.org/pdf/1701.05779v1.pdf | Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks | This work aims to investigate the use of deep neural network to detect
commercial hobby drones in real-life environments by analyzing their sound
data. The purpose of work is to contribute to a system for detecting drones
used for malicious purposes, such as for terrorism. Specifically, we present a
method capable of detecting the presence of commercial hobby drones as a binary
classification problem based on sound event detection. We recorded the sound
produced by a few popular commercial hobby drones, and then augmented this data
with diverse environmental sound data to remedy the scarcity of drone sound
data in diverse environments. We investigated the effectiveness of
state-of-the-art event sound classification methods, i.e., a Gaussian Mixture
Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network
(RNN), for drone sound detection. Our empirical results, which were obtained
with a testing dataset collected on an urban street, confirmed the
effectiveness of these models for operating in a real environment. In summary,
our RNN models showed the best detection performance with an F-Score of 0.8009
with 240 ms of input audio with a short processing time, indicating their
applicability to real-time detection systems. | ['Hae-Yong Yang', 'Woong-Hee Kim', 'Young-Jun Lee', 'Jong-Woo Shin', 'YoungHyoun Kwon', 'Sungho Jeon'] | 2017-01-20 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 1.34327533e-02 -6.12784445e-01 6.36929929e-01 1.54576182e-01
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54a5f8fe-2796-4e61-994c-77388043c907 | safe-reinforcement-learning-with-contrastive | 2209.09648 | null | https://arxiv.org/abs/2209.09648v1 | https://arxiv.org/pdf/2209.09648v1.pdf | Safe Reinforcement Learning with Contrastive Risk Prediction | As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. We conduct experiments in robotic simulation environments. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches. | ['Yuhong Guo', 'Hanping Zhang'] | 2022-09-10 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.05222717e-01 5.51526725e-01 -6.19499743e-01 -1.84978142e-01
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7060f86a-c0c3-491e-acee-a403eea304f0 | cross-architecture-distillation-for-face | 2306.14662 | null | https://arxiv.org/abs/2306.14662v1 | https://arxiv.org/pdf/2306.14662v1.pdf | Cross Architecture Distillation for Face Recognition | Transformers have emerged as the superior choice for face recognition tasks, but their insufficient platform acceleration hinders their application on mobile devices. In contrast, Convolutional Neural Networks (CNNs) capitalize on hardware-compatible acceleration libraries. Consequently, it has become indispensable to preserve the distillation efficacy when transferring knowledge from a Transformer-based teacher model to a CNN-based student model, known as Cross-Architecture Knowledge Distillation (CAKD). Despite its potential, the deployment of CAKD in face recognition encounters two challenges: 1) the teacher and student share disparate spatial information for each pixel, obstructing the alignment of feature space, and 2) the teacher network is not trained in the role of a teacher, lacking proficiency in handling distillation-specific knowledge. To surmount these two constraints, 1) we first introduce a Unified Receptive Fields Mapping module (URFM) that maps pixel features of the teacher and student into local features with unified receptive fields, thereby synchronizing the pixel-wise spatial information of teacher and student. Subsequently, 2) we develop an Adaptable Prompting Teacher network (APT) that integrates prompts into the teacher, enabling it to manage distillation-specific knowledge while preserving the model's discriminative capacity. Extensive experiments on popular face benchmarks and two large-scale verification sets demonstrate the superiority of our method. | ['Zhen Lei', 'Xiao-Yu Zhang', 'Zhixiang He', 'Xiangyu Zhu', 'Weisong Zhao'] | 2023-06-26 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 5.23382947e-02 -1.36539191e-01 -1.01994328e-01 -4.82498556e-01
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559265a6-7259-4d07-bfde-429b31bc2812 | ualberta-at-semeval-2021-task-2-determining | null | null | https://aclanthology.org/2021.semeval-1.101 | https://aclanthology.org/2021.semeval-1.101.pdf | UAlberta at SemEval-2021 Task 2: Determining Sense Synonymy via Translations | We describe the University of Alberta systems for the SemEval-2021 Word-in-Context (WiC) disambiguation task. We explore the use of translation information for deciding whether two different tokens of the same word correspond to the same sense of the word. Our focus is on developing principled theoretical approaches which are grounded in linguistic phenomena, leading to more explainable models. We show that translations from multiple languages can be leveraged to improve the accuracy on the WiC task. | ['Grzegorz Kondrak', 'Arnob Mallik', 'Hongchang Bao', 'Bradley Hauer'] | 2021-08-01 | null | null | null | semeval-2021 | ['explainable-models'] | ['computer-vision'] | [ 2.92830914e-01 1.11522697e-01 -6.42133772e-01 -5.31927645e-01
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17f855e5-36f6-4431-b082-e4e388d6e668 | avoiding-catastrophic-forgetting-in | 2004.14366 | null | https://arxiv.org/abs/2004.14366v2 | https://arxiv.org/pdf/2004.14366v2.pdf | Elastic weight consolidation for better bias inoculation | The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. While fine-tuning models on additional data has been used to mitigate them, a common issue is that of catastrophic forgetting of the original training dataset. In this paper, we show that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases while being less susceptible to catastrophic forgetting. In our evaluation on fact verification and NLI stress tests, we show that fine-tuning with EWC dominates standard fine-tuning, yielding models with lower levels of forgetting on the original (biased) dataset for equivalent gains in accuracy on the fine-tuning (unbiased) dataset. | ['James Thorne', 'Andreas Vlachos'] | 2020-04-29 | null | https://aclanthology.org/2021.eacl-main.82 | https://aclanthology.org/2021.eacl-main.82.pdf | eacl-2021-2 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 1.50410280e-01 3.14246744e-01 -3.01621258e-01 -7.26576328e-01
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1d7fa2be-3cea-481a-b961-d0475c2b6467 | simple-unsupervised-object-centric-learning | 2205.14065 | null | https://arxiv.org/abs/2205.14065v1 | https://arxiv.org/pdf/2205.14065v1.pdf | Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos | Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional single-vector representations such as poor systematic generalization. Although there have been many remarkable advances in recent years, one of the most critical problems in this direction has been that previous methods work only with simple and synthetic scenes but not with complex and naturalistic images or videos. In this paper, we propose STEVE, an unsupervised model for object-centric learning in videos. Our proposed model makes a significant advancement by demonstrating its effectiveness on various complex and naturalistic videos unprecedented in this line of research. Interestingly, this is achieved by neither adding complexity to the model architecture nor introducing a new objective or weak supervision. Rather, it is achieved by a surprisingly simple architecture that uses a transformer-based image decoder conditioned on slots and the learning objective is simply to reconstruct the observation. Our experiment results on various complex and naturalistic videos show significant improvements compared to the previous state-of-the-art. | ['Sungjin Ahn', 'Yi-Fu Wu', 'Gautam Singh'] | 2022-05-27 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 4.45839435e-01 1.61108091e-01 -2.31154338e-01 -2.82007754e-01
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fc21fb44-f8d1-4ada-afc1-7c468748fa0a | improving-speaker-discrimination-of-target | 2001.08378 | null | https://arxiv.org/abs/2001.08378v1 | https://arxiv.org/pdf/2001.08378v1.pdf | Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam | Target speech extraction, which extracts a single target source in a mixture given clues about the target speaker, has attracted increasing attention. We have recently proposed SpeakerBeam, which exploits an adaptation utterance of the target speaker to extract his/her voice characteristics that are then used to guide a neural network towards extracting speech of that speaker. SpeakerBeam presents a practical alternative to speech separation as it enables tracking speech of a target speaker across utterances, and achieves promising speech extraction performance. However, it sometimes fails when speakers have similar voice characteristics, such as in same-gender mixtures, because it is difficult to discriminate the target speaker from the interfering speakers. In this paper, we investigate strategies for improving the speaker discrimination capability of SpeakerBeam. First, we propose a time-domain implementation of SpeakerBeam similar to that proposed for a time-domain audio separation network (TasNet), which has achieved state-of-the-art performance for speech separation. Besides, we investigate (1) the use of spatial features to better discriminate speakers when microphone array recordings are available, (2) adding an auxiliary speaker identification loss for helping to learn more discriminative voice characteristics. We show experimentally that these strategies greatly improve speech extraction performance, especially for same-gender mixtures, and outperform TasNet in terms of target speech extraction. | ['Tsubasa Ochiai', 'Shoko Araki', 'Marc Delcroix', 'Naohiro Tawara', 'Keisuke Kinoshita', 'Tomohiro Nakatani', 'Katerina Zmolikova'] | 2020-01-23 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 2.08971724e-01 -9.96697098e-02 -3.36204022e-02 -1.68062285e-01
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8c26e88b-3d10-4fc5-9557-5405c9daaa2d | the-global-information-for-land-cover | 2006.00234 | null | https://arxiv.org/abs/2006.00234v2 | https://arxiv.org/pdf/2006.00234v2.pdf | Integrating global spatial features in CNN based Hyperspectral/SAR imagery classification | The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dual-branch convolutional neural network (CNN) classification method is designed in combination with the global information to mine the pixel features of the image. Then, the features of the two neural networks are fused with another fully neural network to realize the classification of remote sensing images. Finally, two remote sensing images are used to verify the effectiveness of our method, including hyperspectral imaging (HSI) and polarimetric synthetic aperture radar (PolSAR) imagery. The result of the proposed method is superior to the traditional single-channel convolutional neural network. | ['Chen Hu', 'MinChao Yan', 'Fei Ma', 'Jun Ni', 'Fan Zhang'] | 2020-05-30 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.21271360e-01 -6.40789211e-01 3.16742100e-02 -5.91453254e-01
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81c3b234-9ef5-45e8-bdf5-cb094c6dc6ff | on-multiple-intelligences-and-learning-styles | 2008.04793 | null | https://arxiv.org/abs/2008.04793v4 | https://arxiv.org/pdf/2008.04793v4.pdf | Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles | This article discusses some trends and concepts in developing new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Using the concept of 'multiple intelligences' rather than a single type of intelligence, we categorize and provide working definitions of various AGI depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom with abilities of cooperation, collaboration and even co-creating something new and valuable and have meta-learning capacities. Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent. Key words: Artificial General Intelligence (AGI), multiple intelligences, learning styles, physical intelligence, emotional intelligence, social intelligence, attentional intelligence, moral-ethical intelligence, responsible decision making, creative-innovative intelligence, cognitive functions, meta-learning of AI systems. | ['Alexander P. Kuleshov', 'Andrzej Cichocki'] | 2020-08-07 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-2.56650627e-01 4.14807260e-01 2.89915472e-01 1.04526607e-02
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2d72fab4-e761-440d-b8f4-bbc7da3705ae | weakly-supervised-action-localization-with | 1908.06552 | null | https://arxiv.org/abs/1908.06552v1 | https://arxiv.org/pdf/1908.06552v1.pdf | Weakly-supervised Action Localization with Background Modeling | We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, our framework uses an attention model to extract both foreground and background frames whose appearance is explicitly modeled. Most prior works ignore the background, but we show that modeling it allows our system to learn a richer notion of actions and their temporal extents. Second, we combine bottom-up, class-agnostic attention modules with top-down, class-specific activation maps, using the latter as form of self-supervision for the former. Doing so allows our model to learn a more accurate model of attention without explicit temporal supervision. These modifications lead to 10% AP@IoU=0.5 improvement over existing systems on THUMOS14. Our proposed weaklysupervised system outperforms recent state-of-the-arts by at least 4.3% AP@IoU=0.5. Finally, we demonstrate that weakly-supervised learning can be used to aggressively scale-up learning to in-the-wild, uncurated Instagram videos. The addition of these videos significantly improves localization performance of our weakly-supervised model | ['Charless C. Fowlkes', 'Deva Ramanan', 'Phuc Xuan Nguyen'] | 2019-08-19 | weakly-supervised-action-localization-with-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Nguyen_Weakly-Supervised_Action_Localization_With_Background_Modeling_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Nguyen_Weakly-Supervised_Action_Localization_With_Background_Modeling_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 1.02962583e-01 6.99904263e-02 -7.87274122e-01 -3.79934192e-01
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86e6b3af-add5-4ebf-add6-ca30607208fd | specular-and-diffuse-reflection-based-face | 1907.12400 | null | https://arxiv.org/abs/1907.12400v5 | https://arxiv.org/pdf/1907.12400v5.pdf | Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices | In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible light camera, the proposed algorithm takes two facial photos, one taken with a flash, the other without a flash. The proposed $SpecDiff$ descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with $SpecDiff$ descriptor outperforms other flash-based PAD algorithms on both an in-house database and on publicly available NUAA, Replay-Attack, and SiW databases. Moreover, the proposed algorithm achieves statistically significantly better accuracy to that of an end-to-end, deep neural network classifier, while being approximately six-times faster execution speed. The code is publicly available at https://github.com/Akinori-F-Ebihara/SpecDiff-spoofing-detector. | ['Akinori F. Ebihara', 'Kazuyuki Sakurai', 'Hitoshi Imaoka'] | 2019-07-29 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.96205491e-01 -3.84263694e-01 -2.14307439e-02 -2.09714159e-01
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8e45244b-1959-4133-a746-fef56a5ace5e | cerebrum-7t-fast-and-fully-volumetric-brain | null | null | https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25636 | https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.25636 | CEREBRUM‐7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes | Ultra high-field MRI enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7 Tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end Convolutional Neural Network (CNN), that allows fully automatic segmentation of a whole 7T T1w MRI brain volume at once, without partitioning the volume, preprocessing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on, and is suitable for neuroimaging studies, such as layer fMRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test. For more, please visit: https://rocknroll87q.github.io/cerebrum7t/. | ['L', 'Muckli', 'D.', 'Bontempi', 'S.', 'Benini', 'M.', 'Svanera'] | 2021-01-28 | null | null | null | human-brain-mapping-2021-1 | ['brain-segmentation'] | ['medical'] | [ 8.17570239e-02 1.43765509e-01 5.03872752e-01 -4.66231436e-01
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a8fde303-274c-4147-a96a-0db15867ed32 | unsupervised-acoustic-unit-discovery-for | 1904.07556 | null | https://arxiv.org/abs/1904.07556v2 | https://arxiv.org/pdf/1904.07556v2.pdf | Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks | For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsupervised discrete subword modelling could be useful for studies of phonetic category learning in infants or in low-resource speech technology requiring symbolic input. We use an autoencoder (AE) architecture with intermediate discretisation. We decouple acoustic unit discovery from speaker modelling by conditioning the AE's decoder on the training speaker identity. At test time, unit discovery is performed on speech from an unseen speaker, followed by unit decoding conditioned on a known target speaker to obtain reconstructed filterbanks. This output is fed to a neural vocoder to synthesise speech in the target speaker's voice. For discretisation, categorical variational autoencoders (CatVAEs), vector-quantised VAEs (VQ-VAEs) and straight-through estimation are compared at different compression levels on two languages. Our final model uses convolutional encoding, VQ-VAE discretisation, deconvolutional decoding and an FFTNet vocoder. We show that decoupled speaker conditioning intrinsically improves discrete acoustic representations, yielding competitive synthesis quality compared to the challenge baseline. | ['Ewald van der Westhuizen', 'Elan van Biljon', 'Avashna Govender', 'André Nortje', 'Lisa van Staden', 'Leanne Nortje', 'Arnu Pretorius', 'Ryan Eloff', 'Herman Kamper', 'Benjamin van Niekerk'] | 2019-04-16 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 4.12716776e-01 5.81987202e-01 1.61939431e-02 -3.93205345e-01
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64c0530b-d239-49a9-b640-8a305f0431da | tackling-low-resourced-sign-language | 2212.01140 | null | https://arxiv.org/abs/2212.01140v1 | https://arxiv.org/pdf/2212.01140v1.pdf | Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22 | This paper describes the system developed at the Universitat Polit\`ecnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation techniques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers' baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings. | ['Jordi Torres', 'Xavier Giró-i-Nieto', 'Gerard I. Gàllego', 'Laia Tarrés'] | 2022-12-02 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.46000117e-01 1.49078041e-01 -1.70531705e-01 -3.04997534e-01
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d5e0cb4f-c2b2-4dc0-9bf4-f9d5962d419a | explainable-ai-and-visual-reasoning-insights | 2304.03318 | null | https://arxiv.org/abs/2304.03318v1 | https://arxiv.org/pdf/2304.03318v1.pdf | Explainable AI And Visual Reasoning: Insights From Radiology | Why do explainable AI (XAI) explanations in radiology, despite their promise of transparency, still fail to gain human trust? Current XAI approaches provide justification for predictions, however, these do not meet practitioners' needs. These XAI explanations lack intuitive coverage of the evidentiary basis for a given classification, posing a significant barrier to adoption. We posit that XAI explanations that mirror human processes of reasoning and justification with evidence may be more useful and trustworthy than traditional visual explanations like heat maps. Using a radiology case study, we demonstrate how radiology practitioners get other practitioners to see a diagnostic conclusion's validity. Machine-learned classifications lack this evidentiary grounding and consequently fail to elicit trust and adoption by potential users. Insights from this study may generalize to guiding principles for human-centered explanation design based on human reasoning and justification of evidence. | ['David Kirsh', 'Robert Kaufman'] | 2023-04-06 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.84883738e-01 1.44256175e+00 -7.37561464e-01 -7.31901169e-01
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8e681ec8-9133-4577-b575-b486d2e52aa1 | scorpiano-a-system-for-automatic-music | 2108.10689 | null | https://arxiv.org/abs/2108.10689v1 | https://arxiv.org/pdf/2108.10689v1.pdf | Scorpiano -- A System for Automatic Music Transcription for Monophonic Piano Music | Music transcription is the process of transcribing music audio into music notation. It is a field in which the machines still cannot beat human performance. The main motivation for automatic music transcription is to make it possible for anyone playing a musical instrument, to be able to generate the music notes for a piece of music quickly and accurately. It does not matter if the person is a beginner and simply struggles to find the music score by searching, or an expert who heard a live jazz improvisation and would like to reproduce it without losing time doing manual transcription. We propose Scorpiano -- a system that can automatically generate a music score for simple monophonic piano melody tracks using digital signal processing. The system integrates multiple digital audio processing methods: notes onset detection, tempo estimation, beat detection, pitch detection and finally generation of the music score. The system has proven to give good results for simple piano melodies, comparable to commercially available neural network based systems. | ['Branislav Gerazov', 'Bojan Sofronievski'] | 2021-08-24 | null | null | null | null | ['music-transcription'] | ['music'] | [ 5.62804639e-01 -1.59816578e-01 3.22032571e-01 1.98279753e-01
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44a715a9-c2c1-43b0-9269-ad852b16ab87 | learning-optimized-risk-scores | 1610.00168 | null | https://arxiv.org/abs/1610.00168v5 | https://arxiv.org/pdf/1610.00168v5.pdf | Learning Optimized Risk Scores | Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey application-specific operational constraints. In this paper, we present a new machine learning approach to learn risk scores. We formulate the risk score problem as a mixed integer nonlinear program, and present a cutting plane algorithm for non-convex settings to efficiently recover its optimal solution. We improve our algorithm with specialized techniques to generate feasible solutions, narrow the optimality gap, and reduce data-related computation. Our approach can fit risk scores in a way that scales linearly in the number of samples, provides a certificate of optimality, and obeys real-world constraints without parameter tuning or post-processing. We benchmark the performance benefits of this approach through an extensive set of numerical experiments, comparing to risk scores built using heuristic approaches. We also discuss its practical benefits through a real-world application where we build a customized risk score for ICU seizure prediction in collaboration with the Massachusetts General Hospital. | ['Berk Ustun', 'Cynthia Rudin'] | 2016-10-01 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.55816928e-01 1.76643610e-01 -6.17247581e-01 -4.79753673e-01
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96089798-2fb0-4831-be22-4efb55b27288 | cgc-contrastive-graph-clustering-for | 2204.08504 | null | https://arxiv.org/abs/2204.08504v4 | https://arxiv.org/pdf/2204.08504v4.pdf | CGC: Contrastive Graph Clustering for Community Detection and Tracking | Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clustering perspective. Recently, state-of-the-art clustering performance in various domains has been achieved by deep clustering methods. Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework. Despite some differences in modeling choices (e.g., encoder architectures), existing DGC methods are mainly based on autoencoders and use the same clustering objective with relatively minor adaptations. Also, while many real-world graphs are dynamic, previous DGC methods considered only static graphs. In this work, we develop CGC, a novel end-to-end framework for graph clustering, which fundamentally differs from existing methods. CGC learns node embeddings and cluster assignments in a contrastive graph learning framework, where positive and negative samples are carefully selected in a multi-level scheme such that they reflect hierarchical community structures and network homophily. Also, we extend CGC for time-evolving data, where temporal graph clustering is performed in an incremental learning fashion, with the ability to detect change points. Extensive evaluation on real-world graphs demonstrates that the proposed CGC consistently outperforms existing methods. | ['Christos Faloutsos', 'Nesreen Ahmed', 'Fan Du', 'Sungchul Kim', 'Iftikhar Ahamath Burhanuddin', 'Eunyee Koh', 'Ryan Rossi', 'Namyong Park'] | 2022-04-05 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-5.10601699e-01 -1.00190304e-01 -7.38935322e-02 -2.69625813e-01
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e425c2a6-76ad-4ebd-9493-6f4533c49797 | learn-to-explain-multimodal-reasoning-via | 2209.09513 | null | https://arxiv.org/abs/2209.09513v2 | https://arxiv.org/pdf/2209.09513v2.pdf | Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering | When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA), a new benchmark that consists of ~21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations. We further design language models to learn to generate lectures and explanations as the chain of thought (CoT) to mimic the multi-hop reasoning process when answering ScienceQA questions. ScienceQA demonstrates the utility of CoT in language models, as CoT improves the question answering performance by 1.20% in few-shot GPT-3 and 3.99% in fine-tuned UnifiedQA. We also explore the upper bound for models to leverage explanations by feeding those in the input; we observe that it improves the few-shot performance of GPT-3 by 18.96%. Our analysis further shows that language models, similar to humans, benefit from explanations to learn from fewer data and achieve the same performance with just 40% of the data. The data and code are available at https://scienceqa.github.io. | ['Ashwin Kalyan', 'Peter Clark', 'Oyvind Tafjord', 'Song-Chun Zhu', 'Kai-Wei Chang', 'Liang Qiu', 'Tony Xia', 'Swaroop Mishra', 'Pan Lu'] | 2022-09-20 | null | null | null | null | ['science-question-answering', 'visual-commonsense-reasoning'] | ['miscellaneous', 'reasoning'] | [ 1.97965484e-02 5.05385876e-01 -7.74357989e-02 -5.30391812e-01
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c528887a-7c42-4bc1-b299-ecf44eeaed5a | self-supervised-video-representation-learning | 1811.09795 | null | http://arxiv.org/abs/1811.09795v1 | http://arxiv.org/pdf/1811.09795v1.pdf | Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles | Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have
been utilized for visual representation learning for still images, when the
number of labeled images is limited or absent at all. Recently, this worthwhile
stream of study extends to video domain where the cost of human labeling is
even more expensive. However, the most of existing methods are still based on
2D CNN architectures that can not directly capture spatio-temporal information
for video applications. In this paper, we introduce a new self-supervised task
called as \textit{Space-Time Cubic Puzzles} to train 3D CNNs using large scale
video dataset. This task requires a network to arrange permuted 3D
spatio-temporal crops. By completing \textit{Space-Time Cubic Puzzles}, the
network learns both spatial appearance and temporal relation of video frames,
which is our final goal. In experiments, we demonstrate that our learned 3D
representation is well transferred to action recognition tasks, and outperforms
state-of-the-art 2D CNN-based competitors on UCF101 and HMDB51 datasets. | ['Dahun Kim', 'In So Kweon', 'Donghyeon Cho'] | 2018-11-24 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 6.15732670e-02 -3.87810498e-01 -3.80675256e-01 -1.18475787e-01
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cd9ca85c-7d4b-4a7c-9b15-85574dec67f2 | longitudinal-analysis-of-mask-and-no-mask-on | 2111.00121 | null | https://arxiv.org/abs/2111.00121v5 | https://arxiv.org/pdf/2111.00121v5.pdf | Longitudinal Analysis of Mask and No-Mask on Child Face Recognition | Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems (FRS). However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on FRS. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease approximately 25%, 22%, 18%, 12%, respectively. | ['Neeta Nain', 'Zahid Akhtar', 'Praveen Kumar Chandaliya'] | 2021-10-29 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 4.85535972e-02 -1.76179588e-01 1.24710403e-01 -7.17170715e-01
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81c8a918-588a-44e7-9a77-fa0c70e42554 | tov-the-original-vision-model-for-optical | 2204.04716 | null | https://arxiv.org/abs/2204.04716v1 | https://arxiv.org/pdf/2204.04716v1.pdf | TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning | Do we on the right way for remote sensing image understanding (RSIU) by training models via supervised data-dependent and task-dependent way, instead of human vision in a label-free and task-independent way? We argue that a more desirable RSIU model should be trained with intrinsic structure from data rather that extrinsic human labels to realize generalizability across a wide range of RSIU tasks. According to this hypothesis, we proposed \textbf{T}he \textbf{O}riginal \textbf{V}ision model (TOV) in remote sensing filed. Trained by massive unlabeled optical data along a human-like self-supervised learning (SSL) path that is from general knowledge to specialized knowledge, TOV model can be easily adapted to various RSIU tasks, including scene classification, object detection, and semantic segmentation, and outperforms dominant ImageNet supervised pretrained method as well as two recently proposed SSL pretrained methods on majority of 12 publicly available benchmarks. Moreover, we analyze the influences of two key factors on the performance of building TOV model for RSIU, including the influence of using different data sampling methods and the selection of learning paths during self-supervised optimization. We believe that a general model which is trained by a label-free and task-independent way may be the next paradigm for RSIU and hope the insights distilled from this study can help to foster the development of an original vision model for RSIU. | ['Haifeng Li', 'Weipeng Lu', 'Qing Zhu', 'Guo Zhang', 'Ji Qia', 'Chao Tao'] | 2022-04-10 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 5.10004222e-01 8.32697377e-02 -2.81190574e-01 -6.43568993e-01
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34a66a23-415a-45cd-a9da-6ccfaa3dc6ea | thermodynamics-of-protein-folding | 2307.02175 | null | https://arxiv.org/abs/2307.02175v2 | https://arxiv.org/pdf/2307.02175v2.pdf | Thermodynamics of Protein Folding | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter, "Introduction to Protein Folding", we introduced the concept of free energy and the protein folding landscape. Here, we provide a deeper, more formal underpinning of free energy in terms of the entropy and enthalpy; to this end, we will first need to better define the meaning of equilibrium, entropy and enthalpy. When we understand these concepts, we will come back for a more quantitative explanation of protein folding and dynamics. We will discuss the influence of temperature on the free energy landscape, and the difference between microstates and macrostates. | ['K. Anton Feenstra', 'Sanne Abeln', 'Arthur Goetzee', 'Isabel Houtkamp', 'Maurits Dijkstra', 'Erik van Dijk', 'Halima Mouhib', 'Juami H. M. van Gils'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction', 'protein-folding'] | ['miscellaneous', 'natural-language-processing'] | [ 2.38445729e-01 -1.19647525e-01 -1.42193958e-01 -3.85668665e-01
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2589b6fe-1013-41d1-b0d7-1299c5db8939 | learning-to-regulate-3d-head-shape-by | 2208.12078 | null | https://arxiv.org/abs/2208.12078v1 | https://arxiv.org/pdf/2208.12078v1.pdf | Learning to regulate 3D head shape by removing occluding hair from in-the-wild images | Recent 3D face reconstruction methods reconstruct the entire head compared to earlier approaches which only model the face. Although these methods accurately reconstruct facial features, they do not explicitly regulate the upper part of the head. Extracting information about this part of the head is challenging due to varying degrees of occlusion by hair. We present a novel approach for modeling the upper head by removing occluding hair and reconstructing the skin, revealing information about the head shape. We introduce three objectives: 1) a dice consistency loss that enforces similarity between the overall head shape of the source and rendered image, 2) a scale consistency loss to ensure that head shape is accurately reproduced even if the upper part of the head is not visible, and 3) a 71 landmark detector trained using a moving average loss function to detect additional landmarks on the head. These objectives are used to train an encoder in an unsupervised manner to regress FLAME parameters from in-the-wild input images. Our unsupervised 3DMM model achieves state-of-the-art results on popular benchmarks and can be used to infer the head shape, facial features, and textures for direct use in animation or avatar creation. | ['Cai Yiyu', 'Varsha Saravanabavan', 'Sohan Anisetty'] | 2022-08-25 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.82552490e-02 5.29525280e-01 1.90623969e-01 -4.42330956e-01
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eec72c04-4e91-4c8b-949c-add9f9525a56 | a-hybrid-end-to-end-spatio-temporal-attention | 2307.03068 | null | https://arxiv.org/abs/2307.03068v1 | https://arxiv.org/pdf/2307.03068v1.pdf | A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition | Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli. | ['Mujdat Cetin', 'Mastaneh Torkamani-Azar', 'Shadi Sartipi'] | 2023-07-06 | null | null | null | null | ['emotion-recognition', 'emotion-classification', 'transfer-learning', 'eeg-emotion-recognition', 'emotion-classification'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 1.72010995e-02 -1.39359146e-01 4.64179337e-01 -7.13650346e-01
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bbb585f2-954e-48fe-9f3d-2809336eb1a7 | sketch-and-refine-towards-faithful-and | 2105.14778 | null | https://arxiv.org/abs/2105.14778v1 | https://arxiv.org/pdf/2105.14778v1.pdf | Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation | Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from low coverage and poor faithfulness problems. To mitigate these problems, we propose a novel Skeleton-based two-stage method that combines both Autoregressive and Non-Autoregressive generations (SANA). Our approach includes: (1) skeleton generation with an autoregressive pointer network to select key tokens from the source table; (2) edit-based non-autoregressive generation model to produce texts via iterative insertion and deletion operations. By integrating hard constraints from the skeleton, the non-autoregressive model improves the generation's coverage over the source table and thus enhances its faithfulness. We conduct automatic and human evaluations on both WikiPerson and WikiBio datasets. Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness. In particular, we achieve PARENT-T recall of 99.47 in WikiPerson, improving over the existing best results by more than 10 points. | ['Hongxia Yang', 'Jingren Zhou', 'Yichang Zhang', 'Chang Zhou', 'An Yang', 'Junyang Lin', 'Peng Wang'] | 2021-05-31 | null | https://aclanthology.org/2021.findings-acl.427 | https://aclanthology.org/2021.findings-acl.427.pdf | findings-acl-2021-8 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.96602130e-01 6.55889511e-01 -1.91267818e-01 -2.68508404e-01
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36dafb2f-4062-47dd-a54f-594c38149e60 | scene-text-image-super-resolution-via-content | 2210.06924 | null | https://arxiv.org/abs/2210.06924v1 | https://arxiv.org/pdf/2210.06924v1.pdf | Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks | Text image super-resolution is a unique and important task to enhance readability of text images to humans. It is widely used as pre-processing in scene text recognition. However, due to the complex degradation in natural scenes, recovering high-resolution texts from the low-resolution inputs is ambiguous and challenging. Existing methods mainly leverage deep neural networks trained with pixel-wise losses designed for natural image reconstruction, which ignore the unique character characteristics of texts. A few works proposed content-based losses. However, they only focus on text recognizers' accuracy, while the reconstructed images may still be ambiguous to humans. Further, they often have weak generalizability to handle cross languages. To this end, we present TATSR, a Text-Aware Text Super-Resolution framework, which effectively learns the unique text characteristics using Criss-Cross Transformer Blocks (CCTBs) and a novel Content Perceptual (CP) Loss. The CCTB extracts vertical and horizontal content information from text images by two orthogonal transformers, respectively. The CP Loss supervises the text reconstruction with content semantics by multi-scale text recognition features, which effectively incorporates content awareness into the framework. Extensive experiments on various language datasets demonstrate that TATSR outperforms state-of-the-art methods in terms of both recognition accuracy and human perception. | ['Yu-Wing Tai', 'Bin Wang', 'Rui Qin'] | 2022-10-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 8.06429982e-01 -6.34715796e-01 -9.36233178e-02 -4.75696295e-01
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ce3325b3-8789-4109-867e-5f2a9069ffd5 | a-lightweight-music-texture-transfer-system-1 | 1810.01248 | null | https://arxiv.org/abs/1810.01248v3 | https://arxiv.org/pdf/1810.01248v3.pdf | A Lightweight Music Texture Transfer System | Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance. | ['Yidan Liu', 'Faqiang Shi', 'Zhi Cai', 'JianXin Li', 'Chen Li', 'Xutan Peng'] | 2018-09-27 | a-lightweight-music-texture-transfer-system | https://arxiv.org/abs/1810.01248 | https://arxiv.org/pdf/1810.01248 | arxiv-preprint-2018-9 | ['music-texture-transfer'] | ['music'] | [ 2.63294101e-01 -6.25586092e-01 2.29157597e-01 -6.19163699e-02
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6e5fa786-26de-4747-83ea-0dd70cf3f9ff | evaluating-gpt-3-5-and-gpt-4-on-grammatical | 2306.15788 | null | https://arxiv.org/abs/2306.15788v1 | https://arxiv.org/pdf/2306.15788v1.pdf | Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese | We investigate the effectiveness of GPT-3.5 and GPT-4, two large language models, as Grammatical Error Correction (GEC) tools for Brazilian Portuguese and compare their performance against Microsoft Word and Google Docs. We introduce a GEC dataset for Brazilian Portuguese with four categories: Grammar, Spelling, Internet, and Fast typing. Our results show that while GPT-4 has higher recall than other methods, LLMs tend to have lower precision, leading to overcorrection. This study demonstrates the potential of LLMs as practical GEC tools for Brazilian Portuguese and encourages further exploration of LLMs for non-English languages and other educational settings. | ['Fábio Perez', 'Maria Carolina Penteado'] | 2023-06-27 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-4.92593437e-01 4.38452736e-02 -7.48907849e-02 3.63128670e-02
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32e1ad44-2770-4871-8561-78dc36225d87 | towards-more-suitable-personalization-in | 2305.15157 | null | https://arxiv.org/abs/2305.15157v1 | https://arxiv.org/pdf/2305.15157v1.pdf | Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training | Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems. However, almost all existing works have to face large communication burdens and the risk of disruption if the central server fails. Only limited efforts have been used in a decentralized way but still suffers from inferior representation ability due to sharing the full model with its neighbors. Therefore, in this paper, we propose a personalized FL framework with a decentralized partial model training called DFedAlt. It personalizes the "right" components in the modern deep models by alternately updating the shared and personal parameters to train partially personalized models in a peer-to-peer manner. To further promote the shared parameters aggregation process, we propose DFedSalt integrating the local Sharpness Aware Minimization (SAM) optimizer to update the shared parameters. It adds proper perturbation in the direction of the gradient to overcome the shared model inconsistency across clients. Theoretically, we provide convergence analysis of both algorithms in the general non-convex setting for decentralized partial model training in PFL. Our experiments on several real-world data with various data partition settings demonstrate that (i) decentralized training is more suitable for partial personalization, which results in state-of-the-art (SOTA) accuracy compared with the SOTA PFL baselines; (ii) the shared parameters with proper perturbation make partial personalized FL more suitable for decentralized training, where DFedSalt achieves most competitive performance. | ['DaCheng Tao', 'Xueqian Wang', 'Li Shen', 'Zihao Lin', 'Yan Sun', 'Yingqi Liu', 'Yifan Shi'] | 2023-05-24 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-6.23668969e-01 -1.10297829e-01 -3.13914925e-01 -5.98121107e-01
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47593dde-3ed4-496d-9352-37509f280ba2 | unseen-object-instance-segmentation-with | 2204.09847 | null | https://arxiv.org/abs/2204.09847v2 | https://arxiv.org/pdf/2204.09847v2.pdf | Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation | Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features of large-scale synthetic data and directly applying the model to unseen real-world scenarios. However, the domain shift caused by the sim2real gap is inevitable, posing a crucial challenge to the segmentation model. In this paper, we emphasize the adaptation process across sim2real domains and model it as a learning problem on the BatchNorm parameters of a simulation-trained model. Specifically, we propose a novel non-parametric entropy objective, which formulates the learning objective for the test-time adaptation in an open-world manner. Then, a cross-modality knowledge distillation objective is further designed to encourage the test-time knowledge transfer for feature enhancement. Our approach can be efficiently implemented with only test images, without requiring annotations or revisiting the large-scale synthetic training data. Besides significant time savings, the proposed method consistently improves segmentation results on the overlap and boundary metrics, achieving state-of-the-art performance on unseen object instance segmentation. | ['Zhiyong Liu', 'Hong Qiao', 'Xu Yang', 'Siqi Zhang', 'Lu Zhang'] | 2022-04-21 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 3.80200386e-01 3.57171834e-01 1.47279650e-01 -5.29615879e-01
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92007bc1-ff8d-407e-a9ad-0f802ccd2217 | keyword-extraction-in-scientific-documents | 2207.01888 | null | https://arxiv.org/abs/2207.01888v2 | https://arxiv.org/pdf/2207.01888v2.pdf | Keyword Extraction in Scientific Documents | The scientific publication output grows exponentially. Therefore, it is increasingly challenging to keep track of trends and changes. Understanding scientific documents is an important step in downstream tasks such as knowledge graph building, text mining, and discipline classification. In this workshop, we provide a better understanding of keyword and keyphrase extraction from the abstract of scientific publications. | ['Ce Zhang', 'Peter Egger', 'Vanya Brucker', 'Michael Wechner', 'Sandra Mitrović', 'Emmanuel de Salis', 'Sara Nasirian', 'Parijat Ghoshal', 'Piriyakorn Piriyatamwong', 'Susie Xi Rao'] | 2022-07-05 | null | null | null | null | ['keyword-extraction', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-9.82662514e-02 -2.47457609e-01 -4.92543906e-01 1.71373561e-01
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0808ec01-32ca-4bf0-849a-01467ca92db9 | multi-view-multi-label-anomaly-network | 2210.16719 | null | https://arxiv.org/abs/2210.16719v2 | https://arxiv.org/pdf/2210.16719v2.pdf | Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network | Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. In this paper, we propose a MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, the packet is divided into the packet header and the packet body, together with the flow features of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. Taking advantage of the above characteristics, we propose an end-to-end network traffic classification method. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance. | ['Xinbo Gao', 'Chao Yang', 'Chunlei Peng', 'Zhangxuan Dang', 'Yu Zheng'] | 2022-10-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.89289071e-03 -7.88692713e-01 -6.14145458e-01 -5.18473566e-01
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57a82ba3-8dd7-4a9a-a342-1d6ac70d5bf0 | data-augmentation-for-low-resource-neural | 1705.00440 | null | http://arxiv.org/abs/1705.00440v1 | http://arxiv.org/pdf/1705.00440v1.pdf | Data Augmentation for Low-Resource Neural Machine Translation | The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequency words by generating new sentence pairs containing rare words in
new, synthetically created contexts. Experimental results on simulated
low-resource settings show that our method improves translation quality by up
to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation. | ['Arianna Bisazza', 'Marzieh Fadaee', 'Christof Monz'] | 2017-05-01 | data-augmentation-for-low-resource-neural-1 | https://aclanthology.org/P17-2090 | https://aclanthology.org/P17-2090.pdf | acl-2017-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.97198111e-01 -1.29152574e-02 -2.10146084e-01 -2.54756421e-01
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26652383-adeb-46b5-ae4e-84e9d0ab1918 | norm-in-norm-loss-with-faster-convergence-and | 2008.03889 | null | https://arxiv.org/abs/2008.03889v1 | https://arxiv.org/pdf/2008.03889v1.pdf | Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment | Currently, most image quality assessment (IQA) models are supervised by the MAE or MSE loss with empirically slow convergence. It is well-known that normalization can facilitate fast convergence. Therefore, we explore normalization in the design of loss functions for IQA. Specifically, we first normalize the predicted quality scores and the corresponding subjective quality scores. Then, the loss is defined based on the norm of the differences between these normalized values. The resulting "Norm-in-Norm'' loss encourages the IQA model to make linear predictions with respect to subjective quality scores. After training, the least squares regression is applied to determine the linear mapping from the predicted quality to the subjective quality. It is shown that the new loss is closely connected with two common IQA performance criteria (PLCC and RMSE). Through theoretical analysis, it is proved that the embedded normalization makes the gradients of the loss function more stable and more predictable, which is conducive to the faster convergence of the IQA model. Furthermore, to experimentally verify the effectiveness of the proposed loss, it is applied to solve a challenging problem: quality assessment of in-the-wild images. Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster and the final model achieves better performance. The proposed model also achieves state-of-the-art prediction performance on this challenging problem. For reproducible scientific research, our code is publicly available at https://github.com/lidq92/LinearityIQA. | ['Dingquan Li', 'Tingting Jiang', 'Ming Jiang'] | 2020-08-10 | null | null | null | null | ['blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.06395148e-02 -2.95008123e-01 -1.47086218e-01 -4.26205099e-01
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a919a1d9-c360-4122-986f-b9bd4c501517 | a-dual-branch-network-for-infrared-and | 2101.09643 | null | https://arxiv.org/abs/2101.09643v1 | https://arxiv.org/pdf/2101.09643v1.pdf | A Dual-branch Network for Infrared and Visible Image Fusion | Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and we directly insert the input image-visible light image in each layer of the entire network. We use SSIM and gradient loss functions that are more consistent with perception instead of mean square error loss. After the adversarial training between the generator and the discriminator, we show that a trained end-to-end fusion network -- the generator network -- is finally obtained. Our experiments show that the fused images obtained by our approach achieve good score based on multiple evaluation indicators. Further, our fused images have better visual effects in multiple sets of contrasts, which are more satisfying to human visual perception. | ['Xiao-Jun Wu', 'Yu Fu'] | 2021-01-24 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 2.84292251e-01 -2.07455590e-01 2.38041282e-01 -2.98877746e-01
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7b9e4c74-d7a4-4d44-8b4a-6c37ac17c5ff | a-bayesian-approach-to-uncertainty-in-word | 2306.09066 | null | https://arxiv.org/abs/2306.09066v1 | https://arxiv.org/pdf/2306.09066v1.pdf | A Bayesian approach to uncertainty in word embedding bias estimation | Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and employing bootstrapping techniques with low sample sizes. We show that similar results can be easily obtained using such methods even if the data are generated by a null model lacking the intended bias. Consequently, we argue that this approach generates false confidence. To address this issue, we propose a Bayesian alternative: hierarchical Bayesian modeling, which enables a more uncertainty-sensitive inspection of bias in word embeddings at different levels of granularity. To showcase our method, we apply it to Religion, Gender, and Race word lists from the original research, together with our control neutral word lists. We deploy the method using Google, Glove, and Reddit embeddings. Further, we utilize our approach to evaluate a debiasing technique applied to Reddit word embedding. Our findings reveal a more complex landscape than suggested by the proponents of single-number metrics. The datasets and source code for the paper are publicly available. | ['Rafal Urbaniak', 'Alicja Dobrzeniecka'] | 2023-06-15 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-2.23861430e-02 -4.88902479e-02 -4.66216534e-01 -2.61084110e-01
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8cd0b376-f3af-4041-9676-9262350cc7fc | scalenet-a-shallow-architecture-for-scale | 2112.04846 | null | https://arxiv.org/abs/2112.04846v3 | https://arxiv.org/pdf/2112.04846v3.pdf | ScaleNet: A Shallow Architecture for Scale Estimation | In this paper, we address the problem of estimating scale factors between images. We formulate the scale estimation problem as a prediction of a probability distribution over scale factors. We design a new architecture, ScaleNet, that exploits dilated convolutions as well as self and cross-correlation layers to predict the scale between images. We demonstrate that rectifying images with estimated scales leads to significant performance improvements for various tasks and methods. Specifically, we show how ScaleNet can be combined with sparse local features and dense correspondence networks to improve camera pose estimation, 3D reconstruction, or dense geometric matching in different benchmarks and datasets. We provide an extensive evaluation on several tasks and analyze the computational overhead of ScaleNet. The code, evaluation protocols, and trained models are publicly available at https://github.com/axelBarroso/ScaleNet. | ['Krystian Mikolajczyk', 'Yurun Tian', 'Axel Barroso-Laguna'] | 2021-12-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['geometric-matching'] | ['computer-vision'] | [-7.90492371e-02 -1.06493779e-01 -4.96694557e-02 -6.94201350e-01
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da09b666-b400-48ff-9a14-5072eee51b49 | scaneru-interactive-3d-visual-grounding-based | 2303.13186 | null | https://arxiv.org/abs/2303.13186v1 | https://arxiv.org/pdf/2303.13186v1.pdf | ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding | Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances.To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding. Specifically, a new task termed Embodied Reference Understanding (ERU) is first designed for this concern. Then a new dataset called ScanERU is constructed to evaluate the effectiveness of this idea. Different from existing datasets, our ScanERU is the first to cover semi-synthetic scene integration with textual, real-world visual, and synthetic gestural information. Additionally, this paper formulates a heuristic framework based on attention mechanisms and human body movements to enlighten the research of ERU. Experimental results demonstrate the superiority of the proposed method, especially in the recognition of multiple identical objects. Our codes and dataset are ready to be available publicly. | ['Heng Tao Shen', 'Zheng Wang', 'Yang Yang', 'Guoqing Wang', 'Yunqiang Pei', 'Ziyang Lu'] | 2023-03-23 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 2.20270172e-01 2.46316418e-01 -1.76838368e-01 -2.70151287e-01
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cbc85854-164c-4fe7-9f86-3f1a98e98f2c | rotationnet-joint-object-categorization-and | 1603.06208 | null | http://arxiv.org/abs/1603.06208v4 | http://arxiv.org/pdf/1603.06208v4.pdf | RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints | We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnet | ['Yoshifumi Nishida', 'Yasuyuki Matsushita', 'Asako Kanezaki'] | 2016-03-20 | rotationnet-joint-object-categorization-and-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kanezaki_RotationNet_Joint_Object_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kanezaki_RotationNet_Joint_Object_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-object-classification', 'object-categorization'] | ['computer-vision', 'computer-vision'] | [-3.17469805e-01 -1.82281524e-01 -4.20149624e-01 -6.58445060e-01
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91fc00d8-a93b-4d82-9c9b-4520ec4579f6 | speech-to-speech-translation-for-a-real-world | null | null | https://research.facebook.com/publications/hokkien-direct-speech-to-speech-translation/ | https://research.facebook.com/micro_site/url/?click_from_context_menu=true&country=US&destination=https%3A%2F%2Fresearch.facebook.com%2Ffile%2F799432337944526%2FSpeech-to-speech-translation-for-a-real-world-unwritten-language.pdf&event_type=click&last_nav_impression_id=0ZiwMSAu7fV8ZZvIz&max_percent_page_viewed=40&max_viewport_height_px=969&max_viewport_width_px=1440&orig_http_referrer=https%3A%2F%2Fgithub.com%2Ffacebookresearch%2Ffairseq%2Ftree%2Fust%2Fexamples%2Fhokkien&orig_request_uri=https%3A%2F%2Fresearch.facebook.com%2Fpublications%2Fhokkien-direct-speech-to-speech-translation%2F®ion=noam&scrolled=true&session_id=0UzpNNeZaaKZiUxnS&site=mc_research | Speech-to-speech translation for a real-world unwritten language | We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard text writing systems. We use English-Taiwanese Hokkien as a case study, and present an end-to-end solution from training data collection, modeling choices to benchmark dataset release. First, we present efforts on creating human annotated data, automatically mining data from large unlabeled speech datasets, and adopting pseudo-labeling to produce weakly supervised data. On the modeling, we take advantage of recent advances in applying self-supervised discrete representations as target for prediction in S2ST and show the effectiveness of leveraging additional text supervision from Mandarin, a language similar to Hokkien, in model training. Finally, we release an S2ST benchmark set to facilitate future research in this field. | ['Ann Lee', 'Wei-Ning Hsu', 'Juan Pino', 'Changhan Wang', 'Sravya Popuri', 'Hirofumi Inaguma', 'Hongyu Gong', 'Holger Schwenk', 'Paul-Ambroise Duquenne', 'Paden Tomasello', 'Yu-An Chung', 'Justine Kao', 'Jingfei Du', 'Yilin Yang', 'Kevin Tran', 'Peng-Jen Chen'] | 2022-10-19 | null | null | null | arxiv-2022-10 | ['speech-to-speech-translation'] | ['speech'] | [ 4.88468200e-01 5.54951847e-01 -5.80787063e-01 -7.74839878e-01
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33f3debb-2e09-4388-ada0-62a4d349d7c5 | program-repair-with-repeated-learning | null | null | https://openreview.net/forum?id=l5NavnRLD0A | https://openreview.net/pdf?id=l5NavnRLD0A | Program Repair with Repeated Learning | A key challenge in generate-and-validate automated program repair is directing the search for fixes so that it can efficiently find those that are more likely to be correct. To this end, several techniques use machine learning to capture the features of programmer-written fixes. In existing approaches, fitting the model typically takes place before fix generation and independent of it: the fix generation process uses the learned model as one of its inputs. However, the intermediate outcomes of an ongoing fix generation process often provide valuable information about which fixes were "better"; this information could profitably be used to retrain the model, so that each new iteration of the fixing process would also learn from the outcome of previous ones. In this paper, we propose the Liana technique for automated program repair, which is based on this idea of repeatedly learning the features of generated fixes. To this end, Liana uses a fine-grained model that combines information about fix characteristics, their relations to the fixing context, and the result of test executions. The model is initially trained offline, and then repeatedly updated online as the fix generation process unravels; at any step, the most up to date model is used to guide the search for fixes—prioritizing those that are more likely to include the right ingredients. In an experimental evaluation on 732 real-world Java bugs from 3 popular benchmarks, Liana built correct fixes for 134 faults (83 ranked as first in its output)—improving over several other generate-and-validate program repair tools according to various measures. | ['Anonymous'] | 2021-04-24 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.60832539e-01 -1.68007314e-01 -5.05125523e-01 -2.01207861e-01
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80e7fde5-f5a5-4019-94ca-2b6a1d85c0fa | end-to-end-environmental-sound-classification | 1904.08990 | null | http://arxiv.org/abs/1904.08990v1 | http://arxiv.org/pdf/1904.08990v1.pdf | End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network | In this paper, we present an end-to-end approach for environmental sound
classification based on a 1D Convolution Neural Network (CNN) that learns a
representation directly from the audio signal. Several convolutional layers are
used to capture the signal's fine time structure and learn diverse filters that
are relevant to the classification task. The proposed approach can deal with
audio signals of any length as it splits the signal into overlapped frames
using a sliding window. Different architectures considering several input sizes
are evaluated, including the initialization of the first convolutional layer
with a Gammatone filterbank that models the human auditory filter response in
the cochlea. The performance of the proposed end-to-end approach in classifying
environmental sounds was assessed on the UrbanSound8k dataset and the
experimental results have shown that it achieves 89% of mean accuracy.
Therefore, the propose approach outperforms most of the state-of-the-art
approaches that use handcrafted features or 2D representations as input.
Furthermore, the proposed approach has a small number of parameters compared to
other architectures found in the literature, which reduces the amount of data
required for training. | ['Sajjad Abdoli', 'Patrick Cardinal', 'Alessandro Lameiras Koerich'] | 2019-04-18 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.04186520e-01 -2.60208607e-01 6.70436919e-01 -3.80966514e-01
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f46dbf42-b5a5-450c-b115-5042b9f8b31b | context-aware-semantic-similarity-measurement | 2305.03520 | null | https://arxiv.org/abs/2305.03520v1 | https://arxiv.org/pdf/2305.03520v1.pdf | Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation | The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotated data. This research proposes a new context-aware approach to unsupervised word sense disambiguation, which provides a flexible mechanism for incorporating contextual information into the similarity measurement process. We experiment with a popular benchmark dataset to evaluate the proposed strategy and compare its performance with state-of-the-art unsupervised word sense disambiguation techniques. The experimental results indicate that our approach substantially enhances disambiguation accuracy and surpasses the performance of several existing techniques. Our findings underscore the significance of integrating contextual information in semantic similarity measurements to manage word sense ambiguity in unsupervised scenarios effectively. | ['Jorge Martinez-Gil'] | 2023-05-05 | null | null | null | null | ['word-sense-disambiguation', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.65264916e-01 -1.41121492e-01 -9.53858346e-02 -3.49363446e-01
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02afc2f8-2ea8-4137-af83-c438ae357604 | a-comparative-study-of-pretrained-language-1 | 2301.11847 | null | https://arxiv.org/abs/2301.11847v1 | https://arxiv.org/pdf/2301.11847v1.pdf | A Comparative Study of Pretrained Language Models for Long Clinical Text | Objective: Clinical knowledge enriched transformer models (e.g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (e.g., Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts. Materials and Methods: Inspired by the success of long sequence transformer models and the fact that clinical notes are mostly long, we introduce two domain enriched language models, Clinical-Longformer and Clinical-BigBird, which are pre-trained on a large-scale clinical corpus. We evaluate both language models using 10 baseline tasks including named entity recognition, question answering, natural language inference, and document classification tasks. Results: The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT and other short-sequence transformers in all 10 downstream tasks and achieve new state-of-the-art results. Discussion: Our pre-trained language models provide the bedrock for clinical NLP using long texts. We have made our source code available at https://github.com/luoyuanlab/Clinical-Longformer, and the pre-trained models available for public download at: https://huggingface.co/yikuan8/Clinical-Longformer. Conclusion: This study demonstrates that clinical knowledge enriched long-sequence transformers are able to learn long-term dependencies in long clinical text. Our methods can also inspire the development of other domain-enriched long-sequence transformers. | ['Yuan Luo', 'Hanyin Wang', 'Faraz S. Ahmad', 'Ramsey M. Wehbe', 'Yikuan Li'] | 2023-01-27 | null | null | null | null | ['clinical-knowledge', 'document-classification'] | ['miscellaneous', 'natural-language-processing'] | [-8.53132159e-02 1.81750983e-01 -3.26733083e-01 -2.17286080e-01
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f5325eab-60aa-464b-a49c-d6454c3f2791 | replay-and-synthetic-speech-detection-with | 2010.15006 | null | https://arxiv.org/abs/2010.15006v3 | https://arxiv.org/pdf/2010.15006v3.pdf | Replay and Synthetic Speech Detection with Res2net Architecture | Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-called Res2Net, to improve the anti-spoofing countermeasure's generalizability. Res2Net mainly modifies the ResNet block to enable multiple feature scales. Specifically, it splits the feature maps within one block into multiple channel groups and designs a residual-like connection across different channel groups. Such connection increases the possible receptive fields, resulting in multiple feature scales. This multiple scaling mechanism significantly improves the countermeasure's generalizability to unseen spoofing attacks. It also decreases the model size compared to ResNet-based models. Experimental results show that the Res2Net model consistently outperforms ResNet34 and ResNet50 by a large margin in both physical access (PA) and logical access (LA) of the ASVspoof 2019 corpus. Moreover, integration with the squeeze-and-excitation (SE) block can further enhance performance. For feature engineering, we investigate the generalizability of Res2Net combined with different acoustic features, and observe that the constant-Q transform (CQT) achieves the most promising performance in both PA and LA scenarios. Our best single system outperforms other state-of-the-art single systems in both PA and LA of the ASVspoof 2019 corpus. | ['Helen Meng', 'Dong Yu', 'Dan Su', 'Xunying Liu', 'Chao Weng', 'Na Li', 'Xu Li'] | 2020-10-28 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 3.71264368e-02 -4.04587746e-01 -2.39826247e-01 2.68488854e-01
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171f14fb-42d9-429a-b125-36fe219ceae0 | a-novel-discourse-parser-based-on-support | null | null | https://aclanthology.org/P09-1075 | https://aclanthology.org/P09-1075.pdf | A Novel Discourse Parser Based on Support Vector Machine Classification | This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statistical machine learning (multivariate capabilities of Support Vector Machines) and a rich feature space. RST offers a formal framework for hierarchical text organization with strong applications in discourse analysis and text generation. We demonstrate automated annotation of a text with RST hierarchically organised relations, with results comparable to those achieved by specially trained human annotators. Using a rich set of shallow lexical, syntactic and structural features from the input text, our parser achieves, in linear time, 73.9% of professional annotators’ human agreement F-score. The parser is 5% to 12% more accurate than current state-of-the-art parsers. | ['Helmut Prendinger', 'David duVerle'] | 2009-08-02 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.12360394e-01 1.16386390e+00 -2.82603562e-01 -3.48148435e-01
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a4015666-acf0-43db-b6a1-830a4010e5e1 | anytime-sampling-for-autoregressive-models-1 | 2102.11495 | null | https://arxiv.org/abs/2102.11495v1 | https://arxiv.org/pdf/2102.11495v1.pdf | Anytime Sampling for Autoregressive Models via Ordered Autoencoding | Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to real-time computational resources. This challenge impedes the deployment of powerful autoregressive models, which involve a slow sampling process that is sequential in nature and typically scales linearly with respect to the data dimension. To address this difficulty, we propose a new family of autoregressive models that enables anytime sampling. Inspired by Principal Component Analysis, we learn a structured representation space where dimensions are ordered based on their importance with respect to reconstruction. Using an autoregressive model in this latent space, we trade off sample quality for computational efficiency by truncating the generation process before decoding into the original data space. Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling. The approach suffers almost no loss in sample quality (measured by FID) using only 60\% to 80\% of all latent dimensions for image data. Code is available at https://github.com/Newbeeer/Anytime-Auto-Regressive-Model . | ['Stefano Ermon', 'Aditya Grover', 'Rui Shu', 'Linyuan Gong', 'Sahaj Garg', 'Yang song', 'Yilun Xu'] | 2021-02-23 | anytime-sampling-for-autoregressive-models | https://openreview.net/forum?id=TSRTzJnuEBS | https://openreview.net/pdf?id=TSRTzJnuEBS | iclr-2021-1 | ['audio-generation'] | ['audio'] | [ 1.85659260e-01 5.42336283e-03 1.55596346e-01 -1.72358274e-01
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b5c77fc8-d2a4-439a-82a6-7e6a12662b57 | information-recovery-driven-deep-incomplete | 2304.00429 | null | https://arxiv.org/abs/2304.00429v3 | https://arxiv.org/pdf/2304.00429v3.pdf | Information Recovery-Driven Deep Incomplete Multiview Clustering Network | Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods. | ['Yong Xu', 'Chao Huang', 'Xiaoling Luo', 'Zhihao Wu', 'Jie Wen', 'Chengliang Liu'] | 2023-04-02 | null | null | null | null | ['incomplete-multi-view-clustering', 'graph-reconstruction'] | ['computer-vision', 'graphs'] | [-9.11063850e-02 4.25929390e-02 -1.62424222e-01 -4.23258245e-01
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02aee6be-f101-4c81-b070-edc3f922b91d | named-entity-inclusion-in-abstractive-text-1 | 2307.02570 | null | https://arxiv.org/abs/2307.02570v1 | https://arxiv.org/pdf/2307.02570v1.pdf | Named Entity Inclusion in Abstractive Text Summarization | We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model's attention on the named entities in a text. At first, the named entity recognition model RoBERTa is trained to determine named entities in the text. After that, this model is used to mask named entities in the text and the BART model is trained to reconstruct them. Next, the BART model is fine-tuned on the summarization task. Our experiments showed that this pretraining approach improves named entity inclusion precision and recall metrics. | ['Tatiana Batura', 'Sergey Berezin'] | 2023-07-05 | named-entity-inclusion-in-abstractive-text | https://aclanthology.org/2022.sdp-1.17 | https://aclanthology.org/2022.sdp-1.17.pdf | sdp-coling-2022-10 | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.96158513e-01 6.94148719e-01 -4.12985772e-01 -4.22739714e-01
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6dbd3e2c-3c84-460f-8fbe-64f9804d5a62 | image-set-querying-based-localization | 1509.06016 | null | http://arxiv.org/abs/1509.06016v1 | http://arxiv.org/pdf/1509.06016v1.pdf | Image Set Querying Based Localization | Conventional single image based localization methods usually fail to localize
a querying image when there exist large variations between the querying image
and the pre-built scene. To address this, we propose an image-set querying
based localization approach. When the localization by a single image fails to
work, the system will ask the user to capture more auxiliary images. First, a
local 3D model is established for the querying image set. Then, the pose of the
querying image set is estimated by solving a nonlinear optimization problem,
which aims to match the local 3D model against the pre-built scene. Experiments
have shown the effectiveness and feasibility of the proposed approach. | ['Jie zhou', 'Baohua Chen', 'Yueqi Duan', 'Siyuan Huang', 'Lei Deng'] | 2015-09-20 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-3.67073677e-02 -3.70187342e-01 -1.04645588e-01 -4.33812678e-01
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987629a7-e63f-47d5-aff3-daa3d57069ac | on-the-effectiveness-of-neural-text | 2006.05129 | null | https://arxiv.org/abs/2006.05129v1 | https://arxiv.org/pdf/2006.05129v1.pdf | On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech | Advanced neural network models have penetrated Automatic Speech Recognition (ASR) in recent years, however, in language modeling many systems still rely on traditional Back-off N-gram Language Models (BNLM) partly or entirely. The reason for this are the high cost and complexity of training and using neural language models, mostly possible by adding a second decoding pass (rescoring). In our recent work we have significantly improved the online performance of a conversational speech transcription system by transferring knowledge from a Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with text generation based data augmentation. In the present paper we analyze the amount of transferable knowledge and demonstrate that the neural augmented LM (RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by dropping the second decoding pass and making the system real-time capable. We also systematically compare word and subword LMs and show that subword-based neural text augmentation can be especially beneficial in under-resourced conditions. In addition, we show that using the RNN-BNLM in the first pass followed by a neural second pass, offline ASR results can be even significantly improved. | ['Péter Mihajlik', 'Tibor Fegyó', 'György Szaszák', 'Balázs Tarján'] | 2020-06-09 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 5.18807054e-01 6.10411525e-01 1.47165731e-01 -2.04325005e-01
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aed67dbb-70dc-4317-a02e-0d64eef908cc | glm-dialog-noise-tolerant-pre-training-for | 2302.14401 | null | https://arxiv.org/abs/2302.14401v1 | https://arxiv.org/pdf/2302.14401v1.pdf | GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation | We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics.Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. The additional easy-to-use toolkit that consists of short text entity linking, query generation, and helpful knowledge classification is also released to enable diverse applications. All the source code is available on Github. | ['Jie Tang', 'Juanzi Li', 'Sunrui Lu', 'Nianyi Lin', 'Xiaohan Zhang', 'Haohua Wang', 'Yiqi Xu', 'Zeyao Ma', 'Zijun Yao', 'Jifan Yu', 'Daniel Zhang-li', 'Xiaokang Zhang', 'Jing Zhang'] | 2023-02-28 | null | null | null | null | ['dialogue-evaluation', 'dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [-6.07834697e-01 5.22842407e-01 6.25113817e-03 -2.91519076e-01
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99f95b7c-a3a7-4a02-aab2-4d9cf69d8625 | dch-2-a-parallel-customer-helpdesk-dialogue | 2104.08755 | null | https://arxiv.org/abs/2104.08755v2 | https://arxiv.org/pdf/2104.08755v2.pdf | DCH-2: A Parallel Customer-Helpdesk Dialogue Corpus with Distributions of Annotators' Labels | We introduce a data set called DCH-2, which contains 4,390 real customer-helpdesk dialogues in Chinese and their English translations. DCH-2 also contains dialogue-level annotations and turn-level annotations obtained independently from either 19 or 20 annotators. The data set was built through our effort as organisers of the NTCIR-14 Short Text Conversation and NTCIR-15 Dialogue Evaluation tasks, to help researchers understand what constitutes an effective customer-helpdesk dialogue, and thereby build efficient and helpful helpdesk systems that are available to customers at all times. In addition, DCH-2 may be utilised for other purposes, for example, as a repository for retrieval-based dialogue systems, or as a parallel corpus for machine translation in the helpdesk domain. | ['Tetsuya Sakai', 'Zhaohao Zeng'] | 2021-04-18 | null | null | null | null | ['dialogue-evaluation', 'short-text-conversation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.93635631e-02 6.01556301e-01 7.34668896e-02 -5.20191610e-01
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e987e5db-9d1f-4ba4-b682-8b82d173bdb7 | fast-rir-fast-neural-diffuse-room-impulse | 2110.04057 | null | https://arxiv.org/abs/2110.04057v2 | https://arxiv.org/pdf/2110.04057v2.pdf | FAST-RIR: Fast neural diffuse room impulse response generator | We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RIR takes rectangular room dimensions, listener and speaker positions, and reverberation time as inputs and generates specular and diffuse reflections for a given acoustic environment. Our FAST-RIR is capable of generating RIRs for a given input reverberation time with an average error of 0.02s. We evaluate our generated RIRs in automatic speech recognition (ASR) applications using Google Speech API, Microsoft Speech API, and Kaldi tools. We show that our proposed FAST-RIR with batch size 1 is 400 times faster than a state-of-the-art diffuse acoustic simulator (DAS) on a CPU and gives similar performance to DAS in ASR experiments. Our FAST-RIR is 12 times faster than an existing GPU-based RIR generator (gpuRIR). We show that our FAST-RIR outperforms gpuRIR by 2.5% in an AMI far-field ASR benchmark. | ['Dong Yu', 'Dinesh Manocha', 'Zhenyu Tang', 'Meng Yu', 'Shi-Xiong Zhang', 'Anton Ratnarajah'] | 2021-10-07 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [-5.21794520e-02 -4.88971889e-01 1.32064021e+00 -2.66497970e-01
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92529a1d-8cae-4003-a760-a018d3c560f5 | micro-expression-recognition-based-on | 2205.14643 | null | https://arxiv.org/abs/2205.14643v1 | https://arxiv.org/pdf/2205.14643v1.pdf | Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning | Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions. Therefore, recognizing micro-expressions is a challenge task. In this paper, we propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We use 3D CNN to extract RGB features and FLOW features of micro-expression sequences and fuse them, and use BERT network to extract text information in Facial Action Coding System. Through cross-modal contrastive loss, we embed attribute information in the visual network, thereby improving the representation ability of micro-expression recognition in the case of limited samples. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively. The comparative experiments show that this method has better recognition effect than other methods for micro-expression recognition. | ['Jing Xiao', 'Zhangcheng Huang', 'Tianbo Wu', 'Jianzong Wang', 'Yanxin Song'] | 2022-05-29 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 4.00851145e-02 -5.74718535e-01 -2.28492066e-01 -5.30397654e-01
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a2bb4996-d637-4073-a2f6-19d85b82acf2 | signal-novelty-detection-as-an-intrinsic | null | null | https://www.mdpi.com/1424-8220/23/8/3985 | https://www.mdpi.com/1424-8220/23/8/3985/pdf | Signal Novelty Detection as an Intrinsic Reward for Robotics | In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task’s completion or failure, leading to slow convergence. Additional intrinsic rewards based on the state visitation frequency can provide more feedback. In this study, an Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space. The neural network processed signals from various types of sensors simultaneously. It was tested on simulated robotic agents in a benchmark set of classic control OpenAI Gym test environments (including Mountain Car, Acrobot, CartPole, and LunarLander), achieving more efficient and accurate robot control in three of the four tasks (with only slight degradation in the Lunar Lander task) when purely intrinsic rewards were used compared to standard extrinsic rewards. By incorporating autoencoder-based intrinsic rewards, robots could potentially become more dependable in autonomous operations like space or underwater exploration or during natural disaster response. This is because the system could better adapt to changing environments or unexpected situations. | ['Jiří Pospíchal', 'Iveta Dirgová Luptáková', 'Martin Kubovčík'] | 2023-04-14 | null | null | null | mdpi-sensors-2023-4 | ['acrobot'] | ['playing-games'] | [-1.12372555e-01 2.06864342e-01 4.04418167e-03 -1.63090184e-01
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83803577-2353-4f04-9b36-c8513c26842d | analysis-of-different-losses-for-deep | 2204.02980 | null | https://arxiv.org/abs/2204.02980v3 | https://arxiv.org/pdf/2204.02980v3.pdf | Analysis of Different Losses for Deep Learning Image Colorization | Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output. Some of them are based on a specific type of error between the predicted image and ground truth one, while other losses rely on the comparison of perceptual properties. But, is the choice of the objective function that crucial, i.e., does it play an important role in the results? In this chapter, we aim to answer this question by analyzing the impact of the loss function on the estimated colorization results. To that goal, we review the different losses and evaluation metrics that are used in the literature. We then train a baseline network with several of the reviewed objective functions: classic L1 and L2 losses, as well as more complex combinations such as Wasserstein GAN and VGG-based LPIPS loss. Quantitative results show that the models trained with VGG-based LPIPS provide overall slightly better results for most evaluation metrics. Qualitative results exhibit more vivid colors when with Wasserstein GAN plus the L2 loss or again with the VGG-based LPIPS. Finally, the convenience of quantitative user studies is also discussed to overcome the difficulty of properly assessing on colorized images, notably for the case of old archive photographs where no ground truth is available. | ['Patricia Vitoria', 'Lara Raad', 'Rémi Giraud', 'Michaël Clément', 'Hernan Carrillo', 'Aurélie Bugeau', 'Coloma Ballester'] | 2022-04-06 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.02227077e-01 4.18420695e-02 1.40241235e-01 -2.63765037e-01
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a3ccec13-a63d-4807-83fc-2d7eb579468c | audio-denoising-with-deep-network-priors | 1904.07612 | null | https://arxiv.org/abs/1904.07612v3 | https://arxiv.org/pdf/1904.07612v3.pdf | Speech Denoising by Accumulating Per-Frequency Modeling Fluctuations | We present a method for audio denoising that combines processing done in both the time domain and the time-frequency domain. Given a noisy audio clip, the method trains a deep neural network to fit this signal. Since the fitting is only partly successful and is able to better capture the underlying clean signal than the noise, the output of the network helps to disentangle the clean audio from the rest of the signal. This is done by accumulating a fitting score per time-frequency bin and applying the time-frequency domain filtering based on the obtained scores. The method is completely unsupervised and only trains on the specific audio clip that is being denoised. Our experiments demonstrate favorable performance in comparison to the literature methods. Our code and samples are available at github.com/mosheman5/DNP and as supplementary. Index Terms: Audio denoising; Unsupervised learning | ['Michael Michelashvili', 'Lior Wolf'] | 2019-04-16 | null | null | null | null | ['audio-denoising', 'speech-denoising'] | ['audio', 'speech'] | [ 3.10017258e-01 -1.97349161e-01 4.20521259e-01 -1.93587273e-01
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8e0ce48a-dfb4-4543-bc55-bf9dff108d43 | accelerated-training-for-matrix-norm | null | null | http://papers.nips.cc/paper/4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach | http://papers.nips.cc/paper/4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach.pdf | Accelerated Training for Matrix-norm Regularization: A Boosting Approach | Sparse learning models typically combine a smooth loss with a nonsmooth penalty, such as trace norm. Although recent developments in sparse approximation have offered promising solution methods, current approaches either apply only to matrix-norm constrained problems or provide suboptimal convergence rates. In this paper, we propose a boosting method for regularized learning that guarantees $\epsilon$ accuracy within $O(1/\epsilon)$ iterations. Performance is further accelerated by interlacing boosting with fixed-rank local optimization---exploiting a simpler local objective than previous work. The proposed method yields state-of-the-art performance on large-scale problems. We also demonstrate an application to latent multiview learning for which we provide the first efficient weak-oracle. | ['Yao-Liang Yu', 'Xinhua Zhang', 'Dale Schuurmans'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['multiview-learning'] | ['computer-vision'] | [-5.84721714e-02 -1.00756526e-01 -4.39628094e-01 -6.22058988e-01
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ba0068d6-22ce-49dd-b417-aca7236467d7 | semi-automating-knowledge-base-construction | 2005.08146 | null | https://arxiv.org/abs/2005.08146v2 | https://arxiv.org/pdf/2005.08146v2.pdf | Semi-Automating Knowledge Base Construction for Cancer Genetics | In this work, we consider the exponentially growing subarea of genetics in cancer. The need to synthesize and centralize this evidence for dissemination has motivated a team of physicians to manually construct and maintain a knowledge base that distills key results reported in the literature. This is a laborious process that entails reading through full-text articles to understand the study design, assess study quality, and extract the reported cancer risk estimates associated with particular hereditary cancer genes (i.e., penetrance). In this work, we propose models to automatically surface key elements from full-text cancer genetics articles, with the ultimate aim of expediting the manual workflow currently in place. We propose two challenging tasks that are critical for characterizing the findings reported cancer genetics studies: (i) Extracting snippets of text that describe \emph{ascertainment mechanisms}, which in turn inform whether the population studied may introduce bias owing to deviations from the target population; (ii) Extracting reported risk estimates (e.g., odds or hazard ratios) associated with specific germline mutations. The latter task may be viewed as a joint entity tagging and relation extraction problem. To train models for these tasks, we induce distant supervision over tokens and snippets in full-text articles using the manually constructed knowledge base. We propose and evaluate several model variants, including a transformer-based joint entity and relation extraction model to extract <germline mutation, risk-estimate>} pairs. We observe strong empirical performance, highlighting the practical potential for such models to aid KB construction in this space. We ablate components of our model, observing, e.g., that a joint model for <germline mutation, risk-estimate> fares substantially better than a pipelined approach. | ['Kevin S. Hughes', 'Kanhua Yin', 'Somin Wadhwa', 'Byron C. Wallace'] | 2020-05-17 | null | https://openreview.net/forum?id=EQrvONEwh | https://openreview.net/pdf?id=EQrvONEwh | akbc-2020-6 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 6.48127079e-01 5.27483463e-01 -7.63260126e-01 -2.41041139e-01
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ff96c786-6351-4831-952e-a95d47e0b304 | probabilistic-model-of-narratives-over | 2004.06793 | null | https://arxiv.org/abs/2004.06793v1 | https://arxiv.org/pdf/2004.06793v1.pdf | Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model | Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data. | ['Ivan Garibay', 'Toktam A. Oghaz', 'Niloofar Yousefi', 'Jasser Jasser', 'Ece C. Mutlu'] | 2020-04-14 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [-3.97669002e-02 -8.19270611e-02 -4.83020425e-01 -2.77553853e-02
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d57ed381-270e-4785-86ed-39a2dae8b706 | gaze-estimation-approach-using-deep | 2208.04298 | null | https://arxiv.org/abs/2208.04298v1 | https://arxiv.org/pdf/2208.04298v1.pdf | Gaze Estimation Approach Using Deep Differential Residual Network | Gaze estimation, which is a method to determine where a person is looking at given the person's full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with $angular-error$ of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images. | ['Ahmad Chaddad', 'Ahmed Bouridane', 'Haoyu Wang', 'Xu Wang', 'Yujie Li', 'Longzhao Huang'] | 2022-08-08 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.39749482e-01 -2.91711255e-03 -7.06829205e-02 -5.18290162e-01
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8d187836-7eff-47eb-9eb3-54c72abbd292 | increasing-the-usefulness-of-already-existing | 2303.06727 | null | https://arxiv.org/abs/2303.06727v1 | https://arxiv.org/pdf/2303.06727v1.pdf | Increasing the usefulness of already existing annotations through WSI registration | Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the analysis of whole-slide-images (WSIs) of immunohistochemically (IHC) stained tissue sections could benefit from computational pathology methods. However, scoring biomarkers such as KI67 in IHC WSIs often necessitates the detection of areas of invasive cancer. Training cancer detection models often requires annotations, which is time-consuming and therefore costly. Currently, cancer regions are typically annotated in WSIs of haematoxylin and eosin (H&E) stained tissue sections. In this study, we investigate the possibility to register annotations that were made in H&E WSIs to their IHC counterparts. Two pathologists annotated regions of invasive cancer in WSIs of 272 breast cancer cases. For each case, a matched H&E and KI67 WSI are available, resulting in 544 WSIs with invasive cancer annotations. We find that cancer detection CNNs that were trained with annotations registered from the H&E to the KI67 WSIs only differ slightly in calibration but not in performance compared to cancer detection models trained on annotations made directly in the KI67 WSIs in a test set consisting of 54 cases. The mean slide-level AUROC is 0.974 [0.964, 0.982] for models trained with the KI67 annotations and 0.974 [0.965, 0.982] for models trained using registered annotations. This indicates that WSI registration has the potential to reduce the need for IHC-specific annotations. This could significantly increase the usefulness of already existing annotations. | ['Mattias Rantalainen', 'Johan Hartman', 'Daniel Budelmann', 'Stephanie Robertson', 'Balazs Acs', 'Viktoria Sartor', 'Philippe Weitz'] | 2023-03-12 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.39417121e-01 4.18433994e-01 -2.87295312e-01 -1.18615977e-01
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22be240b-2f79-41b4-920e-84fcdda89a74 | truncated-tensor-schatten-p-norm-based | 2205.09390 | null | https://arxiv.org/abs/2205.09390v1 | https://arxiv.org/pdf/2205.09390v1.pdf | Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns | Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in making better decisions. However, traffic data in reality often has corrupted or incomplete values due to detector and communication malfunctions. Data imputation is thus required to ensure the effectiveness of downstream data-driven applications. To this end, numerous tensor-based methods treating the imputation problem as the low-rank tensor completion (LRTC) have been attempted in previous works. To tackle rank minimization, which is at the core of the LRTC, most of aforementioned methods utilize the tensor nuclear norm (NN) as a convex surrogate for the minimization. However, the over-relaxation issue in NN refrains it from desirable performance in practice. In this paper, we define an innovative nonconvex truncated Schatten p-norm for tensors (TSpN) to approximate tensor rank and impute missing spatiotemporal traffic data under the LRTC framework. We model traffic data into a third-order tensor structure of (time intervals,locations (sensors),days) and introduce four complicated missing patterns, including random missing and three fiber-like missing cases according to the tensor mode-n fibers. Despite nonconvexity of the objective function in our model, we derive the global optimal solutions by integrating the alternating direction method of multipliers (ADMM) with generalized soft-thresholding (GST). In addition, we design a truncation rate decay strategy to deal with varying missing rate scenarios. Comprehensive experiments are finally conducted using real-world spatiotemporal datasets, which demonstrate that the proposed LRTC-TSpN method performs well under various missing cases, meanwhile outperforming other SOTA tensor-based imputation models in almost all scenarios. | ['Jian Sun', 'Guoyang Qin', 'Tong Nie'] | 2022-05-19 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [ 1.73315912e-01 -6.00273073e-01 -2.96509713e-01 -3.11534435e-01
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afde3a5e-9610-4766-8c22-4c0522cc96da | progressive-residual-learning-for-single | 2103.07973 | null | https://arxiv.org/abs/2103.07973v1 | https://arxiv.org/pdf/2103.07973v1.pdf | Progressive residual learning for single image dehazing | The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when applied to real scenarios due to the unavailable or insufficient data problems. On the other hand, the physical model-based methods have better interpretability but suffer from multi-objective optimizations of parameters, which may lead to sub-optimal dehazing results. In this paper, a progressive residual learning strategy has been proposed to combine the physical model-free dehazing process with reformulated scattering model-based dehazing operations, which enjoys the merits of dehazing methods in both categories. Specifically, the global atmosphere light and transmission maps are interactively optimized with the aid of accurate residual information and preliminary dehazed restorations from the initial physical model-free dehazing process. The proposed method performs favorably against the state-of-the-art methods on public dehazing benchmarks with better model interpretability and adaptivity for complex hazy data. | ['Wenqi Ren', 'Yuhua Qian', 'Deyu Li', 'Jiaying Liu', 'Bin Wang', 'Yudong Liang'] | 2021-03-14 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.27312645e-01 -2.14202449e-01 6.84598923e-01 -3.60355258e-01
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b1c181b6-2888-43f7-adc3-ca7d7360f063 | lungattn-advanced-lung-sound-classification | null | null | https://iopscience.iop.org/article/10.1088/1361-6579/ac27b9 | https://iopscience.iop.org/article/10.1088/1361-6579/ac27b9 | LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram | Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians.Approach. We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound. We adopt a feature extraction method based on dual tunable Q-factor wavelet transform and triple short-time Fourier transform to obtain a multi-channel spectrogram. Mixup method is introduced to augment adventitious lung sound recordings to address the imbalance dataset problem.Main results. Based on the ICBHI 2017 challenge dataset, we implement our framework and compare with the state-of-the-art works. Experimental results show that LungAttn has achieved the Sensitivity, Se, Specificity, Sp and Score of 36.36%, 71.44% and 53.90%, respectively. Of which, our work has improved the Scoreby 1.69% compared to the state-of-the-art models based on the official ICBHI 2017 dataset splitting method.Significance. Multi-channel spectrogram based on different oscillatory behavior of adventitious lung sound provides necessary information of lung sound recordings. Attention mechanism is introduced to lung sound classification methods and has proved to be effective. The proposed LungAttn model can potentially improve the speed and accuracy of lung sound classification in clinical practice. | ['Guoxing Wang', 'Liebin Zhao', 'Yongfu Li', 'Yi Ma', 'Qianyu Guo', 'Shijian Liu', 'Hansong Wang', 'Jiajun Yuan', 'Jizuo Li'] | 2021-10-29 | null | null | null | physiological-measurement-2021-10 | ['sound-classification'] | ['audio'] | [-1.25615209e-01 -3.11544746e-01 2.63884128e-03 3.23746622e-01
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6247b2c0-9b63-4696-bd77-a8e867497f81 | differentiable-mathematical-programming-for | 2210.02159 | null | https://arxiv.org/abs/2210.02159v1 | https://arxiv.org/pdf/2210.02159v1.pdf | Differentiable Mathematical Programming for Object-Centric Representation Learning | We propose topology-aware feature partitioning into $k$ disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a partitioning method which is represented as a linear program. The method is topologically aware since it explicitly encodes neighborhood relationships in the image graph. To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation. Optimizations specific to cut problems allow us to solve the quadratic programs and compute their gradients significantly more efficiently compared with the general quadratic programming approach. Our results show that our approach is scalable and outperforms existing methods on object discovery tasks with textured scenes and objects. | ['Efstratios Gavves', 'Phillip Lippe', 'Adeel Pervez'] | 2022-10-05 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [-1.53383613e-01 1.24565102e-01 -4.27096158e-01 -7.12146759e-01
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3f809d56-6674-4d24-b57f-1b3b15c1aca4 | detecting-gan-generated-imagery-using-color | 1812.08247 | null | http://arxiv.org/abs/1812.08247v1 | http://arxiv.org/pdf/1812.08247v1.pdf | Detecting GAN-generated Imagery using Color Cues | Image forensics is an increasingly relevant problem, as it can potentially
address online disinformation campaigns and mitigate problematic aspects of
social media. Of particular interest, given its recent successes, is the
detection of imagery produced by Generative Adversarial Networks (GANs), e.g.
`deepfakes'. Leveraging large training sets and extensive computing resources,
recent work has shown that GANs can be trained to generate synthetic imagery
which is (in some ways) indistinguishable from real imagery. We analyze the
structure of the generating network of a popular GAN implementation, and show
that the network's treatment of color is markedly different from a real camera
in two ways. We further show that these two cues can be used to distinguish
GAN-generated imagery from camera imagery, demonstrating effective
discrimination between GAN imagery and real camera images used to train the
GAN. | ['Michael Albright', 'Scott McCloskey'] | 2018-12-19 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 9.35834229e-01 2.01206937e-01 -2.85655018e-02 1.01801626e-01
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b2b19169-1824-4982-bf98-38b9f1cc990e | does-black-box-attribute-inference-attacks-on | 2306.00578 | null | https://arxiv.org/abs/2306.00578v1 | https://arxiv.org/pdf/2306.00578v1.pdf | Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk? | Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education. However, recent studies have shown that GNNs are highly vulnerable to attacks such as membership inference attack and link reconstruction attack. Surprisingly, attribute inference attacks has received little attention. In this paper, we initiate the first investigation into attribute inference attack where an attacker aims to infer the sensitive user attributes based on her public or non-sensitive attributes. We ask the question whether black-box attribute inference attack constitutes a significant privacy risk for graph-structured data and their corresponding GNN model. We take a systematic approach to launch the attacks by varying the adversarial knowledge and assumptions. Our findings reveal that when an attacker has black-box access to the target model, GNNs generally do not reveal significantly more information compared to missing value estimation techniques. Code is available. | ['Megha Khosla', 'Oliver Sihlovec', 'Anmar Hizber', 'Iyiola E. Olatunji'] | 2023-06-01 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 4.60127920e-01 7.75867164e-01 -3.20921987e-01 -5.19500554e-01
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1d2676b3-ba5f-4ff4-8880-2d61f82d70eb | echovpr-echo-state-networks-for-visual-place | 2110.05572 | null | https://arxiv.org/abs/2110.05572v3 | https://arxiv.org/pdf/2110.05572v3.pdf | EchoVPR: Echo State Networks for Visual Place Recognition | Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models to recover the position of a query image (or images) from sequential datasets that include both spatial and temporal components. Recently, Echo State Network (ESN) varieties have proven particularly powerful at solving machine learning tasks that require spatio-temporal modelling. These networks are simple, yet powerful neural architectures that--exhibiting memory over multiple time-scales and non-linear high-dimensional representations--can discover temporal relations in the data while still maintaining linearity in the learning time. In this paper, we present a series of ESNs and analyse their applicability to the VPR problem. We report that the addition of ESNs to pre-processed convolutional neural networks led to a dramatic boost in performance in comparison to non-recurrent networks in five out of six standard benchmarks (GardensPoint, SPEDTest, ESSEX3IN1, Oxford RobotCar, and Nordland), demonstrating that ESNs are able to capture the temporal structure inherent in VPR problems. Moreover, we show that models that include ESNs can outperform class-leading VPR models which also exploit the sequential dynamics of the data. Finally, our results demonstrate that ESNs improve generalisation abilities, robustness, and accuracy further supporting their suitability to VPR applications. | ['Luca Manneschi', 'Eleni Vasilaki', 'Michael Mangan', 'Andrew Philippides', 'Andrew B. Barron', 'Mark Scerri', 'Anil Ozdemir'] | 2021-10-11 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 9.97191742e-02 -4.04639363e-01 -5.64553514e-02 -1.73221469e-01
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e0c43bed-a9a4-45e0-8111-c5e59a84b327 | semeval-2014-task-8-broad-coverage-semantic | null | null | https://aclanthology.org/S14-2008 | https://aclanthology.org/S14-2008.pdf | SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing | null | ['Jan Haji{\\v{c}}', 'Daniel Zeman', 'Yusuke Miyao', 'Stephan Oepen', 'Angelina Ivanova', 'Yi Zhang', 'Marco Kuhlmann', 'Dan Flickinger'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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875fac6f-ab9d-4f66-b1a7-f9878960857f | fseval-a-benchmarking-framework-for-feature | null | null | https://joss.theoj.org/papers/10.21105/joss.04611 | https://www.theoj.org/joss-papers/joss.04611/10.21105.joss.04611.pdf | fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms | The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables users to run experiments in parallel and distributed over multiple machines, and export the results to an SQL database. The execution of an experiment can be fully determined by a configuration file, which means the experiment results can be reproduced easily, given only the configuration file. fseval has high test coverage, continuous integration, and rich documentation. The package is open source and can be installed through PyPI. The source code is available at: https://github.com/dunnkers/fseval. | ['George Azzopardi', 'Ahmad Alsahaf', 'Jeroen G. S. Overschie'] | 2022-11-23 | null | null | null | journal-of-open-source-software-2022-11 | ['automated-feature-engineering', 'feature-engineering', 'classification-with-costly-features'] | ['methodology', 'methodology', 'miscellaneous'] | [-4.15864468e-01 -8.01459014e-01 -2.22067639e-01 -4.63483155e-01
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e2117266-6f76-4689-94f6-b166fe72c5aa | scandmm-a-deep-markov-model-of-scanpath | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.pdf | ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images | Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism. Most existing scanpath prediction methods for 360deg images do not give a complete treatment of the time-dependency when predicting human scanpath, resulting in inferior performance and poor generalizability. In this paper, we present a scanpath prediction method for 360deg images by designing a novel Deep Markov Model (DMM) architecture, namely ScanDMM. We propose a semantics-guided transition function to learn the nonlinear dynamics of time-dependent attentional landscape. Moreover, a state initialization strategy is proposed by considering the starting point of viewing, enabling the model to learn the dynamics with the correct "launcher". We further demonstrate that our model achieves state-of-the-art performance on four 360deg image databases, and exhibit its generalizability by presenting two applications of applying scanpath prediction models to other visual tasks - saliency detection and image quality assessment, expecting to provide profound insights into these fields. | ['Zhou Wang', 'Shiqi Wang', 'Hanwei Zhu', 'Yuming Fang', 'Xiangjie Sui'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['saliency-detection', 'image-quality-assessment', 'scanpath-prediction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.44815663e-01 2.03704625e-01 -4.79856730e-01 -5.09685636e-01
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58bae9e7-134b-42e9-8840-769b93002981 | fusion-gcn-multimodal-action-recognition | 2109.12946 | null | https://arxiv.org/abs/2109.12946v1 | https://arxiv.org/pdf/2109.12946v1.pdf | Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks | In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance for skeleton-based action recognition. With Fusion-GCN, we propose to integrate various sensor data modalities into a graph that is trained using a GCN model for multi-modal action recognition. Additional sensor measurements are incorporated into the graph representation, either on a channel dimension (introducing additional node attributes) or spatial dimension (introducing new nodes). Fusion-GCN was evaluated on two public available datasets, the UTD-MHAD- and MMACT datasets, and demonstrates flexible fusion of RGB sequences, inertial measurements and skeleton sequences. Our approach gets comparable results on the UTD-MHAD dataset and improves the baseline on the large-scale MMACT dataset by a significant margin of up to 12.37% (F1-Measure) with the fusion of skeleton estimates and accelerometer measurements. | ['Dietrich Paulus', 'Raphael Memmesheimer', 'Michael Duhme'] | 2021-09-27 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 5.67188025e-01 1.52337447e-01 -3.05111080e-01 -3.51223260e-01
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c878610d-1df3-4d04-a20b-6daed2e36711 | cross-lingual-transfer-learning-for-phrase | 2306.02579 | null | https://arxiv.org/abs/2306.02579v1 | https://arxiv.org/pdf/2306.02579v1.pdf | Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model | Phrase break prediction is a crucial task for improving the prosody naturalness of a text-to-speech (TTS) system. However, most proposed phrase break prediction models are monolingual, trained exclusively on a large amount of labeled data. In this paper, we address this issue for low-resource languages with limited labeled data using cross-lingual transfer. We investigate the effectiveness of zero-shot and few-shot cross-lingual transfer for phrase break prediction using a pre-trained multilingual language model. We use manually collected datasets in four Indo-European languages: one high-resource language and three with limited resources. Our findings demonstrate that cross-lingual transfer learning can be a particularly effective approach, especially in the few-shot setting, for improving performance in low-resource languages. This suggests that cross-lingual transfer can be inexpensive and effective for developing TTS front-end in resource-poor languages. | ['Jae-Min Kim', 'Jong-Hwan Kim', 'Hyun-Wook Yoon', 'Hoyeon Lee'] | 2023-06-05 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.11200818e-01 -2.88465410e-01 -7.50216663e-01 -2.75918007e-01
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668a89b7-9d2b-4883-b617-3ba0b20caf26 | autoselect-automatic-and-dynamic-detection | 2012.05894 | null | https://arxiv.org/abs/2012.05894v1 | https://arxiv.org/pdf/2012.05894v1.pdf | AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking | 3D multi-object tracking is an important component in robotic perception systems such as self-driving vehicles. Recent work follows a tracking-by-detection pipeline, which aims to match past tracklets with detections in the current frame. To avoid matching with false positive detections, prior work filters out detections with low confidence scores via a threshold. However, finding a proper threshold is non-trivial, which requires extensive manual search via ablation study. Also, this threshold is sensitive to many factors such as target object category so we need to re-search the threshold if these factors change. To ease this process, we propose to automatically select high-quality detections and remove the efforts needed for manual threshold search. Also, prior work often uses a single threshold per data sequence, which is sub-optimal in particular frames or for certain objects. Instead, we dynamically search threshold per frame or per object to further boost performance. Through experiments on KITTI and nuScenes, our method can filter out $45.7\%$ false positives while maintaining the recall, achieving new S.O.T.A. performance and removing the need for manually threshold tuning. | ['Kris Kitani', 'Xinshuo Weng'] | 2020-12-10 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 5.36783524e-02 -5.36218762e-01 -1.77480727e-01 -2.96589464e-01
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88ee5fb4-f26c-4db1-8534-111a9ebbab18 | fidnet-lidar-point-cloud-semantic | 2109.03787 | null | https://arxiv.org/abs/2109.03787v1 | https://arxiv.org/pdf/2109.03787v1.pdf | FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding | Projecting the point cloud on the 2D spherical range image transforms the LiDAR semantic segmentation to a 2D segmentation task on the range image. However, the LiDAR range image is still naturally different from the regular 2D RGB image; for example, each position on the range image encodes the unique geometry information. In this paper, we propose a new projection-based LiDAR semantic segmentation pipeline that consists of a novel network structure and an efficient post-processing step. In our network structure, we design a FID (fully interpolation decoding) module that directly upsamples the multi-resolution feature maps using bilinear interpolation. Inspired by the 3D distance interpolation used in PointNet++, we argue this FID module is a 2D version distance interpolation on $(\theta, \phi)$ space. As a parameter-free decoding module, the FID largely reduces the model complexity by maintaining good performance. Besides the network structure, we empirically find that our model predictions have clear boundaries between different semantic classes. This makes us rethink whether the widely used K-nearest-neighbor post-processing is still necessary for our pipeline. Then, we realize the many-to-one mapping causes the blurring effect that some points are mapped into the same pixel and share the same label. Therefore, we propose to process those occluded points by assigning the nearest predicted label to them. This NLA (nearest label assignment) post-processing step shows a better performance than KNN with faster inference speed in the ablation study. On the SemanticKITTI dataset, our pipeline achieves the best performance among all projection-based methods with $64 \times 2048$ resolution and all point-wise solutions. With a ResNet-34 as the backbone, both the training and testing of our model can be finished on a single RTX 2080 Ti with 11G memory. The code is released. | ['Xinming Huang', 'Lin Bai', 'Yiming Zhao'] | 2021-09-08 | null | null | null | null | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.01561683e-01 1.62197396e-01 -8.88906419e-02 -8.06059718e-01
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83cea8ea-8c77-47f6-a56e-caa4c20a2932 | multi-objective-consensus-clustering | 2002.10241 | null | https://arxiv.org/abs/2002.10241v2 | https://arxiv.org/pdf/2002.10241v2.pdf | Multi-objective Consensus Clustering Framework for Flight Search Recommendation | In the travel industry, online customers book their travel itinerary according to several features, like cost and duration of the travel or the quality of amenities. To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required. Clustering ensemble approaches were developed to overcome well-known problems of classical clustering approaches, that each rely on a different theoretical model and can thus identify in the data space only clusters corresponding to this model. Clustering ensemble approaches combine multiple clustering results, each from a different algorithmic configuration, for generating more robust consensus clusters corresponding to agreements between initial clusters. We present a new clustering ensemble multi-objective optimization-based framework developed for analyzing Amadeus customer search data and improve personalized recommendations. This framework optimizes diversity in the clustering ensemble search space and automatically determines an appropriate number of clusters without requiring user's input. Experimental results compare the efficiency of this approach with other existing approaches on Amadeus customer search data in terms of internal (Adjusted Rand Index) and external (Amadeus business metric) validations. | ['Nicolas Pasquier', 'Simon Nanty', 'Sujoy Chatterjee', 'Maria A. Zuluaga'] | 2020-02-20 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-3.22345197e-01 -3.99819285e-01 -1.75857142e-01 -7.99730420e-01
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f0035e3e-daaf-4b08-b6ab-6b6cd4511313 | an-efficient-multilingual-language-model | 2305.15020 | null | https://arxiv.org/abs/2305.15020v1 | https://arxiv.org/pdf/2305.15020v1.pdf | An Efficient Multilingual Language Model Compression through Vocabulary Trimming | Multilingual language model (LM) have become a powerful tool in NLP especially for non-English languages. Nevertheless, model parameters of multilingual LMs remain large due to the larger embedding matrix of the vocabulary covering tokens in different languages. On the contrary, monolingual LMs can be trained in a target language with the language-specific vocabulary only, but this requires a large budget and availability of reliable corpora to achieve a high-quality LM from scratch. In this paper, we propose vocabulary-trimming (VT), a method to reduce a multilingual LM vocabulary to a target language by deleting irrelevant tokens from its vocabulary. In theory, VT can compress any existing multilingual LM to build monolingual LMs in any language covered by the multilingual LM. In our experiments, we show that VT can retain the original performance of the multilingual LM, while being smaller in size (in general around 50% of the original vocabulary size is enough) than the original multilingual LM. The evaluation is performed over four NLP tasks (two generative and two classification tasks) among four widely used multilingual LMs in seven languages. Finally, we show that this methodology can keep the best of both monolingual and multilingual worlds by keeping a small size as monolingual models without the need for specifically retraining them, and even limiting potentially harmful social biases. | ['Jose Camacho-Collados', 'Yi Zhou', 'Asahi Ushio'] | 2023-05-24 | null | null | null | null | ['model-compression'] | ['methodology'] | [-3.74746978e-01 2.81454355e-01 -4.24572498e-01 -3.39496098e-02
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8ac9c5a4-163d-4a01-b344-4f13b0c6677c | the-brain-tumor-segmentation-brats-challenge-3 | 2305.19369 | null | https://arxiv.org/abs/2305.19369v1 | https://arxiv.org/pdf/2305.19369v1.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa) | Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely. | ['Udunna C Anazodo', 'Abiodun Fatade', 'Farouk Dako', 'Spyridon Bakas', 'Ujjwal Baid', 'Bjoern H Menze', 'Zeke Meier', 'Elaine Johansson', 'Gian-Marco Conte', 'Maire Piraud', 'Christina Bukas', 'Koen van Leemput', 'Ariana Familiar', 'Zhifan Jiang', 'Xinyang Liu', 'Chunhao Wang', 'Zachary Reitman', 'Walter Wiggins', 'Russell Takeshi Shinohara', 'Verena Chung', 'Timothy Bergquist', 'James Eddy', 'Keyvan Farahani', 'Juan Eugenio Iglesias', 'Hongwei Bran Li', 'Anahita Fathi Kzerooni', 'Dominic LaBella', 'Anastasia Janas', 'Florian Kofler', 'Benedikt Wiestler', 'Jake Albrecht', 'Marius Linguraru', 'Mariam Aboian', 'Evan Calabrese', 'Kator Iorpagher', 'Yewande Gbadamosi', 'Afolabi Ogunleye', 'Gabriel Babatunde', 'Chinasa Kalaiwo', 'Kenneth Aguh', 'Nancy Ojo', 'Adaobi Emegoakor', 'Mohammad Abba Suwaid', 'Rachel Akinola', 'Olubukola Omidiji', 'Dong Zhang', 'Confidence Raymond', 'Oluyemisi Toyobo', 'Anu Gbadamosi', 'Jeffrey D. Rudie', 'Maruf Adewole'] | 2023-05-30 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 2.77692489e-02 -2.64011994e-02 -1.93749085e-01 -3.74119193e-03
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6b2feeaa-4be0-4674-a5e8-1009822ad931 | perceiving-unseen-3d-objects-by-poking-the | 2302.13375 | null | https://arxiv.org/abs/2302.13375v1 | https://arxiv.org/pdf/2302.13375v1.pdf | Perceiving Unseen 3D Objects by Poking the Objects | We present a novel approach to interactive 3D object perception for robots. Unlike previous perception algorithms that rely on known object models or a large amount of annotated training data, we propose a poking-based approach that automatically discovers and reconstructs 3D objects. The poking process not only enables the robot to discover unseen 3D objects but also produces multi-view observations for 3D reconstruction of the objects. The reconstructed objects are then memorized by neural networks with regular supervised learning and can be recognized in new test images. The experiments on real-world data show that our approach could unsupervisedly discover and reconstruct unseen 3D objects with high quality, and facilitate real-world applications such as robotic grasping. The code and supplementary materials are available at the project page: https://zju3dv.github.io/poking_perception. | ['Xiaowei Zhou', 'Hujun Bao', 'Yunzhou Song', 'Linghao Chen'] | 2023-02-26 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 2.19615418e-02 3.42563957e-01 -2.64170486e-02 -3.08273435e-01
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2143b651-8de4-453f-9976-ad64aa98f650 | w2kpe-keyphrase-extraction-with-word-word | 2303.13463 | null | https://arxiv.org/abs/2303.13463v1 | https://arxiv.org/pdf/2303.13463v1.pdf | W2KPE: Keyphrase Extraction with Word-Word Relation | This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials. We model the challenge as a single-class Named Entity Recognition task and developed techniques for better performance on the challenge: For the data preprocessing, we encode the split keyphrases after word segmentation. In addition, we increase the amount of input information that the model can accept at one time by fusing multiple preprocessed sentences into one segment. We replace the loss function with the multi-class focal loss to address the sparseness of keyphrases. Besides, we score each appearance of keyphrases and add an extra output layer to fit the score to rank keyphrases. Exhaustive evaluations are performed to find the best combination of the word segmentation tool, the pre-trained embedding model, and the corresponding hyperparameters. With these proposals, we scored 45.04 on the final test set. | ['Wei Wang', 'Shichen Dong', 'Wen Cheng'] | 2023-03-22 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.50598556e-01 5.39846383e-02 -3.18652272e-01 -1.88791975e-01
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c93b94cf-c2a8-43df-8758-f185e07a1a06 | neural-network-training-with-asymmetric | 2201.13377 | null | https://arxiv.org/abs/2201.13377v1 | https://arxiv.org/pdf/2201.13377v1.pdf | Neural Network Training with Asymmetric Crosspoint Elements | Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically degrades the classification performance of networks trained with conventional algorithms. Here, we describe and experimentally demonstrate an alternative fully-parallel training algorithm: Stochastic Hamiltonian Descent. Instead of conventionally tuning weights in the direction of the error function gradient, this method programs the network parameters to successfully minimize the total energy (Hamiltonian) of the system that incorporates the effects of device asymmetry. We provide critical intuition on why device asymmetry is fundamentally incompatible with conventional training algorithms and how the new approach exploits it as a useful feature instead. Our technique enables immediate realization of analog deep learning accelerators based on readily available device technologies. | ['Seyoung Kim', 'Wilfried Haensch', 'John Rozen', 'Jesus A. del Alamo', 'Tomasz Nowicki', 'Teodor K. Todorov', 'Tayfun Gokmen', 'Murat Onen'] | 2022-01-31 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 4.24826384e-01 -9.23300385e-02 -1.67469695e-01 -2.43548527e-01
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8.34580421e-01 -5.74001253e-01 9.82640505e-01 1.14569974e+00
5.32243311e-01 -1.00018203e+00 -2.07723841e-01 4.38686341e-01
1.41408384e-01 -6.52838290e-01 2.36061573e-01 4.79935668e-02
-3.56379114e-02 1.25967526e+00 7.33153999e-01 -4.04570997e-01
6.11027420e-01 9.18134093e-01 -3.98655355e-01 -2.92529285e-01
-7.45685577e-01 6.48607612e-01 -2.31169954e-01 3.94267797e-01
4.02069926e-01 -8.85247428e-04 -3.26688141e-01 4.42174733e-01
-2.32442468e-01 3.75698581e-02 6.78095639e-01 1.25712371e+00
-2.83443570e-01 -8.94228697e-01 3.76948178e-01 7.65318334e-01
-2.96814919e-01 -1.84378520e-01 -3.54016900e-01 3.79869401e-01
-3.15103173e-01 9.37862620e-02 2.21494138e-01 -6.94290280e-01
3.11309427e-01 4.39630240e-01 9.41277385e-01 5.08715883e-02
-9.32428002e-01 -7.65504539e-01 -2.24753290e-01 -2.83239633e-01
3.46171498e-01 -1.88867301e-01 -1.63581324e+00 -7.29238272e-01
-2.40533367e-01 1.27136454e-01 1.31903505e+00 7.26425290e-01
5.52105606e-01 5.92094600e-01 2.85466462e-01 -1.00214303e+00
-6.86657190e-01 -3.09905022e-01 -4.12209719e-01 -2.76160687e-01
1.83173656e-01 -6.41496003e-01 -4.52051550e-01 -8.90971482e-01] | [8.247868537902832, 2.555638074874878] |
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