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fc810c22-502c-489f-b848-b745fafee04b | near-zero-shot-suggestion-mining-with-a | 2111.12956 | null | https://arxiv.org/abs/2111.12956v1 | https://arxiv.org/pdf/2111.12956v1.pdf | Near-Zero-Shot Suggestion Mining with a Little Help from WordNet | In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest. To reduce training costs and annotation efforts needed to build a classifier for a specific label set, we present and evaluate several entailment-based zero-shot approaches to suggestion classification in a label-fully-unseen fashion. In particular, we introduce the strategy of assigning target class labels to sentences in English language with user intentions, which significantly improves prediction quality. The proposed strategies are evaluated with a comprehensive experimental study that validated our results both quantitatively and qualitatively. | ['Sergey Nikolenko', 'Sejeong Kwon', 'Elena Tutubalina', 'Anton Alekseev'] | 2021-11-25 | null | null | null | null | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.68833449e-01 2.72742629e-01 -7.95757771e-01 -9.60916698e-01
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a57c1625-2943-49e9-bd39-2def6dc398ba | investigating-emotion-color-association-in | 2011.11058 | null | https://arxiv.org/abs/2011.11058v1 | https://arxiv.org/pdf/2011.11058v1.pdf | Investigating Emotion-Color Association in Deep Neural Networks | It has been found that representations learned by Deep Neural Networks (DNNs) correlate very well to neural responses measured in primates' brains and psychological representations exhibited by human similarity judgment. On another hand, past studies have shown that particular colors can be associated with specific emotion arousal in humans. Do deep neural networks also learn this behavior? In this study, we investigate if DNNs can learn implicit associations in stimuli, particularly, an emotion-color association between image stimuli. Our study was conducted in two parts. First, we collected human responses on a forced-choice decision task in which subjects were asked to select a color for a specified emotion-inducing image. Next, we modeled this decision task on neural networks using the similarity between deep representation (extracted using DNNs trained on object classification tasks) of the images and images of colors used in the task. We found that our model showed a fuzzy linear relationship between the two decision probabilities. This results in two interesting findings, 1. The representations learned by deep neural networks can indeed show an emotion-color association 2. The emotion-color association is not just random but involves some cognitive phenomena. Finally, we also show that this method can help us in the emotion classification task, specifically when there are very few examples to train the model. This analysis can be relevant to psychologists studying emotion-color associations and artificial intelligence researchers modeling emotional intelligence in machines or studying representations learned by deep neural networks. | ['Shashi Kant Gupta', 'Shivi Gupta'] | 2020-11-22 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 1.63242772e-01 -2.03752130e-01 1.12630449e-01 -8.01884055e-01
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c37a33a3-be52-4ab1-8f28-6b6de7fe7e20 | syntactic-methods-for-negation-detection-in | null | null | https://aclanthology.org/W16-2921 | https://aclanthology.org/W16-2921.pdf | Syntactic methods for negation detection in radiology reports in Spanish | null | ['Jorge Vivaldi', 'Vanesa Stricker', 'Viviana Cotik', 'Horacio Rodriguez'] | 2016-08-01 | null | null | null | ws-2016-8 | ['negation-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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4d9b0030-dc65-4184-82c8-3113a6147ef5 | deep-reinforced-attention-regression-for | 2111.10917 | null | https://arxiv.org/abs/2111.10917v1 | https://arxiv.org/pdf/2111.10917v1.pdf | Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval | Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) aims at finding a specific image from a large gallery given a query sketch. Despite the widespread applicability of FG-SBIR in many critical domains (e.g., crime activity tracking), existing approaches still suffer from a low accuracy while being sensitive to external noises such as unnecessary strokes in the sketch. The retrieval performance will further deteriorate under a more practical on-the-fly setting, where only a partially complete sketch with only a few (noisy) strokes are available to retrieve corresponding images. We propose a novel framework that leverages a uniquely designed deep reinforcement learning model that performs a dual-level exploration to deal with partial sketch training and attention region selection. By enforcing the model's attention on the important regions of the original sketches, it remains robust to unnecessary stroke noises and improve the retrieval accuracy by a large margin. To sufficiently explore partial sketches and locate the important regions to attend, the model performs bootstrapped policy gradient for global exploration while adjusting a standard deviation term that governs a locator network for local exploration. The training process is guided by a hybrid loss that integrates a reinforcement loss and a supervised loss. A dynamic ranking reward is developed to fit the on-the-fly image retrieval process using partial sketches. The extensive experimentation performed on three public datasets shows that our proposed approach achieves the state-of-the-art performance on partial sketch based image retrieval. | ['Qi Yu', 'Xumin Liu', 'Hitesh Sapkota', 'Dingrong Wang'] | 2021-11-21 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.19893187e-01 -4.28062975e-01 -5.12211561e-01 -2.32960492e-01
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f2f34b32-d913-4816-9639-9382ecf0dda2 | recovering-remote-photoplethysmograph-signal | 1905.02419 | null | https://arxiv.org/abs/1905.02419v2 | https://arxiv.org/pdf/1905.02419v2.pdf | Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks | Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG). However for many medical applications (e.g., atrial fibrillation (AF) detection) knowing only the average HR is not sufficient, and measuring precise rPPG signals from face for heart rate variability (HRV) analysis is needed. Here we propose an rPPG measurement method, which is the first work to use deep spatio-temporal networks for reconstructing precise rPPG signals from raw facial videos. With the constraint of trend-consistency with ground truth pulse curves, our method is able to recover rPPG signals with accurate pulse peaks. Comprehensive experiments are conducted on two benchmark datasets, and results demonstrate that our method can achieve superior performance on both HR and HRV levels comparing to the state-of-the-art methods. We also achieve promising results of using reconstructed rPPG signals for AF detection and emotion recognition. | ['Xiaobai Li', 'Zitong Yu', 'Guoying Zhao'] | 2019-05-07 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 6.88832924e-02 -1.76040202e-01 -4.71120849e-02 -4.64344233e-01
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d0a045fc-2c28-414f-9584-e24017589d98 | spine-a-scalable-log-parser-with-feedback | null | null | https://dl.acm.org/doi/abs/10.1145/3540250.3549176 | https://dl.acm.org/doi/abs/10.1145/3540250.3549176 | SPINE: a scalable log parser with feedback guidance | Log parsing, which extracts log templates and parameters, is a critical prerequisite step for automated log analysis techniques. Though existing log parsers have achieved promising accuracy on public log datasets, they still face many challenges when applied in the industry. Through studying the characteristics of real-world log data and analyzing the limitations of existing log parsers, we identify two problems. Firstly, it is non-trivial to scale a log parser to a vast number of logs, especially in real-world scenarios where the log data is extremely imbalanced. Secondly, existing log parsers overlook the importance of user feedback, which is imperative for parser fine-tuning under the continuous evolution of log data. To overcome the challenges, we propose SPINE, which is a highly scalable log parser with user feedback guidance. Based on our log parser equipped with initial grouping and progressive clustering,we propose a novel log data scheduling algorithm to improve the efficiency of parallelization under the large-scale imbalanced log data. Besides, we introduce user feedback to make the parser fast adapt to the evolving logs. We evaluated SPINE on 16 public log datasets. SPINE achieves more than 0.90 parsing accuracy on average with the highest parsing efficiency, which outperforms the state-of-the-art log parsers. We also evaluated SPINE in the production environment of Microsoft, in which SPINE can parse 30million logs in less than 8 minutes under 16 executors, achieving near real-time performance. In addition, our evaluations show that SPINE can consistently achieve good accuracy under log evolution with a moderate number of user feedback. | ['Xuheng Wang'] | 2022-11-01 | null | null | null | acm-conferences-2022-11 | ['log-parsing'] | ['computer-code'] | [-2.08885819e-01 -4.08604711e-01 -2.46871606e-01 -4.22430575e-01
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0148579b-e791-4117-a7e1-cab3664d9333 | reproducibility-report-explainable-deep-one | 2206.02598 | null | https://arxiv.org/abs/2206.02598v1 | https://arxiv.org/pdf/2206.02598v1.pdf | [Reproducibility Report] Explainable Deep One-Class Classification | Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FCDD achieves results comparable with the state-of-the-art in sample-wise AD on Fashion-MNIST and CIFAR-10 and exceeds the state-of-the-art on the pixel-wise task on MVTec-AD. We reproduced the main results of the paper using the author's code with minor changes and provide runtime requirements to achieve if (CPU memory, GPU memory, and training time). We propose another analysis methodology using a critical difference diagram, and further investigate the test performance of the model during the training phase. | ['Etienne Decencière', 'Joao P. C. Bertoldo'] | 2022-06-06 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-2.03064919e-01 3.44525099e-01 1.38020411e-01 -4.76790667e-01
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a97493ab-5f5c-4191-a9de-994a1d949bb6 | palr-personalization-aware-llms-for | 2305.07622 | null | https://arxiv.org/abs/2305.07622v3 | https://arxiv.org/pdf/2305.07622v3.pdf | PALR: Personalization Aware LLMs for Recommendation | Large language models (LLMs) have recently received significant attention for their exceptional capabilities. Despite extensive efforts in developing general-purpose LLMs that can be utilized in various natural language processing (NLP) tasks, there has been less research exploring their potential in recommender systems. In this paper, we propose a novel framework, named PALR, which aiming to combine user history behaviors (such as clicks, purchases, ratings, etc.) with LLMs to generate user preferred items. Specifically, we first use user/item interactions as guidance for candidate retrieval. Then we adopt a LLM-based ranking model to generate recommended items. Unlike existing approaches that typically adopt general-purpose LLMs for zero/few-shot recommendation testing or training on small-sized language models (with less than 1 billion parameters), which cannot fully elicit LLMs' reasoning abilities and leverage rich item side parametric knowledge, we fine-tune a 7 billion parameters LLM for the ranking purpose. This model takes retrieval candidates in natural language format as input, with instruction which explicitly asking to select results from input candidates during inference. Our experimental results demonstrate that our solution outperforms state-of-the-art models on various sequential recommendation tasks. | ['Yanbin Lu', 'Xiaojiang Huang', 'Eunah Cho', 'Ziyan Jiang', 'Fan Yang', 'Zheng Chen'] | 2023-05-12 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.30539069e-01 -5.22893257e-02 -5.88039100e-01 -6.10921443e-01
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9bea0c7c-d67a-4535-9aca-420be0e191a0 | an-online-semantic-mapping-system-for | 2203.03944 | null | https://arxiv.org/abs/2203.03944v1 | https://arxiv.org/pdf/2203.03944v1.pdf | An Online Semantic Mapping System for Extending and Enhancing Visual SLAM | We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Besides the semantic map enrichment the associated detections are further introduced as semantic constraints into a simultaneous localization and mapping (SLAM) system for pose correction purposes. This way, we are able generate additional meaningful information that allows to achieve higher-level tasks, while simultaneously leveraging the view-invariance of object detections to improve the accuracy and the robustness of the odometry estimation. We propose tracklets of locally associated object observations to handle ambiguous and false predictions and an uncertainty-based greedy association scheme for an accelerated processing time. Our system reaches real-time capabilities with an average iteration duration of 65~ms and is able to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a public dataset. Additionally, we implemented our approach as a modular ROS package that makes it straightforward for integration in arbitrary graph-based SLAM methods. | ['Ayoub Al-Hamadi', 'Thorsten Hempel'] | 2022-03-08 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 9.82687473e-02 2.77692139e-01 2.38560423e-01 -4.39789772e-01
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03ba9a77-e689-4b4b-8c0a-86b2d55a7bfa | on-the-interplay-between-misspecification-and | 2303.09390 | null | https://arxiv.org/abs/2303.09390v1 | https://arxiv.org/pdf/2303.09390v1.pdf | On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits | We study linear contextual bandits in the misspecified setting, where the expected reward function can be approximated by a linear function class up to a bounded misspecification level $\zeta>0$. We propose an algorithm based on a novel data selection scheme, which only selects the contextual vectors with large uncertainty for online regression. We show that, when the misspecification level $\zeta$ is dominated by $\tilde O (\Delta / \sqrt{d})$ with $\Delta$ being the minimal sub-optimality gap and $d$ being the dimension of the contextual vectors, our algorithm enjoys the same gap-dependent regret bound $\tilde O (d^2/\Delta)$ as in the well-specified setting up to logarithmic factors. In addition, we show that an existing algorithm SupLinUCB (Chu et al., 2011) can also achieve a gap-dependent constant regret bound without the knowledge of sub-optimality gap $\Delta$. Together with a lower bound adapted from Lattimore et al. (2020), our result suggests an interplay between misspecification level and the sub-optimality gap: (1) the linear contextual bandit model is efficiently learnable when $\zeta \leq \tilde O(\Delta / \sqrt{d})$; and (2) it is not efficiently learnable when $\zeta \geq \tilde \Omega({\Delta} / {\sqrt{d}})$. Experiments on both synthetic and real-world datasets corroborate our theoretical results. | ['Quanquan Gu', 'Zhiyuan Fan', 'Jiafan He', 'Weitong Zhang'] | 2023-03-16 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.06320819e-02 2.29080111e-01 -6.55532002e-01 -9.81411412e-02
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81a29ff7-3667-4591-a405-50d83f1af467 | disco-a-system-leveraging-semantic-search-in | null | null | https://aclanthology.org/C16-2014 | https://aclanthology.org/C16-2014.pdf | DISCO: A System Leveraging Semantic Search in Document Review | This paper presents Disco, a prototype for supporting knowledge workers in exploring, reviewing and sorting collections of textual data. The goal is to facilitate, accelerate and improve the discovery of information. To this end, it combines Semantic Relatedness techniques with a review workflow developed in a tangible environment. Disco uses a semantic model that is leveraged on-line in the course of search sessions, and accessed through natural hand-gesture, in a simple and intuitive way. | ['Fabien Guillot', 'Caroline Privault', 'Ngoc Phuoc An Vo'] | 2016-12-01 | disco-a-system-leveraging-semantic-search-in-1 | https://aclanthology.org/C16-2014 | https://aclanthology.org/C16-2014.pdf | coling-2016-12 | ['text-clustering'] | ['natural-language-processing'] | [-1.70503497e-01 -1.16059273e-01 -2.72637278e-01 -1.42106131e-01
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671e8b90-2f75-43bb-bcac-4a1de99c008e | on-cropped-versus-uncropped-training-sets-in | 2110.02933 | null | https://arxiv.org/abs/2110.02933v2 | https://arxiv.org/pdf/2110.02933v2.pdf | On Cropped versus Uncropped Training Sets in Tabular Structure Detection | Automated document processing for tabular information extraction is highly desired in many organizations, from industry to government. Prior works have addressed this problem under table detection and table structure detection tasks. Proposed solutions leveraging deep learning approaches have been giving promising results in these tasks. However, the impact of dataset structures on table structure detection has not been investigated. In this study, we provide a comparison of table structure detection performance with cropped and uncropped datasets. The cropped set consists of only table images that are cropped from documents assuming tables are detected perfectly. The uncropped set consists of regular document images. Experiments show that deep learning models can improve the detection performance by up to 9% in average precision and average recall on the cropped versions. Furthermore, the impact of cropped images is negligible under the Intersection over Union (IoU) values of 50%-70% when compared to the uncropped versions. However, beyond 70% IoU thresholds, cropped datasets provide significantly higher detection performance. | ['Shahzad Khan', 'Burak Kantarci', 'Murat Simsek', 'Yakup Akkaya'] | 2021-10-06 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 1.72310978e-01 5.11735827e-02 -6.12515844e-02 -7.87524208e-02
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ba135738-d526-4db0-a300-b8a04736a326 | pop-music-transformer-generating-music-with | 2002.00212 | null | https://arxiv.org/abs/2002.00212v3 | https://arxiv.org/pdf/2002.00212v3.pdf | Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions | A great number of deep learning based models have been recently proposed for automatic music composition. Among these models, the Transformer stands out as a prominent approach for generating expressive classical piano performance with a coherent structure of up to one minute. The model is powerful in that it learns abstractions of data on its own, without much human-imposed domain knowledge or constraints. In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model. In particular, we seek to impose a metrical structure in the input data, so that Transformers can be more easily aware of the beat-bar-phrase hierarchical structure in music. The new data representation maintains the flexibility of local tempo changes, and provides hurdles to control the rhythmic and harmonic structure of music. With this approach, we build a Pop Music Transformer that composes Pop piano music with better rhythmic structure than existing Transformer models. | ['Yi-Hsuan Yang', 'Yu-Siang Huang'] | 2020-02-01 | null | null | null | null | ['music-modeling'] | ['music'] | [-7.13016540e-02 1.63597822e-01 -3.68331708e-02 -1.44157887e-01
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f43d11c5-cd5a-49c7-a81f-9603e5834990 | learning-local-displacements-for-point-cloud | 2203.16600 | null | https://arxiv.org/abs/2203.16600v1 | https://arxiv.org/pdf/2203.16600v1.pdf | Learning Local Displacements for Point Cloud Completion | We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand. The first one carries out feature extraction by matching the point features to a set of pre-trained local descriptors. Then, to avoid losing individual descriptors as part of standard operations such as max-pooling, we propose an alternative neighbor-pooling operation that relies on adopting the feature vectors with the highest activations. Finally, up-sampling in the decoder modifies our feature extraction in order to increase the output dimension. While this model is already able to achieve competitive results with the state of the art, we further propose a way to increase the versatility of our approach to process point clouds. To this aim, we introduce a second model that assembles our layers within a transformer architecture. We evaluate both architectures on object and indoor scene completion tasks, achieving state-of-the-art performance. | ['Federico Tombari', 'Nassir Navab', 'David Joseph Tan', 'Yida Wang'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 3.24127316e-01 2.27073371e-01 3.51064891e-01 -5.76098740e-01
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8d901171-6ee7-44e8-9985-df234b30a582 | delving-into-the-cyclic-mechanism-in-semi | 2010.12176 | null | https://arxiv.org/abs/2010.12176v1 | https://arxiv.org/pdf/2010.12176v1.pdf | Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation | In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, we introduce a simple gradient correction module, which extends the offline pipeline to an online method while maintaining the efficiency of the former. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is beneficial to segmentation quality. | ['Weiyao Lin', 'John See', 'Jinlong Peng', 'Ning Xu', 'Yuxi Li'] | 2020-10-23 | null | http://proceedings.neurips.cc/paper/2020/hash/0d5bd023a3ee11c7abca5b42a93c4866-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0d5bd023a3ee11c7abca5b42a93c4866-Paper.pdf | neurips-2020-12 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.62368083e-01 -2.20714901e-02 -1.40132934e-01 -5.18405735e-01
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a2f64b32-8ba2-46a3-bd5d-5e817291ef34 | geomvsnet-learning-multi-view-stereo-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.pdf | GeoMVSNet: Learning Multi-View Stereo With Geometry Perception | Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-resolution depth maps through narrowing hypothesis ranges. However, previous methods ignored the vital geometric information embedded in coarse stages, leading to vulnerable cost matching and sub-optimal reconstruction results. In this paper, we propose a geometry awareness model, termed GeoMVSNet, to explicitly integrate geometric clues implied in coarse stages for delicate depth estimation. In particular, we design a two-branch geometry fusion network to extract geometric priors from coarse estimations to enhance structural feature extraction at finer stages. Besides, we embed the coarse probability volumes, which encode valuable depth distribution attributes, into the lightweight regularization network to further strengthen depth-wise geometry intuition. Meanwhile, we apply the frequency domain filtering to mitigate the negative impact of the high-frequency regions and adopt the curriculum learning strategy to progressively boost the geometry integration of the model. To intensify the full-scene geometry perception of our model, we present the depth distribution similarity loss based on the Gaussian-Mixture Model assumption. Extensive experiments on DTU and Tanks and Temples (T&T) datasets demonstrate that our GeoMVSNet achieves state-of-the-art results and ranks first on the T&T-Advanced set. Code is available at https://github.com/doubleZ0108/GeoMVSNet. | ['Ronggang Wang', 'Yuxi Hu', 'Rui Peng', 'Zhe Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [-1.44486055e-01 8.79682973e-02 1.01664357e-01 -3.99162680e-01
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35e0cb45-3b55-4fc9-9a9a-f1af5d57d33a | unknown-intent-detection-using-multi | null | null | https://aclanthology.org/2021.ranlp-main.127 | https://aclanthology.org/2021.ranlp-main.127.pdf | Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers | Modelling and understanding dialogues in a conversation depends on identifying the user intent from the given text. Unknown or new intent detection is a critical task, as in a realistic scenario a user intent may frequently change over time and divert even to an intent previously not encountered. This task of separating the unknown intent samples from known intents one is challenging as the unknown user intent can range from intents similar to the predefined intents to something completely different. Prior research on intent discovery often consider it as a classification task where an unknown intent can belong to a predefined set of known intent classes. In this paper we tackle the problem of detecting a completely unknown intent without any prior hints about the kind of classes belonging to unknown intents. We propose an effective post-processing method using multi-objective optimization to tune an existing neural network based intent classifier and make it capable of detecting unknown intents. We perform experiments using existing state-of-the-art intent classifiers and use our method on top of them for unknown intent detection. Our experiments across different domains and real-world datasets show that our method yields significant improvements compared with the state-of-the-art methods for unknown intent detection. | ['Roshni Ramnani', 'Sakshi C. Jain', 'Shubhashis Sengupta', 'Asif Ekbal', 'Zishan Ahmad', 'Prerna Prem'] | null | null | https://aclanthology.org/2021.ranlp-1.127 | https://aclanthology.org/2021.ranlp-1.127.pdf | ranlp-2021-9 | ['intent-discovery'] | ['natural-language-processing'] | [ 5.29366791e-01 -1.08470105e-01 -7.55841732e-02 -7.63062179e-01
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735322b6-7702-4ae4-a518-4057161e78c7 | quantized-sparse-weight-decomposition-for | 2207.11048 | null | https://arxiv.org/abs/2207.11048v1 | https://arxiv.org/pdf/2207.11048v1.pdf | Quantized Sparse Weight Decomposition for Neural Network Compression | In this paper, we introduce a novel method of neural network weight compression. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is computed on the fly during inference to generate the target model's weights. We use projected gradient descent methods to find quantized and sparse factorization of the weight tensors. We show that this approach can be seen as a unification of weight SVD, vector quantization, and sparse PCA. Combined with end-to-end fine-tuning our method exceeds or is on par with previous state-of-the-art methods in terms of the trade-off between accuracy and model size. Our method is applicable to both moderate compression regimes, unlike vector quantization, and extreme compression regimes. | ['Arash Behboodi', 'Markus Nagel', 'Mart van Baalen', 'Andrey Kuzmin'] | 2022-07-22 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.24624956e-01 1.05307914e-01 -4.05401379e-01 -3.79293859e-01
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93392afd-b820-49a1-b0f0-3e6195de14f1 | unibuckernel-a-kernel-based-learning-method | 1803.07602 | null | http://arxiv.org/abs/1803.07602v4 | http://arxiv.org/pdf/1803.07602v4.pdf | UnibucKernel: A kernel-based learning method for complex word identification | In this paper, we present a kernel-based learning approach for the 2018
Complex Word Identification (CWI) Shared Task. Our approach is based on
combining multiple low-level features, such as character n-grams, with
high-level semantic features that are either automatically learned using word
embeddings or extracted from a lexical knowledge base, namely WordNet. After
feature extraction, we employ a kernel method for the learning phase. The
feature matrix is first transformed into a normalized kernel matrix. For the
binary classification task (simple versus complex), we employ Support Vector
Machines. For the regression task, in which we have to predict the complexity
level of a word (a word is more complex if it is labeled as complex by more
annotators), we employ v-Support Vector Regression. We applied our approach
only on the three English data sets containing documents from Wikipedia,
WikiNews and News domains. Our best result during the competition was the third
place on the English Wikipedia data set. However, in this paper, we also report
better post-competition results. | ['Radu Tudor Ionescu', 'Andrei M. Butnaru'] | 2018-03-20 | unibuckernel-a-kernel-based-learning-method-1 | https://aclanthology.org/W18-0519 | https://aclanthology.org/W18-0519.pdf | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.28500506e-01 3.05726454e-02 -4.01819855e-01 -3.11822563e-01
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3085373d-adfa-4bde-adf9-d8af806e2acb | searcher-shared-embedding-architecture-for | null | null | https://aclanthology.org/2020.clssts-1.4 | https://aclanthology.org/2020.clssts-1.4.pdf | SEARCHER: Shared Embedding Architecture for Effective Retrieval | We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms. Instead, both queries and documents are mapped into a shared embedding space where retrieval is performed. We discuss potential advantages of the approach in handling polysemy and synonymy. We present a method for training the model, and give details of the model implementation. We present experimental results for two cases: Somali-English and Bulgarian-English CLIR. | ['Scott Miller', 'Marjorie Freedman', 'Elizabeth Boschee', 'Joel Barry'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.42636636e-01 -4.04709876e-01 -4.68657762e-01 -4.84159768e-01
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d27802ee-6bb2-4de7-95d6-58f01dd94944 | topic-discovery-via-latent-space-clustering | 2202.04582 | null | https://arxiv.org/abs/2202.04582v1 | https://arxiv.org/pdf/2202.04582v1.pdf | Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations | Topic models have been the prominent tools for automatic topic discovery from text corpora. Despite their effectiveness, topic models suffer from several limitations including the inability of modeling word ordering information in documents, the difficulty of incorporating external linguistic knowledge, and the lack of both accurate and efficient inference methods for approximating the intractable posterior. Recently, pretrained language models (PLMs) have brought astonishing performance improvements to a wide variety of tasks due to their superior representations of text. Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models. In this paper, we begin by analyzing the challenges of using PLM representations for topic discovery, and then propose a joint latent space learning and clustering framework built upon PLM embeddings. In the latent space, topic-word and document-topic distributions are jointly modeled so that the discovered topics can be interpreted by coherent and distinctive terms and meanwhile serve as meaningful summaries of the documents. Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery, and is conceptually simpler than topic models. On two benchmark datasets in different domains, our model generates significantly more coherent and diverse topics than strong topic models, and offers better topic-wise document representations, based on both automatic and human evaluations. | ['Jiawei Han', 'Yu Zhang', 'Jiaxin Huang', 'Yunyi Zhang', 'Yu Meng'] | 2022-02-09 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.67908952e-01 1.01503439e-01 -7.65071690e-01 -4.25635606e-01
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b10c01df-9c92-4624-ba4f-a7b5cbee57de | learning-functions-to-study-the-benefit-of | 2006.05561 | null | https://arxiv.org/abs/2006.05561v2 | https://arxiv.org/pdf/2006.05561v2.pdf | Learning Functions to Study the Benefit of Multitask Learning | We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. MTL models are trained to optimize a set of related tasks jointly. Although multitask learning has achieved improved performance in some problems, there are also tasks that lose performance when trained together. These mixed results motivate us to study the factors that impact the performance of MTL models. We note that theoretical bounds and convergence rates for MTL models exist, but they rely on strong assumptions such as task relatedness and the use of balanced datasets. To remedy these limitations, we propose the creation of a task simulator and the use of Symbolic Regression to learn expressions relating model performance to possible factors of influence. For MTL, we study the model performance against the number of tasks (T), the number of samples per task (n) and the task relatedness measured by the adjusted mutual information (AMI). In our experiments, we could empirically find formulas relating model performance with factors of sqrt(n), sqrt(T), which are equivalent to sound mathematical proofs in Maurer[2016], and we went beyond by discovering that performance relates to a factor of sqrt(AMI). | ['Dietrich Klakow', 'Gabriele Bettgenhäuser', 'Michael A. Hedderich'] | 2020-06-09 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 3.38355273e-01 2.07063686e-02 -7.74360523e-02 -5.38949490e-01
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f407a7b0-1405-4747-81f6-0188cba9a235 | beyond-rgb-scene-property-synthesis-with | 2206.04669 | null | https://arxiv.org/abs/2206.04669v1 | https://arxiv.org/pdf/2206.04669v1.pdf | Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields | Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused on developing data-driven discriminative models for scene understanding. This paper provides a new approach to scene understanding, from a synthesis model perspective, by leveraging the recent progress on implicit 3D representation and neural rendering. Building upon the great success of Neural Radiance Fields (NeRFs), we introduce Scene-Property Synthesis with NeRF (SS-NeRF) that is able to not only render photo-realistic RGB images from novel viewpoints, but also render various accurate scene properties (e.g., appearance, geometry, and semantics). By doing so, we facilitate addressing a variety of scene understanding tasks under a unified framework, including semantic segmentation, surface normal estimation, reshading, keypoint detection, and edge detection. Our SS-NeRF framework can be a powerful tool for bridging generative learning and discriminative learning, and thus be beneficial to the investigation of a wide range of interesting problems, such as studying task relationships within a synthesis paradigm, transferring knowledge to novel tasks, facilitating downstream discriminative tasks as ways of data augmentation, and serving as auto-labeller for data creation. | ['Yu-Xiong Wang', 'Martial Hebert', 'Zhipeng Bao', 'Shuhong Zheng', 'Mingtong Zhang'] | 2022-06-09 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 7.05084145e-01 1.62684254e-03 1.57138824e-01 -5.86862803e-01
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ca46993d-bb7a-41a6-a548-30a07531a4b4 | cmir-net-a-deep-learning-based-model-for | 1904.04794 | null | https://arxiv.org/abs/1904.04794v2 | https://arxiv.org/pdf/1904.04794v2.pdf | CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing | We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases. | ['Mihai Datcu', 'Biplab Banerjee', 'Ushasi Chaudhuri', 'Avik Bhattacharya'] | 2019-04-09 | null | null | null | null | ['multi-label-image-retrieval', 'cross-modal-information-retrieval'] | ['computer-vision', 'miscellaneous'] | [ 5.47227740e-01 -8.15220356e-01 -9.28551555e-02 -4.13085729e-01
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b16b9256-5408-461b-9a61-80629584945f | semail-eliminating-distractors-in-visual | 2306.10695 | null | https://arxiv.org/abs/2306.10695v1 | https://arxiv.org/pdf/2306.10695v1.pdf | SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models | Model-based imitation learning (MBIL) is a popular reinforcement learning method that improves sample efficiency on high-dimension input sources, such as images and videos. Following the convention of MBIL research, existing algorithms are highly deceptive by task-irrelevant information, especially moving distractors in videos. To tackle this problem, we propose a new algorithm - named Separated Model-based Adversarial Imitation Learning (SeMAIL) - decoupling the environment dynamics into two parts by task-relevant dependency, which is determined by agent actions, and training separately. In this way, the agent can imagine its trajectories and imitate the expert behavior efficiently in task-relevant state space. Our method achieves near-expert performance on various visual control tasks with complex observations and the more challenging tasks with different backgrounds from expert observations. | ['De-Chuan Zhan', 'Ruying Chen', 'Minghao Shao', 'Yucen Wang', 'Shenghua Wan'] | 2023-06-19 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-4.14262488e-02 -9.59966555e-02 -1.59552246e-01 3.63773406e-01
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83d90232-92e9-4cf5-ab02-1f06674d98a7 | an-unpaired-sketch-to-photo-translation-model | 1909.08313 | null | https://arxiv.org/abs/1909.08313v3 | https://arxiv.org/pdf/1909.08313v3.pdf | Unsupervised Sketch-to-Photo Synthesis | Humans can envision a realistic photo given a free-hand sketch that is not only spatially imprecise and geometrically distorted but also without colors and visual details. We study unsupervised sketch-to-photo synthesis for the first time, learning from unpaired sketch-photo data where the target photo for a sketch is unknown during training. Existing works only deal with style change or spatial deformation alone, synthesizing photos from edge-aligned line drawings or transforming shapes within the same modality, e.g., color images. Our key insight is to decompose unsupervised sketch-to-photo synthesis into a two-stage translation task: First shape translation from sketches to grayscale photos and then content enrichment from grayscale to color photos. We also incorporate a self-supervised denoising objective and an attention module to handle abstraction and style variations that are inherent and specific to sketches. Our synthesis is sketch-faithful and photo-realistic to enable sketch-based image retrieval in practice. An exciting corollary product is a universal and promising sketch generator that captures human visual perception beyond the edge map of a photo. | ['Qian Yu', 'Stella Yu', 'Runtao Liu'] | 2019-09-18 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 6.53684139e-01 6.91169649e-02 1.66931719e-01 -5.51619411e-01
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ab2a75c9-49f7-4b2a-9bfb-265d02c1039a | recasting-self-attention-with-holographic | 2305.19534 | null | https://arxiv.org/abs/2305.19534v1 | https://arxiv.org/pdf/2305.19534v1.pdf | Recasting Self-Attention with Holographic Reduced Representations | In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs can make using transformers infeasible. Motivated by problems in malware detection, where sequence lengths of $T \geq 100,000$ are a roadblock to deep learning, we re-cast self-attention using the neuro-symbolic approach of Holographic Reduced Representations (HRR). In doing so we perform the same high-level strategy of the standard self-attention: a set of queries matching against a set of keys, and returning a weighted response of the values for each key. Implemented as a ``Hrrformer'' we obtain several benefits including $\mathcal{O}(T H \log H)$ time complexity, $\mathcal{O}(T H)$ space complexity, and convergence in $10\times$ fewer epochs. Nevertheless, the Hrrformer achieves near state-of-the-art accuracy on LRA benchmarks and we are able to learn with just a single layer. Combined, these benefits make our Hrrformer the first viable Transformer for such long malware classification sequences and up to $280\times$ faster to train on the Long Range Arena benchmark. Code is available at \url{https://github.com/NeuromorphicComputationResearchProgram/Hrrformer} | ['James Holt', 'Tim Oates', 'Stella Biderman', 'Edward Raff', 'Mohammad Mahmudul Alam'] | 2023-05-31 | null | null | null | null | ['malware-classification'] | ['miscellaneous'] | [ 3.26074749e-01 -3.08751166e-01 1.49026945e-01 -9.85741839e-02
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ba56378d-cf8c-4a34-ba5f-475fef820d58 | no-fear-of-heterogeneity-classifier | 2106.05001 | null | https://arxiv.org/abs/2106.05001v2 | https://arxiv.org/pdf/2106.05001v2.pdf | No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data | A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggregation scheme at the server. Other works also share public datasets or synthesized samples to supplement the training of under-represented classes or introduce a certain level of personalization. Though effective, they lack a deep understanding of how the data heterogeneity affects each layer of a deep classification model. In this paper, we bridge this gap by performing an experimental analysis of the representations learned by different layers. Our observations are surprising: (1) there exists a greater bias in the classifier than other layers, and (2) the classification performance can be significantly improved by post-calibrating the classifier after federated training. Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated gaussian mixture model. Experimental results demonstrate that CCVR achieves state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10. We hope that our simple yet effective method can shed some light on the future research of federated learning with non-IID data. | ['Jiashi Feng', 'Jian Liang', 'Yifan Zhang', 'Dapeng Hu', 'Fei Chen', 'Mi Luo'] | 2021-06-09 | null | http://proceedings.neurips.cc/paper/2021/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2f2b265625d76a6704b08093c652fd79-Paper.pdf | neurips-2021-12 | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [-4.15715277e-01 -3.28418672e-01 -3.96442264e-01 -8.43750000e-01
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aabed015-e3fd-4958-b6c8-20435d4c630b | few-shot-text-classification-with-pre-trained | 1804.02063 | null | http://arxiv.org/abs/1804.02063v1 | http://arxiv.org/pdf/1804.02063v1.pdf | Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop | Most of the literature around text classification treats it as a supervised
learning problem: given a corpus of labeled documents, train a classifier such
that it can accurately predict the classes of unseen documents. In industry,
however, it is not uncommon for a business to have entire corpora of documents
where few or none have been classified, or where existing classifications have
become meaningless. With web content, for example, poor taxonomy management can
result in labels being applied indiscriminately, making filtering by these
labels unhelpful. Our work aims to make it possible to classify an entire
corpus of unlabeled documents using a human-in-the-loop approach, where the
content owner manually classifies just one or two documents per category and
the rest can be automatically classified. This "few-shot" learning approach
requires rich representations of the documents such that those that have been
manually labeled can be treated as prototypes, and automatic classification of
the rest is a simple case of measuring the distance to prototypes. This
approach uses pre-trained word embeddings, where documents are represented
using a simple weighted average of constituent word embeddings. We have tested
the accuracy of the approach on existing labeled datasets and provide the
results here. We have also made code available for reproducing the results we
got on the 20 Newsgroups dataset. | ['Sunny Chopra', 'Katherine Bailey'] | 2018-04-05 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.30075648e-01 2.83191562e-01 -3.40199977e-01 -5.28551936e-01
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971ca2e0-fe01-499a-aaea-d43d0740dbec | accessible-instruction-following-agent | 2305.06358 | null | https://arxiv.org/abs/2305.06358v1 | https://arxiv.org/pdf/2305.06358v1.pdf | Accessible Instruction-Following Agent | Humans can collaborate and complete tasks based on visual signals and instruction from the environment. Training such a robot is difficult especially due to the understanding of the instruction and the complicated environment. Previous instruction-following agents are biased to English-centric corpus, making it unrealizable to be applied to users that use multiple languages or even low-resource languages. Nevertheless, the instruction-following agents are pre-trained in a mode that assumes the user can observe the environment, which limits its accessibility. In this work, we're trying to generalize the success of instruction-following agents to non-English languages with little corpus resources, and improve its intractability and accessibility. We introduce UVLN (Universal Vision-Language Navigation), a novel machine-translation instructional augmented framework for cross-lingual vision-language navigation, with a novel composition of state-of-the-art large language model (GPT3) with the image caption model (BLIP). We first collect a multilanguage vision-language navigation dataset via machine translation. Then we extend the standard VLN training objectives to a multilingual setting via a cross-lingual language encoder. The alignment between different languages is captured through a shared vision and action context via a cross-modal transformer, which encodes the inputs of language instruction, visual observation, and action decision sequences. To improve the intractability, we connect our agent with the large language model that informs the situation and current state to the user and also explains the action decisions. Experiments over Room Across Room Dataset prove the effectiveness of our approach. And the qualitative results show the promising intractability and accessibility of our instruction-following agent. | ['Kairui Zhou'] | 2023-05-08 | null | null | null | null | ['vision-language-navigation', 'instruction-following'] | ['computer-vision', 'natural-language-processing'] | [-1.11138001e-01 2.23850086e-01 -1.59324422e-01 -2.22473890e-01
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0ea95bf7-2940-4e44-a197-c91f440aedda | 3d-modelling-of-survey-scene-from-images | 2111.05541 | null | https://arxiv.org/abs/2111.05541v1 | https://arxiv.org/pdf/2111.05541v1.pdf | 3D modelling of survey scene from images enhanced with a multi-exposure fusion | In current practice, scene survey is carried out by workers using total stations. The method has high accuracy, but it incurs high costs if continuous monitoring is needed. Techniques based on photogrammetry, with the relatively cheaper digital cameras, have gained wide applications in many fields. Besides point measurement, photogrammetry can also create a three-dimensional (3D) model of the scene. Accurate 3D model reconstruction depends on high quality images. Degraded images will result in large errors in the reconstructed 3D model. In this paper, we propose a method that can be used to improve the visibility of the images, and eventually reduce the errors of the 3D scene model. The idea is inspired by image dehazing. Each original image is first transformed into multiple exposure images by means of gamma-correction operations and adaptive histogram equalization. The transformed images are analyzed by the computation of the local binary patterns. The image is then enhanced, with each pixel generated from the set of transformed image pixels weighted by a function of the local pattern feature and image saturation. Performance evaluation has been performed on benchmark image dehazing datasets. Experimentations have been carried out on outdoor and indoor surveys. Our analysis finds that the method works on different types of degradation that exist in both outdoor and indoor images. When fed into the photogrammetry software, the enhanced images can reconstruct 3D scene models with sub-millimeter mean errors. | ['Ho-Yin Chan', 'Arthur Wing-Tak Leung', 'Liping Li', 'Kwok-Leung Chan'] | 2021-11-10 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 7.98533499e-01 -4.45906073e-01 4.01122987e-01 -1.45544529e-01
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730fd741-ba68-4328-9aa2-b7364ca9ae72 | robust-occlusion-aware-pose-estimation-for | 2003.03518 | null | https://arxiv.org/abs/2003.03518v1 | https://arxiv.org/pdf/2003.03518v1.pdf | Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands | Many manipulation tasks, such as placement or within-hand manipulation, require the object's pose relative to a robot hand. The task is difficult when the hand significantly occludes the object. It is especially hard for adaptive hands, for which it is not easy to detect the finger's configuration. In addition, RGB-only approaches face issues with texture-less objects or when the hand and the object look similar. This paper presents a depth-based framework, which aims for robust pose estimation and short response times. The approach detects the adaptive hand's state via efficient parallel search given the highest overlap between the hand's model and the point cloud. The hand's point cloud is pruned and robust global registration is performed to generate object pose hypotheses, which are clustered. False hypotheses are pruned via physical reasoning. The remaining poses' quality is evaluated given agreement with observed data. Extensive evaluation on synthetic and real data demonstrates the accuracy and computational efficiency of the framework when applied on challenging, highly-occluded scenarios for different object types. An ablation study identifies how the framework's components help in performance. This work also provides a dataset for in-hand 6D object pose estimation. Code and dataset are available at: https://github.com/wenbowen123/icra20-hand-object-pose | ['Kostas E. Bekris', 'Sruthi Soorian', 'Avishai Sintov', 'Chaitanya Mitash', 'Bowen Wen', 'Andrew Kimmel'] | 2020-03-07 | null | null | null | null | ['6d-pose-estimation-using-rgbd', 'hand-object-pose'] | ['computer-vision', 'computer-vision'] | [ 2.96390932e-02 -2.02313289e-01 1.26043618e-01 1.64680313e-02
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fdfdd821-b784-4556-a2a9-662c9af7dc12 | voxeltrack-multi-person-3d-human-pose | 2108.02452 | null | https://arxiv.org/abs/2108.02452v1 | https://arxiv.org/pdf/2108.02452v1.pdf | VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild | We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. In contrast to previous efforts which require to establish cross-view correspondence based on noisy 2D pose estimates, it directly estimates and tracks 3D poses from a 3D voxel-based representation constructed from multi-view images. We first discretize the 3D space by regular voxels and compute a feature vector for each voxel by averaging the body joint heatmaps that are inversely projected from all views. We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint. Similarly, a Re-ID feature is computed for each voxel which is used to track the estimated 3D poses over time. The main advantage of the approach is that it avoids making any hard decisions based on individual images. The approach can robustly estimate and track 3D poses even when people are severely occluded in some cameras. It outperforms the state-of-the-art methods by a large margin on three public datasets including Shelf, Campus and CMU Panoptic. | ['Wenjun Zeng', 'Wenyu Liu', 'Xinggang Wang', 'Chunyu Wang', 'Yifu Zhang'] | 2021-08-05 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.30904743e-01 -3.27716559e-01 -6.55294582e-03 -4.19074804e-01
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c0509ff1-3a62-4ad4-a70a-363aecf7067b | complexity-based-prompting-for-multi-step | 2210.00720 | null | https://arxiv.org/abs/2210.00720v2 | https://arxiv.org/pdf/2210.00720v2.pdf | Complexity-Based Prompting for Multi-Step Reasoning | We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer, large language models can generate new reasoning chains and predict answers for new inputs. A central question is which reasoning examples make the most effective prompts. In this work, we propose complexity-based prompting, a simple and effective example selection scheme for multi-step reasoning. We show that prompts with higher reasoning complexity, i.e., chains with more reasoning steps, achieve substantially better performance on multi-step reasoning tasks over strong baselines. We further extend our complexity-based criteria from prompting (selecting inputs) to decoding (selecting outputs), where we sample multiple reasoning chains from the model, then choose the majority of generated answers from complex reasoning chains (over simple chains). When used to prompt GPT-3 and Codex, our approach substantially improves multi-step reasoning accuracy and achieves new state-of-the-art (SOTA) performance on three math benchmarks (GSM8K, MultiArith, and MathQA) and two BigBenchHard tasks (Date Understanding and Penguins), with an average +5.3 and up to +18 accuracy improvements. Compared with existing example selection schemes like manual tuning or retrieval-based selection, selection based on reasoning complexity is intuitive, easy to implement, and annotation-efficient. Further results demonstrate the robustness of performance gains from complex prompts under format perturbation and distribution shift. | ['Tushar Khot', 'Peter Clark', 'Ashish Sabharwal', 'Hao Peng', 'Yao Fu'] | 2022-10-03 | null | null | null | null | ['gsm8k', 'date-understanding'] | ['natural-language-processing', 'reasoning'] | [ 1.76087633e-01 2.50005692e-01 -1.21329464e-01 -6.00245178e-01
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9d740d38-0972-47bf-b953-45a570170121 | de-novo-visual-proteomics-in-single-cells | 1512.09347 | null | http://arxiv.org/abs/1512.09347v3 | http://arxiv.org/pdf/1512.09347v3.pdf | De novo visual proteomics in single cells through pattern mining | Cryo-electron tomography enables 3D visualization of cells in a near native
state at molecular resolution. The produced cellular tomograms contain detailed
information about all macromolecular complexes, their structures, their
abundances and their specific spatial locations in the cell. However,
extracting this information is very challenging and current methods usually
rely on templates of known structure. Here, we formulate a template-free visual
proteomics analysis as a de novo pattern mining problem and propose a new
framework called "Multi Pattern Pursuit" for supporting proteome-scale de novo
discovery of macromolecular complexes in cellular tomograms without using
templates of known structures. Our tests on simulated and experimental
tomograms show that our method is a promising tool for template-free visual
proteomics analysis. | [] | 2016-02-11 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.59100667e-01 -5.58645546e-01 -5.12158964e-03 1.46267831e-01
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24282088-3e06-4b9f-85f8-e61b2917aa42 | music-separation-enhancement-with-generative | 2208.12387 | null | https://arxiv.org/abs/2208.12387v1 | https://arxiv.org/pdf/2208.12387v1.pdf | Music Separation Enhancement with Generative Modeling | Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous noise or removing harmonics. We propose a post-processing model (the Make it Sound Good (MSG) post-processor) to enhance the output of music source separation systems. We apply our post-processing model to state-of-the-art waveform-based and spectrogram-based music source separators, including a separator unseen by MSG during training. Our analysis of the errors produced by source separators shows that waveform models tend to introduce more high-frequency noise, while spectrogram models tend to lose transients and high frequency content. We introduce objective measures to quantify both kinds of errors and show MSG improves the source reconstruction of both kinds of errors. Crowdsourced subjective evaluations demonstrate that human listeners prefer source estimates of bass and drums that have been post-processed by MSG. | ['Bryan Pardo', 'Prem Seetharaman', 'Max Morrison', 'Ethan Manilow', 'Boaz Cogan', 'Noah Schaffer'] | 2022-08-26 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.92668721e-01 -4.36825752e-01 3.31952780e-01 1.85982257e-01
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933b53c8-62a0-4e6c-98e1-de4e6f4eb143 | conquer-contextualized-query-reduction-using | 2305.12662 | null | https://arxiv.org/abs/2305.12662v1 | https://arxiv.org/pdf/2305.12662v1.pdf | ConQueR: Contextualized Query Reduction using Search Logs | Query reformulation is a key mechanism to alleviate the linguistic chasm of query in ad-hoc retrieval. Among various solutions, query reduction effectively removes extraneous terms and specifies concise user intent from long queries. However, it is challenging to capture hidden and diverse user intent. This paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained language model (PLM). Specifically, it reduces verbose queries with two different views: core term extraction and sub-query selection. One extracts core terms from an original query at the term level, and the other determines whether a sub-query is a suitable reduction for the original query at the sequence level. Since they operate at different levels of granularity and complement each other, they are finally aggregated in an ensemble manner. We evaluate the reduction quality of ConQueR on real-world search logs collected from a commercial web search engine. It achieves up to 8.45% gains in exact match scores over the best competing model. | ['Jongwuk Lee', 'Young-In Song', 'Eunseong Choi', 'Sunkyung Lee', 'Minjin Choi', 'Hye-Young Kim'] | 2023-05-22 | null | null | null | null | ['term-extraction'] | ['natural-language-processing'] | [ 2.41178393e-01 -4.04278934e-01 -5.58058858e-01 -1.89685076e-01
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49c7d18f-5504-478a-9973-dc65421aa9ce | discobox-weakly-supervised-instance | 2105.06464 | null | https://arxiv.org/abs/2105.06464v2 | https://arxiv.org/pdf/2105.06464v2.pdf | DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision | We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensembling framework where instance segmentation and semantic correspondence are jointly guided by a structured teacher in addition to the bounding box supervision. The teacher is a structured energy model incorporating a pairwise potential and a cross-image potential to model the pairwise pixel relationships both within and across the boxes. Minimizing the teacher energy simultaneously yields refined object masks and dense correspondences between intra-class objects, which are taken as pseudo-labels to supervise the task network and provide positive/negative correspondence pairs for dense constrastive learning. We show a symbiotic relationship where the two tasks mutually benefit from each other. Our best model achieves 37.9% AP on COCO instance segmentation, surpassing prior weakly supervised methods and is competitive to supervised methods. We also obtain state of the art weakly supervised results on PASCAL VOC12 and PF-PASCAL with real-time inference. | ['Anima Anandkumar', 'Larry S. Davis', 'Yuke Zhu', 'Guilin Liu', 'Subhashree Radhakrishnan', 'Christopher Choy', 'Zhiding Yu', 'Shiyi Lan'] | 2021-05-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lan_DiscoBox_Weakly_Supervised_Instance_Segmentation_and_Semantic_Correspondence_From_Box_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lan_DiscoBox_Weakly_Supervised_Instance_Segmentation_and_Semantic_Correspondence_From_Box_ICCV_2021_paper.pdf | iccv-2021-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.99195051e-01 6.10712051e-01 -3.12742293e-01 -1.00382125e+00
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5d037c99-1fdc-458e-872c-29d3d46524b5 | noise-level-estimation-from-single-color | 1904.02566 | null | http://arxiv.org/abs/1904.02566v1 | http://arxiv.org/pdf/1904.02566v1.pdf | Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels | We propose a simple method for estimating noise level from a single color
image. In most image-denoising algorithms, an accurate noise-level estimate
results in good denoising performance; however, it is difficult to estimate
noise level from a single image because it is an ill-posed problem. We tackle
this problem by using prior knowledge that textures are highly correlated
between RGB channels and noise is uncorrelated to other signals. We also
extended our method for RAW images because they are available in almost all
digital cameras and often used in practical situations. Experiments show the
high noise-estimation performance of our method in synthetic noisy images. We
also applied our method to natural images including RAW images and achieved
better noise-estimation performance than conventional methods. | ['Michihiro Kobayashi', 'Akihiro Nakamura'] | 2019-04-04 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 3.81610036e-01 -7.80126572e-01 5.26329577e-01 -1.90647304e-01
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34506fa6-d7c6-4df1-a373-e03c62cca9d7 | recurrent-u-net-for-resource-constrained | 1906.04913 | null | https://arxiv.org/abs/1906.04913v1 | https://arxiv.org/pdf/1906.04913v1.pdf | Recurrent U-Net for Resource-Constrained Segmentation | State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on standard GPUs. In this paper, we introduce a novel recurrent U-Net architecture that preserves the compactness of the original U-Net, while substantially increasing its performance to the point where it outperforms the state of the art on several benchmarks. We will demonstrate its effectiveness for several tasks, including hand segmentation, retina vessel segmentation, and road segmentation. We also introduce a large-scale dataset for hand segmentation. | ['Joachim Hugonot', 'Pascal Fua', 'Mathieu Salzmann', 'Wei Wang', 'Kaicheng Yu'] | 2019-06-11 | recurrent-u-net-for-resource-constrained-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['road-segementation', 'hand-segmentation'] | ['computer-vision', 'computer-vision'] | [-5.03969975e-02 9.52552855e-02 -2.78432935e-01 -2.06333041e-01
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66f0bf78-f959-43dd-9916-7f05963e3ca9 | from-shadow-segmentation-to-shadow-removal | 2008.00267 | null | https://arxiv.org/abs/2008.00267v1 | https://arxiv.org/pdf/2008.00267v1.pdf | From Shadow Segmentation to Shadow Removal | The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets and hinders the possibility of training large-scale, robust shadow removal algorithms. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves. Our method is trained via an adversarial framework, following a physical model of shadow formation. Our central contribution is a set of physics-based constraints that enables this adversarial training. Our method achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. The advantages of our training regime are even more pronounced in shadow removal for videos. Our method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and outperforms state-of-the-art methods on this challenging test. We illustrate the advantages of our method on our proposed video shadow removal dataset. | ['Dimitris Samaras', 'Hieu Le'] | 2020-08-01 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1321_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560256.pdf | eccv-2020-8 | ['shadow-removal'] | ['computer-vision'] | [ 9.06232834e-01 2.46971890e-01 4.42985952e-01 -1.59166589e-01
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a3c78a13-1124-4cf0-8a76-9c89aded68ae | fast-user-guided-video-object-segmentation-by | 1904.09791 | null | http://arxiv.org/abs/1904.09791v2 | http://arxiv.org/pdf/1904.09791v2.pdf | Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks | We present a deep learning method for the interactive video object
segmentation. Our method is built upon two core operations, interaction and
propagation, and each operation is conducted by Convolutional Neural Networks.
The two networks are connected both internally and externally so that the
networks are trained jointly and interact with each other to solve the complex
video object segmentation problem. We propose a new multi-round training scheme
for the interactive video object segmentation so that the networks can learn
how to understand the user's intention and update incorrect estimations during
the training. At the testing time, our method produces high-quality results and
also runs fast enough to work with users interactively. We evaluated the
proposed method quantitatively on the interactive track benchmark at the DAVIS
Challenge 2018. We outperformed other competing methods by a significant margin
in both the speed and the accuracy. We also demonstrated that our method works
well with real user interactions. | ['Ning Xu', 'Joon-Young Lee', 'Seoung Wug Oh', 'Seon Joo Kim'] | 2019-04-22 | fast-user-guided-video-object-segmentation-by-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Oh_Fast_User-Guided_Video_Object_Segmentation_by_Interaction-And-Propagation_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Oh_Fast_User-Guided_Video_Object_Segmentation_by_Interaction-And-Propagation_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 1.64558277e-01 2.01353561e-02 -1.88016102e-01 -5.45453250e-01
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cc7b41e6-07b7-4c33-92ee-48127a8cdd6d | repbert-contextualized-text-embeddings-for | 2006.15498 | null | https://arxiv.org/abs/2006.15498v2 | https://arxiv.org/pdf/2006.15498v2.pdf | RepBERT: Contextualized Text Embeddings for First-Stage Retrieval | Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings. The inner products of query and document embeddings are regarded as relevance scores. On MS MARCO Passage Ranking task, RepBERT achieves state-of-the-art results among all initial retrieval techniques. And its efficiency is comparable to bag-of-words methods. | ['Min Zhang', 'Yiqun Liu', 'Shaoping Ma', 'Jingtao Zhan', 'Jiaxin Mao'] | 2020-06-28 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-4.14580345e-01 -7.67998397e-01 -7.00076580e-01 -2.02677146e-01
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0fae5e52-b35e-4d47-86ac-9ac64b185eec | graphical-representation-for-heterogeneous | 1503.00488 | null | http://arxiv.org/abs/1503.00488v3 | http://arxiv.org/pdf/1503.00488v3.pdf | Graphical Representation for Heterogeneous Face Recognition | Heterogeneous face recognition (HFR) refers to matching face images acquired
from different sources (i.e., different sensors or different wavelengths) for
identification. HFR plays an important role in both biometrics research and
industry. In spite of promising progresses achieved in recent years, HFR is
still a challenging problem due to the difficulty to represent two
heterogeneous images in a homogeneous manner. Existing HFR methods either
represent an image ignoring the spatial information, or rely on a
transformation procedure which complicates the recognition task. Considering
these problems, we propose a novel graphical representation based HFR method
(G-HFR) in this paper. Markov networks are employed to represent heterogeneous
image patches separately, which takes the spatial compatibility between
neighboring image patches into consideration. A coupled representation
similarity metric (CRSM) is designed to measure the similarity between obtained
graphical representations. Extensive experiments conducted on multiple HFR
scenarios (viewed sketch, forensic sketch, near infrared image, and thermal
infrared image) show that the proposed method outperforms state-of-the-art
methods. | ['Chunlei Peng', 'Nannan Wang', 'Jie Li', 'Xinbo Gao'] | 2015-03-02 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 7.22480953e-01 -5.36933601e-01 -4.92220651e-03 -2.93098509e-01
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52a6ae0c-645d-4ee5-96bc-60ba91d15d9a | private-graph-extraction-via-feature | 2206.14724 | null | https://arxiv.org/abs/2206.14724v1 | https://arxiv.org/pdf/2206.14724v1.pdf | Private Graph Extraction via Feature Explanations | Privacy and interpretability are two of the important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks. We show that additional knowledge of post-hoc feature explanations substantially increases the success rate of these attacks. Further, we investigate in detail the differences between attack performance with respect to three different classes of explanation methods for graph neural networks: gradient-based, perturbation-based, and surrogate model-based methods. While gradient-based explanations reveal the most in terms of the graph structure, we find that these explanations do not always score high in utility. For the other two classes of explanations, privacy leakage increases with an increase in explanation utility. Finally, we propose a defense based on a randomized response mechanism for releasing the explanations which substantially reduces the attack success rate. Our anonymized code is available. | ['Megha Khosla', 'Thorben Funke', 'Mandeep Rathee', 'Iyiola E. Olatunji'] | 2022-06-29 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 3.47190559e-01 8.16327393e-01 -2.85488933e-01 -2.74420559e-01
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ff779e50-f93b-4235-b29c-493b2ac98269 | rethinking-label-smoothing-on-multi-hop | 2212.09512 | null | https://arxiv.org/abs/2212.09512v1 | https://arxiv.org/pdf/2212.09512v1.pdf | Rethinking Label Smoothing on Multi-hop Question Answering | Label smoothing is a regularization technique widely used in supervised learning to improve the generalization of models on various tasks, such as image classification and machine translation. However, the effectiveness of label smoothing in multi-hop question answering (MHQA) has yet to be well studied. In this paper, we systematically analyze the role of label smoothing on various modules of MHQA and propose F1 smoothing, a novel label smoothing technique specifically designed for machine reading comprehension (MRC) tasks. We evaluate our method on the HotpotQA dataset and demonstrate its superiority over several strong baselines, including models that utilize complex attention mechanisms. Our results suggest that label smoothing can be effective in MHQA, but the choice of smoothing strategy can significantly affect performance. | ['Xipeng Qiu', 'Xuanjing Huang', 'Zhao Cao', 'Xinyu Zhang', 'Xiannian Hu', 'Hang Yan', 'Yiguang Wu', 'Yuxin Wang', 'Zhangyue Yin'] | 2022-12-19 | null | null | null | null | ['multi-hop-question-answering', 'machine-reading-comprehension'] | ['knowledge-base', 'natural-language-processing'] | [ 4.32340503e-01 3.24072987e-01 -3.06379646e-01 -6.20502532e-01
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af466257-7cd9-4c82-af4e-45d1d6558e73 | frnet-flattened-residual-network-for-infant | 1904.05578 | null | http://arxiv.org/abs/1904.05578v1 | http://arxiv.org/pdf/1904.05578v1.pdf | FRNET: Flattened Residual Network for Infant MRI Skull Stripping | Skull stripping for brain MR images is a basic segmentation task. Although
many methods have been proposed, most of them focused mainly on the adult MR
images. Skull stripping for infant MR images is more challenging due to the
small size and dynamic intensity changes of brain tissues during the early
ages. In this paper, we propose a novel CNN based framework to robustly extract
brain region from infant MR image without any human assistance. Specifically,
we propose a simplified but more robust flattened residual network architecture
(FRnet). We also introduce a new boundary loss function to highlight ambiguous
and low contrast regions between brain and non-brain regions. To make the whole
framework more robust to MR images with different imaging quality, we further
introduce an artifact simulator for data augmentation. We have trained and
tested our proposed framework on a large dataset (N=343), covering newborns to
48-month-olds, and obtained performance better than the state-of-the-art
methods in all age groups. | ['Qian Zhang', 'Weili Lin', 'Dinggang Shen', 'Xiaopeng Zong', 'Li Wang', 'Gang Li'] | 2019-04-11 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 2.99961865e-01 1.77845076e-01 3.31842840e-01 -7.87061870e-01
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a1dd4b23-8244-4822-8fa3-711819c574ee | learning-non-maximum-suppression | 1705.02950 | null | http://arxiv.org/abs/1705.02950v2 | http://arxiv.org/pdf/1705.02950v2.pdf | Learning non-maximum suppression | Object detectors have hugely profited from moving towards an end-to-end
learning paradigm: proposals, features, and the classifier becoming one neural
network improved results two-fold on general object detection. One
indispensable component is non-maximum suppression (NMS), a post-processing
algorithm responsible for merging all detections that belong to the same
object. The de facto standard NMS algorithm is still fully hand-crafted,
suspiciously simple, and -- being based on greedy clustering with a fixed
distance threshold -- forces a trade-off between recall and precision. We
propose a new network architecture designed to perform NMS, using only boxes
and their score. We report experiments for person detection on PETS and for
general object categories on the COCO dataset. Our approach shows promise
providing improved localization and occlusion handling. | ['Jan Hosang', 'Rodrigo Benenson', 'Bernt Schiele'] | 2017-05-08 | learning-non-maximum-suppression-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Hosang_Learning_Non-Maximum_Suppression_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Hosang_Learning_Non-Maximum_Suppression_CVPR_2017_paper.pdf | cvpr-2017-7 | ['occlusion-handling'] | ['computer-vision'] | [ 4.37727645e-02 7.95262828e-02 5.26881143e-02 -6.29958451e-01
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e7185e4c-275a-48ea-bb1e-cdf2a939ae0d | clelfpc-a-large-open-multi-speaker-corpus-of | null | null | https://aclanthology.org/2022.lrec-1.104 | https://aclanthology.org/2022.lrec-1.104.pdf | CLeLfPC: a Large Open Multi-Speaker Corpus of French Cued Speech | Cued Speech is a communication system developed for deaf people to complement speechreading at the phonetic level with hands. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements of speech to make the phonemes of spoken language look different from each other. This paper describes CLeLfPC - Corpus de Lecture en Langue française Parlée Complétée, a corpus of French Cued Speech. It consists in about 4 hours of audio and HD video recordings of 23 participants. The recordings are 160 different isolated ‘CV’ syllables repeated 5 times, 320 words or phrases repeated 2-3 times and about 350 sentences repeated 2-3 times. The corpus is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. It can be used for any further research or teaching purpose. The corpus includes orthographic transliteration and other phonetic annotations on 5 of the recorded topics, i.e. syllables, words, isolated sentences and a text. The early results are encouraging: it seems that 1/ the hand position has a high influence on the key audio duration; and 2/ the hand shape has not. | ['Carine André', 'Maryvonne Zimmermann', 'Brigitte Bigi'] | null | null | null | null | lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [ 1.79958139e-02 2.08542064e-01 -4.45349663e-02 -1.63788155e-01
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8130781c-93d9-42db-9bde-7711079b8a47 | distribution-aware-testing-of-neural-networks | 2102.13602 | null | https://arxiv.org/abs/2102.13602v1 | https://arxiv.org/pdf/2102.13602v1.pdf | Distribution-Aware Testing of Neural Networks Using Generative Models | The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications being deployed with DNNs. The need for reliability raises a need for rigorous testing of the safety and trustworthiness of these systems. In the last few years, there have been a number of research efforts focused on testing DNNs. However the test generation techniques proposed so far lack a check to determine whether the test inputs they are generating are valid, and thus invalid inputs are produced. To illustrate this situation, we explored three recent DNN testing techniques. Using deep generative model based input validation, we show that all the three techniques generate significant number of invalid test inputs. We further analyzed the test coverage achieved by the test inputs generated by the DNN testing techniques and showed how invalid test inputs can falsely inflate test coverage metrics. To overcome the inclusion of invalid inputs in testing, we propose a technique to incorporate the valid input space of the DNN model under test in the test generation process. Our technique uses a deep generative model-based algorithm to generate only valid inputs. Results of our empirical studies show that our technique is effective in eliminating invalid tests and boosting the number of valid test inputs generated. | ['Mary Lou Soffa', 'Matthew B. Dwyer', 'Swaroopa Dola'] | 2021-02-26 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 1.37447581e-01 1.58840299e-01 1.43475115e-01 -4.26299721e-01
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4a4db427-db46-4481-ab02-5bc0fd4f4691 | meet-in-the-middle-multi-scale-upsampling-and | 2211.15225 | null | https://arxiv.org/abs/2211.15225v2 | https://arxiv.org/pdf/2211.15225v2.pdf | Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition | In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset. | ['Hazim Kemal Ekenel', 'Vitomir Štruc', 'Berk Kemal Özata', 'Klemen Grm'] | 2022-11-28 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 7.38832116e-01 -4.65464741e-01 1.29904926e-01 -4.49505389e-01
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2e24c813-84d2-49bd-9c0c-57ad82d34724 | zero-shot-dense-video-captioning-by-jointly | 2307.02682 | null | https://arxiv.org/abs/2307.02682v1 | https://arxiv.org/pdf/2307.02682v1.pdf | Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment | Dense video captioning, a task of localizing meaningful moments and generating relevant captions for videos, often requires a large, expensive corpus of annotated video segments paired with text. In an effort to minimize the annotation cost, we propose ZeroTA, a novel method for dense video captioning in a zero-shot manner. Our method does not require any videos or annotations for training; instead, it localizes and describes events within each input video at test time by optimizing solely on the input. This is accomplished by introducing a soft moment mask that represents a temporal segment in the video and jointly optimizing it with the prefix parameters of a language model. This joint optimization aligns a frozen language generation model (i.e., GPT-2) with a frozen vision-language contrastive model (i.e., CLIP) by maximizing the matching score between the generated text and a moment within the video. We also introduce a pairwise temporal IoU loss to let a set of soft moment masks capture multiple distinct events within the video. Our method effectively discovers diverse significant events within the video, with the resulting captions appropriately describing these events. The empirical results demonstrate that ZeroTA surpasses zero-shot baselines and even outperforms the state-of-the-art few-shot method on the widely-used benchmark ActivityNet Captions. Moreover, our method shows greater robustness compared to supervised methods when evaluated in out-of-domain scenarios. This research provides insight into the potential of aligning widely-used models, such as language generation models and vision-language models, to unlock a new capability: understanding temporal aspects of videos. | ['Minjoon Seo', 'Hanseok Oh', 'Hyunji Lee', 'Aiden SJ Lee', 'Seongyun Lee', 'Yongrae Jo'] | 2023-07-05 | null | null | null | null | ['video-captioning', 'dense-video-captioning', 'text-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.11165327e-01 -2.62140650e-02 -4.36919808e-01 -2.33075127e-01
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fb4eaf6a-1981-4b07-8a2a-e4628414d72c | a-deep-neural-network-for-chinese-zero | 1604.05800 | null | http://arxiv.org/abs/1604.05800v3 | http://arxiv.org/pdf/1604.05800v3.pdf | A Deep Neural Network for Chinese Zero Pronoun Resolution | Existing approaches for Chinese zero pronoun resolution overlook semantic
information. This is because zero pronouns have no descriptive information,
which results in difficulty in explicitly capturing their semantic similarities
with antecedents. Moreover, when dealing with candidate antecedents,
traditional systems simply take advantage of the local information of a single
candidate antecedent while failing to consider the underlying information
provided by the other candidates from a global perspective. To address these
weaknesses, we propose a novel zero pronoun-specific neural network, which is
capable of representing zero pronouns by utilizing the contextual information
at the semantic level. In addition, when dealing with candidate antecedents, a
two-level candidate encoder is employed to explicitly capture both the local
and global information of candidate antecedents. We conduct experiments on the
Chinese portion of the OntoNotes 5.0 corpus. Experimental results show that our
approach substantially outperforms the state-of-the-art method in various
experimental settings. | ['Wei-Nan Zhang', 'Qingyu Yin', 'Ting Liu', 'Yu Zhang'] | 2016-04-20 | null | null | null | null | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [ 3.07952851e-01 1.32503465e-01 -5.03818452e-01 -5.03727615e-01
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f208ce83-08a2-4fb3-9b94-2806a839576b | 190406472 | 1904.06472 | null | https://arxiv.org/abs/1904.06472v2 | https://arxiv.org/pdf/1904.06472v2.pdf | A Repository of Conversational Datasets | Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy'. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set. | ['Tsung-Hsien Wen', 'Ivan Vulić', 'Pei-Hao Su', 'Nikola Mrkšić', 'Iñigo Casanueva', 'Paweł Budzianowski', 'Matthew Henderson', 'Girish Kumar', 'Georgios Spithourakis', 'Daniela Gerz', 'Sam Coope'] | 2019-04-13 | a-repository-of-conversational-datasets | https://aclanthology.org/W19-4101 | https://aclanthology.org/W19-4101.pdf | ws-2019-8 | ['dialogue-understanding', 'conversational-response-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.07546139e-01 1.99592095e-02 -3.31376851e-01 -1.00954211e+00
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7c501ac9-af20-492a-bf14-9dd906d7445d | stimulating-the-diffusion-model-for-image | 2307.03992 | null | https://arxiv.org/abs/2307.03992v1 | https://arxiv.org/pdf/2307.03992v1.pdf | Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling | Image denoising is a fundamental problem in computational photography, where achieving high-quality perceptual performance with low distortion is highly demanding. Current methods either struggle with perceptual performance or suffer from significant distortion. Recently, the emerging diffusion model achieves state-of-the-art performance in various tasks, and its denoising mechanism demonstrates great potential for image denoising. However, stimulating diffusion models for image denoising is not straightforward and requires solving several critical problems. On the one hand, the input inconsistency hinders the connection of diffusion models and image denoising. On the other hand, the content inconsistency between the generated image and the desired denoised image introduces additional distortion. To tackle these problems, we present a novel strategy called Diffusion Model for Image Denoising (DMID) by understanding and rethinking the diffusion model from a denoising perspective. Our DMID strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained diffusion model, and an adaptive ensembling method that reduces distortion in the denoised image. Our DMID strategy achieves state-of-the-art performance on all distortion-based and perceptual metrics, for both Gaussian and real-world image denoising. | ['Hua Huang', 'Zhiwei Xiong', 'Lizhi Wang', 'Hansen Feng', 'Tong Li'] | 2023-07-08 | null | null | null | null | ['image-denoising', 'denoising'] | ['computer-vision', 'computer-vision'] | [ 4.14390981e-01 -2.45493650e-01 4.08543408e-01 -1.68736562e-01
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19292227-f338-474e-94da-691c74f8ef97 | modeling-fine-grained-information-via | 2211.10991 | null | https://arxiv.org/abs/2211.10991v1 | https://arxiv.org/pdf/2211.10991v1.pdf | Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval | Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a \textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph information bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models. The code has been available at https://github.com/wutaiqiang/GER-WSDM2023. | ['Yujiu Yang', 'Siheng Li', 'Weijie Liu', 'Weigang Guo', 'Xingyu Bai', 'Taiqiang Wu'] | 2022-11-20 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-3.19227725e-01 5.39420664e-01 -2.92380065e-01 -2.82159358e-01
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754e5c1a-3f33-49cb-b5a3-6fef10a67e9e | non-parametric-online-market-regime-detection | 2306.15835 | null | https://arxiv.org/abs/2306.15835v1 | https://arxiv.org/pdf/2306.15835v1.pdf | Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures | In this work we present a non-parametric online market regime detection method for multidimensional data structures using a path-wise two-sample test derived from a maximum mean discrepancy-based similarity metric on path space that uses rough path signatures as a feature map. The latter similarity metric has been developed and applied as a discriminator in recent generative models for small data environments, and has been optimised here to the setting where the size of new incoming data is particularly small, for faster reactivity. On the same principles, we also present a path-wise method for regime clustering which extends our previous work. The presented regime clustering techniques were designed as ex-ante market analysis tools that can identify periods of approximatively similar market activity, but the new results also apply to path-wise, high dimensional-, and to non-Markovian settings as well as to data structures that exhibit autocorrelation. We demonstrate our clustering tools on easily verifiable synthetic datasets of increasing complexity, and also show how the outlined regime detection techniques can be used as fast on-line automatic regime change detectors or as outlier detection tools, including a fully automated pipeline. Finally, we apply the fine-tuned algorithms to real-world historical data including high-dimensional baskets of equities and the recent price evolution of crypto assets, and we show that our methodology swiftly and accurately indicated historical periods of market turmoil. | ['Blanka Horvath', 'Zacharia Issa'] | 2023-06-27 | null | null | null | null | ['clustering', 'outlier-detection'] | ['methodology', 'methodology'] | [-2.43375540e-01 -6.10359371e-01 -8.89491662e-02 7.72350505e-02
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5ca5aa92-cd87-47b7-abb7-64c53b4dbf25 | sequential-batch-learning-in-finite-action | 2004.06321 | null | https://arxiv.org/abs/2004.06321v1 | https://arxiv.org/pdf/2004.06321v1.pdf | Sequential Batch Learning in Finite-Action Linear Contextual Bandits | We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end. Compared to both standard online contextual bandits learning or offline policy learning in contexutal bandits, this sequential batch learning problem provides a finer-grained formulation of many personalized sequential decision making problems in practical applications, including medical treatment in clinical trials, product recommendation in e-commerce and adaptive experiment design in crowdsourcing. We study two settings of the problem: one where the contexts are arbitrarily generated and the other where the contexts are \textit{iid} drawn from some distribution. In each setting, we establish a regret lower bound and provide an algorithm, whose regret upper bound nearly matches the lower bound. As an important insight revealed therefrom, in the former setting, we show that the number of batches required to achieve the fully online performance is polynomial in the time horizon, while for the latter setting, a pure-exploitation algorithm with a judicious batch partition scheme achieves the fully online performance even when the number of batches is less than logarithmic in the time horizon. Together, our results provide a near-complete characterization of sequential decision making in linear contextual bandits when batch constraints are present. | ['Zhengyuan Zhou', 'Zhengqing Zhou', 'Yinyu Ye', 'Yanjun Han', 'Jose Blanchet', 'Peter W. Glynn'] | 2020-04-14 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 2.96996087e-01 4.18217152e-01 -1.03618836e+00 -3.15593421e-01
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e5a277ed-943e-4eda-94ca-9c23c7e2dd91 | continuous-sign-language-recognition-with | 2303.03202 | null | https://arxiv.org/abs/2303.03202v3 | https://arxiv.org/pdf/2303.03202v3.pdf | Continuous Sign Language Recognition with Correlation Network | Human body trajectories are a salient cue to identify actions in the video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition (CSLR) usually process frames independently, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly capture and leverage body trajectories across frames to identify signs. In specific, a correlation module is first proposed to dynamically compute correlation maps between the current frame and adjacent frames to identify trajectories of all spatial patches. An identification module is then presented to dynamically emphasize the body trajectories within these correlation maps. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison with previous spatial-temporal reasoning methods verifies the effectiveness of CorrNet. Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames. | ['Wei Feng', 'Zekang Liu', 'Liqing Gao', 'Lianyu Hu'] | 2023-03-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sign-language-recognition'] | ['computer-vision'] | [-1.44955724e-01 -4.50942934e-01 -2.36573756e-01 -7.26991594e-02
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9d764e4d-348b-4bdc-b60b-900b12472115 | few-bit-backward-quantized-gradients-of | 2202.00441 | null | https://arxiv.org/abs/2202.00441v2 | https://arxiv.org/pdf/2202.00441v2.pdf | Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction | Memory footprint is one of the main limiting factors for large neural network training. In backpropagation, one needs to store the input to each operation in the computational graph. Every modern neural network model has quite a few pointwise nonlinearities in its architecture, and such operation induces additional memory costs which -- as we show -- can be significantly reduced by quantization of the gradients. We propose a systematic approach to compute optimal quantization of the retained gradients of the pointwise nonlinear functions with only a few bits per each element. We show that such approximation can be achieved by computing optimal piecewise-constant approximation of the derivative of the activation function, which can be done by dynamic programming. The drop-in replacements are implemented for all popular nonlinearities and can be used in any existing pipeline. We confirm the memory reduction and the same convergence on several open benchmarks. | ['Ivan Oseledets', 'Denis Dimitrov', 'Alex Shonenkov', 'Julia Gusak', 'Daniel Bershatsky', 'Georgii Novikov'] | 2022-02-01 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-5.71866706e-02 9.89594981e-02 -1.72299236e-01 -5.16177535e-01
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a89c8279-82d0-4a08-9d15-6cae58c4f324 | adaptive-residue-wise-profile-fusion-for-low | 2108.04176 | null | https://arxiv.org/abs/2108.04176v1 | https://arxiv.org/pdf/2108.04176v1.pdf | Adaptive Residue-wise Profile Fusion for Low Homologous Protein SecondaryStructure Prediction Using External Knowledge | Protein secondary structure prediction (PSSP) is essential for protein function analysis. However, for low homologous proteins, the PSSP suffers from insufficient input features. In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise profile fusion. In practice, we firstly demonstrate the superiority of profile over Position-Specific Scoring Matrix (PSSM) for low homologous PSSP. Based on this observation, we introduce the novel self-supervised BERT features as the pseudo profile, which implicitly involves the residue distribution in all native discovered sequences as the complementary features. Further-more, a novel residue-wise attention is specially designed to adaptively fuse different features (i.e.,original low-quality profile, BERT based pseudo profile), which not only takes full advantage of each feature but also avoids noise disturbance. Be-sides, the feature consistency loss is proposed to accelerate the model learning from multiple semantic levels. Extensive experiments confirm that our method outperforms state-of-the-arts (i.e.,4.7%forextremely low homologous cases on BC40 dataset). | ['Shuguang Cu', 'Sheng Wang', 'Zhen Li1', 'Boyuan Wang', 'Jun Wei', 'Qin Wang'] | 2021-08-05 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 5.70420504e-01 4.82135899e-02 -1.57594010e-01 -5.20348072e-01
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079c929c-038c-40ce-8566-8cd5ed0e02a2 | did-you-offend-me-classification-of-offensive | null | null | https://aclanthology.org/W18-5118 | https://aclanthology.org/W18-5118.pdf | Did you offend me? Classification of Offensive Tweets in Hinglish Language | The use of code-switched languages (\textit{e.g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult to understand the text automatically. This paper presents the Multi-Input Multi-Channel Transfer Learning based model (MIMCT) to detect offensive (hate speech or abusive) Hinglish tweets from the proposed Hinglish Offensive Tweet (HOT) dataset using transfer learning coupled with multiple feature inputs. Specifically, it takes multiple primary word embedding along with secondary extracted features as inputs to train a multi-channel CNN-LSTM architecture that has been pre-trained on English tweets through transfer learning. The proposed MIMCT model outperforms the baseline supervised classification models, transfer learning based CNN and LSTM models to establish itself as the state of the art in the unexplored domain of Hinglish offensive text classification. | ['Ramit Sawhney', 'Meghna Ayyar', 'Rajiv Shah', 'Puneet Mathur'] | 2018-10-01 | null | null | null | ws-2018-10 | ['abuse-detection'] | ['natural-language-processing'] | [-1.35176703e-01 9.92820114e-02 -2.35141933e-01 -4.26815957e-01
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bb7d4c2c-82a4-4f2a-86f2-06864acea732 | composing-rnns-and-fsts-for-small-data | null | null | https://openreview.net/forum?id=rkgj0tNfom | https://openreview.net/pdf?id=rkgj0tNfom | Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text | In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN). We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data. | ['Anonymous'] | 2018-10-15 | null | null | null | nips-workshop-irasl-2018 | ['transliteration'] | ['natural-language-processing'] | [ 1.98795214e-01 2.77878672e-01 1.20411597e-01 -2.95409381e-01
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f5ac5fbe-fd66-4538-9d5f-e245201bf4be | enhancing-medical-image-segmentation-with | 2301.10847 | null | https://arxiv.org/abs/2301.10847v1 | https://arxiv.org/pdf/2301.10847v1.pdf | Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach | While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available at \url{https://github.com/mindflow-institue/TransCeption}. | ['Dorit Merhof', 'Julien Cohen-Adad', 'Ehsan Khodapanah Aghdam', 'Yiwei Jia', 'Reza Azad'] | 2023-01-25 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 2.89005458e-01 1.16631396e-01 -2.66029924e-01 -3.54851753e-01
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426c314d-294d-494d-a47a-53a7c8e259ed | readprobe-a-demo-of-retrieval-enhanced-large | 2306.07875 | null | https://arxiv.org/abs/2306.07875v1 | https://arxiv.org/pdf/2306.07875v1.pdf | ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading | With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may be an effective approach to achieving this goal. In this paper, we present ReadProbe, a tool to support lateral reading, powered by generative large language models from OpenAI and the Bing search engine. Our tool is able to generate useful questions for lateral reading, scour the web for relevant documents, and generate well-attributed answers to help people better evaluate online information. We made a web-based application to demonstrate how ReadProbe can help reduce the risk of being misled by false information. The code is available at https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won the first prize in a national AI misinformation hackathon. | ['Ronak Pradeep', 'Dake Zhang'] | 2023-06-13 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-3.80896747e-01 2.78439224e-01 -1.82316408e-01 -1.51896968e-01
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e56c431c-3af9-49fa-a029-5ae3904b7aaf | evolutionary-generation-of-visual-motion | 2112.13243 | null | https://arxiv.org/abs/2112.13243v1 | https://arxiv.org/pdf/2112.13243v1.pdf | Evolutionary Generation of Visual Motion Illusions | Why do we sometimes perceive static images as if they were moving? Visual motion illusions enjoy a sustained popularity, yet there is no definitive answer to the question of why they work. We present a generative model, the Evolutionary Illusion GENerator (EIGen), that creates new visual motion illusions. The structure of EIGen supports the hypothesis that illusory motion might be the result of perceiving the brain's own predictions rather than perceiving raw visual input from the eyes. The scientific motivation of this paper is to demonstrate that the perception of illusory motion could be a side effect of the predictive abilities of the brain. The philosophical motivation of this paper is to call attention to the untapped potential of "motivated failures", ways for artificial systems to fail as biological systems fail, as a worthy outlet for Artificial Intelligence and Artificial Life research. | ['Eiji Watanabe', 'Lana Sinapayen'] | 2021-12-25 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.44933715e-01 2.71861345e-01 6.33288398e-02 2.71569267e-02
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b5261c15-7006-4250-8e56-5034baba0ede | ipg-net-image-pyramid-guidance-network-for | 1912.00632 | null | https://arxiv.org/abs/1912.00632v3 | https://arxiv.org/pdf/1912.00632v3.pdf | IPG-Net: Image Pyramid Guidance Network for Small Object Detection | For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious information imbalance. To acquire enough semantic information for shallow layers, Feature Pyramid Networks (FPN) is used to build a top-down propagated path. In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer. Our IPG-Net has two main parts: the image pyramid guidance transformation module and the image pyramid guidance fusion module. Our main idea is to introduce the image pyramid guidance into the backbone stream to solve the information imbalance problem, which alleviates the vanishment of the small object features. This IPG transformation module promises even in the deepest stage of the backbone, there is enough spatial information for bounding box regression and classification. Furthermore, we designed an effective fusion module to fuse the features from the image pyramid and features from the backbone stream. We have tried to apply this novel network to both one-stage and two-stage detection models, state of the art results are obtained on the most popular benchmark data sets, i.e. MS COCO and Pascal VOC. | ['Guangyu Gao', 'Li Fang', 'Ziming Liu', 'Lin Sun'] | 2019-12-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.23027869e-01 -1.36189424e-02 -5.02488529e-03 -3.26409459e-01
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d1dc73c0-b59b-4a79-bd8e-a98e327bce3c | mapfast-a-deep-algorithm-selector-for-multi | 2102.12461 | null | https://arxiv.org/abs/2102.12461v1 | https://arxiv.org/pdf/2102.12461v1.pdf | MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings | Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm. In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms' strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs. | ['Nora Ayanian', 'Baskin Senbaslar', 'Eric Ewing', 'Vikraman Sathiyanarayanan', 'Jingyao Ren'] | 2021-02-24 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-7.66772553e-02 -2.01642439e-01 -4.87809151e-01 -3.87978345e-01
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8152701b-7d0a-48a2-84f4-03a7f80f64ed | an-open-source-multi-goal-reinforcement | 2105.05985 | null | https://arxiv.org/abs/2105.05985v1 | https://arxiv.org/pdf/2105.05985v1.pdf | An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet | This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementation of the original environment. Besides, we provide users with new APIs to access a joint control mode, image observations and goals with customisable camera and a built-in on-hand camera. We further design a set of multi-step, multi-goal, long-horizon and sparse reward robotic manipulation tasks, aiming to inspire new goal-conditioned reinforcement learning algorithms for such challenges. We use a simple, human-prior-based curriculum learning method to benchmark the multi-step manipulation tasks. Discussions about future research opportunities regarding this kind of tasks are also provided. | ['Yu-Kun Lai', 'Jing Wu', 'Ze Ji', 'Xintong Yang'] | 2021-05-12 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-3.83091360e-01 -2.32364759e-02 8.70535225e-02 -1.42296195e-01
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0e50b341-c60b-4578-a4d3-33ecea95b35d | deepfd-automated-fault-diagnosis-and | 2205.01938 | null | https://arxiv.org/abs/2205.01938v1 | https://arxiv.org/pdf/2205.01938v1.pdf | DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs | As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN), which, unfortunately, might be a detour. Specifically, several existing studies have reported that many unsatisfactory behaviors are actually originated from the faults residing in DL programs. Besides, locating faulty neurons is not actionable for developers, while locating the faulty statements in DL programs can provide developers with more useful information for debugging. Though a few recent studies were proposed to pinpoint the faulty statements in DL programs or the training settings (e.g. too large learning rate), they were mainly designed based on predefined rules, leading to many false alarms or false negatives, especially when the faults are beyond their capabilities. In view of these limitations, in this paper, we proposed DeepFD, a learning-based fault diagnosis and localization framework which maps the fault localization task to a learning problem. In particular, it infers the suspicious fault types via monitoring the runtime features extracted during DNN model training and then locates the diagnosed faults in DL programs. It overcomes the limitations by identifying the root causes of faults in DL programs instead of neurons and diagnosing the faults by a learning approach instead of a set of hard-coded rules. The evaluation exhibits the potential of DeepFD. It correctly diagnoses 52% faulty DL programs, compared with around half (27%) achieved by the best state-of-the-art works. Besides, for fault localization, DeepFD also outperforms the existing works, correctly locating 42% faulty programs, which almost doubles the best result (23%) achieved by the existing works. | ['Shing-Chi Cheung', 'Bo Wu', 'Yongqiang Tian', 'Ming Wen', 'Xiao Chen', 'Meiziniu Li', 'Jialun Cao'] | 2022-05-04 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-2.88294464e-01 4.01669703e-02 -3.33625555e-01 -9.58318934e-02
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397c8e21-0987-4251-9af6-714eac89e46f | a-survey-of-word-embeddings-evaluation | 1801.09536 | null | http://arxiv.org/abs/1801.09536v1 | http://arxiv.org/pdf/1801.09536v1.pdf | A Survey of Word Embeddings Evaluation Methods | Word embeddings are real-valued word representations able to capture lexical
semantics and trained on natural language corpora. Models proposing these
representations have gained popularity in the recent years, but the issue of
the most adequate evaluation method still remains open. This paper presents an
extensive overview of the field of word embeddings evaluation, highlighting
main problems and proposing a typology of approaches to evaluation, summarizing
16 intrinsic methods and 12 extrinsic methods. I describe both widely-used and
experimental methods, systematize information about evaluation datasets and
discuss some key challenges. | ['Amir Bakarov'] | 2018-01-21 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [-9.01810527e-02 -1.11633293e-01 -9.44524944e-01 -4.30895209e-01
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3eb19d86-5aa0-4230-9fa9-bcc8ced173ae | beat-a-large-scale-semantic-and-emotional | 2203.05297 | null | https://arxiv.org/abs/2203.05297v5 | https://arxiv.org/pdf/2203.05297v5.pdf | BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis | Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with facial expressions, emotions, and semantics, in addition to the known correlation with audio, text, and speaker identity. Based on this observation, we propose a baseline model, Cascaded Motion Network (CaMN), which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (SRGR). Qualitative and quantitative experiments demonstrate metrics' validness, ground truth data quality, and baseline's state-of-the-art performance. To the best of our knowledge, BEAT is the largest motion capture dataset for investigating human gestures, which may contribute to a number of different research fields, including controllable gesture synthesis, cross-modality analysis, and emotional gesture recognition. The data, code and model are available on https://pantomatrix.github.io/BEAT/. | ['Bo Zheng', 'Elif Bozkurt', 'You Zhou', 'Zhengqing Li', 'Yichen Peng', 'Naoya Iwamoto', 'Zihao Zhu', 'Haiyang Liu'] | 2022-03-10 | null | null | null | null | ['gesture-recognition', 'gesture-generation'] | ['computer-vision', 'robots'] | [ 7.90504292e-02 -2.79006064e-01 -3.54742646e-01 -4.85189348e-01
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f7e43eb5-316c-4b3b-a21d-d4bddeac57b3 | image-label-based-semantic-segmentation | 2303.07892 | null | https://arxiv.org/abs/2303.07892v3 | https://arxiv.org/pdf/2303.07892v3.pdf | SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters | Achieving high-quality semantic segmentation predictions using only image-level labels enables a new level of real-world applicability. Although state-of-the-art networks deliver reliable predictions, the amount of handcrafted pixel-wise annotations to enable these results are not feasible in many real-world applications. Hence, several works have already targeted this bottleneck, using classifier-based networks like Class Activation Maps~\cite{CAM} (CAMs) as a base. Addressing CAM's weaknesses of fuzzy borders and incomplete predictions, state-of-the-art approaches rely only on adding regulations to the classifier loss or using pixel-similarity-based refinement after the fact. We propose a framework that introduces an additional module using object perimeters for improved saliency. We define object perimeter information as the line separating the object and background. Our new PerimeterFit module will be applied to pre-refine the CAM predictions before using the pixel-similarity-based network. In this way, our PerimeterFit increases the quality of the CAM prediction while simultaneously improving the false negative rate. We investigated a wide range of state-of-the-art unsupervised semantic segmentation networks and edge detection techniques to create useful perimeter maps, which enable our framework to predict object locations with sharper perimeters. We achieved up to 1.5% improvement over frameworks without our PerimeterFit module. We conduct an exhaustive analysis to illustrate that SILOP enhances existing state-of-the-art frameworks for image-level-based semantic segmentation. The framework is open-source and accessible online at https://github.com/ErikOstrowski/SILOP. | ['Muhammad Shafique', 'Bharath Srinivas Prabakaran', 'Erik Ostrowski'] | 2023-03-14 | null | null | null | null | ['edge-detection', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.78044200e-01 4.73840296e-01 -3.64629060e-01 -4.92402762e-01
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00e0945d-2a5e-4e0a-86ad-82815b75befa | a-unified-framework-for-tumor-proliferation | 1612.07180 | null | http://arxiv.org/abs/1612.07180v2 | http://arxiv.org/pdf/1612.07180v2.pdf | A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology | We present a unified framework to predict tumor proliferation scores from
breast histopathology whole slide images. Our system offers a fully automated
solution to predicting both a molecular data-based, and a mitosis
counting-based tumor proliferation score. The framework integrates three
modules, each fine-tuned to maximize the overall performance: An image
processing component for handling whole slide images, a deep learning based
mitosis detection network, and a proliferation scores prediction module. We
have achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based
score prediction and 0.652 F1-score in mitosis detection. On Spearman's
correlation coefficient, which evaluates predictive accuracy on the molecular
data based score, the system obtained 0.6171. Our approach won first place in
all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which
is MICCAI grand challenge. | ['Minsoo Kim', 'Kyunghyun Paeng', 'Sunggyun Park', 'Sangheum Hwang'] | 2016-12-21 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 1.47792518e-01 2.36555442e-01 -5.24136007e-01 -1.51497960e-01
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163cec3a-a314-4981-bce2-5937640c8d4f | mm-fi-multi-modal-non-intrusive-4d-human | 2305.10345 | null | https://arxiv.org/abs/2305.10345v1 | https://arxiv.org/pdf/2305.10345v1.pdf | MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing | 4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals for device-free human sensing. In this paper, we propose MM-Fi, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks. MM-Fi consists of over 320k synchronized frames of five modalities from 40 human subjects. Various annotations are provided to support potential sensing tasks, e.g., human pose estimation and action recognition. Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks. We envision that MM-Fi can contribute to wireless sensing research with respect to action recognition, human pose estimation, multi-modal learning, cross-modal supervision, and interdisciplinary healthcare research. | ['Lihua Xie', 'Chris Xiaoxuan Lu', 'Han Zou', 'Shenghai Yuan', 'Yuecong Xu', 'Xinyan Chen', 'Yunjiao Zhou', 'He Huang', 'Jianfei Yang'] | 2023-05-12 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 5.28458714e-01 3.05007286e-02 -1.29865214e-01 -2.39268512e-01
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bdb49b8c-7880-415a-b54a-9effafdf3992 | query-focused-sentence-compression-in-linear-1 | null | null | https://aclanthology.org/D19-1612 | https://aclanthology.org/D19-1612.pdf | Query-focused Sentence Compression in Linear Time | Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU. | ["Brendan O{'}Connor", 'H', 'Abram ler'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['sentence-compression'] | ['natural-language-processing'] | [ 5.97805679e-01 5.18634692e-02 -5.97140551e-01 -4.36153859e-01
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db8a5a1f-d355-4d15-9027-ff100059c68e | user-centric-evaluation-of-ocr-systems-for | 2302.13410 | null | https://arxiv.org/abs/2302.13410v1 | https://arxiv.org/pdf/2302.13410v1.pdf | User-Centric Evaluation of OCR Systems for Kwak'wala | There has been recent interest in improving optical character recognition (OCR) for endangered languages, particularly because a large number of documents and books in these languages are not in machine-readable formats. The performance of OCR systems is typically evaluated using automatic metrics such as character and word error rates. While error rates are useful for the comparison of different models and systems, they do not measure whether and how the transcriptions produced from OCR tools are useful to downstream users. In this paper, we present a human-centric evaluation of OCR systems, focusing on the Kwak'wala language as a case study. With a user study, we show that utilizing OCR reduces the time spent in the manual transcription of culturally valuable documents -- a task that is often undertaken by endangered language community members and researchers -- by over 50%. Our results demonstrate the potential benefits that OCR tools can have on downstream language documentation and revitalization efforts. | ['Graham Neubig', 'Antonios Anastasopoulos', 'Michayla King', 'Daisy Rosenblum', 'Shruti Rijhwani'] | 2023-02-26 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 2.73039311e-01 -4.13292974e-01 2.29292605e-02 -1.64861128e-01
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6612c2c7-bdc6-4b2d-8ea4-8f2849119804 | parameters-sharing-exploration-and-hetero | 2008.06223 | null | https://arxiv.org/abs/2008.06223v2 | https://arxiv.org/pdf/2008.06223v2.pdf | Parameter Sharing Exploration and Hetero-Center based Triplet Loss for Visible-Thermal Person Re-Identification | This paper focuses on the visible-thermal cross-modality person re-identification (VT Re-ID) task, whose goal is to match person images between the daytime visible modality and the nighttime thermal modality. The two-stream network is usually adopted to address the cross-modality discrepancy, the most challenging problem for VT Re-ID, by learning the multi-modality person features. In this paper, we explore how many parameters of two-stream network should share, which is still not well investigated in the existing literature. By well splitting the ResNet50 model to construct the modality-specific feature extracting network and modality-sharing feature embedding network, we experimentally demonstrate the effect of parameters sharing of two-stream network for VT Re-ID. Moreover, in the framework of part-level person feature learning, we propose the hetero-center based triplet loss to relax the strict constraint of traditional triplet loss through replacing the comparison of anchor to all the other samples by anchor center to all the other centers. With the extremely simple means, the proposed method can significantly improve the VT Re-ID performance. The experimental results on two datasets show that our proposed method distinctly outperforms the state-of-the-art methods by large margins, especially on RegDB dataset achieving superior performance, rank1/mAP/mINP 91.05%/83.28%/68.84%. It can be a new baseline for VT Re-ID, with a simple but effective strategy. | ['Xichuan Zhou', 'Xiaoheng Tan', 'Haijun Liu'] | 2020-08-14 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [-1.13077993e-02 -6.14162326e-01 8.74217153e-02 -2.85200685e-01
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5b72635a-d855-4a88-9952-7c30b5be9d25 | language-understanding-for-text-based-games | 1506.08941 | null | http://arxiv.org/abs/1506.08941v2 | http://arxiv.org/pdf/1506.08941v2.pdf | Language Understanding for Text-based Games Using Deep Reinforcement Learning | In this paper, we consider the task of learning control policies for
text-based games. In these games, all interactions in the virtual world are
through text and the underlying state is not observed. The resulting language
barrier makes such environments challenging for automatic game players. We
employ a deep reinforcement learning framework to jointly learn state
representations and action policies using game rewards as feedback. This
framework enables us to map text descriptions into vector representations that
capture the semantics of the game states. We evaluate our approach on two game
worlds, comparing against baselines using bag-of-words and bag-of-bigrams for
state representations. Our algorithm outperforms the baselines on both worlds
demonstrating the importance of learning expressive representations. | ['Regina Barzilay', 'tejas kulkarni', 'Karthik Narasimhan'] | 2015-06-30 | language-understanding-for-text-based-games-1 | https://aclanthology.org/D15-1001 | https://aclanthology.org/D15-1001.pdf | emnlp-2015-9 | ['text-based-games'] | ['playing-games'] | [-1.82114661e-01 3.29121649e-01 -4.94058460e-01 -7.35477507e-02
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d8fdd113-290d-4968-94f3-77f6e8f87e4d | blessing-of-dimensionality-high-dimensional | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Chen_Blessing_of_Dimensionality_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Chen_Blessing_of_Dimensionality_2013_CVPR_paper.pdf | Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification | Making a high-dimensional (e.g., 100K-dim) feature for face recognition seems not a good idea because it will bring difficulties on consequent training, computation, and storage. This prevents further exploration of the use of a highdimensional feature. In this paper, we study the performance of a highdimensional feature. We first empirically show that high dimensionality is critical to high performance. A 100K-dim feature, based on a single-type Local Binary Pattern (LBP) descriptor, can achieve significant improvements over both its low-dimensional version and the state-of-the-art. We also make the high-dimensional feature practical. With our proposed sparse projection method, named rotated sparse regression, both computation and model storage can be reduced by over 100 times without sacrificing accuracy quality. | ['Dong Chen', 'Xudong Cao', 'Jian Sun', 'Fang Wen'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.17700228e-01 -4.34358925e-01 -4.85635959e-02 -3.72562468e-01
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928b4694-352d-4d3c-bd8c-e6aca892b03d | af-2-s3net-attentive-feature-fusion-with | 2102.04530 | null | https://arxiv.org/abs/2102.04530v1 | https://arxiv.org/pdf/2102.04530v1.pdf | (AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network | Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one the essential components of environmental perception that provides semantic information of the scene. Recently, several methods have been introduced for 3D LiDAR semantic segmentation. While, they can lead to improved performance, they are either afflicted by high computational complexity, therefore are inefficient, or lack fine details of smaller instances. To alleviate this problem, we propose AF2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation. We present a novel multi-branch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder. Our AF2-S3Net fuses the voxel based learning and point-based learning into a single framework to effectively process the large 3D scene. Our experimental results show that the proposed method outperforms the state-of-the-art approaches on the large-scale SemanticKITTI benchmark, ranking 1st on the competitive public leaderboard competition upon publication. | ['Bingbing Liu', 'Enxu Li', 'Ehsan Taghavi', 'Ryan Razani', 'Ran Cheng'] | 2021-02-08 | af-2-s3net-attentive-feature-fusion-with-1 | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.pdf | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 9.65998918e-02 2.70408988e-02 -1.85946211e-01 -7.71203518e-01
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104156e3-9168-442c-84a2-44a12b0f28d1 | ballistocardiogram-signal-processing-a | 1807.00951 | null | http://arxiv.org/abs/1807.00951v1 | http://arxiv.org/pdf/1807.00951v1.pdf | Ballistocardiogram Signal Processing: A Literature Review | Time-domain algorithms are focused on detecting local maxima or local minima
using a moving window, and therefore finding the interval between the dominant
J-peaks of ballistocardiogram (BCG) signal. However, this approach has many
limitations due to the nonlinear and nonstationary behavior of the BCG signal.
This is because the BCG signal does not display consistent J-peaks, which can
usually be the case for overnight, in-home monitoring, particularly with frail
elderly. Additionally, its accuracy will be undoubtedly affected by motion
artifacts. Second, frequency-domain algorithms do not provide information about
interbeat intervals. Nevertheless, they can provide information about heart
rate variability. This is usually done by taking the fast Fourier transform or
the inverse Fourier transform of the logarithm of the estimated spectrum, i.e.,
cepstrum of the signal using a sliding window. Thereafter, the dominant
frequency is obtained in a particular frequency range. The limit of these
algorithms is that the peak in the spectrum may get wider and multiple peaks
may appear, which might cause a problem in measuring the vital signs. At last,
the objective of wavelet-domain algorithms is to decompose the signal into
different components, hence the component which shows an agreement with the
vital signs can be selected i.e., the selected component contains only
information about the heart cycles or respiratory cycles, respectively. An
empirical mode decomposition is an alternative approach to wavelet
decomposition, and it is also a very suitable approach to cope with nonlinear
and nonstationary signals such as cardiorespiratory signals. Apart from the
above-mentioned algorithms, machine learning approaches have been implemented
for measuring heartbeats. However, manual labeling of training data is a
restricting property. | ['Ibrahim Sadek'] | 2018-07-03 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.16640109e-01 -3.68506521e-01 -8.88466313e-02 1.14272282e-01
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4d785e07-521f-448d-a5d8-41753d746107 | consistent-explanations-by-contrastive | 2110.00527 | null | https://arxiv.org/abs/2110.00527v2 | https://arxiv.org/pdf/2110.00527v2.pdf | Consistent Explanations by Contrastive Learning | Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human priors, such as consistency across image transformations. Given an interpretation algorithm, e.g., Grad-CAM, we introduce a novel training method to train the model to produce more consistent explanations. Since obtaining the ground truth for a desired model interpretation is not a well-defined task, we adopt ideas from contrastive self-supervised learning, and apply them to the interpretations of the model rather than its embeddings. We show that our method, Contrastive Grad-CAM Consistency (CGC), results in Grad-CAM interpretation heatmaps that are more consistent with human annotations while still achieving comparable classification accuracy. Moreover, our method acts as a regularizer and improves the accuracy on limited-data, fine-grained classification settings. In addition, because our method does not rely on annotations, it allows for the incorporation of unlabeled data into training, which enables better generalization of the model. Our code is available here: https://github.com/UCDvision/CGC | ['Hamed Pirsiavash', 'Dennis Fong', 'Ashley Ouligian', 'Soroush Abbasi Koohpayegani', 'Vipin Pillai'] | 2021-10-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['explainable-models'] | ['computer-vision'] | [ 2.61901736e-01 5.50852716e-01 -5.24255097e-01 -6.69705272e-01
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2838669a-dc0c-4a51-ad08-fb489f357387 | data-efficient-learning-for-sim-to-real | 1906.08989 | null | https://arxiv.org/abs/1906.08989v1 | https://arxiv.org/pdf/1906.08989v1.pdf | Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks | Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data. To work well in the real world, the policy needs to see many instances of the task, including various object arrangements in the scene as well as variations in object geometry, texture, material, and environmental illumination. In this paper, we propose a method that learns to perform table-top instance grasping of a wide variety of objects while using no real world grasping data, outperforming the baseline using 2.5D shape by 10%. Our method learns 3D point cloud of object, and use that to train a domain-invariant grasping policy. We formulate the learning process as a two-step procedure: 1) Learning a domain-invariant 3D shape representation of objects from about 76K episodes in simulation and about 530 episodes in the real world, where each episode lasts less than a minute and 2) Learning a critic grasping policy in simulation only based on the 3D shape representation from step 1. Our real world data collection in step 1 is both cheaper and faster compared to existing approaches as it only requires taking multiple snapshots of the scene using a RGBD camera. Finally, the learned 3D representation is not specific to grasping, and can potentially be used in other interaction tasks. | ['Sören Pirk', 'Yuanzheng Gong', 'Yunfei Bai', 'Xinchen Yan', 'Mohi Khansari', 'Honglak Lee', 'Jasmine Hsu'] | 2019-06-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-1.37219816e-01 -1.33585274e-01 1.68844745e-01 -2.50120908e-01
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70b5dc3d-17e3-49c3-afc8-d9dc751d9370 | similarity-guided-deep-face-image-retrieval | 2107.05025 | null | https://arxiv.org/abs/2107.05025v1 | https://arxiv.org/pdf/2107.05025v1.pdf | Similarity Guided Deep Face Image Retrieval | Face image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly. In order to conduct fast and accurate retrieval, a compact hash code-based methods have been proposed, and recently, deep face image hashing methods with supervised classification training have shown outstanding performance. However, classification-based scheme has a disadvantage in that it cannot reveal complex similarities between face images into the hash code learning. In this paper, we attempt to improve the face image retrieval quality by proposing a Similarity Guided Hashing (SGH) method, which gently considers self and pairwise-similarity simultaneously. SGH employs various data augmentations designed to explore elaborate similarities between face images, solving both intra and inter identity-wise difficulties. Extensive experimental results on the protocols with existing benchmarks and an additionally proposed large scale higher resolution face image dataset demonstrate that our SGH delivers state-of-the-art retrieval performance. | ['Nam Ik Cho', 'Young Kyun Jang'] | 2021-07-11 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 1.21133476e-01 -5.05185902e-01 -2.29565099e-01 -6.80086255e-01
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419d9287-0cc3-49aa-bfa5-c5188b1eea65 | fault-signature-identification-for-bldc-motor | 2209.03159 | null | https://arxiv.org/abs/2209.03159v1 | https://arxiv.org/pdf/2209.03159v1.pdf | Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach | A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current signature analysis (MCSA) is widely used for fault detection now-a-days, the proposed hybrid method qualifies as one of the most powerful online/offline techniques for diagnosing the process faults. Existing approaches have some drawbacks that can degrade the performance and accuracy of a process-diagnosis system. In particular, it is very difficult to detect random stochastic noise due to the nonlinear behavior of valve controller. Using only Short Time Fourier Transform (STFT), frequency leakage and the small amplitude of the current components related to the fault can be observed, but the fault due to the controller behavior cannot be observed. Therefore, a framework of advanced multirate signal and data-processing aided with sensor fusion algorithms is proposed in this article and satisfactory results are obtained. For implementing the system, a DSP-based BLDC motor controller with three-phase inverter module (TMS 320F2812) is used and the performance of the proposed method is validated on real time data. | ['B. K. Panigrahi', 'Susanta Roy', 'Tribeni Prasad Banerjee'] | 2022-09-07 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.24481547e-01 -6.00806653e-01 2.54675239e-01 2.37158880e-01
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420b3e3f-7834-42ce-88ec-ce15b0914a7c | learning-shared-semantic-space-for-speech-to | 2105.03095 | null | https://arxiv.org/abs/2105.03095v3 | https://arxiv.org/pdf/2105.03095v3.pdf | Learning Shared Semantic Space for Speech-to-Text Translation | Having numerous potential applications and great impact, end-to-end speech translation (ST) has long been treated as an independent task, failing to fully draw strength from the rapid advances of its sibling - text machine translation (MT). With text and audio inputs represented differently, the modality gap has rendered MT data and its end-to-end models incompatible with their ST counterparts. In observation of this obstacle, we propose to bridge this representation gap with Chimera. By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art. Specifically, Chimera obtains 27.1 BLEU on MuST-C EN-DE, improving the SOTA by a +1.9 BLEU margin. Further experimental analyses demonstrate that the shared semantic space indeed conveys common knowledge between these two tasks and thus paves a new way for augmenting training resources across modalities. Code, data, and resources are available at https://github.com/Glaciohound/Chimera-ST. | ['Lei LI', 'Heng Ji', 'Mingxuan Wang', 'Chi Han'] | 2021-05-07 | null | https://aclanthology.org/2021.findings-acl.195 | https://aclanthology.org/2021.findings-acl.195.pdf | findings-acl-2021-8 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.68360341e-01 2.24825904e-01 -4.39525962e-01 -2.86189497e-01
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53b1f4d8-3de7-4cb8-96a3-2ea06936eca8 | sick-nl-a-dataset-for-dutch-natural-language | null | null | https://aclanthology.org/2021.eacl-main.126 | https://aclanthology.org/2021.eacl-main.126.pdf | SICK-NL: A Dataset for Dutch Natural Language Inference | We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of (Marelli et al., 2014) from English into Dutch. Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch on the two tasks. In the paper, we motivate and detail the translation process, perform a baseline evaluation on both the original SICK dataset and its Dutch incarnation SICK-NL, taking inspiration from Dutch skipgram embeddings and contextualised embedding models. In addition, we encapsulate two phenomena encountered in the translation to formulate stress tests and verify how well the Dutch models capture syntactic restructurings that do not affect semantics. Our main finding is all models perform worse on SICK-NL than on SICK, indicating that the Dutch dataset is more challenging than the English original. Results on the stress tests show that models don{'}t fully capture word order freedom in Dutch, warranting future systematic studies. | ['Michael Moortgat', 'Gijs Wijnholds'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 1.29959837e-01 4.00957555e-01 -3.77718389e-01 -5.69426119e-01
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6094c57e-29a3-49e3-ad2e-a83b2707e66b | incorporating-unlabelled-data-into-bayesian | 2304.01762 | null | https://arxiv.org/abs/2304.01762v2 | https://arxiv.org/pdf/2304.01762v2.pdf | Incorporating Unlabelled Data into Bayesian Neural Networks | Conventional Bayesian Neural Networks (BNNs) cannot leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian Neural Networks, which use unlabelled data to learn improved prior predictive distributions by maximising an evidence lower bound during an unsupervised pre-training step. With a novel methodology developed to better understand prior predictive distributions, we then show that self-supervised prior predictives capture image semantics better than conventional BNN priors. In our empirical evaluations, we see that self-supervised BNNs offer the label efficiency of self-supervised methods and the uncertainty estimates of Bayesian methods, particularly outperforming conventional BNNs in low-to-medium data regimes. | ['Vincent Fortuin', 'Yee Whye Teh', 'Tom Rainforth', 'Mrinank Sharma'] | 2023-04-04 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 6.58841193e-01 9.18951750e-01 -6.13861978e-01 -1.02255726e+00
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c1f23a17-10b2-45f5-b4b8-a770af2cfb0e | interactive-segmentation-of-radiance-fields | 2212.13545 | null | https://arxiv.org/abs/2212.13545v2 | https://arxiv.org/pdf/2212.13545v2.pdf | Interactive Segmentation of Radiance Fields | Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understanding and manipulating scenes represented as RFs, with semantic segmentation of objects as an important step. Prior segmentation efforts show promise but don't scale to complex objects with diverse appearance. We present the ISRF method to interactively segment objects with fine structure and appearance. Nearest neighbor feature matching using distilled semantic features identifies high-confidence seed regions. Bilateral search in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., and an interactive segmentation tool that others can use. Project Page: https://rahul-goel.github.io/isrf/ | ['PJ Narayanan', 'Saurabh Saini', 'Dhawal Sirikonda', 'Rahul Goel'] | 2022-12-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Goel_Interactive_Segmentation_of_Radiance_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['mixed-reality', 'interactive-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.11414772e-01 -3.26952338e-02 2.87733711e-02 -6.97941661e-01
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1064e53f-f5e0-4f79-bb7c-bb3914c60968 | stable-long-term-recurrent-video-super | 2112.08950 | null | https://arxiv.org/abs/2112.08950v1 | https://arxiv.org/pdf/2112.08950v1.pdf | Stable Long-Term Recurrent Video Super-Resolution | Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. However, when inferring on long video sequences presenting low motion (i.e. in which some parts of the scene barely move), recurrent models diverge through recurrent processing, generating high frequency artifacts. To the best of our knowledge, no study about VSR pointed out this instability problem, which can be critical for some real-world applications. Video surveillance is a typical example where such artifacts would occur, as both the camera and the scene stay static for a long time. In this work, we expose instabilities of existing recurrent VSR networks on long sequences with low motion. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. Finally, we introduce a new framework of recurrent VSR networks that is both stable and competitive, based on Lipschitz stability theory. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. We empirically show its competitive performance on long sequences with low motion. | ['Jean-Luc Starck', 'Joana Frontera-Pons', 'Arnaud Woiselle', 'Benjamin Naoto Chiche'] | 2021-12-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chiche_Stable_Long-Term_Recurrent_Video_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chiche_Stable_Long-Term_Recurrent_Video_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution'] | ['computer-vision'] | [ 3.93049061e-01 -3.12648982e-01 -6.32188693e-02 1.37366712e-01
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db21db24-661a-4024-b937-f7a822b2fc1d | paradise-exploiting-parallel-data-for-1 | null | null | https://aclanthology.org/2022.repl4nlp-1.3 | https://aclanthology.org/2022.repl4nlp-1.3.pdf | PARADISE”:" Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining | Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel &Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence. Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost. | ['Mikel Artetxe', 'Machel Reid'] | null | null | null | null | repl4nlp-acl-2022-5 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 3.52549285e-01 -3.34068388e-01 -1.52867123e-01 -3.34989399e-01
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51d458b8-4311-4eb8-8028-6ab49aa019c2 | progressive-image-deraining-networks-a-better | 1901.09221 | null | https://arxiv.org/abs/1901.09221v3 | https://arxiv.org/pdf/1901.09221v3.pdf | Progressive Image Deraining Networks: A Better and Simpler Baseline | Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the contribution of various network modules when developing new deraining networks. To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions. Specifically, by repeatedly unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take advantage of recursive computation. A recurrent layer is further introduced to exploit the dependencies of deep features across stages, forming our progressive recurrent network (PReNet). Furthermore, intra-stage recursive computation of ResNet can be adopted in PRN and PReNet to notably reduce network parameters with graceful degradation in deraining performance. For network input and output, we take both stage-wise result and original rainy image as input to each ResNet and finally output the prediction of {residual image}. As for loss functions, single MSE or negative SSIM losses are sufficient to train PRN and PReNet. Experiments show that PRN and PReNet perform favorably on both synthetic and real rainy images. Considering its simplicity, efficiency and effectiveness, our models are expected to serve as a suitable baseline in future deraining research. The source codes are available at https://github.com/csdwren/PReNet. | ['QinGhua Hu', 'WangMeng Zuo', 'Pengfei Zhu', 'Dongwei Ren', 'Deyu Meng'] | 2019-01-26 | progressive-image-deraining-networks-a-better-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ren_Progressive_Image_Deraining_Networks_A_Better_and_Simpler_Baseline_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ren_Progressive_Image_Deraining_Networks_A_Better_and_Simpler_Baseline_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [ 6.78524673e-02 -1.44485086e-01 2.19925672e-01 -5.07318556e-01
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2ebff300-d6c9-4296-9ab5-43d4b721cb33 | on-learning-contrastive-representations-for | 2203.01785 | null | https://arxiv.org/abs/2203.01785v3 | https://arxiv.org/pdf/2203.01785v3.pdf | On Learning Contrastive Representations for Learning with Noisy Labels | Deep neural networks are able to memorize noisy labels easily with a softmax cross-entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a noise-robust loss function to the CE loss. However, the memorization issue is alleviated but still remains due to the non-robust CE loss. To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where label noise does not dominate the representation learning. By theoretically investigating the representations induced by the proposed regularization function, we reveal that the learned representations keep information related to true labels and discard information related to corrupted labels. Moreover, our theoretical results also indicate that the learned representations are robust to the label noise. The effectiveness of this method is demonstrated with experiments on benchmark datasets. | ['Boyu Wang', 'A. Ian McLeod', 'Qi She', 'Sheng Liu', 'Li Yi'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yi_On_Learning_Contrastive_Representations_for_Learning_With_Noisy_Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yi_On_Learning_Contrastive_Representations_for_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 5.48467398e-01 1.65374890e-01 -2.86715431e-03 -5.22921503e-01
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c287d167-f7b4-4de3-aa73-f1b0f0e3c988 | self-regression-learning-for-blind | 2103.16806 | null | https://arxiv.org/abs/2103.16806v1 | https://arxiv.org/pdf/2103.16806v1.pdf | Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label | Hyperspectral image fusion (HIF) is critical to a wide range of applications in remote sensing and many computer vision applications. Most traditional HIF methods assume that the observation model is predefined or known. However, in real applications, the observation model involved are often complicated and unknown, which leads to the serious performance drop of many advanced HIF methods. Also, deep learning methods can achieve outstanding performance, but they generally require a large number of image pairs for model training, which are difficult to obtain in realistic scenarios. Towards these issues, we proposed a self-regression learning method that alternatively reconstructs hyperspectral image (HSI) and estimate the observation model. In particular, we adopt an invertible neural network (INN) for restoring the HSI, and two fully-connected network (FCN) for estimating the observation model. Moreover, \emph{SoftMax} nonlinearity is applied to the FCN for satisfying the non-negative, sparsity and equality constraints. Besides, we proposed a local consistency loss function to constrain the observation model by exploring domain specific knowledge. Finally, we proposed an angular loss function to improve spectral reconstruction accuracy. Extensive experiments on both synthetic and real-world dataset show that our model can outperform the state-of-the-art methods | ['Xinhao Ding', 'Yue Huang', 'Wu Wang'] | 2021-03-31 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.21679330e-01 -3.88632238e-01 2.54081246e-02 -4.49610382e-01
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780b8f0a-a833-483a-b122-488b88da3442 | a-comparative-study-of-pretrained-language | 1912.01580 | null | https://arxiv.org/abs/1912.01580v2 | https://arxiv.org/pdf/1912.01580v2.pdf | A Comparative Study of Pretrained Language Models on Thai Social Text Categorization | The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language model on a large noisy Thai social media corpus of over 1.26 billion tokens and later fine-tuned on the downstream classification tasks. Due to the linguistically noisy and domain-specific nature of the content, our unique data preprocessing steps designed for Thai social media were utilized to ease the training comprehension of the model. We compared four modern language models: ULMFiT, ELMo with biLSTM, OpenAI GPT, and BERT. We systematically compared the models across different dimensions including speed of pretraining and fine-tuning, perplexity, downstream classification benchmarks, and performance in limited pretraining data. | ['Boonserm Kijsirikul', 'Thanapapas Horsuwan', 'Kasidis Kanwatchara', 'Peerapon Vateekul'] | 2019-12-03 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-2.31557302e-02 2.74685800e-01 -1.76372796e-01 -6.31185234e-01
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57c29942-8139-4648-94bb-71dec910a9b5 | convsearch-a-open-domain-conversational | 2204.02659 | null | https://arxiv.org/abs/2204.02659v1 | https://arxiv.org/pdf/2204.02659v1.pdf | ConvSearch: A Open-Domain Conversational Search Behavior Dataset | Conversational Search has been paid much attention recently with the increasing popularity of intelligent user interfaces. However, compared with the endeavour in designing effective conversational search algorithms, relatively much fewer researchers have focused on the construction of benchmark datasets. For most existing datasets, the information needs are defined by researchers and search requests are not proposed by actual users. Meanwhile, these datasets usually focus on the conversations between users and agents (systems), while largely ignores the search behaviors of agents before they return response to users. To overcome these problems, we construct a Chinese Open-Domain Conversational Search Behavior Dataset (ConvSearch) based on Wizard-of-Oz paradigm in the field study scenario. We develop a novel conversational search platform to collect dialogue contents, annotate dialogue quality and candidate search results and record agent search behaviors. 25 search agents and 51 users are recruited for the field study that lasts about 45 days. The ConvSearch dataset contains 1,131 dialogues together with annotated search results and corresponding search behaviors. We also provide the intent labels of each search behavior iteration to support intent understanding related researches. The dataset is already open to public for academic usage. | ['Shaoping Ma', 'Min Zhang', 'Yingye Huang', 'Yiqun Liu', 'Zhihong Wang', 'Zhumin Chu'] | 2022-04-06 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.50296375e-01 -3.57758225e-04 -4.47736233e-01 -4.72648382e-01
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35295cae-9bc1-46f3-a8f3-60f4f1c62cc8 | fine-tune-bert-for-extractive-summarization | 1903.10318 | null | https://arxiv.org/abs/1903.10318v2 | https://arxiv.org/pdf/1903.10318v2.pdf | Fine-tune BERT for Extractive Summarization | BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at https://github.com/nlpyang/BertSum | ['Yang Liu'] | 2019-03-25 | fine-tune-bert-for-extractive-summarization-1 | null | null | arxiv-2019-3 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-1.41757458e-01 2.27917984e-01 -3.79045427e-01 -1.38502523e-01
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8d5b1256-6fef-4114-a3cb-e28bc488c0fc | investigation-of-densely-connected | 2112.10108 | null | https://arxiv.org/abs/2112.10108v1 | https://arxiv.org/pdf/2112.10108v1.pdf | Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition | We investigate densely connected convolutional networks (DenseNets) and their extension with domain adversarial training for noise robust speech recognition. DenseNets are very deep, compact convolutional neural networks which have demonstrated incredible improvements over the state-of-the-art results in computer vision. Our experimental results reveal that DenseNets are more robust against noise than other neural network based models such as deep feed forward neural networks and convolutional neural networks. Moreover, domain adversarial learning can further improve the robustness of DenseNets against both, known and unknown noise conditions. | ['Ngoc Thang Vu', 'Chia Yu Li'] | 2021-12-19 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [-9.58857015e-02 1.90427914e-01 3.00439924e-01 -2.15300918e-01
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f2a751c8-9bee-416b-94c5-d9a8b2f6adca | shadowdiffusion-diffusion-based-shadow | 2211.08089 | null | https://arxiv.org/abs/2211.08089v2 | https://arxiv.org/pdf/2211.08089v2.pdf | DeS3: Attention-driven Self and Soft Shadow Removal using ViT Similarity and Color Convergence | Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing methods rely on binary shadow masks, without considering the ambiguous boundaries of soft and self shadows. In this paper, we present DeS3, a method that removes hard, soft and self shadows based on the self-tuned ViT feature similarity and color convergence. Our novel ViT similarity loss utilizes features extracted from a pre-trained Vision Transformer. This loss helps guide the reverse diffusion process towards recovering scene structures. We also introduce a color convergence loss to constrain the surface colors in the reverse inference process to avoid any color shifts. Our DeS3 is able to differentiate shadow regions from the underlying objects, as well as shadow regions from the object casting the shadow. This capability enables DeS3 to better recover the structures of objects even when they are partially occluded by shadows. Different from existing methods that rely on constraints during the training phase, we incorporate the ViT similarity and color convergence loss during the sampling stage. This enables our DeS3 model to effectively integrate its strong modeling capabilities with input-specific knowledge in a self-tuned manner. Our method outperforms state-of-the-art methods on the SRD, AISTD, LRSS, USR and UIUC datasets, removing hard, soft, and self shadows robustly. Specifically, our method outperforms the SOTA method by 20% of the RMSE of the whole image on the SRD dataset. | ['Robby T. Tan', 'Yuan Yuan', 'Wei Ye', 'Wenhan Yang', 'Yeying Jin'] | 2022-11-15 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 5.43224573e-01 -1.60815194e-01 2.64198959e-01 -2.42735639e-01
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