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34180d00-7184-4e2c-8665-b71d2d7ff1cb | multi-loss-convolutional-network-with-time | 2306.08956 | null | https://arxiv.org/abs/2306.08956v1 | https://arxiv.org/pdf/2306.08956v1.pdf | Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement | The Dual-Path Convolution Recurrent Network (DPCRN) was proposed to effectively exploit time-frequency domain information. By combining the DPRNN module with Convolution Recurrent Network (CRN), the DPCRN obtained a promising performance in speech separation with a limited model size. In this paper, we explore self-attention in the DPCRN module and design a model called Multi-Loss Convolutional Network with Time-Frequency Attention(MNTFA) for speech enhancement. We use self-attention modules to exploit the long-time information, where the intra-chunk self-attentions are used to model the spectrum pattern and the inter-chunk self-attention are used to model the dependence between consecutive frames. Compared to DPRNN, axial self-attention greatly reduces the need for memory and computation, which is more suitable for long sequences of speech signals. In addition, we propose a joint training method of a multi-resolution STFT loss and a WavLM loss using a pre-trained WavLM network. Experiments show that with only 0.23M parameters, the proposed model achieves a better performance than DPCRN. | ['Jie Ji', 'Yi Zhou', 'Hongqing Liu', 'Liang Wan'] | 2023-06-15 | null | null | null | null | ['speech-separation', 'speech-enhancement'] | ['speech', 'speech'] | [ 8.52911621e-02 -1.18350551e-01 1.09053731e-01 -1.72274068e-01
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95c0bf84-d1bd-4997-ba10-3e00a6a72f1c | evaluating-bert-based-pre-training-language | 2203.07731 | null | https://arxiv.org/abs/2203.07731v1 | https://arxiv.org/pdf/2203.07731v1.pdf | Evaluating BERT-based Pre-training Language Models for Detecting Misinformation | It is challenging to control the quality of online information due to the lack of supervision over all the information posted online. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection techniques to limit the adverse effects of spreading misinformation. Previous studies mainly focused on finding and extracting the significant features of text data. However, extracting features is time-consuming and not a highly effective process. This study proposes the BERT- based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation. Furthermore, different language models (LM) ' performance with different trainable parameters was compared. The proposed technique is tested on different short and long text datasets. The result of the proposed technique has been compared with the state-of-the-art techniques on the same datasets. The results show that the proposed technique performs better than the state-of-the-art techniques. We also tested the proposed technique by combining the datasets. The results demonstrated that the large data training and testing size considerably improves the technique's performance. | ['Amitava Datta', 'Ghulam Mubashar Hassan', 'Rini Anggrainingsih'] | 2022-03-15 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-1.17494855e-02 -1.91400364e-01 -1.49259806e-01 -1.27237692e-01
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d0490626-4b42-49fb-9cbf-253aa25c6ef4 | uncertainty-guided-mixup-for-semi-supervised | 2107.06707 | null | https://arxiv.org/abs/2107.06707v1 | https://arxiv.org/pdf/2107.06707v1.pdf | Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data | Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data. However, the source data are not always available due to the privacy preserving consideration or bandwidth limitation. Source-free domain adaptation aims to solve the above problem by performing domain adaptation without accessing the source data. The adaptation paradigm is receiving more and more attention in recent years, and multiple works have been proposed for unsupervised source-free domain adaptation. However, without utilizing any supervised signal and source data at the adaptation stage, the optimization of the target model is unstable and fragile. To alleviate the problem, we focus on semi-supervised domain adaptation under source-free setting. More specifically, we propose uncertainty-guided Mixup to reduce the representation's intra-domain discrepancy and perform inter-domain alignment without directly accessing the source data. Finally, we conduct extensive semi-supervised domain adaptation experiments on various datasets. Our method outperforms the recent semi-supervised baselines and the unsupervised variant also achieves competitive performance. The experiment codes will be released in the future. | ['Sheng Zhou', 'Zhen Zhang', 'Jiajun Bu', 'Ning Ma'] | 2021-07-14 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.40012693e-01 -6.46511018e-02 -6.25355244e-01 -6.12906814e-01
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9c543843-82fd-4d18-97ec-e57e5c98639a | swinir-image-restoration-using-swin | 2108.10257 | null | https://arxiv.org/abs/2108.10257v1 | https://arxiv.org/pdf/2108.10257v1.pdf | SwinIR: Image Restoration Using Swin Transformer | Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by $\textbf{up to 0.14$\sim$0.45dB}$, while the total number of parameters can be reduced by $\textbf{up to 67%}$. | ['Radu Timofte', 'Luc van Gool', 'Kai Zhang', 'Guolei Sun', 'JieZhang Cao', 'Jingyun Liang'] | 2021-08-23 | null | null | null | null | ['color-image-denoising', 'video-super-resolution', 'jpeg-compression-artifact-reduction', 'grayscale-image-denoising'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.82437301e-01 -3.96221608e-01 2.53797531e-01 -3.40964168e-01
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003c6ddc-a32e-40fa-b6a9-abf3c0ed01e0 | qmsum-a-new-benchmark-for-query-based-multi | 2104.05938 | null | https://arxiv.org/abs/2104.05938v1 | https://arxiv.org/pdf/2104.05938v1.pdf | QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization | Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at \url{https://github.com/Yale-LILY/QMSum}. | ['Dragomir Radev', 'Xipeng Qiu', 'Yang Liu', 'Asli Celikyilmaz', 'Ahmed Hassan Awadallah', 'Rahul Jha', 'Mutethia Mutuma', 'Ahmad Zaidi', 'Tao Yu', 'Da Yin', 'Ming Zhong'] | 2021-04-13 | null | https://aclanthology.org/2021.naacl-main.472 | https://aclanthology.org/2021.naacl-main.472.pdf | naacl-2021-4 | ['meeting-summarization'] | ['natural-language-processing'] | [ 4.16868240e-01 1.77361801e-01 -3.06987576e-02 -5.12146294e-01
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d5ad56c9-85ca-4b61-9eb2-dd8cd1192f2a | structure-guided-image-outpainting | 2212.12326 | null | https://arxiv.org/abs/2212.12326v1 | https://arxiv.org/pdf/2212.12326v1.pdf | Structure-guided Image Outpainting | Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulfilled, owing to difficulties caused by large-scale area loss and less legitimate neighboring information. These difficulties have made outpainted images handled by most of the existing models unrealistic to human eyes and spatially inconsistent. When upsampling through deconvolution to generate fake content, the naive generation methods may lead to results lacking high-frequency details and structural authenticity. Therefore, as our novelties to handle image outpainting problems, we introduce structural prior as a condition to optimize the generation quality and a new semantic embedding term to enhance perceptual sanity. we propose a deep learning method based on Generative Adversarial Network (GAN) and condition edges as structural prior in order to assist the generation. We use a multi-phase adversarial training scheme that comprises edge inference training, contents inpainting training, and joint training. The newly added semantic embedding loss is proved effective in practice. | ['Wenliang Jia', 'Weixi Cheng', 'Xi Wang'] | 2022-12-21 | null | null | null | null | ['image-outpainting', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.81446832e-01 1.29036963e-01 8.32384750e-02 -1.68509968e-02
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75ab6cfd-afc3-4466-bba8-3d126a49eeb8 | spatial-mixup-directional-loudness | 2110.06126 | null | https://arxiv.org/abs/2110.06126v1 | https://arxiv.org/pdf/2110.06126v1.pdf | Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection | Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain. For sound event localization and detection (SELD) tasks several augmentation methods have been proposed, with most borrowing ideas from other domains such as images, speech, or monophonic audio. However, only a few exploit the spatial properties of a full 3D audio scene. We propose Spatial Mixup, as an application of parametric spatial audio effects for data augmentation, which modifies the directional properties of a multi-channel spatial audio signal encoded in the ambisonics domain. Similarly to beamforming, these modifications enhance or suppress signals arriving from certain directions, although the effect is less pronounced. Therefore enabling deep learning models to achieve invariance to small spatial perturbations. The method is evaluated with experiments in the DCASE 2021 Task 3 dataset, where spatial mixup increases performance over a non-augmented baseline, and compares to other well known augmentation methods. Furthermore, combining spatial mixup with other methods greatly improves performance. | ['Yuki Mitsufuji', 'Shusuke Takahashi', 'Yuichiro Koyama', 'Kazuki Shimada', 'Ricardo Falcon-Perez'] | 2021-10-12 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 4.58452523e-01 -1.54213324e-01 1.38315782e-01 -8.67389143e-02
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87f3d47f-e8eb-49cf-98b6-78a6089e5300 | a-chinese-multi-type-complex-questions | 2111.06086 | null | https://arxiv.org/abs/2111.06086v1 | https://arxiv.org/pdf/2111.06086v1.pdf | A Chinese Multi-type Complex Questions Answering Dataset over Wikidata | Complex Knowledge Base Question Answering is a popular area of research in the past decade. Recent public datasets have led to encouraging results in this field, but are mostly limited to English and only involve a small number of question types and relations, hindering research in more realistic settings and in languages other than English. In addition, few state-of-the-art KBQA models are trained on Wikidata, one of the most popular real-world knowledge bases. We propose CLC-QuAD, the first large scale complex Chinese semantic parsing dataset over Wikidata to address these challenges. Together with the dataset, we present a text-to-SPARQL baseline model, which can effectively answer multi-type complex questions, such as factual questions, dual intent questions, boolean questions, and counting questions, with Wikidata as the background knowledge. We finally analyze the performance of SOTA KBQA models on this dataset and identify the challenges facing Chinese KBQA. | ['Zhou Zhao', 'Songfang Huang', 'Yifan He', 'Shushu Wang', 'Ran Qin', 'Fengqing Jiang', 'Qifan Pan', 'Yechen Xu', 'Lichao Zhang', 'Min Yang', 'Jianyun Zou'] | 2021-11-11 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-5.26240528e-01 3.12116921e-01 -1.66871622e-01 -5.03154516e-01
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23a1a3a5-38df-448d-89c8-15423833778f | reaching-kesten-stigum-threshold-in-the | 2305.10227 | null | https://arxiv.org/abs/2305.10227v1 | https://arxiv.org/pdf/2305.10227v1.pdf | Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions | We study robust community detection in the context of node-corrupted stochastic block model, where an adversary can arbitrarily modify all the edges incident to a fraction of the $n$ vertices. We present the first polynomial-time algorithm that achieves weak recovery at the Kesten-Stigum threshold even in the presence of a small constant fraction of corrupted nodes. Prior to this work, even state-of-the-art robust algorithms were known to break under such node corruption adversaries, when close to the Kesten-Stigum threshold. We further extend our techniques to the $Z_2$ synchronization problem, where our algorithm reaches the optimal recovery threshold in the presence of similar strong adversarial perturbations. The key ingredient of our algorithm is a novel identifiability proof that leverages the push-out effect of the Grothendieck norm of principal submatrices. | ['David Steurer', 'Yiding Hua', "Tommaso d'Orsi", 'Jingqiu Ding'] | 2023-05-17 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 5.09937704e-01 2.78598905e-01 -2.06872877e-02 4.54303920e-01
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6c354141-f434-4f8f-ad4c-9b9f336f8c13 | win-fail-action-recognition | 2102.07355 | null | https://arxiv.org/abs/2102.07355v1 | https://arxiv.org/pdf/2102.07355v1.pdf | Win-Fail Action Recognition | Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a truer, deeper understanding of videos, we introduce the task of win-fail action recognition -- differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," and "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. We systematically analyze the characteristics of the win-fail task/dataset with prototypical action recognition networks and a novel video retrieval task. While current action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from https://github.com/ParitoshParmar/Win-Fail-Action-Recognition. | ['Brendan Morris', 'Paritosh Parmar'] | 2021-02-15 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 4.13461536e-01 -3.98692936e-01 -8.69673491e-01 -3.50610137e-01
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94dbaa38-263f-44a9-aee8-84c6dc7ae753 | unified-multi-modal-landmark-tracking-for | 2011.06838 | null | https://arxiv.org/abs/2011.06838v3 | https://arxiv.org/pdf/2011.06838v3.pdf | Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry | We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames. The lightweight formulation of the 3D features allows for real-time execution on a single CPU. Our proposed system has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device, for a total duration of 96 min and 2.4 km traveled distance. In these test sequences, using only one exteroceptive sensor leads to failure due to either underconstrained geometry (affecting lidar) or textureless areas caused by aggressive lighting changes (affecting vision). In these conditions, our factor graph naturally uses the best information available from each sensor modality without any hard switches. | ['Maurice Fallon', 'Sandipan Das', 'Marco Camurri', 'David Wisth'] | 2020-11-13 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 2.18836784e-01 -1.98033154e-02 5.52491397e-02 -1.66134745e-01
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e39d4342-ab3d-4b3d-801e-85317af5d137 | verimedi-pill-identification-using-proxy | 2104.11231 | null | https://arxiv.org/abs/2104.11231v1 | https://arxiv.org/pdf/2104.11231v1.pdf | VeriMedi: Pill Identification using Proxy-based Deep Metric Learning and Exact Solution | We present the system that we have developed for the identification and verification of pills using images that are taken by the VeriMedi device. The VeriMedi device is an Internet of Things device that takes pictures of a filled pill vial from the bottom of the vial and uses the solution that is presented in this research to identify the pills in the vials. The solution has two serially connected deep learning solutions which do segmentation and identification. The segmentation solution creates the masks for each pill in the vial image by using the Mask R-CNN model, then segments and crops the pills and blurs the background. After that, the segmented pill images are sent to the identification solution where a Deep Metric Learning model that is trained with Proxy Anchor Loss (PAL) function generates embedding vectors for each pill image. The generated embedding vectors are fed into a one-layer fully connected network that is trained with the exact solution to predict each single pill image. Then, the aggregation/verification function aggregates the multiple predictions coming from multiple single pill images and verifies the correctness of the final prediction with respect to predefined rules. Besides, we enhanced the PAL with a better proxy initialization that increased the performance of the models and let the model learn the new classes of images continually without retraining the model with the whole dataset. When the model that is trained with initial classes is retrained only with new classes, the accuracy of the model increases for both old and new classes. The identification solution that we have presented in this research can also be reused for other problem domains which require continual learning and/or Fine-Grained Visual Categorization. | ['Alina Nescerecka', 'Stanislavs Hilcuks', 'Viktors Roze', 'Tekin Evrim Ozmermer'] | 2021-04-22 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.05263749e-01 1.53987408e-01 -2.47170422e-02 -3.85217100e-01
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0a0fa9c2-0c12-4af7-9e0e-dfaca11cb5f4 | jointly-learning-propagating-features-on-the | null | null | https://link.springer.com/chapter/10.1007/978-3-031-12423-5_1 | https://link.springer.com/chapter/10.1007/978-3-031-12423-5_1 | Jointly Learning Propagating Features on the Knowledge Graph for Movie Recommendation | Knowledge graphs are widely used as auxiliary information to improve the performance in recommender systems. This enables items to be aligned with knowledge entities and provides additional item attributes to facilitate learning interactions between users and items. However, the lack of user connections in the knowledge graph may degrade the profiling of user preferences, especially for explicit user behaviors. Furthermore, learning knowledge graph embeddings is not entirely consistent with recommendation tasks due to different objectives. To solve the aforementioned problems, we extract knowledge entities from users' explicit reviews and propose a multi-task framework to jointly learn propagating features on the knowledge graph for movie recommendations. The review-based heterogeneous graph can provide substantial information for learning user preferences. In the proposed framework, we use an attention-based multi-hop propagation mechanism to take users and movies as center nodes and extend their attributes along with the connections of the knowledge graph by recursively calculating the different contributions of their neighbors. We use two real-world datasets to show the effectiveness of our proposed model in comparison with state-of-the-art baselines. Additionally, we investigate two aspects of the proposed model in extended ablation studies. | ['Qiong Chang', 'Jun Miyazaki', 'Yun Liu'] | 2022-08-22 | null | null | null | database-and-expert-systems-applications-2022 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'movie-recommendation'] | ['graphs', 'methodology', 'miscellaneous'] | [-1.95387453e-01 7.49383271e-02 -8.63837957e-01 -5.19205332e-01
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3829547a-3c23-4311-a57c-7d96e5eb79ea | red-sfa-relation-discovery-based-slow-feature | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.pdf | ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering | For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReD-SFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art. | ['Jun Li', 'Zhang Zhang', 'Kaiqi Huang', 'Tieniu Tan', 'Peipei Yang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['time-series-clustering'] | ['time-series'] | [-3.28834802e-01 -1.56824693e-01 -9.41244140e-02 -1.72898665e-01
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6a16db71-a07d-4ff9-8ff6-9771ea85ce32 | sok-pragmatic-assessment-of-machine-learning | 2305.00550 | null | https://arxiv.org/abs/2305.00550v1 | https://arxiv.org/pdf/2305.00550v1.pdf | SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection | Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For Network Intrusion Detection (NID), however, scientific advances in ML are still seen with skepticism by practitioners. This disconnection is due to the intrinsically limited scope of research papers, many of which primarily aim to demonstrate new methods ``outperforming'' prior work -- oftentimes overlooking the practical implications for deploying the proposed solutions in real systems. Unfortunately, the value of ML for NID depends on a plethora of factors, such as hardware, that are often neglected in scientific literature. This paper aims to reduce the practitioners' skepticism towards ML for NID by "changing" the evaluation methodology adopted in research. After elucidating which "factors" influence the operational deployment of ML in NID, we propose the notion of "pragmatic assessment", which enable practitioners to gauge the real value of ML methods for NID. Then, we show that the state-of-research hardly allows one to estimate the value of ML for NID. As a constructive step forward, we carry out a pragmatic assessment. We re-assess existing ML methods for NID, focusing on the classification of malicious network traffic, and consider: hundreds of configuration settings; diverse adversarial scenarios; and four hardware platforms. Our large and reproducible evaluations enable estimating the quality of ML for NID. We also validate our claims through a user-study with security practitioners. | ['Johannes Schneider', 'Pavel Laskov', 'Giovanni Apruzzese'] | 2023-04-30 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 6.80494756e-02 -2.53479719e-01 -3.83007497e-01 -1.07652657e-01
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1a8255d9-b127-4324-be20-c801b9c85803 | minimax-rates-in-network-analysis-graphon | 1811.06055 | null | http://arxiv.org/abs/1811.06055v2 | http://arxiv.org/pdf/1811.06055v2.pdf | Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing | This paper surveys some recent developments in fundamental limits and optimal
algorithms for network analysis. We focus on minimax optimal rates in three
fundamental problems of network analysis: graphon estimation, community
detection, and hypothesis testing. For each problem, we review state-of-the-art
results in the literature followed by general principles behind the optimal
procedures that lead to minimax estimation and testing. This allows us to
connect problems in network analysis to other statistical inference problems
from a general perspective. | ['Zongming Ma', 'Chao Gao'] | 2018-11-14 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 5.28526247e-01 2.32993960e-01 -7.31581748e-01 -1.19402952e-01
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d7e3b217-b7e2-40b1-9db6-11269b21b596 | translating-translationese-a-two-step | 1906.05683 | null | https://arxiv.org/abs/1906.05683v1 | https://arxiv.org/pdf/1906.05683v1.pdf | Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation | Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then `translating' the resulting pseudo-translation, or `Translationese' into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario. | ['Jonathan May', 'Nima Pourdamghani', 'Kevin Knight', 'Nada Aldarrab', 'Marjan Ghazvininejad'] | 2019-06-11 | translating-translationese-a-two-step-1 | https://aclanthology.org/P19-1293 | https://aclanthology.org/P19-1293.pdf | acl-2019-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.98410970e-01 2.66056299e-01 -4.50611830e-01 -5.11778951e-01
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5746436f-4700-4b5f-a64b-0332bbc15853 | vqfr-blind-face-restoration-with-vector | 2205.06803 | null | https://arxiv.org/abs/2205.06803v3 | https://arxiv.org/pdf/2205.06803v3.pdf | VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder | Although generative facial prior and geometric prior have recently demonstrated high-quality results for blind face restoration, producing fine-grained facial details faithful to inputs remains a challenging problem. Motivated by the classical dictionary-based methods and the recent vector quantization (VQ) technique, we propose a VQ-based face restoration method - VQFR. VQFR takes advantage of high-quality low-level feature banks extracted from high-quality faces and can thus help recover realistic facial details. However, the simple application of the VQ codebook cannot achieve good results with faithful details and identity preservation. Therefore, we further introduce two special network designs. 1). We first investigate the compression patch size in the VQ codebook and find that the VQ codebook designed with a proper compression patch size is crucial to balance the quality and fidelity. 2). To further fuse low-level features from inputs while not "contaminating" the realistic details generated from the VQ codebook, we proposed a parallel decoder consisting of a texture decoder and a main decoder. Those two decoders then interact with a texture warping module with deformable convolution. Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods. | ['Ming-Ming Cheng', 'Ying Shan', 'Gen Li', 'Chao Dong', 'Liangbin Xie', 'Xintao Wang', 'YuChao Gu'] | 2022-05-13 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 1.96714830e-02 -2.36605778e-02 1.46657275e-02 -1.16218910e-01
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d67118cd-1063-441c-b2c0-b20f3c34ee4c | finite-volume-least-squares-neural-network-fv | 2110.10895 | null | https://arxiv.org/abs/2110.10895v3 | https://arxiv.org/pdf/2110.10895v3.pdf | Least-Squares Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Laws: Discrete Divergence Operator | A least-squares neural network (LSNN) method was introduced for solving scalar linear and nonlinear hyperbolic conservation laws (HCLs) in [7, 6]. This method is based on an equivalent least-squares (LS) formulation and uses ReLU neural network as approximating functions, making it ideal for approximating discontinuous functions with unknown interface location. In the design of the LSNN method for HCLs, the numerical approximation of differential operators is a critical factor, and standard numerical or automatic differentiation along coordinate directions can often lead to a failed NN-based method. To overcome this challenge, this paper rewrites HCLs in their divergence form of space and time and introduces a new discrete divergence operator. As a result, the proposed LSNN method is free of penalization of artificial viscosity. Theoretically, the accuracy of the discrete divergence operator is estimated even for discontinuous solutions. Numerically, the LSNN method with the new discrete divergence operator was tested for several benchmark problems with both convex and non-convex fluxes, and was able to compute the correct physical solution for problems with rarefaction, shock or compound waves. The method is capable of capturing the shock of the underlying problem without oscillation or smearing, even without any penalization of the entropy condition, total variation, and/or artificial viscosity. | ['Min Liu', 'Jingshuang Chen', 'Zhiqiang Cai'] | 2021-10-21 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.89554691e-01 -1.19046345e-01 3.32605809e-01 1.29168943e-01
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a8b72856-c16d-427b-9ad2-a929c295645a | diverse-facial-inpainting-guided-by-exemplars | 2202.06358 | null | https://arxiv.org/abs/2202.06358v3 | https://arxiv.org/pdf/2202.06358v3.pdf | Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars | We present EXE-GAN, a novel exemplar-guided facial inpainting framework using generative adversarial networks. Our approach can not only preserve the quality of the input facial image but also complete the image with exemplar-like facial attributes. We achieve this by simultaneously leveraging the global style of the input image, the stochastic style generated from the random latent code, and the exemplar style of exemplar image. We introduce a novel attribute similarity metric to encourage networks to learn the style of facial attributes from the exemplar in a self-supervised way. To guarantee the natural transition across the boundaries of inpainted regions, we introduce a novel spatial variant gradient backpropagation technique to adjust the loss gradients based on the spatial location. Extensive evaluations and practical applications on public CelebA-HQ and FFHQ datasets validate the superiority of EXE-GAN in terms of the visual quality in facial inpainting. | ['Jiankai Lyu', 'Min Wang', 'YongLiang Yang', 'Kaijie Shi', 'Xiaogang Jin', 'Xianta Jiang', 'Hanli Zhao', 'Wanglong Lu'] | 2022-02-13 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 1.62627622e-01 1.50342911e-01 -5.95410876e-02 -6.80968761e-01
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1991903b-f7b0-4a2e-a004-f8f2d077d652 | selfdocseg-a-self-supervised-vision-based | 2305.00795 | null | https://arxiv.org/abs/2305.00795v2 | https://arxiv.org/pdf/2305.00795v2.pdf | SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation | Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regarding the scarcity of labeled data. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain and thus making data annotation a tedious task. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches which use text mining and textual labels, we use a complete vision-based approach in pre-training without any ground-truth label or its derivative. Instead, we generate pseudo-layouts from the document images to pre-train an image encoder to learn the document object representation and localization in a self-supervised framework before fine-tuning it with an object detection model. We show that our pipeline sets a new benchmark in this context and performs at par with the existing methods and the supervised counterparts, if not outperforms. The code is made publicly available at: https://github.com/MaitySubhajit/SelfDocSeg | ['Umapada Pal', 'Saumik Bhattacharya', 'Josep Lladós', 'Ayan Banerjee', 'Siladittya Manna', 'Sanket Biswas', 'Subhajit Maity'] | 2023-05-01 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 4.29795742e-01 -1.22288410e-02 -3.67853254e-01 -4.44048226e-01
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5e2532fa-a949-443e-a87c-43624860ca1e | a-neural-span-based-continual-named-entity | 2302.12200 | null | https://arxiv.org/abs/2302.12200v1 | https://arxiv.org/pdf/2302.12200v1.pdf | A Neural Span-Based Continual Named Entity Recognition Model | Named Entity Recognition (NER) models capable of Continual Learning (CL) are realistically valuable in areas where entity types continuously increase (e.g., personal assistants). Meanwhile the learning paradigm of NER advances to new patterns such as the span-based methods. However, its potential to CL has not been fully explored. In this paper, we propose SpanKL1, a simple yet effective Span-based model with Knowledge distillation (KD) to preserve memories and multi-Label prediction to prevent conflicts in CL-NER. Unlike prior sequence labeling approaches, the inherently independent modeling in span and entity level with the designed coherent optimization on SpanKL promotes its learning at each incremental step and mitigates the forgetting. Experiments on synthetic CL datasets derived from OntoNotes and Few-NERD show that SpanKL significantly outperforms previous SoTA in many aspects, and obtains the smallest gap from CL to the upper bound revealing its high practiced value. | ['Qingcai Chen', 'Yunan Zhang'] | 2023-02-23 | null | null | null | null | ['continual-named-entity-recognition'] | ['natural-language-processing'] | [-1.40200198e-01 1.41988993e-01 -3.77372533e-01 -2.73670346e-01
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2ad6c393-5070-4713-9653-d05e93646600 | self-supervised-novel-2d-view-synthesis-of | 2306.14709 | null | https://arxiv.org/abs/2306.14709v1 | https://arxiv.org/pdf/2306.14709v1.pdf | Self-supervised novel 2D view synthesis of large-scale scenes with efficient multi-scale voxel carving | The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it is important for novel view synthesis methods to stay grounded in the physical world as much as possible, while also being able to imagine it from previously unseen views. While most current methods are developed and tested in virtual environments with small scenes and no errors in pose and depth information, we push the boundaries to the real-world domain of large scales in the new context of UAVs. Our algorithmic contributions are two folds. First, we manage to stay anchored in the real 3D world, by introducing an efficient multi-scale voxel carving method, which is able to accommodate significant noises in pose, depth, and illumination variations, while being able to reconstruct the view of the world from drastically different poses at test time. Second, our final high-resolution output is efficiently self-trained on data automatically generated by the voxel carving module, which gives it the flexibility to adapt efficiently to any scene. We demonstrated the effectiveness of our method on highly complex and large-scale scenes in real environments while outperforming the current state-of-the-art. Our code is publicly available: https://github.com/onorabil/MSVC. | ['Marius Leordeanu', 'Alina Marcu', 'Dragos Costea', 'Alexandra Budisteanu'] | 2023-06-26 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 2.43788749e-01 7.88044482e-02 5.46499014e-01 -7.53378794e-02
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10b54210-33dd-41fe-a71b-fdadea7796a9 | m2h2-a-multimodal-multiparty-hindi-dataset | 2108.01260 | null | https://arxiv.org/abs/2108.01260v1 | https://arxiv.org/pdf/2108.01260v1.pdf | M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations | Humor recognition in conversations is a challenging task that has recently gained popularity due to its importance in dialogue understanding, including in multimodal settings (i.e., text, acoustics, and visual). The few existing datasets for humor are mostly in English. However, due to the tremendous growth in multilingual content, there is a great demand to build models and systems that support multilingual information access. To this end, we propose a dataset for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations containing 6,191 utterances from 13 episodes of a very popular TV series "Shrimaan Shrimati Phir Se". Each utterance is annotated with humor/non-humor labels and encompasses acoustic, visual, and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations. The empirical results on M2H2 dataset demonstrate that multimodal information complements unimodal information for humor recognition. The dataset and the baselines are available at http://www.iitp.ac.in/~ai-nlp-ml/resources.html and https://github.com/declare-lab/M2H2-dataset. | ['Soujanya Poria', 'Louis-Philippe Morency', 'Pushpak Bhattacharyya', 'Asif Ekbal', 'Amir Zadeh', 'Navonil Majumder', 'Gopendra Vikram Singh', 'Dushyant Singh Chauhan'] | 2021-08-03 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [-4.75731045e-01 -3.05409044e-01 9.88890007e-02 -2.28720218e-01
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84c97538-c1fa-41c1-9739-e9d74c9f27b4 | iexam-a-novel-online-exam-monitoring-and | 2206.13356 | null | https://arxiv.org/abs/2206.13356v1 | https://arxiv.org/pdf/2206.13356v1.pdf | iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition | Online exams via video conference software like Zoom have been adopted in many schools due to COVID-19. While it is convenient, it is challenging for teachers to supervise online exams from simultaneously displayed student Zoom windows. In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis. To build such a novel system in its first kind, we overcome three challenges. First, we discover a lightweight approach to capturing exam video streams and analyzing them in real time. Second, we utilize the left-corner names that are displayed on each student's Zoom window and propose an improved OCR (optical character recognition) technique to automatically gather the ground truth for the student faces with dynamic positions. Third, we perform several experimental comparisons and optimizations to efficiently shorten the training and testing time required on teachers' PC. Our evaluation shows that iExam achieves high accuracy, 90.4% for real-time face detection and 98.4% for post-exam face recognition, while maintaining acceptable runtime performance. We have made iExam's source code available at https://github.com/VPRLab/iExam. | ['Tan Lee', 'Jimmy H. M. Lee', 'Xiao Yi', 'Daoyuan Wu', 'Xu Yang'] | 2022-06-27 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-1.14732636e-02 -5.19779623e-01 -3.88727710e-02 -3.68064255e-01
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838a121d-e6b1-4094-b9e7-631ac7ddc15e | autoregressive-neural-tensornet-bridging | 2304.01996 | null | https://arxiv.org/abs/2304.01996v2 | https://arxiv.org/pdf/2304.01996v2.pdf | ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation | Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of the Hilbert space with respect to the particle number, a direct simulation is intractable. While representing quantum states with tensor networks and neural networks are the two state-of-the-art methods for approximate simulations, each has its own limitations in terms of expressivity and inductive bias. To address these challenges, we develop a novel architecture, Autoregressive Neural TensorNet (ANTN), which bridges tensor networks and autoregressive neural networks. We show that Autoregressive Neural TensorNet parameterizes normalized wavefunctions, allows for exact sampling, generalizes the expressivity of tensor networks and autoregressive neural networks, and inherits a variety of symmetries from autoregressive neural networks. We demonstrate our approach on quantum state learning as well as finding the ground state of the challenging 2D $J_1$-$J_2$ Heisenberg model with different systems sizes and coupling parameters, outperforming both tensor networks and autoregressive neural networks. Our work opens up new opportunities for scientific simulations of quantum many-body physics and quantum technology. | ['Marin Soljačić', 'Di Luo', 'Eddie Chen', 'Laker Newhouse', 'Zhuo Chen'] | 2023-04-04 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 2.66676825e-02 -2.81575531e-01 -8.84919465e-02 -5.47354296e-02
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8f6f990d-3765-40ec-b52e-bcbac41fe761 | learning-to-transduce-with-unbounded-memory | 1506.02516 | null | http://arxiv.org/abs/1506.02516v3 | http://arxiv.org/pdf/1506.02516v3.pdf | Learning to Transduce with Unbounded Memory | Recently, strong results have been demonstrated by Deep Recurrent Neural
Networks on natural language transduction problems. In this paper we explore
the representational power of these models using synthetic grammars designed to
exhibit phenomena similar to those found in real transduction problems such as
machine translation. These experiments lead us to propose new memory-based
recurrent networks that implement continuously differentiable analogues of
traditional data structures such as Stacks, Queues, and DeQues. We show that
these architectures exhibit superior generalisation performance to Deep RNNs
and are often able to learn the underlying generating algorithms in our
transduction experiments. | ['Mustafa Suleyman', 'Edward Grefenstette', 'Karl Moritz Hermann', 'Phil Blunsom'] | 2015-06-08 | learning-to-transduce-with-unbounded-memory-1 | http://papers.nips.cc/paper/5648-learning-to-transduce-with-unbounded-memory | http://papers.nips.cc/paper/5648-learning-to-transduce-with-unbounded-memory.pdf | neurips-2015-12 | ['natural-language-transduction'] | ['natural-language-processing'] | [ 6.04630768e-01 3.72248083e-01 2.03911543e-01 -5.11536039e-02
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e4f33d7d-c74a-425a-aeb8-9c1c924a720b | gmml-is-all-you-need | 2205.14986 | null | https://arxiv.org/abs/2205.14986v1 | https://arxiv.org/pdf/2205.14986v1.pdf | GMML is All you Need | Vision transformers have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply confined local, or long range global. However, they are known to be data hungry. This has motivated the research in self-supervised transformer pretraining, which does not need to decode the semantic information conveyed by labels to link it to the image properties, but rather focuses directly on extracting a concise representation of the image data that reflects the notion of similarity, and is invariant to nuisance factors. The key vehicle for the self-learning process used by the majority of self-learning methods is the generation of multiple views of the training data and the creation of pretext tasks which use these views to define the notion of image similarity, and data integrity. However, this approach lacks the natural propensity to extract contextual information. We propose group masked model learning (GMML), a self-supervised learning (SSL) mechanism for pretraining vision transformers with the ability to extract the contextual information present in all the concepts in an image. GMML achieves this by manipulating randomly groups of connected tokens, ensuingly covering a meaningful part of a semantic concept, and then recovering the hidden semantic information from the visible part of the concept. GMML implicitly introduces a novel data augmentation process. Unlike most of the existing SSL approaches, GMML does not require momentum encoder, nor rely on careful implementation details such as large batches and gradient stopping, which are all artefacts of most of the current self-supervised learning techniques. The source code is publicly available for the community to train on bigger corpora: https://github.com/Sara-Ahmed/GMML. | ['Josef Kittler', 'Muhammad Awais', 'Sara Atito'] | 2022-05-30 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.46752036e-01 4.03183103e-01 -2.37093121e-01 -5.04009664e-01
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ff44ba7c-1bba-451b-920e-4734ed762779 | supervised-and-unsupervised-machine | null | null | https://aclanthology.org/D19-5206 | https://aclanthology.org/D19-5206.pdf | Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English | This paper presents the NICT{'}s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them. | ['Masao Utiyama', 'Aye Myat Mon', 'Atsushi Fujita', 'Hour Kaing', 'Benjamin Marie', 'Eiichiro Sumita', 'Chenchen Ding'] | 2019-11-01 | null | null | null | ws-2019-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.91976154e-01 5.62939756e-02 -5.84318638e-01 -5.20390630e-01
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dcb1457c-6a58-4244-8994-dff56a0df3f8 | challenges-and-opportunities-in-multi-device | 2206.15432 | null | https://arxiv.org/abs/2206.15432v1 | https://arxiv.org/pdf/2206.15432v1.pdf | Challenges and Opportunities in Multi-device Speech Processing | We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices". We also identify the datasets needed to support these research areas. Based on the review and our research experience in the multi-device domain, we conclude with an outlook on the future evolution | ['Tao Zhang', 'Israel Cohen', 'Arun Nair', 'Jarred Barber', 'Gregory Ciccarelli'] | 2022-06-27 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.79476690e-01 -3.13938797e-01 -4.01658654e-01 -2.79687822e-01
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755d87c2-e41f-4957-a019-74057e31c967 | the-classification-of-optical-galaxy | 2206.06165 | null | https://arxiv.org/abs/2206.06165v2 | https://arxiv.org/pdf/2206.06165v2.pdf | The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques | In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount of data generated by modern surveys. This has brought into question the viability of human-based methods for classifying galaxy morphology. While supervised learning methods require datasets with existing labels, unsupervised learning techniques do not. Therefore, this paper implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS dataset. A convolutional autoencoder feature extractor was trained and implemented. The resulting features were then clustered via k-means, fuzzy c-means and agglomerative clustering. These clusters were compared against the true volunteer classifications provided by the Galaxy Zoo DECaLS project. The best results, in general, were produced by the agglomerate clustering method. However, the increase in performance compared to k-means clustering was not significant considering the increase in clustering time. After undergoing the appropriate clustering algorithm optimizations, this approach could prove useful for classifying the better performing questions and could serve as the basis for a novel approach to generating more "human-like" galaxy morphology classifications from unsupervised techniques. | ['Mattia Vaccari', 'Clement N. Nyirenda', 'Ezra Fielding'] | 2022-06-13 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-4.08874154e-01 -4.24183626e-03 5.62907517e-01 -6.27164900e-01
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7cf284d1-4186-4d99-9d49-083793aeb974 | exploring-the-value-of-multi-view-learning-1 | null | null | https://aclanthology.org/2022.findings-naacl.23 | https://aclanthology.org/2022.findings-naacl.23.pdf | Exploring the Value of Multi-View Learning for Session-Aware Query Representation | Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user’s query reformulations or system’s rankings whereas previous findings show that user’s query behavior and knowledge change depending on the system’s results, intertwine and affect each other during the completion of a search task. In this paper, we explore the value of multi-view learning for generic and unsupervised session-aware query representation learning. First, single-view query embeddings are obtained in separate spaces from query reformulations and document ranking representations using transformers. Then, we investigate the use of linear (CCA) and non linear (UMAP) multi-view learning methods, to align those spaces with the aim of revealing similarity traits in the multi-view shared space. Experimental evaluation is carried out in a query classification and session-based retrieval downstream tasks using respectively the KDD and TREC session datasets. The results show that multi-view learning is an effective and controllable approach for unsupervised learning of generic query representations and can reflect search behavior patterns. | ['Lynda Tamine', 'Karen Pinel-Sauvagnat', 'Gilles Hubert', 'Jose Moreno', 'Diego Ortiz'] | null | null | null | null | findings-naacl-2022-7 | ['multi-view-learning', 'document-ranking'] | ['computer-vision', 'natural-language-processing'] | [-2.93080751e-02 -5.20337880e-01 -7.10079670e-01 -4.76598710e-01
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3baafc22-5a28-44d2-8e1b-01a1440100fa | docee-a-large-scale-and-fine-grained | null | null | https://openreview.net/forum?id=bC5NiJmbK2 | https://openreview.net/pdf?id=bC5NiJmbK2 | DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction | Event extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentence-level event extraction, events are more naturally presented in the form of documents, with event arguments scattering in multiple sentences. However, a major barrier to promote document-level event extraction has been the lack of large-scale and practical training and evaluation datasets. In this paper, we present DocEE, a new document-level event extraction dataset including 20,000+ events, 100,000+ arguments. We highlight three features: large-scale manual annotations, fine-grained argument types and application-oriented settings. Experiments show that there is still a big gap between state-of-the-art models and human beings (43\% Vs 85\% in F1 score), indicating that DocEE is an open issue. We will publish DocEE upon acceptance. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 3.97588998e-01 5.52834451e-01 -1.31061703e-01 -2.71972656e-01
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0e0be9fd-70ae-4943-95eb-ff2ade0120be | spatially-multi-conditional-image-generation | 2203.13812 | null | https://arxiv.org/abs/2203.13812v2 | https://arxiv.org/pdf/2203.13812v2.pdf | Spatially Multi-conditional Image Generation | In most scenarios, conditional image generation can be thought of as an inversion of the image understanding process. Since generic image understanding involves solving multiple tasks, it is natural to aim at generating images via multi-conditioning. However, multi-conditional image generation is a very challenging problem due to the heterogeneity and the sparsity of the (in practice) available conditioning labels. In this work, we propose a novel neural architecture to address the problem of heterogeneity and sparsity of the spatially multi-conditional labels. Our choice of spatial conditioning, such as by semantics and depth, is driven by the promise it holds for better control of the image generation process. The proposed method uses a transformer-like architecture operating pixel-wise, which receives the available labels as input tokens to merge them in a learned homogeneous space of labels. The merged labels are then used for image generation via conditional generative adversarial training. In this process, the sparsity of the labels is handled by simply dropping the input tokens corresponding to the missing labels at the desired locations, thanks to the proposed pixel-wise operating architecture. Our experiments on three benchmark datasets demonstrate the clear superiority of our method over the state-of-the-art and compared baselines. The source code will be made publicly available. | ['Luc van Gool', 'Thomas Probst', 'Danda Pani Paudel', 'Nikola Popovic', 'Ritika Chakraborty'] | 2022-03-25 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 7.17674792e-01 2.32816607e-01 -1.21776812e-01 -1.64515346e-01
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b01e3cd7-9ad1-4c77-8ab9-2f12cf0faa38 | data-augmentation-for-improving-tail-traffic | 2306.04823 | null | https://arxiv.org/abs/2306.04823v1 | https://arxiv.org/pdf/2306.04823v1.pdf | Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems | Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request. In such system, the agent is responsible for serving thousands of skills and interpretations which create a long-tail distribution due to the natural frequency of requests. For example, the samples related to play music might be a thousand times more frequent than those asking for theatre show times. Moreover, inputs used for ML-based skill routing are often a heterogeneous mix of strings, embedding vectors, categorical and scalar features which makes employing augmentation-based long-tail learning approaches challenging. To improve the skill-routing robustness, we propose an augmentation of heterogeneous skill-routing data and training targeted for robust operation in long-tail data regimes. We explore a variety of conditional encoder-decoder generative frameworks to perturb original data fields and create synthetic training data. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments using real-world data from a commercial conversational system. Based on the experiment results, the proposed approach improves more than 80% (51 out of 63) of intents with less than 10K of traffic instances in the skill-routing replication task. | ['Sungjin Lee', 'Jaeyoung Do', 'Mohammad Kachuee', 'Fatemeh Sheikholeslami', 'Ting-Wei Wu'] | 2023-06-07 | null | null | null | null | ['long-tail-learning'] | ['methodology'] | [ 3.24478477e-01 -1.51605815e-01 -1.10387959e-01 -6.01891696e-01
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f51dcd8f-1422-4521-bc6e-82f23db47696 | boosting-radiology-report-generation-by | 2305.04561 | null | https://arxiv.org/abs/2305.04561v2 | https://arxiv.org/pdf/2305.04561v2.pdf | Boosting Radiology Report Generation by Infusing Comparison Prior | Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. This discrepancy can be attributed to a knowledge gap between radiologists and the generation models. While radiologists possess patient-specific prior information, the models solely receive X-ray images at a specific time point. To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports. This extracted comparison prior is then seamlessly integrated into state-of-the-art transformer-based models, enabling them to produce more realistic and comprehensive reports. Our method is evaluated on English report datasets, such as IU X-ray and MIMIC-CXR. The results demonstrate that our approach surpasses baseline models in terms of natural language generation metrics. Notably, our model generates reports that are free from false references to non-existent prior exams, setting it apart from previous models. By addressing this limitation, our approach represents a significant step towards bridging the gap between radiologists and generation models in the domain of medical report generation. | ['Michael Krauthammer', 'Fabio Rinaldi', 'Ryo Sakamoto', 'Mizuho Nishio', 'Koji Fujimoto', 'Morteza Rohanian', 'Farhad Nooralahzadeh', 'Sanghwan Kim'] | 2023-05-08 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.78956997e-01 6.76500499e-01 -2.98964679e-01 -3.56493652e-01
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6a1af8f1-f501-495f-b08f-65f8100f95ad | on-predictive-information-sub-optimality-of-1 | 1910.09578 | null | https://arxiv.org/abs/1910.09578v2 | https://arxiv.org/pdf/1910.09578v2.pdf | On Predictive Information in RNNs | Certain biological neurons demonstrate a remarkable capability to optimally compress the history of sensory inputs while being maximally informative about the future. In this work, we investigate if the same can be said of artificial neurons in recurrent neural networks (RNNs) trained with maximum likelihood. Empirically, we find that RNNs are suboptimal in the information plane. Instead of optimally compressing past information, they extract additional information that is not relevant for predicting the future. We show that constraining past information by injecting noise into the hidden state can improve RNNs in several ways: optimality in the predictive information plane, sample quality, heldout likelihood, and downstream classification performance. | ['Zhe Dong', 'Alexander A. Alemi', 'Deniz Oktay', 'Ben Poole'] | 2019-10-21 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 5.42623460e-01 5.05455732e-01 -3.44663769e-01 -4.85697359e-01
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c0e1e644-4b48-47f7-a494-5a16c21f50d1 | deepprivacy-a-generative-adversarial-network | 1909.04538 | null | https://arxiv.org/abs/1909.04538v1 | https://arxiv.org/pdf/1909.04538v1.pdf | DeepPrivacy: A Generative Adversarial Network for Face Anonymization | We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information. Our model is based on a conditional generative adversarial network, generating images considering the original pose and image background. The conditional information enables us to generate highly realistic faces with a seamless transition between the generated face and the existing background. Furthermore, we introduce a diverse dataset of human faces, including unconventional poses, occluded faces, and a vast variability in backgrounds. Finally, we present experimental results reflecting the capability of our model to anonymize images while preserving the data distribution, making the data suitable for further training of deep learning models. As far as we know, no other solution has been proposed that guarantees the anonymization of faces while generating realistic images. | ['Rudolf Mester', 'Håkon Hukkelås', 'Frank Lindseth'] | 2019-09-10 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 2.27533743e-01 5.23813665e-01 3.96142304e-01 -5.52553177e-01
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c877763b-1bac-47b7-97a0-610f43903fca | ppdl-predicate-probability-distribution-based | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_PPDL_Predicate_Probability_Distribution_Based_Loss_for_Unbiased_Scene_Graph_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_PPDL_Predicate_Probability_Distribution_Based_Loss_for_Unbiased_Scene_Graph_CVPR_2022_paper.pdf | PPDL: Predicate Probability Distribution Based Loss for Unbiased Scene Graph Generation | Scene Graph Generation (SGG) has attracted more and more attention from visual researchers in recent years, since Scene Graph (SG) is valuable in many downstream tasks due to its rich structural-semantic details. However, the application value of SG on downstream tasks is severely limited by the predicate classification bias, which is caused by long-tailed data and presented as semantic bias of predicted relation predicates. Existing methods mainly reduce the prediction bias by better aggregating contexts and integrating external priori knowledge, but rarely take the semantic similarities between predicates into account. In this paper, we propose a Predicate Probability Distribution based Loss (PPDL) to train the biased SGG models and obtain unbiased Scene Graphs ultimately. Firstly, we propose a predicate probability distribution as the semantic representation of a particular predicate class. Afterwards, we re-balance the biased training loss according to the similarity between the predicted probability distribution and the estimated one, and eventually eliminate the long-tailed bias on predicate classification. Notably, the PPDL training method is model-agnostic, and extensive experiments and qualitative analyses on the Visual Genome dataset reveal significant performance improvements of our method on tail classes compared to the state-of-the-art methods. | ['Xiaojie Yuan', 'Ning Jiang', 'Guoqing Zhao', 'Qijie Bai', 'Haiwei Zhang', 'Wei Li'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.64423740e-01 3.71927917e-01 -2.51948923e-01 -4.43895638e-01
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f7816713-5d5a-43d0-93cb-f89ba72b5027 | pnat-non-autoregressive-transformer-by | null | null | https://openreview.net/forum?id=BJe932EYwS | https://openreview.net/pdf?id=BJe932EYwS | PNAT: Non-autoregressive Transformer by Position Learning | Non-autoregressive generation is a new paradigm for text generation. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling of output words is an essential problem in non-autoregressive text generation. In this paper, we propose PNAT, which explicitly models positions of output words as latent variables in text generation. The proposed PNATis simple yet effective. Experimental results show that PNATgives very promising results in machine translation and paraphrase generation tasks, outperforming many strong baselines. | ['Lei LI', 'Jiajun Chen', 'ShuJian Huang', 'Mingxuan Wang', 'Jiangtao Feng', 'Hao Zhou', 'Yu Bao'] | 2019-09-25 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.50893790e-01 3.52319181e-01 -2.92066485e-01 2.86234226e-02
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1352c9e0-68bb-484a-b6cb-e96f32f4852b | rgb-d-mapping-and-tracking-in-a-plenoxel | 2307.03404 | null | https://arxiv.org/abs/2307.03404v1 | https://arxiv.org/pdf/2307.03404v1.pdf | RGB-D Mapping and Tracking in a Plenoxel Radiance Field | Building on the success of Neural Radiance Fields (NeRFs), recent years have seen significant advances in the domain of novel view synthesis. These models capture the scene's volumetric radiance field, creating highly convincing dense photorealistic models through the use of simple, differentiable rendering equations. Despite their popularity, these algorithms suffer from severe ambiguities in visual data inherent to the RGB sensor, which means that although images generated with view synthesis can visually appear very believable, the underlying 3D model will often be wrong. This considerably limits the usefulness of these models in practical applications like Robotics and Extended Reality (XR), where an accurate dense 3D reconstruction otherwise would be of significant value. In this technical report, we present the vital differences between view synthesis models and 3D reconstruction models. We also comment on why a depth sensor is essential for modeling accurate geometry in general outward-facing scenes using the current paradigm of novel view synthesis methods. Focusing on the structure-from-motion task, we practically demonstrate this need by extending the Plenoxel radiance field model: Presenting an analytical differential approach for dense mapping and tracking with radiance fields based on RGB-D data without a neural network. Our method achieves state-of-the-art results in both the mapping and tracking tasks while also being faster than competing neural network-based approaches. | ['Rudolf Mester', 'Annette Stahl', 'Yeonsoo Park', 'Andreas L. Teigen'] | 2023-07-07 | null | null | null | null | ['3d-reconstruction', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.80320019e-01 1.06711447e-01 3.18157464e-01 -1.91972896e-01
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bffe246a-ff37-4376-8785-cb3e49710513 | improving-low-resource-question-answering | 2211.14880 | null | https://arxiv.org/abs/2211.14880v1 | https://arxiv.org/pdf/2211.14880v1.pdf | Improving Low-Resource Question Answering using Active Learning in Multiple Stages | Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain. We also investigate Active Learning for question answering in different stages, overall reducing the annotation effort of humans. For this purpose, we consider target domains in realistic settings, with an extremely low amount of annotated samples but with many unlabeled documents, which we assume can be obtained with little effort. Additionally, we assume sufficient amount of labeled data from the source domain is available. We perform extensive experiments to find the best setup for incorporating domain experts. Our findings show that our novel approach, where humans are incorporated as early as possible in the process, boosts performance in the low-resource, domain-specific setting, allowing for low-labeling-effort question answering systems in new, specialized domains. They further demonstrate how human annotation affects the performance of QA depending on the stage it is performed. | ['Thang Vu', 'A. Cristiano I. Malossi', 'Jasmina Bogojeska', 'Andrea Bartezzaghi', 'Maximilian Schmidt'] | 2022-11-27 | null | null | null | null | ['question-answer-generation', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.54742324e-01 3.77383888e-01 -1.10490695e-01 -5.15911162e-01
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78a9eb60-e2a5-49da-9313-645a5e06cb75 | multimodal-search-on-iconclass-using-vision | 2306.16529 | null | https://arxiv.org/abs/2306.16529v1 | https://arxiv.org/pdf/2306.16529v1.pdf | Multimodal Search on Iconclass using Vision-Language Pre-Trained Models | Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search, which can be conveyed through multiple expression modalities (e.g., images, keywords or textual descriptions). This paper presents the implementation of a new search engine for one of the most widely used iconography classification system, Iconclass. The novelty of this system is the use of a pre-trained vision-language model, namely CLIP, to retrieve and explore Iconclass concepts using visual or textual queries. | ['Harald Sack', 'Tabea Tietz', 'Oleksandra Bruns', 'Mary Ann Tan', 'Etienne Posthumus', 'Cristian Santini'] | 2023-06-23 | null | null | null | null | ['retrieval', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [-3.40820141e-02 -1.32295325e-01 -4.39573467e-01 1.20335318e-01
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c375c28d-d189-41c7-8748-32bd6226a821 | conslt-a-token-level-contrastive-framework | 2204.04916 | null | https://arxiv.org/abs/2204.04916v3 | https://arxiv.org/pdf/2204.04916v3.pdf | A Token-level Contrastive Framework for Sign Language Translation | Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine Translation (NMT) methods, which usually require large-scale corpus for training, to achieve SLT. However, the publicly available SLT corpus is very limited, which causes the collapse of the token representations and the inaccuracy of the generated tokens. To alleviate this issue, we propose ConSLT, a novel token-level \textbf{Con}trastive learning framework for \textbf{S}ign \textbf{L}anguage \textbf{T}ranslation , which learns effective token representations by incorporating token-level contrastive learning into the SLT decoding process. Concretely, ConSLT treats each token and its counterpart generated by different dropout masks as positive pairs during decoding, and then randomly samples $K$ tokens in the vocabulary that are not in the current sentence to construct negative examples. We conduct comprehensive experiments on two benchmarks (PHOENIX14T and CSL-Daily) for both end-to-end and cascaded settings. The experimental results demonstrate that ConSLT can achieve better translation quality than the strong baselines. | ['Xiaodong Shi', 'Yidong Chen', 'Cong Hu', 'Pei Yu', 'Liang Zhang', 'PeiGen Ye', 'Biao Fu'] | 2022-04-11 | null | null | null | null | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 4.78575468e-01 -1.84352726e-01 -3.21445286e-01 -4.91638809e-01
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3f2f4a8c-368f-4994-a8e8-f91793b348e5 | conversational-product-search-based-on | 1909.02071 | null | https://arxiv.org/abs/1909.02071v1 | https://arxiv.org/pdf/1909.02071v1.pdf | Conversational Product Search Based on Negative Feedback | Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. During the interactions, the system could ask questions on certain aspects of the ideal products to clarify the users' needs. For example, previous work proposed to ask users the exact characteristics of their ideal items before showing results. However, users may not have clear ideas about what an ideal item looks like, especially when they have not seen any item. So it is more feasible to facilitate the conversational search by showing example items and asking for feedback instead. In addition, when the users provide negative feedback for the presented items, it is easier to collect their detailed feedback on certain properties (aspect-value pairs) of the non-relevant items. By breaking down the item-level negative feedback to fine-grained feedback on aspect-value pairs, more information is available to help clarify users' intents. So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration. We then propose an aspect-value likelihood model to incorporate both positive and negative feedback on fine-grained aspect-value pairs of the non-relevant items. Experimental results show that our model is significantly better than state-of-the-art product search baselines without using feedback and those baselines using item-level negative feedback. | ['Qingyao Ai', 'W. Bruce Croft', 'Keping Bi', 'Yongfeng Zhang'] | 2019-09-04 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.86781883e-01 6.79240003e-02 -5.07142246e-01 -7.75159895e-01
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d179e3ef-132f-43e0-a4e2-8eb61fe249b8 | graphmdn-leveraging-graph-structure-and-deep | 2010.13668 | null | https://arxiv.org/abs/2010.13668v1 | https://arxiv.org/pdf/2010.13668v1.pdf | GraphMDN: Leveraging graph structure and deep learning to solve inverse problems | The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean, graph-structured data. GNNs have achieved state-of-the-art results across an impressive array of graph-based machine learning problems. Nevertheless, despite their rapid pace of development, much of the work on GNNs has focused on graph classification and embedding techniques, largely ignoring regression tasks over graph data. In this paper, we develop a Graph Mixture Density Network (GraphMDN), which combines graph neural networks with mixture density network (MDN) outputs. By combining these techniques, GraphMDNs have the advantage of naturally being able to incorporate graph structured information into a neural architecture, as well as the ability to model multi-modal regression targets. As such, GraphMDNs are designed to excel on regression tasks wherein the data are graph structured, and target statistics are better represented by mixtures of densities rather than singular values (so-called ``inverse problems"). To demonstrate this, we extend an existing GNN architecture known as Semantic GCN (SemGCN) to a GraphMDN structure, and show results from the Human3.6M pose estimation task. The extended model consistently outperforms both GCN and MDN architectures on their own, with a comparable number of parameters. | ['Sohrob Kazerounian', 'Daniel C. Hannah', 'Tuomas P. Oikarinen'] | 2020-10-26 | null | null | null | null | ['multi-hypotheses-3d-human-pose-estimation'] | ['computer-vision'] | [-7.00588599e-02 3.83188695e-01 -1.36928931e-01 -2.93144643e-01
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12c043eb-96cb-4220-8508-cf1f7840eb28 | drcove-an-augmented-word-representation | null | null | https://aclanthology.org/2019.icon-1.26 | https://aclanthology.org/2019.icon-1.26.pdf | DRCoVe: An Augmented Word Representation Approach using Distributional and Relational Context | Word representation using the distributional information of words from a sizeable corpus is considered efficacious in many natural language processing and text mining applications. However, distributional representation of a word is unable to capture distant relational knowledge, representing the relational semantics. In this paper, we propose a novel word representation approach using distributional and relational contexts, DRCoVe, which augments the distributional representation of a word using the relational semantics extracted as syntactic and semantic association among entities from the underlying corpus. Unlike existing approaches that use external knowledge bases representing the relational semantics for enhanced word representation, DRCoVe uses typed dependencies (aka syntactic dependencies) to extract relational knowledge from the underlying corpus. The proposed approach is applied over a biomedical text corpus to learn word representation and compared with GloVe, which is one of the most popular word embedding approaches. The evaluation results on various benchmark datasets for word similarity and word categorization tasks demonstrate the effectiveness of DRCoVe over the GloVe. | ['Mohd Fazil', 'Muhammad Abulaish', 'Md. Aslam Parwez'] | null | null | null | null | icon-2019-12 | ['word-similarity'] | ['natural-language-processing'] | [ 1.25331342e-01 2.39640146e-01 -4.22567904e-01 -4.50380653e-01
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0dc91104-df5f-44c9-bc34-f7548252efa6 | contrastively-reinforced-attention | null | null | https://www.bmvc2020-conference.com/conference/papers/paper_0656.html | https://www.bmvc2020-conference.com/assets/papers/0656.pdf | Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image Recognition | Fine-grained visual classification is inherently challenging because of its inter-class similarity and intra-class variance. However, by contrasting the images with same/different labels, a human can instinctively notice that the key clues lie in certain objects while other objects are ignorable. Inspired by this, we propose Contrastively-reinforced Attention Convolutional Neural Network (CRA-CNN), which reinforces the attention awareness of deep activations. CRA-CNN mainly contains two parts: the classification stream and attention regularization stream. The former classifies the input image and simultaneously divides the visual information of the input into attention and redundancy. The latter evaluates the attention/redundancy proposal by classifying the attention and contrasting the attention/redundancy of various inputs. The evaluation information is backpropagated and forces the classification stream to improve its awareness of visual attention, which helps classification. Experimental results on CUB-Birds and Stanford Cars show that CRA-CNN distinctly outperforms the baselines and is comparable with state-of-art studies despite its simplicity. | ['Kenji Mase', 'Jien Kato', 'Yu Wang', 'Dichao Liu'] | 2020-09-08 | null | null | null | bmvc-2020-9 | ['fine-grained-image-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [-6.48552254e-02 -2.37972736e-01 -3.20962779e-02 -3.33010405e-01
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43f20597-6e83-44ef-84b8-04d0499cf3af | strategy-for-rapid-diabetic-retinopathy | 2305.04724 | null | https://arxiv.org/abs/2305.04724v1 | https://arxiv.org/pdf/2305.04724v1.pdf | Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing | In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent optimizer. The EDLM effectiveness is assessed on the KAG GLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50. | ['S. Anusuya', 'V. Banupriya'] | 2023-05-08 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-9.64299217e-02 -2.40700468e-01 5.36321700e-02 -2.52388537e-01
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85951139-d8d9-4f4a-8c4a-86d4dcb33b5c | clireval-evaluating-machine-translation-as-a | null | null | https://aclanthology.org/2020.acl-demos.18 | https://aclanthology.org/2020.acl-demos.18.pdf | CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task | We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved. The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR. As a case study, we run CLIReval on the {``}metrics shared task{''} of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality. CLIReval is publicly available at https://github.com/ssun32/CLIReval. | ['Suzanna Sia', 'Kevin Duh', 'Shuo Sun'] | 2020-07-01 | null | null | null | acl-2020-6 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-5.08912392e-02 -2.24412709e-01 -5.56395590e-01 -1.78160429e-01
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8b6bc2f3-c219-49d1-900a-cac176f2c594 | coverless-information-hiding-based-on-1 | 1802.03528 | null | http://arxiv.org/abs/1802.03528v1 | http://arxiv.org/pdf/1802.03528v1.pdf | Coverless information hiding based on Generative Model | A new coverless image information hiding method based on generative model is
proposed, we feed the secret image to the generative model database, and
generate a meaning-normal and independent image different from the secret
image, then, the generated image is transmitted to the receiver and is fed to
the generative model database to generate another image visually the same as
the secret image. So we only need to transmit the meaning-normal image which is
not related to the secret image, and we can achieve the same effect as the
transmission of the secret image. This is the first time to propose the
coverless image information hiding method based on generative model, compared
with the traditional image steganography, the transmitted image does not embed
any information of the secret image in this method, therefore, can effectively
resist steganalysis tools. Experimental results show that our method has high
capacity, safety and reliability. | ['Xintao Duan', 'Haoxian Song'] | 2018-02-10 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 9.49439645e-01 1.04408320e-02 2.41601691e-01 1.61269203e-01
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8fdd5c6c-c024-4df1-995f-c6626e2a9dca | optimization-derived-learning-with-essential | 2206.07875 | null | https://arxiv.org/abs/2206.07875v1 | https://arxiv.org/pdf/2206.07875v1.pdf | Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training | Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, previous ODL approaches regard the training and hyper-training procedures as two separated stages, meaning that the hyper-training variables have to be fixed during the training process, and thus it is also impossible to simultaneously obtain the convergence of training and hyper-training variables. In this work, we design a Generalized Krasnoselskii-Mann (GKM) scheme based on fixed-point iterations as our fundamental ODL module, which unifies existing ODL methods as special cases. Under the GKM scheme, a Bilevel Meta Optimization (BMO) algorithmic framework is constructed to solve the optimal training and hyper-training variables together. We rigorously prove the essential joint convergence of the fixed-point iteration for training and the process of optimizing hyper-parameters for hyper-training, both on the approximation quality, and on the stationary analysis. Experiments demonstrate the efficiency of BMO with competitive performance on sparse coding and real-world applications such as image deconvolution and rain streak removal. | ['Yixuan Zhang', 'Jin Zhang', 'Shangzhi Zeng', 'Xuan Liu', 'Risheng Liu'] | 2022-06-16 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [-8.77196267e-02 -1.44450054e-01 -1.16509654e-01 -7.24088252e-02
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caf88a98-4632-4647-b216-41d8a3ec32d6 | histopathology-image-classification-using | 2306.14459 | null | https://arxiv.org/abs/2306.14459v1 | https://arxiv.org/pdf/2306.14459v1.pdf | Histopathology Image Classification using Deep Manifold Contrastive Learning | Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in contrastive learning, is not well suited to represent the distance between two data points, especially on a nonlinear feature manifold. Inspired by manifold learning, we propose a novel extension of contrastive learning that leverages geodesic distance between features as a similarity metric for histopathology whole slide image classification. To reduce the computational overhead in manifold learning, we propose geodesic-distance-based feature clustering for efficient contrastive loss evaluation using prototypes without time-consuming pairwise feature similarity comparison. The efficacy of the proposed method is evaluated on two real-world histopathology image datasets. Results demonstrate that our method outperforms state-of-the-art cosine-distance-based contrastive learning methods. | ['Won-Ki Jeong', 'Jing Wei Tan'] | 2023-06-26 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'clustering', 'classification-1'] | ['computer-vision', 'methodology', 'methodology', 'methodology'] | [-6.48044273e-02 -4.26600218e-01 -1.23447679e-01 -4.24666494e-01
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a6f1fa12-e8c6-4b06-9295-41a4b4e88e29 | deep-inside-outside-recursive-autoencoder | null | null | https://aclanthology.org/2020.coling-main.322 | https://aclanthology.org/2020.coling-main.322.pdf | Deep Inside-outside Recursive Autoencoder with All-span Objective | Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing. During its forward computation, it provides phrase and contextual representations for all spans in the input sentence. By utilizing the contextual representation of each leaf-level span, the span of length 1, to reconstruct the word inside the span, the model is trained without labeled data. In this work, we extend the training objective of DIORA by making use of all spans instead of only leaf-level spans. We test our new training objective on datasets of two languages: English and Japanese, and empirically show that our method achieves improvement in parsing accuracy over the original DIORA. | ['Kewei Tu', 'Jiong Cai', 'Ruyue Hong'] | 2020-12-01 | null | null | null | coling-2020-8 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.62006542e-01 5.68173528e-01 -2.56370783e-01 -4.67315555e-01
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976f2f05-e6a7-4bf1-823a-5a36bee6383d | chatgpt-vs-state-of-the-art-models-a | 2304.14177 | null | https://arxiv.org/abs/2304.14177v2 | https://arxiv.org/pdf/2304.14177v2.pdf | ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task | Transformer-based language models, including ChatGPT, have demonstrated exceptional performance in various natural language generation tasks. However, there has been limited research evaluating ChatGPT's keyphrase generation ability, which involves identifying informative phrases that accurately reflect a document's content. This study seeks to address this gap by comparing ChatGPT's keyphrase generation performance with state-of-the-art models, while also testing its potential as a solution for two significant challenges in the field: domain adaptation and keyphrase generation from long documents. We conducted experiments on six publicly available datasets from scientific articles and news domains, analyzing performance on both short and long documents. Our results show that ChatGPT outperforms current state-of-the-art models in all tested datasets and environments, generating high-quality keyphrases that adapt well to diverse domains and document lengths. | ['José Portela', 'Alvaro J. López-López', 'Roberto Martínez-Cruz'] | 2023-04-27 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 9.28229466e-02 2.60540992e-02 -3.24974924e-01 1.81280330e-01
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fbf21530-23ff-42c3-9635-7b041cb47bd5 | deep-feature-statistics-mapping-for | 2209.05321 | null | https://arxiv.org/abs/2209.05321v2 | https://arxiv.org/pdf/2209.05321v2.pdf | Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment | The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined. The underlying mechanism of the proposed approach is based upon the wild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion. We empirically show that the statistics deviation could be effectively leveraged in quality assessment, and the proposed method is superior when evaluated in different settings. Extensive experimental results demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA) model delivers promising performance compared with existing NR-IQA models and shows a high generalization capability in the cross-dataset settings. The implementation of our method is publicly available at https://github.com/Baoliang93/DFSS-IQA. | ['Sam Kwong', 'Shiqi Wang', 'Lingyu Zhu', 'Hanwei Zhu', 'Baoliang Chen'] | 2022-09-12 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.74019203e-01 -5.13654590e-01 -5.47713637e-02 -3.44694465e-01
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f454c166-6e05-46cd-9010-9c5e7fba28c2 | on-the-robustness-of-alphafold-a-covid-19 | 2301.04093 | null | https://arxiv.org/abs/2301.04093v2 | https://arxiv.org/pdf/2301.04093v2.pdf | On the Robustness of AlphaFold: A COVID-19 Case Study | Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations in the protein sequence do not generally lead to drastic changes in the protein structure. In this paper, we demonstrate that AlphaFold does not exhibit such robustness despite its high accuracy. This raises the challenge of detecting and quantifying the extent to which these predicted protein structures can be trusted. To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version. We prove that the problem of minimally perturbing protein sequences to fool protein folding neural networks is NP-complete. Based on the well-established BLOSUM62 sequence alignment scoring matrix, we generate adversarial protein sequences and show that the RMSD between the predicted protein structure and the structure of the original sequence are very large when the adversarial changes are bounded by (i) 20 units in the BLOSUM62 distance, and (ii) five residues (out of hundreds or thousands of residues) in the given protein sequence. In our experimental evaluation, we consider 111 COVID-19 proteins in the Universal Protein resource (UniProt), a central resource for protein data managed by the European Bioinformatics Institute, Swiss Institute of Bioinformatics, and the US Protein Information Resource. These result in an overall GDT similarity test score average of around 34%, demonstrating a substantial drop in the performance of AlphaFold. | ['Susmit Jha', 'Arvind Ramanathan', 'Rickard Ewetz', 'Alvaro Velasquez', 'George Atia', 'Andre Beckus', 'Sumit Jha', 'Ismail Alkhouri'] | 2023-01-10 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 6.45034850e-01 3.66582721e-01 3.35792005e-01 -1.66608259e-01
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13dd7228-df0d-48c1-9075-3ec1b5e06ca4 | gait-recognition-using-fmcw-radar-and | null | null | https://ieeexplore.ieee.org/abstract/document/9160199 | https://ieeexplore.ieee.org/abstract/document/9160199 | Gait Recognition using FMCW Radar and Temporal Convolutional Deep Neural Networks | The capability of human identification in specific scenarios and in a quickly and accurately manner, is a critical aspect in various surveillance applications. In particular, in this context, classical survaillance systems are based on videocameras, requiring high computational/storing resources, which are very sensitive to light and weather conditions. In this paper, an efficient classifier based on deep learning is used for the purpose of identifying individuals features by resorting to the micro-Doppler data extracted from low-power frequency-modulated continuous-wave radar measurements. Results obtained through the application of a deep temporal convolutional neural networks confirms the applicability of deep learning to the problem at hand. Best obtained identification accuracy is 0.949 with an F-measure of 0.88 using a temporal window of four seconds. | ['Danilo Orlando', 'Carmine Clemente', 'Marta Cimitile', 'Filippo Biondi', 'Mario Luca Bernardi', 'Pia Addabbo'] | 2020-08-06 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.05133837e-02 -7.28222430e-01 2.56197006e-01 -2.32650533e-01
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593ff917-1663-4295-8790-f8f1a0f183f4 | task-agnostic-distillation-of-encoder-decoder | 2305.12330 | null | https://arxiv.org/abs/2305.12330v1 | https://arxiv.org/pdf/2305.12330v1.pdf | Task-agnostic Distillation of Encoder-Decoder Language Models | Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks. The intriguing task-agnostic property has driven a shifted focus from task-specific to task-agnostic distillation of LMs. While task-agnostic, compute-efficient, performance-preserved LMs can be yielded by task-agnostic distillation, previous studies mainly sit in distillation of either encoder-only LMs (e.g., BERT) or decoder-only ones (e.g., GPT) yet largely neglect that distillation of encoder-decoder LMs (e.g., T5) can posit very distinguished behaviors. Frustratingly, we discover that existing task-agnostic distillation methods can fail to handle the distillation of encoder-decoder LMs. To the demand, we explore a few paths and uncover a path named as MiniEnD that successfully tackles the distillation of encoder-decoder LMs in a task-agnostic fashion. We examine MiniEnD on language understanding and abstractive summarization. The results showcase that MiniEnD is generally effective and is competitive compared to other alternatives. We further scale MiniEnD up to distillation of 3B encoder-decoder language models with interpolated distillation. The results imply the opportunities and challenges in distilling large language models (e.g., LLaMA). | ['Dawei Song', 'Jingang Wang', 'Yang Yang', 'Chen Zhang'] | 2023-05-21 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.76878631e-01 1.94956213e-01 -2.39625841e-01 -1.54259562e-01
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4f10cf6b-d806-4da6-bcc6-c63beb55a717 | fpga-implementation-of-convolutional-neural-1 | 2306.13557 | null | https://arxiv.org/abs/2306.13557v2 | https://arxiv.org/pdf/2306.13557v2.pdf | FPGA Implementation of Convolutional Neural Network for Real-Time Handwriting Recognition | Machine Learning (ML) has recently been a skyrocketing field in Computer Science. As computer hardware engineers, we are enthusiastic about hardware implementations of popular software ML architectures to optimize their performance, reliability, and resource usage. In this project, we designed a highly-configurable, real-time device for recognizing handwritten letters and digits using an Altera DE1 FPGA Kit. We followed various engineering standards, including IEEE-754 32-bit Floating-Point Standard, Video Graphics Array (VGA) display protocol, Universal Asynchronous Receiver-Transmitter (UART) protocol, and Inter-Integrated Circuit (I2C) protocols to achieve the project goals. These significantly improved our design in compatibility, reusability, and simplicity in verifications. Following these standards, we designed a 32-bit floating-point (FP) instruction set architecture (ISA). We developed a 5-stage RISC processor in System Verilog to manage image processing, matrix multiplications, ML classifications, and user interfaces. Three different ML architectures were implemented and evaluated on our design: Linear Classification (LC), a 784-64-10 fully connected neural network (NN), and a LeNet-like Convolutional Neural Network (CNN) with ReLU activation layers and 36 classes (10 for the digits and 26 for the case-insensitive letters). The training processes were done in Python scripts, and the resulting kernels and weights were stored in hex files and loaded into the FPGA's SRAM units. Convolution, pooling, data management, and various other ML features were guided by firmware in our custom assembly language. This paper documents the high-level design block diagrams, interfaces between each System Verilog module, implementation details of our software and firmware components, and further discussions on potential impacts. | ['Qikun Liu', 'Lingkai Zhao', 'Shichen Qiao', 'Eric J. Hoffman', 'Haining Qiu'] | 2023-06-23 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 9.86032560e-02 -4.52001452e-01 -1.81166753e-01 -7.10735679e-01
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dfb3c801-ebbb-40dc-9214-9f5de1fb1173 | doubleadapt-a-meta-learning-approach-to | 2306.09862 | null | https://arxiv.org/abs/2306.09862v1 | https://arxiv.org/pdf/2306.09862v1.pdf | DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting | Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new patterns recurring in the future stock market. However, incremental learning for stock trend forecasting still remains under-explored due to the challenge of distribution shifts (a.k.a. concept drifts). With the stock market dynamically evolving, the distribution of future data can slightly or significantly differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts. Our key insight is to automatically learn how to adapt stock data into a locally stationary distribution in favor of profitable updates. Complemented by data adaptation, we can confidently adapt the model parameters under mitigated distribution shifts. We cast each incremental learning task as a meta-learning task and automatically optimize the adapters for desirable data adaptation and parameter initialization. Experiments on real-world stock datasets demonstrate that DoubleAdapt achieves state-of-the-art predictive performance and shows considerable efficiency. | ['Yanyan Shen', 'Shuming Kong', 'Lifan Zhao'] | 2023-06-16 | null | null | null | null | ['meta-learning', 'incremental-learning'] | ['methodology', 'methodology'] | [-3.90490323e-01 -4.24314827e-01 -4.63191390e-01 -4.76293981e-01
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9b4039e0-947d-4f44-a199-1267db156bcb | robust-android-malware-detection-system | 2101.12031 | null | https://arxiv.org/abs/2101.12031v1 | https://arxiv.org/pdf/2101.12031v1.pdf | Robust Android Malware Detection System against Adversarial Attacks using Q-Learning | The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attacks. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against adversarial attacks. For this purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in the adversary's shoes, we designed adversarial attacks on the detection models with the goal of maximizing fooling rate, while making minimum modifications to the Android application and ensuring that the app's functionality and behavior do not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with a maximum of five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree-based model using the multiple policy approach. Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks. | ['Mohit Sewak', 'Piyush Nikam', 'Sanjay K. Sahay', 'Hemant Rathore'] | 2021-01-27 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.82654798e-01 9.61559117e-02 -3.37171912e-01 2.21688136e-01
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f25aa696-cb32-4d42-993a-c4ea75f7e3d3 | 6d-pose-estimation-with-combined-deep | 2111.06276 | null | https://arxiv.org/abs/2111.06276v1 | https://arxiv.org/pdf/2111.06276v1.pdf | 6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping | Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://sites.google.com/view/dl-ppf6dpose/. | ['Chyi-Yeu Lin', 'Joel Vidal', 'Yu-Ru Chen', 'Trung-Son Le', 'Tuan-Tang Le'] | 2021-11-11 | null | null | null | null | ['3d-object-recognition', 'robotic-grasping'] | ['computer-vision', 'robots'] | [-8.33556056e-02 -2.89428324e-01 2.61557758e-01 -3.88920009e-01
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a254d767-341c-423b-902e-974d974e005e | real-time-video-highlights-for-yahoo-esports | 1611.08780 | null | http://arxiv.org/abs/1611.08780v1 | http://arxiv.org/pdf/1611.08780v1.pdf | Real-Time Video Highlights for Yahoo Esports | Esports has gained global popularity in recent years and several companies
have started offering live streaming videos of esports games and events. This
creates opportunities to develop large scale video understanding systems for
new product features and services. We present a technique for detecting
highlights from live streaming videos of esports game matches. Most video games
use pronounced visual effects to emphasize highlight moments; we use CNNs to
learn convolution filters of those visual effects for detecting highlights. We
propose a cascaded prediction approach that allows us to deal with several
challenges arise in a production environment. We demonstrate our technique on
our new dataset of three popular game titles, Heroes of the Storm, League of
Legends, and Dota 2. Our technique achieves 18 FPS on a single CPU with an
average precision of up to 83.18%. Part of our technique is currently deployed
in production on Yahoo Esports. | ['Yale Song'] | 2016-11-27 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-1.41430780e-01 -3.11241329e-01 -3.25253874e-01 -1.60151795e-01
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cb801c82-4146-4622-941b-33169fc1c6c1 | retinal-vessel-segmentation-via-a-multi | 2304.12856 | null | https://arxiv.org/abs/2304.12856v1 | https://arxiv.org/pdf/2304.12856v1.pdf | Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning | Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score (and correspondingly Jaccard index) while keeping the number of trainable parameters comparatively low. We have evaluated our method on three benchmark datasets, including DRIVE, STARE, and CHASE, demonstrating its superior performance as compared with competitive approaches elsewhere in the literature. | ['Imran Razzak', 'Antonio Robles-Kelly', 'Syed S. Naqvi', 'Tariq M. Khan'] | 2023-04-25 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 4.17092532e-01 -1.00805119e-01 1.60119757e-01 -2.42649913e-01
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76209560-e97a-4a0c-a915-54d85b3f8e28 | gusum-graph-based-unsupervised-summarization-1 | null | null | https://aclanthology.org/2022.textgraphs-1.5 | https://aclanthology.org/2022.textgraphs-1.5.pdf | GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT | Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are usually created by calculating sentence similarities. In this paper, we develop a Graph-Based Unsupervised Summarization(GUSUM) method for extractive text summarization based on the principle of including the most important sentences while excluding sentences with similar meanings in the summary. We modify traditional graph ranking algorithms with recent sentence embedding models and sentence features and modify how sentence centrality is computed. We first define the sentence feature scores represented at the vertices, indicating the importance of each sentence in the document. After this stage, we use Sentence-BERT for obtaining sentence embeddings to better capture the sentence meaning. In this way, we define the edges of a graph where semantic similarities are represented. Next we create an undirected graph that includes sentence significance and similarities between sentences. In the last stage, we determine the most important sentences in the document with the ranking method we suggested on the graph created. Experiments on CNN/Daily Mail, New York Times, arXiv, and PubMed datasets show our approach achieves high performance on unsupervised graph-based summarization when evaluated both automatically and by humans. | ['Mark Lee', 'Phillip Smith', 'Tuba Gokhan'] | null | null | null | null | coling-textgraphs-2022-10 | ['graph-ranking', 'sentence-embeddings', 'sentence-embeddings', 'extractive-document-summarization', 'document-summarization'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.29644966e-01 4.30887550e-01 -1.09270543e-01 -3.70985806e-01
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bb27dd9c-dc87-44d0-98e7-430fd90ae04a | one-sided-matrix-completion-from-two | 2306.04049 | null | https://arxiv.org/abs/2306.04049v1 | https://arxiv.org/pdf/2306.04049v1.pdf | One-sided Matrix Completion from Two Observations Per Row | Given only a few observed entries from a low-rank matrix $X$, matrix completion is the problem of imputing the missing entries, and it formalizes a wide range of real-world settings that involve estimating missing data. However, when there are too few observed entries to complete the matrix, what other aspects of the underlying matrix can be reliably recovered? We study one such problem setting, that of "one-sided" matrix completion, where our goal is to recover the right singular vectors of $X$, even in the regime where recovering the left singular vectors is impossible, which arises when there are more rows than columns and very few observations. We propose a natural algorithm that involves imputing the missing values of the matrix $X^TX$ and show that even with only two observations per row in $X$, we can provably recover $X^TX$ as long as we have at least $\Omega(r^2 d \log d)$ rows, where $r$ is the rank and $d$ is the number of columns. We evaluate our algorithm on one-sided recovery of synthetic data and low-coverage genome sequencing. In these settings, our algorithm substantially outperforms standard matrix completion and a variety of direct factorization methods. | ['Gregory Valiant', 'Percy Liang', 'Steven Cao'] | 2023-06-06 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.74843532e-01 7.92330876e-02 -5.47591820e-02 4.52949591e-02
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9f2e7e9a-11f9-45f7-8959-ba7ceca6546a | citeprompt-using-prompts-to-identify-citation | 2304.12730 | null | https://arxiv.org/abs/2304.12730v2 | https://arxiv.org/pdf/2304.12730v2.pdf | CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers | Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the citation in a given context. In this paper, we present CitePrompt, a framework which uses the hitherto unexplored approach of prompt-based learning for citation intent classification. We argue that with the proper choice of the pretrained language model, the prompt template, and the prompt verbalizer, we can not only get results that are better than or comparable to those obtained with the state-of-the-art methods but also do it with much less exterior information about the scientific document. We report state-of-the-art results on the ACL-ARC dataset, and also show significant improvement on the SciCite dataset over all baseline models except one. As suitably large labelled datasets for citation intent classification can be quite hard to find, in a first, we propose the conversion of this task to the few-shot and zero-shot settings. For the ACL-ARC dataset, we report a 53.86% F1 score for the zero-shot setting, which improves to 63.61% and 66.99% for the 5-shot and 10-shot settings, respectively. | ['Imon Mukherjee', 'Debarshi Kumar Sanyal', 'Avishek Lahiri'] | 2023-04-25 | null | null | null | null | ['citation-intent-classification', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.04848564e-02 -3.90665606e-02 -6.41759396e-01 -6.81594312e-02
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117fd6de-9485-489a-af4d-139b87206eae | taking-actions-separately-a-bidirectionally | null | null | https://aclanthology.org/2022.coling-1.395 | https://aclanthology.org/2022.coling-1.395.pdf | Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation | Training Neural Machine Translation (NMT) models suffers from sparse parallel data, in the infrequent translation scenarios towards low-resource source languages. The existing solutions primarily concentrate on the utilization of Parent-Child (PC) transfer learning. It transfers well-trained NMT models on high-resource languages (namely Parent NMT) to low-resource languages, so as to produce Child NMT models by fine-tuning. It has been carefully demonstrated that a variety of PC variants yield significant improvements for low-resource NMT. In this paper, we intend to enhance PC-based NMT by a bidirectionally-adaptive learning strategy. Specifically, we divide inner constituents (6 transformers) of Parent encoder into two “teams”, i.e., T1 and T2. During representation learning, T1 learns to encode low-resource languages conditioned on bilingual shareable latent space. Generative adversarial network and masked language modeling are used for space-shareable encoding. On the other hand, T2 is straightforwardly transferred to low-resource languages, and fine-tuned together with T1 for low-resource translation. Briefly, T1 and T2 take actions separately for different goals. The former aims to adapt to characteristics of low-resource languages during encoding, while the latter adapts to translation experiences learned from high-resource languages. We experiment on benchmark corpora SETIMES, conducting low-resource NMT for Albanian (Sq), Macedonian (Mk), Croatian (Hr) and Romanian (Ro). Experimental results show that our method yields substantial improvements, which allows the NMT performance to reach BLEU4-scores of 62.24%, 56.93%, 50.53% and 54.65% for Sq, Mk, Hr and Ro, respectively. | ['Guodong Zhou', 'Jianmin Yao', 'Minhan Xu', 'Yu Hong', 'Xiaolin Xing'] | null | null | null | null | coling-2022-10 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.91593164e-01 2.68508270e-02 -3.46557707e-01 -1.49308741e-01
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29554054-ae46-45bd-a9e3-11b7dcafe9fa | evaluation-of-a-tree-based-pipeline | 1603.06212 | null | http://arxiv.org/abs/1603.06212v1 | http://arxiv.org/pdf/1603.06212v1.pdf | Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science | As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement an open source Tree-based Pipeline
Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a
series of simulated and real-world benchmark data sets. In particular, we show
that TPOT can design machine learning pipelines that provide a significant
improvement over a basic machine learning analysis while requiring little to no
input nor prior knowledge from the user. We also address the tendency for TPOT
to design overly complex pipelines by integrating Pareto optimization, which
produces compact pipelines without sacrificing classification accuracy. As
such, this work represents an important step toward fully automating machine
learning pipeline design. | ['Randal S. Olson', 'Nathan Bartley', 'Jason H. Moore', 'Ryan J. Urbanowicz'] | 2016-03-20 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 3.81507911e-02 -1.64808705e-02 1.56486392e-01 -4.42628354e-01
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c452af14-ba39-4c06-98bc-6e4b195719ef | intermediate-and-future-frame-prediction-of | 2303.04405 | null | https://arxiv.org/abs/2303.04405v1 | https://arxiv.org/pdf/2303.04405v1.pdf | Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network | Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions. To solve this problem, we proposed warp and refine network (WR-Net). WR-Net is divided into an optical flow warp component and a warp image refinement component. We used the TV-L1 algorithm instead of deep learning-based approaches to extract the optical flow warp component. The deep-learning-based model is trained on the human-centric view of the RGB channel and does not work on geostationary satellites, which is gray-scale one-channel imagery. The refinement network refines the warped image through a multi-temporal fusion layer. We evaluated WR-Net by interpolation of temporal resolution at 4 min intervals to 2 min intervals in large-scale GK2A geostationary meteorological satellite imagery. Furthermore, we applied WR-Net to the future frame prediction task and showed that the explicit use of optical flow can help future frame prediction. | ['Wanseok Seo', 'Hyesook Lee', 'Hyungkun Bae', 'Heesun Park', 'Hyungon Ry', 'Yeji Choi', 'Minseok Seo'] | 2023-03-08 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-7.92919174e-02 -4.83718157e-01 -3.26036178e-02 -1.79752901e-01
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401e7d48-5816-4873-b059-b3ce7f11bbcd | deep-learning-for-environmentally-robust | 1705.10874 | null | http://arxiv.org/abs/1705.10874v3 | http://arxiv.org/pdf/1705.10874v3.pdf | Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments | Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks. | ['Björn Schuller', 'Amr El-Desoky Mousa', 'Jouni Pohjalainen', 'Jürgen Geiger', 'Wenyu Jin', 'Zixing Zhang'] | 2017-05-30 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 2.89917916e-01 -1.56457335e-01 4.34875727e-01 -3.40700448e-01
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9842fe24-69fd-44e0-bd13-08934fddcfc8 | hierarchical-3d-feature-learning-for-pancreas | 2109.01667 | null | https://arxiv.org/abs/2109.01667v1 | https://arxiv.org/pdf/2109.01667v1.pdf | Hierarchical 3D Feature Learning for Pancreas Segmentation | We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to multiple 3D decoders that individually predict intermediate segmentation maps. Finally, all segmentation maps are combined to obtain a unique detailed segmentation mask. We test our model on both CT and MRI imaging data: the publicly available NIH Pancreas-CT dataset (consisting of 82 contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans). Experimental results show that our model outperforms existing methods on CT pancreas segmentation, obtaining an average Dice score of about 88%, and yields promising segmentation performance on a very challenging MRI data set (average Dice score is about 77%). Additional control experiments demonstrate that the achieved performance is due to the combination of our 3D fully-convolutional deep network and the hierarchical representation decoding, thus substantiating our architectural design. | ['Concetto Spampinato', 'Ulas Bagci', 'Simone Palazzo', 'Ismail Irmakci', 'Giovanni Bellitto', 'Federica Proietto Salanitri'] | 2021-09-03 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [ 2.27880880e-01 3.59043926e-01 -1.49844229e-01 -5.83876610e-01
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48d8e3ea-9438-4c83-94b3-03aa50cc009b | gsdf-geometry-driven-signed-distance | 2304.11970 | null | https://arxiv.org/abs/2304.11970v1 | https://arxiv.org/pdf/2304.11970v1.pdf | gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction | Signed distance functions (SDFs) is an attractive framework that has recently shown promising results for 3D shape reconstruction from images. SDFs seamlessly generalize to different shape resolutions and topologies but lack explicit modelling of the underlying 3D geometry. In this work, we exploit the hand structure and use it as guidance for SDF-based shape reconstruction. In particular, we address reconstruction of hands and manipulated objects from monocular RGB images. To this end, we estimate poses of hands and objects and use them to guide 3D reconstruction. More specifically, we predict kinematic chains of pose transformations and align SDFs with highly-articulated hand poses. We improve the visual features of 3D points with geometry alignment and further leverage temporal information to enhance the robustness to occlusion and motion blurs. We conduct extensive experiments on the challenging ObMan and DexYCB benchmarks and demonstrate significant improvements of the proposed method over the state of the art. | ['Ivan Laptev', 'Cordelia Schmid', 'ShiZhe Chen', 'Zerui Chen'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-reconstruction', '3d-shape-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.22812487e-01 -4.40506279e-01 7.53976181e-02 -3.40325177e-01
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65c0aad4-3f9c-4015-a1a7-43c1c85a5835 | diagnosing-and-remedying-shot-sensitivity | 2207.03398 | null | https://arxiv.org/abs/2207.03398v1 | https://arxiv.org/pdf/2207.03398v1.pdf | Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners | Few-shot recognition involves training an image classifier to distinguish novel concepts at test time using few examples (shot). Existing approaches generally assume that the shot number at test time is known in advance. This is not realistic, and the performance of a popular and foundational method has been shown to suffer when train and test shots do not match. We conduct a systematic empirical study of this phenomenon. In line with prior work, we find that shot sensitivity is broadly present across metric-based few-shot learners, but in contrast to prior work, larger neural architectures provide a degree of built-in robustness to varying test shot. More importantly, a simple, previously known but greatly overlooked class of approaches based on cosine distance consistently and greatly improves robustness to shot variation, by removing sensitivity to sample noise. We derive cosine alternatives to popular and recent few-shot classifiers, broadening their applicability to realistic settings. These cosine models consistently improve shot-robustness, outperform prior shot-robust state of the art, and provide competitive accuracy on a range of benchmarks and architectures, including notable gains in the very-low-shot regime. | ['Bharath Hariharan', 'Luming Tang', 'Davis Wertheimer'] | 2022-07-07 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.28206474e-01 -5.94386995e-01 -4.69621718e-01 -4.24962252e-01
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93d3ff16-9b62-4e0f-8ece-fc129c97180a | location-based-training-for-multi-channel | 2110.04289 | null | https://arxiv.org/abs/2110.04289v1 | https://arxiv.org/pdf/2110.04289v1.pdf | Location-based training for multi-channel talker-independent speaker separation | Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial information afforded by microphone arrays, we propose a new training approach to resolving permutation ambiguities for multi-channel speaker separation. The proposed approach, named location-based training (LBT), assigns speakers on the basis of their spatial locations. This training strategy is easy to apply, and organizes speakers according to their positions in physical space. Specifically, this study investigates azimuth angles and source distances for location-based training. Evaluation results on separating two- and three-speaker mixtures show that azimuth-based training consistently outperforms PIT, and distance-based training further improves the separation performance when speaker azimuths are close. Furthermore, we dynamically select azimuth-based or distance-based training by estimating the azimuths of separated speakers, which further improves separation performance. LBT has a linear training complexity with respect to the number of speakers, as opposed to the factorial complexity of PIT. We further demonstrate the effectiveness of LBT for the separation of four and five concurrent speakers. | ['DeLiang Wang', 'Ke Tan', 'Hassan Taherian'] | 2021-10-08 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 7.05752969e-02 -6.88032985e-01 3.35337874e-03 -4.02412355e-01
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a26e617f-26cb-4f4b-92f5-7a936be0e01a | towards-a-unified-approach-to-single-image | 2103.14204 | null | https://arxiv.org/abs/2103.14204v1 | https://arxiv.org/pdf/2103.14204v1.pdf | Towards a Unified Approach to Single Image Deraining and Dehazing | We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit. Via depth-aware fusion of multi-layer rain streaks according to the camera imaging mechanism, the new model can better capture the sophisticated non-deterministic degradation patterns commonly seen in real rainy images. We also propose a Densely Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining and dehazing tasks. Our design alleviates the bottleneck issue existent in conventional multi-scale networks and enables more effective information exchange and aggregation. Extensive experimental results demonstrate that the proposed DSCAN is able to deliver superior derained/dehazed results on both synthetic and real images as compared to the state-of-the-art. Moreover, it is shown that for our DSCAN, the synthetic dataset built using the new physical model yields better generalization performance on real images in comparison with the existing datasets based on over-simplified models. | ['Jun Chen', 'Linhui Dai', 'Zhihao Shi', 'Yongrui Ma', 'Xiaohong Liu'] | 2021-03-26 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.15267055e-02 -1.73190594e-01 5.90756238e-01 -3.51945579e-01
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cf8b6c9a-9d15-42c9-b684-47859333f97e | more-than-classification-a-unified-framework | 2305.17607 | null | https://arxiv.org/abs/2305.17607v1 | https://arxiv.org/pdf/2305.17607v1.pdf | More than Classification: A Unified Framework for Event Temporal Relation Extraction | Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events. For example, relation \textit{Includes} could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3\% on both datasets. By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios. When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations. The code is released at \url{https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE}. | ['Dongyan Zhao', 'Chang Liu', 'Yansong Feng', 'Shengqi Zhu', 'Yutong Hu', 'Quzhe Huang'] | 2023-05-28 | null | null | null | null | ['temporal-relation-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.86059850e-02 9.33309942e-02 -6.48309946e-01 -6.55410528e-01
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8756ee41-4123-464e-aea6-c61be932e183 | discrete-wavelet-transform-for-generative | null | null | https://ieeexplore.ieee.org/document/9716101 | https://ieeexplore.ieee.org/document/9716101 | Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors | Driver identification is a central research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as the main sensor devices. After extracting features from smartphone-embedded sensors, various machine learning methods can be used to identify the driver. However, the accuracy often degrades as the number of drivers increases. This paper uses a Generative Adversarial Network (GAN) for data augmentation to obtain a driver identification algorithm that maintains excellent performance also when the number of drivers increases. Since GAN diversifies the drivers’ data, it makes it possible to apply the identification algorithm on a larger number of drivers without overfitting. Although GANs are commonly used in image processing for image augmentation, their use for driving signal augmentation is novel. However, GAN’s training on raw driving signals diverges. This challenge is solved by getting the Discrete Wavelet Transform (DWT) on driving signals before feeding to GAN. Our experiments prove the usefulness of GAN model for generating driving signals emanating from DWT on smartphones’ accelerometer and gyroscope signals. After collecting the augmented data, their histograms along the overlapped windows are fed to machine learning methods covered by a Stacked Generalization Method (SGM). The presented hybrid GAN-SGM approach identifies drivers with 97% accuracy, 98% precision, 97% recall, and 97% F1-measure that outperforms standard machine learning methods that utilize features extracted by the statistical, spectral, and temporal approaches. | ['Johan Wahlström', 'Mehdi Ghatee', 'Rouhollah Ahmadian'] | 2022-04-01 | null | null | null | ieee-sensors-journal-2022-4 | ['image-augmentation'] | ['computer-vision'] | [ 4.95512098e-01 -8.63391906e-02 -2.41459742e-01 -2.33162716e-01
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7263628e-5599-4d91-981e-79913b18addf | leveraging-rationales-to-improve-human-task | 2002.04202 | null | https://arxiv.org/abs/2002.04202v1 | https://arxiv.org/pdf/2002.04202v1.pdf | Leveraging Rationales to Improve Human Task Performance | Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning methods. In this work, we consider a question not previously explored within the XAI and ML communities: Given a computational system whose performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We study this question in the context of the game of Chess, for which computational game engines that surpass the performance of the average player are widely available. We introduce the Rationale-Generating Algorithm, an automated technique for generating rationales for utility-based computational methods, which we evaluate with a multi-day user study against two baselines. The results show that our approach produces rationales that lead to statistically significant improvement in human task performance, demonstrating that rationales automatically generated from an AI's internal task model can be used not only to explain what the system is doing, but also to instruct the user and ultimately improve their task performance. | ['Sonia Chernova', 'Devleena Das'] | 2020-02-11 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [ 4.64545071e-01 7.17416108e-01 -2.50066407e-02 -3.00525010e-01
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ba7cd129-5415-411f-8252-3bcb73ec50a8 | gcfagg-global-and-cross-view-feature | 2305.06799 | null | https://arxiv.org/abs/2305.06799v1 | https://arxiv.org/pdf/2305.06799v1.pdf | GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering | Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary ofsimilar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks. | ['Weisi Lin', 'Liang Liao', 'Guanghui Yue', 'Chang Tang', 'Chenlei Lv', 'Yuanyang Zhang', 'Weiqing Yan'] | 2023-05-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-4.33084339e-01 -3.87788057e-01 2.64887530e-02 -5.82994938e-01
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a30fbddb-262e-430f-8889-ebf3abc29418 | mtldesc-looking-wider-to-describe-better | 2203.07003 | null | https://arxiv.org/abs/2203.07003v1 | https://arxiv.org/pdf/2203.07003v1.pdf | MTLDesc: Looking Wider to Describe Better | Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context. In this work, we focus on making local descriptors "look wider to describe better" by learning local Descriptors with More Than just Local information (MTLDesc). Specifically, we resort to context augmentation and spatial attention mechanisms to make our MTLDesc obtain non-local awareness. First, Adaptive Global Context Augmented Module and Diverse Local Context Augmented Module are proposed to construct robust local descriptors with context information from global to local. Second, Consistent Attention Weighted Triplet Loss is designed to integrate spatial attention awareness into both optimization and matching stages of local descriptors learning. Third, Local Features Detection with Feature Pyramid is given to obtain more stable and accurate keypoints localization. With the above innovations, the performance of our MTLDesc significantly surpasses the prior state-of-the-art local descriptors on HPatches, Aachen Day-Night localization and InLoc indoor localization benchmarks. | ['Xiaopeng Zhang', 'Bin Fan', 'Weiliang Meng', 'Shibiao Xu', 'Yuyang Zhang', 'Rongtao Xu', 'Changwei Wang'] | 2022-03-14 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-4.04017806e-01 -7.36162364e-01 -3.69147956e-01 -8.12689543e-01
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956f3a59-0213-4bd5-86a4-c2942982b251 | vesselness-features-and-the-inverse | 1306.1609 | null | http://arxiv.org/abs/1306.1609v1 | http://arxiv.org/pdf/1306.1609v1.pdf | Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR | Over the course of the last decade, infrared (IR) and particularly thermal IR
imaging based face recognition has emerged as a promising complement to
conventional, visible spectrum based approaches which continue to struggle when
applied in the real world. While inherently insensitive to visible spectrum
illumination changes, IR images introduce specific challenges of their own,
most notably sensitivity to factors which affect facial heat emission patterns,
e.g. emotional state, ambient temperature, and alcohol intake. In addition,
facial expression and pose changes are more difficult to correct in IR images
because they are less rich in high frequency detail which is an important cue
for fitting any deformable model. We describe a novel method which addresses
these challenges. To normalize for pose and facial expression changes we
generate a synthetic frontal image of a face in a canonical, neutral facial
expression from an image of the face in an arbitrary pose and facial
expression. This is achieved by piecewise affine warping which follows active
appearance model (AAM) fitting. This is the first publication which explores
the use of an AAM on thermal IR images; we propose a pre-processing step which
enhances detail in thermal images, making AAM convergence faster and more
accurate. To overcome the problem of thermal IR image sensitivity to the
pattern of facial temperature emissions we describe a representation based on
reliable anatomical features. In contrast to previous approaches, our
representation is not binary; rather, our method accounts for the reliability
of the extracted features. This makes the proposed representation much more
robust both to pose and scale changes. The effectiveness of the proposed
approach is demonstrated on the largest public database of thermal IR images of
faces on which it achieved 100% identification, significantly outperforming
previous methods. | ['Reza Shoja Ghiass', 'Xavier Maldague', 'Hakim Bendada', 'Ognjen Arandjelovic'] | 2013-06-07 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 6.60937130e-01 -9.55176130e-02 -1.36326347e-02 -3.64884168e-01
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ad306c9c-5ead-4cee-be2c-82e43d845fb9 | deep-learning-for-handling-kernel-model | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.pdf | Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution | Most existing non-blind image deconvolution methods assume that the given blurring kernel is error-free. In practice, blurring kernel often is estimated via some blind deblurring algorithm which is not exactly the truth. Also, the convolution model is only an approximation to practical blurring effect. It is known that non-blind deconvolution is susceptible to such a kernel/model error. Based on an error-in-variable (EIV) model of image blurring that takes kernel error into consideration, this paper presents a deep learning method for deconvolution, which unrolls a total-least-squares (TLS) estimator whose relating priors are learned by neural networks (NNs). The experiments showed that the proposed method is robust to kernel/model error. It noticeably outperformed existing solutions when deblurring images using noisy kernels, e.g. the ones estimated from existing blind motion deblurring methods.
| [' Hui Ji', 'Yuesong Nan'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-deconvolution'] | ['computer-vision'] | [ 1.21723458e-01 -5.73152184e-01 1.95227712e-01 -1.47088319e-01
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bcdf4c17-d07a-46a2-b790-18fcba0ac0e1 | accurate-fine-grained-segmentation-of-human | 2306.03934 | null | https://arxiv.org/abs/2306.03934v1 | https://arxiv.org/pdf/2306.03934v1.pdf | Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling | Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we propose a novel method for extracting fine-grained anatomical structures in CXR using pseudo-labeling of three-dimensional computed tomography (CT) scans. Methods: We created a large-scale dataset of 10,021 thoracic CTs with 157 labels and applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. These labels were projected onto a two-dimensional plane, similar to the CXR, allowing the training of detailed semantic segmentation models for CXR without any manual annotation effort. Results: Our resulting segmentation models demonstrated remarkable performance on CXR, with a high average model-annotator agreement between two radiologists with mIoU scores of 0.93 and 0.85 for frontal and lateral anatomy, while inter-annotator agreement remained at 0.95 and 0.83 mIoU. Our anatomical segmentations allowed for the accurate extraction of relevant explainable medical features such as the cardio-thoracic-ratio. Conclusion: Our method of volumetric pseudo-labeling paired with CT projection offers a promising approach for detailed anatomical segmentation of CXR with a high agreement with human annotators. This technique may have important clinical implications, particularly in the analysis of various thoracic pathologies. | ['Rainer Stiefelhagen', 'Jens Kleesiek', 'Ken Herrmann', 'Simon Reiß', 'Moon Kim', 'Matthias A. Fink', 'Alexander Jaus', 'Constantin Seibold'] | 2023-06-06 | null | null | null | null | ['computed-tomography-ct', 'anatomy'] | ['methodology', 'miscellaneous'] | [ 1.45617858e-01 4.99208450e-01 -8.45915750e-02 -5.29011309e-01
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78f1200e-abf6-40c2-a8f4-07689bb3c5ee | rudsi-graph-based-word-sense-induction | 2209.13750 | null | https://arxiv.org/abs/2209.13750v1 | https://arxiv.org/pdf/2209.13750v1.pdf | RuDSI: graph-based word sense induction dataset for Russian | We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russian, RuDSI is completely data-driven (based on texts from Russian National Corpus), with no external word senses imposed on annotators. Depending on the parameters of graph clustering, different derivative datasets can be produced from raw annotation. We report the performance that several baseline WSI methods obtain on RuDSI and discuss possibilities for improving these scores. | ['Andrey Kutuzov', 'Elisey Rykov', 'Ekaterina Gavrishina', 'Anna Aksenova'] | 2022-09-28 | null | https://aclanthology.org/2022.textgraphs-1.9 | https://aclanthology.org/2022.textgraphs-1.9.pdf | coling-textgraphs-2022-10 | ['graph-clustering'] | ['graphs'] | [ 3.45319897e-01 6.04446471e-01 -3.86052936e-01 -1.93212405e-01
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753d0e42-6c59-4f06-9bff-c4c2a21959ba | kite-keypoint-conditioned-policies-for | 2306.16605 | null | https://arxiv.org/abs/2306.16605v2 | https://arxiv.org/pdf/2306.16605v2.pdf | KITE: Keypoint-Conditioned Policies for Semantic Manipulation | While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in manipulation. A crucial step to realizing a performant instruction-following robot is achieving semantic manipulation, where a robot interprets language at different specificities, from high-level instructions like "Pick up the stuffed animal" to more detailed inputs like "Grab the left ear of the elephant." To tackle this, we propose Keypoints + Instructions to Execution (KITE), a two-step framework for semantic manipulation which attends to both scene semantics (distinguishing between different objects in a visual scene) and object semantics (precisely localizing different parts within an object instance). KITE first grounds an input instruction in a visual scene through 2D image keypoints, providing a highly accurate object-centric bias for downstream action inference. Provided an RGB-D scene observation, KITE then executes a learned keypoint-conditioned skill to carry out the instruction. The combined precision of keypoints and parameterized skills enables fine-grained manipulation with generalization to scene and object variations. Empirically, we demonstrate KITE in 3 real-world environments: long-horizon 6-DoF tabletop manipulation, semantic grasping, and a high-precision coffee-making task. In these settings, KITE achieves a 75%, 70%, and 71% overall success rate for instruction-following, respectively. KITE outperforms frameworks that opt for pre-trained visual language models over keypoint-based grounding, or omit skills in favor of end-to-end visuomotor control, all while being trained from fewer or comparable amounts of demonstrations. Supplementary material, datasets, code, and videos can be found on our website: http://tinyurl.com/kite-site. | ['Jeannette Bohg', 'Dorsa Sadigh', 'Suneel Belkhale', 'Priya Sundaresan'] | 2023-06-29 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.63822353e-01 4.46792878e-02 -2.45819211e-01 -3.37753564e-01
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00f1ce51-ccee-4757-916e-1342fd31760f | early-mfcc-and-hpcp-fusion-for-robust-cover | 1707.04680 | null | http://arxiv.org/abs/1707.04680v1 | http://arxiv.org/pdf/1707.04680v1.pdf | Early MFCC And HPCP Fusion for Robust Cover Song Identification | While most schemes for automatic cover song identification have focused on
note-based features such as HPCP and chord profiles, a few recent papers
surprisingly showed that local self-similarities of MFCC-based features also
have classification power for this task. Since MFCC and HPCP capture
complementary information, we design an unsupervised algorithm that combines
normalized, beat-synchronous blocks of these features using cross-similarity
fusion before attempting to locally align a pair of songs. As an added bonus,
our scheme naturally incorporates structural information in each song to fill
in alignment gaps where both feature sets fail. We show a striking jump in
performance over MFCC and HPCP alone, achieving a state of the art mean
reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new
medium-sized hand designed benchmark dataset called "Covers 1000," which
consists of 395 cliques of cover songs for a total of 1000 songs, and we show
that our algorithm achieves an MRR of 0.9 on this dataset for the first
correctly identified song in a clique. We provide the precomputed HPCP and MFCC
features, as well as beat intervals, for all songs in the Covers 1000 dataset
for use in further research. | ['Tralie Christopher J.'] | 2017-07-15 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 1.90452978e-01 -5.39417744e-01 -1.11304987e-02 -1.99180469e-01
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0acebeb5-eb41-4b29-aa50-100bf7786ad5 | sparse-local-embeddings-for-extreme-multi | null | null | http://papers.nips.cc/paper/5969-sparse-local-embeddings-for-extreme-multi-label-classification | http://papers.nips.cc/paper/5969-sparse-local-embeddings-for-extreme-multi-label-classification.pdf | Sparse Local Embeddings for Extreme Multi-label Classification | The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace. Still, leading embedding approaches have been unable to deliver high prediction accuracies or scale to large problems as the low rank assumption is violated in most real world applications.This paper develops the SLEEC classifier to address both limitations. The main technical contribution in SLEEC is a formulation for learning a small ensemble of local distance preserving embeddings which can accurately predict infrequently occurring (tail) labels. This allows SLEEC to break free of the traditional low-rank assumption and boost classification accuracy by learning embeddings which preserve pairwise distances between only the nearest label vectors. We conducted extensive experiments on several real-world as well as benchmark data sets and compare our method against state-of-the-art methods for extreme multi-label classification. Experiments reveal that SLEEC can make significantly more accurate predictions then the state-of-the-art methods including both embeddings (by as much as 35%) as well as trees (by as much as 6%). SLEEC can also scale efficiently to data sets with a million labels which are beyond the pale of leading embedding methods. | ['Manik Varma', 'Kush Bhatia', 'Purushottam Kar', 'Prateek Jain', 'Himanshu Jain'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['extreme-multi-label-classification'] | ['methodology'] | [ 3.70858878e-01 7.00062737e-02 -3.75029922e-01 -4.73119438e-01
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7883f75d-d99b-4fe7-819d-aa736840eeea | receptive-field-analysis-of-temporal | 2204.06439 | null | https://arxiv.org/abs/2204.06439v3 | https://arxiv.org/pdf/2204.06439v3.pdf | Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation | Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. Temporal convolutional networks (TCNs) are commonly used for sequence modelling in speech enhancement tasks. A feature of TCNs is that they have a receptive field (RF) dependent on the specific model configuration which determines the number of input frames that can be observed to produce an individual output frame. It has been shown that TCNs are capable of performing dereverberation of simulated speech data, however a thorough analysis, especially with focus on the RF is yet lacking in the literature. This paper analyses dereverberation performance depending on the model size and the RF of TCNs. Experiments using the WHAMR corpus which is extended to include room impulse responses (RIRs) with larger T60 values demonstrate that a larger RF can have significant improvement in performance when training smaller TCN models. It is also demonstrated that TCNs benefit from a wider RF when dereverberating RIRs with larger RT60 values. | ['Thomas Hain', 'Stefan Goetze', 'William Ravenscroft'] | 2022-04-13 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.72821331e-01 -2.38964930e-01 3.75415802e-01 -2.13978052e-01
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428191da-aa45-4126-a506-52209e91e8d6 | robust-matrix-completion-with-heavy-tailed | 2206.04276 | null | https://arxiv.org/abs/2206.04276v1 | https://arxiv.org/pdf/2206.04276v1.pdf | Robust Matrix Completion with Heavy-tailed Noise | This paper studies low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of theoretical understanding when the observations are contaminated by heavy-tailed noises. Prior theory falls short of explaining the empirical results and is unable to capture the optimal dependence of the estimation error on the noise level. In this paper, we adopt an adaptive Huber loss to accommodate heavy-tailed noise, which is robust against large and possibly asymmetric errors when the parameter in the loss function is carefully designed to balance the Huberization biases and robustness to outliers. Then, we propose an efficient nonconvex algorithm via a balanced low-rank Burer-Monteiro matrix factorization and gradient decent with robust spectral initialization. We prove that under merely bounded second moment condition on the error distributions, rather than the sub-Gaussian assumption, the Euclidean error of the iterates generated by the proposed algorithm decrease geometrically fast until achieving a minimax-optimal statistical estimation error, which has the same order as that in the sub-Gaussian case. The key technique behind this significant advancement is a powerful leave-one-out analysis framework. The theoretical results are corroborated by our simulation studies. | ['Jianqing Fan', 'Bingyan Wang'] | 2022-06-09 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 8.17120597e-02 -1.82240218e-01 -2.01069135e-02 -7.09882081e-02
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f093e7db-c0f2-4449-9308-150bd07e2792 | eeg-and-emg-dataset-for-the-detection-of | 2305.11996 | null | https://arxiv.org/abs/2305.11996v2 | https://arxiv.org/pdf/2305.11996v2.pdf | EEG and EMG dataset for the detection of errors introduced by an active orthosis device | This paper presents a dataset containing recordings of the electroencephalogram (EEG) and the electromyogram (EMG) from eight subjects who were assisted in moving their right arm by an active orthosis device. The supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. In this paper, we explain the experimental setup and present some behavioral analyses across all subjects. Additionally, we present an average event-related potential analysis for one subject to offer insights into the data quality and the EEG activity caused by the error introduction. The dataset described herein is openly accessible. The aim of this study was to provide a dataset to the research community, particularly for the development of new methods in the asynchronous detection of erroneous events from the EEG. We are especially interested in the tactile and haptic-mediated recognition of errors, which has not yet been sufficiently investigated in the literature. We hope that the detailed description of the orthosis and the experiment will enable its reproduction and facilitate a systematic investigation of the influencing factors in the detection of erroneous behavior of assistive systems by a large community. | ['Elsa Andrea Kirchner', 'Frank Kirchner', 'Marc Tabie', 'Tobias Rossol', 'Su Kyoung Kim', 'Julia Habenicht', 'Judith Bütefür', 'Kartik Chari', 'Niklas Kueper'] | 2023-05-19 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.34844434e-01 1.90462887e-01 2.18686625e-01 -8.69934540e-03
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0e610e68-1079-4656-8e16-7de7e40e8d0e | minimal-adversarial-examples-for-deep | 2008.12066 | null | https://arxiv.org/abs/2008.12066v4 | https://arxiv.org/pdf/2008.12066v4.pdf | Minimal Adversarial Examples for Deep Learning on 3D Point Clouds | With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e.g., object recognition, semantic segmentation. In a safety-critical environment, it is however not well understood how such deep learning models are vulnerable to adversarial examples. In this work, we explore adversarial attacks for point cloud-based neural networks. We propose a unified formulation for adversarial point cloud generation that can generalise two different attack strategies. Our method generates adversarial examples by attacking the classification ability of point cloud-based networks while considering the perceptibility of the examples and ensuring the minimal level of point manipulations. Experimental results show that our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively, while manipulating only about 4% of the total points. | ['Sai-Kit Yeung', 'Duc Thanh Nguyen', 'Binh-Son Hua', 'Jaeyeon Kim'] | 2020-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kim_Minimal_Adversarial_Examples_for_Deep_Learning_on_3D_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kim_Minimal_Adversarial_Examples_for_Deep_Learning_on_3D_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-recognition', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 1.61188304e-01 2.91724980e-01 4.56537783e-01 -9.85298157e-02
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4c76a597-4ca9-4425-8b9a-1eec1a02d84f | inferring-the-ground-truth-through | 1807.11836 | null | http://arxiv.org/abs/1807.11836v1 | http://arxiv.org/pdf/1807.11836v1.pdf | Inferring the ground truth through crowdsourcing | Universally valid ground truth is almost impossible to obtain or would come
at a very high cost. For supervised learning without universally valid ground
truth, a recommended approach is applying crowdsourcing: Gathering a large data
set annotated by multiple individuals of varying possibly expertise levels and
inferring the ground truth data to be used as labels to train the classifier.
Nevertheless, due to the sensitivity of the problem at hand (e.g. mitosis
detection in breast cancer histology images), the obtained data needs
verification and proper assessment before being used for classifier training.
Even in the context of organic computing systems, an indisputable ground truth
might not always exist. Therefore, it should be inferred through the
aggregation and verification of the local knowledge of each autonomous agent. | ['Jean Pierre Char'] | 2018-07-31 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.77689689e-01 5.64331234e-01 -4.50472087e-02 -2.57900953e-01
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7f071ab5-b56b-4c37-a0e1-f62759880db5 | disc-a-dataset-for-integrated-sensing-and | 2306.09469 | null | https://arxiv.org/abs/2306.09469v1 | https://arxiv.org/pdf/2306.09469v1.pdf | DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems | In this paper we present DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response including reflections of the signal on the moving body parts of subjects moving in an indoor environment. The provided data contain the contribution of 7 subjects performing 4 different activities. Differently from available radar-based millimeter-wave sensing datasets, our measurements are collected using both uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. Thanks to these unique characteristics, DISC serves as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communication. | ['Joerg Widmer', 'Michele Rossi', 'Jesus Omar Lacruz', 'Jacopo Pegoraro'] | 2023-06-15 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'benchmarking', 'benchmarking', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'robots', 'time-series'] | [ 6.19376957e-01 2.55672429e-02 -1.55435234e-01 -1.30095422e-01
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4836cfe1-573e-4215-b026-fffe5fc42ebd | toward-semi-automatic-misconception-discovery | 2103.04448 | null | https://arxiv.org/abs/2103.04448v1 | https://arxiv.org/pdf/2103.04448v1.pdf | Toward Semi-Automatic Misconception Discovery Using Code Embeddings | Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highlighting patterns in student data, which domain experts can then inspect to identify misconceptions. In this work, we present a novel method for the semi-automated discovery of problem-specific misconceptions from students' program code in computing courses, using a state-of-the-art code classification model. We trained the model on a block-based programming dataset and used the learned embedding to cluster incorrect student submissions. We found these clusters correspond to specific misconceptions about the problem and would not have been easily discovered with existing approaches. We also discuss potential applications of our approach and how these misconceptions inform domain-specific insights into students' learning processes. | ['Thomas W. Price', 'Poorvaja Penmetsa', 'Samiha Marwan', 'Wengran Wang', 'Krupal Shah', 'Yang Shi'] | 2021-03-07 | null | null | null | null | ['code-classification', 'misconceptions'] | ['computer-code', 'miscellaneous'] | [-8.23012590e-02 1.73079044e-01 -1.06536865e-01 -3.67588401e-01
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a920e84a-2d7a-4b65-b252-6261f11d7ba0 | interpretable-deep-clustering | 2306.04785 | null | https://arxiv.org/abs/2306.04785v1 | https://arxiv.org/pdf/2306.04785v1.pdf | Interpretable Deep Clustering | Clustering is a fundamental learning task widely used as a first step in data analysis. For example, biologists often use cluster assignments to analyze genome sequences, medical records, or images. Since downstream analysis is typically performed at the cluster level, practitioners seek reliable and interpretable clustering models. We propose a new deep-learning framework that predicts interpretable cluster assignments at the instance and cluster levels. First, we present a self-supervised procedure to identify a subset of informative features from each data point. Then, we design a model that predicts cluster assignments and a gate matrix that leads to cluster-level feature selection. We show that the proposed method can reliably predict cluster assignments using synthetic and real data. Furthermore, we verify that our model leads to interpretable results at a sample and cluster level. | ['Ofir Lindenbaum', 'Jonathan Svirsky'] | 2023-06-07 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 1.10224679e-01 -1.53514192e-01 -1.34698629e-01 -7.73109317e-01
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4c0aae86-91d2-4af5-8ee3-c8e4dc60800b | minute-ventilation-measurement-using | 2208.13319 | null | https://arxiv.org/abs/2208.13319v1 | https://arxiv.org/pdf/2208.13319v1.pdf | Minute ventilation measurement using Plethysmographic Imaging and lighting parameters | Breathing disorders such as sleep apnea is a critical disorder that affects a large number of individuals due to the insufficient capacity of the lungs to contain/exchange oxygen and carbon dioxide to ensure that the body is in the stable state of homeostasis. Respiratory Measurements such as minute ventilation can be used in correlation with other physiological measurements such as heart rate and heart rate variability for remote monitoring of health and detecting symptoms of such breathing related disorders. In this work, we formulate a deep learning based approach to measure remote ventilation on a private dataset. The dataset will be made public upon acceptance of this work. We use two versions of a deep neural network to estimate the minute ventilation from data streams obtained through wearable heart rate and respiratory devices. We demonstrate that the simple design of our pipeline - which includes lightweight deep neural networks - can be easily incorporate into real time health monitoring systems. | ['Krishna Vardhan', 'Bo Ji', 'Karen Li', 'Ludwik Sams', 'Daniel Minati'] | 2022-08-29 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.68069485e-02 -2.60674767e-02 -1.35317519e-01 -5.38371563e-01
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796994bb-50c8-4209-b91a-9c7a72645c90 | what-if-generating-code-to-answer-simulation | 2204.07835 | null | https://arxiv.org/abs/2204.07835v1 | https://arxiv.org/pdf/2204.07835v1.pdf | What If: Generating Code to Answer Simulation Questions | Many texts, especially in chemistry and biology, describe complex processes. We focus on texts that describe a chemical reaction process and questions that ask about the process's outcome under different environmental conditions. To answer questions about such processes, one needs to understand the interactions between the different entities involved in the process and to simulate their state transitions during the process execution under different conditions. A state transition is defined as the memory modification the program does to the variables during the execution. We hypothesize that generating code and executing it to simulate the process will allow answering such questions. We, therefore, define a domain-specific language (DSL) to represent processes. We contribute to the community a unique dataset curated by chemists and annotated by computer scientists. The dataset is composed of process texts, simulation questions, and their corresponding computer codes represented by the DSL.We propose a neural program synthesis approach based on reinforcement learning with a novel state-transition semantic reward. The novel reward is based on the run-time semantic similarity between the predicted code and the reference code. This allows simulating complex process transitions and thus answering simulation questions. Our approach yields a significant boost in accuracy for simulation questions: 88\% accuracy as opposed to 83\% accuracy of the state-of-the-art neural program synthesis approaches and 54\% accuracy of state-of-the-art end-to-end text-based approaches. | ['Kira Radinsky', 'Gal Peretz'] | 2022-04-16 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.43014586e-01 2.26645157e-01 6.81141391e-02 -2.28674680e-01
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2acb498d-bab5-4c84-81a7-90c1baf9c6bd | learning-to-score-olympic-events | 1611.05125 | null | http://arxiv.org/abs/1611.05125v3 | http://arxiv.org/pdf/1611.05125v3.pdf | Learning To Score Olympic Events | Estimating action quality, the process of assigning a "score" to the
execution of an action, is crucial in areas such as sports and health care.
Unlike action recognition, which has millions of examples to learn from, the
action quality datasets that are currently available are small -- typically
comprised of only a few hundred samples. This work presents three frameworks
for evaluating Olympic sports which utilize spatiotemporal features learned
using 3D convolutional neural networks (C3D) and perform score regression with
i) SVR, ii) LSTM, and iii) LSTM followed by SVR. An efficient training
mechanism for the limited data scenarios is presented for clip-based training
with LSTM. The proposed systems show significant improvement over existing
quality assessment approaches on the task of predicting scores of Olympic
events {diving, vault, figure skating}. While the SVR-based frameworks yield
better results, LSTM-based frameworks are more natural for describing an action
and can be used for improvement feedback. | ['Brendan Tran Morris', 'Paritosh Parmar'] | 2016-11-16 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 2.11493168e-02 -1.32483974e-01 -5.98397374e-01 -6.29630566e-01
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1.69556722e-01 -7.48779118e-01 1.83581769e-01 -5.56140170e-02] | [8.032525062561035, 0.6317633390426636] |
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