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9e016405-ec18-4f07-962a-1b75fe258c2e | joint-visual-and-wireless-signal-feature | 2008.08790 | null | https://arxiv.org/abs/2008.08790v1 | https://arxiv.org/pdf/2008.08790v1.pdf | Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization | The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time. | ['Shugong Xu', 'Shunqing Zhang', 'Chenlu Xiang', 'Guangbing Zhou', 'Yu Wang'] | 2020-08-20 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-1.50381178e-01 -6.87592745e-01 -1.35193184e-01 -3.14261585e-01
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ba42c4a9-7bc8-472a-afcf-fc630822fe72 | sliced-optimal-partial-transport | 2212.08049 | null | https://arxiv.org/abs/2212.08049v8 | https://arxiv.org/pdf/2212.08049v8.pdf | Sliced Optimal Partial Transport | Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mass in source and target measures, which limits its application. Optimal Partial Transport (OPT) is a recently proposed solution to this limitation. Similar to the OT problem, the computation of OPT relies on solving a linear programming problem (often in high dimensions), which can become computationally prohibitive. In this paper, we propose an efficient algorithm for calculating the OPT problem between two non-negative measures in one dimension. Next, following the idea of sliced OT distances, we utilize slicing to define the sliced OPT distance. Finally, we demonstrate the computational and accuracy benefits of the sliced OPT-based method in various numerical experiments. In particular, we show an application of our proposed Sliced-OPT in noisy point cloud registration. | ['Berhnard Schmitzer', 'Soheil Kolouri', 'Mathew Thorpe', 'Yikun Bai'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_Sliced_Optimal_Partial_Transport_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-registration'] | ['computer-vision'] | [ 3.37647647e-02 -2.54923254e-01 1.28332049e-01 -9.65298414e-02
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f702a492-6490-4b9b-b4f5-64192f010491 | is-your-perspective-also-my-perspective | null | null | https://aclanthology.org/2022.argmining-1.11 | https://aclanthology.org/2022.argmining-1.11.pdf | Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity | Although argumentation can be highly subjective, the common practice with supervised machine learning is to construct and learn from an aggregated ground truth formed from individual judgments by majority voting, averaging, or adjudication. This approach leads to a neglect of individual, but potentially important perspectives and in many cases cannot do justice to the subjective character of the tasks. One solution to this shortcoming are multi-perspective approaches, which have received very little attention in the field of argument mining so far. In this work we present PerspectifyMe, a method to incorporate perspectivism by enriching a task with subjectivity information from the data annotation process. We exemplify our approach with the use case of classifying argument concreteness, and provide first promising results for the recently published CIMT PartEval Argument Concreteness Corpus. | ['Julia Romberg'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 3.48731816e-01 9.32886183e-01 -2.85123348e-01 -5.05137086e-01
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ffa8fa52-d736-4971-b1d0-e88956bb4e77 | automatic-tuning-of-loss-trade-offs-without | 2305.16699 | null | https://arxiv.org/abs/2305.16699v1 | https://arxiv.org/pdf/2305.16699v1.pdf | Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis | Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot modifications of the VITS model have shown superior performance, while having useful properties inherited from VITS. However, the performance of VITS and VITS-based zero-shot models vary dramatically depending on how the losses are balanced. This can be problematic, as it requires a burdensome procedure of tuning loss balance hyper-parameters to find the optimal balance. In this work, we propose a novel framework that finds this optimum without search, by inducing the decoder of VITS-based models to its full reconstruction ability. With our framework, we show superior performance compared to baselines in zero-shot TTS and VC, achieving state-of-the-art performance. Furthermore, we show the robustness of our framework in various settings. We provide an explanation for the results in the discussion. | ['Tae-Hyun Oh', 'Bohyung Kim', 'Seongyeon Park'] | 2023-05-26 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.64438352e-01 2.06543550e-01 -1.51228622e-01 -6.23403937e-02
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cd87ac0e-8ac4-42e8-b2e9-0a1b27a35977 | vector-quantized-diffusion-model-with | 2208.09141 | null | https://arxiv.org/abs/2208.09141v2 | https://arxiv.org/pdf/2208.09141v2.pdf | Vector Quantized Diffusion Model with CodeUnet for Text-to-Sign Pose Sequences Generation | Sign Language Production (SLP) aims to translate spoken languages into sign sequences automatically. The core process of SLP is to transform sign gloss sequences into their corresponding sign pose sequences (G2P). Most existing G2P models usually perform this conditional long-range generation in an autoregressive manner, which inevitably leads to an accumulation of errors. To address this issue, we propose a vector quantized diffusion method for conditional pose sequences generation, called PoseVQ-Diffusion, which is an iterative non-autoregressive method. Specifically, we first introduce a vector quantized variational autoencoder (Pose-VQVAE) model to represent a pose sequence as a sequence of latent codes. Then we model the latent discrete space by an extension of the recently developed diffusion architecture. To better leverage the spatial-temporal information, we introduce a novel architecture, namely CodeUnet, to generate higher quality pose sequence in the discrete space. Moreover, taking advantage of the learned codes, we develop a novel sequential k-nearest-neighbours method to predict the variable lengths of pose sequences for corresponding gloss sequences. Consequently, compared with the autoregressive G2P models, our model has a faster sampling speed and produces significantly better results. Compared with previous non-autoregressive G2P methods, PoseVQ-Diffusion improves the predicted results with iterative refinements, thus achieving state-of-the-art results on the SLP evaluation benchmark. | ['Xiaohui Hu', 'Yao Du', 'Hao Tang', 'Zexian Li', 'Qipeng Zhang', 'Pan Xie'] | 2022-08-19 | null | null | null | null | ['sign-language-production'] | ['natural-language-processing'] | [ 2.21259013e-01 -8.14559758e-02 -7.96835497e-02 -1.74955949e-01
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f3416e01-8872-416b-8694-f564d2e6f779 | dlow-diversifying-latent-flows-for-diverse | 2003.08386 | null | https://arxiv.org/abs/2003.08386v2 | https://arxiv.org/pdf/2003.08386v2.pdf | DLow: Diversifying Latent Flows for Diverse Human Motion Prediction | Deep generative models are often used for human motion prediction as they are able to model multi-modal data distributions and characterize diverse human behavior. While much care has been taken into designing and learning deep generative models, how to efficiently produce diverse samples from a deep generative model after it has been trained is still an under-explored problem. To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples. Clearly, this random sampling strategy is not guaranteed to produce diverse samples for two reasons: (1) The independent sampling cannot force the samples to be diverse; (2) The sampling is based solely on likelihood which may only produce samples that correspond to the major modes of the data distribution. To address these problems, we propose a novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model. Unlike random (independent) sampling, the proposed DLow sampling method samples a single random variable and then maps it with a set of learnable mapping functions to a set of correlated latent codes. The correlated latent codes are then decoded into a set of correlated samples. During training, DLow uses a diversity-promoting prior over samples as an objective to optimize the latent mappings to improve sample diversity. The design of the prior is highly flexible and can be customized to generate diverse motions with common features (e.g., similar leg motion but diverse upper-body motion). Our experiments demonstrate that DLow outperforms state-of-the-art baseline methods in terms of sample diversity and accuracy. Our code is released on the project page: https://www.ye-yuan.com/dlow. | ['Ye Yuan', 'Kris Kitani'] | 2020-03-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/794_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540324.pdf | eccv-2020-8 | ['human-pose-forecasting'] | ['computer-vision'] | [ 7.10836798e-02 -1.77475259e-01 -5.46461523e-01 -2.54327327e-01
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3272713f-bc63-41f9-81cc-865a365035a5 | host-based-network-intrusion-detection-via | 2306.09451 | null | https://arxiv.org/abs/2306.09451v1 | https://arxiv.org/pdf/2306.09451v1.pdf | Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier | Network Intrusion Detection Systems (NIDS) have been extensively investigated by monitoring real network traffic and analyzing suspicious activities. However, there are limitations in detecting specific types of attacks with NIDS, such as Advanced Persistent Threats (APT). Additionally, NIDS is restricted in observing complete traffic information due to encrypted traffic or a lack of authority. To address these limitations, a Host-based Intrusion Detection system (HIDS) evaluates resources in the host, including logs, files, and folders, to identify APT attacks that routinely inject malicious files into victimized nodes. In this study, a hybrid network intrusion detection system that combines NIDS and HIDS is proposed to improve intrusion detection performance. The feature flattening technique is applied to flatten two-dimensional host-based features into one-dimensional vectors, which can be directly used by traditional Machine Learning (ML) models. A two-stage collaborative classifier is introduced that deploys two levels of ML algorithms to identify network intrusions. In the first stage, a binary classifier is used to detect benign samples. All detected attack types undergo a multi-class classifier to reduce the complexity of the original problem and improve the overall detection performance. The proposed method is shown to generalize across two well-known datasets, CICIDS 2018 and NDSec-1. Performance of XGBoost, which represents conventional ML, is evaluated. Combining host and network features enhances attack detection performance (macro average F1 score) by 8.1% under the CICIDS 2018 dataset and 3.7% under the NDSec-1 dataset. Meanwhile, the two-stage collaborative classifier improves detection performance for most single classes, especially for DoS-LOIC-UDP and DoS-SlowHTTPTest, with improvements of 30.7% and 84.3%, respectively, when compared with the traditional ML XGBoost. | ['Petar Djukic', 'Mehran Bagheri', 'Burak Kantarci', 'Murat Simsek', 'Zhiyan Chen'] | 2023-06-15 | null | null | null | null | ['intrusion-detection', 'network-intrusion-detection'] | ['miscellaneous', 'miscellaneous'] | [ 4.47718315e-02 -6.42807662e-01 -4.72358346e-01 -1.81916475e-01
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c12476df-d398-498e-9ab9-e5c30814fff3 | pixel-level-explanation-of-multiple-instance | 2303.08632 | null | https://arxiv.org/abs/2303.08632v1 | https://arxiv.org/pdf/2303.08632v1.pdf | Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images | Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagation (LRP), Information Bottleneck Attribution (IBA), and InputIBA. With this collection of methods, we can derive pixel-level explanations on for the task of diagnosing blood cancer from patients' blood smears. We study two datasets of acute myeloid leukemia with over 100 000 single cell images and observe how each attribution method performs on the multiple instance learning architecture focusing on different properties of the white blood single cells. Additionally, we compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard. Our study addresses the challenge of implementing pixel-level explainability in multiple instance learning models and provides insights for clinicians to better understand and trust decisions from computer-aided diagnosis systems. | ['Carsten Marr', 'Nassir Navab', 'Daniel Rueckert', 'Rudolf Matthias Hehr', 'Peter Lienemann', 'Ashkan Khakzar', 'Oleksandra Adonkina', 'Ario Sadafi'] | 2023-03-15 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 6.45010173e-01 1.05877411e+00 -3.57822776e-01 -6.93162024e-01
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9e75a4fb-7d36-41e1-841c-79f3cd4bd037 | training-data-generating-networks-linking-3d-1 | 2010.08276 | null | https://arxiv.org/abs/2010.08276v2 | https://arxiv.org/pdf/2010.08276v2.pdf | Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization | We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning algorithm to define the shape. The nested optimization problem can be modeled by bi-level optimization. Specifically, the algorithms for bi-level optimization are also being used in meta learning approaches for few-shot learning. Our framework establishes a link between 3D shape analysis and few-shot learning. We combine training data generating networks with bi-level optimization algorithms to obtain a complete framework for which all components can be jointly trained. We improve upon recent work on standard benchmarks for 3d shape reconstruction. | ['Peter Wonka', 'Biao Zhang'] | 2020-10-16 | training-data-generating-networks-shape | https://openreview.net/forum?id=dDo8druYppX | https://openreview.net/pdf?id=dDo8druYppX | iclr-2022-4 | ['3d-shape-representation'] | ['computer-vision'] | [ 2.95448065e-01 1.04845673e-01 -2.53085732e-01 -4.45494592e-01
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27ca032c-bb09-41d6-ab9f-8cb37b430f13 | minimum-delay-moving-object-detection | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Lao_Minimum_Delay_Moving_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lao_Minimum_Delay_Moving_CVPR_2017_paper.pdf | Minimum Delay Moving Object Detection | We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
| ['Dong Lao', 'Ganesh Sundaramoorthi'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['moving-object-detection'] | ['computer-vision'] | [ 2.09070116e-01 -2.35454053e-01 -3.29701751e-01 -1.07417852e-02
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62a9f1bf-1078-478a-884e-be9d64793990 | integrating-machine-learning-concepts-into | 2211.06491 | null | https://arxiv.org/abs/2211.06491v1 | https://arxiv.org/pdf/2211.06491v1.pdf | Integrating machine learning concepts into undergraduate classes | In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level elective in several curricula, this does not mean all students are exposed to it. Exposure to the concepts and practical applications of machine learning will assist in the creation of a workforce ready to tackle problems related to machine learning, currently a hot topic in industry. Preliminary assessments indicate that this approach promotes student learning. While students prefer the proposed side-by-side teaching approach, numerical comparisons show that the workshop approach may be more effective for student learning, indicating that further work in this area is required. | ['Mahesh K. Banavar', 'Blaine Ayotte', 'Chinmay Sahu'] | 2022-11-09 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 3.53670985e-01 4.30489838e-01 -1.12370312e-01 -4.95805442e-01
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d813f2bc-b709-4a05-8bb9-854b787a7b78 | invariant-grounding-for-video-question-1 | 2206.02349 | null | https://arxiv.org/abs/2206.02349v1 | https://arxiv.org/pdf/2206.02349v1.pdf | Invariant Grounding for Video Question Answering | Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches on superficial correlations between video-question pairs and answers as the alignments. However, ERM can be problematic, because it tends to over-exploit the spurious correlations between question-irrelevant scenes and answers, instead of inspecting the causal effect of question-critical scenes. As a result, the VideoQA models suffer from unreliable reasoning. In this work, we first take a causal look at VideoQA and argue that invariant grounding is the key to ruling out the spurious correlations. Towards this end, we propose a new learning framework, Invariant Grounding for VideoQA (IGV), to ground the question-critical scene, whose causal relations with answers are invariant across different interventions on the complement. With IGV, the VideoQA models are forced to shield the answering process from the negative influence of spurious correlations, which significantly improves the reasoning ability. Experiments on three benchmark datasets validate the superiority of IGV in terms of accuracy, visual explainability, and generalization ability over the leading baselines. | ['Tat-Seng Chua', 'Wei Ji', 'Junbin Xiao', 'Xiang Wang', 'Yicong Li'] | 2022-06-06 | invariant-grounding-for-video-question | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.06771022e-01 3.01605463e-01 3.56039032e-02 -3.14990491e-01
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939e37be-9859-4fcf-9fc2-16824d95bfe5 | wavelet-based-reflection-symmetry-detection | 1707.02931 | null | http://arxiv.org/abs/1707.02931v4 | http://arxiv.org/pdf/1707.02931v4.pdf | Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms | Symmetry is one of the significant visual properties inside an image plane,
to identify the geometrically balanced structures through real-world objects.
Existing symmetry detection methods rely on descriptors of the local image
features and their neighborhood behavior, resulting incomplete symmetrical axis
candidates to discover the mirror similarities on a global scale. In this
paper, we propose a new reflection symmetry detection scheme, based on a
reliable edge-based feature extraction using Log-Gabor filters, plus an
efficient voting scheme parameterized by their corresponding textural and color
neighborhood information. Experimental evaluation on four single-case and three
multiple-case symmetry detection datasets validates the superior achievement of
the proposed work to find global symmetries inside an image. | ['Philippe Colantoni', 'Olivier Alata', 'Cecile Barat', 'Christophe Ducottet', 'Mohamed Elawady'] | 2017-07-10 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 8.87018219e-02 -4.24714386e-01 -3.56938422e-01 -3.16407502e-01
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aae62a96-456d-45cc-9d4c-c35425843f6a | vertex-centric-visual-programming-for-graph | null | null | https://dl.acm.org/doi/10.1145/3448016.3452770 | https://dl.acm.org/doi/pdf/10.1145/3448016.3452770 | Vertex-Centric Visual Programming for Graph Neural Networks | Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering. Existing GNN systems (e.g., PyG and DGL) adopt a tensor-centric programming model and train GNNs with manually written operators. Such design results in poor usability due to the large semantic gap between the API and the GNN models, and suffers from inferior efficiency because of high memory consumption and massive data movement. We demonstrateSeastar, a novel GNN training framework that adopts avertex-centric programming paradigm and supportsautomatic kernel generation, to simplify model development and improve training efficiency. We will (i) show how to express GNN models succinctly using a visual "drag-and-drop'' interface or Seastar's vertex-centric python API; (ii) demonstrate the performance advantage of Seastar over existing GNN systems in convergence speed, training throughput and memory consumption; and (iii) illustrate how Seastar's optimizations (e.g., operator fusion and constant folding) improve training efficiency by profiling the run-time performance. | ['Fan Yu', 'Bo Tang', 'Yufei Cai', 'Peiqi Yin', 'Xiao Yan', 'James Cheng', 'Tatiana Jin', 'Yuntao Gui', 'Yidi Wu'] | 2021-06-09 | null | null | null | proceedings-of-the-2021-international | ['graph-clustering'] | ['graphs'] | [-4.23654258e-01 6.43033534e-02 -2.45683342e-01 -2.44796336e-01
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2e6378cf-ec85-4e90-9224-c8dcac45287b | evaluating-covid-19-sequence-data-using | 2211.10546 | null | https://arxiv.org/abs/2211.10546v2 | https://arxiv.org/pdf/2211.10546v2.pdf | Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model | The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-known that the major SARS-CoV-2 lineages are characterized by mutations that happen predominantly in the spike protein. Understanding the spike protein structure and how it can be perturbed is vital for understanding and determining if a lineage is of concern. These are crucial to identifying and controlling current outbreaks and preventing future pandemics. Machine learning (ML) methods are a viable solution to this effort, given the volume of available sequencing data, much of which is unaligned or even unassembled. However, such ML methods require fixed-length numerical feature vectors in Euclidean space to be applicable. Similarly, euclidean space is not considered the best choice when working with the classification and clustering tasks for biological sequences. For this purpose, we design a method that converts the protein (spike) sequences into the sequence similarity network (SSN). We can then use SSN as an input for the classical algorithms from the graph mining domain for the typical tasks such as classification and clustering to understand the data. We show that the proposed alignment-free method is able to outperform the current SOTA method in terms of clustering results. Similarly, we are able to achieve higher classification accuracy using well-known Node2Vec-based embedding compared to other baseline embedding approaches. | ['Sarwan Ali'] | 2022-11-19 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 3.66216660e-01 -3.60090554e-01 -1.97628457e-02 -1.88949153e-01
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bf7b9c9a-c1fc-4c89-b231-df2c2fb7c518 | subsidiary-prototype-alignment-for-universal | 2210.15909 | null | https://arxiv.org/abs/2210.15909v1 | https://arxiv.org/pdf/2210.15909v1.pdf | Subsidiary Prototype Alignment for Universal Domain Adaptation | Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in UniDA is negative transfer, i.e. misalignment of "known" and "unknown" classes. To this end, we first uncover an intriguing tradeoff between negative-transfer-risk and domain-invariance exhibited at different layers of a deep network. It turns out we can strike a balance between these two metrics at a mid-level layer. Towards designing an effective framework based on this insight, we draw motivation from Bag-of-visual-Words (BoW). Word-prototypes in a BoW-like representation of a mid-level layer would represent lower-level visual primitives that are likely to be unaffected by the category-shift in the high-level features. We develop modifications that encourage learning of word-prototypes followed by word-histogram based classification. Following this, subsidiary prototype-space alignment (SPA) can be seen as a closed-set alignment problem, thereby avoiding negative transfer. We realize this with a novel word-histogram-related pretext task to enable closed-set SPA, operating in conjunction with goal task UniDA. We demonstrate the efficacy of our approach on top of existing UniDA techniques, yielding state-of-the-art performance across three standard UniDA and Open-Set DA object recognition benchmarks. | ['R. Venkatesh Babu', 'Varun Jampani', 'Hiran Sarkar', 'Akshay Kulkarni', 'Suvaansh Bhambri', 'Jogendra Nath Kundu'] | 2022-10-28 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 3.96335512e-01 6.16685003e-02 -1.18102930e-01 -4.09668416e-01
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2c9fe0b2-d78c-40c9-be80-ba50ddec3913 | edin-an-end-to-end-benchmark-and-pipeline-for | 2205.12570 | null | https://arxiv.org/abs/2205.12570v1 | https://arxiv.org/pdf/2205.12570v1.pdf | EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing | Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and because novel concepts arise constantly. This paper created the Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities, that is entities without a description in the knowledge base and labeled mentions, have to be integrated into an existing entity linking system. By contrasting EDIN with zero-shot entity linking, we provide insight on the additional challenges it poses. Building on dense-retrieval based entity linking, we introduce the end-to-end EDIN pipeline that detects, clusters, and indexes mentions of unknown entities in context. Experiments show that indexing a single embedding per entity unifying the information of multiple mentions works better than indexing mentions independently. | ['Nicola Cancedda', 'Sebastian Riedel', 'Mikhail Plekhanov', 'Fabio Petroni', 'Nora Kassner'] | 2022-05-25 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-5.04108012e-01 5.13313055e-01 -4.00235742e-01 -1.07420571e-01
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d0be8972-7abb-45be-8167-04bf7413f429 | 190910145 | 1909.10145 | null | https://arxiv.org/abs/1909.10145v1 | https://arxiv.org/pdf/1909.10145v1.pdf | PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs | Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accurately while alleviating the need for supervised learning to a great degree. While effective for relatively short-term time integration, when long time integration of the time-dependent PDEs is sought, the time-space domain may become arbitrarily large and hence training of the neural network may become prohibitively expensive. To this end, we develop a parareal physics-informed neural network (PPINN), hence decomposing a long-time problem into many independent short-time problems supervised by an inexpensive/fast coarse-grained (CG) solver. In particular, the serial CG solver is designed to provide approximate predictions of the solution at discrete times, while initiate many fine PINNs simultaneously to correct the solution iteratively. There is a two-fold benefit from training PINNs with small-data sets rather than working on a large-data set directly, i.e., training of individual PINNs with small-data is much faster, while training the fine PINNs can be readily parallelized. Consequently, compared to the original PINN approach, the proposed PPINN approach may achieve a significant speedup for long-time integration of PDEs, assuming that the CG solver is fast and can provide reasonable predictions of the solution, hence aiding the PPINN solution to converge in just a few iterations. To investigate the PPINN performance on solving time-dependent PDEs, we first apply the PPINN to solve the Burgers equation, and subsequently we apply the PPINN to solve a two-dimensional nonlinear diffusion-reaction equation. Our results demonstrate that PPINNs converge in a couple of iterations with significant speed-ups proportional to the number of time-subdomains employed. | ['George Em. Karniadakis', 'Xuhui Meng', 'Zhen Li', 'Dongkun Zhang'] | 2019-09-23 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.10907622e-01 -1.80924982e-02 2.89942861e-01 -1.94210699e-03
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cde4a356-4d85-4cfb-a184-7bb761d4f7d6 | prediction-of-drug-effectiveness-in | 2210.08016 | null | https://arxiv.org/abs/2210.08016v3 | https://arxiv.org/pdf/2210.08016v3.pdf | Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms | Rheumatoid arthritis (RA) is an autoimmune condition caused when patients' immune system mistakenly targets their own tissue. Machine learning (ML) has the potential to identify patterns in patient electronic health records (EHR) to forecast the best clinical treatment to improve patient outcomes. This study introduced a Drug Response Prediction (DRP) framework with two main goals: 1) design a data processing pipeline to extract information from tabular clinical data, and then preprocess it for functional use, and 2) predict RA patient's responses to drugs and evaluate classification models' performance. We propose a novel two-stage ML framework based on European Alliance of Associations for Rheumatology (EULAR) criteria cutoffs to model drug effectiveness. Our model Stacked-Ensemble DRP was developed and cross-validated using data from 425 RA patients. The evaluation used a subset of 124 patients (30%) from the same data source. In the evaluation of the test set, two-stage DRP leads to improved classification accuracy over other end-to-end classification models for binary classification. Our proposed method provides a complete pipeline to predict disease activity scores and identify the group that does not respond well to anti-TNF treatments, thus showing promise in supporting clinical decisions based on EHR information. | ['Jacopo Cirrone', 'Valay Shah', 'Woodward B. Galbraith', 'Nikunj Gupta', 'Shengjia Chen'] | 2022-10-14 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.22688341e-01 -3.22004765e-01 -5.72259724e-01 -3.85003865e-01
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7a2ebab0-9589-4b2d-931e-b616693c6bcb | graphcfc-a-directed-graph-based-cross-modal | 2207.12261 | null | https://arxiv.org/abs/2207.12261v3 | https://arxiv.org/pdf/2207.12261v3.pdf | GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition | Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches. | ['Zhigang Zeng', 'Guoqing Lv', 'XiaoPing Wang', 'Jiang Li'] | 2022-07-06 | null | null | null | null | ['multimodal-emotion-recognition', 'emotion-classification', 'emotion-recognition-in-conversation', 'emotion-classification', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.62091845e-01 -1.07624449e-01 -9.35044438e-02 -3.34275156e-01
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7fed3843-6d32-4377-b9fe-cda740cc4226 | link-prediction-with-attention-applied-on | 2302.06229 | null | https://arxiv.org/abs/2302.06229v1 | https://arxiv.org/pdf/2302.06229v1.pdf | Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models | Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincar\'e ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches. | ['Steffen Staab', 'Daniel Hernández', 'Mojtaba Nayyeri', 'Cosimo Gregucci'] | 2023-02-13 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [-2.67062455e-01 4.16195571e-01 -7.05778182e-01 -9.14678350e-02
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cbb5f7a5-1257-443d-8f6c-d4e8c00d0890 | confidence-measure-guided-single-image-de | 1909.04207 | null | https://arxiv.org/abs/1909.04207v1 | https://arxiv.org/pdf/1909.04207v1.pdf | Confidence Measure Guided Single Image De-raining | Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density. This varying characteristic of rain streaks affect different parts of the image differently. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Image Quality-based single image Deraining using Confidence measure (QuDeC), network addresses this issue by learning the quality or distortion level of each patch in the rainy image, and further processes this information to learn the rain content at different scales. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate of both quality at each location and residual rain streak information (residual map). Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. | ['Rajeev Yasarla', 'Vishal M. Patel'] | 2019-09-10 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.17013901e-01 -5.56479812e-01 4.14019406e-01 -7.02433288e-01
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2cb5ea9a-7012-4465-9145-d9b461ebe606 | every-pixel-counts-unsupervised-geometry | 1806.10556 | null | http://arxiv.org/abs/1806.10556v2 | http://arxiv.org/pdf/1806.10556v2.pdf | Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding | Learning to estimate 3D geometry in a single image by watching unlabeled
videos via deep convolutional network has made significant process recently.
Current state-of-the-art (SOTA) methods, are based on the learning framework of
rigid structure-from-motion, where only 3D camera ego motion is modeled for
geometry estimation.However, moving objects also exist in many videos, e.g.
moving cars in a street scene. In this paper, we tackle such motion by
additionally incorporating per-pixel 3D object motion into the learning
framework, which provides holistic 3D scene flow understanding and helps single
image geometry estimation. Specifically, given two consecutive frames from a
video, we adopt a motion network to predict their relative 3D camera pose and a
segmentation mask distinguishing moving objects and rigid background. An
optical flow network is used to estimate dense 2D per-pixel correspondence. A
single image depth network predicts depth maps for both images. The four types
of information, i.e. 2D flow, camera pose, segment mask and depth maps, are
integrated into a differentiable holistic 3D motion parser (HMP), where
per-pixel 3D motion for rigid background and moving objects are recovered. We
design various losses w.r.t. the two types of 3D motions for training the depth
and motion networks, yielding further error reduction for estimated geometry.
Finally, in order to solve the 3D motion confusion from monocular videos, we
combine stereo images into joint training. Experiments on KITTI 2015 dataset
show that our estimated geometry, 3D motion and moving object masks, not only
are constrained to be consistent, but also significantly outperforms other SOTA
algorithms, demonstrating the benefits of our approach. | ['Wei Xu', 'Peng Wang', 'Yang Wang', 'Ram Nevatia', 'Zhenheng Yang'] | 2018-06-27 | null | null | null | null | ['depth-and-camera-motion', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.51523384e-02 -1.07451133e-01 -1.67471930e-01 -3.73332739e-01
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a60f6f10-c261-4744-9911-607fcc15fde2 | direct-comparative-analysis-of-nature | 2212.10797 | null | https://arxiv.org/abs/2212.10797v1 | https://arxiv.org/pdf/2212.10797v1.pdf | Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks | Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the communities. To apply NIOAs, either of the two, or both objectives are explored. Since NIOAs mostly exploit randomness in their strategies, it is necessary to analyze their performance for specific applications. In this paper, NIOAs are analyzed on the community detection problem. A direct comparison approach is followed to perform pairwise comparison of NIOAs. The performance is measured in terms of five scores designed based on prasatul matrix and also with average isolability. Three widely used real-world social networks and four NIOAs are considered for analyzing the quality of communities generated by NIOAs. | ['Anupam Biswas', 'Alberto Tonda', 'Bijita Singha', 'Soumita Das'] | 2022-12-21 | null | null | null | null | ['community-detection'] | ['graphs'] | [-1.25947120e-02 -1.20463341e-01 5.93751967e-02 1.95063099e-01
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2382d8dd-5ace-44e0-b29c-7ba5b9d91e58 | a-domain-knowledge-enhanced-pre-trained | null | null | https://aclanthology.org/2022.coling-1.85 | https://aclanthology.org/2022.coling-1.85.pdf | A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products | We present a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search. Following ELECTRA’s replaced token detection (RTD) pre-training, we leverage biomedical entity masking (EM) strategy to learn better contextual word representations. Furthermore, we propose a novel pre-training task, product attribute prediction (PAP), to inject product knowledge into the pre-trained language model efficiently by leveraging medicinal product databases directly. By sharing the parameters of PAP’s transformer encoder with that of RTD’s main transformer, these two pre-training tasks are jointly learned. Experiments demonstrate the effectiveness of PAP task for pre-trained language model on medicinal product vertical search scenario, which includes query-title relevance, query intent classification, and named entity recognition in query. | ['Feifei Lyu', 'Jianhui Jiang', 'Kesong Liu'] | null | null | null | null | coling-2022-10 | ['intent-classification'] | ['natural-language-processing'] | [ 5.86393595e-01 2.14216337e-01 -9.87621725e-01 -1.84257746e-01
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3e5105a0-741e-4df9-9a1d-4124a38b60c3 | physiological-parameter-monitoring-from | null | null | https://ieeexplore.ieee.org/document/5963704 | https://ieeexplore.ieee.org/document/5963704/ | Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone | We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.
Index Terms: non-invasive monitoring, telemonitoring, heart rate variability (HRV), breathing rate, oxygen saturation | ['Senior Member', 'and Ki H. Chon', 'Yitzhak Mendelson', 'Member', 'Domhnull Granquist-Fraser', 'Alexander M. Gorbach', 'Joseph Meyer', 'Jinseok Lee', 'IEEE', 'Student Member', 'Christopher G. Scully'] | 2011-07-29 | null | null | null | null | ['spo2-estimation', 'heart-rate-variability', 'heart-rate-estimation'] | ['medical', 'medical', 'medical'] | [ 7.34215200e-01 -4.37304109e-01 1.44822985e-01 -2.07612917e-01
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2f40743c-7ed9-4771-9a9a-7156b40d67ce | improving-punctuation-restoration-for-speech | 2110.00560 | null | https://arxiv.org/abs/2110.00560v1 | https://arxiv.org/pdf/2110.00560v1.pdf | Improving Punctuation Restoration for Speech Transcripts via External Data | Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream language models, it is necessary to add punctuation marks. In this paper, we tackle the punctuation restoration problem specifically for the noisy text (e.g., phone conversation scenarios). To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data. Moreover, we propose a two-stage fine-tuning approach that utilizes the sampled external data as well as our in-domain dataset for models based on BERT. Extensive experiments show that the proposed approach outperforms the baseline with an improvement of 1:12% F1 score. | ['Simon Corston-Oliver', 'Shashi Bhushan TN', 'Md Tahmid Rahman Laskar', 'Cheng Chen', 'Xue-Yong Fu'] | 2021-10-01 | null | https://aclanthology.org/2021.wnut-1.19 | https://aclanthology.org/2021.wnut-1.19.pdf | wnut-acl-2021-11 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 4.91169602e-01 1.79653496e-01 4.11221981e-02 -6.01402044e-01
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ee181499-4168-42a5-bb9a-86a4c4f5f1cb | syntax-guided-program-reduction-for | 2205.14374 | null | https://arxiv.org/abs/2205.14374v2 | https://arxiv.org/pdf/2205.14374v2.pdf | Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models | Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. Recently, input program reduction techniques have been proposed to identify key features in the input programs to improve the transparency of CI models. However, this approach is syntax-unaware and does not consider the grammar of the programming language. In this paper, we apply a syntax-guided program reduction technique that considers the grammar of the input programs during reduction. Our experiments on multiple models across different types of input programs show that the syntax-guided program reduction technique is faster and provides smaller sets of key tokens in reduced programs. We also show that the key tokens could be used in generating adversarial examples for up to 65% of the input programs. | ['Mohammad Amin Alipour', 'Aftab Hussain', 'Md Rafiqul Islam Rabin'] | 2022-05-28 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 3.34829658e-01 4.74892408e-01 -2.59628892e-01 -2.80055851e-01
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0af919c8-9eb1-4360-bd8f-dbb98850f226 | semantic-invariant-multi-view-clustering-with | 2305.12743 | null | https://arxiv.org/abs/2305.12743v1 | https://arxiv.org/pdf/2305.12743v1.pdf | Semantic Invariant Multi-view Clustering with Fully Incomplete Information | Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples. The resulting consensus semantics remains unaffected by cross-view distribution shifts, making them useful for realigning/imputing defective instances and forming clusters. We demonstrate the effectiveness of SMILE through extensive comparison experiments with 13 state-of-the-art baselines on five benchmarks. Our approach improves the clustering accuracy of NoisyMNIST from 19.3\%/23.2\% to 82.7\%/69.0\% when the correspondences/instances are fully incomplete. We will release the code after acceptance. | ['Xi Peng', 'Peng Hu', 'Changqing Zhang', 'Yiding Lu', 'Mouxing Yang', 'Pengxin Zeng'] | 2023-05-22 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.09067896e-01 -2.44259968e-01 -1.37822643e-01 -5.60701787e-01
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165bc4a9-26ed-4f80-bb30-7dc3d47bbf92 | uncertainty-based-offline-reinforcement | 2110.01548 | null | https://arxiv.org/abs/2110.01548v2 | https://arxiv.org/pdf/2110.01548v2.pdf | Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble | Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approximation errors from out-of-distribution (OOD) data points. To this end, offline RL algorithms adopt either a constraint or a penalty term that explicitly guides the policy to stay close to the given dataset. However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem. Moreover, these methods under-utilize the generalization ability of deep neural networks and often fall into suboptimal solutions too close to the given dataset. In this work, we propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution. We show that the clipped Q-learning, a technique widely used in online RL, can be leveraged to successfully penalize OOD data points with high prediction uncertainties. Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning. Based on this observation, we propose an ensemble-diversified actor-critic algorithm that reduces the number of required ensemble networks down to a tenth compared to the naive ensemble while achieving state-of-the-art performance on most of the D4RL benchmarks considered. | ['Hyun Oh Song', 'Jang-Hyun Kim', 'Seungyong Moon', 'Gaon An'] | 2021-10-04 | null | http://proceedings.neurips.cc/paper/2021/hash/3d3d286a8d153a4a58156d0e02d8570c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/3d3d286a8d153a4a58156d0e02d8570c-Paper.pdf | neurips-2021-12 | ['value-prediction', 'd4rl'] | ['computer-code', 'robots'] | [-1.89389527e-01 6.06556907e-02 -3.70090991e-01 -1.53044894e-01
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089934a1-5441-4525-bed0-2690cff1d4d3 | sparsity-agnostic-depth-completion | 2212.00790 | null | https://arxiv.org/abs/2212.00790v1 | https://arxiv.org/pdf/2212.00790v1.pdf | Sparsity Agnostic Depth Completion | We present a novel depth completion approach agnostic to the sparsity of depth points, that is very likely to vary in many practical applications. State-of-the-art approaches yield accurate results only when processing a specific density and distribution of input points, i.e. the one observed during training, narrowing their deployment in real use cases. On the contrary, our solution is robust to uneven distributions and extremely low densities never witnessed during training. Experimental results on standard indoor and outdoor benchmarks highlight the robustness of our framework, achieving accuracy comparable to state-of-the-art methods when tested with density and distribution equal to the training one while being much more accurate in the other cases. Our pretrained models and further material are available in our project page. | ['Stefano Mattoccia', 'Matteo Poggi', 'Andrea Conti'] | 2022-12-01 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 2.02172086e-01 1.07006215e-01 -1.05161250e-01 -7.83313587e-02
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801fc086-7c4f-4b63-a4fb-4769638c4afc | slot-vae-object-centric-scene-generation-with | 2306.06997 | null | https://arxiv.org/abs/2306.06997v1 | https://arxiv.org/pdf/2306.06997v1.pdf | Slot-VAE: Object-Centric Scene Generation with Slot Attention | Slot attention has shown remarkable object-centric representation learning performance in computer vision tasks without requiring any supervision. Despite its object-centric binding ability brought by compositional modelling, as a deterministic module, slot attention lacks the ability to generate novel scenes. In this paper, we propose the Slot-VAE, a generative model that integrates slot attention with the hierarchical VAE framework for object-centric structured scene generation. For each image, the model simultaneously infers a global scene representation to capture high-level scene structure and object-centric slot representations to embed individual object components. During generation, slot representations are generated from the global scene representation to ensure coherent scene structures. Our extensive evaluation of the scene generation ability indicates that Slot-VAE outperforms slot representation-based generative baselines in terms of sample quality and scene structure accuracy. | ['Justin Dauwels', 'Letao Liu', 'Yanbo Wang'] | 2023-06-12 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 6.20706022e-01 6.55019939e-01 3.83934565e-02 -4.44619596e-01
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4ee4656f-0718-4b8a-9349-a50abe647cd4 | leveraging-deep-learning-approaches-for | 2304.01908 | null | https://arxiv.org/abs/2304.01908v1 | https://arxiv.org/pdf/2304.01908v1.pdf | Leveraging Deep Learning Approaches for Deepfake Detection: A Review | Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various social media platforms, hence advertising it to the world got easy, calling for an efficacious countermeasure. Thus, one of the optimistic counter steps against deepfake would be deepfake detection. To undertake this threat, researchers in the past have created models to detect deepfakes based on ML/DL techniques like Convolutional Neural Networks. This paper aims to explore different methodologies with an intention to achieve a cost-effective model with a higher accuracy with different types of the datasets, which is to address the generalizability of the dataset. | ['Mounika Vanamala', 'Rushit Dave', 'Aniruddha Tiwari'] | 2023-04-04 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-1.44683838e-01 3.76662433e-01 -2.15741441e-01 7.40322247e-02
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6113c8da-7a1f-4bfa-a880-c65a6d9add34 | attention-based-models-for-text-dependent | 1710.10470 | null | http://arxiv.org/abs/1710.10470v3 | http://arxiv.org/pdf/1710.10470v3.pdf | Attention-Based Models for Text-Dependent Speaker Verification | Attention-based models have recently shown great performance on a range of
tasks, such as speech recognition, machine translation, and image captioning
due to their ability to summarize relevant information that expands through the
entire length of an input sequence. In this paper, we analyze the usage of
attention mechanisms to the problem of sequence summarization in our end-to-end
text-dependent speaker recognition system. We explore different topologies and
their variants of the attention layer, and compare different pooling methods on
the attention weights. Ultimately, we show that attention-based models can
improves the Equal Error Rate (EER) of our speaker verification system by
relatively 14% compared to our non-attention LSTM baseline model. | ['Li Wan', 'Ignacio Lopez Moreno', 'F A Rezaur Rahman Chowdhury', 'Quan Wang'] | 2017-10-28 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 6.08055115e-01 1.25264153e-01 -1.22184053e-01 -4.70051944e-01
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cdd16f8a-7dc8-4695-ba46-a75cbf0581f2 | adaptive-low-rank-and-sparse-decomposition-of | 1302.1610 | null | http://arxiv.org/abs/1302.1610v2 | http://arxiv.org/pdf/1302.1610v2.pdf | Adaptive low rank and sparse decomposition of video using compressive sensing | We address the problem of reconstructing and analyzing surveillance videos
using compressive sensing. We develop a new method that performs video
reconstruction by low rank and sparse decomposition adaptively. Background
subtraction becomes part of the reconstruction. In our method, a background
model is used in which the background is learned adaptively as the compressive
measurements are processed. The adaptive method has low latency, and is more
robust than previous methods. We will present experimental results to
demonstrate the advantages of the proposed method. | ['Wei Deng', 'Fei Yang', 'Dimitris Metaxas', 'Zuowei Shen', 'Hong Jiang'] | 2013-02-06 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 7.83128440e-01 -7.22043633e-01 -1.01020552e-01 -1.20135218e-01
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4796a0b4-af4d-4680-88fa-df7670933101 | deep-successor-reinforcement-learning | 1606.02396 | null | http://arxiv.org/abs/1606.02396v1 | http://arxiv.org/pdf/1606.02396v1.pdf | Deep Successor Reinforcement Learning | Learning robust value functions given raw observations and rewards is now
possible with model-free and model-based deep reinforcement learning
algorithms. There is a third alternative, called Successor Representations
(SR), which decomposes the value function into two components -- a reward
predictor and a successor map. The successor map represents the expected future
state occupancy from any given state and the reward predictor maps states to
scalar rewards. The value function of a state can be computed as the inner
product between the successor map and the reward weights. In this paper, we
present DSR, which generalizes SR within an end-to-end deep reinforcement
learning framework. DSR has several appealing properties including: increased
sensitivity to distal reward changes due to factorization of reward and world
dynamics, and the ability to extract bottleneck states (subgoals) given
successor maps trained under a random policy. We show the efficacy of our
approach on two diverse environments given raw pixel observations -- simple
grid-world domains (MazeBase) and the Doom game engine. | ['Simanta Gautam', 'Samuel J. Gershman', 'Ardavan Saeedi', 'Tejas D. Kulkarni'] | 2016-06-08 | null | null | null | null | ['game-of-doom', 'fps-games'] | ['playing-games', 'playing-games'] | [ 7.61744156e-02 1.92952439e-01 -3.31275731e-01 -3.02193701e-01
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582473fe-9922-4735-82f9-e449da7f740b | indices-matter-learning-to-index-for-deep | 1908.00672 | null | https://arxiv.org/abs/1908.00672v1 | https://arxiv.org/pdf/1908.00672v1.pdf | Indices Matter: Learning to Index for Deep Image Matting | We show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can recover boundary details much better than other upsampling operators such as bilinear interpolation. By looking at the indices as a function of the feature map, we introduce the concept of learning to index, and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the pooling and upsampling operators, without the need of supervision. At the core of this framework is a flexible network module, termed IndexNet, which dynamically predicts indices given an input. Due to its flexibility, IndexNet can be used as a plug-in applying to any off-the-shelf convolutional networks that have coupled downsampling and upsampling stages. We demonstrate the effectiveness of IndexNet on the task of natural image matting where the quality of learned indices can be visually observed from predicted alpha mattes. Results on the Composition-1k matting dataset show that our model built on MobileNetv2 exhibits at least $16.1\%$ improvement over the seminal VGG-16 based deep matting baseline, with less training data and lower model capacity. Code and models has been made available at: https://tinyurl.com/IndexNetV1 | ['Hao Lu', 'Chunhua Shen', 'Songcen Xu', 'Yutong Dai'] | 2019-08-02 | indices-matter-learning-to-index-for-deep-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lu_Indices_Matter_Learning_to_Index_for_Deep_Image_Matting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lu_Indices_Matter_Learning_to_Index_for_Deep_Image_Matting_ICCV_2019_paper.pdf | iccv-2019-10 | ['semantic-image-matting'] | ['computer-vision'] | [ 3.85259420e-01 5.66301823e-01 -1.39182448e-01 -3.69997591e-01
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eb7e30dd-380e-40bd-ad0c-f78f0a2d5043 | minihack-the-planet-a-sandbox-for-open-ended | 2109.13202 | null | https://arxiv.org/abs/2109.13202v2 | https://arxiv.org/pdf/2109.13202v2.pdf | MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research | Progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted by the community are not explicitly designed for evaluating specific capabilities of RL methods. While there exist environments for assessing particular open problems in RL (such as exploration, transfer learning, unsupervised environment design, or even language-assisted RL), it is generally difficult to extend these to richer, more complex environments once research goes beyond proof-of-concept results. We present MiniHack, a powerful sandbox framework for easily designing novel RL environments. MiniHack is a one-stop shop for RL experiments with environments ranging from small rooms to complex, procedurally generated worlds. By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use. With this sandbox framework, novel environments can be designed easily, either using a human-readable description language or a simple Python interface. In addition to a variety of RL tasks and baselines, MiniHack can wrap existing RL benchmarks and provide ways to seamlessly add additional complexity. | ['Tim Rocktäschel', 'Edward Grefenstette', 'Heinrich Küttler', 'Fabio Petroni', 'Eric Hambro', 'Minqi Jiang', 'Jack Parker-Holder', 'Vitaly Kurin', 'Robert Kirk', 'Mikayel Samvelyan'] | 2021-09-27 | null | null | null | null | ['nethack'] | ['playing-games'] | [-6.07218385e-01 -1.04718663e-01 8.77742190e-03 -1.27395794e-01
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20812b7c-5546-4e6f-8fb7-11b6f0928f97 | action-anticipation-for-collaborative | 1910.00714 | null | https://arxiv.org/abs/1910.00714v2 | https://arxiv.org/pdf/1910.00714v2.pdf | Action Anticipation for Collaborative Environments: The Impact of Contextual Information and Uncertainty-Based Prediction | To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the observation of the human limb movements may not be sufficient to anticipate their actions unambiguously. In this work, we consider two additional sources of information (i.e., context) over time, gaze, movement and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e., deterministic) criterion. The main contributions of this paper are four-fold: (i) We propose a novel and effective decision-making criterion that can be used to anticipate actions even in situations of high ambiguity; (ii) we propose a deep architecture that outperforms previous results in the action anticipation task when using the Acticipate collaborative dataset; (iii) we show that contextual information is important to disambiguate the interpretation of similar actions; and (iv) we also provide a formal description of three existing performance metrics that can be easily used to evaluate action anticipation models.Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations. | ['José Santos-Victor', 'Raquel Vassallo', 'Plinio Moreno', 'Clebeson Canuto', 'Jorge Samatelo'] | 2019-10-01 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 6.30813420e-01 1.61390543e-01 -6.50841966e-02 -5.17483652e-01
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5037b394-d97e-4a14-b993-310dd9a0e5a5 | fine-tuning-convolutional-neural-networks-for-1 | null | null | https://www.sciencedirect.com/science/article/pii/S0957417418304421?casa_token=SuXq40lwuksAAAAA:Q73-Pe0oYbRuzuyqljxps1qkZScWER4-FTKgukOhLZ1hKYWPogAOYsFoNIihz9hw7PgKtJEHaA | https://www.sciencedirect.com/science/article/abs/pii/S0957417418304421 | Fine-tuning Convolutional Neural Networks for fine art classification | The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections. | ['Tomislav Lipic', 'Sonja Grgic', 'Eva Cetinic'] | 2018-12-30 | null | null | null | expert-systems-with-applications-2018-12 | ['scene-recognition', 'artistic-style-classification', 'genre-classification', 'artist-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.40085471e-01 -3.94000769e-01 -8.27304199e-02 -3.09466362e-01
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8f9592e0-00ed-48fe-a468-166e254002af | intelligent-diagnostic-scheme-for-lung-cancer | 2303.06340 | null | https://arxiv.org/abs/2303.06340v1 | https://arxiv.org/pdf/2303.06340v1.pdf | Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning | Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability. | ['Cong Wang', 'Shi-Ju Ran', 'Gang Su', 'Xiao-Dong Han', 'Cheng-en Wang', 'Xiao-Guang Li', 'Lin Cheng', 'Sheng-Chen Bai', 'Yu-Jia An'] | 2023-03-11 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.53468984e-01 3.92851293e-01 -1.80551380e-01 -2.79380977e-01
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8b7e2d81-cd92-4ebd-809f-2dadfef940a3 | music-transcription-based-on-bayesian-piece | 1908.06969 | null | https://arxiv.org/abs/1908.06969v2 | https://arxiv.org/pdf/1908.06969v2.pdf | Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions | Most work on musical score models (a.k.a. musical language models) for music transcription has focused on describing the local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music. Focusing on rhythm, we formulate several classes of Bayesian Markov models of musical scores that describe repetitions indirectly using the sparse transition probabilities of notes or note patterns. This enables us to construct piece-specific models for unseen scores with an unfixed repetitive structure and to derive tractable inference algorithms. Moreover, to describe approximate repetitions, we explicitly incorporate a process for modifying the repeated notes/note patterns. We apply these models as prior musical score models for rhythm transcription, where piece-specific score models are inferred from performed MIDI data by Bayesian learning, in contrast to the conventional supervised construction of score models. Evaluations using the vocal melodies of popular music showed that the Bayesian models improved the transcription accuracy for most of the tested model types, indicating the universal efficacy of the proposed approach. Moreover, we found an effective data representation for modelling rhythms that maximizes the transcription accuracy and computational efficiency. | ['Eita Nakamura', 'Kazuyoshi Yoshii'] | 2019-08-18 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.27807724e-01 -3.08901034e-02 -7.45341405e-02 -1.13372002e-02
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99fe3c6a-8d3a-4cb4-bcbe-882ea6640bde | contrastive-semi-supervised-learning-for-1 | 2303.09101 | null | https://arxiv.org/abs/2303.09101v4 | https://arxiv.org/pdf/2303.09101v4.pdf | Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank | Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network training. However, the naive mean-teacher method suffers from two main problems: (1) The consistency loss used in training might become ineffective when the teacher's prediction is wrong. (2) Using L1 distance may cause the network to overfit wrong labels, resulting in confirmation bias. To address the above problems, we first introduce a reliable bank to store the "best-ever" outputs as pseudo ground truth. To assess the quality of outputs, we conduct an empirical analysis based on the monotonicity property to select the most trustworthy NR-IQA method. Besides, in view of the confirmation bias problem, we incorporate contrastive regularization to prevent the overfitting on wrong labels. Experimental results on both full-reference and non-reference underwater benchmarks demonstrate that our algorithm has obvious improvement over SOTA methods quantitatively and qualitatively. Code has been released at https://github.com/Huang-ShiRui/Semi-UIR. | ['Yunsong Li', 'Jun Chen', 'Huan Liu', 'Keyan Wang', 'Shirui Huang'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.pdf | cvpr-2023-1 | ['underwater-image-restoration'] | ['computer-vision'] | [ 1.72065109e-01 1.34036809e-01 1.64129511e-01 -6.29319310e-01
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98702eb8-b067-4a3a-8be0-29872832f3f4 | a-novel-improved-fuzzy-support-vector-machine | 1801.00681 | null | http://arxiv.org/abs/1801.00681v1 | http://arxiv.org/pdf/1801.00681v1.pdf | A novel improved fuzzy support vector machine based stock price trend forecast model | Application of fuzzy support vector machine in stock price forecast. Support
vector machine is a new type of machine learning method proposed in 1990s. It
can deal with classification and regression problems very successfully. Due to
the excellent learning performance of support vector machine, the technology
has become a hot research topic in the field of machine learning, and it has
been successfully applied in many fields. However, as a new technology, there
are many limitations to support vector machines. There is a large amount of
fuzzy information in the objective world. If the training of support vector
machine contains noise and fuzzy information, the performance of the support
vector machine will become very weak and powerless. As the complexity of many
factors influence the stock price prediction, the prediction results of
traditional support vector machine cannot meet people with precision, this
study improved the traditional support vector machine fuzzy prediction
algorithm is proposed to improve the new model precision. NASDAQ Stock Market,
Standard & Poor's (S&P) Stock market are considered. Novel advanced- fuzzy
support vector machine (NA-FSVM) is the proposed methodology. | ['Guohao Li', 'Yifan Bao', 'Shuheng Wang'] | 2018-01-02 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.25885904e-01 -6.82087481e-01 -2.79903203e-01 -2.16047093e-01
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7cf7d5dd-ba20-4d3f-9172-24e507724268 | mix-em-unsupervised-image-classification | 2007.09502 | null | https://arxiv.org/abs/2007.09502v2 | https://arxiv.org/pdf/2007.09502v2.pdf | MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings | We present MIX'EM, a novel solution for unsupervised image classification. MIX'EM generates representations that by themselves are sufficient to drive a general-purpose clustering algorithm to deliver high-quality classification. This is accomplished by building a mixture of embeddings module into a contrastive visual representation learning framework in order to disentangle representations at the category level. It first generates a set of embedding and mixing coefficients from a given visual representation, and then combines them into a single embedding. We introduce three techniques to successfully train MIX'EM and avoid degenerate solutions; (i) diversify mixture components by maximizing entropy, (ii) minimize instance conditioned component entropy to enforce a clustered embedding space, and (iii) use an associative embedding loss to enforce semantic separability. By applying (i) and (ii), semantic categories emerge through the mixture coefficients, making it possible to apply (iii). Subsequently, we run K-means on the representations to acquire semantic classification. We conduct extensive experiments and analyses on STL10, CIFAR10, and CIFAR100-20 datasets, achieving state-of-the-art classification accuracy of 78\%, 82\%, and 44\%, respectively. To achieve robust and high accuracy, it is essential to use the mixture components to initialize K-means. Finally, we report competitive baselines (70\% on STL10) obtained by applying K-means to the "normalized" representations learned using the contrastive loss. | ['Tinne Tuytelaars', 'Ali Varamesh'] | 2020-07-18 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 2.13723511e-01 1.73990000e-02 -9.60038900e-02 -3.21139902e-01
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7d48d01d-2d5a-47f9-975b-0c3ef7894479 | crime-scene-classification-from-skeletal | 2207.01687 | null | https://arxiv.org/abs/2207.01687v1 | https://arxiv.org/pdf/2207.01687v1.pdf | Crime scene classification from skeletal trajectory analysis in surveillance settings | Video anomaly analysis is a core task actively pursued in the field of computer vision, with applications extending to real-world crime detection in surveillance footage. In this work, we address the task of human-related crime classification. In our proposed approach, the human body in video frames, represented as skeletal joints trajectories, is used as the main source of exploration. First, we introduce the significance of extending the ground truth labels for HR-Crime dataset and hence, propose a supervised and unsupervised methodology to generate trajectory-level ground truth labels. Next, given the availability of the trajectory-level ground truth, we introduce a trajectory-based crime classification framework. Ablation studies are conducted with various architectures and feature fusion strategies for the representation of the human trajectories. The conducted experiments demonstrate the feasibility of the task and pave the path for further research in the field. | ['Maya Aghaei', 'Estefania Talavera', 'Alina-Daniela Matei'] | 2022-07-04 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 4.69952345e-01 5.98073900e-02 -2.04170533e-02 -2.30510086e-01
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f4f99b4e-a5e3-417d-9b4f-480bb00b3cd4 | dynamic-refinement-network-for-oriented-and | 2005.09973 | null | https://arxiv.org/abs/2005.09973v2 | https://arxiv.org/pdf/2005.09973v2.pdf | Dynamic Refinement Network for Oriented and Densely Packed Object Detection | Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task. To resolve the first two issues, we present a dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH). Our FSM enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, whereas the DRH empowers our model to refine the prediction dynamically in an object-aware manner. To address the limited availability of related benchmarks, we collect an extensive and fully annotated dataset, namely, SKU110K-R, which is relabeled with oriented bounding boxes based on SKU110K. We perform quantitative evaluations on several publicly available benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset. Experimental results show that our method achieves consistent and substantial gains compared with baseline approaches. The code and dataset are available at https://github.com/Anymake/DRN_CVPR2020. | ['Wei-Ming Dong', 'Kekai Sheng', 'Xiaowei Guo', 'Yuqiang Ren', 'Haolei Yuan', 'Chongyang Ma', 'Changsheng Xu', 'Xingjia Pan'] | 2020-05-20 | dynamic-refinement-network-for-oriented-and-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 1.44745614e-02 -2.58188754e-01 -7.82150850e-02 -4.66370970e-01
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a7691f11-9715-411b-940e-4bd0c0075040 | disentangled-contrastive-collaborative | 2305.02759 | null | https://arxiv.org/abs/2305.02759v2 | https://arxiv.org/pdf/2305.02759v2.pdf | Disentangled Contrastive Collaborative Filtering | Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF. | ['Chao Huang', 'Dawei Yin', 'Jiashu Zhao', 'Lianghao Xia', 'Xubin Ren'] | 2023-05-04 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 3.62416863e-01 1.20846450e-01 -4.40017045e-01 -4.26059991e-01
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ae375951-555c-4f78-ab66-ed9a5112b1c7 | ss-cxr-multitask-representation-learning | 2211.12944 | null | https://arxiv.org/abs/2211.12944v2 | https://arxiv.org/pdf/2211.12944v2.pdf | SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model | Chest X-rays (CXRs) are a widely used imaging modality for the diagnosis and prognosis of lung disease. The image analysis tasks vary. Examples include pathology detection and lung segmentation. There is a large body of work where machine learning algorithms are developed for specific tasks. A significant recent example is Coronavirus disease (covid-19) detection using CXR data. However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes. Here, we propose an alternative solution, a new self-supervised paradigm, where a general representation from CXRs is learned using a group-masked self-supervised framework. The pre-trained model is then fine-tuned for domain-specific tasks such as covid-19, pneumonia detection, and general health screening. We show that the same pre-training can be used for the lung segmentation task. Our proposed paradigm shows robust performance in multiple downstream tasks which demonstrates the success of the pre-training. Moreover, the performance of the pre-trained models on data with significant drift during test time proves the learning of a better generic representation. The methods are further validated by covid-19 detection in a unique small-scale pediatric data set. The performance gain in accuracy (~25%) is significant when compared to a supervised transformer-based method. This adds credence to the strength and reliability of our proposed framework and pre-training strategy. | ['Marius George Linguraru', 'Josef Kitler', 'Gustavo Nino', 'Muhammad Awais', 'Sara Atito', 'Abhijeet Parida', 'Syed Muhammad Anwar'] | 2022-11-23 | null | null | null | null | ['covid-19-detection', 'pneumonia-detection'] | ['medical', 'medical'] | [ 4.88753259e-01 -3.93665433e-02 -3.28785181e-01 -3.81448418e-01
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8a565271-60da-4798-a230-d482d0ed964e | nested-named-entity-recognition-as-corpus | null | null | https://aclanthology.org/2022.coling-1.218 | https://aclanthology.org/2022.coling-1.218.pdf | Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing | As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previous studies on nested NER usually apply linear structure to model the nested NEs which are actually accommodated in a hierarchical structure. Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Besides, there is no research on applying corpus-level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpus-level. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art. Further empirical studies show that our proposed corpus-aware features can substantially improve NER domain adaptation, which demonstrates the surprising advantage of our proposed corpus-level holistic structure modeling. | ['Hai Zhao', 'Zuchao Li', 'Yifei Yang'] | null | null | null | null | coling-2022-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.74746043e-03 9.40935910e-02 -3.48754376e-01 -4.05753851e-01
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ba92756d-e3e5-4a6f-92c7-b687e172c54e | unsupervised-dependency-parsing-with | null | null | https://aclanthology.org/P14-1126 | https://aclanthology.org/P14-1126.pdf | Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization | null | ['Fei Xia', 'Xuezhe Ma'] | 2014-06-01 | null | null | null | acl-2014-6 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f26b36e4-f1d4-40ed-8b08-cb70cae28b86 | text2video-text-driven-talking-head-video | 2104.14631 | null | https://arxiv.org/abs/2104.14631v3 | https://arxiv.org/pdf/2104.14631v3.pdf | Text2Video: Text-driven Talking-head Video Synthesis with Personalized Phoneme-Pose Dictionary | With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach. | ['Liangjun Zhang', 'Miao Liao', 'Jiahong Yuan', 'Sibo Zhang'] | 2021-04-29 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 4.14786249e-01 -2.52869842e-03 2.36565605e-01 -2.86164105e-01
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45a74616-e120-4b96-9df6-3a003d694561 | enhanced-memory-network-the-novel-network | 2110.03392 | null | https://arxiv.org/abs/2110.03392v1 | https://arxiv.org/pdf/2110.03392v1.pdf | Enhanced Memory Network: The novel network structure for Symbolic Music Generation | Symbolic melodies generation is one of the essential tasks for automatic music generation. Recently, models based on neural networks have had a significant influence on generating symbolic melodies. However, the musical context structure is complicated to capture through deep neural networks. Although long short-term memory (LSTM) is attempted to solve this problem through learning order dependence in the musical sequence, it is not capable of capturing musical context with only one note as input for each time step of LSTM. In this paper, we propose a novel Enhanced Memory Network (EMN) with several recurrent units, named Enhanced Memory Unit (EMU), to explicitly modify the internal architecture of LSTM for containing music beat information and reinforces the memory of the latest musical beat through aggregating beat inside the memory gate. In addition, to increase the diversity of generated musical notes, cosine distance among adjacent time steps of hidden states is considered as part of loss functions to avoid a high similarity score that harms the diversity of generated notes. Objective and subjective evaluation results show that the proposed method achieves state-of-the-art performance. Code and music demo are available at https://github.com/qrqrqrqr/EMU | ['Lan Wang', 'Nan Yan', 'Haibin Liu', 'Jin Li'] | 2021-10-07 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.58234686e-01 -3.05975914e-01 3.60826738e-02 1.42708672e-02
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c0d15580-9862-446e-b005-751d9a71f617 | structured-siamese-network-for-real-time | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yunhua_Zhang_Structured_Siamese_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yunhua_Zhang_Structured_Siamese_Network_ECCV_2018_paper.pdf | Structured Siamese Network for Real-Time Visual Tracking | Local structure of target objects are essential for robust tracking. However, existing methods based on deep neural networks mostly describe the target appearance from the global view, leading to high sensitivity to non-rigid appearance change and partial occlusion. In this paper, we circumvent this issue by proposing a local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking. To this end, a local pattern detection module is designed to automatically identify discriminative regions of the target objects. The detection results are further refined by a message passing module, which enforces the structural context among local patterns to construct local structures. We show that the message passing module can be formulated as the inference process of a conditional random field (CRF) and implemented by differentiable operations, allowing the entire model to be trained in an end-to-end manner. By considering various combinations of the local structures, our tracker is able to form various types of structure patterns. Target tracking is finally achieved by a matching procedure of the structure patterns between target template and candidates. Extensive evaluations on three benchmark data sets demonstrate that the proposed tracking algorithm performs favorably against state-of-the-art methods while running at a highly efficient speed of 45 fps. | ['Mengyang Feng', 'Lijun Wang', 'Huchuan Lu', 'Dong Wang', 'Yunhua Zhang', 'Jinqing Qi'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['real-time-visual-tracking'] | ['computer-vision'] | [ 1.06826060e-01 -4.21294659e-01 -3.67803395e-01 -3.73196751e-01
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c0ff3504-dbed-4534-bae9-2485aa26d719 | neural-bandits-for-data-mining-searching-for | 2212.05190 | null | https://arxiv.org/abs/2212.05190v3 | https://arxiv.org/pdf/2212.05190v3.pdf | Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy | Polypharmacy, most often defined as the simultaneous consumption of five or more drugs at once, is a prevalent phenomenon in the older population. Some of these polypharmacies, deemed inappropriate, may be associated with adverse health outcomes such as death or hospitalization. Considering the combinatorial nature of the problem as well as the size of claims database and the cost to compute an exact association measure for a given drug combination, it is impossible to investigate every possible combination of drugs. Therefore, we propose to optimize the search for potentially inappropriate polypharmacies (PIPs). To this end, we propose the OptimNeuralTS strategy, based on Neural Thompson Sampling and differential evolution, to efficiently mine claims datasets and build a predictive model of the association between drug combinations and health outcomes. We benchmark our method using two datasets generated by an internally developed simulator of polypharmacy data containing 500 drugs and 100 000 distinct combinations. Empirically, our method can detect up to 72% of PIPs while maintaining an average precision score of 99% using 30 000 time steps. | ['Caroline Sirois', 'Richard Khoury', 'Audrey Durand', 'Alexandre Larouche'] | 2022-12-10 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.23945439e-01 -2.54107237e-01 -2.84006327e-01 -4.10857145e-03
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8205e873-a7b4-4a64-847e-f7cd78f5480f | multimodal-emotion-recognition-using | 1602.08225 | null | http://arxiv.org/abs/1602.08225v1 | http://arxiv.org/pdf/1602.08225v1.pdf | Multimodal Emotion Recognition Using Multimodal Deep Learning | To enhance the performance of affective models and reduce the cost of
acquiring physiological signals for real-world applications, we adopt
multimodal deep learning approach to construct affective models from multiple
physiological signals. For unimodal enhancement task, we indicate that the best
recognition accuracy of 82.11% on SEED dataset is achieved with shared
representations generated by Deep AutoEncoder (DAE) model. For multimodal
facilitation tasks, we demonstrate that the Bimodal Deep AutoEncoder (BDAE)
achieves the mean accuracies of 91.01% and 83.25% on SEED and DEAP datasets,
respectively, which are much superior to the state-of-the-art approaches. For
cross-modal learning task, our experimental results demonstrate that the mean
accuracy of 66.34% is achieved on SEED dataset through shared representations
generated by EEG-based DAE as training samples and shared representations
generated by eye-based DAE as testing sample, and vice versa. | ['Bao-liang Lu', 'Wei-Long Zheng', 'Wei Liu'] | 2016-02-26 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-2.78204888e-01 -1.40386075e-01 3.70965958e-01 -3.78421098e-01
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e3251a2d-128e-40c0-abf1-e1f9c74da38c | the-lmu-munich-unsupervised-machine | null | null | https://aclanthology.org/W18-6428 | https://aclanthology.org/W18-6428.pdf | The LMU Munich Unsupervised Machine Translation Systems | We describe LMU Munich{'}s unsupervised machine translation systems for English↔German translation. These systems were used to participate in the WMT18 news translation shared task and more specifically, for the unsupervised learning sub-track. The systems are trained on English and German monolingual data only and exploit and combine previously proposed techniques such as using word-by-word translated data based on bilingual word embeddings, denoising and on-the-fly backtranslation. | ['er', 'Viktor Hangya', 'Alex Fraser', 'Matthias Huck', 'Dario Stojanovski'] | 2018-10-01 | null | null | null | ws-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.14536785e-01 9.14066061e-02 -6.45692289e-01 -4.07611251e-01
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afbdd65f-7615-4fd8-9313-9d48581e1bd5 | image-specific-information-suppression-and | 2208.14365 | null | https://arxiv.org/abs/2208.14365v1 | https://arxiv.org/pdf/2208.14365v1.pdf | Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search | Text-based person search is a challenging task that aims to search pedestrian images with the same identity from the image gallery given a query text description. In recent years, text-based person search has made good progress, and state-of-the-art methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, the existing methods explicitly extract image parts and text phrases from images and texts by hand-crafted split or external tools and then conduct complex cross-modal local matching. Moreover, the existing methods seldom consider the problem of information inequality between modalities caused by image-specific information. In this paper, we propose an efficient joint Information and Semantic Alignment Network (ISANet) for text-based person search. Specifically, we first design an image-specific information suppression module, which suppresses image background and environmental factors by relation-guide localization and channel attention filtration respectively. This design can effectively alleviate the problem of information inequality and realize the information alignment between images and texts. Secondly, we propose an implicit local alignment module to adaptively aggregate image and text features to a set of modality-shared semantic topic centers, and implicitly learn the local fine-grained correspondence between images and texts without additional supervision information and complex cross-modal interactions. Moreover, a global alignment is introduced as a supplement to the local perspective. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of the proposed ISANet. | ['Jinhui Tang', 'Liyan Zhang', 'Hao Tang', 'Shuanglin Yan'] | 2022-08-30 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.45298988e-01 -5.33628643e-01 -2.86846399e-01 -5.10448515e-01
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a845fe61-fb8e-4b03-89b0-aa854a5c32c3 | an-ensemble-of-transfer-semi-supervised-and | 1806.06506 | null | http://arxiv.org/abs/1806.06506v2 | http://arxiv.org/pdf/1806.06506v2.pdf | An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification | In this work, we propose an ensemble of classifiers to distinguish between
various degrees of abnormalities of the heart using Phonocardiogram (PCG)
signals acquired using digital stethoscopes in a clinical setting, for the
INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats
SubChallenge. Our primary classification framework constitutes a convolutional
neural network with 1D-CNN time-convolution (tConv) layers, which uses features
transferred from a model trained on the 2016 Physionet Heart Sound Database. We
also employ a Representation Learning (RL) approach to generate features in an
unsupervised manner using Deep Recurrent Autoencoders and use Support Vector
Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we
utilize an SVM classifier on a high-dimensional segment-level feature extracted
using various functionals on short-term acoustic features, i.e., Low-Level
Descriptors (LLD). An ensemble of the three different approaches provides a
relative improvement of 11.13% compared to our best single sub-system in terms
of the Unweighted Average Recall (UAR) performance metric on the evaluation
dataset. | ['Shabnam Ghaffarzadegan', 'Md. Tauhiduzzaman Khan', 'Ahmed Imtiaz Humayun', 'Taufiq Hasan', 'Zhe Feng'] | 2018-06-18 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 5.10047913e-01 6.59430474e-02 3.36249709e-01 -2.48920530e-01
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5f789216-5df9-4b02-af2c-2f8df94d10b2 | deep-convolutional-neural-network-for-1 | 2007.02393 | null | https://arxiv.org/abs/2007.02393v2 | https://arxiv.org/pdf/2007.02393v2.pdf | Deep Convolutional Neural Network for Identifying Seam-Carving Forgery | Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing and duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion. To attain the ability to learn low-level features, we designed a CNN architecture comprising five types of network blocks specialized for capturing subtle signals. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas of the given image. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). In addition, our model with the ensemble module is robust for various unseen cases. The experimental results also demonstrate that our method can be applied to localize both seam-removed and seam-inserted areas. | ['Heung-Kyu Lee', 'Myung-Joon Kwon', 'In-Jae Yu', 'Seung-Hun Nam', 'Minseok Son', 'Wonhyuk Ahn'] | 2020-07-05 | null | null | null | null | ['image-retargeting', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 4.64886010e-01 -1.89854562e-01 1.18316151e-01 -1.23977661e-01
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318e106f-8705-4a7e-bfe4-465b21b9db86 | manga109dialog-a-large-scale-dialogue-dataset | 2306.17469 | null | https://arxiv.org/abs/2306.17469v1 | https://arxiv.org/pdf/2306.17469v1.pdf | Manga109Dialog A Large-scale Dialogue Dataset for Comics Speaker Detection | The expanding market for e-comics has spurred interest in the development of automated methods to analyze comics. For further understanding of comics, an automated approach is needed to link text in comics to characters speaking the words. Comics speaker detection research has practical applications, such as automatic character assignment for audiobooks, automatic translation according to characters' personalities, and inference of character relationships and stories. To deal with the problem of insufficient speaker-to-text annotations, we created a new annotation dataset Manga109Dialog based on Manga109. Manga109Dialog is the world's largest comics speaker annotation dataset, containing 132,692 speaker-to-text pairs. We further divided our dataset into different levels by prediction difficulties to evaluate speaker detection methods more appropriately. Unlike existing methods mainly based on distances, we propose a deep learning-based method using scene graph generation models. Due to the unique features of comics, we enhance the performance of our proposed model by considering the frame reading order. We conducted experiments using Manga109Dialog and other datasets. Experimental results demonstrate that our scene-graph-based approach outperforms existing methods, achieving a prediction accuracy of over 75%. | ['Yusuke Matsui', 'Kiyoharu Aizawa', 'Yingxuan Li'] | 2023-06-30 | null | null | null | null | ['scene-graph-generation', 'graph-generation'] | ['computer-vision', 'graphs'] | [-1.29320934e-01 -2.09673241e-01 5.88029549e-02 -3.49333256e-01
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29aa5349-5150-407b-b730-d714197a982a | a-laboratory-created-dataset-with-ground | 1902.08347 | null | http://arxiv.org/abs/1902.08347v1 | http://arxiv.org/pdf/1902.08347v1.pdf | A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation | Spectral unmixing is an important and challenging problem in hyperspectral
data processing. This topic has been extensively studied and a variety of
unmixing algorithms have been proposed in the literature. However, the lack of
publicly available dataset with ground-truth makes it difficult to evaluate and
compare the performance of unmixing algorithms in a quantitative and objective
manner. Most of the existing works rely on the use of numerical synthetic data
and an intuitive inspection of the results of real data. To alleviate this
dilemma, in this study, we design several experimental scenes in our
laboratory, including printed checkerboards, mixed quartz sands, and reflection
with a vertical board. A dataset is then created by imaging these scenes with
the hyperspectral camera in our laboratory, providing 36 mixtures with more
than 130, 000 pixels with 256 wavelength bands ranging from 400nm to 1000nm.
The experimental settings are strictly controlled so that pure material
spectral signatures and material compositions are known. To the best of our
knowledge, this dataset is the first publicly available dataset created in a
systematic manner with ground-truth for spectral unmixing. Some typical linear
and nonlinear unmixing algorithms are also tested with this dataset and lead to
meaningful results. | ['Zhe He', 'Jie Chen', 'Min Zhao'] | 2019-02-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.36297607e-01 -7.21833110e-01 7.65515566e-02 3.20070684e-02
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221b7460-b434-4bd8-9b8e-8f5b7f8b4a4f | randomized-3d-scene-generation-for | 2306.04237 | null | https://arxiv.org/abs/2306.04237v1 | https://arxiv.org/pdf/2306.04237v1.pdf | Randomized 3D Scene Generation for Generalizable Self-supervised Pre-training | Capturing and labeling real-world 3D data is laborious and time-consuming, which makes it costly to train strong 3D models. To address this issue, previous works generate randomized 3D scenes and pre-train models on generated data. Although the pre-trained models gain promising performance boosts, previous works have two major shortcomings. First, they focus on only one downstream task (i.e., object detection). Second, a fair comparison of generated data is still lacking. In this work, we systematically compare data generation methods using a unified setup. To clarify the generalization of the pre-trained models, we evaluate their performance in multiple tasks (e.g., object detection and semantic segmentation) and with different pre-training methods (e.g., masked autoencoder and contrastive learning). Moreover, we propose a new method to generate 3D scenes with spherical harmonics. It surpasses the previous formula-driven method with a clear margin and achieves on-par results with methods using real-world scans and CAD models. | ['Michael Heizmann', 'Lanxiao Li'] | 2023-06-07 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.00098979e-01 2.37064496e-01 1.95952475e-01 -3.65451783e-01
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ffe000b9-572d-4e6e-b1d0-5cebf7241e94 | leveraging-distributional-semantics-for-multi | 1709.05976 | null | http://arxiv.org/abs/1709.05976v3 | http://arxiv.org/pdf/1709.05976v3.pdf | Leveraging Distributional Semantics for Multi-Label Learning | We present a novel and scalable label embedding framework for large-scale
multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using
Distributional Semantics). Our approach draws inspiration from ideas rooted in
distributional semantics, specifically the Skip Gram Negative Sampling (SGNS)
approach, widely used to learn word embeddings for natural language processing
tasks. Learning such embeddings can be reduced to a certain matrix
factorization. Our approach is novel in that it highlights interesting
connections between label embedding methods used for multi-label learning and
paragraph/document embedding methods commonly used for learning representations
of text data. The framework can also be easily extended to incorporate
auxiliary information such as label-label correlations; this is crucial
especially when there are a lot of missing labels in the training data. We
demonstrate the effectiveness of our approach through an extensive set of
experiments on a variety of benchmark datasets, and show that the proposed
learning methods perform favorably compared to several baselines and
state-of-the-art methods for large-scale multi-label learning. To facilitate
end-to-end learning, we develop a joint learning algorithm that can learn the
embeddings as well as a regression model that predicts these embeddings given
input features, via efficient gradient-based methods. | ['Nagarajan Natarajan', 'Vivek Gupta', 'Rahul Wadbude', 'Prateek Jain', 'Piyush Rai', 'Harish Karnick'] | 2017-09-18 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 2.94319451e-01 -1.85469568e-01 -5.95771730e-01 -6.06259465e-01
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2f27a73a-ae7f-4804-86be-dad202e0c227 | consecutive-question-generation-via-dynamic | 2211.08850 | null | https://arxiv.org/abs/2211.08850v1 | https://arxiv.org/pdf/2211.08850v1.pdf | Consecutive Question Generation via Dynamic Multitask Learning | In this paper, we propose the task of consecutive question generation (CQG), which generates a set of logically related question-answer pairs to understand a whole passage, with a comprehensive consideration of the aspects including accuracy, coverage, and informativeness. To achieve this, we first examine the four key elements of CQG, i.e., question, answer, rationale, and context history, and propose a novel dynamic multitask framework with one main task generating a question-answer pair, and four auxiliary tasks generating other elements. It directly helps the model generate good questions through both joint training and self-reranking. At the same time, to fully explore the worth-asking information in a given passage, we make use of the reranking losses to sample the rationales and search for the best question series globally. Finally, we measure our strategy by QA data augmentation and manual evaluation, as well as a novel application of generated question-answer pairs on DocNLI. We prove that our strategy can improve question generation significantly and benefit multiple related NLP tasks. | ['Xing Shi', 'Sujian Li', 'Yunji Li'] | 2022-11-16 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.60442069e-01 3.64448935e-01 2.89139450e-01 -3.62424284e-01
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f641703c-df23-4cff-a0ca-b8c815d905ad | variational-graph-generator-for-multi-view | 2210.07011 | null | https://arxiv.org/abs/2210.07011v2 | https://arxiv.org/pdf/2210.07011v2.pdf | Variational Graph Generator for Multi-View Graph Clustering | Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then a simple yet effective graph encoder in conjunction with the multi-view clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of VGMGC by analyzing the uncertainty of the inferred consensus graph with information bottleneck principle. Extensive experiments demonstrate the superior performance of our VGMGC over SOTAs. | ['Lifang He', 'Philip S. Yu', 'Zhifeng Hao', 'Xiaorong Pu', 'Shudong Huang', 'Yazhou Ren', 'Jie Xu', 'Yawen Ling', 'Jianpeng Chen'] | 2022-10-13 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-2.13409290e-01 1.12095661e-01 -5.31128049e-02 -2.13843137e-01
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28dbff23-06a2-4a3e-a511-49cbdef3d166 | neuralmind-unicamp-at-2022-trec-neuclir-large | 2303.16145 | null | https://arxiv.org/abs/2303.16145v1 | https://arxiv.org/pdf/2303.16145v1.pdf | NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval | This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs are in different languages, even in the presence of suboptimal first-stage retrieval performance. The results of the study show outstanding performance across all tasks and languages, leading to a high number of winning positions. Finally, this study provides valuable insights into the use of mT5 in CLIR tasks and highlights its potential as a viable solution. For reproduction refer to https://github.com/unicamp-dl/NeuCLIR22-mT5 | ['Rodrigo Nogueira', 'Roberto Lotufo', 'Vitor Jeronymo'] | 2023-03-28 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-4.34628695e-01 -3.80108267e-01 -5.63423336e-01 7.83704445e-02
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44cb024c-e971-4eed-a0e1-20c2af354d1e | image-classifiers-leak-sensitive-attributes | 2303.09289 | null | https://arxiv.org/abs/2303.09289v2 | https://arxiv.org/pdf/2303.09289v2.pdf | Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations | Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy. To investigate this privacy leakage, we introduce the first Class Attribute Inference Attack (CAIA), which leverages recent advances in text-to-image synthesis to infer sensitive attributes of individual classes in a black-box setting, while remaining competitive with related white-box attacks. Our extensive experiments in the face recognition domain show that CAIA can accurately infer undisclosed sensitive attributes, such as an individual's hair color, gender, and racial appearance, which are not part of the training labels. Interestingly, we demonstrate that adversarial robust models are even more vulnerable to such privacy leakage than standard models, indicating that a trade-off between robustness and privacy exists. | ['Kristian Kersting', 'Patrick Schramowski', 'Manuel Brack', 'Felix Friedrich', 'Dominik Hintersdorf', 'Lukas Struppek'] | 2023-03-16 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 9.43395257e-01 3.61305863e-01 -1.36317506e-01 -9.36907053e-01
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4f525ffe-351b-48c3-b6ed-e5d41eb7d232 | baseline-method-for-the-sport-task-of | 2302.02752 | null | https://arxiv.org/abs/2302.02752v1 | https://arxiv.org/pdf/2302.02752v1.pdf | Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms | This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2022 benchmark. This task proposes two subtasks: stroke classification from trimmed videos, and stroke detection from untrimmed videos. This baseline addresses both subtasks. We propose two types of 3D-CNN architectures to solve the two subtasks. Both 3D-CNNs use Spatio-temporal convolutions and attention mechanisms. The architectures and the training process are tailored to solve the addressed subtask. This baseline method is shared publicly online to help the participants in their investigation and alleviate eventually some aspects of the task such as video processing, training method, evaluation and submission routine. The baseline method reaches 86.4% of accuracy with our v2 model for the classification subtask. For the detection subtask, the baseline reaches a mAP of 0.131 and IoU of 0.515 with our v1 model. | ['Pierre-Etienne Martin'] | 2023-02-06 | null | null | null | null | ['action-classification', 'stroke-classification'] | ['computer-vision', 'methodology'] | [-3.56932804e-02 -5.95456995e-02 -2.16264814e-01 -2.47534681e-02
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5f2bf354-cc11-4b2d-a026-0fd207131638 | a-visual-slam-with-moving-object-trajectory | 2303.02257 | null | https://arxiv.org/abs/2303.02257v1 | https://arxiv.org/pdf/2303.02257v1.pdf | A Visual SLAM with Moving Object Trajectory Prediction | Visual Simultaneous Localization and Mapping (SLAM) has received significant attention in recent years due to its ability to estimate camera trajectory and create an environment map using visual data alone, making a substantial contribution to autonomous driving applications, in particular, a real-world scenario with moving crowds and vehicles. In this work, we propose a visual SLAM system that incorporates moving object trajectory tracking and prediction. We take into account the motion clues of the pedestrians to track and predict their movement, as long as mapping the environment. Such an integrated system solves the localization of the camera and other moving objects in the scene, and further creates a sparse map to support the potential navigation of the vehicle. In the experiment, we demonstrate the effectiveness and robustness of our approach through a comprehensive evaluation on both our simulation and real-world KITTI datasets. | ['Wenbin Li', 'Siyuan Gou', 'Qi Zhang'] | 2023-03-03 | null | null | null | null | ['trajectory-prediction', 'simultaneous-localization-and-mapping'] | ['computer-vision', 'computer-vision'] | [-3.34226906e-01 -5.06384432e-01 -5.81437014e-02 -4.06652689e-01
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448527f1-6a7e-4cce-8632-fcff814eea4e | make-one-shot-video-object-segmentation-1 | 2012.01866 | null | https://arxiv.org/abs/2012.01866v1 | https://arxiv.org/pdf/2012.01866v1.pdf | Make One-Shot Video Object Segmentation Efficient Again | Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is provided at test time. Following the one-shot principle, fine-tuning VOS methods train a segmentation model separately on each given object mask. However, recently the VOS community has deemed such a test time optimization and its impact on the test runtime as unfeasible. To mitigate the inefficiencies of previous fine-tuning approaches, we present efficient One-Shot Video Object Segmentation (e-OSVOS). In contrast to most VOS approaches, e-OSVOS decouples the object detection task and predicts only local segmentation masks by applying a modified version of Mask R-CNN. The one-shot test runtime and performance are optimized without a laborious and handcrafted hyperparameter search. To this end, we meta learn the model initialization and learning rates for the test time optimization. To achieve optimal learning behavior, we predict individual learning rates at a neuron level. Furthermore, we apply an online adaptation to address the common performance degradation throughout a sequence by continuously fine-tuning the model on previous mask predictions supported by a frame-to-frame bounding box propagation. e-OSVOS provides state-of-the-art results on DAVIS 2016, DAVIS 2017, and YouTube-VOS for one-shot fine-tuning methods while reducing the test runtime substantially. Code is available at https://github.com/dvl-tum/e-osvos. | ['Laura Leal-Taixe', 'Tim Meinhardt'] | 2020-12-03 | make-one-shot-video-object-segmentation | http://proceedings.neurips.cc/paper/2020/hash/781397bc0630d47ab531ea850bddcf63-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/781397bc0630d47ab531ea850bddcf63-Paper.pdf | neurips-2020-12 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.12374566e-01 -1.01326577e-01 -2.55234629e-01 -4.16138738e-01
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6286d588-f731-4cbd-b084-b265b7218dbf | bisyn-gat-bi-syntax-aware-graph-attention | 2204.03117 | null | https://arxiv.org/abs/2204.03117v2 | https://arxiv.org/pdf/2204.03117v2.pdf | BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference. It is challenging because a sentence may contain multiple aspects or complicated (e.g., conditional, coordinating, or adversative) relations. Recently, exploiting dependency syntax information with graph neural networks has been the most popular trend. Despite its success, methods that heavily rely on the dependency tree pose challenges in accurately modeling the alignment of the aspects and their words indicative of sentiment, since the dependency tree may provide noisy signals of unrelated associations (e.g., the "conj" relation between "great" and "dreadful" in Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully exploits the syntax information (e.g., phrase segmentation and hierarchical structure) of the constituent tree of a sentence to model the sentiment-aware context of every single aspect (called intra-context) and the sentiment relations across aspects (called inter-context) for learning. Experiments on four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the state-of-the-art methods consistently. | ['Zhiyong He', 'Fei Wang', 'Xian-Ling Mao', 'Wei Wei', 'Shuo Liang'] | 2022-04-06 | null | https://aclanthology.org/2022.findings-acl.144 | https://aclanthology.org/2022.findings-acl.144.pdf | findings-acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 4.48158793e-02 1.62007511e-01 -3.42892408e-01 -7.71672845e-01
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e5c3b69a-d77b-44b4-8ba0-25736ae3e842 | inherent-consistent-learning-for-accurate | 2303.14175 | null | https://arxiv.org/abs/2303.14175v4 | https://arxiv.org/pdf/2303.14175v4.pdf | Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation | Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of labeled and unlabeled data to help segmentation. In practice, we introduce two external modules, namely Supervised Semantic Proxy Adaptor (SSPA) and Unsupervised Semantic Consistent Learner (USCL) that is based on the attention mechanism to align the semantic category representations of labeled and unlabeled data, as well as update the global semantic representations over the entire training set. The proposed ICL is a plug-and-play scheme for various network architectures, and the two modules are not involved in the testing stage. Experimental results on three public benchmarks show that the proposed method can outperform the state-of-the-art, especially when the number of annotated data is extremely limited. Code is available at: https://github.com/zhuye98/ICL.git. | ['Ruimao Zhang', 'Si-Qi Liu', 'Jie Yang', 'Ye Zhu'] | 2023-03-24 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.84905663e-01 4.55545992e-01 -4.57613260e-01 -7.30603158e-01
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f2bb5527-8c2b-481e-bae6-d2b6e1a1bfbf | learning-joint-multilingual-sentence | 1704.04154 | null | http://arxiv.org/abs/1704.04154v2 | http://arxiv.org/pdf/1704.04154v2.pdf | Learning Joint Multilingual Sentence Representations with Neural Machine Translation | In this paper, we use the framework of neural machine translation to learn
joint sentence representations across six very different languages. Our aim is
that a representation which is independent of the language, is likely to
capture the underlying semantics. We define a new cross-lingual similarity
measure, compare up to 1.4M sentence representations and study the
characteristics of close sentences. We provide experimental evidence that
sentences that are close in embedding space are indeed semantically highly
related, but often have quite different structure and syntax. These relations
also hold when comparing sentences in different languages. | ['Matthijs Douze', 'Holger Schwenk'] | 2017-04-13 | learning-joint-multilingual-sentence-1 | https://aclanthology.org/W17-2619 | https://aclanthology.org/W17-2619.pdf | ws-2017-8 | ['joint-multilingual-sentence-representations'] | ['natural-language-processing'] | [-9.64141041e-02 -7.21132085e-02 -3.48160356e-01 -9.27994370e-01
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e00b9874-4e04-4a73-9d9d-05d17f3ae20f | trace-table-reconstruction-aligned-to-corner | 2305.00630 | null | https://arxiv.org/abs/2305.00630v1 | https://arxiv.org/pdf/2305.00630v1.pdf | TRACE: Table Reconstruction Aligned to Corner and Edges | A table is an object that captures structured and informative content within a document, and recognizing a table in an image is challenging due to the complexity and variety of table layouts. Many previous works typically adopt a two-stage approach; (1) Table detection(TD) localizes the table region in an image and (2) Table Structure Recognition(TSR) identifies row- and column-wise adjacency relations between the cells. The use of a two-stage approach often entails the consequences of error propagation between the modules and raises training and inference inefficiency. In this work, we analyze the natural characteristics of a table, where a table is composed of cells and each cell is made up of borders consisting of edges. We propose a novel method to reconstruct the table in a bottom-up manner. Through a simple process, the proposed method separates cell boundaries from low-level features, such as corners and edges, and localizes table positions by combining the cells. A simple design makes the model easier to train and requires less computation than previous two-stage methods. We achieve state-of-the-art performance on the ICDAR2013 table competition benchmark and Wired Table in the Wild(WTW) dataset. | ['Seonghyeon Kim', 'Seung Shin', 'Jaeheung Surh', 'Daehyun Nam', 'Youngmin Baek'] | 2023-05-01 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 2.65817165e-01 -1.47863597e-01 -1.28393963e-01 -2.28928208e-01
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597ccb79-3cff-4ab5-8627-e498d6b8dd55 | rangeseg-range-aware-real-time-segmentation | 2205.01570 | null | https://arxiv.org/abs/2205.01570v1 | https://arxiv.org/pdf/2205.01570v1.pdf | RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds | Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone with two range dependent decoders. A heavy decoder only computes top of a range image where the far and small objects locate to improve small object detection accuracy, and a light decoder computes whole range image for low computational cost. The results are further clustered by the DBSCAN method with a resolution weighted distance function to get instance-level segmentation results. Experiments on the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic segmentation methods with enormous speedup and improves the instance-level segmentation performance on small and far objects. The whole RangeSeg pipeline meets the real time requirement on NVIDIA\textsuperscript{\textregistered} JETSON AGX Xavier with 19 frames per second in average. | ['Tian Sheuan Chang', 'Tzu-Hsuan Chen'] | 2022-05-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 8.12914595e-02 -2.50179827e-01 -1.21330740e-02 -9.34880793e-01
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062b602a-e57c-4ce3-b7f8-6856002e5500 | universal-proposition-bank-2-0 | null | null | https://aclanthology.org/2022.lrec-1.181 | https://aclanthology.org/2022.lrec-1.181.pdf | Universal Proposition Bank 2.0 | Semantic role labeling (SRL) represents the meaning of a sentence in the form of predicate-argument structures. Such shallow semantic analysis is helpful in a wide range of downstream NLP tasks and real-world applications. As treebanks enabled the development of powerful syntactic parsers, the accurate predicate-argument analysis demands training data in the form of propbanks. Unfortunately, most languages simply do not have corresponding propbanks due to the high cost required to construct such resources. To overcome such challenges, Universal Proposition Bank 1.0 (UP1.0) was released in 2017, with high-quality propbank data generated via a two-stage method exploiting monolingual SRL and multilingual parallel data. In this paper, we introduce Universal Proposition Bank 2.0 (UP2.0), with significant enhancements over UP1.0: (1) propbanks with higher quality by using a state-of-the-art monolingual SRL and improved auto-generation of annotations; (2) expanded language coverage (from 7 to 9 languages); (3) span annotation for the decoupling of syntactic analysis; and (4) Gold data for a subset of the languages. We also share our experimental results that confirm the significant quality improvements of the generated propbanks. In addition, we present a comprehensive experimental evaluation on how different implementation choices impact the quality of the resulting data. We release these resources to the research community and hope to encourage more research on cross-lingual SRL. | ['Yunyao Li', 'Huaiyu Zhu', 'Khoi-Nguyen Tran', 'Huyen Nguyen', 'Ha Linh', 'Michał Ulewicz', 'Alexandre Rademaker', 'Ishan Jindal'] | null | null | null | null | lrec-2022-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.32193780e-02 5.80240607e-01 -5.33963084e-01 -4.92883921e-01
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cc0a1c82-580a-4c67-aea4-b6d5fd4ce0fa | masked-reconstruction-contrastive-learning | 2211.09013 | null | https://arxiv.org/abs/2211.09013v1 | https://arxiv.org/pdf/2211.09013v1.pdf | Masked Reconstruction Contrastive Learning with Information Bottleneck Principle | Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased to learn only the ability to discriminate positive and negative pairs due to the discriminative task setting. However, this bias would lead to ignoring its sufficiency for other downstream tasks, which we call the discriminative information overfitting problem. In this paper, we propose to tackle the above problems from the aspect of the Information Bottleneck (IB) principle, further pushing forward the frontier of CL. Specifically, we present a new perspective that CL is an instantiation of the IB principle, including information compression and expression. We theoretically analyze the optimal information situation and demonstrate that minimum sufficient augmentation and information-generalized representation are the optimal requirements for achieving maximum compression and generalizability to downstream tasks. Therefore, we propose the Masked Reconstruction Contrastive Learning~(MRCL) model to improve CL models. For implementation in practice, MRCL utilizes the masking operation for stronger augmentation, further eliminating redundant and noisy information. In order to alleviate the discriminative information overfitting problem effectively, we employ the reconstruction task to regularize the discriminative task. We conduct comprehensive experiments and show the superiority of the proposed model on multiple tasks, including image classification, semantic segmentation and objective detection. | ['Xuecheng Nie', 'Tiande Guo', 'Congying Han', 'Bonan Li', 'Ziwen Liu'] | 2022-11-15 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 7.47958779e-01 -2.26877872e-02 -5.58810174e-01 -4.69571084e-01
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292d7577-0c70-4b8e-87d5-e738a9f2711f | graph-information-aggregation-cross-domain | null | null | https://doi.org/10.1109/TNNLS.2022.3185795 | https://doi.org/10.1109/TNNLS.2022.3185795 | Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification | Most domain adaptation (DA) methods in cross-scene hyperspectral image classification focus on cases where source data (SD) and target data (TD) with the same classes are obtained by the same sensor. However, the classification performance is significantly reduced when there are new classes in TD. In addition, domain alignment, as one of the main approaches in DA, is carried out based on local spatial information, rarely taking into account nonlocal spatial information (nonlocal relationships) with strong correspondence. A graph information aggregation cross-domain few-shot learning (Gia-CFSL) framework is proposed, intending to make up for the above-mentioned shortcomings by combining FSL with domain alignment based on graph information aggregation. SD with all label samples and TD with a few label samples are implemented for FSL episodic training. Meanwhile, intradomain distribution extraction block (IDE-block) and cross-domain similarity aware block (CSA-block) are designed. The IDE-block is used to characterize and aggregate the intradomain nonlocal relationships and the interdomain feature and distribution similarities are captured in the CSA-block. Furthermore, feature-level and distribution-level cross-domain graph alignments are used to mitigate the impact of domain shift on FSL. Experimental results on three public HSI datasets demonstrate the superiority of the proposed method. The codes will be available from the website: https://github.com/YuxiangZhang-BIT/IEEE_TNNLS_Gia-CFSL. | ['Qian Du', 'Ran Tao', 'Shuai Wang', 'Mengmeng Zhang', 'Wei Li', 'Yuxiang Zhang'] | 2022-06-30 | null | null | null | ieee-transactions-on-neural-networks-and-15 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.71878797e-01 -4.87744749e-01 -2.10621715e-01 -3.45950097e-01
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37c9b5b2-ea8b-4a0c-9a80-7dcaa72cf200 | decoupling-knowledge-from-memorization | 2205.14704 | null | https://arxiv.org/abs/2205.14704v3 | https://arxiv.org/pdf/2205.14704v3.pdf | Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning | Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unstable generalization issues. Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data. To alleviate such limitations, we develop RetroPrompt with the motivation of decoupling knowledge from memorization to help the model strike a balance between generalization and memorization. In contrast with vanilla prompt learning, RetroPrompt constructs an open-book knowledge-store from training instances and implements a retrieval mechanism during the process of input, training and inference, thus equipping the model with the ability to retrieve related contexts from the training corpus as cues for enhancement. Extensive experiments demonstrate that RetroPrompt can obtain better performance in both few-shot and zero-shot settings. Besides, we further illustrate that our proposed RetroPrompt can yield better generalization abilities with new datasets. Detailed analysis of memorization indeed reveals RetroPrompt can reduce the reliance of language models on memorization; thus, improving generalization for downstream tasks. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt. | ['Huajun Chen', 'Luo Si', 'Fei Huang', 'Chuanqi Tan', 'Shumin Deng', 'Xiaozhuan Liang', 'Ningyu Zhang', 'Lei LI', 'Xiang Chen'] | 2022-05-29 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.91702221e-02 -7.36035919e-03 -3.65966916e-01 -2.63504714e-01
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3016302f-fc73-4d91-9d3b-10915d3b0d05 | bayesian-inference-for-jump-diffusion | 2304.06592 | null | https://arxiv.org/abs/2304.06592v1 | https://arxiv.org/pdf/2304.06592v1.pdf | Bayesian Inference for Jump-Diffusion Approximations of Biochemical Reaction Networks | Biochemical reaction networks are an amalgamation of reactions where each reaction represents the interaction of different species. Generally, these networks exhibit a multi-scale behavior caused by the high variability in reaction rates and abundances of species. The so-called jump-diffusion approximation is a valuable tool in the modeling of such systems. The approximation is constructed by partitioning the reaction network into a fast and slow subgroup of fast and slow reactions, respectively. This enables the modeling of the dynamics using a Langevin equation for the fast group, while a Markov jump process model is kept for the dynamics of the slow group. Most often biochemical processes are poorly characterized in terms of parameters and population states. As a result of this, methods for estimating hidden quantities are of significant interest. In this paper, we develop a tractable Bayesian inference algorithm based on Markov chain Monte Carlo. The presented blocked Gibbs particle smoothing algorithm utilizes a sequential Monte Carlo method to estimate the latent states and performs distinct Gibbs steps for the parameters of a biochemical reaction network, by exploiting a jump-diffusion approximation model. The presented blocked Gibbs sampler is based on the two distinct steps of state inference and parameter inference. We estimate states via a continuous-time forward-filtering backward-smoothing procedure in the state inference step. By utilizing bootstrap particle filtering within a backward-smoothing procedure, we sample a smoothing trajectory. For estimating the hidden parameters, we utilize a separate Markov chain Monte Carlo sampler within the Gibbs sampler that uses the path-wise continuous-time representation of the reaction counters. Finally, the algorithm is numerically evaluated for a partially observed multi-scale birth-death process example. | ['Heinz Koeppl', 'Bastian Alt', 'Derya Altıntan'] | 2023-04-13 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.65640199e-01 -1.64857298e-01 1.05631940e-01 1.50515959e-01
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06f168f3-bc7a-47a3-9416-1a3d5e6829ee | skeleton-prototype-contrastive-learning-with | 2208.11814 | null | https://arxiv.org/abs/2208.11814v1 | https://arxiv.org/pdf/2208.11814v1.pdf | Skeleton Prototype Contrastive Learning with Multi-Level Graph Relation Modeling for Unsupervised Person Re-Identification | Person re-identification (re-ID) via 3D skeletons is an important emerging topic with many merits. Existing solutions rarely explore valuable body-component relations in skeletal structure or motion, and they typically lack the ability to learn general representations with unlabeled skeleton data for person re-ID. This paper proposes a generic unsupervised Skeleton Prototype Contrastive learning paradigm with Multi-level Graph Relation learning (SPC-MGR) to learn effective representations from unlabeled skeletons to perform person re-ID. Specifically, we first construct unified multi-level skeleton graphs to fully model body structure within skeletons. Then we propose a multi-head structural relation layer to comprehensively capture relations of physically-connected body-component nodes in graphs. A full-level collaborative relation layer is exploited to infer collaboration between motion-related body parts at various levels, so as to capture rich body features and recognizable walking patterns. Lastly, we propose a skeleton prototype contrastive learning scheme that clusters feature-correlative instances of unlabeled graph representations and contrasts their inherent similarity with representative skeleton features ("skeleton prototypes") to learn discriminative skeleton representations for person re-ID. Empirical evaluations show that SPC-MGR significantly outperforms several state-of-the-art skeleton-based methods, and it also achieves highly competitive person re-ID performance for more general scenarios. | ['Chunyan Miao', 'Haocong Rao'] | 2022-08-25 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.08219255e-02 1.44633457e-01 -5.18828392e-01 -2.74919063e-01
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eae5939a-6781-4f05-9582-fb46fe804959 | complex-word-identification-using-character-n | null | null | https://aclanthology.org/W18-0541 | https://aclanthology.org/W18-0541.pdf | Complex Word Identification Using Character n-grams | This paper investigates the use of character n-gram frequencies for identifying complex words in English, German and Spanish texts. The approach is based on the assumption that complex words are likely to contain different character sequences than simple words. The multinomial Naive Bayes classifier was used with n-grams of different lengths as features, and the best results were obtained for the combination of 2-grams and 4-grams. This variant was submitted to the Complex Word Identification Shared Task 2018 for all texts and achieved F-scores between 70{\%} and 83{\%}. The system was ranked in the middle range for all English texts, as third of fourteen submissions for German, and as tenth of seventeen submissions for Spanish. The method is not very convenient for the cross-language task, achieving only 59{\%} on the French text. | ["Maja Popovi{\\'c}"] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-1.14454828e-01 -1.60799399e-01 -1.72755376e-01 -7.87988212e-03
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6479f570-a87e-4007-aa89-d32b559e45c3 | pv3d-a-3d-generative-model-for-portrait-video | 2212.06384 | null | https://arxiv.org/abs/2212.06384v3 | https://arxiv.org/pdf/2212.06384v3.pdf | PV3D: A 3D Generative Model for Portrait Video Generation | Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos. In this work, we propose PV3D, the first generative framework that can synthesize multi-view consistent portrait videos. Specifically, our method extends the recent static 3D-aware image GAN to the video domain by generalizing the 3D implicit neural representation to model the spatio-temporal space. To introduce motion dynamics to the generation process, we develop a motion generator by stacking multiple motion layers to generate motion features via modulated convolution. To alleviate motion ambiguities caused by camera/human motions, we propose a simple yet effective camera condition strategy for PV3D, enabling both temporal and multi-view consistent video generation. Moreover, PV3D introduces two discriminators for regularizing the spatial and temporal domains to ensure the plausibility of the generated portrait videos. These elaborated designs enable PV3D to generate 3D-aware motion-plausible portrait videos with high-quality appearance and geometry, significantly outperforming prior works. As a result, PV3D is able to support many downstream applications such as animating static portraits and view-consistent video motion editing. Code and models are released at https://showlab.github.io/pv3d. | ['Zhongcong Xu', 'Mike Zheng Shou', 'Jiashi Feng', 'Song Bai', 'Wenqing Zhang', 'Jun Hao Liew', 'Jianfeng Zhang'] | 2022-12-13 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 3.08954686e-01 -3.32261585e-02 1.72051731e-02 -6.22225553e-03
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046b16f2-15f7-4810-9e1c-51bccb827ccf | document-image-layout-analysis-via-explicit | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0020025521007106 | http://zhengyingbin.cc/index_files/e3net.pdf | Document Image Layout Analysis via Explicit Edge Embedding Network | Layout analysis from a document image plays an important role in document content understanding and information extraction systems. While many existing methods focus on learning knowledge with convolutional networks directly from color channels, we argue the importance of highfrequency structures in document images, especially edge information. In this paper, we present a novel document layout analysis framework with the Explicit Edge Embedding Network. Specifically, the proposed network contains the edge embedding block and dynamic skip connection block to produce detailed features, as well as a lightweight fully convolutional subnet as the backbone for the effectiveness of the framework. The edge embedding block is designed to explicitly incorporate the edge information from the document images. The dynamic skip connection block aims to learn both color and edge representations with learnable weights. In contrast to the previous methods, we harness the model by using a synthetic document approach to overcome data scarcity. The combination of data augmentation and edge embedding is important toward a more compact representation than directly using the training images with only
color channels. We conduct experiments using the proposed
framework on three document layout analysis benchmarks
and demonstrate its superiority in terms of effectiveness and
efficiency over previous approaches. | ['Liang He', 'Hao Ye', 'Tianlong Ma', 'Yingbin Zheng', 'Xingjiao Wu'] | 2021-10-01 | null | null | null | information-sciences-2021-10 | ['document-layout-analysis'] | ['computer-vision'] | [ 2.18959272e-01 -2.02001080e-01 -1.44530565e-01 -3.27155143e-01
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c22328ee-c1f2-495c-a047-5b07e285d323 | hitachi-at-semeval-2022-task-2-on-the | null | null | https://aclanthology.org/2022.semeval-1.15 | https://aclanthology.org/2022.semeval-1.15.pdf | Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection | In this paper, we describe our system for SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. The task aims at detecting idiomaticity in an input sequence (Subtask A) and modeling representation of sentences that contain potential idiomatic multiword expressions (MWEs) (Subtask B) in three languages. We focus on the zero-shot setting of Subtask A and propose two span-based idiomaticity classification methods: MWE span-based classification and idiomatic MWE span prediction-based classification. We use several cross-lingual pre-trained language models (InfoXLM, XLM-R, and others) as our backbone network. Our best-performing system, fine-tuned with the span-based idiomaticity classification, ranked fifth in the zero-shot setting of Subtask A and exhibited a macro F1 score of 0.7466. | ['Yasuhiro Sogawa', 'Hiroaki Ozaki', 'Gaku Morio', 'Atsuki Yamaguchi'] | null | null | null | null | semeval-naacl-2022-7 | ['xlm-r'] | ['natural-language-processing'] | [-5.77876531e-02 -8.22472498e-02 -6.87030077e-01 -4.27149922e-01
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e2ae8e9b-3e7d-4a8c-9ef5-bc592f5ac44d | beyond-admm-a-unified-client-variance-reduced | 2212.01519 | null | https://arxiv.org/abs/2212.01519v3 | https://arxiv.org/pdf/2212.01519v3.pdf | Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework | As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling. | ['Defeng Sun', 'Tony Q. S. Quek', 'Tsung-Hui Chang', 'Zhiguo Wang', 'Yanqing Xu', 'Shuai Wang'] | 2022-12-03 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [-2.84951091e-01 -3.26976001e-01 -3.58733684e-01 -2.96999276e-01
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40ad0acc-a287-4415-955a-dd3654e30172 | speech-enhancement-with-multi-granularity | 2302.08342 | null | https://arxiv.org/abs/2302.08342v1 | https://arxiv.org/pdf/2302.08342v1.pdf | Speech Enhancement with Multi-granularity Vector Quantization | With advances in deep learning, neural network based speech enhancement (SE) has developed rapidly in the last decade. Meanwhile, the self-supervised pre-trained model and vector quantization (VQ) have achieved excellent performance on many speech-related tasks, while they are less explored on SE. As it was shown in our previous work that utilizing a VQ module to discretize noisy speech representations is beneficial for speech denoising, in this work we therefore study the impact of using VQ at different layers with different number of codebooks. Different VQ modules indeed enable to extract multiple-granularity speech features. Following an attention mechanism, the contextual features extracted by a pre-trained model are fused with the local features extracted by the encoder, such that both global and local information are preserved to reconstruct the enhanced speech. Experimental results on the Valentini dataset show that the proposed model can improve the SE performance, where the impact of choosing pre-trained models is also revealed. | ['Jie Zhang', 'Qiu-Shi Zhu', 'Xiao-Ying Zhao'] | 2023-02-16 | null | null | null | null | ['speech-enhancement', 'speech-denoising'] | ['speech', 'speech'] | [ 2.89426655e-01 1.02857187e-01 1.43179089e-01 -4.36849743e-01
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96470085-76af-4242-b11a-2274f28b2812 | weakly-supervised-positional-contrastive | 2307.04617 | null | https://arxiv.org/abs/2307.04617v1 | https://arxiv.org/pdf/2307.04617v1.pdf | Weakly-supervised positional contrastive learning: application to cirrhosis classification | Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histology-based diagnoses, is rare and costly. Pretraining strategies, like contrastive learning (CL) methods, can leverage unlabeled or weakly-annotated datasets. These methods typically require large batch sizes, which poses a difficulty in the case of large 3D images at full resolution, due to limited GPU memory. Nevertheless, volumetric positional information about the spatial context of each 2D slice can be very important for some medical applications. In this work, we propose an efficient weakly-supervised positional (WSP) contrastive learning strategy where we integrate both the spatial context of each 2D slice and a weak label via a generic kernel-based loss function. We illustrate our method on cirrhosis prediction using a large volume of weakly-labeled images, namely radiological low-confidence annotations, and small strongly-labeled (i.e., high-confidence) datasets. The proposed model improves the classification AUC by 5% with respect to a baseline model on our internal dataset, and by 26% on the public LIHC dataset from the Cancer Genome Atlas. The code is available at: https://github.com/Guerbet-AI/wsp-contrastive. | ['Isabelle Bloch', 'Pietro Gori', 'Marc-Michel Rohé', 'Alexandre Bône', 'Emma Sarfati'] | 2023-07-10 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'classification-1'] | ['computer-vision', 'methodology', 'methodology'] | [ 5.30820563e-02 1.76546797e-01 -4.69721794e-01 -4.96179312e-01
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0bbc0d74-8be4-4eea-89ab-022bf9e811cf | any-to-any-generation-via-composable | 2305.11846 | null | https://arxiv.org/abs/2305.11846v1 | https://arxiv.org/pdf/2305.11846v1.pdf | Any-to-Any Generation via Composable Diffusion | We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not limited to a subset of modalities like text or image. Despite the absence of training datasets for many combinations of modalities, we propose to align modalities in both the input and output space. This allows CoDi to freely condition on any input combination and generate any group of modalities, even if they are not present in the training data. CoDi employs a novel composable generation strategy which involves building a shared multimodal space by bridging alignment in the diffusion process, enabling the synchronized generation of intertwined modalities, such as temporally aligned video and audio. Highly customizable and flexible, CoDi achieves strong joint-modality generation quality, and outperforms or is on par with the unimodal state-of-the-art for single-modality synthesis. The project page with demonstrations and code is at https://codi-gen.github.io | ['Mohit Bansal', 'Michael Zeng', 'Chenguang Zhu', 'ZiYi Yang', 'Zineng Tang'] | 2023-05-19 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 5.27980685e-01 1.58614621e-01 7.71776661e-02 9.93820205e-02
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fcb7ae9c-f83c-407c-83f9-470ad663be5c | penelopie-enabling-open-information | 2103.15075 | null | https://arxiv.org/abs/2103.15075v1 | https://arxiv.org/pdf/2103.15075v1.pdf | PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation | In this paper we present our submission for the EACL 2021 SRW; a methodology that aims at bridging the gap between high and low-resource languages in the context of Open Information Extraction, showcasing it on the Greek language. The goals of this paper are twofold: First, we build Neural Machine Translation (NMT) models for English-to-Greek and Greek-to-English based on the Transformer architecture. Second, we leverage these NMT models to produce English translations of Greek text as input for our NLP pipeline, to which we apply a series of pre-processing and triple extraction tasks. Finally, we back-translate the extracted triples to Greek. We conduct an evaluation of both our NMT and OIE methods on benchmark datasets and demonstrate that our approach outperforms the current state-of-the-art for the Greek natural language. | ['Nikolaos Matsatsinis', 'Nikolaos Papadakis', 'Dimitris Papadopoulos'] | 2021-03-28 | null | https://aclanthology.org/2021.eacl-srw.4 | https://aclanthology.org/2021.eacl-srw.4.pdf | eacl-2021-2 | ['open-information-extraction'] | ['natural-language-processing'] | [ 4.23945487e-01 4.71802026e-01 -1.86814010e-01 -4.29915860e-02
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13b6e13b-f794-400d-9090-2bd9666024fc | knowledge-graph-is-in-rescue-task-oriented | null | null | https://openreview.net/forum?id=UQk8XMFAE2u | https://openreview.net/pdf?id=UQk8XMFAE2u | Knowledge Graph is in Rescue: Task Oriented Dialogue System for Response Generation without NLU and DM | Natural language understanding (NLU) and dialogue management (DM) are the standard prerequisites for response generation in a task-oriented dialogue system. In the existing literature, NLU and DM have been tackled as two independent tasks, requiring separate labeled data. Besides this problem of additional data requirements, NLU and DM also introduce errors in the pipe-lined processing of dialogue. Direct generation of responses from the user utterances without using any intermediate NLU and DM modules is, thus, a worthy goal. To accomplish this, the model should be able to (implicitly) understand the intent of the user, fetch the appropriate data from the knowledge base, (implicitly) decide an action by looking at the fetched data and conversation history, and finally, generate the response. In this work, we build an end-to-end dialogue generation system that does not require NLU and DM components or their associated labels in the data. We propose an effective technique based on the pre-trained GPT-2 and Graph Convolution Network (GCN) that takes conversation history and knowledge graph as input and produces appropriate responses. Experiments on three benchmark datasets viz. MultiWOZ, InCar Assist, and CamRest show that our proposed model achieves performance comparable to state-of-the-art systems without using the NLU and DM labels. Human evaluation conducted on the outputs also confirms that our proposed model generates highly fluent and contextually relevant responses. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['dialogue-management'] | ['natural-language-processing'] | [ 4.17554945e-01 6.09123170e-01 3.42428386e-01 -5.94460130e-01
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76297b21-5eca-4ec8-a800-a77278cafa0a | russiansuperglue-a-russian-language | 2010.15925 | null | https://arxiv.org/abs/2010.15925v2 | https://arxiv.org/pdf/2010.15925v2.pdf | RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark | In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We provide baselines, human level evaluation, an open-source framework for evaluating models (https://github.com/RussianNLP/RussianSuperGLUE), and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the adapted diagnostic test set and offer the first steps to further expanding or assessing state-of-the-art models independently of language. | ['Andrey Evlampiev', 'Andrey Chertok', 'Maria Tikhonova', 'Vladislav Mikhailov', 'Valentin Malykh', 'Ekaterina Artemova', 'Denis Shevelev', 'Anton Emelyanov', 'Alena Fenogenova', 'Tatiana Shavrina'] | 2020-10-29 | null | https://aclanthology.org/2020.emnlp-main.381 | https://aclanthology.org/2020.emnlp-main.381.pdf | emnlp-2020-11 | ['logical-reasoning-question-ansering'] | ['natural-language-processing'] | [-7.32482821e-02 3.97407055e-01 1.53080344e-01 -3.27580214e-01
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60b39bb1-dc85-485a-bf5d-258ed5be2155 | the-munich-biovoice-corpus-effects-of | null | null | https://aclanthology.org/L14-1491 | https://aclanthology.org/L14-1491.pdf | The Munich Biovoice Corpus: Effects of Physical Exercising, Heart Rate, and Skin Conductance on Human Speech Production | We introduce a spoken language resource for the analysis of impact that physical exercising has on human speech production. In particular, the database provides heart rate and skin conductance measurement information alongside the audio recordings. It contains recordings from 19 subjects in a relaxed state and after exercising. The audio material includes breathing, sustained vowels, and read text. Further, we describe pre-extracted audio-features from our openSMILE feature extractor together with baseline performances for the recognition of high and low heart rate using these features. The baseline results clearly show the feasibility of automatic estimation of heart rate from the human voice, in particular from sustained vowels. Both regression - in order to predict the exact heart rate value - and a binary classification setting for high and low heart rate classes are investigated. Finally, we give tendencies on feature group relevance in the named contexts of heart rate estimation and skin conductivity estimation. | ['Bj{\\"o}rn Schuller', 'Florian Eyben', 'Felix Friedmann'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['heart-rate-estimation'] | ['medical'] | [ 4.02108431e-01 2.98305482e-01 -2.70047411e-02 -2.60467172e-01
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524192e1-c47b-4946-8014-02565c484980 | english-to-bengali-multimodal-neural-machine | null | null | https://aclanthology.org/2022.wat-1.14 | https://aclanthology.org/2022.wat-1.14.pdf | English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation | Automatic translation of one natural language to another is a popular task of natural language processing. Although the deep learning-based technique known as neural machine translation (NMT) is a widely accepted machine translation approach, it needs an adequate amount of training data, which is a challenging issue for low-resource pair translation. Moreover, the multimodal concept utilizes text and visual features to improve low-resource pair translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) English to Bengali multimodal translation task where we have participated as a team named CNLP-NITS-PP in two tracks: 1) text-only and 2) multimodal translation. Herein, we have proposed a transliteration-based phrase pairs augmentation approach which shows improvement in the multimodal translation task and achieved benchmark results on Bengali Visual Genome 1.0 dataset. We have attained the best results on the challenge and evaluation test set for English to Bengali multimodal translation with BLEU scores of 28.70, 43.90 and RIBES scores of 0.688931, 0.780669, respectively. | ['Sivaji Bandyopadhyay', 'Partha Pakray', 'Riyanka Manna', 'Pankaj Dadure', 'Sahinur Rahman Laskar'] | null | null | null | null | wat-2022-10 | ['transliteration'] | ['natural-language-processing'] | [ 3.19411844e-01 -2.41637468e-01 -7.72548020e-02 -1.90443411e-01
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809029cc-1e4d-485c-9daa-b2f79ae5b032 | recursions-are-all-you-need-towards-efficient | 2305.05505 | null | https://arxiv.org/abs/2305.05505v1 | https://arxiv.org/pdf/2305.05505v1.pdf | Recursions Are All You Need: Towards Efficient Deep Unfolding Networks | The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance the efficiency of deep unfolding models. First, recursions are used to effectively eliminate the redundancies in deep unfolding networks. Secondly, we randomize the number of recursions during training to decrease the overall training time. Finally, to effectively utilize the power of recursions, we introduce a learnable unit to modulate the features of the model based on both the total number of iterations and the current iteration index. To evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST. Extensive testing shows that our proposed framework allows the network to cut down as much as 75% of its learnable parameters while mostly maintaining its performance, and at the same time, it cuts around 21% and 42% from the training time for ISTA-Net+ and COAST respectively. Moreover, when presented with a limited training dataset, the recursive models match or even outperform their respective non-recursive baseline. Codes and pretrained models are available at https://github.com/Rawwad-Alhejaili/Recursions-Are-All-You-Need . | ['Ali Al-Shaikhi', 'Hamzah Luqman', 'Motaz Alfarraj', 'Rawwad Alhejaili'] | 2023-05-09 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 2.90033996e-01 1.43674821e-01 -3.74789420e-03 -3.07695597e-01
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c23e93da-4457-4678-aa71-0938341bbcd6 | an-open-framework-for-remote-ppg-methods-and | null | null | https://ieeexplore.ieee.org/document/9272290 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9272290 | An Open Framework for Remote-PPG Methods and their Assessment | This paper presents a comprehensive framework for studying methods of pulse rate estimation relying on remote photoplethysmography (rPPG). There has been a remarkable development of rPPG techniques in recent years, and the publication of several surveys too, yet a sound assessment of their performance has been overlooked at best, whether not undeveloped. The methodological rationale behind the framework we propose is that in order to study, develop and compare new rPPG methods in a principled and reproducible way, the following conditions should be met: i) a structured pipeline to monitor rPPG algorithms’ input, output, and main control parameters; ii) the availability and the use of multiple datasets; iii) a sound statistical assessment of methods’ performance. The proposed framework is instantiated in the form of a Python package named pyVHR (short for Python tool for Virtual Heart Rate), which is made freely available on GitHub (github.com/phuselab/pyVHR). Here, to substantiate our approach, we evaluate eight well-known rPPG methods, through extensive experiments across five public video datasets, and subsequent nonparametric statistical analysis. Surprisingly, performances achieved by the four best methods, namely POS, CHROM, PCA and SSR, are not significantly different from a statistical standpoint highlighting the importance of evaluate the different approaches with a statistical assessment. | ['Raffaella Lanzarotti', 'Giuliano Grossi', 'Alessandro D’Amelio', 'Vittorio Cuculo', 'Donatello Conte', 'Giuseppe Boccignone'] | 2020-11-26 | null | null | null | null | ['physiological-computing', 'photoplethysmography-ppg', 'heart-rate-estimation'] | ['computer-vision', 'medical', 'medical'] | [ 2.15582445e-01 -2.41895363e-01 7.67881945e-02 -2.37356335e-01
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58fe8d3f-1137-45f7-b9f5-239cb510ed38 | ai-augmentation-of-radiologist-performance-in | null | null | https://pubs.rsna.org/doi/full/10.1148/radiol.2020201491 | https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020201491 | AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT | Background
COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy.
Purpose
To establish and evaluate an artificial intelligence (AI) system in differentiating COVID-19 and other pneumonia on chest CT and assess radiologist performance without and with AI assistance.
Methods
521 patients with positive RT-PCR for COVID-19 and abnormal chest CT findings were retrospectively identified from ten hospitals from January 2020 to April 2020. 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia on chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by two-layer fully-connected neural network to pool slices together. Our final cohort of 1,186 patients (132,583 CT slices) was divided into training, validation and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance on separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance.
Results
Our final model achieved a test accuracy of 96% (95% CI: 90-98%), sensitivity 95% (95% CI: 83-100%) and specificity of 96% (95% CI: 88-99%) with Receiver Operating Characteristic (ROC) AUC of 0.95 and Precision-Recall (PR) AUC of 0.90. On independent testing, our model achieved an accuracy of 87% (95% CI: 82-90%), sensitivity of 89% (95% CI: 81-94%) and specificity of 86% (95% CI: 80-90%) with ROC AUC of 0.90 and PR AUC of 0.87. Assisted by the models’ probabilities, the radiologists achieved a higher average test accuracy (90% vs. 85%, Δ=5, p<0.001), sensitivity (88% vs. 79%, Δ=9, p<0.001) and specificity (91% vs. 88%, Δ=3, p=0.001).
Conclusion
AI assistance improved radiologists' performance in distinguishing COVID-19 from non-COVID-19 pneumonia on chest CT.
Summary
AI assistance improved radiologists’ performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT. | ['Wei-Hua Liao', 'Qi-Zhi Yu', 'Raymond Y. Huang', 'Fei-Xian Fu', 'Yi-Hui Li', 'Ping-Feng Hu', 'Qiu-Hua Zeng', 'Xiao-Long Jiang', 'Lin-Bo Shi', 'Dong-Cui Wang', 'Zeng Xiong', 'Thi My Linh Tran', 'Ji Whae Choi', 'Ben Hsieh', 'Kasey Halsey', 'Ji Mei', 'Ronnie Sebro', 'Robin Wang', 'Michael K. Atalay', 'Ken Chang', 'Ian Pan', 'Harrison X. Bai'] | 2020-04-27 | null | null | null | rsna-2020-4 | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 1.69209167e-01 1.63951945e-02 -3.49069983e-01 -1.00003548e-01
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28675b52-d205-4825-87d1-5c5f4db963f3 | extracting-narrative-timelines-as-temporal | null | null | https://aclanthology.org/P12-1010 | https://aclanthology.org/P12-1010.pdf | Extracting Narrative Timelines as Temporal Dependency Structures | null | ['Marie-Francine Moens', 'R', 'Oleks Kolomiyets', 'Steven Bethard'] | 2012-07-01 | null | null | null | acl-2012-7 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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6.69384480e-01 4.61409837e-02 3.24159935e-02 -7.42885649e-01
5.37211418e-01 -3.75702560e-01 4.78586555e-01 -1.32223868e+00
1.30741090e-01 -9.61415648e-01 6.95283338e-02 9.40189242e-01
2.74610907e-01 1.20424610e-02 8.66204947e-02 5.39500564e-02
-1.44871444e-01 -5.70943117e-01 9.21121240e-01 -4.07591403e-01
-4.92852688e-01 -5.20386267e-03 -1.03226590e+00 8.97834301e-02
9.92593169e-01 -7.84614205e-01 -3.59281451e-01 -4.20183957e-01
-8.29068601e-01 -1.95836127e-02 6.50830388e-01 3.03609259e-02
7.30431557e-01 -1.51530886e+00 -2.30721906e-01 7.18729138e-01
-3.22939813e-01 -3.74400020e-01 -1.70157343e-01 6.58265352e-01
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1.85185194e-01 -9.21545625e-02 -7.54407048e-01 3.55081353e-03
6.88606799e-01 1.05194807e+00 -2.91290224e-01 -6.04341030e-01
-4.56784427e-01 6.65885091e-01 2.32151806e-01 -2.87484169e-01
9.17833567e-01 -2.19243199e-01 -9.89108324e-01 4.28274393e-01
8.38715494e-01 -1.36269778e-01 -3.69319022e-02 4.16855574e-01
1.25943512e-01 -8.02164078e-01 -4.32406247e-01 -3.55811834e-01
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8.57008278e-01 -3.02566767e-01 8.18335712e-01 9.66778398e-01
5.72402716e-01 -1.68019545e+00 -2.13140488e-01 6.89524531e-01
9.12339568e-01 -9.28350806e-01 5.44190466e-01 -7.27165341e-01
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1.27306652e+00 5.74917436e-01 1.14069022e-02 -3.70670199e-01
-9.44949508e-01 1.12705544e-01 3.75366658e-01 1.11147714e+00
5.06513238e-01 5.07374525e-01 -9.66500878e-01 3.21953118e-01
-7.28706717e-01 -2.34555230e-01 4.90474731e-01 1.41972518e+00
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-5.76629937e-02 1.49448860e+00 -1.97535474e-03 -5.03491640e-01
4.11356747e-01 1.41089618e-01 7.21762300e-01 -1.23501487e-01
-9.04846430e-01 4.15413082e-01 3.84890139e-01 -1.23056793e+00
-8.58632207e-01 -1.05834293e+00 -1.40667629e+00 -1.19437599e+00
-5.12782812e-01 1.89310342e-01 3.83317530e-01 3.90289724e-01
-2.06836104e-01 2.85260603e-02 9.80917513e-01 -1.07949340e+00
-3.90341938e-01 -9.39418614e-01 -1.00026262e+00 -7.84516707e-02
2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01] | [-7.321430683135986, 3.6746392250061035] |
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