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51e169df-7e23-491f-893c-affbddde8aad | improving-document-level-relation-extraction-1 | null | null | https://openreview.net/forum?id=wu3ADZaresn | https://openreview.net/pdf?id=wu3ADZaresn | Improving Document-level Relation Extraction via Context Guided Mention Integration and Inter-pair Reasoning | Document-level Relation Extraction (DRE) aims to recognize the relations between two entities.
The entity may correspond to multiple mentions that span beyond sentence boundary.
Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contribute to a specific relation.
Moreover, prior efforts mainly focus on reasoning at entity-level rather than capturing the global interactions between entity pairs.
In this paper, we propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve the DRE.
Instead of simply applying average pooling, the contexts are utilized to guide the integration of coreferential mentions in a weighted sum manner.
Additionally, inter-pair reasoning executes an iterative algorithm on the entity pair graph, so as to model the interdependency of relations.
We evaluate our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR, and GDA.
Experimental results show that our model outperforms previous state-of-the-art models. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.60333980e-02 5.19819796e-01 -3.64306450e-01 -3.73796433e-01
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008a3cda-e3b5-4d4f-9ccb-ac06248acbb2 | the-effect-of-metadata-on-scientific | 2302.03341 | null | https://arxiv.org/abs/2302.03341v1 | https://arxiv.org/pdf/2302.03341v1.pdf | The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study | Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies. | ['Jiawei Han', 'Yu Meng', 'Qi Zhu', 'Bowen Jin', 'Yu Zhang'] | 2023-02-07 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-3.13801229e-01 -3.12530279e-01 -6.52988553e-01 5.36665246e-02
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d5d757a8-820d-4fd6-a7da-88aea97872bf | domain-and-task-adaptation-for-vaccinchatnl-a | null | null | https://aclanthology.org/2022.coling-1.312 | https://aclanthology.org/2022.coling-1.312.pdf | Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model | FAQs are important resources to find information. However, especially if a FAQ concerns many question-answer pairs, it can be a difficult and time-consuming job to find the answer you are looking for. A FAQ chatbot can ease this process by automatically retrieving the relevant answer to a user’s question. We present VaccinChatNL, a Dutch FAQ corpus on the topic of COVID-19 vaccination. Starting with 50 question-answer pairs we built VaccinChat, a FAQ chatbot, which we used to gather more user questions that were also annotated with the appropriate or new answer classes. This iterative process of gathering user questions, annotating them, and retraining the model with the increased data set led to a corpus that now contains 12,883 user questions divided over 181 answers. We provide the first publicly available Dutch FAQ answering data set of this size with large groups of semantically equivalent human-paraphrased questions. Furthermore, our study shows that before fine-tuning a classifier, continued pre-training of Dutch language models with task- and/or domain-specific data improves classification results. In addition, we show that large groups of semantically similar questions are important for obtaining well-performing intent classification models. | ['Walter Daelemans', 'Ehsan Lotfi', 'Maxime De Bruyn', 'Jeska Buhmann'] | null | null | null | null | coling-2022-10 | ['intent-classification'] | ['natural-language-processing'] | [-1.54978096e-01 7.13501498e-02 8.22197460e-03 -4.63120639e-01
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3192d2f4-3114-481c-9b8d-7fa1551e5572 | evolvemt-an-ensemble-mt-engine-improving | 2306.11823 | null | https://arxiv.org/abs/2306.11823v1 | https://arxiv.org/pdf/2306.11823v1.pdf | EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only | This paper presents EvolveMT for efficiently combining multiple machine translation (MT) engines. The proposed system selects the output from a single engine for each segment by utilizing online learning techniques to predict the most suitable system for every translation request. A neural quality estimation metric supervises the method without requiring reference translations. The online learning capability of this system allows for dynamic adaptation to alterations in the domain or machine translation engines, thereby obviating the necessity for additional training. EvolveMT selects a subset of translation engines to be called based on the source sentence features. The degree of exploration is configurable according to the desired quality-cost trade-off. Results from custom datasets demonstrate that EvolveMT achieves similar translation accuracy at a lower cost than selecting the best translation of each segment from all translations using an MT quality estimator. To our knowledge, EvolveMT is the first meta MT system that adapts itself after deployment to incoming translation requests from the production environment without needing costly retraining on human feedback. | ['Hassan Sawaf', 'Shreyas Sharma', 'Mohamed Al-Badrashiny', 'Ahmet Gunduz', 'Kamer Ali Yuksel'] | 2023-06-20 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 1.72212392e-01 -8.69819298e-02 -3.63683313e-01 -3.31365049e-01
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891781b9-74a1-4c4d-9bc1-d66d8735eef1 | self-correction-for-human-parsing | 1910.09777 | null | https://arxiv.org/abs/1910.09777v1 | https://arxiv.org/pdf/1910.09777v1.pdf | Self-Correction for Human Parsing | Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g. human parsing, remains a challenging task. The ambiguous boundary between different semantic parts and those categories with similar appearance usually are confusing, leading to unexpected noises in ground truth masks. To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models. In particular, starting from a model trained with inaccurate annotations as initialization, we design a cyclically learning scheduler to infer more reliable pseudo-masks by iteratively aggregating the current learned model with the former optimal one in an online manner. Besides, those correspondingly corrected labels can in turn to further boost the model performance. In this way, the models and the labels will reciprocally become more robust and accurate during the self-correction learning cycles. Benefiting from the superiority of SCHP, we achieve the best performance on two popular single-person human parsing benchmarks, including LIP and Pascal-Person-Part datasets. Our overall system ranks 1st in CVPR2019 LIP Challenge. Code is available at https://github.com/PeikeLi/Self-Correction-Human-Parsing. | ['Yi Yang', 'Peike Li', 'Yunchao Wei', 'Yunqiu Xu'] | 2019-10-22 | null | null | null | null | ['human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 3.78594846e-01 3.81786168e-01 -1.58072799e-01 -6.09292984e-01
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b3794d9f-2b9a-48dc-9fec-1237d66aff06 | an-internal-learning-approach-to-video | 1909.07957 | null | https://arxiv.org/abs/1909.07957v1 | https://arxiv.org/pdf/1909.07957v1.pdf | An Internal Learning Approach to Video Inpainting | We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images. In extending DIP to video we make two important contributions. First, we show that coherent video inpainting is possible without a priori training. We take a generative approach to inpainting based on internal (within-video) learning without reliance upon an external corpus of visual data to train a one-size-fits-all model for the large space of general videos. Second, we show that such a framework can jointly generate both appearance and flow, whilst exploiting these complementary modalities to ensure mutual consistency. We show that leveraging appearance statistics specific to each video achieves visually plausible results whilst handling the challenging problem of long-term consistency. | ['Long Mai', 'John Collomosse', 'Zhaowen Wang', 'Ning Xu', 'Haotian Zhang', 'Hailin Jin'] | 2019-09-17 | an-internal-learning-approach-to-video-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_An_Internal_Learning_Approach_to_Video_Inpainting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_An_Internal_Learning_Approach_to_Video_Inpainting_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-inpainting'] | ['computer-vision'] | [ 4.26244557e-01 2.98795104e-01 -3.39404028e-03 -1.53750420e-01
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5f97b956-9322-4437-ae2d-fe2eb585449c | towards-segmenting-everything-that-moves | 1902.03715 | null | https://arxiv.org/abs/1902.03715v4 | https://arxiv.org/pdf/1902.03715v4.pdf | Towards Segmenting Anything That Moves | Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal grouping, state-of-the-art approaches do not make use of learning-based methods. To bridge this gap, we propose a simple learning-based approach for spatio-temporal grouping. Our approach leverages motion cues from optical flow as a bottom-up signal for separating objects from each other. Motion cues are then combined with appearance cues that provide a generic objectness prior for capturing the full extent of objects. We show that our approach outperforms all prior work on the benchmark FBMS dataset. One potential worry with learning-based methods is that they might overfit to the particular type of objects that they have been trained on. To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories. | ['Deva Ramanan', 'Pavel Tokmakov', 'Achal Dave'] | 2019-02-11 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 3.02117229e-01 -4.51509207e-01 -3.88452262e-01 -1.98242188e-01
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773973b6-e46b-4837-9b75-d4ec9a7c62db | the-interface-between-readability-and | null | null | https://aclanthology.org/W18-7001 | https://aclanthology.org/W18-7001.pdf | The Interface Between Readability and Automatic Text Simplification | null | ['Thomas Fran{\\c{c}}ois'] | 2018-11-01 | null | null | null | ws-2018-11 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b05f6d49-fd48-49b3-a5cd-780e9289e3e1 | visual-tactile-fusion-for-transparent-object | 2211.16693 | null | https://arxiv.org/abs/2211.16693v1 | https://arxiv.org/pdf/2211.16693v1.pdf | Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds | The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a novel grasping network named TGCNN is proposed for grasping position detection, showing good results in both synthetic and real scenes. In tactile calibration, inspired by human grasping, a fully convolutional network based tactile feature extraction method and a central location based adaptive grasping strategy are designed, improving the success rate by 36.7% compared to direct grasping. Furthermore, a visual-tactile fusion method is proposed for transparent objects classification, which improves the classification accuracy by 34%. The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. | ['Xiao-Ping Zhang', 'Xueqian Wang', 'Chongkun Xia', 'Linqi Ye', 'Houde Liu', 'Wenbo Ding', 'Haixin Yu', 'Shoujie Li'] | 2022-11-30 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 2.53181219e-01 -3.79358500e-01 3.70062768e-01 -2.68426210e-01
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28a07772-f57a-44c4-9675-a86c2c60a4d8 | event-based-motion-segmentation-by-cascaded | 2111.03483 | null | https://arxiv.org/abs/2111.03483v1 | https://arxiv.org/pdf/2111.03483v1.pdf | Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting | Among prerequisites for a synthetic agent to interact with dynamic scenes, the ability to identify independently moving objects is specifically important. From an application perspective, nevertheless, standard cameras may deteriorate remarkably under aggressive motion and challenging illumination conditions. In contrast, event-based cameras, as a category of novel biologically inspired sensors, deliver advantages to deal with these challenges. Its rapid response and asynchronous nature enables it to capture visual stimuli at exactly the same rate of the scene dynamics. In this paper, we present a cascaded two-level multi-model fitting method for identifying independently moving objects (i.e., the motion segmentation problem) with a monocular event camera. The first level leverages tracking of event features and solves the feature clustering problem under a progressive multi-model fitting scheme. Initialized with the resulting motion model instances, the second level further addresses the event clustering problem using a spatio-temporal graph-cut method. This combination leads to efficient and accurate event-wise motion segmentation that cannot be achieved by any of them alone. Experiments demonstrate the effectiveness and versatility of our method in real-world scenes with different motion patterns and an unknown number of independently moving objects. | ['Shaojie Shen', 'Yi Zhou', 'Xiuyuan Lu'] | 2021-11-05 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.84760904e-01 -5.05119205e-01 1.78617284e-01 1.47191599e-01
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63fc1c7a-99a6-4270-9ae7-b107a8298ba0 | effects-of-data-enrichment-with-image | 2306.07724 | null | https://arxiv.org/abs/2306.07724v1 | https://arxiv.org/pdf/2306.07724v1.pdf | Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks | Images cannot always be expected to come in a certain standard format and orientation. Deep networks need to be trained to take into account unexpected variations in orientation or format. For this purpose, training data should be enriched to include different conditions. In this study, the effects of data enrichment on the performance of deep networks in the super resolution problem were investigated experimentally. A total of six basic image transformations were used for the enrichment procedures. In the experiments, two deep network models were trained with variants of the ILSVRC2012 dataset enriched by these six image transformation processes. Considering a single image transformation, it has been observed that the data enriched with 180 degree rotation provides the best results. The most unsuccessful result was obtained when the models were trained on the enriched data generated by the flip upside down process. Models scored highest when trained with a mix of all transformations. | ['Hakan Temiz'] | 2023-06-13 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 3.13695580e-01 2.55161501e-03 2.07291573e-01 -5.28028607e-01
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33b1b072-939b-4d90-907c-63e403e6d123 | target-concept-guided-medical-concept | null | null | https://aclanthology.org/2020.deelio-1.8 | https://aclanthology.org/2020.deelio-1.8.pdf | Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts | Medical concept normalization (MCN) i.e., mapping of colloquial medical phrases to standard concepts is an essential step in analysis of medical social media text. The main drawback in existing state-of-the-art approach (Kalyan and Sangeetha, 2020b) is learning target concept vector representations from scratch which requires more number of training instances. Our model is based on RoBERTa and target concept embeddings. In our model, we integrate a) target concept information in the form of target concept vectors generated by encoding target concept descriptions using SRoBERTa, state-of-the-art RoBERTa based sentence embedding model and b) domain lexicon knowledge by enriching target concept vectors with synonym relationship knowledge using retrofitting algorithm. It is the first attempt in MCN to exploit both target concept information as well as domain lexicon knowledge in the form of retrofitted target concept vectors. Our model outperforms all the existing models with an accuracy improvement up to 1.36% on three standard datasets. Further, our model when trained only on mapping lexicon synonyms achieves up to 4.87% improvement in accuracy. | ['Sivanesan Sangeetha', 'Katikapalli Subramanyam Kalyan'] | null | null | null | null | emnlp-deelio-2020-11 | ['medical-concept-normalization'] | ['medical'] | [ 6.08879685e-01 2.76024073e-01 -3.33514124e-01 -2.55530983e-01
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261652b3-e9aa-4f3b-9a08-8d147027ff05 | application-of-deep-neural-networks-to-assess | 2003.02334 | null | https://arxiv.org/abs/2003.02334v1 | https://arxiv.org/pdf/2003.02334v1.pdf | Application of Deep Neural Networks to assess corporate Credit Rating | Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selected subset of features, or if it is better to allow the algorithms to select features themselves. Second, is the temporal aspect inherent in financial data important for the results obtained by a machine learning algorithm? Third, is there a particular neural network architecture that consistently outperforms others with respect to input features, sectors and holdout set? We create several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedure. | ['Parisa Golbayani', 'Ionut Florescu', 'Dan Wang'] | 2020-03-04 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [-0.17744206 -0.13011084 -0.23143615 -0.44358867 -0.20680556 -0.51451176
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7b91d01e-5e8b-4564-a0fc-3d7c41eedfcc | socially-and-contextually-aware-human-motion | 2007.06843 | null | https://arxiv.org/abs/2007.06843v1 | https://arxiv.org/pdf/2007.06843v1.pdf | Socially and Contextually Aware Human Motion and Pose Forecasting | Smooth and seamless robot navigation while interacting with humans depends on predicting human movements. Forecasting such human dynamics often involves modeling human trajectories (global motion) or detailed body joint movements (local motion). Prior work typically tackled local and global human movements separately. In this paper, we propose a novel framework to tackle both tasks of human motion (or trajectory) and body skeleton pose forecasting in a unified end-to-end pipeline. To deal with this real-world problem, we consider incorporating both scene and social contexts, as critical clues for this prediction task, into our proposed framework. To this end, we first couple these two tasks by i) encoding their history using a shared Gated Recurrent Unit (GRU) encoder and ii) applying a metric as loss, which measures the source of errors in each task jointly as a single distance. Then, we incorporate the scene context by encoding a spatio-temporal representation of the video data. We also include social clues by generating a joint feature representation from motion and pose of all individuals from the scene using a social pooling layer. Finally, we use a GRU based decoder to forecast both motion and skeleton pose. We demonstrate that our proposed framework achieves a superior performance compared to several baselines on two social datasets. | ['Vida Adeli', 'Juan Carlos Niebles', 'Ehsan Adeli', 'Ian Reid', 'Hamid Rezatofighi'] | 2020-07-14 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [ 1.06959999e-01 1.21968538e-01 1.51656047e-01 -5.34465849e-01
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8d295b4c-ddc2-4b56-a8ff-c235da6206c3 | color-constancy-by-gans-an-experimental | 1812.03085 | null | http://arxiv.org/abs/1812.03085v1 | http://arxiv.org/pdf/1812.03085v1.pdf | Color Constancy by GANs: An Experimental Survey | In this paper, we formulate the color constancy task as an image-to-image
translation problem using GANs. By conducting a large set of experiments on
different datasets, an experimental survey is provided on the use of different
types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs).
Based on the experimental review, recommendations are given for the design of
CC-GAN architectures based on different criteria, circumstances and datasets. | ['Theo Gevers', 'Sezer Karaoglu', 'Anil S. Baslamisli', 'Yang Liu', 'Partha Das'] | 2018-12-07 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.60655123e-01 -6.40711561e-02 -1.01836964e-01 -4.79965478e-01
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9e2061c0-cd85-4891-aacf-862768505beb | modeling-and-design-of-heterogeneous | 2304.05137 | null | https://arxiv.org/abs/2304.05137v1 | https://arxiv.org/pdf/2304.05137v1.pdf | Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing | Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties. | ['Markus J. Buehler', 'Nic A. Lee', 'Wei Lu'] | 2023-04-11 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 9.75814834e-02 2.73395777e-01 1.75248742e-01 2.47679889e-01
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00526faa-e6a9-4daf-8490-2a869c7e5241 | hts-at-a-hierarchical-token-semantic-audio | 2202.00874 | null | https://arxiv.org/abs/2202.00874v1 | https://arxiv.org/pdf/2202.00874v1.pdf | HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection | Audio classification is an important task of mapping audio samples into their corresponding labels. Recently, the transformer model with self-attention mechanisms has been adopted in this field. However, existing audio transformers require large GPU memories and long training time, meanwhile relying on pretrained vision models to achieve high performance, which limits the model's scalability in audio tasks. To combat these problems, we introduce HTS-AT: an audio transformer with a hierarchical structure to reduce the model size and training time. It is further combined with a token-semantic module to map final outputs into class featuremaps, thus enabling the model for the audio event detection (i.e. localization in time). We evaluate HTS-AT on three datasets of audio classification where it achieves new state-of-the-art (SOTA) results on AudioSet and ESC-50, and equals the SOTA on Speech Command V2. It also achieves better performance in event localization than the previous CNN-based models. Moreover, HTS-AT requires only 35% model parameters and 15% training time of the previous audio transformer. These results demonstrate the high performance and high efficiency of HTS-AT. | ['Shlomo Dubnov', 'Taylor Berg-Kirkpatrick', 'Zejun Ma', 'Bilei Zhu', 'Xingjian Du', 'Ke Chen'] | 2022-02-02 | null | null | null | null | ['sound-classification', 'keyword-spotting'] | ['audio', 'speech'] | [-1.84678316e-01 -4.67558831e-01 1.78544685e-01 -2.03575715e-01
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1ca91ad1-f516-4c52-a047-6b385c44ba99 | gpu-based-computation-of-2d-least-median-of | 1510.01041 | null | http://arxiv.org/abs/1510.01041v1 | http://arxiv.org/pdf/1510.01041v1.pdf | GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection | The 2D Least Median of Squares (LMS) is a popular tool in robust regression
because of its high breakdown point: up to half of the input data can be
contaminated with outliers without affecting the accuracy of the LMS estimator.
The complexity of 2D LMS estimation has been shown to be $\Omega(n^2)$ where
$n$ is the total number of points. This high theoretical complexity along with
the availability of graphics processing units (GPU) motivates the development
of a fast, parallel, GPU-based algorithm for LMS computation. We present a CUDA
based algorithm for LMS computation and show it to be much faster than the
optimal state of the art single threaded CPU algorithm. We begin by describing
the proposed method and analyzing its performance. We then demonstrate how it
can be used to modify the well-known Hough Transform (HT) in order to
efficiently detect image lines in noisy images. Our method is compared with
standard HT-based line detection methods and shown to overcome their
shortcomings in terms of both efficiency and accuracy. | ['Gil Shapira', 'Tal Hassner'] | 2015-10-05 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.28302827e-01 -4.22030360e-01 5.21528542e-01 -1.27398759e-01
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10b6b639-a332-4e24-80a0-67fa68dc0d31 | collaborative-static-and-dynamic-vision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.pdf | Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding | Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatio-temporal visual-linguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall framework achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets. | ['Wei-Shi Zheng', 'Tiancai Ye', 'Zhi Jin', 'Jian-Fang Hu', 'Chaolei Tan', 'Zihang Lin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [-1.70216486e-01 -3.69945258e-01 -3.10027361e-01 -4.69668031e-01
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c4028d89-ebaf-4616-a5e6-b5e47ca78ee9 | taxonomy-expansion-for-named-entity | 2305.13191 | null | https://arxiv.org/abs/2305.13191v1 | https://arxiv.org/pdf/2305.13191v1.pdf | Taxonomy Expansion for Named Entity Recognition | Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize additional entity types. A simple approach is to re-annotate entire dataset with both existing and additional entity types and then train the model on the re-annotated dataset. However, this is an extremely laborious task. To remedy this, we propose a novel approach called Partial Label Model (PLM) that uses only partially annotated datasets. We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy expansion not considered in prior work. The gap between PLM and all other approaches is especially large in settings where there is limited data available for the additional entity types (as much as 11 F1), thus suggesting a more cost effective approaches to taxonomy expansion. | ['Miguel Ballesteros', 'Dan Roth', 'Vittorio Castelli', 'Yassine Benajiba', 'Shuai Wang', 'Neha Anna John', 'Giovanni Paolini', 'Jie Ma', 'Yogarshi Vyas', 'Karthikeyan K'] | 2023-05-22 | null | null | null | null | ['named-entity-recognition-ner', 'taxonomy-expansion'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.50596090e-03 2.83746243e-01 -2.45148227e-01 -4.68868732e-01
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11ca6d82-95ae-4417-b775-e8b11f2bfe2b | advanced-semantics-for-commonsense-knowledge | 2011.00905 | null | https://arxiv.org/abs/2011.00905v4 | https://arxiv.org/pdf/2011.00905v4.pdf | Advanced Semantics for Commonsense Knowledge Extraction | Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent. A web interface, data and code can be found at https://ascent.mpi-inf.mpg.de/. | ['Gerhard Weikum', 'Simon Razniewski', 'Tuan-Phong Nguyen'] | 2020-11-02 | null | null | null | null | ['commonsense-knowledge-base-construction'] | ['knowledge-base'] | [-4.34728354e-01 4.67438757e-01 -4.35022295e-01 -3.42252135e-01
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d07afb81-bcd9-4c48-a382-8a7c67936631 | impossible-triangle-what-s-next-for-pre | 2204.06130 | null | https://arxiv.org/abs/2204.06130v2 | https://arxiv.org/pdf/2204.06130v2.pdf | Impossible Triangle: What's Next for Pre-trained Language Models? | Recent development of large-scale pre-trained language models (PLM) have significantly improved the capability of models in various NLP tasks, in terms of performance after task-specific fine-tuning and zero-shot / few-shot learning. However, many of such models come with a dauntingly huge size that few institutions can afford to pre-train, fine-tune or even deploy, while moderate-sized models usually lack strong generalized few-shot learning capabilities. In this paper, we first elaborate the current obstacles of using PLM models in terms of the Impossible Triangle: 1) moderate model size, 2) state-of-the-art few-shot learning capability, and 3) state-of-the-art fine-tuning capability. We argue that all existing PLM models lack one or more properties from the Impossible Triangle. To remedy these missing properties of PLMs, various techniques have been proposed, such as knowledge distillation, data augmentation and prompt learning, which inevitably brings additional work to the application of PLMs in real scenarios. We then offer insights into future research directions of PLMs to achieve the Impossible Triangle, and break down the task into several key phases. | ['Michael Zeng', 'Chenguang Zhu'] | 2022-04-13 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 7.51347421e-03 1.21127188e-01 -5.39270699e-01 -1.24012701e-01
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d3bc3fc4-fb3c-4247-9243-70483708894e | llama-open-and-efficient-foundation-language-1 | 2302.13971 | null | https://arxiv.org/abs/2302.13971v1 | https://arxiv.org/pdf/2302.13971v1.pdf | LLaMA: Open and Efficient Foundation Language Models | We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community. | ['Guillaume Lample', 'Edouard Grave', 'Armand Joulin', 'Aurelien Rodriguez', 'Faisal Azhar', 'Eric Hambro', 'Naman Goyal', 'Baptiste Rozière', 'Timothée Lacroix', 'Marie-Anne Lachaux', 'Xavier Martinet', 'Gautier Izacard', 'Thibaut Lavril', 'Hugo Touvron'] | 2023-02-27 | llama-open-and-efficient-foundation-language | https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ | https://research.facebook.com/file/1574548786327032/LLaMA--Open-and-Efficient-Foundation-Language-Models.pdf | arxiv-2023-2 | ['math-word-problem-solving', 'multi-task-language-understanding', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'methodology', 'reasoning', 'time-series'] | [-5.05362093e-01 -3.18572074e-01 -6.82029605e-01 -1.43722504e-01
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793d8276-9881-49e1-b9f7-3611d23225d7 | object-segmentation-by-mining-cross-modal | 2305.10469 | null | https://arxiv.org/abs/2305.10469v2 | https://arxiv.org/pdf/2305.10469v2.pdf | Object Segmentation by Mining Cross-Modal Semantics | Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy. We explore semantics among the multimodal inputs in two aspects: the modality-shared consistency and the modality-specific variation. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision. On the one hand, the AF block explicitly dissociates the shared and specific representation and learns to weight the modal contribution by adjusting the proportion, region, and pattern, depending upon the quality. On the other hand, our CFD initially decodes the shared feature and then refines the output through specificity-aware querying. Further, we enforce semantic consistency across the decoding layers to enable interaction across network hierarchies, improving feature discriminability. Exhaustive comparison on eleven datasets with depth or thermal clues, and on two challenging tasks, namely salient and camouflage object segmentation, validate our effectiveness in terms of both performance and robustness. | ['Radu Timofte', 'Guolei Sun', 'Cédric Demonceaux', 'Qiuping Jiang', 'Zhaochong An', 'Zhuyun Zhou', 'Jingjing Wang', 'Zongwei Wu'] | 2023-05-17 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 4.77672338e-01 -7.78267384e-02 -2.87272632e-01 -7.12949753e-01
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990eba05-e171-41b5-9f6f-d70d61726ba2 | truncated-affinity-maximization-one-class | 2306.00006 | null | https://arxiv.org/abs/2306.00006v2 | https://arxiv.org/pdf/2306.00006v2.pdf | Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection | One prevalent property we find empirically in real-world graph anomaly detection (GAD) datasets is a one-class homophily, i.e., normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker than normal nodes. However, this anomaly-discriminative property is ignored by existing GAD methods that are typically built using a conventional anomaly detection objective, such as data reconstruction. In this work, we explore this property to introduce a novel unsupervised anomaly scoring measure for GAD -- local node affinity -- that assigns a larger anomaly score to nodes that are less affiliated with their neighbors, with the affinity defined as similarity on node attributes/representations. We further propose Truncated Affinity Maximization (TAM) that learns tailored node representations for our anomaly measure by maximizing the local affinity of nodes to their neighbors. Optimizing on the original graph structure can be biased by non-homophily edges (i.e., edges connecting normal and abnormal nodes). Thus, TAM is instead optimized on truncated graphs where non-homophily edges are removed iteratively to mitigate this bias. The learned representations result in significantly stronger local affinity for normal nodes than abnormal nodes. Extensive empirical results on six real-world GAD datasets show that TAM substantially outperforms seven competing models, achieving over 10% increase in AUROC/AUPRC compared to the best contenders on challenging datasets. Our code will be made available at https: //github.com/mala-lab/TAM-master/. | ['Guansong Pang', 'Hezhe Qiao'] | 2023-05-29 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [-4.28111963e-02 4.95297343e-01 -2.49019906e-01 -4.98523861e-01
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7888f508-0d01-4880-8f7d-9e0dc4a6010e | vgr-net-a-view-invariant-gait-recognition | 1710.04803 | null | http://arxiv.org/abs/1710.04803v1 | http://arxiv.org/pdf/1710.04803v1.pdf | VGR-Net: A View Invariant Gait Recognition Network | Biometric identification systems have become immensely popular and important
because of their high reliability and efficiency. However person identification
at a distance, still remains a challenging problem. Gait can be seen as an
essential biometric feature for human recognition and identification. It can be
easily acquired from a distance and does not require any user cooperation thus
making it suitable for surveillance. But the task of recognizing an individual
using gait can be adversely affected by varying view points making this task
more and more challenging. Our proposed approach tackles this problem by
identifying spatio-temporal features and performing extensive experimentation
and training mechanism. In this paper, we propose a 3-D Convolution Deep Neural
Network for person identification using gait under multiple view. It is a
2-stage network, in which we have a classification network that initially
identifies the viewing point angle. After that another set of networks (one for
each angle) has been trained to identify the person under a particular viewing
angle. We have tested this network over CASIA-B publicly available database and
have achieved state-of-the-art results. The proposed system is much more
efficient in terms of time and space and performing better for almost all
angles. | ['Divyansh Aggarwal', 'Daksh Thapar', 'Aditya Nigam', 'Punjal Agarwal'] | 2017-10-13 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-7.62093952e-03 -8.59063447e-01 2.01779306e-01 -3.99010956e-01
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355194fe-d617-455f-a77a-73fb8f598c51 | label-semantic-knowledge-distillation-for | 2208.03763 | null | https://arxiv.org/abs/2208.03763v1 | https://arxiv.org/pdf/2208.03763v1.pdf | Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation | The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over the last few years, almost all existing SGG models follow the same training paradigm: they treat both object and predicate classification in SGG as a single-label classification problem, and the ground-truths are one-hot target labels. However, this prevalent training paradigm has overlooked two characteristics of current SGG datasets: 1) For positive samples, some specific subject-object instances may have multiple reasonable predicates. 2) For negative samples, there are numerous missing annotations. Regardless of the two characteristics, SGG models are easy to be confused and make wrong predictions. To this end, we propose a novel model-agnostic Label Semantic Knowledge Distillation (LS-KD) for unbiased SGG. Specifically, LS-KD dynamically generates a soft label for each subject-object instance by fusing a predicted Label Semantic Distribution (LSD) with its original one-hot target label. LSD reflects the correlations between this instance and multiple predicate categories. Meanwhile, we propose two different strategies to predict LSD: iterative self-KD and synchronous self-KD. Extensive ablations and results on three SGG tasks have attested to the superiority and generality of our proposed LS-KD, which can consistently achieve decent trade-off performance between different predicate categories. | ['Jun Xiao', 'Yi Yang', 'Jian Shao', 'Wenxiao Wang', 'Hanrong Shi', 'Long Chen', 'Lin Li'] | 2022-08-07 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 4.80993390e-01 4.49607223e-01 -4.19759125e-01 -6.10854506e-01
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9feff4dc-f308-40c5-b229-212b58c06eba | uncovering-energy-efficient-practices-in-deep | 2303.13972 | null | https://arxiv.org/abs/2303.13972v1 | https://arxiv.org/pdf/2303.13972v1.pdf | Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI | Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevant tasks or energy usage. We examine the training stage of the deep learning pipeline from a sustainability perspective, through the study of hyperparameter tuning strategies and the model complexity, two factors vastly impacting the overall pipeline's energy consumption. First, we investigate the effectiveness of grid search, random search and Bayesian optimisation during hyperparameter tuning, and we find that Bayesian optimisation significantly dominates the other strategies. Furthermore, we analyse the architecture of convolutional neural networks with the energy consumption of three prominent layer types: convolutional, linear and ReLU layers. The results show that convolutional layers are the most computationally expensive by a strong margin. Additionally, we observe diminishing returns in accuracy for more energy-hungry models. The overall energy consumption of training can be halved by reducing the network complexity. In conclusion, we highlight innovative and promising energy-efficient practices for training deep learning models. To expand the application of Green AI, we advocate for a shift in the design of deep learning models, by considering the trade-off between energy efficiency and accuracy. | ['Arie van Deursen', 'June Sallou', 'Daniel Feitosa', 'Luís Cruz', 'Tim Yarally'] | 2023-03-24 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.34087905e-01 5.93737401e-02 -2.26895228e-01 -1.07735731e-01
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c3556a8c-3e12-4efc-bac9-ef8ccbb1cc8a | boosting-multi-label-image-classification | 2205.10986 | null | https://arxiv.org/abs/2205.10986v1 | https://arxiv.org/pdf/2205.10986v1.pdf | Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation | Multi-Label Image Classification (MLIC) approaches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook discriminative features of the target itself and lead to model overfitting, thus undermining the performance. In this study, we propose a generic framework named Parallel Self-Distillation (PSD) for boosting MLIC models. PSD decomposes the original MLIC task into several simpler MLIC sub-tasks via two elaborated complementary task decomposition strategies named Co-occurrence Graph Partition (CGP) and Dis-occurrence Graph Partition (DGP). Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels. Finally, knowledge distillation is leveraged to learn a compact global ensemble of full categories with these learned patterns for reconciling the label correlation exploitation and model overfitting. Extensive results on MS-COCO and NUS-WIDE datasets demonstrate that our framework can be easily plugged into many MLIC approaches and improve performances of recent state-of-the-art approaches. The explainable visual study also further validates that our method is able to learn both the category-specific and co-occurring features. The source code is released at https://github.com/Robbie-Xu/CPSD. | ['Bo Liu', 'Daniel Zeng', 'Luwen Huangfu', 'Fengtao Zhou', 'Sheng Huang', 'Jiazhi Xu'] | 2022-05-23 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.68123001e-01 -6.01941906e-03 -6.15165412e-01 -4.94272023e-01
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af464d59-bc54-49ff-9e61-9e038018df7d | nafssr-stereo-image-super-resolution-using | 2204.08714 | null | https://arxiv.org/abs/2204.08714v2 | https://arxiv.org/pdf/2204.08714v2.pdf | NAFSSR: Stereo Image Super-Resolution Using NAFNet | Stereo image super-resolution aims at enhancing the quality of super-resolution results by utilizing the complementary information provided by binocular systems. To obtain reasonable performance, most methods focus on finely designing modules, loss functions, and etc. to exploit information from another viewpoint. This has the side effect of increasing system complexity, making it difficult for researchers to evaluate new ideas and compare methods. This paper inherits a strong and simple image restoration model, NAFNet, for single-view feature extraction and extends it by adding cross attention modules to fuse features between views to adapt to binocular scenarios. The proposed baseline for stereo image super-resolution is noted as NAFSSR. Furthermore, training/testing strategies are proposed to fully exploit the performance of NAFSSR. Extensive experiments demonstrate the effectiveness of our method. In particular, NAFSSR outperforms the state-of-the-art methods on the KITTI 2012, KITTI 2015, Middlebury, and Flickr1024 datasets. With NAFSSR, we won 1st place in the NTIRE 2022 Stereo Image Super-resolution Challenge. Codes and models will be released at https://github.com/megvii-research/NAFNet. | ['Wenqing Yu', 'Liangyu Chen', 'Xiaojie Chu'] | 2022-04-19 | null | null | null | null | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 2.18269452e-01 -2.12127090e-01 -1.39320627e-01 -3.96332681e-01
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355a3c2f-f0e8-46fb-a1d7-ccd8c3301091 | pliers-a-popularity-based-recommender-system | 2307.02865 | null | https://arxiv.org/abs/2307.02865v1 | https://arxiv.org/pdf/2307.02865v1.pdf | PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks | In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a good tradeoff between algorithmic complexity and the level of personalization of recommended items. To evaluate PLIERS, we performed a set of experiments on real OSN datasets, demonstrating that it outperforms state-of-the-art solutions in terms of personalization, relevance, and novelty of recommendations. | ['Elena Pagani', 'Franca Delmastro', 'Mattia Giovanni Campana', 'Valerio Arnaboldi'] | 2023-07-06 | null | null | null | null | ['recommendation-systems'] | ['miscellaneous'] | [-4.23433632e-01 -1.89418003e-01 -5.12288749e-01 -2.21538007e-01
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461a069b-bf7f-421a-812e-80a70cc02e7a | multi-scale-progressive-fusion-network-for | 2003.10985 | null | https://arxiv.org/abs/2003.10985v2 | https://arxiv.org/pdf/2003.10985v2.pdf | Multi-Scale Progressive Fusion Network for Single Image Deraining | Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal. For similar rain streaks at different positions, we employ recurrent calculation to capture the global texture, thus allowing to explore the complementary and redundant information at the spatial dimension to characterize target rain streaks. Besides, we construct multi-scale pyramid structure, and further introduce the attention mechanism to guide the fine fusion of this correlated information from different scales. This multi-scale progressive fusion strategy not only promotes the cooperative representation, but also boosts the end-to-end training. Our proposed method is extensively evaluated on several benchmark datasets and achieves state-of-the-art results. Moreover, we conduct experiments on joint deraining, detection, and segmentation tasks, and inspire a new research direction of vision task-driven image deraining. The source code is available at \url{https://github.com/kuihua/MSPFN}. | ['Chen Chen', 'Kui Jiang', 'Junjun Jiang', 'Zhongyuan Wang', 'Jiayi Ma', 'Baojin Huang', 'Yimin Luo', 'Peng Yi'] | 2020-03-24 | multi-scale-progressive-fusion-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.pdf | cvpr-2020-6 | ['single-image-deraining'] | ['computer-vision'] | [ 6.61821365e-02 -7.04121172e-01 3.14156681e-01 -4.03379887e-01
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6e99d627-f7c0-4435-a612-a2cd81b0f087 | 3d-face-arbitrary-style-transfer | 2303.07709 | null | https://arxiv.org/abs/2303.07709v1 | https://arxiv.org/pdf/2303.07709v1.pdf | 3D Face Arbitrary Style Transfer | Style transfer of 3D faces has gained more and more attention. However, previous methods mainly use images of artistic faces for style transfer while ignoring arbitrary style images such as abstract paintings. To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST). To begin with, FDST employs a 3D decoupling module to separate facial geometry and texture. Then we propose a style fusion strategy for facial geometry. Subsequently, we design an optimization-based DDSG mechanism for textures that can guide the style transfer by two style images. Besides the normal style image input, DDSG can utilize the original face input as another style input as the face prior. By this means, high-quality face arbitrary style transfer results can be obtained. Furthermore, FDST can be applied in many downstream tasks, including region-controllable style transfer, high-fidelity face texture reconstruction, large-pose face reconstruction, and artistic face reconstruction. Comprehensive quantitative and qualitative results show that our method can achieve comparable performance. All source codes and pre-trained weights will be released to the public. | ['Haoqian Wang', 'Jiawei Zhou', 'Yuxiao Liu', 'Zhifang Liu', 'Yang Liu', 'Chendong Zhao', 'Yuanhao Cai', 'Yingshuang Zou', 'Xiangwen Deng'] | 2023-03-14 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.20166528e-01 -3.64519656e-03 9.19819325e-02 -5.73004067e-01
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58a2ce9a-d7bf-4993-beda-60a64aa83f94 | team-pku-wict-mipl-pic-makeup-temporal-video | 2207.02687 | null | https://arxiv.org/abs/2207.02687v1 | https://arxiv.org/pdf/2207.02687v1.pdf | Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report | In this technical report, we briefly introduce the solutions of our team `PKU-WICT-MIPL' for the PIC Makeup Temporal Video Grounding (MTVG) Challenge in ACM-MM 2022. Given an untrimmed makeup video and a step query, the MTVG aims to localize a temporal moment of the target makeup step in the video. To tackle this task, we propose a phrase relationship mining framework to exploit the temporal localization relationship relevant to the fine-grained phrase and the whole sentence. Besides, we propose to constrain the localization results of different step sentence queries to not overlap with each other through a dynamic programming algorithm. The experimental results demonstrate the effectiveness of our method. Our final submission ranked 2nd on the leaderboard, with only a 0.55\% gap from the first. | ['Yang Liu', 'Yuxin Peng', 'Ting Lei', 'Zhongjie Ye', 'Dejie Yang', 'Minghang Zheng'] | 2022-07-06 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-1.07337888e-02 -1.49034873e-01 -5.47836423e-01 -1.79628760e-01
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cd5993d0-4e63-4773-a10e-da6931c6b83a | interactive-text-ranking-with-bayesian | 1911.10183 | null | https://arxiv.org/abs/1911.10183v3 | https://arxiv.org/pdf/1911.10183v3.pdf | Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation | For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user's labelling effort on high quality candidates and integrates prior knowledge in a Bayesian manner to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation. | ['Yang Gao', 'Iryna Gurevych', 'Edwin Simpson'] | 2019-11-22 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 6.64301455e-01 6.26569152e-01 -2.12314561e-01 -3.29039395e-01
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49b1ae72-71e8-4719-a18f-626a8abaf8f8 | an-ensemble-based-approach-by-fine-tuning-the | 2011.05543 | null | https://arxiv.org/abs/2011.05543v1 | https://arxiv.org/pdf/2011.05543v1.pdf | An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images | Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely used by radiologists to detect pneumonia. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. In this work, we propose using transfer learning, which can reduce the neural network's training time and minimize the generalization error. We trained, fine-tuned the state-of-the-art deep learning models such as InceptionResNet, MobileNetV2, Xception, DenseNet201, and ResNet152V2 to classify pneumonia accurately. Later, we created a weighted average ensemble of these models and achieved a test accuracy of 98.46%, precision of 98.38%, recall of 99.53%, and f1 score of 98.96%. These performance metrics of accuracy, precision, and f1 score are at their highest levels ever reported in the literature, which can be considered a benchmark for the accurate pneumonia classification. | ['Sagar Kora Venu'] | 2020-11-11 | null | null | null | null | ['pneumonia-detection', 'respiratory-failure'] | ['medical', 'medical'] | [ 8.87079071e-03 -3.88698518e-01 -1.00182720e-01 2.00674519e-01
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89b9db9e-4d80-4762-a21c-f26497492cf3 | ratio-preserving-half-cylindrical-warps-for | 1803.06655 | null | http://arxiv.org/abs/1803.06655v1 | http://arxiv.org/pdf/1803.06655v1.pdf | Ratio-Preserving Half-Cylindrical Warps for Natural Image Stitching | A novel warp for natural image stitching is proposed that utilizes the
property of cylindrical warp and a horizontal pixel selection strategy. The
proposed ratio-preserving half-cylindrical warp is a combination of homography
and cylindrical warps which guarantees alignment by homography and possesses
less projective distortion by cylindrical warp. Unlike previous approaches
applying cylindrical warp before homography, we use partition lines to divide
the image into different parts and apply homography in the overlapping region
while a composition of homography and cylindrical warps in the non-overlapping
region. The pixel selection strategy then samples the points in horizontal and
reconstructs the image via interpolation to further reduce horizontal
distortion by maintaining the ratio as similarity. With applying
half-cylindrical warp and horizontal pixel selection, the projective distortion
in vertical and horizontal is mitigated simultaneously. Experiments show that
our warp is efficient and produces a more natural-looking stitched result than
previous methods. | ['Tianli Liao', 'Yifang Xu', 'Jing Chen'] | 2018-03-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.89314938e-01 -3.71446609e-02 -1.74361065e-01 2.37966135e-01
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3057658c-912c-4c1c-8495-511b1b42b0c3 | tcr-short-video-title-generation-and-cover | 2304.12561 | null | https://arxiv.org/abs/2304.12561v1 | https://arxiv.org/pdf/2304.12561v1.pdf | TCR: Short Video Title Generation and Cover Selection with Attention Refinement | With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab viewers' attention. Existing studies on video captioning mostly focus on generating factual descriptions of actions, which do not conform to video titles intended for catching viewer attention. Furthermore, research for cover selection based on multimodal information is sparse. These problems motivate the need for tailored methods to specifically support the joint task of short video title generation and cover selection (TG-CS) as well as the demand for creating corresponding datasets to support the studies. In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers. We then propose a Title generation and Cover selection with attention Refinement (TCR) method for TG-CS. The refinement procedure progressively selects high-quality samples and highly relevant frames and text tokens within each sample to refine model training. Extensive experiments show that our TCR method is superior to various existing video captioning methods in generating titles and is able to select better covers for noisy real-world short videos. | ['Di Niu', 'Yu Xu', 'Hui Liu', 'Weidong Guo', 'Jiuding Yang', 'Yakun Yu'] | 2023-04-25 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 6.32938087e-01 -2.91261747e-02 -4.83397424e-01 -2.46273011e-01
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c42122c8-71cd-4000-9830-9ffb4d2cc86a | usb-a-unified-semi-supervised-learning | 2208.07204 | null | https://arxiv.org/abs/2208.07204v2 | https://arxiv.org/pdf/2208.07204v2.pdf | USB: A Unified Semi-supervised Learning Benchmark for Classification | Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) for classification by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate the dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation of these SSL methods. We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning. USB enables the evaluation of a single SSL algorithm on more tasks from multiple domains but with less cost. Specifically, on a single NVIDIA V100, only 39 GPU days are required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with TorchSSL. | ['Yue Zhang', 'Xing Xie', 'Jindong Wang', 'Bernt Schiele', 'Takahiro Shinozaki', 'Bhiksha Raj', 'Marios Savvides', 'Wei Ye', 'Satoshi Nakamura', 'Yu-Feng Li', 'Zhen Wu', 'Heli Qi', 'Lan-Zhe Guo', 'Zhi Zhou', 'Linyi Yang', 'RenJie Wang', 'Wenxin Hou', 'Ran Tao', 'Wang Sun', 'Yue Fan', 'Hao Chen', 'Yidong Wang'] | 2022-08-12 | null | null | null | null | ['classification'] | ['methodology'] | [-3.61225903e-02 -4.89627272e-01 -2.28537619e-01 -6.52099133e-01
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9ddaf78e-ab3b-49c0-babe-4ade700a25d9 | luminous-indoor-scene-generation-for-embodied | 2111.05527 | null | https://arxiv.org/abs/2111.05527v1 | https://arxiv.org/pdf/2111.05527v1.pdf | LUMINOUS: Indoor Scene Generation for Embodied AI Challenges | Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts. This paper presents Luminous, the first research framework that employs state-of-the-art indoor scene synthesis algorithms to generate large-scale simulated scenes for Embodied AI challenges. Further, we automatically and quantitatively evaluate the quality of generated indoor scenes via their ability to support complex household tasks. Luminous incorporates a novel scene generation algorithm (Constrained Stochastic Scene Generation (CSSG)), which achieves competitive performance with human-designed scenes. Within Luminous, the EAI task executor, task instruction generation module, and video rendering toolkit can collectively generate a massive multimodal dataset of new scenes for the training and evaluation of Embodied AI agents. Extensive experimental results demonstrate the effectiveness of the data generated by Luminous, enabling the comprehensive assessment of embodied agents on generalization and robustness. | ['Gaurav S. Sukhatme', 'Jesse Thomason', 'Govind Thattai', 'Qiaozi Gao', 'Zhiwei Jia', 'Kaixiang Lin', 'Yizhou Zhao'] | 2021-11-10 | null | null | null | null | ['scene-generation', 'indoor-scene-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.99750328e-01 -1.15167670e-01 8.54268253e-01 -3.99403691e-01
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c90bda37-07ab-4897-8e0a-9620ece1cc7a | linear-to-multi-linear-algebra-and-systems | 2304.10658 | null | https://arxiv.org/abs/2304.10658v1 | https://arxiv.org/pdf/2304.10658v1.pdf | Linear to multi-linear algebra and systems using tensors | In past few decades, tensor algebra also known as multi-linear algebra has been developed and customized as a tool to be used for various engineering applications. In particular, with the help of a special form of tensor contracted product, known as the Einstein Product and its properties, many of the known concepts from Linear Algebra could be extended to a multi-linear setting. This enables to define the notions of multi-linear system theory where the input, output signals and the system are multi-domain in nature. This paper provides an overview of tensor algebra tools which can be seen as an extension of linear algebra, at the same time highlighting the difference and advantages that the multi-linear setting brings forth. In particular, the notion of tensor inversion, tensor singular value and tensor Eigenvalue decomposition using the Einstein product is explained. In addition, this paper also introduces the notion of contracted convolution in both discrete and continuous multi-linear system tensors. Tensor Networks representation of various tensor operations is also presented. Also, application of tensor tools in developing transceiver schemes for multi-domain communication systems, with an example of MIMO CDMA systems, is presented. Thus this paper acts as an entry point tutorial for graduate students whose research involves multi-domain or multi-modal signals and systems. | ['Harry Leib', 'Adithya Venugopal', 'Divyanshu Pandey'] | 2023-04-20 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-7.53593147e-02 -2.75788426e-01 3.44963014e-01 -2.85040718e-02
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597e5503-5aa1-4b75-af1a-71757a9c22f9 | bert-based-multilingual-machine-comprehension | 2006.01432 | null | https://arxiv.org/abs/2006.01432v1 | https://arxiv.org/pdf/2006.01432v1.pdf | BERT Based Multilingual Machine Comprehension in English and Hindi | Multilingual Machine Comprehension (MMC) is a Question-Answering (QA) sub-task that involves quoting the answer for a question from a given snippet, where the question and the snippet can be in different languages. Recently released multilingual variant of BERT (m-BERT), pre-trained with 104 languages, has performed well in both zero-shot and fine-tuned settings for multilingual tasks; however, it has not been used for English-Hindi MMC yet. We, therefore, present in this article, our experiments with m-BERT for MMC in zero-shot, mono-lingual (e.g. Hindi Question-Hindi Snippet) and cross-lingual (e.g. English QuestionHindi Snippet) fine-tune setups. These model variants are evaluated on all possible multilingual settings and results are compared against the current state-of-the-art sequential QA system for these languages. Experiments show that m-BERT, with fine-tuning, improves performance on all evaluation settings across both the datasets used by the prior model, therefore establishing m-BERT based MMC as the new state-of-the-art for English and Hindi. We also publish our results on an extended version of the recently released XQuAD dataset, which we propose to use as the evaluation benchmark for future research. | ['Somil Gupta', 'Nilesh Khade'] | 2020-06-02 | null | null | null | null | ['multilingual-machine-comprehension'] | ['natural-language-processing'] | [-1.78229406e-01 -5.64758964e-02 2.49742791e-01 -5.71541190e-01
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39295f9c-ea6b-47c9-ba9a-8ef18a353e4c | rapid-training-of-quantum-recurrent-neural | 2207.00378 | null | https://arxiv.org/abs/2207.00378v2 | https://arxiv.org/pdf/2207.00378v2.pdf | Rapid training of quantum recurrent neural networks | Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness Recurrent Neural Networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of RRNs by including continuous-variable quantum resources in it, and to use a quantum-enhanced RNN to overcome these obstacles. The design of the Continuous-Variable Quantum RNN (CV-QRNN) is rooted in the continuous-variable quantum computing paradigm. By performing extensive numerical simulations, we demonstrate that the quantum network is capable of learning-time dependence of several types of temporal data, and that it converges to the optimal weights in fewer epochs than a classical network. Furthermore, for a small number of trainable parameters, it can achieve lower losses than its classical counterpart. CV-QRNN can be implemented using commercially available quantum-photonic hardware. | ['Bertrand Le Saux', 'Adam Buraczewski', 'Magdalena Stobińska', 'Michał Siemaszko'] | 2022-07-01 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.55139917e-01 4.28796634e-02 4.63351011e-02 7.23276734e-02
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3d763c11-b6d7-4dd5-a047-f66cf607e9f3 | magnification-prior-a-self-supervised-method | 2203.07707 | null | https://arxiv.org/abs/2203.07707v2 | https://arxiv.org/pdf/2203.07707v2.pdf | Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images | This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus on fully supervised learning approaches that rely heavily on human annotations. However, the scarcity of labeled and unlabeled data is a long-standing challenge in histopathology. Currently, representation learning without labels remains unexplored for the histopathology domain. The proposed method, Magnification Prior Contrastive Similarity (MPCS), enables self-supervised learning of representations without labels on small-scale breast cancer dataset BreakHis by exploiting magnification factor, inductive transfer, and reducing human prior. The proposed method matches fully supervised learning state-of-the-art performance in malignancy classification when only 20% of labels are used in fine-tuning and outperform previous works in fully supervised learning settings. It formulates a hypothesis and provides empirical evidence to support that reducing human-prior leads to efficient representation learning in self-supervision. The implementation of this work is available online on GitHub - https://github.com/prakashchhipa/Magnification-Prior-Self-Supervised-Method | ['Marcus Liwicki', 'Seiichi Uchida', 'Rajkumar Saini', 'Gustav Grund Pihlgren', 'Richa Upadhyay', 'Prakash Chandra Chhipa'] | 2022-03-15 | null | null | null | null | ['breast-cancer-histology-image-classification-1', 'breast-cancer-histology-image-classification', 'classification-of-breast-cancer-histology'] | ['computer-vision', 'medical', 'medical'] | [ 6.52319968e-01 6.85488224e-01 -5.44699669e-01 -5.55913925e-01
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2e25f604-4305-4773-8b76-017b2f1d5314 | set-to-sequence-ranking-based-concept-aware | 2306.04234 | null | https://arxiv.org/abs/2306.04234v1 | https://arxiv.org/pdf/2306.04234v1.pdf | Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation | With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm. Specifically, we first design a concept-aware encoder module which can capture the correlations among the input learning concepts. The outputs are then fed into a decoder module that sequentially generates a path through an attention mechanism that handles correlations between the learning and target concepts. Our recommendation policy is optimized by policy gradient. In addition, we also introduce an auxiliary module based on knowledge tracing to enhance the model's stability by evaluating students' learning effects on learning concepts. We conduct extensive experiments on two real-world public datasets and one industrial dataset, and the experimental results demonstrate the superiority and effectiveness of SRC. Code will be available at https://gitee.com/mindspore/models/tree/master/research/recommend/SRC. | ['Yong Yu', 'Dingyin Xia', 'Kai Dong', 'Ruiming Tang', 'Menghui Zhu', 'Weiwen Liu', 'Weinan Zhang', 'Yakun Song', 'Jiarui Jin', 'Wei Xia', 'Jian Shen', 'Xianyu Chen'] | 2023-06-07 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [ 2.35042274e-02 -8.67774263e-02 -3.53555471e-01 -5.35282731e-01
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d9ec330d-20ef-432f-b11e-f02c9d021f1d | are-you-telling-me-to-put-glasses-on-the-dog | 2306.02377 | null | https://arxiv.org/abs/2306.02377v1 | https://arxiv.org/pdf/2306.02377v1.pdf | "Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Dataset | Instruction Clarification Requests are a mechanism to solve communication problems, which is very functional in instruction-following interactions. Recent work has argued that the CoDraw dataset is a valuable source of naturally occurring iCRs. Beyond identifying when iCRs should be made, dialogue models should also be able to generate them with suitable form and content. In this work, we introduce CoDraw-iCR (v2), which extends the existing iCR identifiers fine-grained information grounded in the underlying dialogue game items and possible actions. Our annotation can serve to model and evaluate repair capabilities of dialogue agents. | ['David Schlangen', 'Brielen Madureira'] | 2023-06-04 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 2.95321010e-02 8.34445894e-01 -2.29866177e-01 -3.17555666e-01
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8118c620-9e9f-4424-aa73-026c7499c04a | exact-bias-correction-and-covariance | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Freundlich_Exact_Bias_Correction_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Freundlich_Exact_Bias_Correction_2015_CVPR_paper.pdf | Exact Bias Correction and Covariance Estimation for Stereo Vision | We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig. Our analysis is based on the observation that, due to quantization error, a 3D point reconstructed by triangulation essentially represents an entire region in space. The true location of the world point that generated the triangulated point could be anywhere in this region. We argue that the reconstructed point, if it is to represent this region in space without bias, should be located at the centroid of this region, which is not what has been done in the literature. We derive the exact geometry of these regions in space, which we call 3D cells, and we show how they can be viewed as uniform distributions of possible pre-images of the pair of corresponding pixels. By assuming a uniform distribution of points in 3D, as opposed to a uniform distribution of the projections of these 3D points on the images, we arrive at a fast and exact computation of the triangulation bias in each cell. In addition, we derive the exact covariance matrices of the 3D cells. We validate our approach in a variety of simulations ranging from 3D reconstruction to camera localization and relative motion estimation. In all cases, we are able to demonstrate a marked improvement compared to conventional techniques for small disparity values, for which bias is significant and the required corrections are large. | ['Michael Zavlanos', 'Philippos Mordohai', 'Charles Freundlich'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['camera-localization'] | ['computer-vision'] | [ 2.50992000e-01 9.56694335e-02 2.92384088e-01 1.68771707e-02
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f552134c-e4bd-45d0-bb41-3d826a4e9203 | on-the-cross-lingual-transferability-of | 1910.11856 | null | https://arxiv.org/abs/1910.11856v3 | https://arxiv.org/pdf/1910.11856v3.pdf | On the Cross-lingual Transferability of Monolingual Representations | State-of-the-art unsupervised multilingual models (e.g., multilingual BERT) have been shown to generalize in a zero-shot cross-lingual setting. This generalization ability has been attributed to the use of a shared subword vocabulary and joint training across multiple languages giving rise to deep multilingual abstractions. We evaluate this hypothesis by designing an alternative approach that transfers a monolingual model to new languages at the lexical level. More concretely, we first train a transformer-based masked language model on one language, and transfer it to a new language by learning a new embedding matrix with the same masked language modeling objective, freezing parameters of all other layers. This approach does not rely on a shared vocabulary or joint training. However, we show that it is competitive with multilingual BERT on standard cross-lingual classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD). Our results contradict common beliefs of the basis of the generalization ability of multilingual models and suggest that deep monolingual models learn some abstractions that generalize across languages. We also release XQuAD as a more comprehensive cross-lingual benchmark, which comprises 240 paragraphs and 1190 question-answer pairs from SQuAD v1.1 translated into ten languages by professional translators. | ['Mikel Artetxe', 'Sebastian Ruder', 'Dani Yogatama'] | 2019-10-25 | on-the-cross-lingual-transferability-of-1 | https://aclanthology.org/2020.acl-main.421 | https://aclanthology.org/2020.acl-main.421.pdf | acl-2020-6 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-4.48884249e-01 1.11786880e-01 -2.64952034e-01 -4.54821616e-01
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ccbb3dda-10c0-401d-8be4-8b42244e2dcc | distantly-supervised-ner-with-partial | null | null | https://aclanthology.org/C18-1183 | https://aclanthology.org/C18-1183.pdf | Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning | A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity. However, this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models. In this paper, we propose a novel approach which can partially solve the above problems of distant supervision for NER. In our approach, to handle the incomplete problem, we apply partial annotation learning to reduce the effect of unknown labels of characters. As for noisy annotation, we design an instance selector based on reinforcement learning to distinguish positive sentences from auto-generated annotations. In experiments, we create two datasets for Chinese named entity recognition in two domains with the help of distant supervision. The experimental results show that the proposed approach obtains better performance than the comparison systems on both two datasets. | ['Yaosheng Yang', 'Zhengqiu He', 'Zhenghua Li', 'Wenliang Chen', 'Min Zhang'] | 2018-08-01 | distantly-supervised-ner-with-partial-1 | https://aclanthology.org/C18-1183 | https://aclanthology.org/C18-1183.pdf | coling-2018-8 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 1.15450755e-01 2.47844428e-01 -8.41174647e-02 -4.53900635e-01
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970910a5-be9f-4d23-b61a-c9182b83348e | new-method-for-optimization-of-license-plate | 1407.6510 | null | http://arxiv.org/abs/1407.6510v1 | http://arxiv.org/pdf/1407.6510v1.pdf | New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component | License Plate recognition plays an important role on the traffic monitoring
and parking management systems. In this paper, a fast and real time method has
been proposed which has an appropriate application to find tilt and poor
quality plates. In the proposed method, at the beginning, the image is
converted into binary mode using adaptive threshold. Then, by using some edge
detection and morphology operations, plate number location has been specified.
Finally, if the plat has tilt, its tilt is removed away. This method has been
tested on another paper data set that has different images of the background,
considering distance, and angel of view so that the correct extraction rate of
plate reached at 98.66%. | ['Hamid Reza Shayegh', 'Reza Azad'] | 2014-07-24 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 8.70154181e-04 -5.64598560e-01 1.14064410e-01 -5.46627603e-02
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dbc404d1-876d-4915-abb0-6cea0c39fa08 | lcdnet-deep-loop-closure-detection-for-lidar | 2103.05056 | null | https://arxiv.org/abs/2103.05056v4 | https://arxiv.org/pdf/2103.05056v4.pdf | LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM | Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task, however their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this paper, we introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds by simultaneously identifying previously visited places and estimating the 6-DoF relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art loop closure detection and point cloud registration techniques by a large margin, especially while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city. | ['Abhinav Valada', 'Matteo Vaghi', 'Daniele Cattaneo'] | 2021-03-08 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-6.02082610e-02 -1.11416712e-01 -1.09820351e-01 -7.60195494e-01
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0584b942-fa84-4bb4-a317-8a415e667523 | glm-130b-an-open-bilingual-pre-trained-model | 2210.02414 | null | https://arxiv.org/abs/2210.02414v1 | https://arxiv.org/pdf/2210.02414v1.pdf | GLM-130B: An Open Bilingual Pre-trained Model | We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected technical and engineering challenges, particularly on loss spikes and disconvergence. In this paper, we introduce the training process of GLM-130B including its design choices, training strategies for both efficiency and stability, and engineering efforts. The resultant GLM-130B model offers significant outperformance over GPT-3 175B on a wide range of popular English benchmarks while the performance advantage is not observed in OPT-175B and BLOOM-176B. It also consistently and significantly outperforms ERNIE TITAN 3.0 260B -- the largest Chinese language model -- across related benchmarks. Finally, we leverage a unique scaling property of GLM-130B to reach INT4 quantization, without quantization aware training and with almost no performance loss, making it the first among 100B-scale models. More importantly, the property allows its effective inference on 4$\times$RTX 3090 (24G) or 8$\times$RTX 2080 Ti (11G) GPUs, the most ever affordable GPUs required for using 100B-scale models. The GLM-130B model weights are publicly accessible and its code, training logs, related toolkit, and lessons learned are open-sourced at https://github.com/THUDM/GLM-130B . | ['Jie Tang', 'Yuxiao Dong', 'Peng Zhang', 'WenGuang Chen', 'Jidong Zhai', 'Yufei Xue', 'Zixuan Ma', 'Weng Lam Tam', 'Xiao Xia', 'Wendi Zheng', 'Yifan Xu', 'Zhuoyi Yang', 'Ming Ding', 'Hanyu Lai', 'Zihan Wang', 'Zhengxiao Du', 'Xiao Liu', 'Aohan Zeng'] | 2022-10-05 | null | null | null | null | ['multi-task-language-understanding'] | ['methodology'] | [-3.35952997e-01 -2.42814660e-01 -4.27917838e-01 -3.23808223e-01
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8281a55b-e57e-4988-a602-b1409f276777 | learning-elimination-ordering-for-tree | null | null | https://openreview.net/forum?id=aZ7wAnYs9v1 | https://openreview.net/pdf?id=aZ7wAnYs9v1 | Learning Elimination Ordering for Tree Decomposition Problem | We propose a Reinforcement Learning-based approach to approximately solve the Tree Decomposition problem.
Recently, it was shown that learned heuristics could successfully solve combinatorial problems.
We establish that our approach successfully generalizes from small graphs, where an optimal Tree Decomposition can be found by exact algorithms, to large instances of practical interest, while still having very low time-to-solution.
On the other hand, the agent-based approach surpasses all classical greedy heuristics by the quality of the solution. | ['Ivan Oseledets', 'Roman Schutski', 'Taras Khakhulin'] | 2020-10-17 | null | null | null | neurips-workshop-lmca-2020-12 | ['tree-decomposition'] | ['graphs'] | [-7.75721148e-02 6.57042861e-01 -4.23496008e-01 1.44534528e-01
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726e000f-9674-4032-b797-8915c256202a | forknet-multi-branch-volumetric-semantic | 1909.01106 | null | https://arxiv.org/abs/1909.01106v1 | https://arxiv.org/pdf/1909.01106v1.pdf | ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image | We propose a novel model for 3D semantic completion from a single depth image, based on a single encoder and three separate generators used to reconstruct different geometric and semantic representations of the original and completed scene, all sharing the same latent space. To transfer information between the geometric and semantic branches of the network, we introduce paths between them concatenating features at corresponding network layers. Motivated by the limited amount of training samples from real scenes, an interesting attribute of our architecture is the capacity to supplement the existing dataset by generating a new training dataset with high quality, realistic scenes that even includes occlusion and real noise. We build the new dataset by sampling the features directly from latent space which generates a pair of partial volumetric surface and completed volumetric semantic surface. Moreover, we utilize multiple discriminators to increase the accuracy and realism of the reconstructions. We demonstrate the benefits of our approach on standard benchmarks for the two most common completion tasks: semantic 3D scene completion and 3D object completion. | ['Federico Tombari', 'Yida Wang', 'David Joseph Tan', 'Nassir Navab'] | 2019-09-03 | forknet-multi-branch-volumetric-semantic-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_ForkNet_Multi-Branch_Volumetric_Semantic_Completion_From_a_Single_Depth_Image_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_ForkNet_Multi-Branch_Volumetric_Semantic_Completion_From_a_Single_Depth_Image_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 3.34355026e-01 4.75385875e-01 2.13609576e-01 -4.50928360e-01
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c2fe8ca1-4f58-4e9d-b138-7cd0dc8fe569 | offline-reinforcement-learning-with-in-sample | null | null | https://openreview.net/forum?id=68n2s9ZJWF8 | https://openreview.net/pdf?id=68n2s9ZJWF8 | Offline Reinforcement Learning with In-sample Q-Learning | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This tradeoff is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values. We propose a new offline RL method that never needs to evaluate actions outside of the dataset, but still enables the learned policy to improve substantially over the best behavior in the data through generalization. The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state. This leverages the generalization capacity of the function approximator to estimate the value of the best available action at a given state without ever directly querying a Q-function with this unseen action. Our algorithm alternates between fitting this upper expectile value function and backing it up into a Q-function, without any explicit policy. Then, we extract the policy via advantage-weighted behavioral cloning, which also avoids querying out-of-sample actions. We dub our method in-sample Q-learning (IQL). IQL is easy to implement, computationally efficient, and only requires fitting an additional critic with an asymmetric L2 loss. IQL demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline reinforcement learning. We also demonstrate that IQL achieves strong performance fine-tuning using online interaction after offline initialization. | ['Sergey Levine', 'Ashvin Nair', 'Ilya Kostrikov'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['d4rl'] | ['robots'] | [-1.56249598e-01 2.31753871e-01 -5.98332405e-01 -2.03694031e-01
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ca2b2349-209d-4dcd-90cf-59d9ea830ff7 | on-differentially-private-federated-linear | 2302.13945 | null | https://arxiv.org/abs/2302.13945v2 | https://arxiv.org/pdf/2302.13945v2.pdf | On Differentially Private Federated Linear Contextual Bandits | We consider cross-silo federated linear contextual bandit (LCB) problem under differential privacy, where multiple silos (agents) interact with the local users and communicate via a central server to realize collaboration while without sacrificing each user's privacy. We identify three issues in the state-of-the-art: (i) failure of claimed privacy protection and (ii) incorrect regret bound due to noise miscalculation and (iii) ungrounded communication cost. To resolve these issues, we take a two-step principled approach. First, we design an algorithmic framework consisting of a generic federated LCB algorithm and flexible privacy protocols. Then, leveraging the proposed framework, we study federated LCBs under two different privacy constraints. We first establish privacy and regret guarantees under silo-level local differential privacy, which fix the issues present in state-of-the-art algorithm. To further improve the regret performance, we next consider shuffle model of differential privacy, under which we show that our algorithm can achieve nearly ``optimal'' regret without a trusted server. We accomplish this via two different schemes -- one relies on a new result on privacy amplification via shuffling for DP mechanisms and another one leverages the integration of a shuffle protocol for vector sum into the tree-based mechanism, both of which might be of independent interest. Finally, we support our theoretical results with numerical evaluations over contextual bandit instances generated from both synthetic and real-life data. | ['Sayak Ray Chowdhury', 'Xingyu Zhou'] | 2023-02-27 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.02758187e-01 2.28888065e-01 -2.63866037e-01 -3.43476593e-01
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44c36d4c-49c2-428e-be9c-ae693e6e4452 | learning-canonical-view-representation-for-3d | 2108.07084 | null | https://arxiv.org/abs/2108.07084v2 | https://arxiv.org/pdf/2108.07084v2.pdf | Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views | In this paper, we focus on recognizing 3D shapes from arbitrary views, i.e., arbitrary numbers and positions of viewpoints. It is a challenging and realistic setting for view-based 3D shape recognition. We propose a canonical view representation to tackle this challenge. We first transform the original features of arbitrary views to a fixed number of view features, dubbed canonical view representation, by aligning the arbitrary view features to a set of learnable reference view features using optimal transport. In this way, each 3D shape with arbitrary views is represented by a fixed number of canonical view features, which are further aggregated to generate a rich and robust 3D shape representation for shape recognition. We also propose a canonical view feature separation constraint to enforce that the view features in canonical view representation can be embedded into scattered points in a Euclidean space. Experiments on the ModelNet40, ScanObjectNN, and RGBD datasets show that our method achieves competitive results under the fixed viewpoint settings, and significantly outperforms the applicable methods under the arbitrary view setting. | ['Jian Sun', 'Xing Sun', 'Fudong Wang', 'Yifei Gong', 'Xin Wei'] | 2021-08-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wei_Learning_Canonical_View_Representation_for_3D_Shape_Recognition_With_Arbitrary_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wei_Learning_Canonical_View_Representation_for_3D_Shape_Recognition_With_Arbitrary_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-recognition', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [-9.41246003e-02 -3.58415753e-01 8.30639452e-02 -7.16169417e-01
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1e6b01f2-248b-48a4-8d9f-b39cef99c25a | explain-your-move-understanding-agent-actions | null | null | https://openreview.net/forum?id=SJgzLkBKPB | https://openreview.net/pdf?id=SJgzLkBKPB | Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency | As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our approach generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweights irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare our approach with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches. | ['Shripad Deshmukh', 'Sukriti Verma', 'Sameer Singh', 'Nikaash Puri', 'Piyush Gupta', 'Balaji Krishnamurthy', 'Dhruv Kayastha'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['board-games'] | ['playing-games'] | [ 1.98212266e-01 3.81234348e-01 9.79114547e-02 -7.04974383e-02
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61438a1c-dffb-4f78-b5ce-bb21b2f53f0a | deep-idempotent-network-for-efficient-single | 2210.07122 | null | https://arxiv.org/abs/2210.07122v2 | https://arxiv.org/pdf/2210.07122v2.pdf | Deep Idempotent Network for Efficient Single Image Blind Deblurring | Single image blind deblurring is highly ill-posed as neither the latent sharp image nor the blur kernel is known. Even though considerable progress has been made, several major difficulties remain for blind deblurring, including the trade-off between high-performance deblurring and real-time processing. Besides, we observe that current single image blind deblurring networks cannot further improve or stabilize the performance but significantly degrades the performance when re-deblurring is repeatedly applied. This implies the limitation of these networks in modeling an ideal deblurring process. In this work, we make two contributions to tackle the above difficulties: (1) We introduce the idempotent constraint into the deblurring framework and present a deep idempotent network to achieve improved blind non-uniform deblurring performance with stable re-deblurring. (2) We propose a simple yet efficient deblurring network with lightweight encoder-decoder units and a recurrent structure that can deblur images in a progressive residual fashion. Extensive experiments on synthetic and realistic datasets prove the superiority of our proposed framework. Remarkably, our proposed network is nearly 6.5X smaller and 6.4X faster than the state-of-the-art while achieving comparable high performance. | ['Xin Yu', 'Yuchao Dai', 'Zhexiong Wan', 'Yuxin Mao'] | 2022-10-13 | null | null | null | null | ['single-image-blind-deblurring'] | ['computer-vision'] | [ 1.65522456e-01 -6.23484433e-01 -9.48840231e-02 3.34493443e-02
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992cdb45-7687-4906-96e4-90e26589bb5d | geomagnetic-field-influences-probabilistic | 2306.16292 | null | https://arxiv.org/abs/2306.16292v1 | https://arxiv.org/pdf/2306.16292v1.pdf | Geomagnetic field influences probabilistic abstract decision-making in humans | To resolve disputes or determine the order of things, people commonly use binary choices such as tossing a coin, even though it is obscure whether the empirical probability equals to the theoretical probability. The geomagnetic field (GMF) is broadly applied as a sensory cue for various movements in many organisms including humans, although our understanding is limited. Here we reveal a GMF-modulated probabilistic abstract decision-making in humans and the underlying mechanism, exploiting the zero-sum binary stone choice of Go game as a proof-of-principle. The large-scale data analyses of professional Go matches and in situ stone choice games showed that the empirical probabilities of the stone selections were remarkably different from the theoretical probability. In laboratory experiments, experimental probability in the decision-making was significantly influenced by GMF conditions and specific magnetic resonance frequency. Time series and stepwise systematic analyses pinpointed the intentionally uncontrollable decision-making as a primary modulating target. Notably, the continuum of GMF lines and anisotropic magnetic interplay between players were crucial to influence the magnetic field resonance-mediated abstract decision-making. Our findings provide unique insights into the impact of sensing GMF in decision-makings at tipping points and the quantum mechanical mechanism for manifesting the gap between theoretical and empirical probability in 3-dimensional living space. | ['Yongkuk Kim', 'Soo-Chan Kim', 'Yong-Hwan Kim', 'Soo Hyun Jeong', 'In-Taek Oh', 'Kwon-Seok Chae'] | 2023-06-28 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 3.66524726e-01 -1.30811438e-01 7.71279782e-02 3.09181251e-02
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21740a03-14d4-45ba-bddc-796921391fff | audio-visual-understanding-of-passenger-1 | 2007.03876 | null | https://arxiv.org/abs/2007.03876v1 | https://arxiv.org/pdf/2007.03876v1.pdf | Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents | Building multimodal dialogue understanding capabilities situated in the in-cabin context is crucial to enhance passenger comfort in autonomous vehicle (AV) interaction systems. To this end, understanding passenger intents from spoken interactions and vehicle vision systems is a crucial component for developing contextual and visually grounded conversational agents for AV. Towards this goal, we explore AMIE (Automated-vehicle Multimodal In-cabin Experience), the in-cabin agent responsible for handling multimodal passenger-vehicle interactions. In this work, we discuss the benefits of a multimodal understanding of in-cabin utterances by incorporating verbal/language input together with the non-verbal/acoustic and visual clues from inside and outside the vehicle. Our experimental results outperformed text-only baselines as we achieved improved performances for intent detection with a multimodal approach. | ['Shachi H. Kumar', 'Eda Okur', 'Saurav Sahay', 'Lama Nachman'] | 2020-07-08 | audio-visual-understanding-of-passenger | https://aclanthology.org/2020.challengehml-1.7 | https://aclanthology.org/2020.challengehml-1.7.pdf | ws-2020-7 | ['dialogue-understanding'] | ['natural-language-processing'] | [-3.33574623e-01 3.40038091e-01 2.78912455e-01 -6.67369664e-01
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0487b625-7952-4aad-a920-d0b0ce7ccbc9 | masil-towards-maximum-separable-class | 2304.05362 | null | https://arxiv.org/abs/2304.05362v1 | https://arxiv.org/pdf/2304.05362v1.pdf | MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning | Few Shot Class Incremental Learning (FSCIL) with few examples per class for each incremental session is the realistic setting of continual learning since obtaining large number of annotated samples is not feasible and cost effective. We present the framework MASIL as a step towards learning the maximal separable classifier. It addresses the common problem i.e forgetting of old classes and over-fitting to novel classes by learning the classifier weights to be maximally separable between classes forming a simplex Equiangular Tight Frame. We propose the idea of concept factorization explaining the collapsed features for base session classes in terms of concept basis and use these to induce classifier simplex for few shot classes. We further adds fine tuning to reduce any error occurred during factorization and train the classifier jointly on base and novel classes without retaining any base class samples in memory. Experimental results on miniImageNet, CIFAR-100 and CUB-200 demonstrate that MASIL outperforms all the benchmarks. | ['Anant Khandelwal'] | 2023-04-08 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.18135887e-01 1.51488334e-01 -3.08789790e-01 -5.68984449e-01
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169f3613-2db9-4beb-8462-7d10d1049052 | st-nsurl-2019-shared-task-semantic-question | null | null | https://aclanthology.org/2019.nsurl-1.12 | https://aclanthology.org/2019.nsurl-1.12.pdf | ST NSURL 2019 Shared Task: Semantic Question Similarity in Arabic | null | ['Abed Alhakim Freihat', 'Besma Benaziz', 'Mourad Abbas', 'Mohamed Lichouri'] | null | null | null | null | nsurl-2019-9 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8b0d8c6c-0bd6-4ac9-94f5-e17a6a9fde38 | the-pitfalls-of-sample-selection-a-case-study | 2108.05386 | null | https://arxiv.org/abs/2108.05386v1 | https://arxiv.org/pdf/2108.05386v1.pdf | The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification | Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results. In lung nodule classification, for example, many works report results on the publicly available LIDC dataset. In theory, this should allow a direct comparison of the performance of proposed methods and assess the impact of individual contributions. When analyzing seven recent works, however, we find that each employs a different data selection process, leading to largely varying total number of samples and ratios between benign and malignant cases. As each subset will have different characteristics with varying difficulty for classification, a direct comparison between the proposed methods is thus not always possible, nor fair. We study the particular effect of truthing when aggregating labels from multiple experts. We show that specific choices can have severe impact on the data distribution where it may be possible to achieve superior performance on one sample distribution but not on another. While we show that we can further improve on the state-of-the-art on one sample selection, we also find that on a more challenging sample selection, on the same database, the more advanced models underperform with respect to very simple baseline methods, highlighting that the selected data distribution may play an even more important role than the model architecture. This raises concerns about the validity of claimed methodological contributions. We believe the community should be aware of these pitfalls and make recommendations on how these can be avoided in future work. | ['Julia A. Schnabel', 'Ben Glocker', 'Sujal Desai', 'Arjun Nair', 'Sam Ellis', 'Octavio E. Martinez Manzanera', 'Loic Le Folgoc', 'Kyriaki-Margarita Bintsi', 'Vasileios Baltatzis'] | 2021-08-11 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.55973309e-01 1.29096970e-01 -3.28245997e-01 -2.41915286e-01
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9a74f5d5-828f-463a-ab5a-c09d209edab2 | mtlts-a-multi-task-framework-to-obtain | 2112.05798 | null | https://arxiv.org/abs/2112.05798v1 | https://arxiv.org/pdf/2112.05798v1.pdf | MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs | Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing disaster-specific summarizers, we model tweet summarization as a supervised task. Such an approach can automatically learn summary-worthy features, and can therefore generalize well across domains. When trained on the PHEME dataset [29], not only do we outperform the strongest baselines for the auxiliary task of verification/rumour detection, we also achieve 21 - 35% gains in the verified ratio of summary tweets, and 16 - 20% gains in ROUGE1-F1 scores over the existing state-of-the-art solutions for the primary task of trustworthy summarization. | ['Niloy Ganguly', 'Pawan Goyal', 'Koustav Rudra', 'Sourangshu Bhattacharya', 'Hari Chandana Peruri', 'Uppada Vishnu', 'Rajdeep Mukherjee'] | 2021-12-10 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.41728346e-02 4.94168073e-01 -1.55033231e-01 -3.50535542e-01
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7a2de21f-e194-445e-a3d8-370c84451823 | comparing-the-performance-of-cnns-and-shallow | null | null | https://aclanthology.org/2021.vardial-1.12 | https://aclanthology.org/2021.vardial-1.12.pdf | Comparing the Performance of CNNs and Shallow Models for Language Identification | In this work we compare the performance of convolutional neural networks and shallow models on three out of the four language identification shared tasks proposed in the VarDial Evaluation Campaign 2021. In our experiments, convolutional neural networks and shallow models yielded comparable performance in the Romanian Dialect Identification (RDI) and the Dravidian Language Identification (DLI) shared tasks, after the training data was augmented, while an ensemble of support vector machines and Naïve Bayes models was the best performing model in the Uralic Language Identification (ULI) task. While the deep learning models did not achieve state-of-the-art performance at the tasks and tended to overfit the data, the ensemble method was one of two methods that beat the existing baseline for the first track of the ULI shared task. | ['Andrea Ceolin'] | null | null | null | null | eacl-vardial-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-4.66508389e-01 -2.31169000e-01 -1.81275263e-01 -4.76313442e-01
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f3b6e5e5-1967-4e18-a499-8311b2e87309 | graphmapper-efficient-visual-navigation-by | 2205.08325 | null | https://arxiv.org/abs/2205.08325v1 | https://arxiv.org/pdf/2205.08325v1.pdf | GraphMapper: Efficient Visual Navigation by Scene Graph Generation | Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments. A sparse, unified representation of the scene topology will allow agents to act efficiently to move through their environment, communicate the environment state with others, and utilize the representation for diverse downstream tasks. To this end, we propose a method to train an autonomous agent to learn to accumulate a 3D scene graph representation of its environment by simultaneously learning to navigate through said environment. We demonstrate that our approach, GraphMapper, enables the learning of effective navigation policies through fewer interactions with the environment than vision-based systems alone. Further, we show that GraphMapper can act as a modular scene encoder to operate alongside existing Learning-based solutions to not only increase navigational efficiency but also generate intermediate scene representations that are useful for other future tasks. | ['Rakesh Kumar', 'Supun Samarasekera', 'Han-Pang Chiu', 'Niluthpol Chowdhury Mithun', 'Zachary Seymour'] | 2022-05-17 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 8.09954479e-02 8.37894380e-02 1.68241173e-01 -4.55982089e-01
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16a4c169-f8af-4ebf-b74a-66f3c74df368 | towards-omni-generalizable-neural-methods-for | 2305.19587 | null | https://arxiv.org/abs/2305.19587v2 | https://arxiv.org/pdf/2305.19587v2.pdf | Towards Omni-generalizable Neural Methods for Vehicle Routing Problems | Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP. | ['Jie Zhang', 'Zhiguang Cao', 'Wen Song', 'Yaoxin Wu', 'Jianan Zhou'] | 2023-05-31 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 6.47995844e-02 7.01365694e-02 -5.34140527e-01 -4.48900372e-01
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cd884e8e-f5b6-4e3a-8fbb-d0a1e7554a99 | vision-based-autonomous-car-racing-using-deep | 2107.08325 | null | https://arxiv.org/abs/2107.08325v1 | https://arxiv.org/pdf/2107.08325v1.pdf | Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning | Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://caipeide.github.io/autorace-dirl/ | ['Ming Liu', 'Yuxuan Liu', 'Huaiyang Huang', 'Hengli Wang', 'Peide Cai'] | 2021-07-18 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-4.11654800e-01 6.41633794e-02 -1.49892882e-01 -1.55785739e-01
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12649243-99c7-4525-b7ee-5d2c0a2a171e | monotonicity-for-ai-ethics-and-society-an | 2301.07060 | null | https://arxiv.org/abs/2301.07060v1 | https://arxiv.org/pdf/2301.07060v1.pdf | Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance | Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning (ML) community has focused on intersectionality as a matter of statistical parity, especially in discrimination issues, an emerging body of literature addresses another facet -- monotonicity. Based on domain expertise, monotonicity plays a vital role in numerous fairness-related areas, where violations could misguide human decisions and lead to disastrous consequences. In this paper, we first systematically evaluate the significance of applying monotonic neural additive models (MNAMs), which use a fairness-aware ML algorithm to enforce both individual and pairwise monotonicity principles, for the fairness of AI ethics and society. We have found, through a hybrid method of theoretical reasoning, simulation, and extensive empirical analysis, that considering monotonicity axioms is essential in all areas of fairness, including criminology, education, health care, and finance. Our research contributes to the interdisciplinary research at the interface of AI ethics, explainable AI (XAI), and human-computer interactions (HCIs). By evidencing the catastrophic consequences if monotonicity is not met, we address the significance of monotonicity requirements in AI applications. Furthermore, we demonstrate that MNAMs are an effective fairness-aware ML approach by imposing monotonicity restrictions integrating human intelligence. | ['Luyao Zhang', 'Dangxing Chen'] | 2023-01-17 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.61691654e-01 4.99352008e-01 -5.23833752e-01 -4.94272441e-01
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810ca3d9-3490-451c-a498-2780cfd28a8d | nonparametric-forest-structured-neural-topic | null | null | https://aclanthology.org/2022.coling-1.228 | https://aclanthology.org/2022.coling-1.228.pdf | Nonparametric Forest-Structured Neural Topic Modeling | Neural topic models have been widely used in discovering the latent semantics from a corpus. Recently, there are several researches on hierarchical neural topic models since the relationships among topics are valuable for data analysis and exploration. However, the existing hierarchical neural topic models are limited to generate a single topic tree. In this study, we present a nonparametric forest-structured neural topic model by firstly applying the self-attention mechanism to capture parent-child topic relationships, and then build a sparse directed acyclic graph to form a topic forest. Experiments indicate that our model can automatically learn a forest-structured topic hierarchy with indefinite numbers of trees and leaves, and significantly outperforms the baseline models on topic hierarchical rationality and affinity. | ['Yanghui Rao', 'Xuewen Zhang', 'Zhihong Zhang'] | null | null | null | null | coling-2022-10 | ['topic-models'] | ['natural-language-processing'] | [-8.26086998e-02 6.21627748e-01 -6.89841449e-01 -6.40078187e-01
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b6ab1c9a-b997-4b17-89f0-9a859cbb3d18 | machine-learning-for-uav-propeller-fault | 2302.01556 | null | https://arxiv.org/abs/2302.01556v1 | https://arxiv.org/pdf/2302.01556v1.pdf | Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model | This paper describes the development of an on-board data-driven system that can monitor and localize the fault in a quadrotor unmanned aerial vehicle (UAV) and at the same time, evaluate the degree of damage of the fault under real scenarios. To achieve offline training data generation, a hybrid approach is proposed for the development of a virtual data-generative model using a combination of data-driven models as well as well-established dynamic models that describe the kinematics of the UAV. To effectively represent the drop in performance of a faulty propeller, a variation of the deep neural network, a LSTM network is proposed. With the RPM of the propeller as input and based on the fault condition of the propeller, the proposed propeller model estimates the resultant torque and thrust. Then, flight datasets of the UAV under various fault scenarios are generated via simulation using the developed data-generative model. Lastly, a fault classifier using a CNN model is proposed to identify as well as evaluate the degree of damage to the damaged propeller. The scope of this paper focuses on the identification of faulty propellers and classification of the fault level for quadrotor UAVs using RPM as well as flight data. Doing so allows for early minor fault detection to prevent serious faults from occurring if the fault is left unrepaired. To further validate the workability of this approach outside of simulation, a real-flight test is conducted indoors. The real flight data is collected and a simulation to real sim-real test is conducted. Due to the imperfections in the build of our experimental UAV, a slight calibration approach to our simulation model is further proposed and the experimental results obtained show that our trained model can identify the location of propeller fault as well as the degree/type of damage. Currently, the diagnosis accuracy on the testing set is over 80%. | ['Y. F. Zhang', 'C. F. Li', 'F. Liao', 'W. Zhang', 'J. J. Tong'] | 2023-02-03 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.68377781e-01 -1.54038787e-01 5.38945556e-01 9.95019227e-02
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3188b9d1-66fc-4c69-a37b-fb99b883afef | dutch-humor-detection-by-generating-negative | 2010.13652 | null | https://arxiv.org/abs/2010.13652v1 | https://arxiv.org/pdf/2010.13652v1.pdf | Dutch Humor Detection by Generating Negative Examples | Detecting if a text is humorous is a hard task to do computationally, as it usually requires linguistic and common sense insights. In machine learning, humor detection is usually modeled as a binary classification task, trained to predict if the given text is a joke or another type of text. Rather than using completely different non-humorous texts, we propose using text generation algorithms for imitating the original joke dataset to increase the difficulty for the learning algorithm. We constructed several different joke and non-joke datasets to test the humor detection abilities of different language technologies. In particular, we compare the humor detection capabilities of classic neural network approaches with the state-of-the-art Dutch language model RobBERT. In doing so, we create and compare the first Dutch humor detection systems. We found that while other language models perform well when the non-jokes came from completely different domains, RobBERT was the only one that was able to distinguish jokes from generated negative examples. This performance illustrates the usefulness of using text generation to create negative datasets for humor recognition, and also shows that transformer models are a large step forward in humor detection. | ['Pieter Delobelle', 'Thomas Winters'] | 2020-10-26 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-2.81460792e-01 2.55691074e-02 1.25373438e-01 1.63308382e-01
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afe26f9c-80be-463f-834e-3060c028b3c4 | a-review-corpus-annotated-for-negation | null | null | https://aclanthology.org/L12-1298 | https://aclanthology.org/L12-1298.pdf | A review corpus annotated for negation, speculation and their scope | This paper presents a freely available resource for research on handling negation and speculation in review texts. The SFU Review Corpus, consisting of 400 documents of movie, book, and consumer product reviews, was annotated at the token level with negative and speculative keywords and at the sentence level with their linguistic scope. We report statistics on corpus size and the consistency of annotations. The annotated corpus will be useful in many applications, such as document mining and sentiment analysis. | ['Manuel J. Ma{\\~n}a', 'Sheila C.M. de Sousa', 'Noa P. Cruz', 'Ruslan Mitkov', 'Natalia Konstantinova', 'Maite Taboada'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['negation-detection'] | ['natural-language-processing'] | [ 1.26491755e-01 2.79252559e-01 -1.04727447e+00 -3.52303267e-01
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e67b0cbc-b877-4f57-81b7-9d3dea6f53da | a-case-study-of-spatiotemporal-forecasting | 2209.14782 | null | https://arxiv.org/abs/2209.14782v1 | https://arxiv.org/pdf/2209.14782v1.pdf | A case study of spatiotemporal forecasting techniques for weather forecasting | The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most important processes that fall under this domain, and forecasting it has become a crucial part of our daily routine. Weather data analysis is considered the most complex and challenging task. Although numerical weather prediction models are currently state-of-the-art, they are resource intensive and time-consuming. Numerous studies have proposed time-series-based models as a viable alternative to numerical forecasts. Recent research has primarily focused on forecasting weather at a specific location. Therefore, models can only capture temporal correlations. This self-contained paper explores various methods for regional data-driven weather forecasting, i.e., forecasting over multiple latitude-longitude points to capture spatiotemporal correlations. The results showed that spatiotemporal prediction models reduced computational cost while improving accuracy; in particular, the proposed tensor train dynamic mode decomposition-based forecasting model has comparable accuracy to ConvLSTM without the need for training. We use the NASA POWER meteorological dataset to evaluate the models and compare them with the current state of the art. | ['Ivan Oseledets', 'Shakir Showkat Sofi'] | 2022-09-29 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-0.5666259 -0.874865 -0.11432971 -0.5123118 0.01733748 -0.54978603
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e12b203f-02aa-41a6-90c3-e217dfd42500 | learning-embeddings-for-transitive-verb | null | null | https://aclanthology.org/W15-4001 | https://aclanthology.org/W15-4001.pdf | Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization | null | ['Kazuma Hashimoto', 'Yoshimasa Tsuruoka'] | 2015-07-01 | null | null | null | ws-2015-7 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1a59bddd-c2b1-4928-83af-3c7855c42b15 | semscribe-natural-language-generation-for | null | null | https://aclanthology.org/L12-1032 | https://aclanthology.org/L12-1032.pdf | SemScribe: Natural Language Generation for Medical Reports | Natural language generation in the medical domain is heavily influenced by domain knowledge and genre-specific text characteristics. We present SemScribe, an implemented natural language generation system that produces doctor's letters, in particular descriptions of cardiological findings. Texts in this domain are characterized by a high density of information and a relatively telegraphic style. Domain knowledge is encoded in a medical ontology of about 80,000 concepts. The ontology is used in particular for concept generalizations during referring expression generation. Architecturally, the system is a generation pipeline that uses a corpus-informed syntactic frame approach for realizing sentences appropriate to the domain. The system reads XML documents conforming to the HL7 Clinical Document Architecture (CDA) Standard and enhances them with generated text and references to the used data elements. We conducted a first clinical trial evaluation with medical staff and report on the findings. | ['Sebastian Varges', 'Lukas C. Faulstich', 'Heike Bieler', 'Manfred Stede', 'Malik Atalla', 'Kristin Irsig'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['referring-expression-generation'] | ['computer-vision'] | [ 5.92215538e-01 9.92633343e-01 -9.08214226e-02 -5.70766389e-01
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11cbeb71-5f40-43ba-8d25-8c9be4c03f18 | long-short-term-memory-and-learning-to-learn | 1803.09574 | null | http://arxiv.org/abs/1803.09574v4 | http://arxiv.org/pdf/1803.09574v4.pdf | Long short-term memory and learning-to-learn in networks of spiking neurons | Recurrent networks of spiking neurons (RSNNs) underlie the astounding
computing and learning capabilities of the brain. But computing and learning
capabilities of RSNN models have remained poor, at least in comparison with
artificial neural networks (ANNs). We address two possible reasons for that.
One is that RSNNs in the brain are not randomly connected or designed according
to simple rules, and they do not start learning as a tabula rasa network.
Rather, RSNNs in the brain were optimized for their tasks through evolution,
development, and prior experience. Details of these optimization processes are
largely unknown. But their functional contribution can be approximated through
powerful optimization methods, such as backpropagation through time (BPTT).
A second major mismatch between RSNNs in the brain and models is that the
latter only show a small fraction of the dynamics of neurons and synapses in
the brain. We include neurons in our RSNN model that reproduce one prominent
dynamical process of biological neurons that takes place at the behaviourally
relevant time scale of seconds: neuronal adaptation. We denote these networks
as LSNNs because of their Long short-term memory. The inclusion of adapting
neurons drastically increases the computing and learning capability of RSNNs if
they are trained and configured by deep learning (BPTT combined with a rewiring
algorithm that optimizes the network architecture). In fact, the computational
performance of these RSNNs approaches for the first time that of LSTM networks.
In addition RSNNs with adapting neurons can acquire abstract knowledge from
prior learning in a Learning-to-Learn (L2L) scheme, and transfer that knowledge
in order to learn new but related tasks from very few examples. We demonstrate
this for supervised learning and reinforcement learning. | ['Anand Subramoney', 'Wolfgang Maass', 'Robert Legenstein', 'Guillaume Bellec', 'Darjan Salaj'] | 2018-03-26 | long-short-term-memory-and-learning-to-learn-1 | http://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons | http://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons.pdf | neurips-2018-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.14266092e-01 2.27722034e-01 3.95434171e-01 8.98007751e-02
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b6141b16-2799-4b77-9b66-3a1d0b1f3c47 | synthesis-of-adversarial-ddos-attacks-using | 2212.14109 | null | https://arxiv.org/abs/2212.14109v1 | https://arxiv.org/pdf/2212.14109v1.pdf | Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks | Network Intrusion Detection Systems (NIDS) are tools or software that are widely used to maintain the computer networks and information systems keeping them secure and preventing malicious traffics from penetrating into them, as they flag when somebody is trying to break into the system. Best effort has been set up on these systems, and the results achieved so far are quite satisfying, however, new types of attacks stand out as the technology of attacks keep evolving, one of these attacks are the attacks based on Generative Adversarial Networks (GAN) that can evade machine learning IDS leaving them vulnerable. This project investigates the impact of the Adversarial Attacks synthesized using real DDoS attacks generated using GANs on the IDS. The objective is to discover how will these systems react towards synthesized attacks. marking the vulnerability and weakness points of these systems so we could fix them. | ['Sarah Hossam Elmowafy', 'Ahmed Shehata AboMoustafa', 'Mohamed Sayed Hussein', 'Abdelmageed Ahmed Hassan'] | 2022-12-14 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 6.82711601e-02 1.66467875e-01 1.07089646e-01 -6.68578222e-02
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dafcac54-ca7f-4ce6-b4a9-c57a2cb0d400 | r-btn-cross-domain-face-composite-and | 1706.00556 | null | http://arxiv.org/abs/1706.00556v2 | http://arxiv.org/pdf/1706.00556v2.pdf | r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches | We start by asking an interesting yet challenging question, "If an eyewitness
can only recall the eye features of the suspect, such that the forensic artist
can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig.
1), can advanced computer vision techniques help generate the whole face
image?" A more generalized question is that if a large proportion (e.g., more
than 50%) of the face/sketch is missing, can a realistic whole face
sketch/image still be estimated. Existing face completion and generation
methods either do not conduct domain transfer learning or can not handle large
missing area. For example, the inpainting approach tends to blur the generated
region when the missing area is large (i.e., more than 50%). In this paper, we
exploit the potential of deep learning networks in filling large missing region
(e.g., as high as 95% missing) and generating realistic faces with
high-fidelity in cross domains. We propose the recursive generation by
bidirectional transformation networks (r-BTN) that recursively generates a
whole face/sketch from a small sketch/face patch. The large missing area and
the cross domain challenge make it difficult to generate satisfactory results
using a unidirectional cross-domain learning structure. On the other hand, a
forward and backward bidirectional learning between the face and sketch domains
would enable recursive estimation of the missing region in an incremental
manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial
constraint to encourage the generation of realistic faces/sketches. Extensive
experiments have been conducted to demonstrate the superior performance from
r-BTN as compared to existing potential solutions. | ['Zhifei Zhang', 'Yang Song', 'Hairong Qi'] | 2017-06-02 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.23187470e-01 4.47636396e-01 1.73739702e-01 -2.02492833e-01
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d0e92623-c5e7-4a2e-b43a-d73afdee2a9e | learning-spatial-regularization-with-image | 1702.05891 | null | http://arxiv.org/abs/1702.05891v2 | http://arxiv.org/pdf/1702.05891v2.pdf | Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification | Multi-label image classification is a fundamental but challenging task in
computer vision. Great progress has been achieved by exploiting semantic
relations between labels in recent years. However, conventional approaches are
unable to model the underlying spatial relations between labels in multi-label
images, because spatial annotations of the labels are generally not provided.
In this paper, we propose a unified deep neural network that exploits both
semantic and spatial relations between labels with only image-level
supervisions. Given a multi-label image, our proposed Spatial Regularization
Network (SRN) generates attention maps for all labels and captures the
underlying relations between them via learnable convolutions. By aggregating
the regularized classification results with original results by a ResNet-101
network, the classification performance can be consistently improved. The whole
deep neural network is trained end-to-end with only image-level annotations,
thus requires no additional efforts on image annotations. Extensive evaluations
on 3 public datasets with different types of labels show that our approach
significantly outperforms state-of-the-arts and has strong generalization
capability. Analysis of the learned SRN model demonstrates that it can
effectively capture both semantic and spatial relations of labels for improving
classification performance. | ['Hongsheng Li', 'Feng Zhu', 'Wanli Ouyang', 'Xiaogang Wang', 'Nenghai Yu'] | 2017-02-20 | learning-spatial-regularization-with-image-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhu_Learning_Spatial_Regularization_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhu_Learning_Spatial_Regularization_CVPR_2017_paper.pdf | cvpr-2017-7 | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.34347838e-01 -5.24392873e-02 -2.65154213e-01 -7.38996804e-01
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e9e5be41-6481-450a-96a7-7d7d60754cdc | self-supervised-scanpath-prediction-framework | null | null | https://openaccess.thecvf.com/content/CVPR2022W/Ego4D-EPIC/html/Tliba_Self_Supervised_Scanpath_Prediction_Framework_for_Painting_Images_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/Ego4D-EPIC/papers/Tliba_Self_Supervised_Scanpath_Prediction_Framework_for_Painting_Images_CVPRW_2022_paper.pdf | Self Supervised Scanpath Prediction Framework for Painting Images | In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiveness of our approach, we firstly propose a lightweight scanpath baseline model and compare its performance to some state-of-the-art methods. Secondly, we train the encoder of our baseline model on large-scale painting images to study the efficiency of the proposed self-supervised strategy. The lightweight decoder proves effective in learning from the self-supervised pre-trained encoder with better performances than the end-to-end fine-tuned supervised baseline on two painting datasets, including a proposed new visual attention modelling dataset. | ['Alessandro Bruno', 'Aladine Chetouani', 'Mohamed Amine Kerkouri', 'Marouane Tliba'] | 2022-06-19 | null | null | null | cvpr-2022-6 | ['scanpath-prediction'] | ['computer-vision'] | [ 6.81182384e-01 3.41296911e-01 -5.84815405e-02 -5.44638038e-01
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e5a0beda-592c-4123-8f00-9038e48293d0 | fast-video-moment-retrieval | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper.pdf | Fast Video Moment Retrieval | This paper targets at fast video moment retrieval (fast VMR), aiming to localize the target moment efficiently and accurately as queried by a given natural language sentence. We argue that most existing VMR approaches can be divided into three modules namely video encoder, text encoder, and cross-modal interaction module, where the last module is the test-time computational bottleneck. To tackle this issue, we replace the cross-modal interaction module with a cross-modal common space, in which moment-query alignment is learned and efficient moment search can be performed. For the sake of robustness in the learned space, we propose a fine-grained semantic distillation framework to transfer knowledge from additional semantic structures. Specifically, we build a semantic role tree that decomposes a query sentence into different phrases (subtrees). A hierarchical semantic-guided attention module is designed to perform message propagation across the whole tree and yield discriminative features. Finally, the important and discriminative semantics are transferred to the common space by a matching-score distillation process. Extensive experimental results on three popular VMR benchmarks demonstrate that our proposed method enjoys the merits of high speed and significant performance. | ['Changsheng Xu', 'Junyu Gao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['moment-retrieval'] | ['computer-vision'] | [ 2.68420398e-01 -2.90026814e-01 -5.98135591e-01 -4.34042752e-01
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230fe43e-56e1-45ef-a237-d1fc6da7bf6b | neural-models-for-predicting-celtic-mutations | null | null | https://aclanthology.org/2020.sltu-1.1 | https://aclanthology.org/2020.sltu-1.1.pdf | Neural Models for Predicting Celtic Mutations | The Celtic languages share a common linguistic phenomenon known as initial mutations; these consist of pronunciation and spelling changes that occur at the beginning of some words, triggered in certain semantic or syntactic contexts. Initial mutations occur quite frequently and all non-trivial NLP systems for the Celtic languages must learn to handle them properly. In this paper we describe and evaluate neural network models for predicting mutations in two of the six Celtic languages: Irish and Scottish Gaelic. We also discuss applications of these models to grammatical error detection and language modeling. | ['Kevin Scannell'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 1.52401049e-02 -8.63444209e-02 1.70853913e-01 -7.04594433e-01
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135fceee-c81f-4938-a311-c408387245d1 | av-taris-online-audio-visual-speech | 2012.07467 | null | https://arxiv.org/abs/2012.07467v1 | https://arxiv.org/pdf/2012.07467v1.pdf | AV Taris: Online Audio-Visual Speech Recognition | In recent years, Automatic Speech Recognition (ASR) technology has approached human-level performance on conversational speech under relatively clean listening conditions. In more demanding situations involving distant microphones, overlapped speech, background noise, or natural dialogue structures, the ASR error rate is at least an order of magnitude higher. The visual modality of speech carries the potential to partially overcome these challenges and contribute to the sub-tasks of speaker diarisation, voice activity detection, and the recovery of the place of articulation, and can compensate for up to 15dB of noise on average. This article develops AV Taris, a fully differentiable neural network model capable of decoding audio-visual speech in real time. We achieve this by connecting two recently proposed models for audio-visual speech integration and online speech recognition, namely AV Align and Taris. We evaluate AV Taris under the same conditions as AV Align and Taris on one of the largest publicly available audio-visual speech datasets, LRS2. Our results show that AV Taris is superior to the audio-only variant of Taris, demonstrating the utility of the visual modality to speech recognition within the real time decoding framework defined by Taris. Compared to an equivalent Transformer-based AV Align model that takes advantage of full sentences without meeting the real-time requirement, we report an absolute degradation of approximately 3% with AV Taris. As opposed to the more popular alternative for online speech recognition, namely the RNN Transducer, Taris offers a greatly simplified fully differentiable training pipeline. As a consequence, AV Taris has the potential to popularise the adoption of Audio-Visual Speech Recognition (AVSR) technology and overcome the inherent limitations of the audio modality in less optimal listening conditions. | ['Naomi Harte', 'George Sterpu'] | 2020-12-14 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 2.54959524e-01 2.53771693e-01 2.86653906e-01 -1.38706982e-01
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1da7fdc7-0a5f-463f-a020-a3752c66581e | accelerated-iterative-tomographic | 2202.08627 | null | https://arxiv.org/abs/2202.08627v1 | https://arxiv.org/pdf/2202.08627v1.pdf | Accelerated iterative tomographic reconstruction with x-ray edge illumination | Compared to standard tomographic reconstruction, iterative approaches offer the possibility to account for extraneous experimental influences, which allows for a suppression of related artifacts. However, the inclusion of corresponding parameters in the iterative forward model typically leads to longer computation times. Here, we demonstrate experimentally for phase sensitive X-ray imaging based on the edge illumination principle that inadequately sampled illumination curves result in ring artifacts in tomographic reconstructions. We take advantage of appropriately sampled illumination curves instead, which enables us to eliminate the corresponding parameter from the forward model and substantially increase computational speed. In addition, we demonstrate a 30\% improvement in spatial resolution of the iterative approach compared with the standard non-iterative single shot approach. Further, we report on several significant improvements in our numerical implementation of the iterative approach, which we make available online with this publication. Finally, we show that the combination of both experimental and algorithmic advancement lead to a total speed increase by one order of magnitude and an improved contrast to noise ratio in the reconstructions. | ['Marco Endrizzi', 'Alessandro Olivo', 'Lorenzo Massimi', 'Jeff Meganck', 'Tomasz Korzec', 'Peter Modregger'] | 2022-02-17 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 6.98517144e-01 -2.65454799e-01 5.87652206e-01 -1.60104290e-01
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ba26f0b9-1dfd-405d-92a7-8eeda8a0e2ed | analytic-automated-essay-scoring-based-on | null | null | https://aclanthology.org/2022.coling-1.257 | https://aclanthology.org/2022.coling-1.257.pdf | Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory | Essay exams have been attracting attention as a way of measuring the higher-order abilities of examinees, but they have two major drawbacks in that grading them is expensive and raises questions about fairness. As an approach to overcome these problems, automated essay scoring (AES) is in increasing need. Many AES models based on deep neural networks have been proposed in recent years and have achieved high accuracy, but most of these models are designed to predict only a single overall score. However, to provide detailed feedback in practical situations, we often require not only the overall score but also analytic scores corresponding to various aspects of the essay.Several neural AES models that can predict both the analytic scores and the overall score have also been proposed for this very purpose. However, conventional models are designed to have complex neural architectures for each analytic score, which makes interpreting the score prediction difficult. To improve the interpretability of the prediction while maintaining scoring accuracy, we propose a new neural model for automated analytic scoring that integrates a multidimensional item response theory model, which is a popular psychometric model. | ['Masaki Uto', 'Takumi Shibata'] | null | null | null | null | coling-2022-10 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-5.02358735e-01 -2.99452931e-01 -2.46725485e-01 -7.47176468e-01
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63a03a34-8373-43ef-b14f-637456f3c167 | temporal-and-heterogeneous-graph-neural | 2305.08740 | null | https://arxiv.org/abs/2305.08740v1 | https://arxiv.org/pdf/2305.08740v1.pdf | Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction | The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by handcraft human labeling or nature language processing, which are suffering from heavy resource requirement and low accuracy. Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. In particular, we first generate the company relation graph for each trading day according to their historic price. Then we leverage a transformer encoder to encode the price movement information into temporal representations. Afterward, we propose a heterogeneous graph attention network to jointly optimize the embeddings of the financial time series data by transformer encoder and infer the probability of target movements. Finally, we conduct extensive experiments on the stock market in the United States and China. The results demonstrate the effectiveness and superior performance of our proposed methods compared with state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a real-world quantitative algorithm trading system, the accumulated portfolio return obtained by our method significantly outperforms other baselines. | ['Yuqi Liang', 'Ying Zhang', 'Chencheng Shang', 'Dawei Cheng', 'Sheng Xiang'] | 2023-05-09 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.13266093e-01 -2.87032396e-01 -1.83213085e-01 -1.38289675e-01
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fb48a16a-b231-41b3-8d95-20c6cc2667c5 | decomposition-enhances-reasoning-via-self | 2305.00633 | null | https://arxiv.org/abs/2305.00633v2 | https://arxiv.org/pdf/2305.00633v2.pdf | Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding | We endow Large Language Models (LLMs) with fine-grained self-evaluation to refine multi-step reasoning inference. We propose an effective prompting approach that integrates self-evaluation guidance through stochastic beam search. Our approach explores the reasoning search space using a well-calibrated automatic criterion. This enables an efficient search to produce higher-quality final predictions. With the self-evaluation guided stochastic beam search, we also balance the quality-diversity trade-off in the generation of reasoning chains. This allows our approach to adapt well with majority voting and surpass the corresponding Codex-backboned baselines by $6.34\%$, $9.56\%$, and $5.46\%$ on the GSM8K, AQuA, and StrategyQA benchmarks, respectively, in few-shot accuracy. Analysis of our decompositional reasoning finds it pinpoints logic failures and leads to higher consistency and robustness. Our code is publicly available at https://github.com/YuxiXie/SelfEval-Guided-Decoding. | ['Qizhe Xie', 'Junxian He', 'Min-Yen Kan', 'Xu Zhao', 'Yiran Zhao', 'Kenji Kawaguchi', 'Yuxi Xie'] | 2023-05-01 | null | null | null | null | ['math-word-problem-solving', 'gsm8k', 'arithmetic-reasoning', 'math-word-problem-solving', 'strategyqa', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'reasoning', 'reasoning', 'time-series'] | [-1.42410100e-01 3.11829031e-01 -4.93293285e-01 -5.23507953e-01
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b3f6545a-284b-41d4-99ee-2c9a7f1b9970 | sato-contextual-semantic-type-detection-in | 1911.06311 | null | https://arxiv.org/abs/1911.06311v3 | https://arxiv.org/pdf/1911.06311v3.pdf | Sato: Contextual Semantic Type Detection in Tables | Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection approaches either perform poorly with dirty data, support only a limited number of semantic types, fail to incorporate the table context of columns or rely on large sample sizes for training data. We introduce Sato, a hybrid machine learning model to automatically detect the semantic types of columns in tables, exploiting the signals from the context as well as the column values. Sato combines a deep learning model trained on a large-scale table corpus with topic modeling and structured prediction to achieve support-weighted and macro average F1 scores of 0.925 and 0.735, respectively, exceeding the state-of-the-art performance by a significant margin. We extensively analyze the overall and per-type performance of Sato, discussing how individual modeling components, as well as feature categories, contribute to its performance. | ['Wang-Chiew Tan', 'Çağatay Demiralp', 'Jinfeng Li', 'Dan Zhang', 'Yoshihiko Suhara', 'Madelon Hulsebos'] | 2019-11-14 | null | null | null | null | ['column-type-annotation'] | ['natural-language-processing'] | [ 1.27644837e-01 3.21969301e-01 -5.84038973e-01 -5.10872543e-01
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794d86cd-03cd-4acd-9259-633a7fdbc2df | oriented-object-detection-in-aerial-images | 2008.07043 | null | https://arxiv.org/abs/2008.07043v2 | https://arxiv.org/pdf/2008.07043v2.pdf | Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors | Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions and are usually densely packed. Current oriented object detection methods mainly rely on two-stage anchor-based detectors. However, the anchor-based detectors typically suffer from a severe imbalance issue between the positive and negative anchor boxes. To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task. In particular, we first detect the center keypoints of the objects, based on which we then regress the box boundary-aware vectors (BBAVectors) to capture the oriented bounding boxes. The box boundary-aware vectors are distributed in the four quadrants of a Cartesian coordinate system for all arbitrarily oriented objects. To relieve the difficulty of learning the vectors in the corner cases, we further classify the oriented bounding boxes into horizontal and rotational bounding boxes. In the experiment, we show that learning the box boundary-aware vectors is superior to directly predicting the width, height, and angle of an oriented bounding box, as adopted in the baseline method. Besides, the proposed method competes favorably with state-of-the-art methods. Code is available at https://github.com/yijingru/BBAVectors-Oriented-Object-Detection. | ['Dimitris Metaxas', 'Hui Qu', 'Jingru Yi', 'Bo Liu', 'Qiaoying Huang', 'Pengxiang Wu'] | 2020-08-17 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.34755015e-01 -2.91474253e-01 -2.15186253e-01 -1.97781935e-01
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85e77d46-32a7-4532-a529-c4d91c57221c | s3e-a-large-scale-multimodal-dataset-for | 2210.13723 | null | https://arxiv.org/abs/2210.13723v3 | https://arxiv.org/pdf/2210.13723v3.pdf | S3E: A Large-scale Multimodal Dataset for Collaborative SLAM | With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E. | ['Hongbo Chen', 'Tao Jiang', 'Qiming Chen', 'Yudu Jiao', 'Zhiqiang Chen', 'Shipeng Zhong', 'Yuhua Qi', 'Dapeng Feng'] | 2022-10-25 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.71504313e-01 -3.91758978e-01 1.52804092e-01 -5.25495291e-01
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542df122-f0de-4103-8069-b7bcb3408198 | dive-into-machine-learning-algorithms-for | 2207.13842 | null | https://arxiv.org/abs/2207.13842v1 | https://arxiv.org/pdf/2207.13842v1.pdf | Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences | Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels. As hemagglutinin is the major protein in the immune response, only hemagglutinin sequences were used and represented by position-specific scoring matrix and word embedding. The results suggest that the 5-grams-transformer neural network is the most effective algorithm for predicting viral sequence origins, with approximately 99.54% AUCPR, 98.01% F1 score and 96.60% MCC at a higher classification level, and approximately 94.74% AUCPR, 87.41% F1 score and 80.79% MCC at a lower classification level. | ['Dominik Wojtczak', 'Yanhua Xu'] | 2022-07-28 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.31282637e-01 -4.54582840e-01 -2.13083863e-01 -5.17417975e-02
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c085afc8-ecf4-4b71-959d-0eff199e9ba4 | acfnet-adaptively-cooperative-fusion-network | 2109.04627 | null | https://arxiv.org/abs/2109.04627v1 | https://arxiv.org/pdf/2109.04627v1.pdf | ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection | The reasonable employment of RGB and depth data show great significance in promoting the development of computer vision tasks and robot-environment interaction. However, there are different advantages and disadvantages in the early and late fusion of the two types of data. Besides, due to the diversity of object information, using a single type of data in a specific scenario tends to result in semantic misleading. Based on the above considerations, we propose an adaptively-cooperative fusion network (ACFNet) with ResinRes structure for salient object detection. This structure is designed to flexibly utilize the advantages of feature fusion in early and late stages. Secondly, an adaptively-cooperative semantic guidance (ACG) scheme is designed to suppress inaccurate features in the guidance phase. Further, we proposed a type-based attention module (TAM) to optimize the network and enhance the multi-scale perception of different objects. For different objects, the features generated by different types of convolution are enhanced or suppressed by the gated mechanism for segmentation optimization. ACG and TAM optimize the transfer of feature streams according to their data attributes and convolution attributes, respectively. Sufficient experiments conducted on RGB-D SOD datasets illustrate that the proposed network performs favorably against 18 state-of-the-art algorithms. | ['Jinchao Zhu'] | 2021-09-10 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.44887730e-01 -1.39095113e-01 1.90258846e-01 -6.14529848e-01
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aa896e8b-9752-484b-996f-d4daf1668100 | lottery-tickets-in-evolutionary-optimization | 2306.00045 | null | https://arxiv.org/abs/2306.00045v1 | https://arxiv.org/pdf/2306.00045v1.pdf | Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability | Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly sparse trainable initializations for evolution strategies (ES) and characterize qualitative differences compared to gradient descent (GD)-based sparse training. We introduce a novel signal-to-noise iterative pruning procedure, which incorporates loss curvature information into the network pruning step. This can enable the discovery of even sparser trainable network initializations when using black-box evolution as compared to GD-based optimization. Furthermore, we find that these initializations encode an inductive bias, which transfers across different ES, related tasks and even to GD-based training. Finally, we compare the local optima resulting from the different optimization paradigms and sparsity levels. In contrast to GD, ES explore diverse and flat local optima and do not preserve linear mode connectivity across sparsity levels and independent runs. The results highlight qualitative differences between evolution and gradient-based learning dynamics, which can be uncovered by the study of iterative pruning procedures. | ['Henning Sprekeler', 'Robert Tjarko Lange'] | 2023-05-31 | null | null | null | null | ['linear-mode-connectivity'] | ['knowledge-base'] | [ 3.87217589e-02 2.80080941e-02 -6.22942485e-02 -1.28737524e-01
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ff95d43d-e040-4903-937c-ee9678a8c8a1 | countering-malicious-content-moderation | 2212.14727 | null | https://arxiv.org/abs/2212.14727v1 | https://arxiv.org/pdf/2212.14727v1.pdf | Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage | Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective. | ['David Camacho', 'Javier Huertas Tato', 'Alejandro Martín', 'Álvaro Huertas-García'] | 2022-12-27 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-7.40657076e-02 1.96442887e-01 -1.20511211e-01 5.23435533e-01
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2a8720a0-fcf8-457f-bb5d-4c8c77088017 | devil-in-the-details-towards-accurate-single | 1809.05996 | null | http://arxiv.org/abs/1809.05996v3 | http://arxiv.org/pdf/1809.05996v3.pdf | Devil in the Details: Towards Accurate Single and Multiple Human Parsing | Human parsing has received considerable interest due to its wide application
potentials. Nevertheless, it is still unclear how to develop an accurate human
parsing system in an efficient and elegant way. In this paper, we identify
several useful properties, including feature resolution, global context
information and edge details, and perform rigorous analyses to reveal how to
leverage them to benefit the human parsing task. The advantages of these useful
properties finally result in a simple yet effective Context Embedding with Edge
Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end
trainable and can be easily adopted for conducting multiple human parsing.
Benefiting the superiority of CE2P, we achieved the 1st places on all three
human parsing benchmarks. Without any bells and whistles, we achieved 56.50\%
(mIoU), 45.31\% (mean $AP^r$) and 33.34\% ($AP^p_{0.5}$) in LIP, CIHP and MHP
v2.0, which outperform the state-of-the-arts more than 2.06\%, 3.81\% and
1.87\%, respectively. We hope our CE2P will serve as a solid baseline and help
ease future research in single/multiple human parsing. Code has been made
available at \url{https://github.com/liutinglt/CE2P}. | ['Shikui Wei', 'Ting Liu', 'Yunchao Wei', 'Zilong Huang', 'Yao Zhao', 'Thomas Huang', 'Tao Ruan'] | 2018-09-17 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 1.35078371e-01 4.62712020e-01 3.93449776e-02 -4.48060304e-01
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7e8a05d3-a2c0-45ba-b81f-c5d9d4340e3e | on-the-evaluations-of-chatgpt-and-emotion | 2304.03347 | null | https://arxiv.org/abs/2304.03347v2 | https://arxiv.org/pdf/2304.03347v2.pdf | Towards Interpretable Mental Health Analysis with ChatGPT | Automated mental health analysis shows great potential for enhancing the efficiency and accessibility of mental health care, with recent methods using pre-trained language models (PLMs) and incorporated emotional information. The latest large language models (LLMs), such as ChatGPT, exhibit dramatic capabilities on diverse natural language processing tasks. However, existing studies on ChatGPT for mental health analysis bear limitations in inadequate evaluations, ignorance of emotional information, and lack of explainability. To bridge these gaps, we comprehensively evaluate the mental health analysis and emotional reasoning ability of ChatGPT on 11 datasets across 5 tasks, and analyze the effects of various emotion-based prompting strategies. Based on these prompts, we further explore LLMs for interpretable mental health analysis by instructing them to also generate explanations for each of their decisions. With an annotation protocol designed by domain experts, we convey human evaluations to assess the quality of explanations generated by ChatGPT and GPT-3. The annotated corpus will be released for future research. Experimental results show that ChatGPT outperforms traditional neural network-based methods but still has a significant gap with advanced task-specific methods. Prompt engineering with emotional cues can be effective in improving performance on mental health analysis but suffers from a lack of robustness and inaccurate reasoning. In addition, ChatGPT significantly outperforms GPT-3 on all criteria in human evaluations of the explanations and approaches to human performance, showing its great potential in explainable mental health analysis. | ['Ziyan Kuang', 'Sophia Ananiadou', 'Qianqian Xie', 'Tianlin Zhang', 'Shaoxiong Ji', 'Kailai Yang'] | 2023-04-06 | null | null | null | null | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 7.89926760e-03 9.62398767e-01 -3.75045478e-01 -7.36934721e-01
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3388bb0d-1dd3-48da-9ecd-776c88b16520 | uni-mol-a-universal-3d-molecular | null | null | https://chemrxiv.org/engage/chemrxiv/article-details/628e5b4d5d948517f5ce6d72 | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/628e5b4d5d948517f5ce6d72/original/uni-mol-a-universal-3d-molecular-representation-learning-framework.pdf | Uni-Mol: A Universal 3D Molecular Representation Learning Framework | Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction or generation. Herein, we propose Uni-Mol, a universal MRL framework that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data. The two models are used independently for separate tasks, and are combined when used in protein-ligand binding tasks. By properly incorporating 3D information, Uni-Mol outperforms SOTA in 14/15 molecular property prediction tasks. Moreover, Uni-Mol achieves superior performance in 3D spatial tasks, including protein-ligand binding pose prediction, molecular conformation generation, etc. Finally, we show that Uni-Mol can be successfully applied to the tasks with few-shot data like pocket druggability prediction. The model and data will be made publicly available at https://github.com/dptech-corp/Uni-Mol. | ['Guolin Ke', 'Linfeng Zhang', 'Zhewei Wei', 'Hongteng Xu', 'Hang Zheng', 'Qiankun Ding', 'Zhifeng Gao', 'Gengmo Zhou'] | 2022-09-08 | null | null | null | chemrxiv-2022-9 | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.44162047e-01 -4.78531495e-02 -6.82325125e-01 -1.62419677e-01
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ace4e89b-995e-4e99-a2a2-425668960549 | demystifying-the-transferability-of | 2110.04488 | null | https://arxiv.org/abs/2110.04488v3 | https://arxiv.org/pdf/2110.04488v3.pdf | Demystifying the Transferability of Adversarial Attacks in Computer Networks | Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by the attacker. This major property is known as transferability, and makes CNNs ill-suited for security applications. In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. Furthermore, we investigate whether the transferability property issue holds in computer networks applications. In our experiments, we first consider five different attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS (L- BFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then, we perform these attacks against three well- known datasets: the Network-based Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA) dataset, and the RIPE Atlas dataset. Our experimental results show clearly that the transferability happens in specific use cases for the I- FGSM, the JSMA, and the LBFGS attack. In such scenarios, the attack success rate on the target network range from 63.00% to 100%. Finally, we suggest two shielding strategies to hinder the attack transferability, by considering the Most Powerful Attacks (MPAs), and the mismatch LSTM architecture. | ['Mauro Conti', 'Yassine Mekdad', 'Abdeslam El Fergougui', 'Mohammad Hajian Berenjestanaki', 'Ehsan Nowroozi'] | 2021-10-09 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.18390247e-01 1.43605620e-01 -8.10507387e-02 2.75755972e-02
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