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df95ed04-36a4-45b5-9acd-b49cdd88acc5 | automl-for-neuromorphic-computing-and | 2302.13210 | null | https://arxiv.org/abs/2302.13210v1 | https://arxiv.org/pdf/2302.13210v1.pdf | AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures | In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model-based search approach with a simulation framework to simulate spiking architectures, we are able to efficiently explore the configuration space of neuromorphic architectures and identify the subset of conditions leading to the highest performance in a targeted application. We have demonstrated this approach on an exemplar case of real time, on-chip learning application. Our results indicate that we can effectively use optimization approaches to optimize complex architectures, therefore providing a viable pathway towards application-driven codesign. | ['Sandeep Madireddy', 'Angel Yanguas-Gil'] | 2023-02-26 | null | null | null | null | ['automl'] | ['methodology'] | [ 3.03293705e-01 -2.12413698e-01 5.37724614e-01 -1.43208250e-01
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2bf52b21-facb-47a2-8af0-adf5dd7b6350 | eqmotion-equivariant-multi-agent-motion | 2303.10876 | null | https://arxiv.org/abs/2303.10876v2 | https://arxiv.org/pdf/2303.10876v2.pdf | EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning | Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle. However, such equivariance and invariance properties are overlooked by most existing methods. To fill this gap, we propose EqMotion, an efficient equivariant motion prediction model with invariant interaction reasoning. To achieve motion equivariance, we propose an equivariant geometric feature learning module to learn a Euclidean transformable feature through dedicated designs of equivariant operations. To reason agent's interactions, we propose an invariant interaction reasoning module to achieve a more stable interaction modeling. To further promote more comprehensive motion features, we propose an invariant pattern feature learning module to learn an invariant pattern feature, which cooperates with the equivariant geometric feature to enhance network expressiveness. We conduct experiments for the proposed model on four distinct scenarios: particle dynamics, molecule dynamics, human skeleton motion prediction and pedestrian trajectory prediction. Experimental results show that our method is not only generally applicable, but also achieves state-of-the-art prediction performances on all the four tasks, improving by 24.0/30.1/8.6/9.2%. Code is available at https://github.com/MediaBrain-SJTU/EqMotion. | ['Yanfeng Wang', 'Xinchao Wang', 'Yu Guang Wang', 'Siheng Chen', 'Yuhong Tan', 'Robby T. Tan', 'Chenxin Xu'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_EqMotion_Equivariant_Multi-Agent_Motion_Prediction_With_Invariant_Interaction_Reasoning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction', 'trajectory-prediction', 'human-pose-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.19801918e-01 -2.53375202e-01 -2.90138364e-01 -1.92429066e-01
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46c2b53c-8748-4347-ad3d-7792833de0c6 | bs-gat-behavior-similarity-based-graph | 2304.07226 | null | https://arxiv.org/abs/2304.07226v1 | https://arxiv.org/pdf/2304.07226v1.pdf | BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection | With the development of the Internet of Things (IoT), network intrusion detection is becoming more complex and extensive. It is essential to investigate an intelligent, automated, and robust network intrusion detection method. Graph neural networks based network intrusion detection methods have been proposed. However, it still needs further studies because the graph construction method of the existing methods does not fully adapt to the characteristics of the practical network intrusion datasets. To address the above issue, this paper proposes a graph neural network algorithm based on behavior similarity (BS-GAT) using graph attention network. First, a novel graph construction method is developed using the behavior similarity by analyzing the characteristics of the practical datasets. The data flows are treated as nodes in the graph, and the behavior rules of nodes are used as edges in the graph, constructing a graph with a relatively uniform number of neighbors for each node. Then, the edge behavior relationship weights are incorporated into the graph attention network to utilize the relationship between data flows and the structure information of the graph, which is used to improve the performance of the network intrusion detection. Finally, experiments are conducted based on the latest datasets to evaluate the performance of the proposed behavior similarity based graph attention network for the network intrusion detection. The results show that the proposed method is effective and has superior performance comparing to existing solutions. | ['Xin He', 'Jie Li', 'Zhijie Han', 'Yalu Wang'] | 2023-04-07 | null | null | null | null | ['graph-construction', 'network-intrusion-detection'] | ['graphs', 'miscellaneous'] | [-1.53262377e-01 -3.88120174e-01 -1.69929311e-01 -2.20815465e-01
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83678da1-bacb-4773-a6fc-80cb3fb61504 | animediffusion-anime-face-line-drawing | 2303.11137 | null | https://arxiv.org/abs/2303.11137v1 | https://arxiv.org/pdf/2303.11137v1.pdf | AnimeDiffusion: Anime Face Line Drawing Colorization via Diffusion Models | It is a time-consuming and tedious work for manually colorizing anime line drawing images, which is an essential stage in cartoon animation creation pipeline. Reference-based line drawing colorization is a challenging task that relies on the precise cross-domain long-range dependency modelling between the line drawing and reference image. Existing learning methods still utilize generative adversarial networks (GANs) as one key module of their model architecture. In this paper, we propose a novel method called AnimeDiffusion using diffusion models that performs anime face line drawing colorization automatically. To the best of our knowledge, this is the first diffusion model tailored for anime content creation. In order to solve the huge training consumption problem of diffusion models, we design a hybrid training strategy, first pre-training a diffusion model with classifier-free guidance and then fine-tuning it with image reconstruction guidance. We find that with a few iterations of fine-tuning, the model shows wonderful colorization performance, as illustrated in Fig. 1. For training AnimeDiffusion, we conduct an anime face line drawing colorization benchmark dataset, which contains 31696 training data and 579 testing data. We hope this dataset can fill the gap of no available high resolution anime face dataset for colorization method evaluation. Through multiple quantitative metrics evaluated on our dataset and a user study, we demonstrate AnimeDiffusion outperforms state-of-the-art GANs-based models for anime face line drawing colorization. We also collaborate with professional artists to test and apply our AnimeDiffusion for their creation work. We release our code on https://github.com/xq-meng/AnimeDiffusion. | ['Ping Li', 'Tong-Yee Lee', 'Xueting Liu', 'P. Y. Mok', 'Xiangqiao Meng', 'Yu Cao'] | 2023-03-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 7.37614855e-02 -9.10398737e-02 1.57296687e-01 -2.82806784e-01
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f9e2af5c-d094-4c86-8af3-16854e5f9d75 | s-2-sql-injecting-syntax-to-question-schema-1 | 2203.06958 | null | https://arxiv.org/abs/2203.06958v1 | https://arxiv.org/pdf/2203.06958v1.pdf | S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers | The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S$^2$SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL to improve the performance. We also employ the decoupling constraint to induce diverse relational edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard. | ['Yongbin Li', 'Jian Sun', 'Bowen Li', 'Bowen Qin', 'Lihan Wang', 'Ruiying Geng', 'Binyuan Hui'] | 2022-03-14 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 8.77349228e-02 4.99896020e-01 -3.45360607e-01 -7.35963523e-01
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e5a428f4-ae3f-4d1a-a706-54adbb03eb30 | learning-open-information-extraction-of | 1905.07471 | null | https://arxiv.org/abs/1905.07471v1 | https://arxiv.org/pdf/1905.07471v1.pdf | Learning Open Information Extraction of Implicit Relations from Reading Comprehension Datasets | The relationship between two entities in a sentence is often implied by word order and common sense, rather than an explicit predicate. For example, it is evident that "Fed chair Powell indicates rate hike" implies (Powell, is a, Fed chair) and (Powell, works for, Fed). These tuples are just as significant as the explicit-predicate tuple (Powell, indicates, rate hike), but have much lower recall under traditional Open Information Extraction (OpenIE) systems. Implicit tuples are our term for this type of extraction where the relation is not present in the input sentence. There is very little OpenIE training data available relative to other NLP tasks and none focused on implicit relations. We develop an open source, parse-based tool for converting large reading comprehension datasets to OpenIE datasets and release a dataset 35x larger than previously available by sentence count. A baseline neural model trained on this data outperforms previous methods on the implicit extraction task. | ['Theodore Christakis', 'Jacob Beckerman'] | 2019-05-15 | null | null | null | null | ['open-information-extraction', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97825903e-01 1.07794631e+00 -3.41411620e-01 -6.90888524e-01
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6a82542f-737e-4d7c-9e96-12bb0939a7af | multi-target-backdoor-attacks-for-code-pre | 2306.08350 | null | https://arxiv.org/abs/2306.08350v1 | https://arxiv.org/pdf/2306.08350v1.pdf | Multi-target Backdoor Attacks for Code Pre-trained Models | Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-specific data for code-related downstream tasks, thereby limiting the scope of attacks. Moreover, the majority of attacks for pre-trained models are designed for understanding tasks. In this paper, we propose task-agnostic backdoor attacks for code pre-trained models. Our backdoored model is pre-trained with two learning strategies (i.e., Poisoned Seq2Seq learning and token representation learning) to support the multi-target attack of downstream code understanding and generation tasks. During the deployment phase, the implanted backdoors in the victim models can be activated by the designed triggers to achieve the targeted attack. We evaluate our approach on two code understanding tasks and three code generation tasks over seven datasets. Extensive experiments demonstrate that our approach can effectively and stealthily attack code-related downstream tasks. | ['Yang Liu', 'Tianwei Zhang', 'Xiaofei Xie', 'Kangjie Chen', 'Shangqing Liu', 'Yanzhou Li'] | 2023-06-14 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.84317142e-01 8.50697085e-02 -2.27048174e-01 -2.34899685e-01
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219340f5-e59b-4bc3-95c4-a2cc9c28ffa4 | lesion-net-skin-lesion-segmentation-using | 2012.14249 | null | https://arxiv.org/abs/2012.14249v1 | https://arxiv.org/pdf/2012.14249v1.pdf | Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units | Skin lesions segmentation is an important step in the process of automated diagnosis of the skin melanoma. However, the accuracy of segmenting melanomas skin lesions is quite a challenging task due to less data for training, irregular shapes, unclear boundaries, and different skin colors. Our proposed approach helps in improving the accuracy of skin lesion segmentation. Firstly, we have introduced the coordinate convolutional layer before passing the input image into the encoder. This layer helps the network to decide on the features related to translation invariance which further improves the generalization capacity of the model. Secondly, we have leveraged the properties of deep residual units along with the convolutional layers. At last, instead of using only cross-entropy or Dice-loss, we have combined the two-loss functions to optimize the training metrics which helps in converging the loss more quickly and smoothly. After training and validating the proposed model on ISIC 2018 (60% as train set + 20% as validation set), we tested the robustness of our trained model on various other datasets like ISIC 2018 (20% as test-set) ISIC 2017, 2016 and PH2 dataset. The results show that the proposed model either outperform or at par with the existing skin lesion segmentation methods. | ['Priya Kansal', 'Sabari Nathan'] | 2020-12-28 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.66925502e-01 -3.01136691e-02 -1.15899712e-01 -1.76098198e-01
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f230c5a9-2263-4863-8ada-5bfc2e7ecadb | macd-r-cnn-an-abnormal-cell-nucleus-detection | null | null | https://ieeexplore.ieee.org/document/9179730 | https://ieeexplore.ieee.org/document/9179730 | MACD R-CNN: An Abnormal Cell Nucleus Detection Method | The detection of abnormal cell nuclei is a key technique of the cytopathic automatic screening system, which directly determines the performance of the system. Although the Mask R-CNN which combines target detection and semantic segmentation has achieved good performance in general target detection tasks, the performance in abnormal cell detection is still unsatisfactory. To solve this problem, we design a new deep neural network for abnormal cell detection based on the Mask R-CNN, named mask abnormal cell detection R-CNN (MACD R-CNN). First, in the classification branch of Mask R-CNN, it generates the same size of feature maps from different size of RoIs as the input. The nuclei in this part of the feature maps will be deformed to varying degrees. We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification. Then we use the attention mechanism to merge the original RoI and Fixed RoI features. Finally, we increase the depth of the convolution layer to further improve the accuracy of cell classification. Experiments show that the MACD R-CNN can effectively improve the performance of abnormal cell detection. | ['Yongjun He', 'Feng Cao', 'Jian Zhang', 'Baoyan Ma'] | 2020-07-28 | null | null | null | null | ['medical-object-detection', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [-3.49303447e-02 -9.79139507e-02 1.44461423e-01 -2.28197023e-01
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c58d0c10-6e38-4495-93cf-510d2b37bb55 | learning-object-placements-for-relational | 2001.08481 | null | https://arxiv.org/abs/2001.08481v2 | https://arxiv.org/pdf/2001.08481v2.pdf | Learning Object Placements For Relational Instructions by Hallucinating Scene Representations | Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this work, we present a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image. During training, our network receives the learning signal by classifying hallucinated high-level scene representations as an auxiliary task. Unlike previous approaches, our method does not require ground truth data for the pixelwise relational probabilities or 3D models of the objects, which significantly expands the applicability in practical applications. Our results obtained using real-world data and human-robot experiments demonstrate the effectiveness of our method in reasoning about the best way to place objects to reproduce a spatial relation. Videos of our experiments can be found at https://youtu.be/zaZkHTWFMKM | ['Johan Vertens', 'Oier Mees', 'Alp Emek', 'Wolfram Burgard'] | 2020-01-23 | null | null | null | null | ['spatial-relation-recognition', 'scene-generation', 'auxiliary-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-3.54472175e-02 4.90726888e-01 5.91152310e-02 -5.30766308e-01
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d4038c53-52ad-4115-a6d7-132204b88cbb | actor-centric-relation-network | 1807.10982 | null | http://arxiv.org/abs/1807.10982v1 | http://arxiv.org/pdf/1807.10982v1.pdf | Actor-Centric Relation Network | Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level and model temporal context with 3D
ConvNets. Here, we go one step further and model spatio-temporal relations to
capture the interactions between human actors, relevant objects and scene
elements essential to differentiate similar human actions. Our approach is
weakly supervised and mines the relevant elements automatically with an
actor-centric relational network (ACRN). ACRN computes and accumulates
pair-wise relation information from actor and global scene features, and
generates relation features for action classification. It is implemented as
neural networks and can be trained jointly with an existing action detection
system. We show that ACRN outperforms alternative approaches which capture
relation information, and that the proposed framework improves upon the
state-of-the-art performance on JHMDB and AVA. A visualization of the learned
relation features confirms that our approach is able to attend to the relevant
relations for each action. | ['Rahul Sukthankar', 'Kevin Murphy', 'Cordelia Schmid', 'Carl Vondrick', 'Chen Sun', 'Abhinav Shrivastava'] | 2018-07-28 | actor-centric-relation-network-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Chen_Sun_Actor-centric_Relation_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chen_Sun_Actor-centric_Relation_Network_ECCV_2018_paper.pdf | eccv-2018-9 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 2.64884114e-01 -7.03262240e-02 -3.56585860e-01 -2.47835249e-01
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b39800d0-a8ae-4c00-bdf1-4bfed5802de6 | generalizing-through-forgetting-domain | 2209.09485 | null | https://arxiv.org/abs/2209.09485v2 | https://arxiv.org/pdf/2209.09485v2.pdf | Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes | Symptom information is primarily documented in free-text clinical notes and is not directly accessible for downstream applications. To address this challenge, information extraction approaches that can handle clinical language variation across different institutions and specialties are needed. In this paper, we present domain generalization for symptom extraction using pretraining and fine-tuning data that differs from the target domain in terms of institution and/or specialty and patient population. We extract symptom events using a transformer-based joint entity and relation extraction method. To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain. Additionally, we pretrain the transformer language model (LM) on task-related unlabeled texts for better representation. Our experiments indicate that masking and adaptive pretraining methods can significantly improve performance when the source domain is more distant from the target domain. | ['Mari Ostendorf', 'Meliha Yetisgen', 'Kevin Lybarger', 'Sitong Zhou'] | 2022-09-20 | null | null | null | null | ['event-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.60379225e-01 1.38728082e-01 -6.00526392e-01 -5.53004384e-01
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98e44961-765c-488f-9805-ee5ef3ef7827 | blind-signal-dereverberation-for-machine | 2210.00117 | null | https://arxiv.org/abs/2210.00117v1 | https://arxiv.org/pdf/2210.00117v1.pdf | Blind Signal Dereverberation for Machine Speech Recognition | We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal windows, which ideally cover the entire room impulse response, we convert room induced convolution to additions in the log spectral domain. Next, we compute a spectral normalization vector from statistics gathered over reverberated as well as over clean speech in the log spectral domain. During operation, this normalization vectors are used to alleviate reverberations from complex speech spectra recorded under the same reverberant conditions . Such dereverberated complex speech spectra are used to compute complex FDLP-spectrograms for use in automatic speech recognition. | ['Hynek Hermansky', 'Samik Sadhu'] | 2022-09-30 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 4.77739125e-01 -6.43657506e-01 1.14498997e+00 -2.34115407e-01
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14a1e200-0027-4791-b73c-d0d59f8442ef | goal-randomization-for-playing-text-based | null | null | https://openreview.net/forum?id=KdcLdLuIjQT | https://openreview.net/pdf?id=KdcLdLuIjQT | Goal Randomization for Playing Text-based Games without a Reward Function | Playing text-based games requires language understanding and sequential decision making. The objective of a reinforcement learning agent is to behave so as to maximise the sum of a suitable scalar reward function. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose a goal randomization method that uses random basic goals to train a policy in the absence of the reward of environments. Specifically, through simple but effective goal generation, our method learns to continuously propose challenging -- yet temporal and achievable -- goals that allow the agent to learn general skills for acting in a new environment, independent of the task to be solved. In a variety of text-based games, we show that this simple method results in competitive performance for agents. We also show that our method can learn policies that generalize across different text-based games. In further, we demonstrate an interesting result that our method works better than one of state-of-the-art agents GATA, which uses environment rewards for some text-based games. | ['Chengqi Zhang', 'Ling Chen', 'Yali Du', 'Yunqiu Xu', 'Meng Fang'] | 2021-09-29 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 9.95076150e-02 3.93486738e-01 2.02807471e-01 -3.57310064e-02
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7d7d4590-e342-4a6a-b14e-3eddb4268f35 | see-your-heart-psychological-states | 2302.10276 | null | https://arxiv.org/abs/2302.10276v2 | https://arxiv.org/pdf/2302.10276v2.pdf | See Your Heart: Psychological states Interpretation through Visual Creations | In psychoanalysis, generating interpretations to one's psychological state through visual creations is facing significant demands. The two main tasks of existing studies in the field of computer vision, sentiment/emotion classification and affective captioning, can hardly satisfy the requirement of psychological interpreting. To meet the demands for psychoanalysis, we introduce a challenging task, \textbf{V}isual \textbf{E}motion \textbf{I}nterpretation \textbf{T}ask (VEIT). VEIT requires AI to generate reasonable interpretations of creator's psychological state through visual creations. To support the task, we present a multimodal dataset termed SpyIn (\textbf{S}and\textbf{p}la\textbf{y} \textbf{In}terpretation Dataset), which is psychological theory supported and professional annotated. Dataset analysis illustrates that SpyIn is not only able to support VEIT, but also more challenging compared with other captioning datasets. Building on SpyIn, we conduct experiments of several image captioning method, and propose a visual-semantic combined model which obtains a SOTA result on SpyIn. The results indicate that VEIT is a more challenging task requiring scene graph information and psychological knowledge. Our work also show a promise for AI to analyze and explain inner world of humanity through visual creations. | ['Kaiqi Huang', 'Shiyu Zhang', 'Xiaotang Chen', 'Xiaokun Feng', 'Likun Yang'] | 2023-02-11 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 3.98006439e-01 5.34736872e-01 1.31994829e-01 -4.77800727e-01
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78c59faa-e7bd-4ab9-b4a3-1ad848aa9eeb | improving-heterogeneous-face-recognition-with | 1709.02848 | null | http://arxiv.org/abs/1709.02848v2 | http://arxiv.org/pdf/1709.02848v2.pdf | Improving Heterogeneous Face Recognition with Conditional Adversarial Networks | Heterogeneous face recognition between color image and depth image is a much
desired capacity for real world applications where shape information is looked
upon as merely involved in gallery. In this paper, we propose a cross-modal
deep learning method as an effective and efficient workaround for this
challenge. Specifically, we begin with learning two convolutional neural
networks (CNNs) to extract 2D and 2.5D face features individually. Once
trained, they can serve as pre-trained models for another two-way CNN which
explores the correlated part between color and depth for heterogeneous
matching. Compared with most conventional cross-modal approaches, our method
additionally conducts accurate depth image reconstruction from single color
image with Conditional Generative Adversarial Nets (cGAN), and further enhances
the recognition performance by fusing multi-modal matching results. Through
both qualitative and quantitative experiments on benchmark FRGC 2D/3D face
database, we demonstrate that the proposed pipeline outperforms
state-of-the-art performance on heterogeneous face recognition and ensures a
drastically efficient on-line stage. | ['Liming Chen', 'Zhixin Shu', 'Wuming Zhang', 'Dimitris Samaras'] | 2017-09-08 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.94069162e-01 -2.04256717e-02 -2.51280498e-02 -5.52822292e-01
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44c8350c-669f-4401-aeca-81f9e4afbd27 | patt-lite-lightweight-patch-and-attention | 2306.09626 | null | https://arxiv.org/abs/2306.09626v1 | https://arxiv.org/pdf/2306.09626v1.pdf | PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition | Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. While existing work has achieved performance improvements in recent years, FER in the wild and under challenging conditions remains a challenge. In this paper, a lightweight patch and attention network based on MobileNetV1, referred to as PAtt-Lite, is proposed to improve FER performance under challenging conditions. A truncated ImageNet-pre-trained MobileNetV1 is utilized as the backbone feature extractor of the proposed method. In place of the truncated layers is a patch extraction block that is proposed for extracting significant local facial features to enhance the representation from MobileNetV1, especially under challenging conditions. An attention classifier is also proposed to improve the learning of these patched feature maps from the extremely lightweight feature extractor. The experimental results on public benchmark databases proved the effectiveness of the proposed method. PAtt-Lite achieved state-of-the-art results on CK+, RAF-DB, FER2013, FERPlus, and the challenging conditions subsets for RAF-DB and FERPlus. The source code for the proposed method will be available at https://github.com/JLREx/PAtt-Lite. | ['Thian Song Ong', 'Chin Poo Lee', 'Kian Ming Lim', 'Jia Le Ngwe'] | 2023-06-16 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 8.61183256e-02 -1.23636469e-01 -5.11977710e-02 -4.85020459e-01
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685950cd-f9d7-4c67-9fdb-3c214ef98195 | a-novel-joint-points-and-silhouette-based | 2012.06109 | null | https://arxiv.org/abs/2012.06109v1 | https://arxiv.org/pdf/2012.06109v1.pdf | A novel joint points and silhouette-based method to estimate 3D human pose and shape | This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes on 2D and 3D space. A novel energy function based on the correspondence is built and minimized to fit parametric model to the silhouettes. Our approach uses sufficient shape information because the energy function of silhouettes is built from both 2D and 3D space. This also means that our method only needs images from sparse views, which balances data used and the required prior information. Results on synthetic data and real data demonstrate the competitive performance of our approach on pose and shape estimation of the human body. | ['Magnus Oskarsson', 'Anders Heyden', 'Zhongguo Li'] | 2020-12-11 | null | null | null | null | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-3.99016887e-01 1.10024497e-01 -5.46308868e-02 -3.73411477e-01
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e3ea51f9-147b-4afe-926f-6b04604e7ecd | weakly-supervised-video-emotion-detection-and | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Weakly_Supervised_Video_Emotion_Detection_and_Prediction_via_Cross-Modal_Temporal_CVPR_2023_paper.pdf | Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network | Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://github.com/nku-zhichengzhang/WECL. | ['Jufeng Yang', 'Lijuan Wang', 'Zhicheng Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-emotion-detection', 'video-emotion-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.84172884e-02 -2.53905714e-01 -4.67640221e-01 -3.94321650e-01
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86ec2783-2d4a-467c-97b3-2a61cc1890bd | infocnf-efficient-conditional-continuous | null | null | https://openreview.net/forum?id=SJgvl6EFwH | https://openreview.net/pdf?id=SJgvl6EFwH | InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers | Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation. However, conditioning CNFs on signals of interest for conditional image generation and downstream predictive tasks is inefficient due to the high-dimensional latent code generated by the model, which needs to be of the same size as the input data. In this paper, we propose InfoCNF, an efficient conditional CNF that partitions the latent space into a class-specific supervised code and an unsupervised code that shared among all classes for efficient use of labeled information. Since the partitioning strategy (slightly) increases the number of function evaluations (NFEs), InfoCNF also employs gating networks to learn the error tolerances of its ordinary differential equation (ODE) solvers for better speed and performance. We show empirically that InfoCNF improves the test accuracy over the baseline while yielding comparable likelihood scores and reducing the NFEs on CIFAR10. Furthermore, applying the same partitioning strategy in InfoCNF on time-series data helps improve extrapolation performance. | ['Anima Anandkumar', 'Richard G. Baraniuk', 'Animesh Garg', 'Tan M. Nguyen'] | 2019-09-25 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 7.55638182e-02 1.77933881e-03 -2.53339652e-02 -4.86826450e-01
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c8457e8f-f3ea-468b-a130-ae39bf1d1d3f | quality-estimation-of-machine-translated | 2306.15399 | null | https://arxiv.org/abs/2306.15399v1 | https://arxiv.org/pdf/2306.15399v1.pdf | Quality Estimation of Machine Translated Texts based on Direct Evidence from Training Data | Current Machine Translation systems achieve very good results on a growing variety of language pairs and data sets. However, it is now well known that they produce fluent translation outputs that often can contain important meaning errors. Quality Estimation task deals with the estimation of quality of translations produced by a Machine Translation system without depending on Reference Translations. A number of approaches have been suggested over the years. In this paper we show that the parallel corpus used as training data for training the MT system holds direct clues for estimating the quality of translations produced by the MT system. Our experiments show that this simple and direct method holds promise for quality estimation of translations produced by any purely data driven machine translation system. | ['Narayana Murthy Kavi', 'Vibhuti Kumari'] | 2023-06-27 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 2.16861248e-01 4.48824652e-03 -3.73788655e-01 -4.98857379e-01
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0bdb508b-7ef1-42ee-886f-a5aa30fec58c | meta-distant-transfer-learning-for-pre | null | null | https://aclanthology.org/2021.emnlp-main.768 | https://aclanthology.org/2021.emnlp-main.768.pdf | Meta Distant Transfer Learning for Pre-trained Language Models | With the wide availability of Pre-trained Language Models (PLMs), multi-task fine-tuning across domains has been extensively applied. For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer. Inspired by meta-learning, we propose the Meta Distant Transfer Learning (Meta-DTL) framework to learn the cross-task knowledge for PLM-based methods. Meta-DTL first employs task representation learning to mine implicit relations among multiple tasks and classes. Based on the results, it trains a PLM-based meta-learner to capture the transferable knowledge across tasks. The weighted maximum entropy regularizers are proposed to make meta-learner more task-agnostic and unbiased. Finally, the meta-learner can be fine-tuned to fit each task with better parameter initialization. We evaluate Meta-DTL using both BERT and ALBERT on seven public datasets. Experiment results confirm the superiority of Meta-DTL as it consistently outperforms strong baselines. We find that Meta-DTL is highly effective when very few data is available for the target task. | ['Yin Zhang', 'Fei Yang', 'Jun Huang', 'Minghui Qiu', 'Haojie Pan', 'Chengyu Wang'] | null | null | null | null | emnlp-2021-11 | ['implicit-relations'] | ['natural-language-processing'] | [ 8.12795535e-02 -1.47682428e-01 -6.60996735e-01 -5.69538593e-01
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eeefa8e1-cb4c-4b91-855b-18c18facfae3 | neural-re-rendering-for-full-frame-video | 2102.06205 | null | https://arxiv.org/abs/2102.06205v4 | https://arxiv.org/pdf/2102.06205v4.pdf | Hybrid Neural Fusion for Full-frame Video Stabilization | Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views. In this work, we present a frame synthesis algorithm to achieve full-frame video stabilization. We first estimate dense warp fields from neighboring frames and then synthesize the stabilized frame by fusing the warped contents. Our core technical novelty lies in the learning-based hybrid-space fusion that alleviates artifacts caused by optical flow inaccuracy and fast-moving objects. We validate the effectiveness of our method on the NUS, selfie, and DeepStab video datasets. Extensive experiment results demonstrate the merits of our approach over prior video stabilization methods. | ['Jia-Bin Huang', 'Yung-Yu Chuang', 'Ming-Hsuan Yang', 'Wei-Sheng Lai', 'Yu-Lun Liu'] | 2021-02-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Hybrid_Neural_Fusion_for_Full-Frame_Video_Stabilization_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-stabilization'] | ['computer-vision'] | [-3.17139104e-02 -3.34193408e-01 -2.09244683e-01 -9.07368809e-02
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d711eb4a-d157-4624-9828-b11d5571b555 | posterior-sampling-with-cnn-based-plug-and | 2212.14595 | null | https://arxiv.org/abs/2212.14595v1 | https://arxiv.org/pdf/2212.14595v1.pdf | Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion | Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data. | ['Matteo Ravasi', 'Nick Luiken', 'Miguel Corrales', 'Juan Romero', 'Tariq Alkhalifah', 'Muhammad Izzatullah'] | 2022-12-30 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 2.11284876e-01 1.13271931e-02 3.85939062e-01 -2.86681533e-01
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65bafda3-bfdf-44c2-ad2e-aeff365b467b | transition-based-dependency-parsing-using-two | null | null | https://aclanthology.org/D15-1215 | https://aclanthology.org/D15-1215.pdf | Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks | null | ['Xuanjing Huang', 'Xinchi Chen', 'Yaqian Zhou', 'Xipeng Qiu', 'Chenxi Zhu'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8f7e1069-c982-4593-8c06-3c6cbbc839b6 | capsule-based-persianarabic-robust | 1912.03634 | null | https://arxiv.org/abs/1912.03634v2 | https://arxiv.org/pdf/1912.03634v2.pdf | Capsule-Based Persian/Arabic Robust Handwritten Digit Recognition Using EM Routing | In this paper, the problem of handwritten digit recognition has been addressed. However, the underlying language is Persian/Arabic, and the system with which this task is a capsule network (CapsNet) has recently emerged as a more advanced architecture than its ancestor, namely CNN (Convolutional Neural Network). The training of the architecture is performed using the Hoda dataset, which has been provided for Persian/Arabic handwritten digits. The output of the system clearly outperforms the results achieved by its ancestors, as well as other previously presented recognition algorithms. | ['Rahil Mahdian Toroghi', 'Ali Ghofrani'] | 2019-12-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.60769269e-01 -3.22073877e-01 -4.81959544e-02 -2.38718942e-01
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a361b3b4-86eb-4661-8321-7bf3c29d3099 | hierarchical-relational-learning-for-few-shot | 2209.01205 | null | https://arxiv.org/abs/2209.01205v3 | https://arxiv.org/pdf/2209.01205v3.pdf | Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion | Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverage of KGs. It aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have mostly focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, valuable pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods. | ['Jie Yin', 'Bala Rajaratnam', 'Jianyuan Guo', 'Han Wu'] | 2022-09-02 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-8.51168782e-02 5.02974510e-01 -6.10277176e-01 -4.83692408e-01
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8ff2ffcb-169c-4401-83d5-f60a94864437 | ji-yu-zhi-shi-qian-yi-de-qing-gan-yuan-yin | null | null | https://aclanthology.org/2022.ccl-1.45 | https://aclanthology.org/2022.ccl-1.45.pdf | 基于知识迁移的情感-原因对抽取(Emotion-Cause Pair Extraction Based on Knowledge-Transfer) | “现有的情感瘭原因对抽取模型均没有通过加入外部知识来提升情感瘭原因对的抽取效果。本文提出基于知识迁移的情感瘭原因对抽取模型瘨癅癃癐癅瘭癋癔瘩,采用知识库获取文本的显性知识编码;随后引入外部情感分类语料库迁移得到子句的隐性知识编码;最后拼接两个知识编码,加入情感瘨原因瘩子句预测概率及相对位置,搭配癔癲癡癮癳癦癯癲癭癥癲机制融合上下文,并采用窗口机制优化计算压力,实现情感瘭原因对抽取。在癅癃癐癅数据集上的实验结果显示,本文提出的方法超过当前最先进的模型癅癃癐癅瘭瘲癄。” | ['Guoqiong Liao', 'Xiping Liu', 'Changxuan Wan', 'Qizhi Wan', 'Dexi Liu', 'Fengyuan Zhao'] | null | null | null | null | ccl-2022-10 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-0.768306 -0.81891614 0.63639235 0.39404333 0.28725553 -1.3841403
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345b9449-13ff-404a-bce6-9183c3d0bdbb | direct-speech-to-speech-translation-with | 2107.05604 | null | https://arxiv.org/abs/2107.05604v2 | https://arxiv.org/pdf/2107.05604v2.pdf | Direct speech-to-speech translation with discrete units | We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We tackle the problem by first applying a self-supervised discrete speech encoder on the target speech and then training a sequence-to-sequence speech-to-unit translation (S2UT) model to predict the discrete representations of the target speech. When target text transcripts are available, we design a joint speech and text training framework that enables the model to generate dual modality output (speech and text) simultaneously in the same inference pass. Experiments on the Fisher Spanish-English dataset show that the proposed framework yields improvement of 6.7 BLEU compared with a baseline direct S2ST model that predicts spectrogram features. When trained without any text transcripts, our model performance is comparable to models that predict spectrograms and are trained with text supervision, showing the potential of our system for translation between unwritten languages. Audio samples are available at https://facebookresearch.github.io/speech_translation/direct_s2st_units/index.html . | ['Sravya Popuri', 'Wei-Ning Hsu', 'Juan Pino', 'Yun Tang', 'Qing He', 'Yossi Adi', 'Adam Polyak', 'Xutai Ma', 'Jiatao Gu', 'Changhan Wang', 'Peng-Jen Chen', 'Ann Lee'] | 2021-07-12 | null | https://aclanthology.org/2022.acl-long.235 | https://aclanthology.org/2022.acl-long.235.pdf | acl-2022-5 | ['speech-to-speech-translation'] | ['speech'] | [ 7.68633902e-01 6.60788596e-01 -1.35156110e-01 -4.72035438e-01
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e8825ca2-588d-4439-b3b9-ea1d805670e0 | scene-coordinate-regression-forests-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Shotton_Scene_Coordinate_Regression_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Shotton_Scene_Coordinate_Regression_2013_CVPR_paper.pdf | Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images | We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image. Our approach employs a regression forest that is capable of inferring an estimate of each pixel's correspondence to 3D points in the scene's world coordinate frame. The forest uses only simple depth and RGB pixel comparison features, and does not require the computation of feature descriptors. The forest is trained to be capable of predicting correspondences at any pixel, so no interest point detectors are required. The camera pose is inferred using a robust optimization scheme. This starts with an initial set of hypothesized camera poses, constructed by applying the forest at a small fraction of image pixels. Preemptive RANSAC then iterates sampling more pixels at which to evaluate the forest, counting inliers, and refining the hypothesized poses. We evaluate on several varied scenes captured with an RGB-D camera and observe that the proposed technique achieves highly accurate relocalization and substantially out-performs two state of the art baselines. | ['Christopher Zach', 'Shahram Izadi', 'Jamie Shotton', 'Andrew Fitzgibbon', 'Ben Glocker', 'Antonio Criminisi'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['camera-relocalization'] | ['computer-vision'] | [ 5.61554849e-01 1.49399554e-02 1.03592323e-02 -2.33389035e-01
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3b5e8c08-ec5d-487a-88e3-cf1496ecc014 | the-event-storyline-corpus-a-new-benchmark | null | null | https://aclanthology.org/W17-2711 | https://aclanthology.org/W17-2711.pdf | The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction | This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems. | ['Tommaso Caselli', 'Piek Vossen'] | 2017-08-01 | null | null | null | ws-2017-8 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.88278332e-01 2.73774922e-01 -7.22554922e-01 -5.13346732e-01
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bf2fcfb0-d6b0-487a-8837-5c9cf0b2a9e3 | instance-as-identity-a-generic-online | 2208.03079 | null | https://arxiv.org/abs/2208.03079v2 | https://arxiv.org/pdf/2208.03079v2.pdf | Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation | Modeling temporal information for both detection and tracking in a unified framework has been proved a promising solution to video instance segmentation (VIS). However, how to effectively incorporate the temporal information into an online model remains an open problem. In this work, we propose a new online VIS paradigm named Instance As Identity (IAI), which models temporal information for both detection and tracking in an efficient way. In detail, IAI employs a novel identification module to predict identification number for tracking instances explicitly. For passing temporal information cross frame, IAI utilizes an association module which combines current features and past embeddings. Notably, IAI can be integrated with different image models. We conduct extensive experiments on three VIS benchmarks. IAI outperforms all the online competitors on YouTube-VIS-2019 (ResNet-101 43.7 mAP) and YouTube-VIS-2021 (ResNet-50 38.0 mAP). Surprisingly, on the more challenging OVIS, IAI achieves SOTA performance (20.6 mAP). Code is available at https://github.com/zfonemore/IAI | ['Yunchao Wei', 'Yi Yang', 'Xin Yu', 'Zongxin Yang', 'Feng Zhu'] | 2022-08-05 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-2.94785291e-01 -4.56305116e-01 -5.68844438e-01 -7.73304179e-02
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c385699d-58a5-498d-9a36-ac99f393e0d5 | monte-carlo-tree-search-algorithms-for-risk | 2211.13032 | null | https://arxiv.org/abs/2211.13032v2 | https://arxiv.org/pdf/2211.13032v2.pdf | Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning | In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a medical setting a patient may only have one opportunity to treat their illness. Making decisions using just the expected future returns -- known in reinforcement learning as the value -- cannot account for the potential range of adverse or positive outcomes a decision may have. Therefore, we should use the distribution over expected future returns differently to represent the critical information that the agent requires at decision time by taking both the future and accrued returns into consideration. In this paper, we propose two novel Monte Carlo tree search algorithms. Firstly, we present a Monte Carlo tree search algorithm that can compute policies for nonlinear utility functions (NLU-MCTS) by optimising the utility of the different possible returns attainable from individual policy executions, resulting in good policies for both risk-aware and multi-objective settings. Secondly, we propose a distributional Monte Carlo tree search algorithm (DMCTS) which extends NLU-MCTS. DMCTS computes an approximate posterior distribution over the utility of the returns, and utilises Thompson sampling during planning to compute policies in risk-aware and multi-objective settings. Both algorithms outperform the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns. | ['Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Mathieu Reymond', 'Conor F. Hayes'] | 2022-11-23 | null | null | null | null | ['multi-objective-reinforcement-learning', 'thompson-sampling'] | ['methodology', 'methodology'] | [ 6.30925894e-02 2.93783516e-01 -6.91542685e-01 -1.62698850e-01
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fdd2d2dd-e74d-4eae-aa8a-45d8ccd42eee | towards-multimodal-emotion-recognition-in | 1909.02764 | null | https://arxiv.org/abs/1909.02764v2 | https://arxiv.org/pdf/1909.02764v2.pdf | Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning | The recognition of emotions by humans is a complex process which considers multiple interacting signals such as facial expressions and both prosody and semantic content of utterances. Commonly, research on automatic recognition of emotions is, with few exceptions, limited to one modality. We describe an in-car experiment for emotion recognition from speech interactions for three modalities: the audio signal of a spoken interaction, the visual signal of the driver's face, and the manually transcribed content of utterances of the driver. We use off-the-shelf tools for emotion detection in audio and face and compare that to a neural transfer learning approach for emotion recognition from text which utilizes existing resources from other domains. We see that transfer learning enables models based on out-of-domain corpora to perform well. This method contributes up to 10 percentage points in F1, with up to 76 micro-average F1 across the emotions joy, annoyance and insecurity. Our findings also indicate that off-the-shelf-tools analyzing face and audio are not ready yet for emotion detection in in-car speech interactions without further adjustments. | ['Sebastian Zepf', 'Deniz Cevher', 'Roman Klinger'] | 2019-09-06 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-7.77324438e-02 1.10986590e-01 1.99956015e-01 -9.29583788e-01
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b8e20720-99ba-4d9d-b584-5e04eada7859 | exposing-deepfake-with-pixel-wise-ar-and-ppg | 2110.15561 | null | https://arxiv.org/abs/2110.15561v1 | https://arxiv.org/pdf/2110.15561v1.pdf | Exposing Deepfake with Pixel-wise AR and PPG Correlation from Faint Signals | Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. In spite of an urgent need for Deepfake identification, existing pixel-level detection methods are increasingly unable to resist the growing realism of fake videos and lack generalization. In this paper, we propose a scheme to expose Deepfake through faint signals hidden in face videos. This scheme extracts two types of minute information hidden between face pixels-photoplethysmography (PPG) features and auto-regressive (AR) features, which are used as the basis for forensics in the temporal and spatial domains, respectively. According to the principle of PPG, tracking the absorption of light by blood cells allows remote estimation of the temporal domains heart rate (HR) of face video, and irregular HR fluctuations can be seen as traces of tampering. On the other hand, AR coefficients are able to reflect the inter-pixel correlation, and can also reflect the traces of smoothing caused by up-sampling in the process of generating fake faces. Furthermore, the scheme combines asymmetric convolution block (ACBlock)-based improved densely connected networks (DenseNets) to achieve face video authenticity forensics. Its asymmetric convolutional structure enhances the robustness of network to the input feature image upside-down and left-right flipping, so that the sequence of feature stitching does not affect detection results. Simulation results show that our proposed scheme provides more accurate authenticity detection results on multiple deep forgery datasets and has better generalization compared to the benchmark strategy. | ['Jun Yang', 'Maoyu Mao'] | 2021-10-29 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 8.55552852e-02 -1.72986537e-01 6.20312393e-02 -2.08974048e-01
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19d3f894-fead-45fb-afe4-c1a3933a2385 | equivariant-and-invariant-grounding-for-video | 2207.12783 | null | https://arxiv.org/abs/2207.12783v1 | https://arxiv.org/pdf/2207.12783v1.pdf | Equivariant and Invariant Grounding for Video Question Answering | Video Question Answering (VideoQA) is the task of answering the natural language questions about a video. Producing an answer requires understanding the interplay across visual scenes in video and linguistic semantics in question. However, most leading VideoQA models work as black boxes, which make the visual-linguistic alignment behind the answering process obscure. Such black-box nature calls for visual explainability that reveals ``What part of the video should the model look at to answer the question?''. Only a few works present the visual explanations in a post-hoc fashion, which emulates the target model's answering process via an additional method. Nonetheless, the emulation struggles to faithfully exhibit the visual-linguistic alignment during answering. Instead of post-hoc explainability, we focus on intrinsic interpretability to make the answering process transparent. At its core is grounding the question-critical cues as the causal scene to yield answers, while rolling out the question-irrelevant information as the environment scene. Taking a causal look at VideoQA, we devise a self-interpretable framework, Equivariant and Invariant Grounding for Interpretable VideoQA (EIGV). Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene. By imposing them on the answering process, EIGV is able to distinguish the causal scene from the environment information, and explicitly present the visual-linguistic alignment. Extensive experiments on three benchmark datasets justify the superiority of EIGV in terms of accuracy and visual interpretability over the leading baselines. | ['Tat-Seng Chua', 'Junbin Xiao', 'Xiang Wang', 'Yicong Li'] | 2022-07-26 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.82432562e-01 4.09233183e-01 -1.14918865e-01 -5.12467861e-01
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9d9c8168-687a-4bd9-ada2-90390eb5616d | application-of-data-encryption-in-chinese | 2208.14627 | null | https://arxiv.org/abs/2208.14627v1 | https://arxiv.org/pdf/2208.14627v1.pdf | Application of Data Encryption in Chinese Named Entity Recognition | Recently, with the continuous development of deep learning, the performance of named entity recognition tasks has been dramatically improved. However, the privacy and the confidentiality of data in some specific fields, such as biomedical and military, cause insufficient data to support the training of deep neural networks. In this paper, we propose an encryption learning framework to address the problems of data leakage and inconvenient disclosure of sensitive data in certain domains. We introduce multiple encryption algorithms to encrypt training data in the named entity recognition task for the first time. In other words, we train the deep neural network using the encrypted data. We conduct experiments on six Chinese datasets, three of which are constructed by ourselves. The experimental results show that the encryption method achieves satisfactory results. The performance of some models trained with encrypted data even exceeds the performance of the unencrypted method, which verifies the effectiveness of the introduced encryption method and solves the problem of data leakage to a certain extent. | ['Weizhi Xu', 'Hui Yu', 'Han Zhao', 'Yang Cao', 'Yanfang Geng', 'Shengyu Fan', 'Jikun Dong', 'Kaifang Long'] | 2022-08-31 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.84708478e-03 -1.90495983e-01 1.08491585e-01 -6.47647560e-01
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061d4f38-3fc8-4407-89a5-ce7b8a8a7bd4 | transformer-transforms-salient-object | 2104.10127 | null | https://arxiv.org/abs/2104.10127v5 | https://arxiv.org/pdf/2104.10127v5.pdf | Generative Transformer for Accurate and Reliable Salient Object Detection | Transformer, which originates from machine translation, is particularly powerful at modeling long-range dependencies. Currently, the transformer is making revolutionary progress in various vision tasks, leading to significant performance improvements compared with the convolutional neural network (CNN) based frameworks. In this paper, we conduct extensive research on exploiting the contributions of transformers for accurate and reliable salient object detection. For the former, we apply transformer to a deterministic model, and explain that the effective structure modeling and global context modeling abilities lead to its superior performance compared with the CNN based frameworks. For the latter, we observe that both CNN and transformer based frameworks suffer greatly from the over-confidence issue, where the models tend to generate wrong predictions with high confidence. To estimate the reliability degree of both CNN- and transformer-based frameworks, we further present a latent variable model, namely inferential generative adversarial network (iGAN), based on the generative adversarial network (GAN). The stochastic attribute of the latent variable makes it convenient to estimate the predictive uncertainty, serving as an auxiliary output to evaluate the reliability of model prediction. Different from the conventional GAN, which defines the distribution of the latent variable as fixed standard normal distribution $\mathcal{N}(0,\mathbf{I})$, the proposed iGAN infers the latent variable by gradient-based Markov Chain Monte Carlo (MCMC), namely Langevin dynamics, leading to an input-dependent latent variable model. We apply our proposed iGAN to both fully and weakly supervised salient object detection, and explain that iGAN within the transformer framework leads to both accurate and reliable salient object detection. | ['Nick Barnes', 'Deng-Ping Fan', 'Xinyu Tian', 'Yunqiu Lv', 'Aixuan Li', 'Yuchao Dai', 'Zhexiong Wan', 'Jing Zhang', 'Yuxin Mao'] | 2021-04-20 | null | null | null | null | ['camouflaged-object-segmentation'] | ['computer-vision'] | [ 3.90115321e-01 2.49187812e-01 9.74388979e-03 -6.35473058e-02
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6f37b384-7496-47fe-badb-95a13b902d02 | post-mortem-iris-recognition-with-deep | 1901.01708 | null | https://arxiv.org/abs/1901.01708v2 | https://arxiv.org/pdf/1901.01708v2.pdf | Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation | This paper proposes the first known to us iris recognition methodology designed specifically for post-mortem samples. We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images. We show how to use segmentation masks predicted by neural networks in conventional, Gabor-based iris recognition method, which employs circular approximations of the pupillary and limbic iris boundaries. As a whole, this method allows for a significant improvement in post-mortem iris recognition accuracy over the methods designed only for ante-mortem irises, including the academic OSIRIS and commercial IriCore implementations. The proposed method reaches the EER less than 1% for samples collected up to 10 hours after death, when compared to 16.89% and 5.37% of EER observed for OSIRIS and IriCore, respectively. For samples collected up to 369 hours post-mortem, the proposed method achieves the EER 21.45%, while 33.59% and 25.38% are observed for OSIRIS and IriCore, respectively. Additionally, the method is tested on a database of iris images collected from ophthalmology clinic patients, for which it also offers an advantage over the two other algorithms. This work is the first step towards post-mortem-specific iris recognition, which increases the chances of identification of deceased subjects in forensic investigations. The new database of post-mortem iris images acquired from 42 subjects, as well as the deep learning-based segmentation models are made available along with the paper, to ensure all the results presented in this manuscript are reproducible. | ['Adam Czajka', 'Piotr Maciejewicz', 'Mateusz Trokielewicz'] | 2019-01-07 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 1.41445786e-01 2.00621020e-02 5.78013286e-02 -1.47335127e-01
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8ee31cb4-03c6-44b8-9a2e-98ce0030c290 | how-do-you-correct-run-on-sentences-its-not | 1809.08298 | null | http://arxiv.org/abs/1809.08298v1 | http://arxiv.org/pdf/1809.08298v1.pdf | How do you correct run-on sentences it's not as easy as it seems | Run-on sentences are common grammatical mistakes but little research has
tackled this problem to date. This work introduces two machine learning models
to correct run-on sentences that outperform leading methods for related tasks,
punctuation restoration and whole-sentence grammatical error correction. Due to
the limited annotated data for this error, we experiment with artificially
generating training data from clean newswire text. Our findings suggest
artificial training data is viable for this task. We discuss implications for
correcting run-ons and other types of mistakes that have low coverage in
error-annotated corpora. | ['Kostiantyn Omelianchuk', 'Courtney Napoles', 'Joel Tetreault', 'Junchao Zheng'] | 2018-09-21 | how-do-you-correct-run-on-sentences-its-not-1 | https://aclanthology.org/W18-6105 | https://aclanthology.org/W18-6105.pdf | ws-2018-11 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.66367376e-01 7.41318047e-01 2.38979205e-01 -8.77718687e-01
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2da72127-c7b1-4ea1-bd3e-3fda2ee7e3bf | augpt-dialogue-with-pre-trained-language | 2102.05126 | null | https://arxiv.org/abs/2102.05126v3 | https://arxiv.org/pdf/2102.05126v3.pdf | AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models | Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via back-translation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation. | ['Ondřej Dušek', 'Tomáš Nekvinda', 'Vojtěch Hudeček', 'Jonáš Kulhánek'] | 2021-02-09 | null | https://aclanthology.org/2021.nlp4convai-1.19 | https://aclanthology.org/2021.nlp4convai-1.19.pdf | emnlp-nlp4convai-2021-11 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [ 1.21543258e-02 6.85201108e-01 -2.40432531e-01 -5.25544703e-01
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86913762-de1c-401a-a474-9559fb62fb9f | challenging-america-modeling-language-in-1 | null | null | https://aclanthology.org/2022.findings-naacl.56 | https://aclanthology.org/2022.findings-naacl.56.pdf | Challenging America: Modeling language in longer time scales | The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i.e. pre-train and fine-tune large Transformer models. This paper introduces an ML challenge, named Challenging America (ChallAm), based on OCR-ed excerpts from historical newspapers collected from the Chronicling America portal. ChallAm provides a dataset of clippings, labeled with metadata on their origin, and paired with their textual contents retrieved by an OCR tool. Three, publicly available, ML tasks are defined in the challenge: to determine the article date, to detect the location of the issue, and to deduce a word in a text gap (cloze test). Strong baselines are provided for all three ChallAm tasks. In particular, we pre-trained a RoBERTa model from scratch from the historical texts. We also discuss the issues of discrimination and hate-speech present in the historical American texts. | ['Piotr Wierzchon', 'Krzysztof Jurkiewicz', 'Karol Kaczmarek', 'Krzysztof Jassem', 'Filip Graliński', 'Jakub Pokrywka'] | null | null | null | null | findings-naacl-2022-7 | ['cloze-test'] | ['natural-language-processing'] | [ 2.84375131e-01 4.31659818e-02 -2.58875966e-01 -1.61364600e-01
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fd5d3838-2af7-439c-8a80-4eb2dbd3261f | pali-a-jointly-scaled-multilingual-language | 2209.06794 | null | https://arxiv.org/abs/2209.06794v4 | https://arxiv.org/pdf/2209.06794v4.pdf | PaLI: A Jointly-Scaled Multilingual Language-Image Model | Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pre-trained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train a large, 4-billion parameter ViT (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design. | ['Radu Soricut', 'Neil Houlsby', 'Xiaohua Zhai', 'Anelia Angelova', 'Andreas Steiner', 'Carlos Riquelme', 'Burcu Karagol Ayan', 'Chao Jia', 'Mojtaba Seyedhosseini', 'Weicheng Kuo', 'James Bradbury', 'Ashish Thapliyal', 'Linting Xue', 'Gaurav Mishra', 'Hassan Akbari', 'Keran Rong', 'Nan Ding', 'Joan Puigcerver', 'Alexander Kolesnikov', 'Lucas Beyer', 'Basil Mustafa', 'Adam Grycner', 'Sebastian Goodman', 'Daniel Salz', 'Piotr Padlewski', 'AJ Piergiovanni', 'Soravit Changpinyo', 'Xiao Wang', 'Xi Chen'] | 2022-09-14 | null | null | null | null | ['zero-shot-transfer-image-classification', 'visual-reasoning', 'few-shot-image-classification', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'reasoning'] | [ 2.12910101e-02 1.33878002e-02 1.48830548e-01 -2.75438160e-01
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e58a91a6-4946-470f-81f7-55ac60ba074c | pairwise-heuristic-sequence-alignment | 2010.13478 | null | https://arxiv.org/abs/2010.13478v1 | https://arxiv.org/pdf/2010.13478v1.pdf | Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning | Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method. | ['Dong Ho Cho', 'Gyu Bum Han', 'Hye In Seo', 'Dong Jin Ji', 'Yong Joon Song'] | 2020-10-26 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 3.23411256e-01 -5.57071388e-01 -3.43759991e-02 -2.51308948e-01
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6d5bca79-300e-4186-96dd-c083d7801c78 | mars-entry-trajectory-planning-with-range | 2201.09455 | null | https://arxiv.org/abs/2201.09455v1 | https://arxiv.org/pdf/2201.09455v1.pdf | Mars Entry Trajectory Planning with Range Discretization and Successive Convexification | This paper develops a sequential convex programming approach for Mars entry trajectory planning by range discretization. To improve the accuracy of numerical integration, the range of entry trajectory is selected as the independent variable rather than time or energy. A dilation factor is employed to normalize the entry dynamics and integration interval of the performance index so that the difficult free-final-time programming problem can be converted to a fixed-final-range optimization problem. The bank angle rate with respect to the range is introduced as the new control input in order to decouple the control from the state and facilitate convexification of constraints on the bank angle and its rate. The nonlinear bank angle rate constraint is further relaxed into a linear one via inequality relaxation. Moreover, the nonconvex minimum-time performance index is convexified by regarding flight time as a state variable. Then, the Mars entry trajectory planning problem can be formulated into the framework of convex programming after linearization. By range discretization and successive convexification, the reformulated Mars entry trajectory planning problem is transcribed into a series of convex optimization sub-problems that can be sequentially solved using the convex programming algorithm. The virtual control and adaptive trust-region techniques are employed to improve the accuracy, robustness, and computation efficiency of the algorithm. Numerical simulations with comparative studies are presented to demonstrate the convergence performance and efficiency of the proposed algorithm. | ['Ming Xin', 'Shuang Li', 'Xu Liu'] | 2022-01-24 | null | null | null | null | ['numerical-integration', 'trajectory-planning'] | ['miscellaneous', 'robots'] | [-1.81121141e-01 1.21333532e-01 -5.81399679e-01 -1.43445894e-01
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5a26ec24-a830-4126-8361-e1ab6b9410f9 | zebrapose-coarse-to-fine-surface-encoding-for | 2203.09418 | null | https://arxiv.org/abs/2203.09418v2 | https://arxiv.org/pdf/2203.09418v2.pdf | ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation | Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases. | ['Federico Tombari', 'Didier Stricker', 'Benjamin Busam', 'Nassir Navab', 'Jason Rambach', 'Torben Fetzer', 'Mahdi Saleh', 'Yongzhi Su'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-to-3d'] | ['computer-vision'] | [ 5.04193082e-02 6.31610230e-02 -2.46948481e-01 -3.74587506e-01
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d055e562-a615-496c-9934-af952a2fe4b9 | tablenet-deep-learning-model-for-end-to-end | 2001.01469 | null | https://arxiv.org/abs/2001.01469v1 | https://arxiv.org/pdf/2001.01469v1.pdf | TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images | With the widespread use of mobile phones and scanners to photograph and upload documents, the need for extracting the information trapped in unstructured document images such as retail receipts, insurance claim forms and financial invoices is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular sub-images presents a unique set of challenges. This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table. While some progress has been made in table detection, extracting the table contents is still a challenge since this involves more fine grained table structure(rows & columns) recognition. Prior approaches have attempted to solve the table detection and structure recognition problems independently using two separate models. In this paper, we propose TableNet: a novel end-to-end deep learning model for both table detection and structure recognition. The model exploits the interdependence between the twin tasks of table detection and table structure recognition to segment out the table and column regions. This is followed by semantic rule-based row extraction from the identified tabular sub-regions. The proposed model and extraction approach was evaluated on the publicly available ICDAR 2013 and Marmot Table datasets obtaining state of the art results. Additionally, we demonstrate that feeding additional semantic features further improves model performance and that the model exhibits transfer learning across datasets. Another contribution of this paper is to provide additional table structure annotations for the Marmot data, which currently only has annotations for table detection. | ['Lovekesh Vig', 'Monika Sharma', 'Vishwanath D', 'Shubham Paliwal', 'Rohit Rahul'] | 2020-01-06 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 3.11385423e-01 6.31654710e-02 -1.97195694e-01 -3.92183930e-01
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03f7b817-90b8-4b64-9058-5fc50346e001 | skipconvnet-skip-convolutional-neural-network | 2007.09131 | null | https://arxiv.org/abs/2007.09131v1 | https://arxiv.org/pdf/2007.09131v1.pdf | SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping | The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications. One of the most popular variants of these FCNs is the `U-Net', which is an encoder-decoder network with skip connections. In this study, we propose `SkipConvNet' where we replace each skip connection with multiple convolutional modules to provide decoder with intuitive feature maps rather than encoder's output to improve the learning capacity of the network. We also propose the use of optimal smoothing of power spectral density (PSD) as a pre-processing step, which helps to further enhance the efficiency of the network. To evaluate our proposed system, we use the REVERB challenge corpus to assess the performance of various enhancement approaches under the same conditions. We focus solely on monitoring improvements in speech quality and their contribution to improving the efficiency of back-end speech systems, such as speech recognition and speaker verification, trained on only clean speech. Experimental findings show that the proposed system consistently outperforms other approaches. | ['Jing Huang', 'Wei Xue', 'John H. L. Hansen', 'Shahram Ghorbani', 'Wei Xia', 'Vinay Kothapally'] | 2020-07-17 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.07238658e-01 8.94803256e-02 3.15784335e-01 -4.82870013e-01
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779f4b52-be4c-4718-92dd-0001f2b042e0 | efficient-graph-deep-learning-in-tensorflow | 2101.11552 | null | https://arxiv.org/abs/2101.11552v1 | https://arxiv.org/pdf/2101.11552v1.pdf | Efficient Graph Deep Learning in TensorFlow with tf_geometric | We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc. These infrastructures enable tf_geometric to support single-graph computation, multi-graph computation, graph mini-batch, distributed training, etc.; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as transductive node classification, inductive node classification, link prediction, and graph classification. Based on the kernel libraries, tf_geometric implements a variety of popular GNN models for different tasks. To facilitate the implementation of GNNs, tf_geometric also provides some other libraries for dataset management, graph sampling, etc. Different from existing popular GNN libraries, tf_geometric provides not only Object-Oriented Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to handle advanced graph deep learning tasks such as graph meta-learning. The APIs of tf_geometric are friendly, and they are suitable for both beginners and experts. In this paper, we first present an overview of tf_geometric's framework. Then, we conduct experiments on some benchmark datasets and report the performance of several popular GNN models implemented by tf_geometric. | ['Changsheng Xu', 'Huaiwen Zhang', 'Quan Zhao', 'Youze Wang', 'Quan Fang', 'Shengsheng Qian', 'Jun Hu'] | 2021-01-27 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-7.99626768e-01 1.17636845e-01 -3.10491979e-01 -3.75729799e-01
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8450ba3b-9104-482d-8b35-0dd32f8d7904 | cwid-hi-a-dataset-for-complex-word | null | null | https://aclanthology.org/2022.lrec-1.604 | https://aclanthology.org/2022.lrec-1.604.pdf | CWID-hi: A Dataset for Complex Word Identification in Hindi Text | Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. Multiple studies have been done to create datasets for text simplification. However, most of these datasets focus on high-resource languages only. In this work, we proposed a complex word dataset for Hindi, a language largely ignored in text simplification literature. We used various Hindi knowledge annotators for annotation to capture the annotator’s language knowledge. Our analysis shows a significant difference between native and non-native annotators’ perception of word complexity. We also built an automatic complex word classifier using a soft voting approach based on the predictions from tree-based ensemble classifiers. These models behave differently for annotations made by different categories of users, such as native and non-native speakers. Our dataset and analysis will help simplify Hindi text depending on the user’s language understanding. The dataset is available at https://zenodo.org/record/5229160. | ['Ravi Shekhar', 'Dhanya Pramod', 'Gayatri Venugopal'] | null | null | null | null | lrec-2022-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-6.4717107e-02 5.2677250e-01 3.7780430e-02 -6.1281121e-01
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e147cef4-4e29-4ed6-983e-00e49e12bb2b | stochastic-marginal-likelihood-gradients | 2306.03968 | null | https://arxiv.org/abs/2306.03968v1 | https://arxiv.org/pdf/2306.03968v1.pdf | Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels | Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace approximations can allow to optimize such hyperparameters just like standard neural network parameters using gradients and on the training data. However, estimating a single hyperparameter gradient requires a pass through the entire dataset, limiting the scalability of such algorithms. In this work, we overcome this issue by introducing lower bounds to the linearized Laplace approximation of the marginal likelihood. In contrast to previous estimators, these bounds are amenable to stochastic-gradient-based optimization and allow to trade off estimation accuracy against computational complexity. We derive them using the function-space form of the linearized Laplace, which can be estimated using the neural tangent kernel. Experimentally, we show that the estimators can significantly accelerate gradient-based hyperparameter optimization. | ['Bernhard Schölkopf', 'Gunnar Rätsch', 'Mark van der Wilk', 'Tycho F. A. van der Ouderaa', 'Alexander Immer'] | 2023-06-06 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-4.03613389e-01 2.09844634e-01 -3.06874424e-01 -6.66963875e-01
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6dce0ddd-567d-4789-966e-d5d8dc166e51 | unsupervised-continual-learning-and-self | 1904.02021 | null | https://arxiv.org/abs/1904.02021v6 | https://arxiv.org/pdf/1904.02021v6.pdf | Unsupervised Progressive Learning and the STAM Architecture | We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using clustering and classification tasks. While there are no existing learning scenarios that are directly comparable to UPL, we compare the STAM architecture with two recent continual learning models, Memory Aware Synapses (MAS) and Gradient Episodic Memories (GEM), after adapting them in the UPL setting. | ['Constantine Dovrolis', 'Seth Baer', 'James Smith', 'Cameron Taylor'] | 2019-04-03 | null | https://openreview.net/forum?id=Skxw-REFwS | https://openreview.net/pdf?id=Skxw-REFwS | null | ['online-clustering'] | ['computer-vision'] | [ 2.98204154e-01 9.00646523e-02 1.35831498e-02 -3.89122784e-01
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48b68d94-d9ef-43f0-8db9-ff5afa28417e | template-kernels-for-dependency-parsing | null | null | https://aclanthology.info/papers/N15-1163/n15-1163 | https://www.aclweb.org/anthology/N15-1163 | Template Kernels for Dependency Parsing | null | ['Hillel Taub-Tabib', 'Amir Globerson', 'Yoav Goldberg'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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5bc0853b-208e-4943-8285-b03912028d3e | s4t-source-free-domain-adaptation-for | 2107.10140 | null | https://arxiv.org/abs/2107.10140v2 | https://arxiv.org/pdf/2107.10140v2.pdf | AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation | Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy constraints. We focus on source-free domain adaptation for semantic segmentation, wherein a source model must adapt itself to a new target domain given only unlabeled target data. We propose Augmentation Consistency-guided Self-training (AUGCO), a source-free adaptation algorithm that uses the model's pixel-level predictive consistency across diverse, automatically generated views of each target image along with model confidence to identify reliable pixel predictions, and selectively self-trains on those. AUGCO achieves state-of-the-art results for source-free adaptation on 3 standard benchmarks for semantic segmentation, all within a simple to implement and fast to converge method. | ['Judy Hoffman', 'Deeksha Kartik', 'Shivam Khare', 'Viraj Prabhu'] | 2021-07-21 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.83006465e-01 3.28161031e-01 -4.42419797e-01 -7.81158388e-01
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18fa0f8d-a8dc-43e0-8baf-ed19bad1bec5 | towards-coupling-full-disk-and-active-region | 2209.07406 | null | https://arxiv.org/abs/2209.07406v1 | https://arxiv.org/pdf/2209.07406v1.pdf | Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting | Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of a wide spectrum of researchers due to recent advances in both remote sensing as well as machine learning and deep learning approaches. The experimental findings based on both machine and deep learning models reveal significant performance improvements for task specific datasets. Along with building models, the practice of deploying such models to production environments under operational settings is a more complex and often time-consuming process which is often not addressed directly in research settings. We present a set of new heuristic approaches to train and deploy an operational solar flare prediction system for $\geq$M1.0-class flares with two prediction modes: full-disk and active region-based. In full-disk mode, predictions are performed on full-disk line-of-sight magnetograms using deep learning models whereas in active region-based models, predictions are issued for each active region individually using multivariate time series data instances. The outputs from individual active region forecasts and full-disk predictors are combined to a final full-disk prediction result with a meta-model. We utilized an equal weighted average ensemble of two base learners' flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model. The major findings of this study are: (i) We successfully coupled two heterogeneous flare prediction models trained with different datasets and model architecture to predict a full-disk flare probability for next 24 hours, (ii) Our proposed ensembling model, i.e., logistic regression, improves on the predictive performance of two base learners and the baseline meta learner measured in terms of two widely used metrics True Skill Statistic (TSS) and Heidke Skill core (HSS), and (iii) Our result analysis suggests that the logistic regression-based ensemble (Meta-FP) improves on the full-disk model (base learner) by $\sim9\%$ in terms TSS and $\sim10\%$ in terms of HSS. Similarly, it improves on the AR-based model (base learner) by $\sim17\%$ and $\sim20\%$ in terms of TSS and HSS respectively. Finally, when compared to the baseline meta model, it improves on TSS by $\sim10\%$ and HSS by $\sim15\%$. | ['Berkay Aydin', 'Manolis K. Georgoulis', 'Rafal A. Angryk', 'Anli Ji', 'Chetraj Pandey'] | 2022-08-11 | null | null | null | null | ['weather-forecasting', 'solar-flare-prediction'] | ['miscellaneous', 'time-series'] | [-1.33770213e-01 -2.49943987e-01 1.71559691e-01 -4.21020448e-01
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fb70f391-3984-4e8a-b16a-76f0303b3d5b | can-ner-convolutional-attention-network | 1904.02141 | null | https://arxiv.org/abs/1904.02141v3 | https://arxiv.org/pdf/1904.02141v3.pdf | CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset. | ['Börje F. Karlsson', 'Yuying Zhu', 'Guoxin Wang'] | 2019-04-03 | can-ner-convolutional-attention-network-for | https://aclanthology.org/N19-1342 | https://aclanthology.org/N19-1342.pdf | naacl-2019-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-3.08653086e-01 -1.73499793e-01 6.96856976e-02 -2.46141866e-01
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bfa41185-6c54-40cc-b454-d8f48e4675b2 | tdls-a-top-down-layer-searching-algorithm-for | 2108.04238 | null | https://arxiv.org/abs/2108.04238v2 | https://arxiv.org/pdf/2108.04238v2.pdf | TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation | Explanation of AI, as well as fairness of algorithms' decisions and the transparency of the decision model, are becoming more and more important. And it is crucial to design effective and human-friendly techniques when opening the black-box model. Counterfactual conforms to the human way of thinking and provides a human-friendly explanation, and its corresponding explanation algorithm refers to a strategic alternation of a given data point so that its model output is "counter-facted", i.e. the prediction is reverted. In this paper, we adapt counterfactual explanation over fine-grained image classification problem. We demonstrated an adaptive method that could give a counterfactual explanation by showing the composed counterfactual feature map using top-down layer searching algorithm (TDLS). We have proved that our TDLS algorithm could provide more flexible counterfactual visual explanation in an efficient way using VGG-16 model on Caltech-UCSD Birds 200 dataset. At the end, we discussed several applicable scenarios of counterfactual visual explanations. | ['Caleb Chen Cao', 'Haocheng Han', 'Cong Wang'] | 2021-08-08 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.10289150e-01 6.04288220e-01 -1.02555864e-01 -5.85147679e-01
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50aef59d-f018-43ca-bf6e-f9608ef02c53 | wastewater-catchment-areas-in-great-britain | null | null | https://www.essoar.org/doi/10.1002/essoar.10510612.2 | https://www.essoar.org/pdfjs/10.1002/essoar.10510612.2 | Wastewater catchment areas in Great Britain | Wastewater catchment area data are essential for wastewater treatment capacity planning and have recently become critical for operationalising wastewater-based epidemiology (WBE) for COVID-19. Owing to the privatised nature of the water industry in the United Kingdom, the required catchment area datasets are not readily available to researchers. Here, we present a consolidated dataset of 7,537 catchment areas from ten sewerage service providers in the Great Britain, covering more than 96% of the population of England and Wales. We develop a geospatial method for estimating the population resident within each catchment from small area population estimates generated by the Office for National Statistics. The method is more widely applicable to matching electronic health records to wastewater infrastructure. Population estimates are highly predictive of population equivalent treatment loads reported under the European Urban Wastewater Treatment Directive. We highlight challenges associated with using geospatial data for wastewater-based epidemiology. | ['Andrew Singer', 'Barbara Kasprzyk-Hordern', 'Sarah Bunney', 'Till Hoffmann'] | 2022-02-25 | null | null | null | essoar-2022-2 | ['epidemiology'] | ['medical'] | [ 2.21890211e-01 2.85633802e-02 2.07162589e-01 1.23951524e-01
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62148378-1f95-40a7-8a61-22b28b104506 | evolution-of-3gpp-standards-towards-true | 2306.04012 | null | https://arxiv.org/abs/2306.04012v1 | https://arxiv.org/pdf/2306.04012v1.pdf | Evolution of 3GPP Standards Towards True Extended Reality (XR) Support in 6G Networks | Extended reality (XR) is a key innovation of 5G-advanced and beyond networks. The diverse XR use-cases, including virtual reality, augmented reality, and mixed reality, transform the way humans interact with surrounding environments. Thus, XR technology enables true immersive experiences of novel services spanning, e.g., e-commerce, healthcare, and education, respectively. However, the efficient support of XR services over existing and future cellular systems is highly challenging and requires multiple radio design improvements, due to the unique XR traffic and performance characteristics. Thus, this article surveys the state-of-art 3GPP standardization activities (release-18) for integrating the XR service class into the 5G-advanced specifications, highlighting the major XR performance challenges. Furthermore, the paper introduces valuable insights and research directions for supporting true XR services over the next-generation 6G networks, where multiple novel radio design mindsets and protocol enhancements are proposed and evaluated using extensive system level simulations, including solutions for application-native dynamic performance reporting, traffic-dependent control channel design, collaborative device aggregation for XR capacity boosting and offload, respectively. | ['Morris Repeta', 'Ali A. Esswie'] | 2023-06-06 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-4.80901673e-02 3.66080850e-02 -3.76417398e-01 -1.86425164e-01
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263f74f0-3bf6-4279-996d-83b52dfba635 | viser-video-specific-surface-embeddings-for | null | null | http://proceedings.neurips.cc/paper/2021/hash/a11f9e533f28593768ebf87075ab34f2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a11f9e533f28593768ebf87075ab34f2-Paper.pdf | ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction | We introduce ViSER, a method for recovering articulated 3D shapes and dense3D trajectories from monocular videos. Previous work on high-quality reconstruction of dynamic 3D shapes typically relies on multiple camera views, strong category-specific priors, or 2D keypoint supervision. We show that none of these are required if one can reliably estimate long-range correspondences in a video, making use of only 2D object masks and two-frame optical flow as inputs. ViSER infers correspondences by matching 2D pixels to a canonical, deformable 3D mesh via video-specific surface embeddings that capture the pixel appearance of each surface point. These embeddings behave as a continuous set of keypoint descriptors defined over the mesh surface, which can be used to establish dense long-range correspondences across pixels. The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses.Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS. Our code is available at viser-shape.github.io. | ['Deva Ramanan', 'Ce Liu', 'Forrester Cole', 'Daniel Vlasic', 'Varun Jampani', 'Deqing Sun', 'Gengshan Yang'] | 2021-12-01 | null | https://openreview.net/forum?id=-JJy-Hw8TFB | https://openreview.net/pdf?id=-JJy-Hw8TFB | neurips-2021-12 | ['3d-shape-reconstruction-from-videos'] | ['computer-vision'] | [-1.22314326e-01 -2.48940796e-01 -2.40700364e-01 -3.15873355e-01
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41231cea-1f5e-4bea-ae26-c9e85bda615a | a-comprehensive-study-of-real-time-object | 2208.10895 | null | https://arxiv.org/abs/2208.10895v2 | https://arxiv.org/pdf/2208.10895v2.pdf | A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey | Deep neural network based object detectors are continuously evolving and are used in a multitude of applications, each having its own set of requirements. While safety-critical applications need high accuracy and reliability, low-latency tasks need resource and energy-efficient networks. Real-time detectors, which are a necessity in high-impact real-world applications, are continuously proposed, but they overemphasize the improvements in accuracy and speed while other capabilities such as versatility, robustness, resource and energy efficiency are omitted. A reference benchmark for existing networks does not exist, nor does a standard evaluation guideline for designing new networks, which results in ambiguous and inconsistent comparisons. We, thus, conduct a comprehensive study on multiple real-time detectors (anchor-, keypoint-, and transformer-based) on a wide range of datasets and report results on an extensive set of metrics. We also study the impact of variables such as image size, anchor dimensions, confidence thresholds, and architecture layers on the overall performance. We analyze the robustness of detection networks against distribution shifts, natural corruptions, and adversarial attacks. Also, we provide a calibration analysis to gauge the reliability of the predictions. Finally, to highlight the real-world impact, we conduct two unique case studies, on autonomous driving and healthcare applications. To further gauge the capability of networks in critical real-time applications, we report the performance after deploying the detection networks on edge devices. Our extensive empirical study can act as a guideline for the industrial community to make an informed choice on the existing networks. We also hope to inspire the research community towards a new direction in the design and evaluation of networks that focuses on a bigger and holistic overview for a far-reaching impact. | ['Bahram Zonooz', 'Senthilkumar Kathiresan', 'Omar Magdy', 'Ratnajit Mukherjee', 'Shruthi Gowda', 'Elahe Arani'] | 2022-08-23 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 9.15656686e-02 -2.72673011e-01 -3.44444394e-01 -1.69117942e-01
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e13a107e-ce5a-45d6-98e6-d7e9efd0332c | robust-leave-one-out-cross-validation-for | 2209.09190 | null | https://arxiv.org/abs/2209.09190v1 | https://arxiv.org/pdf/2209.09190v1.pdf | Robust leave-one-out cross-validation for high-dimensional Bayesian models | Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Bayesian LOO-CV criteria. Our method retains the simplicity and computational convenience of classical approaches, while guaranteeing finite variance of the resulting estimators. Both theoretical and numerical results are provided to illustrate the improved robustness and efficiency. The computational benefits are particularly significant in high-dimensional problems, allowing to perform Bayesian LOO-CV for a broader range of models. The proposed methodology is easily implementable in standard probabilistic programming software and has a computational cost roughly equivalent to fitting the original model once. | ['Giacomo Zanella', 'Luca Silva'] | 2022-09-19 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-2.12905519e-02 -1.72277063e-01 -2.84213513e-01 -5.21095335e-01
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74022fa9-719d-4611-9618-41dd8dfeebfe | towards-transparent-application-of-machine | 2105.12700 | null | https://arxiv.org/abs/2105.12700v2 | https://arxiv.org/pdf/2105.12700v2.pdf | Towards Transparent Application of Machine Learning in Video Processing | Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning. The new techniques, considered as an advanced form of Artificial Intelligence (AI), bring previously unforeseen capabilities. However, they typically come in the form of resource-hungry black-boxes (overly complex with little transparency regarding the inner workings). Their application can therefore be unpredictable and generally unreliable for large-scale use (e.g. in live broadcast). The aim of this work is to understand and optimise learned models in video processing applications so systems that incorporate them can be used in a more trustworthy manner. In this context, the presented work introduces principles for simplification of learned models targeting improved transparency in implementing machine learning for video production and distribution applications. These principles are demonstrated on video compression examples, showing how bitrate savings and reduced complexity can be achieved by simplifying relevant deep learning models. | ['Marta Mrak', 'Fiona Rivera', 'Maria Santamaria', 'Marc Gorriz Blanch', 'Luka Murn'] | 2021-05-26 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 2.60441393e-01 2.46219397e-01 -2.84625351e-01 -4.46573466e-01
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822ca871-95f2-4e09-9dcc-977e97ec7bba | acute-eval-improved-dialogue-evaluation-with | 1909.03087 | null | https://arxiv.org/abs/1909.03087v1 | https://arxiv.org/pdf/1909.03087v1.pdf | ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons | While dialogue remains an important end-goal of natural language research, the difficulty of evaluation is an oft-quoted reason why it remains troublesome to make real progress towards its solution. Evaluation difficulties are actually two-fold: not only do automatic metrics not correlate well with human judgments, but also human judgments themselves are in fact difficult to measure. The two most used human judgment tests, single-turn pairwise evaluation and multi-turn Likert scores, both have serious flaws as we discuss in this work. We instead provide a novel procedure involving comparing two full dialogues, where a human judge is asked to pay attention to only one speaker within each, and make a pairwise judgment. The questions themselves are optimized to maximize the robustness of judgments across different annotators, resulting in better tests. We also show how these tests work in self-play model chat setups, resulting in faster, cheaper tests. We hope these tests become the de facto standard, and will release open-source code to that end. | ['Jason Weston', 'Stephen Roller', 'Margaret Li'] | 2019-09-06 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 4.06976993e-04 3.49133730e-01 9.12498757e-02 -7.34845400e-01
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c1dd7d5d-b45f-4080-b93b-0c21f1e3794d | towards-weakly-supervised-text-spotting-using | 2202.05508 | null | https://arxiv.org/abs/2202.05508v2 | https://arxiv.org/pdf/2202.05508v2.pdf | Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer | Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks. | ['Pietro Perona', 'R. Manmatha', 'Yarin Bar', 'Sharon Fogel', 'Inbal Lavi', 'Yair Kittenplon'] | 2022-02-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-spotting'] | ['computer-vision'] | [ 5.05405128e-01 -2.20554203e-01 -2.52435803e-01 -4.43372428e-01
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5950d3f0-812d-4427-a2a2-65e102b3e6da | beyond-the-model-data-pre-processing-attack | 2305.03963 | null | https://arxiv.org/abs/2305.03963v2 | https://arxiv.org/pdf/2305.03963v2.pdf | Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps | The increasing popularity of deep learning (DL) models and the advantages of computing, including low latency and bandwidth savings on smartphones, have led to the emergence of intelligent mobile applications, also known as DL apps, in recent years. However, this technological development has also given rise to several security concerns, including adversarial examples, model stealing, and data poisoning issues. Existing works on attacks and countermeasures for on-device DL models have primarily focused on the models themselves. However, scant attention has been paid to the impact of data processing disturbance on the model inference. This knowledge disparity highlights the need for additional research to fully comprehend and address security issues related to data processing for on-device models. In this paper, we introduce a data processing-based attacks against real-world DL apps. In particular, our attack could influence the performance and latency of the model without affecting the operation of a DL app. To demonstrate the effectiveness of our attack, we carry out an empirical study on 517 real-world DL apps collected from Google Play. Among 320 apps utilizing MLkit, we find that 81.56\% of them can be successfully attacked. The results emphasize the importance of DL app developers being aware of and taking actions to secure on-device models from the perspective of data processing. | ['Helei Cui', 'Shuo Huang', 'Yujin Huang', 'Ye Sang'] | 2023-05-06 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 4.52851877e-02 -2.62874544e-01 -3.47451270e-01 6.57643005e-02
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7ab38718-4923-4b47-8094-14040025f3e0 | learning-to-match-mathematical-statements | 2102.02110 | null | https://arxiv.org/abs/2102.02110v1 | https://arxiv.org/pdf/2102.02110v1.pdf | Learning to Match Mathematical Statements with Proofs | We introduce a novel task consisting in assigning a proof to a given mathematical statement. The task is designed to improve the processing of research-level mathematical texts. Applying Natural Language Processing (NLP) tools to research level mathematical articles is both challenging, since it is a highly specialized domain which mixes natural language and mathematical formulae. It is also an important requirement for developing tools for mathematical information retrieval and computer-assisted theorem proving. We release a dataset for the task, consisting of over 180k statement-proof pairs extracted from mathematical research articles. We carry out preliminary experiments to assess the difficulty of the task. We first experiment with two bag-of-words baselines. We show that considering the assignment problem globally and using weighted bipartite matching algorithms helps a lot in tackling the task. Finally, we introduce a self-attention-based model that can be trained either locally or globally and outperforms baselines by a wide margin. | ['Shay B. Cohen', 'Maximin Coavoux'] | 2021-02-03 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.62542158e-01 7.19127357e-02 -1.93020090e-01 -2.41966203e-01
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cb4bb5c4-e64b-4a0a-b637-6f38e60b11d3 | towards-knowledge-intensive-text-to-sql | 2301.01067 | null | https://arxiv.org/abs/2301.01067v1 | https://arxiv.org/pdf/2301.01067v1.pdf | Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge | In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL. | ['Jian-Guang Lou', 'Min-Yen Kan', 'Dechen Zhan', 'Wanxiang Che', 'Dingzirui Wang', 'Mingyang Pan', 'Xuqi Liu', 'Yan Gao', 'Longxu Dou'] | 2023-01-03 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-2.51097947e-01 6.65751398e-01 -2.54390210e-01 -8.53464723e-01
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db8cd67b-0171-4ad9-8ad3-5f768e4114b8 | vita-clip-video-and-text-adaptive-clip-via | 2304.03307 | null | https://arxiv.org/abs/2304.03307v1 | https://arxiv.org/pdf/2304.03307v1.pdf | Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting | Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, recent works in this area face a trade-off. Finetuning the pretrained model to achieve strong supervised performance results in low zero-shot generalization. Similarly, freezing the backbone to retain zero-shot capability causes significant drop in supervised accuracy. Because of this, recent works in literature typically train separate models for supervised and zero-shot action recognition. In this work, we propose a multimodal prompt learning scheme that works to balance the supervised and zero-shot performance under a single unified training. Our prompting approach on the vision side caters for three aspects: 1) Global video-level prompts to model the data distribution; 2) Local frame-level prompts to provide per-frame discriminative conditioning; and 3) a summary prompt to extract a condensed video representation. Additionally, we define a prompting scheme on the text side to augment the textual context. Through this prompting scheme, we can achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and UCF101 while remaining competitive in the supervised setting. By keeping the pretrained backbone frozen, we optimize a much lower number of parameters and retain the existing general representation which helps achieve the strong zero-shot performance. Our codes/models are released at https://github.com/TalalWasim/Vita-CLIP. | ['Mubarak Shah', 'Fahad Shahbaz Khan', 'Salman Khan', 'Muzammal Naseer', 'Syed Talal Wasim'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wasim_Vita-CLIP_Video_and_Text_Adaptive_CLIP_via_Multimodal_Prompting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-action-recognition', 'video-classification', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.34806287e-01 -2.12088630e-01 -6.31528676e-01 -4.66156512e-01
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2cd9b356-be64-4a1f-afb3-848b878e2daa | dynamic-facet-selection-by-maximizing-graded | null | null | https://aclanthology.org/2021.internlp-1.5 | https://aclanthology.org/2021.internlp-1.5.pdf | Dynamic Facet Selection by Maximizing Graded Relevance | Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach. It allows users to narrow down search results through facets, where the facets-documents mapping is determined at runtime based on the context of user query instead of pre-indexing the facets statically. In this paper, we propose a new unsupervised approach for dynamic facet generation, namely optimistic facets, which attempts to generate the best possible subset of facets, hence maximizing expected Discounted Cumulative Gain (DCG), a measure of ranking quality that uses a graded relevance scale. We also release code to generate a new evaluation dataset. Through empirical results on two datasets, we show that the proposed DFS approach considerably improves the document ranking in the search results. | ['Nandana Mihindukulasooriya', 'Alfio Gliozzo', 'Nicolas Rodolfo Fauceglia', 'Ruchi Mahindru', 'Yu Deng', 'Md Faisal Mahbub Chowdhury', 'Michael Glass'] | null | null | null | null | acl-internlp-2021-8 | ['document-ranking'] | ['natural-language-processing'] | [ 2.73709536e-01 3.28155428e-01 -3.26036155e-01 -1.78002253e-01
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17a0b9b2-7668-4ee5-ac4f-b05a046333d9 | multitask-learning-for-fine-grained-twitter | 1707.03569 | null | http://arxiv.org/abs/1707.03569v1 | http://arxiv.org/pdf/1707.03569v1.pdf | Multitask Learning for Fine-Grained Twitter Sentiment Analysis | Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learning them. Our study demonstrates the potential of multitask
models on this type of problems and improves the state-of-the-art results in
the fine-grained sentiment classification problem. | ['Massih-Reza Amini', 'Simon Moura', 'Georgios Balikas'] | 2017-07-12 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.69555300e-02 -2.48714924e-01 -3.25382143e-01 -9.09530818e-01
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fd627d51-7368-43be-813b-2ff64ccb6809 | hybrid-optimization-algorithm-for-large-scale | 1509.06254 | null | http://arxiv.org/abs/1509.06254v1 | http://arxiv.org/pdf/1509.06254v1.pdf | Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition | In this paper we present a hybrid approach for automatic composition of Web
services that generates semantic input-output based compositions with optimal
end-to-end QoS, minimizing the number of services of the resulting composition.
The proposed approach has four main steps: 1) generation of the composition
graph for a request; 2) computation of the optimal composition that minimizes a
single objective QoS function; 3) multi-step optimizations to reduce the search
space by identifying equivalent and dominated services; and 4) hybrid
local-global search to extract the optimal QoS with the minimum number of
services. An extensive validation with the datasets of the Web Service
Challenge 2009-2010 and randomly generated datasets shows that: 1) the
combination of local and global optimization is a general and powerful
technique to extract optimal compositions in diverse scenarios; and 2) the
hybrid strategy performs better than the state-of-the-art, obtaining solutions
with less services and optimal QoS. | ['Manuel Mucientes', 'Pablo Rodriguez-Mier', 'Manuel Lama'] | 2015-09-21 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.45284393e-01 -9.55056623e-02 7.52766803e-02 -4.99571115e-01
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d97484ee-8f88-4738-8f75-34a72479e7ee | radiomics-enhanced-deep-multi-task-learning | 2211.05409 | null | https://arxiv.org/abs/2211.05409v1 | https://arxiv.org/pdf/2211.05409v1.pdf | Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer | Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place. | ['Jinman Kim', 'Dagan Feng', 'Lei Bi', 'Mingyuan Meng'] | 2022-11-10 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 6.93293512e-02 3.29294920e-01 -4.64925379e-01 -4.33782279e-01
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193aa77e-1224-4d50-b69a-0baa23986458 | learning-to-parallelize-with-openmp-by | 2305.05779 | null | https://arxiv.org/abs/2305.05779v1 | https://arxiv.org/pdf/2305.05779v1.pdf | Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation | Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis and program synthesis, including parallelization, in light of the success of machine learning in natural language processing. However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack of an adequate dataset for training, an effective code representation with rich information, and a suitable machine learning model to learn the latent features of code for diverse analyses. To address these challenges, we propose a novel graph-based learning approach called Graph2Par that utilizes a heterogeneous augmented abstract syntax tree (Augmented-AST) representation for code. The proposed approach primarily focused on loop-level parallelization with OpenMP. Moreover, we create an OMP\_Serial dataset with 18598 parallelizable and 13972 non-parallelizable loops to train the machine learning models. Our results show that our proposed approach achieves the accuracy of parallelizable code region detection with 85\% accuracy and outperforms the state-of-the-art token-based machine learning approach. These results indicate that our approach is competitive with state-of-the-art tools and capable of handling loops with complex structures that other tools may overlook. | ['Ali Jannesari', 'Nesreen K. Ahmed', 'Hung Phan', 'Quazi Ishtiaque Mahmud', 'Le Chen'] | 2023-05-09 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-1.76510736e-01 -3.58068794e-01 -7.35571504e-01 -1.89020440e-01
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c9abf6c6-53f9-428f-b17d-e50ceaa0c7b7 | codewithzichao-dravidianlangtech-eacl2021-1 | null | null | https://aclanthology.org/2021.dravidianlangtech-1.52 | https://aclanthology.org/2021.dravidianlangtech-1.52.pdf | Codewithzichao@DravidianLangTech-EACL2021: Exploring Multimodal Transformers for Meme Classification in Tamil Language | This paper describes our submission to shared task on Meme Classification for Tamil Language. To address this task, we explore a multimodal transformer for meme classification in Tamil language. According to the characteristics of the image and text, we use different pretrained models to encode the image and text so as to get better representations of the image and text respectively. Besides, we design a multimodal attention layer to make the text and corresponding image interact fully with each other based on cross attention. Our model achieved 0.55 weighted average F1 score and ranked first in this task. | ['Zichao Li'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-2.53470868e-01 -2.79366434e-01 7.77787939e-02 -2.65532941e-01
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fb7b716e-fc03-4638-b21d-d0f5c95284b3 | predicting-3d-human-dynamics-from-video | 1908.04781 | null | https://arxiv.org/abs/1908.04781v2 | https://arxiv.org/pdf/1908.04781v2.pdf | Predicting 3D Human Dynamics from Video | Given a video of a person in action, we can easily guess the 3D future motion of the person. In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input. We do this for periodic motions such as walking and also actions like bowling and squatting seen in sports or workout videos. While there has been a surge of future prediction problems in computer vision, most approaches predict 3D future from 3D past or 2D future from 2D past inputs. In this work, we focus on the problem of predicting 3D future motion from past image sequences, which has a plethora of practical applications in autonomous systems that must operate safely around people from visual inputs. Inspired by the success of autoregressive models in language modeling tasks, we learn an intermediate latent space on which we predict the future. This effectively facilitates autoregressive predictions when the input differs from the output domain. Our approach can be trained on video sequences obtained in-the-wild without 3D ground truth labels. The project website with videos can be found at https://jasonyzhang.com/phd. | ['Jason Y. Zhang', 'Panna Felsen', 'Angjoo Kanazawa', 'Jitendra Malik'] | 2019-08-13 | predicting-3d-human-dynamics-from-video-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Predicting_3D_Human_Dynamics_From_Video_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Predicting_3D_Human_Dynamics_From_Video_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-human-dynamics', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 1.08251445e-01 7.67924404e-03 -2.48258054e-01 -4.90887851e-01
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6df0a9b9-9a20-4d73-b106-927284681987 | 3d-mininet-learning-a-2d-representation-from | 2002.10893 | null | https://arxiv.org/abs/2002.10893v5 | https://arxiv.org/pdf/2002.10893v5.pdf | 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation | LIDAR semantic segmentation, which assigns a semantic label to each 3D point measured by the LIDAR, is becoming an essential task for many robotic applications such as autonomous driving. Fast and efficient semantic segmentation methods are needed to match the strong computational and temporal restrictions of many of these real-world applications. This work presents 3D-MiniNet, a novel approach for LIDAR semantic segmentation that combines 3D and 2D learning layers. It first learns a 2D representation from the raw points through a novel projection which extracts local and global information from the 3D data. This representation is fed to an efficient 2D Fully Convolutional Neural Network (FCNN) that produces a 2D semantic segmentation. These 2D semantic labels are re-projected back to the 3D space and enhanced through a post-processing module. The main novelty in our strategy relies on the projection learning module. Our detailed ablation study shows how each component contributes to the final performance of 3D-MiniNet. We validate our approach on well known public benchmarks (SemanticKITTI and KITTI), where 3D-MiniNet gets state-of-the-art results while being faster and more parameter-efficient than previous methods. | ['Iñigo Alonso', 'Luis Riazuelo', 'Luis Montesano', 'Ana C. Murillo'] | 2020-02-25 | null | null | null | null | ['real-time-3d-semantic-segmentation', 'lidar-semantic-segmentation', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.67441839e-01 1.89884827e-01 -1.96576491e-01 -8.23477924e-01
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f9fabf8b-5112-4d68-9d79-eb03164e504a | bme-submission-for-sigmorphon-2021-shared | null | null | https://aclanthology.org/2021.sigmorphon-1.27 | https://aclanthology.org/2021.sigmorphon-1.27.pdf | BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection | We present the BME submission for the SIGMORPHON 2021 Task 0 Part 1, Generalization Across Typologically Diverse Languages shared task. We use an LSTM encoder-decoder model with three step training that is first trained on all languages, then fine-tuned on each language family and finally fine-tuned on individual languages. We use a different type of data augmentation technique in the first two steps. Our system outperformed the only other submission. Although it remains worse than the Transformer baseline released by the organizers, our model is simpler and our data augmentation techniques are easily applicable to new languages. We perform ablation studies and show that the augmentation techniques and the three training steps often help but sometimes have a negative effect. Our code is publicly available. | ['Judit Ács', 'Dorina Lakatos', 'Botond Barta', 'Gábor Szolnok'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [-5.25128767e-02 -3.78159340e-03 -3.26016188e-01 -4.09166038e-01
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b55e3b36-6d56-4c7f-bc38-0652b216a6d8 | process-knowledge-infused-ai-towards-user | 2206.13349 | null | https://arxiv.org/abs/2206.13349v1 | https://arxiv.org/pdf/2206.13349v1.pdf | Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety | AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for personalized health or food recommendation with a specific purpose (e.g., allergy-aware recipe recommendations), their adoption is unlikely. Firstly, the AI system needs to follow guidelines or well-defined processes set by experts; the data alone will not be adequate. For example, to diagnose the severity of depression, mental healthcare providers use Patient Health Questionnaire (PHQ-9). So if an AI system were to be used for diagnosis, the medical guideline implied by the PHQ-9 needs to be used. Likewise, a nutritionist's knowledge and steps would need to be used for an AI system that guides a diabetic patient in developing a food plan. Second, the BlackBox nature typical of many current AI systems will not work; the user of an AI system will need to be able to give user-understandable explanations, explanations constructed using concepts that humans can understand and are familiar with. This is the key to eliciting confidence and trust in the AI system. For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to. | ['Vedant Khandelwal', 'Revathy Venkataraman', 'Kaushik Roy', 'Manas Gaur', 'Amit Sheth'] | 2022-06-09 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [ 2.73559928e-01 6.10425234e-01 -3.86720687e-01 -7.95175314e-01
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1c4fe427-06f6-4bf5-b8ca-d91076bdc4b3 | a-transformer-based-network-for-deformable | 2202.12104 | null | https://arxiv.org/abs/2202.12104v3 | https://arxiv.org/pdf/2202.12104v3.pdf | A Transformer-based Network for Deformable Medical Image Registration | Deformable medical image registration plays an important role in clinical diagnosis and treatment. Recently, the deep learning (DL) based image registration methods have been widely investigated and showed excellent performance in computational speed. However, these methods cannot provide enough registration accuracy because of insufficient ability in representing both the global and local features of the moving and fixed images. To address this issue, this paper has proposed the transformer based image registration method. This method uses the distinctive transformer to extract the global and local image features for generating the deformation fields, based on which the registered image is produced in an unsupervised way. Our method can improve the registration accuracy effectively by means of self-attention mechanism and bi-level information flow. Experimental results on such brain MR image datasets as LPBA40 and OASIS-1 demonstrate that compared with several traditional and DL based registration methods, our method provides higher registration accuracy in terms of dice values. | ['Xuming Zhang', 'Wen Qian', 'Yibo Wang'] | 2022-02-24 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 2.87560429e-02 -3.79415482e-01 8.68544281e-02 -5.08497953e-01
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606516e0-bafd-45d4-8810-86479fc55520 | safe-exploration-in-finite-markov-decision | 1606.04753 | null | http://arxiv.org/abs/1606.04753v2 | http://arxiv.org/pdf/1606.04753v2.pdf | Safe Exploration in Finite Markov Decision Processes with Gaussian Processes | In classical reinforcement learning, when exploring an environment, agents
accept arbitrary short term loss for long term gain. This is infeasible for
safety critical applications, such as robotics, where even a single unsafe
action may cause system failure. In this paper, we address the problem of
safely exploring finite Markov decision processes (MDP). We define safety in
terms of an, a priori unknown, safety constraint that depends on states and
actions. We aim to explore the MDP under this constraint, assuming that the
unknown function satisfies regularity conditions expressed via a Gaussian
process prior. We develop a novel algorithm for this task and prove that it is
able to completely explore the safely reachable part of the MDP without
violating the safety constraint. To achieve this, it cautiously explores safe
states and actions in order to gain statistical confidence about the safety of
unvisited state-action pairs from noisy observations collected while navigating
the environment. Moreover, the algorithm explicitly considers reachability when
exploring the MDP, ensuring that it does not get stuck in any state with no
safe way out. We demonstrate our method on digital terrain models for the task
of exploring an unknown map with a rover. | ['Andreas Krause', 'Matteo Turchetta', 'Felix Berkenkamp'] | 2016-06-15 | safe-exploration-in-finite-markov-decision-1 | http://papers.nips.cc/paper/6358-safe-exploration-in-finite-markov-decision-processes-with-gaussian-processes | http://papers.nips.cc/paper/6358-safe-exploration-in-finite-markov-decision-processes-with-gaussian-processes.pdf | neurips-2016-12 | ['safe-exploration'] | ['robots'] | [ 3.33680809e-01 6.60468757e-01 8.79013985e-02 1.44001380e-01
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44f58b4f-2af1-431f-bd97-5f193cec642f | shared-representation-learning-for | 1406.1247 | null | https://arxiv.org/abs/1406.1247v1 | https://arxiv.org/pdf/1406.1247v1.pdf | Shared Representation Learning for Heterogeneous Face Recognition | After intensive research, heterogenous face recognition is still a challenging problem. The main difficulties are owing to the complex relationship between heterogenous face image spaces. The heterogeneity is always tightly coupled with other variations, which makes the relationship of heterogenous face images highly nonlinear. Many excellent methods have been proposed to model the nonlinear relationship, but they apt to overfit to the training set, due to limited samples. Inspired by the unsupervised algorithms in deep learning, this paper proposes an novel framework for heterogeneous face recognition. We first extract Gabor features at some localized facial points, and then use Restricted Boltzmann Machines (RBMs) to learn a shared representation locally to remove the heterogeneity around each facial point. Finally, the shared representations of local RBMs are connected together and processed by PCA. Two problems (Sketch-Photo and NIR-VIS) and three databases are selected to evaluate the proposed method. For Sketch-Photo problem, we obtain perfect results on the CUFS database. For NIR-VIS problem, we produce new state-of-the-art performance on the CASIA HFB and NIR-VIS 2.0 databases. | ['Zhen Lei', 'Stan Z. Li', 'Shengcai Liao', 'Dong Yi'] | 2014-06-05 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [-1.31569654e-01 -3.67356539e-01 -7.39969462e-02 -4.50283945e-01
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a9ee074b-c7f2-4ebc-bbc7-39e8d635a8d8 | color-deconvolution-applied-to-domain | 2305.07404 | null | https://arxiv.org/abs/2305.07404v1 | https://arxiv.org/pdf/2305.07404v1.pdf | Color Deconvolution applied to Domain Adaptation in HER2 histopathological images | Breast cancer early detection is crucial for improving patient outcomes. The Institut Catal\`a de la Salut (ICS) has launched the DigiPatICS project to develop and implement artificial intelligence algorithms to assist with the diagnosis of cancer. In this paper, we propose a new approach for facing the color normalization problem in HER2-stained histopathological images of breast cancer tissue, posed as an style transfer problem. We combine the Color Deconvolution technique with the Pix2Pix GAN network to present a novel approach to correct the color variations between different HER2 stain brands. Our approach focuses on maintaining the HER2 score of the cells in the transformed images, which is crucial for the HER2 analysis. Results demonstrate that our final model outperforms the state-of-the-art image style transfer methods in maintaining the cell classes in the transformed images and is as effective as them in generating realistic images. | ['Montse Pardàs', 'Ferran Marqués', 'David Anglada-Rotger'] | 2023-05-12 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 3.04647684e-01 2.19674140e-01 2.25519225e-01 -3.33978571e-02
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7765e4e5-453d-44d5-9d50-e95fd4a651f5 | discriminator-free-unsupervised-domain | 2301.10611 | null | https://arxiv.org/abs/2301.10611v1 | https://arxiv.org/pdf/2301.10611v1.pdf | Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification | In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Over the last two years, some attempts have been made for introducing adversarial-based UDA methods in the context of MLIC. However, these methods which rely on an additional discriminator subnet present two shortcomings. First, the learning of domain-invariant features may harm their task-specific discriminative power, since the classification and discrimination tasks are decoupled. Moreover, the use of an additional discriminator usually induces an increase of the network size. Herein, we propose to overcome these issues by introducing a novel adversarial critic that is directly deduced from the task-specific classifier. Specifically, a two-component Gaussian Mixture Model (GMM) is fitted on the source and target predictions, allowing the distinction of two clusters. This allows extracting a Gaussian distribution for each component. The resulting Gaussian distributions are then used for formulating an adversarial loss based on a Frechet distance. The proposed method is evaluated on three multi-label image datasets. The obtained results demonstrate that DDA-MLIC outperforms existing state-of-the-art methods while requiring a lower number of parameters. | ['Djamila Aouada', 'Arunkumar Rathinam', 'Anis Kacem', 'Enjie Ghorbel', 'Indel Pal Singh'] | 2023-01-25 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.52671140e-01 6.60157651e-02 -1.16604611e-01 -3.13370287e-01
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b7dddff1-3305-4c53-ae1d-9c89d9fec328 | machine-learning-prediction-for-mean-motion | 2201.06743 | null | https://arxiv.org/abs/2201.06743v1 | https://arxiv.org/pdf/2201.06743v1.pdf | Machine learning prediction for mean motion resonance behaviour -- The planar case | Most recently, machine learning has been used to study the dynamics of integrable Hamiltonian systems and the chaotic 3-body problem. In this work, we consider an intermediate case of regular motion in a non-integrable system: the behaviour of objects in the 2:3 mean motion resonance with Neptune. We show that, given initial data from a short 6250 yr numerical integration, the best-trained artificial neural network (ANN) can predict the trajectories of the 2:3 resonators over the subsequent 18750 yr evolution, covering a full libration cycle over the combined time period. By comparing our ANN's prediction of the resonant angle to the outcome of numerical integrations, the former can predict the resonant angle with an accuracy as small as of a few degrees only, while it has the advantage of considerably saving computational time. More specifically, the trained ANN can effectively measure the resonant amplitudes of the 2:3 resonators, and thus provides a fast approach that can identify the resonant candidates. This may be helpful in classifying a huge population of KBOs to be discovered in future surveys. | ['Nikolaos Georgakarakos', 'Zhihong Jeff Xia', 'Jian Li', 'Xin Li'] | 2022-01-18 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.43090624e-01 2.18967304e-01 -3.88001949e-02 2.37662226e-01
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d711d262-a535-4ceb-b47a-6edd7ed0ddab | sf-tmn-slowfast-temporal-modeling-network-for | 2306.08859 | null | https://arxiv.org/abs/2306.08859v1 | https://arxiv.org/pdf/2306.08859v1.pdf | SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition | Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For better temporal modeling, we propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition that can not only achieve frame-level full video temporal modeling but also achieve segment-level full video temporal modeling. We employ a feature extraction network, pre-trained on the target dataset, to extract features from video frames as the training data for SF-TMN. The Slow Path in SF-TMN utilizes all frame features for frame temporal modeling. The Fast Path in SF-TMN utilizes segment-level features summarized from frame features for segment temporal modeling. The proposed paradigm is flexible regarding the choice of temporal modeling networks. We explore MS-TCN and ASFormer models as temporal modeling networks and experiment with multiple combination strategies for Slow and Fast Paths. We evaluate SF-TMN on Cholec80 surgical phase recognition task and demonstrate that SF-TMN can achieve state-of-the-art results on all considered metrics. SF-TMN with ASFormer backbone outperforms the state-of-the-art Not End-to-End(TCN) method by 2.6% in accuracy and 7.4% in the Jaccard score. We also evaluate SF-TMN on action segmentation datasets including 50salads, GTEA, and Breakfast, and achieve state-of-the-art results. The improvement in the results shows that combining temporal information from both frame level and segment level by refining outputs with temporal refinement stages is beneficial for the temporal modeling of surgical phases. | ['Amer Ghanem', 'Svetlana Petculescu', 'Bharti Goel', 'Mohammad Hasan Sarhan', 'Bokai Zhang'] | 2023-06-15 | null | null | null | null | ['surgical-phase-recognition', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.35469753e-01 -5.87198697e-02 -8.89020443e-01 -1.24393567e-01
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4e72ed48-dea6-4bce-b4a4-82ecac5ee4f8 | contextual-knowledge-learning-for-dialogue | 2305.18200 | null | https://arxiv.org/abs/2305.18200v1 | https://arxiv.org/pdf/2305.18200v1.pdf | Contextual Knowledge Learning For Dialogue Generation | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. The context, comprising utterances from previous dialogue exchanges, is used as a source of content for response generation and as a means of selecting external knowledge. However, to avoid introducing irrelevant content, it is key to enable fine-grained scoring of context and knowledge. In this paper, we present a novel approach to context and knowledge weighting as an integral part of model training. We guide the model training through a Contextual Knowledge Learning (CKL) process which involves Latent Vectors for context and knowledge, respectively. CKL Latent Vectors capture the relationship between context, knowledge, and responses through weak supervision and enable differential weighting of context utterances and knowledge sentences during the training process. Experiments with two standard datasets and human evaluation demonstrate that CKL leads to a significant improvement compared with the performance of six strong baseline models and shows robustness with regard to reduced sizes of training sets. | ['Ke Zhou', 'Natasa Milic-Frayling', 'Wen Zheng'] | 2023-05-29 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.78443211e-01 1.85854137e-01 -2.67311573e-01 -5.21807671e-01
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ae1fd3a7-5d20-460b-9c49-2748da933bc1 | neuro-reachability-of-networked-microgrids | 2101.05159 | null | https://arxiv.org/abs/2101.05159v1 | https://arxiv.org/pdf/2101.05159v1.pdf | Neuro-Reachability of Networked Microgrids | A neural ordinary differential equations network (ODE-Net)-enabled reachability method (Neuro-Reachability) is devised for the dynamic verification of networked microgrids (NMs) with unidentified subsystems and heterogeneous uncertainties. Three new contributions are presented: 1) An ODENet-enabled dynamic model discovery approach is devised to construct the data-driven state-space model which preserves the nonlinear and differential structure of the NMs system; 2) A physics-data-integrated (PDI) NMs model is established, which empowers various NM analytics; and 3) A conformance-empowered reachability analysis is developed to enhance the reliability of the PDI-driven dynamic verification. Extensive case studies demonstrate the efficacy of the ODE-Net-enabled method in microgrid dynamic model discovery, and the effectiveness of the Neuro-Reachability approach in verifying the NMs dynamics under multiple uncertainties and various operational scenarios. | ['Peng Zhang', 'Yifan Zhou'] | 2021-01-13 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-6.96325719e-01 1.36788338e-01 2.87093371e-01 2.86635756e-01
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bd798895-66b7-46f5-81ed-17af054e1ea2 | x-paste-revisit-copy-paste-at-scale-with-clip | 2212.03863 | null | https://arxiv.org/abs/2212.03863v2 | https://arxiv.org/pdf/2212.03863v2.pdf | X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion | Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and significantly boosts the segmentation performance, especially for rare object categories. Although diverse, high-quality object instances used in Copy-Paste result in more performance gain, previous works utilize object instances either from human-annotated instance segmentation datasets or rendered from 3D object models, and both approaches are too expensive to scale up to obtain good diversity. In this paper, we revisit Copy-Paste at scale with the power of newly emerged zero-shot recognition models (e.g., CLIP) and text2image models (e.g., StableDiffusion). We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable. To make such success happen, we design a data acquisition and processing framework, dubbed ``X-Paste", upon which a systematic study is conducted. On the LVIS dataset, X-Paste provides impressive improvements over the strong baseline CenterNet2 with Swin-L as the backbone. Specifically, it archives +2.6 box AP and +2.1 mask AP gains on all classes and even more significant gains with +6.8 box AP, +6.5 mask AP on long-tail classes. Our code and models are available at https://github.com/yoctta/XPaste. | ['Nenghai Yu', 'Weiming Zhang', 'Qi Chu', 'Wenbo Zhou', 'Ce Liu', 'Lu Yuan', 'Fang Wen', 'Dong Chen', 'Dongdong Chen', 'Jianmin Bao', 'Dianmo Sheng', 'Hanqing Zhao'] | 2022-12-07 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 4.01480317e-01 6.88609183e-02 -2.69967943e-01 -2.91576058e-01
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bec037da-4036-4d91-be72-4a48aabb6f1f | trimming-feature-extraction-and-inference-for | 2105.10302 | null | https://arxiv.org/abs/2105.10302v1 | https://arxiv.org/pdf/2105.10302v1.pdf | Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach | Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline's implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design. | ['Luca Benini', 'Andrea Acquaviva', 'Davide Brunelli', 'Enrico Tabanelli'] | 2021-05-21 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-8.94960389e-02 -5.60496867e-01 -2.80305505e-01 -3.93251389e-01
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405bd3fd-d788-49ba-85ca-14a4a203cdae | pistol-pupil-invisible-supportive-tool-to | 2201.06799 | null | https://arxiv.org/abs/2201.06799v2 | https://arxiv.org/pdf/2201.06799v2.pdf | Pistol: Pupil Invisible Supportive Tool to extract Pupil, Iris, Eye Opening, Eye Movements, Pupil and Iris Gaze Vector, and 2D as well as 3D Gaze | This paper describes a feature extraction and gaze estimation software, named \textit{Pistol} that can be used with Pupil Invisible projects and other eye trackers in the future. In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector, eye movement types from pupil and iris velocities, marker detection, marker distance, 2D gaze estimation for the pupil center, iris center, pupil vector, and iris vector using Levenberg Marquart fitting and neural networks. The gaze signal is computed in 2D for each eye and each feature separately and for both eyes in 3D also for each feature separately. We hope this software helps other researchers to extract state-of-the-art features for their research out of their recordings. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FPISTOL&mode=list | ['Shahram Eivazi', 'Daniel Weber', 'Wolfgang Fuhl'] | 2022-01-18 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-3.80205750e-01 -2.01440305e-02 1.60928965e-01 -3.43389243e-01
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b6fb4373-85ec-45de-90c8-06ca90f56587 | fed-nilm-a-federated-learning-based-non | 2105.11085 | null | https://arxiv.org/abs/2105.11085v2 | https://arxiv.org/pdf/2105.11085v2.pdf | Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection | Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training. To prevent such potential risks, a novel NILM method named Fed-NILM which is based on Federated Learning (FL) is proposed in this paper. In Fed-NILM, local model parameters instead of local load data are shared among multiple data owners. The global model is obtained by weighted averaging the parameters. Experiments based on two measured load datasets are conducted to explore the generalization ability of Fed-NILM. Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained NILM is conducted. The experimental results show that Fed-NILM has superior performance in scalability and convergence. Fed-NILM outperforms locally-trained NILMs operated by local data owners and approximates the centrally-trained NILM which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modeling capabilities of local data owners while protecting power consumers' privacy. | ['Fushuan Wen', 'Guolong Liu', 'Junhua Zhao', 'Caomingzhe Si', 'Haijin Wang'] | 2021-05-24 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-2.32342348e-01 -5.54306395e-02 -6.86497033e-01 -6.62138641e-01
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868b1345-ec7d-45d7-9add-7ed9ab594310 | representer-theorems-for-metric-and | 2304.03720 | null | https://arxiv.org/abs/2304.03720v1 | https://arxiv.org/pdf/2304.03720v1.pdf | Representer Theorems for Metric and Preference Learning: A Geometric Perspective | We explore the metric and preference learning problem in Hilbert spaces. We obtain a novel representer theorem for the simultaneous task of metric and preference learning. Our key observation is that the representer theorem can be formulated with respect to the norm induced by the inner product inherent in the problem structure. Additionally, we demonstrate how our framework can be applied to the task of metric learning from triplet comparisons and show that it leads to a simple and self-contained representer theorem for this task. In the case of Reproducing Kernel Hilbert Spaces (RKHS), we demonstrate that the solution to the learning problem can be expressed using kernel terms, akin to classical representer theorems. | ['Peyman Morteza'] | 2023-04-07 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.27587646e-01 1.16456658e-01 1.21951126e-01 -4.90965366e-01
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cb359ccb-597f-41b1-bd6b-e2ed3ee7d7f9 | lipnet-end-to-end-sentence-level-lipreading | 1611.01599 | null | http://arxiv.org/abs/1611.01599v2 | http://arxiv.org/pdf/1611.01599v2.pdf | LipNet: End-to-End Sentence-level Lipreading | Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, existing work on models trained end-to-end perform only word
classification, rather than sentence-level sequence prediction. Studies have
shown that human lipreading performance increases for longer words (Easton &
Basala, 1982), indicating the importance of features capturing temporal context
in an ambiguous communication channel. Motivated by this observation, we
present LipNet, a model that maps a variable-length sequence of video frames to
text, making use of spatiotemporal convolutions, a recurrent network, and the
connectionist temporal classification loss, trained entirely end-to-end. To the
best of our knowledge, LipNet is the first end-to-end sentence-level lipreading
model that simultaneously learns spatiotemporal visual features and a sequence
model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level,
overlapped speaker split task, outperforming experienced human lipreaders and
the previous 86.4% word-level state-of-the-art accuracy (Gergen et al., 2016). | ['Brendan Shillingford', 'Shimon Whiteson', 'Nando de Freitas', 'Yannis M. Assael'] | 2016-11-05 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.30040282e-01 2.35429276e-02 -5.80982864e-01 -2.65939385e-01
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8541981f-c1d8-4728-b9d5-cf3f28777b23 | fml-based-prediction-agent-and-its | 1704.04719 | null | http://arxiv.org/abs/1704.04719v1 | http://arxiv.org/pdf/1704.04719v1.pdf | FML-based Prediction Agent and Its Application to Game of Go | In this paper, we present a robotic prediction agent including a darkforest
Go engine, a fuzzy markup language (FML) assessment engine, an FML-based
decision support engine, and a robot engine for game of Go application. The
knowledge base and rule base of FML assessment engine are constructed by
referring the information from the darkforest Go engine located in NUTN and
OPU, for example, the number of MCTS simulations and winning rate prediction.
The proposed robotic prediction agent first retrieves the database of Go
competition website, and then the FML assessment engine infers the winning
possibility based on the information generated by darkforest Go engine. The
FML-based decision support engine computes the winning possibility based on the
partial game situation inferred by FML assessment engine. Finally, the robot
engine combines with the human-friendly robot partner PALRO, produced by
Fujisoft incorporated, to report the game situation to human Go players.
Experimental results show that the FML-based prediction agent can work
effectively. | ['Sheng-Chi Yang', 'Chia-Hsiu Kao', 'Mei-Hui Wang', 'Chang-Shing Lee', 'Yusuke Nojima', 'Nan Shuo', 'Ryosuke Saga', 'Naoyuki Kubota'] | 2017-04-16 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.37222385e-01 4.47507024e-01 -3.26226205e-01 -3.31423432e-01
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74f774f4-a73c-44c7-8a18-5c4d59f305f2 | know-what-i-don-t-know-handling-ambiguous-and | 2212.08902 | null | https://arxiv.org/abs/2212.08902v2 | https://arxiv.org/pdf/2212.08902v2.pdf | Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL | The task of text-to-SQL aims to convert a natural language question into its corresponding SQL query within the context of relational tables. Existing text-to-SQL parsers generate a "plausible" SQL query for an arbitrary user question, thereby failing to correctly handle problematic user questions. To formalize this problem, we conduct a preliminary study on the observed ambiguous and unanswerable cases in text-to-SQL and summarize them into 6 feature categories. Correspondingly, we identify the causes behind each category and propose requirements for handling ambiguous and unanswerable questions. Following this study, we propose a simple yet effective counterfactual example generation approach that automatically produces ambiguous and unanswerable text-to-SQL examples. Furthermore, we propose a weakly supervised DTE (Detecting-Then-Explaining) model for error detection, localization, and explanation. Experimental results show that our model achieves the best result on both real-world examples and generated examples compared with various baselines. We release our data and code at: \href{https://github.com/wbbeyourself/DTE}{https://github.com/wbbeyourself/DTE}. | ['Jian-Guang Lou', 'Zhoujun Li', 'Yan Gao', 'Bing Wang'] | 2022-12-17 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-2.44648661e-02 5.97460866e-01 -8.22200477e-02 -8.29075873e-01
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37341671-5b07-48c7-88e4-5e5c9738c8aa | defect-detection-approaches-based-on | 2303.11971 | null | https://arxiv.org/abs/2303.11971v1 | https://arxiv.org/pdf/2303.11971v1.pdf | Defect Detection Approaches Based on Simulated Reference Image | This work is addressing the problem of defect anomaly detection based on a clean reference image. Specifically, we focus on SEM semiconductor defects in addition to several natural image anomalies. There are well-known methods to create a simulation of an artificial reference image by its defect specimen. In this work, we introduce several applications for this capability, that the simulated reference is beneficial for improving their results. Among these defect detection methods are classic computer vision applied on difference-image, supervised deep-learning (DL) based on human labels, and unsupervised DL which is trained on feature-level patterns of normal reference images. We show in this study how to incorporate correctly the simulated reference image for these defect and anomaly detection applications. As our experiment demonstrates, simulated reference achieves higher performance than the real reference of an image of a defect and anomaly. This advantage of simulated reference occurs mainly due to the less noise and geometric variations together with better alignment and registration to the original defect background. | ['Boris Sherman', 'Ran Badanes', 'Yotam Ben Shoshan', 'Nati Ofir'] | 2023-03-21 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 6.25816524e-01 3.49027872e-01 6.66694224e-01 -8.24736729e-02
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90812db2-5ecd-424b-9744-787bcbd59f87 | 1st-place-solution-for-psg-competition-with | 2302.02651 | null | https://arxiv.org/abs/2302.02651v1 | https://arxiv.org/pdf/2302.02651v1.pdf | 1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop | Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes. Existing PSG methods utilize one-stage paradigm which simultaneously generates scene graphs and predicts semantic segmentation masks or two-stage paradigm that first adopt an off-the-shelf panoptic segmentor, then pairwise relationship prediction between these predicted objects. One-stage approach despite having a simplified training paradigm, its segmentation results are usually under-satisfactory, while two-stage approach lacks global context and leads to low performance on relation prediction. To bridge this gap, in this paper, we propose GRNet, a Global Relation Network in two-stage paradigm, where the pre-extracted local object features and their corresponding masks are fed into a transformer with class embeddings. To handle relation ambiguity and predicate classification bias caused by long-tailed distribution, we formulate relation prediction in the second stage as a multi-class classification task with soft label. We conduct comprehensive experiments on OpenPSG dataset and achieve the state-of-art performance on the leadboard. We also show the effectiveness of our soft label strategy for long-tailed classes in ablation studies. Our code has been released in https://github.com/wangqixun/mfpsg. | ['Haofan Wang', 'Xiaofeng Guo', 'Qixun Wang'] | 2023-02-06 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.79313099e-01 5.46072662e-01 -3.74563754e-01 -5.76499820e-01
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241bd592-0bb0-4c04-b5e1-c081b8d1027d | how-domain-terminology-affects-meeting | 2011.00692 | null | https://arxiv.org/abs/2011.00692v2 | https://arxiv.org/pdf/2011.00692v2.pdf | How Domain Terminology Affects Meeting Summarization Performance | Meetings are essential to modern organizations. Numerous meetings are held and recorded daily, more than can ever be comprehended. A meeting summarization system that identifies salient utterances from the transcripts to automatically generate meeting minutes can help. It empowers users to rapidly search and sift through large meeting collections. To date, the impact of domain terminology on the performance of meeting summarization remains understudied, despite that meetings are rich with domain knowledge. In this paper, we create gold-standard annotations for domain terminology on a sizable meeting corpus; they are known as jargon terms. We then analyze the performance of a meeting summarization system with and without jargon terms. Our findings reveal that domain terminology can have a substantial impact on summarization performance. We publicly release all domain terminology to advance research in meeting summarization. | ['Fei Liu', 'Alec Kerrigan', 'Dillon Burns', 'Xiaojin Dai', 'Alexander Roustai', 'Jia Jin Koay'] | 2020-11-02 | null | https://aclanthology.org/2020.coling-main.499 | https://aclanthology.org/2020.coling-main.499.pdf | coling-2020-8 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.61466098e-01 5.44069648e-01 -1.45351827e-01 -3.08932126e-01
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