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2d5a9f57-3b7b-476b-a6de-b00e0c27bab0 | amr-parsing-with-action-pointer-transformer-1 | null | null | https://openreview.net/forum?id=X9KK-SCmKWn | https://openreview.net/pdf?id=X9KK-SCmKWn | AMR Parsing with Action-Pointer Transformer | Abstract Meaning Representation parsing belongs to a category of sentence-to-graph prediction tasks where the target graph is not explicitly linked to the sentence tokens. However, nodes or subgraphs are semantically related to subsets of the sentence tokens, and locality between words and related nodes is often preserved. Transition-based approaches have recently shown great progress in capturing these inductive biases but still suffer from limited expressiveness. In this work we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations. We model the transitions as well as the pointer mechanism using a single Transformer model. Parser state and graph structure information is efficiently encoded using attention heads. We show that our approach leads to increased expressiveness while capitalizing inductive biases and attains new state-of-the Smatch scores on AMR 1.0 (78.5) and AMR 2.0 (81.8). | ['Anonymous'] | 2020-11-24 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.83801460e-01 7.77489960e-01 -2.78778821e-01 -5.24666250e-01
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ea2011da-55cd-4e5c-99d0-2b2db96a7ab5 | mednext-transformer-driven-scaling-of | 2303.09975 | null | https://arxiv.org/abs/2303.09975v3 | https://arxiv.org/pdf/2303.09975v3.pdf | MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation | There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://github.com/MIC-DKFZ/MedNeXt. | ['Klaus Maier-Hein', 'Paul F. Jaeger', 'Fabian Isensee', 'Jens Petersen', 'Michael Baumgartner', 'Constantin Ulrich', 'Gregor Koehler', 'Saikat Roy'] | 2023-03-17 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 3.13262224e-01 3.19690466e-01 -1.51141644e-01 -5.93803406e-01
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863d7e6e-068d-4f69-95d5-015f8a1320bd | attention-based-multiple-instance-learning-2 | 2212.07724 | null | https://arxiv.org/abs/2212.07724v2 | https://arxiv.org/pdf/2212.07724v2.pdf | Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays | Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage | ['Marc Aubreville', 'Katharina Breininger', 'Christian Schulz', 'Christoph Brochhausen-Delius', 'Tanja Niedermair', 'Jonathan Ganz', 'Lars-Henning Schmidt', 'Jonas Ammeling'] | 2022-12-15 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.88620251e-01 1.49184391e-01 -7.82989323e-01 -5.44939160e-01
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161e27df-8e5b-4b6e-9b36-dd959bcc1262 | low-resource-style-transfer-via-domain-1 | 2205.12475 | null | https://arxiv.org/abs/2205.12475v1 | https://arxiv.org/pdf/2205.12475v1.pdf | Low Resource Style Transfer via Domain Adaptive Meta Learning | Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of non-parallel data to guide transferring different text styles. (ii) colossal performance degradation when fine-tuning the model in new domains. In this work, we propose DAML-ATM (Domain Adaptive Meta-Learning with Adversarial Transfer Model), which consists of two parts: DAML and ATM. DAML is a domain adaptive meta-learning approach to learn general knowledge in multiple heterogeneous source domains, capable of adapting to new unseen domains with a small amount of data. Moreover, we propose a new unsupervised TST approach Adversarial Transfer Model (ATM), composed of a sequence-to-sequence pre-trained language model and uses adversarial style training for better content preservation and style transfer. Results on multi-domain datasets demonstrate that our approach generalizes well on unseen low-resource domains, achieving state-of-the-art results against ten strong baselines. | ['Sujian Li', 'Yu Xia', 'Xiang Long', 'Xiangyang Li'] | 2022-05-25 | null | https://aclanthology.org/2022.naacl-main.220 | https://aclanthology.org/2022.naacl-main.220.pdf | naacl-2022-7 | ['general-knowledge', 'text-style-transfoer'] | ['miscellaneous', 'natural-language-processing'] | [ 6.41654670e-01 -3.01372409e-01 1.06894625e-02 -5.37805676e-01
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cb9cc02f-0eb7-45f8-8475-f3ed456b9e62 | analyzing-the-effect-of-global-learning-and | null | null | https://aclanthology.org/C12-2136 | https://aclanthology.org/C12-2136.pdf | Analyzing the Effect of Global Learning and Beam-Search on Transition-Based Dependency Parsing | null | ['Yue Zhang', 'Joakim Nivre'] | 2012-12-01 | analyzing-the-effect-of-global-learning-and-1 | https://aclanthology.org/C12-2136 | https://aclanthology.org/C12-2136.pdf | coling-2012-12 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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76d77af5-f2ff-491c-82a2-45d582c8ad46 | effects-of-a-differentiating-therapy-on | 2303.04607 | null | https://arxiv.org/abs/2303.04607v1 | https://arxiv.org/pdf/2303.04607v1.pdf | Effects of a Differentiating Therapy on Cancer-Stem-Cell-Driven Tumors | The growth of many solid tumors has been found to be driven by chemo- and radiotherapy-resistant cancer stem cells (CSCs). A suitable therapeutic avenue in these cases may involve the use of a differentiating agent (DA) to force the differentiation of the CSCs and of conventional therapies to eliminate the remaining differentiated cancer cells (DCCs). To describe the effects of a DA that reprograms CSCs into DCCs, we adapt a differential equation model developed to investigate tumorspheres, which are assumed to consist of jointly evolving CSC and DCC populations. We analyze the mathematical properties of the model, finding the equilibria and their stability. We also present numerical solutions and phase diagrams to describe the system evolution and the therapy effects, denoting the DA strength by a parameter \(a_{dif}\).To obtain realistic predictions, we choose the other model parameters to be those determined previously from fits to various experimental datasets. These datasets characterize the progression of the tumor under various culture conditions. Typically, for small values of \(a_{dif}\) the tumor evolves towards a final state that contains a CSC fraction, but a strong therapy leads to the suppression of this phenotype. Nonetheless, different external conditions lead to very diverse behaviors. For some environmental conditions, the model predicts a threshold not only in the therapy strength, but also in its starting time, an early beginning being potentially crucial. In summary, our model shows how the effects of a DA depend critically not only on the dosage and timing of the drug application, but also on the tumor nature and its environment. | ['Carlos A. Condat', 'Lucas Barberis', 'Jerónimo Fotinós'] | 2023-03-08 | null | null | null | null | ['culture'] | ['speech'] | [ 1.04056112e-01 -2.25788414e-01 -2.68101990e-01 5.34050167e-01
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228a8b0e-aa5f-4a50-97b5-687c15c4f386 | cuet-nlp-tamilnlp-acl2022-multi-class-textual | null | null | https://aclanthology.org/2022.dravidianlangtech-1.31 | https://aclanthology.org/2022.dravidianlangtech-1.31.pdf | CUET-NLP@TamilNLP-ACL2022: Multi-Class Textual Emotion Detection from Social Media using Transformer | Recently, emotion analysis has gained increased attention by NLP researchers due to its various applications in opinion mining, e-commerce, comprehensive search, healthcare, personalized recommendations and online education. Developing an intelligent emotion analysis model is challenging in resource-constrained languages like Tamil. Therefore a shared task is organized to identify the underlying emotion of a given comment expressed in the Tamil language. The paper presents our approach to classifying the textual emotion in Tamil into 11 classes: ambiguous, anger, anticipation, disgust, fear, joy, love, neutral, sadness, surprise and trust. We investigated various machine learning (LR, DT, MNB, SVM), deep learning (CNN, LSTM, BiLSTM) and transformer-based models (Multilingual-BERT, XLM-R). Results reveal that the XLM-R model outdoes all other models by acquiring the highest macro f_1-score (0.33). | ['Mohammed Moshiul Hoque', 'Omar Sharif', 'Eftekhar Hossain', 'Golam Md. Mursalin', 'Rabeya Rabu', 'Nasehatul Mustakim'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [-3.60729009e-01 -1.07809372e-01 -3.87064368e-02 -5.58948934e-01
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aa434ece-5ae8-49c2-8260-61d83a3275af | improving-the-intra-class-long-tail-in-3d | 2210.08375 | null | https://arxiv.org/abs/2210.08375v1 | https://arxiv.org/pdf/2210.08375v1.pdf | Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining | Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples. However, even the best performing models suffer from the most naive mistakes when it comes to rare examples that do not appear frequently in the training data, such as vehicles with irregular geometries. Most studies in the long-tail literature focus on class-imbalanced classification problems with known imbalanced label counts per class, but they are not directly applicable to the intra-class long-tail examples in problems with large intra-class variations such as 3D object detection, where instances with the same class label can have drastically varied properties such as shapes and sizes. Other works propose to mitigate this problem using active learning based on the criteria of uncertainty, difficulty, or diversity. In this study, we identify a new conceptual dimension - rareness - to mine new data for improving the long-tail performance of models. We show that rareness, as opposed to difficulty, is the key to data-centric improvements for 3D detectors, since rareness is the result of a lack in data support while difficulty is related to the fundamental ambiguity in the problem. We propose a general and effective method to identify the rareness of objects based on density estimation in the feature space using flow models, and propose a principled cost-aware formulation for mining rare object tracks, which improves overall model performance, but more importantly - significantly improves the performance for rare objects (by 30.97\% | ['Dragomir Anguelov', 'Yin Zhou', 'Charles R. Qi', 'Mahyar Najibi', 'Chiyu Max Jiang'] | 2022-10-15 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.72252715e-01 -1.56843457e-02 -5.18281639e-01 -4.25650269e-01
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62c06a0b-e2c4-432d-8c4e-2ec1df5b1569 | joint-learning-of-representations-for-web | null | null | https://aclanthology.org/2021.eacl-main.102 | https://aclanthology.org/2021.eacl-main.102.pdf | Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network | Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure. We propose TabGCN, that uses Graph Convolutional Networks to capture the complete structure of tables, knowledge graph and the training annotations, and jointly learns embeddings for table elements as well as the entities and types. To account for knowledge incompleteness, TabGCN{'}s embeddings can be used to discover new entities and types. Using experiments on 5 benchmark datasets, we show that TabGCN significantly outperforms multiple state-of-the-art baselines for table annotation, while showing promising performance on downstream table-related applications. | ['Indrajit Bhattacharya', 'Aniket Pramanick'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [-3.26843798e-01 6.10943437e-01 -8.22755635e-01 -3.97247523e-01
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dc801d2e-d330-45c9-bea3-18e0f90e723f | encoder-decoder-with-multi-level-attention | 2109.02303 | null | https://arxiv.org/abs/2109.02303v1 | https://arxiv.org/pdf/2109.02303v1.pdf | Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation | 3D human shape and pose estimation is the essential task for human motion analysis, which is widely used in many 3D applications. However, existing methods cannot simultaneously capture the relations at multiple levels, including spatial-temporal level and human joint level. Therefore they fail to make accurate predictions in some hard scenarios when there is cluttered background, occlusion, or extreme pose. To this end, we propose Multi-level Attention Encoder-Decoder Network (MAED), including a Spatial-Temporal Encoder (STE) and a Kinematic Topology Decoder (KTD) to model multi-level attentions in a unified framework. STE consists of a series of cascaded blocks based on Multi-Head Self-Attention, and each block uses two parallel branches to learn spatial and temporal attention respectively. Meanwhile, KTD aims at modeling the joint level attention. It regards pose estimation as a top-down hierarchical process similar to SMPL kinematic tree. With the training set of 3DPW, MAED outperforms previous state-of-the-art methods by 6.2, 7.2, and 2.4 mm of PA-MPJPE on the three widely used benchmarks 3DPW, MPI-INF-3DHP, and Human3.6M respectively. Our code is available at https://github.com/ziniuwan/maed. | ['Hongsheng Li', 'Shuai Yi', 'Jianbo Liu', 'Maoqing Tian', 'Zhengjia Li', 'Ziniu Wan'] | 2021-09-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wan_Encoder-Decoder_With_Multi-Level_Attention_for_3D_Human_Shape_and_Pose_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wan_Encoder-Decoder_With_Multi-Level_Attention_for_3D_Human_Shape_and_Pose_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-4.27549064e-01 -1.06028125e-01 -1.17666461e-01 1.16639445e-02
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b659aee7-fcfe-453a-bff2-47e8db64df6d | multi-task-recurrent-neural-network-for-1 | 2003.04772 | null | https://arxiv.org/abs/2003.04772v1 | https://arxiv.org/pdf/2003.04772v1.pdf | Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction | Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with robotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration and order of actions. In order to extract discriminative features from the kinematic signals and boost recognition accuracy, we propose a multi-task recurrent neural network for simultaneous recognition of surgical gestures and estimation of a novel formulation of surgical task progress. To show the effectiveness of the presented approach, we evaluate its application on the JIGSAWS dataset, that is currently the only publicly available dataset for surgical gesture recognition featuring robot kinematic data. We demonstrate that recognition performance improves in multi-task frameworks with progress estimation without any additional manual labelling and training. | ['Matthew J. Clarkson', 'Danail Stoyanov', 'Beatrice van Amsterdam'] | 2020-03-10 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 4.24162596e-01 -7.02248514e-02 -6.19892240e-01 -4.20911491e-01
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cabe630e-3247-488b-b23e-fd6490144d91 | hiding-speaker-s-sex-in-speech-using-zero | 2211.16065 | null | https://arxiv.org/abs/2211.16065v2 | https://arxiv.org/pdf/2211.16065v2.pdf | Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline | The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation fed into a HiFiGAN vocoder is protected using a neural-discriminant analysis approach, which is consistent with the zero-evidence concept of privacy. This approach significantly reduces the information in speech related to the speaker's sex while preserving speech content and some consistency in the resulting protected voices. | ['Driss Matrouf', 'Jean-François Bonastre', 'Junichi Yamagishi', 'Xin Wang', 'Xiaoxiao Miao', 'Paul-Gauthier Noé'] | 2022-11-29 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 3.52909803e-01 5.57622910e-01 3.86221595e-02 -3.05968910e-01
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8090067c-4b91-4410-afe0-896a65f51389 | riskyishness-and-pinocchio-s-search-for-a | 2103.03482 | null | https://arxiv.org/abs/2103.03482v2 | https://arxiv.org/pdf/2103.03482v2.pdf | Pilot Investigation for a Comprehensive Taxonomy of Autonomous Entities | This paper documents an exploratory pilot study to define the term Autonomous Entity, and any characteristics that are required to identify or classify an Autonomous Entity. Our solution builds on previous work with regard to philosophical and scientific classification methods but focuses on a novel Design Science Research Methodology (DSRM) and model to help identify those characteristics which make any autonomous entity similar or different from others. We have solved the problem of not having an existing term to define our lens by creating a new combinatorial term: "Riskyishness". We present a DSRM and instrument for initial investigation, as well as observational and statistical descriptions of their use in the real world to solicit domain expertise and statistical evidence. Further, we demonstrate a specific application of the methodology by creating a second artifact - a tool to score existing and future technologies based on Riskyishness. The first artifact also provides a technique to disentangle miscellaneous existing technologies or add dimensions to the tools to capture future additions and paradigm shifts. | ['William Wagner', 'Joseph Santhosh', 'Clement Aladi', 'Anna Źakowska'] | 2021-03-05 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-1.63126975e-01 3.99501055e-01 -3.20249528e-01 -1.91422626e-01
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bd2e8792-767e-4ca3-85fe-4058baad6142 | weakly-supervised-action-localization-via | 2206.11011 | null | https://arxiv.org/abs/2206.11011v2 | https://arxiv.org/pdf/2206.11011v2.pdf | Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning | Weakly Supervised Temporal Action Localization (WSTAL) aims to localize and classify action instances in long untrimmed videos with only video-level category labels. Due to the lack of snippet-level supervision for indicating action boundaries, previous methods typically assign pseudo labels for unlabeled snippets. However, since some action instances of different categories are visually similar, it is non-trivial to exactly label the (usually) one action category for a snippet, and incorrect pseudo labels would impair the localization performance. To address this problem, we propose a novel method from a category exclusion perspective, named Progressive Complementary Learning (ProCL), which gradually enhances the snippet-level supervision. Our method is inspired by the fact that video-level labels precisely indicate the categories that all snippets surely do not belong to, which is ignored by previous works. Accordingly, we first exclude these surely non-existent categories by a complementary learning loss. And then, we introduce the background-aware pseudo complementary labeling in order to exclude more categories for snippets of less ambiguity. Furthermore, for the remaining ambiguous snippets, we attempt to reduce the ambiguity by distinguishing foreground actions from the background. Extensive experimental results show that our method achieves new state-of-the-art performance on two popular benchmarks, namely THUMOS14 and ActivityNet1.3. | ['Ying Shan', 'Xiao-Ming Wu', 'Kun-Yu Lin', 'Jia-Run Du', 'Wei-Shi Zheng', 'Zhongang Qi', 'Fa-Ting Hong', 'Jia-Chang Feng'] | 2022-06-22 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 0.54666483 -0.07386957 -0.6357929 -0.15473358 -0.54590076 -0.562608
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b126ef35-c5df-46c8-8598-b8aea432b839 | image-based-camera-localization-an-overview | 1610.03660 | null | http://arxiv.org/abs/1610.03660v4 | http://arxiv.org/pdf/1610.03660v4.pdf | Image Based Camera Localization: an Overview | Recently, virtual reality, augmented reality, robotics, autonomous driving et
al attract much attention of both academic and industrial community, in which
image based camera localization is a key task. However, there has not been a
complete review on image-based camera localization. It is urgent to map this
topic to help people enter the field quickly. In this paper, an overview of
image based camera localization is presented. A new and complete kind of
classifications for image based camera localization is provided and the related
techniques are introduced. Trends for the future development are also
discussed. It will be useful to not only researchers but also engineers and
other people interested. | ['Fulin Tang', 'Heping Li', 'Yihong Wu'] | 2016-10-12 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-1.33917242e-01 -5.50454855e-01 -3.63912821e-01 -2.68822819e-01
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d850fcd7-c4fa-4cdb-b94a-3f523264a990 | metaslrcl-a-self-adaptive-learning-rate-and | null | null | https://aclanthology.org/2022.coling-1.180 | https://aclanthology.org/2022.coling-1.180.pdf | MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification | Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attention. Such methods usually consist of a learner as the classifier and a meta learner for specializing the learner to different tasks. For the learner, learning rate is crucial to its performance. However, existing methods treat it as a hyper parameter and adjust it manually, which is time-consuming and laborious. Intuitively, for different tasks and neural network layers, the learning rates should be different and self-adaptive. For the meta learner, it requires a good generalization ability so as to quickly adapt to new tasks. Motivated by these issues, we propose a novel meta learning framework, called MetaSLRCL, for few-shot text classification. Specifically, we present a novel meta learning mechanism to obtain different learning rates for different tasks and neural network layers so as to enable the learner to quickly adapt to new training data. Moreover, we propose a task-oriented curriculum learning mechanism to help the meta learner achieve a better generalization ability by learning from different tasks with increasing difficulties. Extensive experiments on three benchmark datasets demonstrate the effectiveness of MetaSLRCL. | ['Xueqi Cheng', 'Jiafeng Guo', 'Saiping Guan', 'Xiaolong Jin', 'Kailin Zhao'] | null | null | null | null | coling-2022-10 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.16765007e-01 -3.30636680e-01 -2.39295930e-01 -4.87698585e-01
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98f01e9a-d5fd-4d8e-88c3-c0c1dc8c8b49 | hyppo-a-surrogate-based-multi-level | 2110.01698 | null | https://arxiv.org/abs/2110.01698v1 | https://arxiv.org/pdf/2110.01698v1.pdf | HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization | We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. Using asynchronous nested parallelism, we are able to significantly alleviate the computational burden of training complex architectures and quantifying the uncertainty. HYPPO is implemented in Python and can be used with both TensorFlow and PyTorch libraries. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction. Finally, we show that (1) we can reduce by an order of magnitude the number of evaluations necessary to find the most optimal region in the hyperparameter space and (2) we can reduce by two orders of magnitude the throughput for such HPO process to complete. | ['Marc Day', 'Mariam Kiran', 'Talita Perciano', 'Juliane Mueller', 'Vidya Ganapati', 'Chelsea Jones', 'Anuradha Trivedi', 'Casey Garner', 'Vincent Dumont'] | 2021-10-04 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.24102455e-01 1.21646501e-01 1.40621126e-01 -3.76977801e-01
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4be9cdd5-eb85-438e-a391-be8a707c083c | getting-the-roles-right-using-framenet-in-nlp | null | null | https://aclanthology.info/papers/N15-4006/n15-4006 | https://www.aclweb.org/anthology/N15-4006 | Getting the Roles Right: Using FrameNet in NLP | null | ['Miriam R. L. Petruck', 'Collin Baker', 'Michael Ellsworth', 'Nathan Schneider'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['text-annotation'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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e0edc839-7d87-45ba-b075-f5691b993f6a | flexvdw-a-machine-learning-approach-to | 2303.11494 | null | https://arxiv.org/abs/2303.11494v1 | https://arxiv.org/pdf/2303.11494v1.pdf | FlexVDW: A machine learning approach to account for protein flexibility in ligand docking | Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to extremely high values of the calculated van der Waals energy term. Traditionally, this problem has been addressed by explicitly searching for receptor conformations to account for the flexibility of the receptor in ligand binding. Here we present a deep learning model trained to take receptor flexibility into account implicitly when predicting van der Waals energy. We show that incorporating this machine-learned energy term into a state-of-the-art physics-based scoring function improves small molecule ligand pose prediction results in cases with substantial protein deformation, without degrading performance in cases with minimal protein deformation. This work demonstrates the feasibility of learning effects of protein flexibility on ligand binding without explicitly modeling changes in protein structure. | ['Ron O. Dror', 'Joseph M. Paggi', 'Patricia Suriana'] | 2023-03-20 | null | null | null | null | ['pose-prediction'] | ['computer-vision'] | [ 2.78598994e-01 1.18088506e-01 -9.24097002e-02 -5.08679211e-01
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7052190a-1544-45fa-af6b-77e9233969ee | periodicity-in-cryptocurrency-volatility-and | 2109.12142 | null | https://arxiv.org/abs/2109.12142v2 | https://arxiv.org/pdf/2109.12142v2.pdf | Periodicity in Cryptocurrency Volatility and Liquidity | We study recurrent patterns in volatility and volume for major cryptocurrencies, Bitcoin and Ether, using data from two centralized exchanges (Coinbase Pro and Binance) and a decentralized exchange (Uniswap V2). We find systematic patterns in both volatility and volume across day-of-the-week, hour-of-the-day, and within the hour. These patterns have grown stronger over the years and can be related to algorithmic trading and funding times in futures markets. We also document that price formation mainly takes place on the centralized exchanges while price adjustments on the decentralized exchanges can be sluggish. | ['Wade Kimbrough', 'Chan Kim', 'Peter Reinhard Hansen'] | 2021-09-24 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-1.07605040e+00 4.67168465e-02 -1.56629980e-01 -2.14537188e-01
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411c6616-fd81-400d-9ed8-01bd37bc9256 | comparing-reinforcement-learning-and-human | 2306.17766 | null | https://arxiv.org/abs/2306.17766v1 | https://arxiv.org/pdf/2306.17766v1.pdf | Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules | Reliable real-world deployment of reinforcement learning (RL) methods requires a nuanced understanding of their strengths and weaknesses and how they compare to those of humans. Human-machine systems are becoming more prevalent and the design of these systems relies on a task-oriented understanding of both human learning (HL) and RL. Thus, an important line of research is characterizing how the structure of a learning task affects learning performance. While increasingly complex benchmark environments have led to improved RL capabilities, such environments are difficult to use for the dedicated study of task structure. To address this challenge we present a learning environment built to support rigorous study of the impact of task structure on HL and RL. We demonstrate the environment's utility for such study through example experiments in task structure that show performance differences between humans and RL algorithms. | ['Vicki Bier', 'Paul Kantor', 'Yonatan Mintz', 'Vladimir Menkov', 'Eric Pulick'] | 2023-06-30 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [ 3.27643454e-02 -4.81463075e-02 -2.30217174e-01 -2.55394638e-01
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995cff51-9342-4dfd-8aa2-bc6ab650fe21 | towards-practical-lipreading-with-distilled | 2007.06504 | null | https://arxiv.org/abs/2007.06504v3 | https://arxiv.org/pdf/2007.06504v3.pdf | Towards Practical Lipreading with Distilled and Efficient Models | Lipreading has witnessed a lot of progress due to the resurgence of neural networks. Recent works have placed emphasis on aspects such as improving performance by finding the optimal architecture or improving generalization. However, there is still a significant gap between the current methodologies and the requirements for an effective deployment of lipreading in practical scenarios. In this work, we propose a series of innovations that significantly bridge that gap: first, we raise the state-of-the-art performance by a wide margin on LRW and LRW-1000 to 88.5% and 46.6%, respectively using self-distillation. Secondly, we propose a series of architectural changes, including a novel Depthwise Separable Temporal Convolutional Network (DS-TCN) head, that slashes the computational cost to a fraction of the (already quite efficient) original model. Thirdly, we show that knowledge distillation is a very effective tool for recovering performance of the lightweight models. This results in a range of models with different accuracy-efficiency trade-offs. However, our most promising lightweight models are on par with the current state-of-the-art while showing a reduction of 8.2x and 3.9x in terms of computational cost and number of parameters, respectively, which we hope will enable the deployment of lipreading models in practical applications. | ['Pingchuan Ma', 'Maja Pantic', 'Stavros Petridis', 'Brais Martinez'] | 2020-07-13 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.85825869e-01 9.73596945e-02 -3.81843776e-01 -5.71579412e-02
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2b47ba79-94a1-4810-9cb9-8c052e687d6d | example-based-explainable-ai-and-its | 2302.01526 | null | https://arxiv.org/abs/2302.01526v1 | https://arxiv.org/pdf/2302.01526v1.pdf | Example-Based Explainable AI and its Application for Remote Sensing Image Classification | We present a method of explainable artificial intelligence (XAI), "What I Know (WIK)", to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task. One of the expected roles of XAI methods is verifying whether inferences of a trained machine learning model are valid for an application, and it is an important factor that what datasets are used for training the model as well as the model architecture. Our data-centric approach can help determine whether the training dataset is sufficient for each inference by checking the selected example data. If the selected example looks similar to the input data, we can confirm that the model was not trained on a dataset with a feature distribution far from the feature of the input data. With this method, the criteria for selecting an example are not merely data similarity with the input data but also data similarity in the context of the model task. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated with reasonably selected examples. This method can be applied to various machine-learning tasks, including classification and regression. | ['Masao Yasui', 'Taiki Ogihara', 'Peihsuan Lin', 'Kazunari Matsunaga', 'Yasunobu Uchiyama', 'Masato Taki', 'Masato Todo', 'Shin-nosuke Ishikawa'] | 2023-02-03 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.92259657e-01 4.06505495e-01 -1.07212707e-01 -7.66635418e-01
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88296edf-7f2c-4af1-9337-06de79208468 | improving-dialog-evaluation-with-a-multi | 2009.11321 | null | https://arxiv.org/abs/2009.11321v1 | https://arxiv.org/pdf/2009.11321v1.pdf | Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining | There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all irrelevant responses. Ideally, such models should be trained using multiple relevant and irrelevant responses for any given context. However, no such data is publicly available, and hence existing models are usually trained using a single relevant response and multiple randomly selected responses from other contexts (random negatives). To allow for better training and robust evaluation of model-based metrics, we introduce the DailyDialog++ dataset, consisting of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context. Using this dataset, we first show that even in the presence of multiple correct references, n-gram based metrics and embedding based metrics do not perform well at separating relevant responses from even random negatives. While model-based metrics perform better than n-gram and embedding based metrics on random negatives, their performance drops substantially when evaluated on adversarial examples. To check if large scale pretraining could help, we propose a new BERT-based evaluation metric called DEB, which is pretrained on 727M Reddit conversations and then finetuned on our dataset. DEB significantly outperforms existing models, showing better correlation with human judgements and better performance on random negatives (88.27% accuracy). However, its performance again drops substantially, when evaluated on adversarial responses, thereby highlighting that even large-scale pretrained evaluation models are not robust to the adversarial examples in our dataset. The dataset and code are publicly available. | ['Mitesh M. Khapra', 'Ananya B. Sai', 'Siddhartha Arora', 'Akash Kumar Mohankumar'] | 2020-09-23 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 4.80435370e-03 1.88896079e-02 6.84335157e-02 -6.96070671e-01
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b5b403f8-d350-447d-96e7-947a14ec0795 | bayesian-inference-and-role-of-astrocytes-in | 2306.12520 | null | https://arxiv.org/abs/2306.12520v1 | https://arxiv.org/pdf/2306.12520v1.pdf | Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data | Alzheimer's disease (AD) is a prominent, worldwide, age-related neurodegenerative disease that currently has no systemic treatment. Strong evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers, astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large amount of Abeta is secreted by astrocytes, which contributes to the total Abeta deposition in the brain. This suggests that astrocytes may also play a role in AD, leading to increased attention to their dynamics and associated mechanisms. Therefore, in the present study, we developed and evaluated novel stochastic models for Abeta growth using ADNI data to predict the effect of astrocytes on AD progression in a clinical trial. In the AD case, accurate prediction is required for a successful clinical treatment plan. Given that AD studies are observational in nature and involve routine patient visits, stochastic models provide a suitable framework for modelling AD. Using the approximate Bayesian computation (ABC) approach, the AD etiology may be modelled as a multi-state disease process. As a result, we use this approach to examine the weak and strong influence of astrocytes at multiple disease progression stages using ADNI data from the baseline to 2-year visits for AD patients whose ages ranged from 50 to 90 years. Based on ADNI data, we discovered that the strong astrocyte effect (i.e., a higher concentration of astrocytes as compared to Abeta) could help to lower or clear the growth of Abeta, which is a key to slowing down AD progression. | ["the Alzheimer's Disease Neuroimaging Initiative", 'Roderick Melnik', 'Hina Shaheen'] | 2023-06-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-6.02751710e-02 -5.26576459e-01 2.40859404e-01 3.52048478e-03
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f553aec9-95e0-406f-b8ef-ac1e1c7646fc | towards-fair-and-decentralized-privacy | 1906.01167 | null | https://arxiv.org/abs/1906.01167v3 | https://arxiv.org/pdf/1906.01167v3.pdf | Towards Fair and Privacy-Preserving Federated Deep Models | The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates. Server-based solutions are prone to the problem of a single-point-of-failure. In this respect, collaborative learning frameworks, such as federated learning (FL), are more robust. Existing federated learning frameworks overlook an important aspect of participation: fairness. All parties are given the same final model without regard to their contributions. To address these issues, we propose a decentralized Fair and Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into federated deep learning models. In particular, we design a local credibility mutual evaluation mechanism to guarantee fairness, and a three-layer onion-style encryption scheme to guarantee both accuracy and privacy. Different from existing FL paradigm, under FPPDL, each participant receives a different version of the FL model with performance commensurate with his contributions. Experiments on benchmark datasets demonstrate that FPPDL balances fairness, privacy and accuracy. It enables federated learning ecosystems to detect and isolate low-contribution parties, thereby promoting responsible participation. | ['Lingjuan Lyu', 'Kee Siong Ng', 'Karthik Nandakumar', 'Han Yu', 'Yitong Li', 'Jiong Jin', 'Xingjun Ma', 'Jiangshan Yu'] | 2019-06-04 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-6.85684383e-01 4.68814634e-02 -2.48023197e-01 -7.86277115e-01
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94d9e05a-b5f4-4108-af19-953034e4c67f | how-do-we-answer-complex-questions-discourse-1 | 2203.11048 | null | https://arxiv.org/abs/2203.11048v1 | https://arxiv.org/pdf/2203.11048v1.pdf | How Do We Answer Complex Questions: Discourse Structure of Long-form Answers | Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our main goal is to understand how humans organize information to craft complex answers. We develop an ontology of six sentence-level functional roles for long-form answers, and annotate 3.9k sentences in 640 answer paragraphs. Different answer collection methods manifest in different discourse structures. We further analyze model-generated answers -- finding that annotators agree less with each other when annotating model-generated answers compared to annotating human-written answers. Our annotated data enables training a strong classifier that can be used for automatic analysis. We hope our work can inspire future research on discourse-level modeling and evaluation of long-form QA systems. | ['Eunsol Choi', 'Junyi Jessy Li', 'Fangyuan Xu'] | 2022-03-21 | null | https://aclanthology.org/2022.acl-long.249 | https://aclanthology.org/2022.acl-long.249.pdf | acl-2022-5 | ['natural-questions'] | ['miscellaneous'] | [ 4.55443561e-02 8.78790617e-01 -2.08090231e-01 -4.94714409e-01
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640e7b07-a440-4ab6-8389-2d26a2972d74 | efficient-determination-of-safety | 2307.01371 | null | https://arxiv.org/abs/2307.01371v1 | https://arxiv.org/pdf/2307.01371v1.pdf | Efficient Determination of Safety Requirements for Perception Systems | Perception systems operate as a subcomponent of the general autonomy stack, and perception system designers often need to optimize performance characteristics while maintaining safety with respect to the overall closed-loop system. For this reason, it is useful to distill high-level safety requirements into component-level requirements on the perception system. In this work, we focus on efficiently determining sets of safe perception system performance characteristics given a black-box simulator of the fully-integrated, closed-loop system. We combine the advantages of common black-box estimation techniques such as Gaussian processes and threshold bandits to develop a new estimation method, which we call smoothing bandits. We demonstrate our method on a vision-based aircraft collision avoidance problem and show improvements in terms of both accuracy and efficiency over the Gaussian process and threshold bandit baselines. | ['Mykel J. Kochenderfer', 'Esen Yel', 'Anthony L. Corso', 'Sydney M. Katz'] | 2023-07-03 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 3.98479924e-02 1.82313666e-01 -8.99778977e-02 -2.13614196e-01
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63a40134-0718-438b-b232-a5c7d1fd04dd | predicting-risk-of-dementia-with-survival | 2306.10330 | null | https://arxiv.org/abs/2306.10330v1 | https://arxiv.org/pdf/2306.10330v1.pdf | Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort | Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algorithms of Random Forest and Elastic Net are applied to train, optimise, and validate predictive models based on the English Longitudinal Study of Ageing ELSA cohort. The two survival machine learning models are compared with the conventional statistical Cox proportional hazard model, proving their superior predictive capability and stability on the ELSA data, as demonstrated by computationally intensive procedures such as nested cross-validation and Monte Carlo validation. This study is the first to apply survival machine learning to the ELSA data, and demonstrates in this case the superiority of AI based predictive modelling approaches over the widely employed Cox statistical approach in survival analysis. Implications, methodological considerations, and future research directions are discussed. | ['Daniel Stahl', 'Olesya Ajnakina', 'Henry Musto', 'Daniel Stamate'] | 2023-06-17 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.32917452e-01 8.03378969e-03 -5.13838232e-01 -4.32429641e-01
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da9b0ed7-05fd-48f9-b44d-50ea4024a244 | single-image-super-resolution-using-1 | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhu_Single_Image_Super-resolution_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhu_Single_Image_Super-resolution_2014_CVPR_paper.pdf | Single Image Super-resolution using Deformable Patches | We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods. | ['Alan L. Yuille', 'Yanning Zhang', 'Yu Zhu'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['patch-matching'] | ['computer-vision'] | [ 2.35198587e-01 -6.80798218e-02 6.75301105e-02 -1.22228920e-01
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39c8ae10-5daa-4d49-b0f9-4c05a821e2d9 | surfnet-generating-3d-shape-surfaces-using | 1703.04079 | null | http://arxiv.org/abs/1703.04079v1 | http://arxiv.org/pdf/1703.04079v1.pdf | SurfNet: Generating 3D shape surfaces using deep residual networks | 3D shape models are naturally parameterized using vertices and faces, \ie,
composed of polygons forming a surface. However, current 3D learning paradigms
for predictive and generative tasks using convolutional neural networks focus
on a voxelized representation of the object. Lifting convolution operators from
the traditional 2D to 3D results in high computational overhead with little
additional benefit as most of the geometry information is contained on the
surface boundary. Here we study the problem of directly generating the 3D shape
surface of rigid and non-rigid shapes using deep convolutional neural networks.
We develop a procedure to create consistent `geometry images' representing the
shape surface of a category of 3D objects. We then use this consistent
representation for category-specific shape surface generation from a parametric
representation or an image by developing novel extensions of deep residual
networks for the task of geometry image generation. Our experiments indicate
that our network learns a meaningful representation of shape surfaces allowing
it to interpolate between shape orientations and poses, invent new shape
surfaces and reconstruct 3D shape surfaces from previously unseen images. | ['Qi-Xing Huang', 'Karthik Ramani', 'Ayan Sinha', 'Asim Unmesh'] | 2017-03-12 | surfnet-generating-3d-shape-surfaces-using-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Sinha_SurfNet_Generating_3D_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Sinha_SurfNet_Generating_3D_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-shape-generation'] | ['computer-vision'] | [ 3.16208661e-01 6.55728221e-01 3.38931620e-01 -5.59230745e-01
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150354c4-3973-42dd-9d03-5f996cfd4d9b | probabilistic-learning-of-multivariate-time | 2306.09147 | null | https://arxiv.org/abs/2306.09147v2 | https://arxiv.org/pdf/2306.09147v2.pdf | Probabilistic Learning of Multivariate Time Series with Temporal Irregularity | Multivariate sequential data collected in practice often exhibit temporal irregularities, including nonuniform time intervals and component misalignment. However, if uneven spacing and asynchrony are endogenous characteristics of the data rather than a result of insufficient observation, the information content of these irregularities plays a defining role in characterizing the multivariate dependence structure. Existing approaches for probabilistic forecasting either overlook the resulting statistical heterogeneities, are susceptible to imputation biases, or impose parametric assumptions on the data distribution. This paper proposes an end-to-end solution that overcomes these limitations by allowing the observation arrival times to play the central role of model construction, which is at the core of temporal irregularities. To acknowledge temporal irregularities, we first enable unique hidden states for components so that the arrival times can dictate when, how, and which hidden states to update. We then develop a conditional flow representation to non-parametrically represent the data distribution, which is typically non-Gaussian, and supervise this representation by carefully factorizing the log-likelihood objective to select conditional information that facilitates capturing time variation and path dependency. The broad applicability and superiority of the proposed solution are confirmed by comparing it with existing approaches through ablation studies and testing on real-world datasets. | ['Qi Wu', 'Cheuk Hang Leung', 'Yijun Li'] | 2023-06-15 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 7.02872351e-02 -4.68016714e-01 -5.02241492e-01 -3.70288521e-01
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4746e249-814f-4c2f-8ba9-3ed89680174c | learning-regional-attention-over-multi | 2104.07240 | null | https://arxiv.org/abs/2104.07240v1 | https://arxiv.org/pdf/2104.07240v1.pdf | Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval | Large-scale trademark retrieval is an important content-based image retrieval task. A recent study shows that off-the-shelf deep features aggregated with Regional-Maximum Activation of Convolutions (R-MAC) achieve state-of-the-art results. However, R-MAC suffers in the presence of background clutter/trivial regions and scale variance, and discards important spatial information. We introduce three simple but effective modifications to R-MAC to overcome these drawbacks. First, we propose the use of both sum and max pooling to minimise the loss of spatial information. We also employ domain-specific unsupervised soft-attention to eliminate background clutter and unimportant regions. Finally, we add multi-resolution inputs to enhance the scale-invariance of R-MAC. We evaluate these three modifications on the million-scale METU dataset. Our results show that all modifications bring non-trivial improvements, and surpass previous state-of-the-art results. | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Osman Tursun'] | 2021-04-15 | null | null | null | null | ['trademark-retrieval', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [-7.01617682e-04 -6.45354450e-01 2.43575454e-01 -3.85618865e-01
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4f247350-32a8-440f-a586-00b5e42d37ed | superdisco-super-class-discovery-improves | 2304.00101 | null | https://arxiv.org/abs/2304.00101v1 | https://arxiv.org/pdf/2304.00101v1.pdf | SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail | Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long-tailed recognition challenge, since they can learn the tail representation based on different levels of semantic abstraction, making the learned tail features more discriminative. This phenomenon motivated us to propose SuperDisco, an algorithm that discovers super-class representations for long-tailed recognition using a graph model. We learn to construct the super-class graph to guide the representation learning to deal with long-tailed distributions. Through message passing on the super-class graph, image representations are rectified and refined by attending to the most relevant entities based on the semantic similarity among their super-classes. Moreover, we propose to meta-learn the super-class graph under the supervision of a prototype graph constructed from a small amount of imbalanced data. By doing so, we obtain a more robust super-class graph that further improves the long-tailed recognition performance. The consistent state-of-the-art experiments on the long-tailed CIFAR-100, ImageNet, Places and iNaturalist demonstrate the benefit of the discovered super-class graph for dealing with long-tailed distributions. | ['Cees G. M. Snoek', 'XianTong Zhen', 'Jiayi Shen', 'Yingjun Du'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.01477169e-01 1.46585718e-01 -5.04157126e-01 -8.42512131e-01
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cfe41fe7-ca30-4313-9b5d-3e2cf5e8f6f3 | continuous-risk-measures-for-driving-support | 2303.08007 | null | https://arxiv.org/abs/2303.08007v1 | https://arxiv.org/pdf/2303.08007v1.pdf | Continuous Risk Measures for Driving Support | In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD. | ['Tim Puphal', 'Julian Eggert'] | 2023-03-14 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-3.61740142e-01 3.86451115e-03 3.89701165e-02 -1.30482838e-01
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52c949c9-33e3-4e74-bec9-64cad64194af | carpal-confidence-aware-intent-recognition | 2003.08003 | null | https://arxiv.org/abs/2003.08003v2 | https://arxiv.org/pdf/2003.08003v2.pdf | CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy | Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this paper, we propose a novel multi-task intent recognition neural network that predicts not only probabilistic driver trajectories, but also utility statistics associated with the predictions for a given downstream task. We establish a decision criterion for parallel autonomy that takes into account the role of driver trajectory prediction in real-time decision making by reasoning about estimated task-specific utility statistics. We further improve the robustness of our system by considering uncertainties in downstream planning tasks that may lead to unsafe decisions. We test our online system on a realistic urban driving dataset, and demonstrate its advantage in terms of recall and fall-out metrics compared to baseline methods, and demonstrate its effectiveness in intervention and warning use cases. | ['John J. Leonard', 'Luke Fletcher', 'Jonathan A. DeCastro', 'Stephen G. McGill', 'Guy Rosman', 'Brian C. Williams', 'Xin Huang'] | 2020-03-18 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [-2.41799988e-02 3.76102030e-01 -4.38329428e-01 -9.74383414e-01
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12e6256b-df35-434b-adf5-55c4cb928c03 | stability-via-adversarial-training-of-neural | 2210.00874 | null | https://arxiv.org/abs/2210.00874v1 | https://arxiv.org/pdf/2210.00874v1.pdf | Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type | In this paper, we present an approach to neural network mean-field-type control and its stochastic stability analysis by means of adversarial inputs (aka adversarial attacks). This is a class of data-driven mean-field-type control where the distribution of the variables such as the system states and control inputs are incorporated into the problem. Besides, we present a methodology to validate the feasibility of the approximations of the solutions via neural networks and evaluate their stability. Moreover, we enhance the stability by enlarging the training set with adversarial inputs to obtain a more robust neural network. Finally, a worked-out example based on the linear-quadratic mean-field type control problem (LQ-MTC) is presented to illustrate our methodology. | ['Boualem Djehiche', 'Salah Eddine Choutri', 'Julian Barreiro-Gomez'] | 2022-09-27 | null | null | null | null | ['type'] | ['speech'] | [ 2.78116226e-01 3.61694485e-01 -1.93489358e-01 1.55465305e-01
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330a1dce-cf34-4e39-ad7e-2a59374a2f3f | optimal-algorithms-for-stochastic-bilevel | 2306.12067 | null | https://arxiv.org/abs/2306.12067v1 | https://arxiv.org/pdf/2306.12067v1.pdf | Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions | Stochastic Bilevel optimization usually involves minimizing an upper-level (UL) function that is dependent on the arg-min of a strongly-convex lower-level (LL) function. Several algorithms utilize Neumann series to approximate certain matrix inverses involved in estimating the implicit gradient of the UL function (hypergradient). The state-of-the-art StOchastic Bilevel Algorithm (SOBA) [16] instead uses stochastic gradient descent steps to solve the linear system associated with the explicit matrix inversion. This modification enables SOBA to match the lower bound of sample complexity for the single-level counterpart in non-convex settings. Unfortunately, the current analysis of SOBA relies on the assumption of higher-order smoothness for the UL and LL functions to achieve optimality. In this paper, we introduce a novel fully single-loop and Hessian-inversion-free algorithmic framework for stochastic bilevel optimization and present a tighter analysis under standard smoothness assumptions (first-order Lipschitzness of the UL function and second-order Lipschitzness of the LL function). Furthermore, we show that by a slight modification of our approach, our algorithm can handle a more general multi-objective robust bilevel optimization problem. For this case, we obtain the state-of-the-art oracle complexity results demonstrating the generality of both the proposed algorithmic and analytic frameworks. Numerical experiments demonstrate the performance gain of the proposed algorithms over existing ones. | ['Krishnakumar Balasubramanian', 'Tesi Xiao', 'Xuxing Chen'] | 2023-06-21 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.05303878e-01 2.62431484e-02 -7.89076760e-02 -2.23512486e-01
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8c71e037-8b56-4642-bc3f-42d85850200e | comprehensive-multi-modal-interactions-for | 2104.10412 | null | https://arxiv.org/abs/2104.10412v4 | https://arxiv.org/pdf/2104.10412v4.pdf | Comprehensive Multi-Modal Interactions for Referring Image Segmentation | We investigate Referring Image Segmentation (RIS), which outputs a segmentation map corresponding to the natural language description. Addressing RIS efficiently requires considering the interactions happening across visual and linguistic modalities and the interactions within each modality. Existing methods are limited because they either compute different forms of interactions sequentially (leading to error propagation) or ignore intramodal interactions. We address this limitation by performing all three interactions simultaneously through a Synchronous Multi-Modal Fusion Module (SFM). Moreover, to produce refined segmentation masks, we propose a novel Hierarchical Cross-Modal Aggregation Module (HCAM), where linguistic features facilitate the exchange of contextual information across the visual hierarchy. We present thorough ablation studies and validate our approach's performance on four benchmark datasets, showing considerable performance gains over the existing state-of-the-art (SOTA) methods. | ['Vineet Gandhi', 'Kanishk Jain'] | 2021-04-21 | comprehensive-multi-modal-interactions-for-1 | https://aclanthology.org/2022.findings-acl.270 | https://aclanthology.org/2022.findings-acl.270.pdf | findings-acl-2022-5 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.64028656e-01 2.17904657e-01 -1.42099962e-01 -3.69885951e-01
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a5b2f9b3-956e-46a8-b97c-eae802741f66 | automatic-prompt-augmentation-and-selection | 2302.12822 | null | https://arxiv.org/abs/2302.12822v1 | https://arxiv.org/pdf/2302.12822v1.pdf | Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data | Chain-of-thought prompting (CoT) advances the reasoning abilities of large language models (LLMs) and achieves superior performance in arithmetic, commonsense, and symbolic reasoning tasks. However, most CoT studies rely on carefully designed human-annotated rational chains to prompt the language model, which poses challenges for real-world applications where labeled training data is available without human-annotated rational chains. This creates barriers to applications of CoT prompting to these general tasks. This paper proposes a new strategy, Automate-CoT (Automatic Prompt Augmentation and Selection with Chain-of-Thought), that can bypass human engineering of CoTs by automatically augmenting rational chains from a small labeled dataset, and then pruning low-quality chains to construct a candidate pool of machine-generated rationale chains based on the labels. Finally, it selects the optimal combination of several rationale chains from the pool for CoT prompting by employing a variance-reduced policy gradient strategy to estimate the significance of each example in a black-box language model. Automate-CoT enables a quick adaptation of the CoT technique to different tasks. Experimental results demonstrate the effectiveness of our method, where state-of-the-art results are achieved on arithmetic reasoning (+2.7\%), commonsense reasoning (+3.4\%), symbolic reasoning (+3.2\%), and non-reasoning tasks (+2.5\%). Our code will be available at https://github.com/shizhediao/automate-cot. | ['Tong Zhang', 'Shizhe Diao', 'Kashun Shum'] | 2023-02-24 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 1.71229541e-01 3.44009697e-01 -3.32894444e-01 -2.92923450e-01
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e1bfa615-eb8f-4584-9619-b8c17578877b | ifqa-a-dataset-for-open-domain-question | 2305.14010 | null | https://arxiv.org/abs/2305.14010v1 | https://arxiv.org/pdf/2305.14010v1.pdf | IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions | Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and improve models on this ability. To address this void, we introduce the first such dataset, named IfQA, where each question is based on a counterfactual presupposition via an "if" clause. For example, if Los Angeles was on the east coast of the U.S., what would be the time difference between Los Angeles and Paris? Such questions require models to go beyond retrieving direct factual knowledge from the Web: they must identify the right information to retrieve and reason about an imagined situation that may even go against the facts built into their parameters. The IfQA dataset contains over 3,800 questions that were annotated annotated by crowdworkers on relevant Wikipedia passages. Empirical analysis reveals that the IfQA dataset is highly challenging for existing open-domain QA methods, including supervised retrieve-then-read pipeline methods (EM score 36.2), as well as recent few-shot approaches such as chain-of-thought prompting with GPT-3 (EM score 27.4). The unique challenges posed by the IfQA benchmark will push open-domain QA research on both retrieval and counterfactual reasoning fronts. | ['Ashish Sabharwal', 'Peter Clark', 'Meng Jiang', 'Wenhao Yu'] | 2023-05-23 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 7.53253624e-02 6.77094579e-01 -2.78050959e-01 -3.07162732e-01
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2c5189e4-049d-4ed9-a9f9-39fd453e4421 | deconvolving-convolution-neural-network-for | 1806.06970 | null | http://arxiv.org/abs/1806.06970v1 | http://arxiv.org/pdf/1806.06970v1.pdf | Deconvolving convolution neural network for cell detection | Automatic cell detection in histology images is a challenging task due to
varying size, shape and features of cells and stain variations across a large
cohort. Conventional deep learning methods regress the probability of each
pixel belonging to the centre of a cell followed by detection of local maxima.
We present deconvolution as an alternate approach to local maxima detection.
The ground truth points are convolved with a mapping filter to generate
artifical labels. A convolutional neural network (CNN) is modified to convolve
it's output with the same mapping filter and is trained for the mapped labels.
Output of the trained CNN is then deconvolved to generate points as cell
detection. We compare our method with state-of-the-art deep learning approaches
where the results show that the proposed approach detects cells with
comparatively high precision and F1-score. | ['Mariam Jamal-Hanjani', 'Khalid AbdulJabbar', 'John Le Quesne', 'Charles Swanton', 'Yinyin Yuan', 'Shan E Ahmed Raza', 'Selvaraju Veeriah'] | 2018-06-18 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.67325217e-01 7.47150183e-02 4.35748309e-01 -1.84343442e-01
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3970d909-6d33-40ac-a191-878b73294342 | real-time-low-cost-multi-person-3d-pose | 2110.11414 | null | https://arxiv.org/abs/2110.11414v3 | https://arxiv.org/pdf/2110.11414v3.pdf | Real-time, low-cost multi-person 3D pose estimation | The process of tracking human anatomy in computer vision is referred to pose estimation, and it is used in fields ranging from gaming to surveillance. Three-dimensional pose estimation traditionally requires advanced equipment, such as multiple linked intensity cameras or high-resolution time-of-flight cameras to produce depth images. However, there are applications, e.g.~consumer electronics, where significant constraints are placed on the size, power consumption, weight and cost of the usable technology. Here, we demonstrate that computational imaging methods can achieve accurate pose estimation and overcome the apparent limitations of time-of-flight sensors designed for much simpler tasks. The sensor we use is already widely integrated in consumer-grade mobile devices, and despite its low spatial resolution, only 4$\times$4 pixels, our proposed Pixels2Pose system transforms its data into accurate depth maps and 3D pose data of multiple people up to a distance of 3 m from the sensor. We are able to generate depth maps at a resolution of 32$\times$32 and 3D localization of a body parts with an error of only $\approx$10 cm at a frame rate of 7 fps. This work opens up promising real-life applications in scenarios that were previously restricted by the advanced hardware requirements and cost of time-of-flight technology. | ['Jonathan Leach', 'Abderrahim Halimi', 'Steve McLaughlin', 'Brent Hearn', 'Istvan Gyongy', 'Feng Zhu', 'Stirling Scholes', 'Germán Mora Martín', 'Max Tyler', 'Alice Ruget'] | 2021-10-11 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.10979712e-01 2.49876827e-01 2.86947250e-01 -1.93620816e-01
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7a91787a-d44b-4d18-925f-09942b813105 | an-approach-to-improving-sound-based-vehicle | 2204.05082 | null | https://arxiv.org/abs/2204.05082v1 | https://arxiv.org/pdf/2204.05082v1.pdf | An approach to improving sound-based vehicle speed estimation | We consider improving the performance of a recently proposed sound-based vehicle speed estimation method. In the original method, an intermediate feature, referred to as the modified attenuation (MA), has been proposed for both vehicle detection and speed estimation. The MA feature maximizes at the instant of the vehicle's closest point of approach, which represents a training label extracted from video recording of the vehicle's pass by. In this paper, we show that the original labeling approach is suboptimal and propose a method for label correction. The method is tested on the VS10 dataset, which contains 304 audio-video recordings of ten different vehicles. The results show that the proposed label correction method reduces average speed estimation error from 7.39 km/h to 6.92 km/h. If the speed is discretized into 10 km/h classes, the accuracy of correct class prediction is improved from 53.2% to 53.8%, whereas when tolerance of one class offset is allowed, accuracy is improved from 93.4% to 94.3%. | ['Slobodan Djukanovic', 'Nikola Bulatovic'] | 2022-04-08 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [ 1.50716916e-01 -1.37346417e-01 -4.51141298e-01 -3.70048374e-01
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5b2438e3-8098-4c1e-8c2a-b3978e70b621 | latent-complete-row-space-recovery-for-multi | 1912.07248 | null | https://arxiv.org/abs/1912.07248v1 | https://arxiv.org/pdf/1912.07248v1.pdf | Latent Complete Row Space Recovery for Multi-view Subspace Clustering | Multi-view subspace clustering has been applied to applications such as image processing and video surveillance, and has attracted increasing attention. Most existing methods learn view-specific self-representation matrices, and construct a combined affinity matrix from multiple views. The affinity construction process is time-consuming, and the combined affinity matrix is not guaranteed to reflect the whole true subspace structure. To overcome these issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed. Concretely, LCRSR is based on the assumption that the multi-view observations are generated from an underlying latent representation, which is further assumed to collect the authentic samples drawn exactly from multiple subspaces. LCRSR is able to recover the row space of the latent representation, which not only carries complete information from multiple views but also determines the subspace membership under certain conditions. LCRSR does not involve the graph construction procedure and is solved with an efficient and convergent algorithm, thereby being more scalable to large-scale datasets. The effectiveness and efficiency of LCRSR are validated by clustering various kinds of multi-view data and illustrated in the background subtraction task. | ['Chenping Hou', 'Hong Tao', 'Jubo Zhu', 'Dongyun Yi', 'Yuhua Qian'] | 2019-12-16 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [ 4.31602336e-02 -5.43441176e-01 -1.64370567e-01 -1.03504909e-02
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0d0d7e58-e20d-45c2-8635-4ed4f2a4fd0a | user-engagement-prediction-for-clarification | 2102.04163 | null | https://arxiv.org/abs/2102.04163v1 | https://arxiv.org/pdf/2102.04163v1.pdf | User Engagement Prediction for Clarification in Search | Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval massively. However, it comes with a very high risk of frustrating the user in case the system fails in asking decent clarifying questions. Therefore, it is of great importance to determine when and how to ask for clarification. To this aim, in this work, we model search clarification prediction as user engagement problem. We assume that the better a clarification is, the higher user engagement with it would be. We propose a Transformer-based model to tackle the task. The comparison with competitive baselines on large-scale real-life clarification engagement data proves the effectiveness of our model. Also, we analyse the effect of all result page elements on the performance and find that, among others, the ranked list of the search engine leads to considerable improvements. Our extensive analysis of task-specific features guides future research. | ['Fabio Crestani', 'Mohammad Aliannejadi', 'Ivan Sekulić'] | 2021-02-08 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.03294030e-01 4.80583534e-02 -3.18785667e-01 -2.57995129e-01
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0ed24109-5cc7-49bc-b42b-6e9401d3bb54 | boosting-video-captioning-with-dynamic-loss | 2107.11707 | null | https://arxiv.org/abs/2107.11707v3 | https://arxiv.org/pdf/2107.11707v3.pdf | Boosting Video Captioning with Dynamic Loss Network | Video captioning is one of the challenging problems at the intersection of vision and language, having many real-life applications in video retrieval, video surveillance, assisting visually challenged people, Human-machine interface, and many more. Recent deep learning based methods have shown promising results but are still on the lower side than other vision tasks (such as image classification, object detection). A significant drawback with existing video captioning methods is that they are optimized over cross-entropy loss function, which is uncorrelated to the de facto evaluation metrics (BLEU, METEOR, CIDER, ROUGE). In other words, cross-entropy is not a proper surrogate of the true loss function for video captioning. To mitigate this, methods like REINFORCE, Actor-Critic, and Minimum Risk Training (MRT) have been applied but have limitations and are not very effective. This paper proposes an alternate solution by introducing a dynamic loss network (DLN), providing an additional feedback signal that reflects the evaluation metrics directly. Our solution proves to be more efficient than other solutions and can be easily adapted to similar tasks. Our results on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSRVTT) datasets outperform previous methods. | ['Nasib Ullah', 'Partha Pratim Mohanta'] | 2021-07-25 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 9.71658379e-02 -1.10239111e-01 -3.98180664e-01 -4.34244394e-01
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c1f08c01-6d5f-46a3-b9ae-67b6bce946ef | a-fast-machine-learning-model-for-ecg-based | null | null | https://doi.org/10.3389/fphy.2019.00103 | https://www.frontiersin.org/articles/10.3389/fphy.2019.00103/pdf | A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection | We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches. | ['Silvia Ortín', 'Miguel C. Soriano', 'Miquel Alfaras'] | 2019-07-18 | null | null | null | frontiers-in-physics-2019-7 | ['arrhythmia-detection', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 3.37694615e-01 8.31100419e-02 1.21532187e-01 -4.90943730e-01
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e16d6e0f-c1ff-41a1-ac92-55c6ff78a0a5 | automated-detection-of-equine-facial-action | 2102.08983 | null | https://arxiv.org/abs/2102.08983v2 | https://arxiv.org/pdf/2102.08983v2.pdf | Automated Detection of Equine Facial Action Units | The recently developed Equine Facial Action Coding System (EquiFACS) provides a precise and exhaustive, but laborious, manual labelling method of facial action units of the horse. To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images. We use a cascade framework; we firstly train several object detectors to detect the predefined Region-of-Interest (ROI), and secondly apply binary classifiers for each action unit in related regions. We experiment with both regular CNNs and a more tailored model transferred from human facial action unit recognition. Promising initial results are presented for nine action units in the eye and lower face regions. Code for the project is publicly available. | ['Hedvig Kjellström', 'Pia Haubro Andersen', 'Sofia Broomé', 'Zhenghong Li'] | 2021-02-17 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 4.89494950e-01 5.69212377e-01 -1.06514893e-01 -5.31268656e-01
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3cca82eb-6fe3-44e8-bafc-ea3f824dccc4 | capsule-network-based-contrastive-learning-of | 2209.11276 | null | https://arxiv.org/abs/2209.11276v1 | https://arxiv.org/pdf/2209.11276v1.pdf | Capsule Network based Contrastive Learning of Unsupervised Visual Representations | Capsule Networks have shown tremendous advancement in the past decade, outperforming the traditional CNNs in various task due to it's equivariant properties. With the use of vector I/O which provides information of both magnitude and direction of an object or it's part, there lies an enormous possibility of using Capsule Networks in unsupervised learning environment for visual representation tasks such as multi class image classification. In this paper, we propose Contrastive Capsule (CoCa) Model which is a Siamese style Capsule Network using Contrastive loss with our novel architecture, training and testing algorithm. We evaluate the model on unsupervised image classification CIFAR-10 dataset and achieve a top-1 test accuracy of 70.50% and top-5 test accuracy of 98.10%. Due to our efficient architecture our model has 31 times less parameters and 71 times less FLOPs than the current SOTA in both supervised and unsupervised learning. | ['Ioannis Patras', 'Harsh Panwar'] | 2022-09-22 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [-3.61808002e-01 -4.47704382e-02 -4.91390526e-02 -2.35865429e-01
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a6de49d2-f147-4fd7-8d46-3e0f95d60a41 | best-vision-technologies-submission-to | 1806.09278 | null | http://arxiv.org/abs/1806.09278v1 | http://arxiv.org/pdf/1806.09278v1.pdf | Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos | This note describes the details of our solution to the dense-captioning
events in videos task of ActivityNet Challenge 2018. Specifically, we solve
this problem with a two-stage way, i.e., first temporal event proposal and then
sentence generation. For temporal event proposal, we directly leverage the
three-stage workflow in [13, 16]. For sentence generation, we capitalize on
LSTM-based captioning framework with temporal attention mechanism (dubbed as
LSTM-T). Moreover, the input visual sequence to the LSTM-based video captioning
model is comprised of RGB and optical flow images. At inference, we adopt a
late fusion scheme to fuse the two LSTM-based captioning models for sentence
generation. | ['Yuan Liu', 'Moyini Yao'] | 2018-06-25 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 4.75291580e-01 1.11192860e-01 3.25161181e-02 -3.19641948e-01
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29c2caa6-fa40-4bb8-a57c-d8d27f6402b1 | mlp-3d-a-mlp-like-3d-architecture-with-1 | 2206.06292 | null | https://arxiv.org/abs/2206.06292v1 | https://arxiv.org/pdf/2206.06292v1.pdf | MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing | Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networks for video recognition due to the large variations and complexities in video data. In this paper, we present MLP-3D networks, a novel MLP-like 3D architecture for video recognition. Specifically, the architecture consists of MLP-3D blocks, where each block contains one MLP applied across tokens (i.e., token-mixing MLP) and one MLP applied independently to each token (i.e., channel MLP). By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling. GTM divides the input tokens into several temporal groups and linearly maps the tokens in each group with the shared projection matrix. Furthermore, we devise several variants of GTM with different grouping strategies, and compose each variant in different blocks of MLP-3D network by greedy architecture search. Without the dependence on convolutions or attention mechanisms, our MLP-3D networks achieves 68.5\%/81.4\% top-1 accuracy on Something-Something V2 and Kinetics-400 datasets, respectively. Despite with fewer computations, the results are comparable to state-of-the-art widely-used 3D CNNs and video transformers. Source code is available at https://github.com/ZhaofanQiu/MLP-3D. | ['Tao Mei', 'Chong-Wah Ngo', 'Ting Yao', 'Zhaofan Qiu'] | 2022-06-13 | mlp-3d-a-mlp-like-3d-architecture-with | http://openaccess.thecvf.com//content/CVPR2022/html/Qiu_MLP-3D_A_MLP-Like_3D_Architecture_With_Grouped_Time_Mixing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Qiu_MLP-3D_A_MLP-Like_3D_Architecture_With_Grouped_Time_Mixing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-classification'] | ['computer-vision'] | [-1.04200609e-01 -4.03508872e-01 -2.94022828e-01 -1.23894513e-01
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bc281609-c0fc-4303-905f-5e692a037c13 | neural-exploitation-and-exploration-of | 2305.03784 | null | https://arxiv.org/abs/2305.03784v1 | https://arxiv.org/pdf/2305.03784v1.pdf | Neural Exploitation and Exploration of Contextual Bandits | In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, Thompson Sampling (TS), and Upper Confidence Bound (UCB). In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration. In this paper, instead of calculating a large-deviation based statistical bound for exploration like previous methods, we propose, ``EE-Net,'' a novel neural-based exploitation and exploration strategy. In addition to using a neural network (Exploitation network) to learn the reward function, EE-Net uses another neural network (Exploration network) to adaptively learn the potential gains compared to the currently estimated reward for exploration. We provide an instance-based $\widetilde{\mathcal{O}}(\sqrt{T})$ regret upper bound for EE-Net and show that EE-Net outperforms related linear and neural contextual bandit baselines on real-world datasets. | ['Jingrui He', 'Arindam Banerjee', 'Yuchen Yan', 'Yikun Ban'] | 2023-05-05 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 8.39848220e-02 5.37735084e-03 -1.02289355e+00 -3.97800595e-01
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05462e49-8286-4def-a217-04585dabf2ef | a-multivariate-semi-parametric-portfolio-risk | 2207.04595 | null | https://arxiv.org/abs/2207.04595v2 | https://arxiv.org/pdf/2207.04595v2.pdf | A multivariate semi-parametric portfolio risk optimization and forecasting framework | We develop a novel multivariate semi-parametric modelling approach to portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting. Differently from existing univariate semi-parametric approaches, the proposed framework involves explicit modelling of the dependence structure among portfolio asset returns through a dynamic conditional correlation (DCC) parameterization. Model parameters are estimated through a two-step procedure based on the minimization of strictly consistent scoring functions derived from the Asymmetric Laplace (AL) distribution. The proposed model is consequently denominated as DCC-AL. The performance of the DCC-AL in risk forecasting and portfolio optimization is assessed by means of a forecasting study on the components of the Dow Jones index for an out-of-sample period from December 2016 to September 2021. The empirical results support the proposed model class, comparing to a variety of existing approaches, in terms of risk forecasting and portfolio optimization. | ['Chao Wang', 'Giuseppe Storti'] | 2022-07-11 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.65133211e-02 -2.99994648e-02 1.69986859e-01 -4.83704209e-01
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e823fa2b-db8a-4eb2-92bd-0b2f2a8808dd | word-level-loss-extensions-for-neural | 1808.02374 | null | http://arxiv.org/abs/1808.02374v1 | http://arxiv.org/pdf/1808.02374v1.pdf | Word-Level Loss Extensions for Neural Temporal Relation Classification | Unsupervised pre-trained word embeddings are used effectively for many tasks
in natural language processing to leverage unlabeled textual data. Often these
embeddings are either used as initializations or as fixed word representations
for task-specific classification models. In this work, we extend our
classification model's task loss with an unsupervised auxiliary loss on the
word-embedding level of the model. This is to ensure that the learned word
representations contain both task-specific features, learned from the
supervised loss component, and more general features learned from the
unsupervised loss component. We evaluate our approach on the task of temporal
relation extraction, in particular, narrative containment relation extraction
from clinical records, and show that continued training of the embeddings on
the unsupervised objective together with the task objective gives better
task-specific embeddings, and results in an improvement over the state of the
art on the THYME dataset, using only a general-domain part-of-speech tagger as
linguistic resource. | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2018-08-07 | word-level-loss-extensions-for-neural-1 | https://aclanthology.org/C18-1291 | https://aclanthology.org/C18-1291.pdf | coling-2018-8 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.15784389e-01 5.36588311e-01 -7.51656234e-01 -5.62386990e-01
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73b63fa1-c468-4421-b1ed-acf9802b5d51 | deep-collective-knowledge-distillation | 2304.08878 | null | https://arxiv.org/abs/2304.08878v1 | https://arxiv.org/pdf/2304.08878v1.pdf | Deep Collective Knowledge Distillation | Many existing studies on knowledge distillation have focused on methods in which a student model mimics a teacher model well. Simply imitating the teacher's knowledge, however, is not sufficient for the student to surpass that of the teacher. We explore a method to harness the knowledge of other students to complement the knowledge of the teacher. We propose deep collective knowledge distillation for model compression, called DCKD, which is a method for training student models with rich information to acquire knowledge from not only their teacher model but also other student models. The knowledge collected from several student models consists of a wealth of information about the correlation between classes. Our DCKD considers how to increase the correlation knowledge of classes during training. Our novel method enables us to create better performing student models for collecting knowledge. This simple yet powerful method achieves state-of-the-art performances in many experiments. For example, for ImageNet, ResNet18 trained with DCKD achieves 72.27\%, which outperforms the pretrained ResNet18 by 2.52\%. For CIFAR-100, the student model of ShuffleNetV1 with DCKD achieves 6.55\% higher top-1 accuracy than the pretrained ShuffleNetV1. | ['Sungwoo Cho', 'Yongkeun Yun', 'Chanho Min', 'Kyusam Oh', 'Jihyeon Seo'] | 2023-04-18 | null | null | null | null | ['model-compression'] | ['methodology'] | [-3.68307494e-02 1.87776044e-01 -1.15491465e-01 -2.27008104e-01
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84600a21-90e5-4b81-90e0-fa5daab139c7 | deepsurv-personalized-treatment-recommender | 1606.00931 | null | http://arxiv.org/abs/1606.00931v3 | http://arxiv.org/pdf/1606.00931v3.pdf | DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network | Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (e.g. clinical and genetic features)
and the effectiveness of various treatment options. Standard survival models
like the linear Cox proportional hazards model require extensive feature
engineering or prior medical knowledge to model treatment interaction at an
individual level. While nonlinear survival methods, such as neural networks and
survival forests, can inherently model these high-level interaction terms, they
have yet to be shown as effective treatment recommender systems. We introduce
DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art
survival method for modeling interactions between a patient's covariates and
treatment effectiveness in order to provide personalized treatment
recommendations. We perform a number of experiments training DeepSurv on
simulated and real survival data. We demonstrate that DeepSurv performs as well
as or better than other state-of-the-art survival models and validate that
DeepSurv successfully models increasingly complex relationships between a
patient's covariates and their risk of failure. We then show how DeepSurv
models the relationship between a patient's features and effectiveness of
different treatment options to show how DeepSurv can be used to provide
individual treatment recommendations. Finally, we train DeepSurv on real
clinical studies to demonstrate how it's personalized treatment recommendations
would increase the survival time of a set of patients. The predictive and
modeling capabilities of DeepSurv will enable medical researchers to use deep
neural networks as a tool in their exploration, understanding, and prediction
of the effects of a patient's characteristics on their risk of failure. | ['Jared Katzman', 'Tingting Jiang', 'Jonathan Bates', 'Alexander Cloninger', 'Yuval Kluger', 'Uri Shaham'] | 2016-06-02 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [-1.05344042e-01 -5.64642400e-02 -7.76520073e-01 -6.85276568e-01
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60a4aea2-1afc-4e3d-9c04-b54145309ac5 | don-t-generate-discriminate-a-proposal-for | 2212.09736 | null | https://arxiv.org/abs/2212.09736v2 | https://arxiv.org/pdf/2212.09736v2.pdf | Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments | A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects. It thereby casts the burden of ensuring grammaticality, faithfulness, and controllability all on the LMs. We propose Pangu, a generic framework for grounded language understanding that capitalizes on the discriminative ability of LMs instead of their generative ability. Pangu consists of a symbolic agent and a neural LM working in a concerted fashion: The agent explores the environment to incrementally construct valid plans, and the LM evaluates the plausibility of the candidate plans to guide the search process. A case study on the challenging problem of knowledge base question answering (KBQA), which features a massive environment, demonstrates the remarkable effectiveness and flexibility of Pangu: A BERT-base LM is sufficient for setting a new record on standard KBQA datasets, and larger LMs further bring substantial gains. Pangu also enables, for the first time, effective few-shot in-context learning for KBQA with large LMs such as Codex. | ['Yu Su', 'Xiang Deng', 'Yu Gu'] | 2022-12-19 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 1.01063326e-01 5.78716934e-01 -5.71480878e-02 -2.93792397e-01
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6801fa53-6597-45cc-9948-82522a4be626 | sg-fcn-a-motion-and-memory-based-deep | 1809.07988 | null | http://arxiv.org/abs/1809.07988v1 | http://arxiv.org/pdf/1809.07988v1.pdf | SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection | Data-driven saliency detection has attracted strong interest as a result of
applying convolutional neural networks to the detection of eye fixations.
Although a number of imagebased salient object and fixation detection models
have been proposed, video fixation detection still requires more exploration.
Different from image analysis, motion and temporal information is a crucial
factor affecting human attention when viewing video sequences. Although
existing models based on local contrast and low-level features have been
extensively researched, they failed to simultaneously consider interframe
motion and temporal information across neighboring video frames, leading to
unsatisfactory performance when handling complex scenes. To this end, we
propose a novel and efficient video eye fixation detection model to improve the
saliency detection performance. By simulating the memory mechanism and visual
attention mechanism of human beings when watching a video, we propose a
step-gained fully convolutional network by combining the memory information on
the time axis with the motion information on the space axis while storing the
saliency information of the current frame. The model is obtained through
hierarchical training, which ensures the accuracy of the detection. Extensive
experiments in comparison with 11 state-of-the-art methods are carried out, and
the results show that our proposed model outperforms all 11 methods across a
number of publicly available datasets. | ['Meijun Sun', 'Ziqi Zhou', 'Zheng Wang', 'QinGhua Hu', 'Jianmin Jiang'] | 2018-09-21 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 2.73549378e-01 -7.82524824e-01 -3.25295389e-01 -8.36117789e-02
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46dcc3b3-e5ee-4484-8cc6-41bdf001760d | a-spatio-temporal-network-for-video-semantic | 2306.11052 | null | https://arxiv.org/abs/2306.11052v1 | https://arxiv.org/pdf/2306.11052v1.pdf | A spatio-temporal network for video semantic segmentation in surgical videos | Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Segmentation models need to provide accurate and consistent predictions since temporally inconsistent identification of anatomical structures can impair usability and hinder patient safety. Video information can alleviate these challenges leading to reliable models suitable for clinical use. We propose a novel architecture for modelling temporal relationships in videos. The proposed model includes a spatio-temporal decoder to enable video semantic segmentation by improving temporal consistency across frames. The encoder processes individual frames whilst the decoder processes a temporal batch of adjacent frames. The proposed decoder can be used on top of any segmentation encoder to improve temporal consistency. Model performance was evaluated on the CholecSeg8k dataset and a private dataset of robotic Partial Nephrectomy procedures. Segmentation performance was improved when the temporal decoder was applied across both datasets. The proposed model also displayed improvements in temporal consistency. | ['Imanol Luengo', 'Danail Stoyanov', 'Karen Kerr', 'Lucy Culshaw', 'David Owen', 'Felix Bragman', 'Ricardo Sanchez-Matilla', 'Maria Grammatikopoulou'] | 2023-06-19 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 1.24084800e-01 3.70008618e-01 -2.98146039e-01 -3.93279493e-01
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aa85c68c-7fd2-4fe2-96d8-a59cc870aed2 | relevance-guided-supervision-for-openqa-with | 2007.00814 | null | https://arxiv.org/abs/2007.00814v2 | https://arxiv.org/pdf/2007.00814v2.pdf | Relevance-guided Supervision for OpenQA with ColBERT | Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those passages. In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages. We argue that this modeling choice is insufficiently expressive for dealing with the complexity of natural language questions. To address this, we define ColBERT-QA, which adapts the scalable neural retrieval model ColBERT to OpenQA. ColBERT creates fine-grained interactions between questions and passages. We propose an efficient weak supervision strategy that iteratively uses ColBERT to create its own training data. This greatly improves OpenQA retrieval on Natural Questions, SQuAD, and TriviaQA, and the resulting system attains state-of-the-art extractive OpenQA performance on all three datasets. | ['Christopher Potts', 'Omar Khattab', 'Matei Zaharia'] | 2020-07-01 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-1.30332291e-01 2.06083804e-01 1.31226014e-02 -1.57178283e-01
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ef9915b0-e61e-4b83-ad20-f82086e4d58a | a-pov-based-highway-vehicle-trajectory | 2303.06202 | null | https://arxiv.org/abs/2303.06202v1 | https://arxiv.org/pdf/2303.06202v1.pdf | A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture | Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for various traffic safety and management applications. Despite the abundance of trajectory datasets, few offer a comprehensive and diverse range of driving scenes, capturing multiple viewpoints of various highway layouts, merging lanes, and configurations. This limits their ability to capture the nuanced interactions between drivers, vehicles, and the roadway infrastructure. We introduce the \emph{Carolinas Highway Dataset (CHD\footnote{\emph{CHD} available at: \url{https://github.com/TeCSAR-UNCC/Carolinas\_Dataset}})}, a vehicle trajectory, detection, and tracking dataset. \emph{CHD} is a collection of 1.6 million frames captured in highway-based videos from eye-level and high-angle POVs at eight locations across Carolinas with 338,000 vehicle trajectories. The locations, timing of recordings, and camera angles were carefully selected to capture various road geometries, traffic patterns, lighting conditions, and driving behaviors. We also present \emph{PishguVe}\footnote{\emph{PishguVe} code available at: \url{https://github.com/TeCSAR-UNCC/PishguVe}}, a novel vehicle trajectory prediction architecture that uses attention-based graph isomorphism and convolutional neural networks. The results demonstrate that \emph{PishguVe} outperforms existing algorithms to become the new state-of-the-art (SotA) in bird's-eye, eye-level, and high-angle POV trajectory datasets. Specifically, it achieves a 12.50\% and 10.20\% improvement in ADE and FDE, respectively, over the current SotA on NGSIM dataset. Compared to best-performing models on CHD, \emph{PishguVe} achieves lower ADE and FDE on eye-level data by 14.58\% and 27.38\%, respectively, and improves ADE and FDE on high-angle data by 8.3\% and 6.9\%, respectively. | ['Hamed Tabkhi', 'Armin Danesh Pazho', 'Ghazal Alinezhad Noghre', 'Vinit Katariya'] | 2023-03-10 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-5.17638624e-01 -3.98857504e-01 -1.60111129e-01 -3.21173608e-01
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a6678ab6-f327-4b96-8a9d-5313b986dc9d | towards-rich-feature-discovery-with-class | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Towards_Rich_Feature_Discovery_With_Class_Activation_Maps_Augmentation_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Towards_Rich_Feature_Discovery_With_Class_Activation_Maps_Augmentation_for_CVPR_2019_paper.pdf | Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification | The fundamental challenge of small inter-person variation requires Person Re-Identification (Re-ID) models to capture sufficient fine-grained information. This paper proposes to discover diverse discriminative visual cues without extra assistance, e.g., pose estimation, human parsing. Specifically, a Class Activation Maps (CAM) augmentation model is proposed to expand the activation scope of baseline Re-ID model to explore rich visual cues, where the backbone network is extended by a series of ordered branches which share the same input but output complementary CAM. A novel Overlapped Activation Penalty is proposed to force the new branch to pay more attention to the image regions less activated by the old ones, such that spatial diverse visual features can be discovered. The proposed model achieves state-of-the-art results on three person Re-ID benchmarks. Moreover, a visualization approach termed ranking activation map (RAM) is proposed to explicitly interpret the ranking results in the test stage, which gives qualitative validations of the proposed method.
| [' Shu Zhang', ' Kaiqi Huang', ' Xiaotang Chen', ' Zhang Zhang', ' Houjing Huang', 'Wenjie Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['human-parsing'] | ['computer-vision'] | [ 3.50490175e-02 1.87115639e-01 2.75411755e-02 -4.31091756e-01
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17616cab-58d6-4ce5-88e1-c7d2a6e41449 | personalizing-lexical-simplification | null | null | https://aclanthology.org/C18-1019 | https://aclanthology.org/C18-1019.pdf | Personalizing Lexical Simplification | A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users{'} differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output. | ['John Lee', 'Chak Yan Yeung'] | 2018-08-01 | personalizing-lexical-simplification-1 | https://aclanthology.org/C18-1019 | https://aclanthology.org/C18-1019.pdf | coling-2018-8 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.09145194e-01 1.06708616e-01 -2.55742580e-01 -3.08104277e-01
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165c47fd-bd1a-4615-96aa-4ac864c57ab1 | time-efficient-training-of-progressive | 2202.12337 | null | https://arxiv.org/abs/2202.12337v1 | https://arxiv.org/pdf/2202.12337v1.pdf | Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network | Generative Adversarial Networks have been employed successfully to generate high-resolution augmented images of size 1024^2. Although the augmented images generated are unprecedented, the training time of the model is exceptionally high. Conventional GAN requires training of both Discriminator as well as the Generator. In Progressive GAN, which is the current state-of-the-art GAN for image augmentation, instead of training the GAN all at once, a new concept of progressing growing of Discriminator and Generator simultaneously, was proposed. Although the lower stages such as 4x4 and 8x8 train rather quickly, the later stages consume a tremendous amount of time which could take days to finish the model training. In our paper, we propose a novel pipeline that combines Progressive GAN with slight modifications and Super Resolution GAN. Super Resolution GAN up samples low-resolution images to high-resolution images which can prove to be a useful resource to reduce the training time exponentially. | ['Soham Kamble', 'Akshay Joshi', 'Tejas Kolhe', 'Pranesh Kulkarni', 'Atharva Karwande'] | 2022-02-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 7.66635001e-01 4.39569026e-01 3.57163161e-01 -5.37387468e-02
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4ac47a2f-0d21-4ef8-88dd-ec2662ceacd0 | jcdnet-joint-of-common-and-definite-phases | 2303.17294 | null | https://arxiv.org/abs/2303.17294v1 | https://arxiv.org/pdf/2303.17294v1.pdf | JCDNet: Joint of Common and Definite phases Network for Weakly Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to localize action instances in untrimmed videos with only video-level supervision. We witness that different actions record common phases, e.g., the run-up in the HighJump and LongJump. These different actions are defined as conjoint actions, whose rest parts are definite phases, e.g., leaping over the bar in a HighJump. Compared with the common phases, the definite phases are more easily localized in existing researches. Most of them formulate this task as a Multiple Instance Learning paradigm, in which the common phases are tended to be confused with the background, and affect the localization completeness of the conjoint actions. To tackle this challenge, we propose a Joint of Common and Definite phases Network (JCDNet) by improving feature discriminability of the conjoint actions. Specifically, we design a Class-Aware Discriminative module to enhance the contribution of the common phases in classification by the guidance of the coarse definite-phase features. Besides, we introduce a temporal attention module to learn robust action-ness scores via modeling temporal dependencies, distinguishing the common phases from the background. Extensive experiments on three datasets (THUMOS14, ActivityNetv1.2, and a conjoint-action subset) demonstrate that JCDNet achieves competitive performance against the state-of-the-art methods. Keywords: weakly-supervised learning, temporal action localization, conjoint action | ['Wei Zhou', 'Zhiling Luo', 'Xiaoxia Li', 'Yifu Liu'] | 2023-03-30 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.58997355e-02 -3.64498287e-01 -7.36682892e-01 -6.57226294e-02
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df6bb72a-3399-40f0-b305-045f2d9af8a9 | representation-learning-for-weakly-supervised | 2105.00815 | null | https://arxiv.org/abs/2105.00815v1 | https://arxiv.org/pdf/2105.00815v1.pdf | Representation Learning for Weakly Supervised Relation Extraction | Recent years have seen rapid development in Information Extraction, as well as its subtask, Relation Extraction. Relation Extraction is able to detect semantic relations between entities in sentences. Currently, many efficient approaches have been applied to relation extraction tasks. Supervised learning approaches especially have good performance. However, there are still many difficult challenges. One of the most serious problems is that manually labeled data is difficult to acquire. In most cases, limited data for supervised approaches equals lousy performance. Thus here, under the situation with only limited training data, we focus on how to improve the performance of our supervised baseline system with unsupervised pre-training. Feature is one of the key components in improving the supervised approaches. Traditional approaches usually apply hand-crafted features, which require expert knowledge and expensive human labor. However, this type of feature might suffer from data sparsity: when the training set size is small, the model parameters might be poorly estimated. In this thesis, we present several novel unsupervised pre-training models to learn the distributed text representation features, which are encoded with rich syntactic-semantic patterns of relation expressions. The experiments have demonstrated that this type of feature, combine with the traditional hand-crafted features, could improve the performance of the logistic classification model for relation extraction, especially on the classification of relations with only minor training instances. | ['Zhuang Li'] | 2021-04-10 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.07413778e-01 3.05418670e-01 -4.30630684e-01 -6.25571787e-01
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bd77063c-2213-45fc-80fc-b68118d4a376 | towards-discriminative-representation | 2108.03439 | null | https://arxiv.org/abs/2108.03439v2 | https://arxiv.org/pdf/2108.03439v2.pdf | Towards Discriminative Representation Learning for Unsupervised Person Re-identification | In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods typically follow a two-stage optimization pipeline, where the network is first pre-trained on source and then fine-tuned on target with pseudo labels created by feature clustering. Such methods sustain two main limitations. (1) The label noise may hinder the learning of discriminative features for recognizing target classes. (2) The domain gap may hinder knowledge transferring from source to target. We propose three types of technical schemes to alleviate these issues. First, we propose a cluster-wise contrastive learning algorithm (CCL) by iterative optimization of feature learning and cluster refinery to learn noise-tolerant representations in the unsupervised manner. Second, we adopt a progressive domain adaptation (PDA) strategy to gradually mitigate the domain gap between source and target data. Third, we propose Fourier augmentation (FA) for further maximizing the class separability of re-ID models by imposing extra constraints in the Fourier space. We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations. Experiments demonstrate that our method consistently achieves notable improvements over the state-of-the-art unsupervised re-ID methods on multiple benchmarks, e.g., surpassing MMT largely by 8.1\%, 9.9\%, 11.4\% and 11.1\% mAP on the Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT tasks, respectively. | ['Shengjin Wang', 'Yi Shan', 'Weihua Chen', 'Lu Tian', 'Dong Li', 'Takashi Isobe'] | 2021-08-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Isobe_Towards_Discriminative_Representation_Learning_for_Unsupervised_Person_Re-Identification_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Isobe_Towards_Discriminative_Representation_Learning_for_Unsupervised_Person_Re-Identification_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.04033986e-01 -2.17836261e-01 -1.24713384e-01 -4.24021780e-01
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4e05a794-15d0-4ae0-9979-b4ccd0113807 | dibb-distributing-black-box-optimization | null | null | https://openreview.net/forum?id=WYDzDksK5b | https://openreview.net/pdf?id=WYDzDksK5b | DiBB: Distributing Black-Box Optimization | We present a novel framework for Distributing Black-Box Optimization (DiBB). DiBB can encapsulate any Black Box Optimization (BBO) method, making it of particular interest for scaling and distributing modern Evolution Strategies (ES), such as CMA-ES and its variants, which maintain a sampling covariance matrix throughout the run. Due to high algorithmic complexity however, such methods are unsuitable alone to address high-dimensional problems, e.g. for sophisticated Reinforcement Learning (RL) control. This limits the applicable methods to simpler ES, which trade off faster updates for lowered sample efficiency. DiBB overcomes this limitation by means of problem decomposition, leveraging expert knowledge in the problem structure such as a known topology for a neural network controller. This allows to distribute the workload across an arbitrary number of nodes in a cluster, while maintaining the feasibility of second order (covariance) learning on high-dimensional problems. The computational complexity per node is bounded by the (arbitrary) size of blocks of variables, which is independent of the problem size. | ['Tobias Glasmachers', 'Philippe Cudre-Mauroux', 'Fabien Vorpe', 'Luca Sven Rolshoven', 'Giuseppe Cuccu'] | 2021-09-29 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-2.69720316e-01 1.25590339e-01 -1.12774096e-01 4.04776931e-01
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8375332f-03f1-4d9d-af81-7c8cda880ac3 | bique-biquaternionic-embeddings-of-knowledge | 2109.14401 | null | https://arxiv.org/abs/2109.14401v1 | https://arxiv.org/pdf/2109.14401v1.pdf | BiQUE: Biquaternionic Embeddings of Knowledge Graphs | Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics. In contrast, hyperbolic models are effective at modeling hierarchical relations, but do not perform as well on patterns on which circular rotation excels. It is crucial for KGE models to unify multiple geometric transformations so as to fully cover the multifarious relations in KGs. To do so, we propose BiQUE, a novel model that employs biquaternions to integrate multiple geometric transformations, viz., scaling, translation, Euclidean rotation, and hyperbolic rotation. BiQUE makes the best trade-offs among geometric operators during training, picking the best one (or their best combination) for each relation. Experiments on five datasets show BiQUE's effectiveness. | ['Stanley Kok', 'Jia Guo'] | 2021-09-29 | null | https://aclanthology.org/2021.emnlp-main.657 | https://aclanthology.org/2021.emnlp-main.657.pdf | emnlp-2021-11 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.81985009e-01 1.80737332e-01 -4.27427500e-01 -1.57513976e-01
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7d6d5cd8-0564-4512-a830-4571f4d60b18 | semantic-instance-segmentation-with-a | 1708.02551 | null | http://arxiv.org/abs/1708.02551v1 | http://arxiv.org/pdf/1708.02551v1.pdf | Semantic Instance Segmentation with a Discriminative Loss Function | Semantic instance segmentation remains a challenging task. In this work we
propose to tackle the problem with a discriminative loss function, operating at
the pixel level, that encourages a convolutional network to produce a
representation of the image that can easily be clustered into instances with a
simple post-processing step. The loss function encourages the network to map
each pixel to a point in feature space so that pixels belonging to the same
instance lie close together while different instances are separated by a wide
margin. Our approach of combining an off-the-shelf network with a principled
loss function inspired by a metric learning objective is conceptually simple
and distinct from recent efforts in instance segmentation. In contrast to
previous works, our method does not rely on object proposals or recurrent
mechanisms. A key contribution of our work is to demonstrate that such a simple
setup without bells and whistles is effective and can perform on par with more
complex methods. Moreover, we show that it does not suffer from some of the
limitations of the popular detect-and-segment approaches. We achieve
competitive performance on the Cityscapes and CVPPP leaf segmentation
benchmarks. | ['Luc van Gool', 'Bert De Brabandere', 'Davy Neven'] | 2017-08-08 | null | null | null | null | ['multi-human-parsing'] | ['computer-vision'] | [ 4.39543039e-01 4.83967960e-01 -9.30913091e-02 -5.19501388e-01
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4a9079cf-ea00-4f25-b0b8-33741820346b | local-hdp-interactive-open-ended-3d-object | 2009.01152 | null | https://arxiv.org/abs/2009.01152v3 | https://arxiv.org/pdf/2009.01152v3.pdf | Local-HDP: Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios | We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, the online variational inference method has been adapted for fast posterior approximation in the Local-HDP model. Experiments show that the proposed Local-HDP method outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency by a large margin. Moreover, two robotic experiments have been conducted to show the applicability of the proposed approach in real-time applications. | ['H. Ayoobi', 'R. Verbrugge', 'B. Verheij', 'H. Kasaei', 'M. Cao'] | 2020-09-02 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-6.05939269e-01 7.77549669e-02 -1.03601657e-01 -4.16898131e-01
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8af85ade-7131-47fe-b216-dfd68e6b181c | a-multi-oriented-chinese-keyword-spotter | 2001.00722 | null | https://arxiv.org/abs/2001.00722v2 | https://arxiv.org/pdf/2001.00722v2.pdf | A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection | Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words. Different from English words which are split naturally by visual blanks, Chinese words are generally split only by semantic information. In this paper, we propose a new Chinese keyword spotter for natural images, which is inspired by Mask R-CNN. We propose to predict the keyword masks guided by text line detection. Firstly, proposals of text lines are generated by Faster R-CNN;Then, text line masks and keyword masks are predicted by segmentation in the proposals. In this way, the text lines and keywords are predicted in parallel. We create two Chinese keyword datasets based on RCTW-17 and ICPR MTWI2018 to verify the effectiveness of our method. | ['Hao Song', 'Hongzhen Wang', 'Pei Xu', 'Shen Huang', 'Qi Ju', 'Shan Huang'] | 2020-01-03 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.26349288e-01 -1.45310432e-01 -2.28279516e-01 -3.53616178e-01
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0d0c0b87-079b-4e22-bd1b-af87959305da | smooth-trajectron-augmenting-the-trajectron | 2305.19678 | null | https://arxiv.org/abs/2305.19678v2 | https://arxiv.org/pdf/2305.19678v2.pdf | Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention | Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown promise in other domains but is lacking in the trajectory forecasting of multiple traffic agents in large-scale autonomous driving datasets. This work investigates the state-of-the-art trajectory forecasting model Trajectron++ which we enhance by incorporating a smoothing term in its attention module. This attention mechanism mimics human attention inspired by cognitive science research indicating limits to attention switching. We evaluate the performance of the resulting Smooth-Trajectron++ model and compare it to the original model on various benchmarks, revealing the potential of incorporating insights from human cognition into trajectory prediction models. | ['Arkady Zgonnikov', 'Julian F. Schumann', 'Frederik S. B. Westerhout'] | 2023-05-31 | null | null | null | null | ['trajectory-prediction', 'autonomous-vehicles', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.89381376e-01 2.70466864e-01 -3.34494293e-01 -3.95638555e-01
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2cd0e77d-72c3-40ca-b55d-2909370c7282 | double-dip-unsupervised-image-decomposition | 1812.00467 | null | http://arxiv.org/abs/1812.00467v2 | http://arxiv.org/pdf/1812.00467v2.pdf | "Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors | Many seemingly unrelated computer vision tasks can be viewed as a special
case of image decomposition into separate layers. For example, image
segmentation (separation into foreground and background layers); transparent
layer separation (into reflection and transmission layers); Image dehazing
(separation into a clear image and a haze map), and more. In this paper we
propose a unified framework for unsupervised layer decomposition of a single
image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown
[Ulyanov et al] that the structure of a single DIP generator network is
sufficient to capture the low-level statistics of a single image. We show that
coupling multiple such DIPs provides a powerful tool for decomposing images
into their basic components, for a wide variety of applications. This
capability stems from the fact that the internal statistics of a mixture of
layers is more complex than the statistics of each of its individual
components. We show the power of this approach for Image-Dehazing, Fg/Bg
Segmentation, Watermark-Removal, Transparency Separation in images and video,
and more. These capabilities are achieved in a totally unsupervised way, with
no training examples other than the input image/video itself. | ['Assaf Shocher', 'Michal Irani', 'Yossi Gandelsman'] | 2018-12-02 | double-dip-unsupervised-image-decomposition-2 | null | null | computer-vision-foundation-2018-12 | ['transparency-separation', 'unsupervised-image-decomposition'] | ['computer-vision', 'computer-vision'] | [ 7.92421877e-01 1.99248940e-01 2.91501909e-01 4.78897523e-03
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f63462ec-253c-403f-b8a8-26fa6cf1afca | elastic-numerical-reasoning-with-adaptive | 2210.10105 | null | https://arxiv.org/abs/2210.10105v2 | https://arxiv.org/pdf/2210.10105v2.pdf | ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler | Numerical reasoning over text is a challenging task of Artificial Intelligence (AI), requiring reading comprehension and numerical reasoning abilities. Previous approaches use numerical reasoning programs to represent the reasoning process. However, most works do not separate the generation of operators and operands, which are key components of a numerical reasoning program, thus limiting their ability to generate such programs for complicated tasks. In this paper, we introduce the numEricaL reASoning with adapTive symbolIc Compiler (ELASTIC) model, which is constituted of the RoBERTa as the Encoder and a Compiler with four modules: Reasoning Manager, Operator Generator, Operands Generator, and Memory Register. ELASTIC is robust when conducting complicated reasoning. Also, it is domain agnostic by supporting the expansion of diverse operators without caring about the number of operands it contains. Experiments show that ELASTIC achieves 68.96 and 65.21 of execution accuracy and program accuracy on the FinQA dataset and 83.00 program accuracy on the MathQA dataset, outperforming previous state-of-the-art models significantly. | ['Yashar Moshfeghi', 'Jiaxin Zhang'] | 2022-10-18 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-1.47336066e-01 5.59457876e-02 -2.45472774e-01 -3.72743130e-01
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92069f73-bf2a-438f-9c55-8f6855ba0d49 | efficientsrface-an-efficient-network-with | 2306.02277 | null | https://arxiv.org/abs/2306.02277v1 | https://arxiv.org/pdf/2306.02277v1.pdf | EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection | In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts methods. | ['Bo Yang', 'Jianhua Xu', 'Jie Xie', 'Jun Li', 'Guangtao Wang'] | 2023-06-04 | null | null | null | null | ['image-super-resolution', 'face-detection', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.04924378e-02 -1.89733833e-01 -2.14537550e-02 -4.58244383e-01
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f8f9956f-bdbf-4e3b-954c-27cd2baefc11 | web-scale-language-independent-cataloging-of | null | null | https://aclanthology.org/E17-1091 | https://aclanthology.org/E17-1091.pdf | Web-Scale Language-Independent Cataloging of Noisy Product Listings for E-Commerce | The cataloging of product listings through taxonomy categorization is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search recommendations to query understanding. However, manual and rule based approaches to categorization are not scalable. In this paper, we compare several classifiers for categorizing listings in both English and Japanese product catalogs. We show empirically that a combination of words from product titles, navigational breadcrumbs, and list prices, when available, improves results significantly. We outline a novel method using correspondence topic models and a lightweight manual process to reduce noise from mis-labeled data in the training set. We contrast linear models, gradient boosted trees (GBTs) and convolutional neural networks (CNNs), and show that GBTs and CNNs yield the highest gains in error reduction. Finally, we show GBTs applied in a language-agnostic way on a large-scale Japanese e-commerce dataset have improved taxonomy categorization performance over current state-of-the-art based on deep belief network models. | ['Giuseppe Di Fabbrizio', 'i', 'Pradipto Das', 'Y Xia', 'Aaron Levine', 'Ankur Datta'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['product-categorization'] | ['miscellaneous'] | [-3.58942330e-01 -2.68086314e-01 -6.42351270e-01 -8.20399523e-01
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b540997d-3511-4918-a313-425d7586e6b3 | improving-fairness-in-deepfake-detection | 2306.16635 | null | https://arxiv.org/abs/2306.16635v1 | https://arxiv.org/pdf/2306.16635v1.pdf | Improving Fairness in Deepfake Detection | Despite the development of effective deepfake detection models in recent years, several recent studies have demonstrated that biases in the training data utilized to develop deepfake detection models can lead to unfair performance for demographic groups of different races and/or genders. Such can result in these groups being unfairly targeted or excluded from detection, allowing misclassified deepfakes to manipulate public opinion and erode trust in the model. While these studies have focused on identifying and evaluating the unfairness in deepfake detection, no methods have been developed to address the fairness issue of deepfake detection at the algorithm level. In this work, we make the first attempt to improve deepfake detection fairness by proposing novel loss functions to train fair deepfake detection models in ways that are agnostic or aware of demographic factors. Extensive experiments on four deepfake datasets and five deepfake detectors demonstrate the effectiveness and flexibility of our approach in improving the deepfake detection fairness. | ['Siwei Lyu', 'George H. Chen', 'Shan Jia', 'Shu Hu', 'Yan Ju'] | 2023-06-29 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'fairness', 'fairness'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [-4.04311031e-01 9.94937196e-02 -3.73622388e-01 -8.70724440e-01
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b96a0b48-c29b-406f-a278-cc8bb4f67e12 | pbsm-backdoor-attack-against-keyword-spotting | 2211.08697 | null | https://arxiv.org/abs/2211.08697v1 | https://arxiv.org/pdf/2211.08697v1.pdf | PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking | Keyword spotting (KWS) has been widely used in various speech control scenarios. The training of KWS is usually based on deep neural networks and requires a large amount of data. Manufacturers often use third-party data to train KWS. However, deep neural networks are not sufficiently interpretable to manufacturers, and attackers can manipulate third-party training data to plant backdoors during the model training. An effective backdoor attack can force the model to make specified judgments under certain conditions, i.e., triggers. In this paper, we design a backdoor attack scheme based on Pitch Boosting and Sound Masking for KWS, called PBSM. Experimental results demonstrated that PBSM is feasible to achieve an average attack success rate close to 90% in three victim models when poisoning less than 1% of the training data. | ['Shunhui Ji', 'Yan Xiao', 'Hai Dong', 'Pengcheng Zhang', 'Hanbo Cai'] | 2022-11-16 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-4.95997742e-02 -5.80146238e-02 -2.93415248e-01 -1.91015691e-01
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97994f02-8e23-4519-b183-95a5399e7964 | efficient-and-deterministic-search-strategy | 2305.11716 | null | https://arxiv.org/abs/2305.11716v1 | https://arxiv.org/pdf/2305.11716v1.pdf | Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration | Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approaches commonly lead to numerous outlier correspondences, making outlier-robust registration techniques indispensable. Many recent studies have adopted the branch and bound (BnB) optimization framework to solve the correspondence-based point cloud registration problem globally and deterministically. Nonetheless, BnB-based methods are time-consuming to search the entire 6-dimensional parameter space, since their computational complexity is exponential to the dimension of the solution domain. In order to enhance algorithm efficiency, existing works attempt to decouple the 6 degrees of freedom (DOF) original problem into two 3-DOF sub-problems, thereby reducing the dimension of the parameter space. In contrast, our proposed approach introduces a novel pose decoupling strategy based on residual projections, effectively decomposing the raw problem into three 2-DOF rotation search sub-problems. Subsequently, we employ a novel BnB-based search method to solve these sub-problems, achieving efficient and deterministic registration. Furthermore, our method can be adapted to address the challenging problem of simultaneous pose and correspondence registration (SPCR). Through extensive experiments conducted on synthetic and real-world datasets, we demonstrate that our proposed method outperforms state-of-the-art methods in terms of efficiency, while simultaneously ensuring robustness. | ['Alois Knoll', 'Feihu Zhang', 'Xueli Liu', 'Hu Cao', 'Yinlong Liu', 'Xinyi Li'] | 2023-05-19 | null | null | null | null | ['3d-feature-matching', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [ 3.76067944e-02 -5.14740288e-01 -4.40076105e-02 -7.00113252e-02
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47f965c5-f221-4724-91e7-13c148424fc1 | geneface-generalized-and-stable-real-time | 2305.00787 | null | https://arxiv.org/abs/2305.00787v1 | https://arxiv.org/pdf/2305.00787v1.pdf | GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation | Generating talking person portraits with arbitrary speech audio is a crucial problem in the field of digital human and metaverse. A modern talking face generation method is expected to achieve the goals of generalized audio-lip synchronization, good video quality, and high system efficiency. Recently, neural radiance field (NeRF) has become a popular rendering technique in this field since it could achieve high-fidelity and 3D-consistent talking face generation with a few-minute-long training video. However, there still exist several challenges for NeRF-based methods: 1) as for the lip synchronization, it is hard to generate a long facial motion sequence of high temporal consistency and audio-lip accuracy; 2) as for the video quality, due to the limited data used to train the renderer, it is vulnerable to out-of-domain input condition and produce bad rendering results occasionally; 3) as for the system efficiency, the slow training and inference speed of the vanilla NeRF severely obstruct its usage in real-world applications. In this paper, we propose GeneFace++ to handle these challenges by 1) utilizing the pitch contour as an auxiliary feature and introducing a temporal loss in the facial motion prediction process; 2) proposing a landmark locally linear embedding method to regulate the outliers in the predicted motion sequence to avoid robustness issues; 3) designing a computationally efficient NeRF-based motion-to-video renderer to achieves fast training and real-time inference. With these settings, GeneFace++ becomes the first NeRF-based method that achieves stable and real-time talking face generation with generalized audio-lip synchronization. Extensive experiments show that our method outperforms state-of-the-art baselines in terms of subjective and objective evaluation. Video samples are available at https://genefaceplusplus.github.io . | ['Zhou Zhao', 'Zejun Ma', 'Xiang Yin', 'Yi Ren', 'Jinglin Liu', 'Jiawei Huang', 'Rongjie Huang', 'Ziyue Jiang', 'Jinzheng He', 'Zhenhui Ye'] | 2023-05-01 | null | null | null | null | ['motion-prediction', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-6.14954494e-02 -4.02304471e-01 -1.24265373e-01 -1.01392850e-01
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d1f6f474-e7c4-4ac3-af2b-c864e7cfa8c2 | eamdrift-an-interpretable-self-retrain-model | 2305.19837 | null | https://arxiv.org/abs/2305.19837v1 | https://arxiv.org/pdf/2305.19837v1.pdf | EAMDrift: An interpretable self retrain model for time series | The use of machine learning for time series prediction has become increasingly popular across various industries thanks to the availability of time series data and advancements in machine learning algorithms. However, traditional methods for time series forecasting rely on pre-optimized models that are ill-equipped to handle unpredictable patterns in data. In this paper, we present EAMDrift, a novel method that combines forecasts from multiple individual predictors by weighting each prediction according to a performance metric. EAMDrift is designed to automatically adapt to out-of-distribution patterns in data and identify the most appropriate models to use at each moment through interpretable mechanisms, which include an automatic retraining process. Specifically, we encode different concepts with different models, each functioning as an observer of specific behaviors. The activation of the overall model then identifies which subset of the concept observers is identifying concepts in the data. This activation is interpretable and based on learned rules, allowing to study of input variables relations. Our study on real-world datasets shows that EAMDrift outperforms individual baseline models by 20% and achieves comparable accuracy results to non-interpretable ensemble models. These findings demonstrate the efficacy of EAMDrift for time-series prediction and highlight the importance of interpretability in machine learning models. | ['António Rodrigues', 'João Leitão', 'Cláudia Soares', 'Gonçalo Mateus'] | 2023-05-31 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 6.16037659e-02 1.67835932e-02 -2.28171304e-01 -5.06622255e-01
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3f077786-4b9e-4067-adce-f11cda41d4d7 | flight-mode-on-a-feather-light-network-for | 2305.10889 | null | https://arxiv.org/abs/2305.10889v1 | https://arxiv.org/pdf/2305.10889v1.pdf | FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement | Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noise ratio. In order to address these problems, we propose a novel approach named FLIGHT-Net using a sequence of neural architecture blocks. The first block regulates illumination conditions through pixel-wise scene dependent illumination adjustment. The output image is produced in the output of the second block, which includes channel attention and denoising sub-blocks. Our highly efficient neural network architecture delivers state-of-the-art performance with only 25K parameters. The method's code, pretrained models and resulting images will be publicly available. | ['Mustafa Ayazaoglu', 'Hamza Ergezer', 'Mustafa Ozcan'] | 2023-05-18 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 7.09941566e-01 -6.14758968e-01 3.78413230e-01 -3.92948538e-01
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df620d28-bd45-4940-84dc-c43720fc2c20 | facial-micro-expression-spotting-and | 1902.03514 | null | http://arxiv.org/abs/1902.03514v2 | http://arxiv.org/pdf/1902.03514v2.pdf | Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory | Facial micro-expressions are sudden involuntary minute muscle movements which
reveal true emotions that people try to conceal. Spotting a micro-expression
and recognizing it is a major challenge owing to its short duration and
intensity. Many works pursued traditional and deep learning based approaches to
solve this issue but compromised on learning low-level features and higher
accuracy due to unavailability of datasets. This motivated us to propose a
novel joint architecture of spatial and temporal network which extracts
time-contrasted features from the feature maps to contrast out micro-expression
from rapid muscle movements. The usage of time contrasted features greatly
improved the spotting of micro-expression from inconspicuous facial movements.
Also, we include a memory module to predict the class and intensity of the
micro-expression across the temporal frames of the micro-expression clip. Our
method achieves superior performance in comparison to other conventional
approaches on CASMEII dataset. | ['Ayan Kumar Bhunia', 'Sauradip Nag', 'Aishik Konwer', 'Partha Pratim Roy'] | 2019-02-09 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.10528988e-02 -4.05357331e-01 -1.22141033e-01 -5.42986333e-01
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b80e8e1a-1fba-401b-83bb-62c39b06b812 | handling-cold-start-problem-in-review-spam | null | null | https://aclanthology.org/P17-1034 | https://aclanthology.org/P17-1034.pdf | Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors | Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by learning to represent the new reviewers{'} review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large scale dataset in an unsupervised way. | ['Xuepeng Wang', 'Jun Zhao', 'Kang Liu'] | 2017-07-01 | null | null | null | acl-2017-7 | ['spam-detection'] | ['natural-language-processing'] | [-8.30482319e-02 -3.58520478e-01 -4.74393427e-01 -5.28470635e-01
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a0e5e73a-4e4e-41f9-b575-acbe725b6e6c | exploring-the-capacity-of-a-large-scale | 2108.12216 | null | https://arxiv.org/abs/2108.12216v1 | https://arxiv.org/pdf/2108.12216v1.pdf | Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors | In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to a non-language model-based method can achieve with the full training data; recall improves much faster with respect to training data size in the BERT-based method than in the non-language model method while precision behaves similarly. These suggest that (i) the BERT-based method should have a good knowledge of grammar required to recognize certain types of error and that (ii) it can transform the knowledge into error detection rules by fine-tuning with a few training samples, which explains its high generalization ability in grammatical error detection. We further show with pseudo error data that it actually exhibits such nice properties in learning rules for recognizing various types of error. Finally, based on these findings, we explore a cost-effective method for detecting grammatical errors with feedback comments explaining relevant grammatical rules to learners. | ['Kazuaki Hanawa', 'Manabu Kimura', 'Ryo Nagata'] | 2021-08-27 | null | https://aclanthology.org/2022.findings-acl.324 | https://aclanthology.org/2022.findings-acl.324.pdf | findings-acl-2022-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.80579501e-02 5.38803756e-01 1.93636432e-01 -7.11457014e-01
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90c08039-2c3e-4aa8-a363-7f79719cf576 | asking-the-right-question-inferring-advice | 1904.01587 | null | http://arxiv.org/abs/1904.01587v1 | http://arxiv.org/pdf/1904.01587v1.pdf | Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives | People often share personal narratives in order to seek advice from others.
To properly infer the narrator's intention, one needs to apply a certain degree
of common sense and social intuition. To test the capabilities of NLP systems
to recover such intuition, we introduce the new task of inferring what is the
advice-seeking goal behind a personal narrative. We formulate this as a cloze
test, where the goal is to identify which of two advice-seeking questions was
removed from a given narrative.
The main challenge in constructing this task is finding pairs of semantically
plausible advice-seeking questions for given narratives. To address this
challenge, we devise a method that exploits commonalities in experiences people
share online to automatically extract pairs of questions that are appropriate
candidates for the cloze task. This results in a dataset of over 20,000
personal narratives, each matched with a pair of related advice-seeking
questions: one actually intended by the narrator, and the other one not. The
dataset covers a very broad array of human experiences, from dating, to career
options, to stolen iPads. We use human annotation to determine the degree to
which the task relies on common sense and social intuition in addition to a
semantic understanding of the narrative. By introducing several baselines for
this new task we demonstrate its feasibility and identify avenues for better
modeling the intention of the narrator. | ['Cristian Danescu-Niculescu-Mizil', 'Liye Fu', 'Jonathan P. Chang'] | 2019-04-02 | asking-the-right-question-inferring-advice-1 | https://aclanthology.org/N19-1052 | https://aclanthology.org/N19-1052.pdf | naacl-2019-6 | ['cloze-test'] | ['natural-language-processing'] | [ 3.33319873e-01 5.03226936e-01 -1.44163206e-01 -5.74039280e-01
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541f48ba-f755-48a3-83af-360aaa96e9b5 | designing-efficient-pair-trading-strategies | 2211.07080 | null | https://arxiv.org/abs/2211.07080v1 | https://arxiv.org/pdf/2211.07080v1.pdf | Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market | A pair-trading strategy is an approach that utilizes the fluctuations between prices of a pair of stocks in a short-term time frame, while in the long-term the pair may exhibit a strong association and co-movement pattern. When the prices of the stocks exhibit significant divergence, the shares of the stock that gains in price are sold (a short strategy) while the shares of the other stock whose price falls are bought (a long strategy). This paper presents a cointegration-based approach that identifies stocks listed in the five sectors of the National Stock Exchange (NSE) of India for designing efficient pair-trading portfolios. Based on the stock prices from Jan 1, 2018, to Dec 31, 2020, the cointegrated stocks are identified and the pairs are formed. The pair-trading portfolios are evaluated on their annual returns for the year 2021. The results show that the pairs of stocks from the auto and the realty sectors, in general, yielded the highest returns among the five sectors studied in the work. However, two among the five pairs from the information technology (IT) sector are found to have yielded negative returns. | ['Jaydip Sen'] | 2022-11-14 | null | null | null | null | ['pair-trading'] | ['time-series'] | [-6.48257792e-01 -3.24327141e-01 -3.25690389e-01 3.98191303e-01
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8b87a091-cd59-47ab-89a5-dde51b4e8156 | spatiotemporally-consistent-hdr-indoor | 2305.04374 | null | https://arxiv.org/abs/2305.04374v1 | https://arxiv.org/pdf/2305.04374v1.pdf | Spatiotemporally Consistent HDR Indoor Lighting Estimation | We propose a physically-motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refines the lighting prediction as it sees more regions, but also preserves temporal consistency by keeping the refinement smooth. Our framework reconstructs a spherical Gaussian lighting volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially consistent lighting prediction through volume ray tracing, a hybrid blending network for detailed environment maps, an in-network Monte-Carlo rendering layer to enhance photorealism for virtual object insertion, and recurrent neural networks (RNN) to achieve temporally consistent lighting prediction with a video sequence as the input. For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360K HDR environment maps of much higher resolution and 38K video sequences, rendered with GPU-based path tracing. Experiments show that our framework achieves lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods, leading to photorealistic AR applications such as object insertion. | ['Zhao Dong', 'Manmohan Chandraker', 'Mikhail Okunev', 'Li Yu', 'Zhengqin Li'] | 2023-05-07 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 7.87194222e-02 -7.75680020e-02 5.69100380e-01 -4.16911513e-01
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6169c006-e44c-4e77-b3ce-366b14dedb62 | event-based-video-reconstruction-via | 2201.10943 | null | https://arxiv.org/abs/2201.10943v3 | https://arxiv.org/pdf/2201.10943v3.pdf | Event-based Video Reconstruction via Potential-assisted Spiking Neural Network | Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called `events' with high temporal resolution and high dynamic range. So far, the event-based image reconstruction methods are based on artificial neural networks (ANN) or hand-crafted spatiotemporal smoothing techniques. In this paper, we first implement the image reconstruction work via fully spiking neural network (SNN) architecture. As the bio-inspired neural networks, SNNs operating with asynchronous binary spikes distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron. We find that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks. Furthermore, to better utilize the temporal information, we propose a hybrid potential-assisted framework (PA-EVSNN) using the membrane potential of spiking neuron. The proposed neuron is referred as Adaptive Membrane Potential (AMP) neuron, which adaptively updates the membrane potential according to the input spikes. The experimental results demonstrate that our models achieve comparable performance to ANN-based models on IJRR, MVSEC, and HQF datasets. The energy consumptions of EVSNN and PA-EVSNN are 19.36$\times$ and 7.75$\times$ more computationally efficient than their ANN architectures, respectively. | ['Yonghong Tian', 'Tiejun Huang', 'Jianing Li', 'Yi Chang', 'Xiao Wang', 'Lin Zhu'] | 2022-01-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-reconstruction'] | ['computer-vision'] | [ 5.54111183e-01 -6.78305030e-01 5.35201967e-01 -1.06074259e-01
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6a26136d-60f9-43f8-a513-da49e7cd1c90 | weakly-supervised-crack-detection | 2206.06743 | null | https://arxiv.org/abs/2206.06743v2 | https://arxiv.org/pdf/2206.06743v2.pdf | Weakly-Supervised Crack Detection | Pixel-level crack segmentation is widely studied due to its high impact on building and road inspections. While recent studies have made significant improvements in accuracy, they typically heavily depend on pixel-level crack annotations, which are time-consuming to obtain. In earlier work, we proposed to reduce the annotation cost bottleneck by reformulating the crack segmentation problem as a weakly-supervised problem -- i.e. the annotation process is expedited by sacrificing the annotation quality. The loss in annotation quality was remedied by refining the inference with per-pixel brightness values, which was effective when the pixel brightness distribution between cracks and non-cracks are well separated, but struggled greatly for lighter-colored cracks as well as non-crack targets in which the brightness distribution is less articulated. In this work, we propose an annotation refinement approach which takes advantage of the fact that the regions falsely annotated as cracks have similar local visual features as the background. Because the proposed approach is data-driven, it is effective regardless of a dataset's pixel brightness profile. The proposed method is evaluated on three crack segmentation datasets as well as one blood vessel segmentation dataset to test for domain robustness, and the results show that it speeds up the annotation process by factors of 10 to 30, while the detection accuracy stays at a comparable level. | ['Hiroto Nagayoshi', 'Yuki Inoue'] | 2022-06-14 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.49676514e-01 2.00165525e-01 2.09103581e-02 -1.80477381e-01
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6da2afed-1df6-43e4-8e59-d73d56571075 | lift-language-interfaced-fine-tuning-for-non | 2206.06565 | null | https://arxiv.org/abs/2206.06565v4 | https://arxiv.org/pdf/2206.06565v4.pdf | LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks | Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to employ task-specific designs for input, output layers, and loss functions. For instance, it is possible to fine-tune an LM into an MNIST classifier by replacing the word embedding layer with an image patch embedding layer, the word token output layer with a 10-way output layer, and the word prediction loss with a 10-way classification loss, respectively. A natural question arises: Can LM fine-tuning solve non-language downstream tasks without changing the model architecture or loss function? To answer this, we propose Language-Interfaced Fine-Tuning (LIFT) and study its efficacy and limitations by conducting an extensive empirical study on a suite of non-language classification and regression tasks. LIFT does not make any changes to the model architecture or loss function, and it solely relies on the natural language interface, enabling "no-code machine learning with LMs." We find that LIFT performs comparably well across a wide range of low-dimensional classification and regression tasks, matching the performances of the best baselines in many cases, especially for the classification tasks. We also report experimental results on the fundamental properties of LIFT, including inductive bias, robustness, and sample complexity. We also analyze the effect of pretraining on LIFT and a few properties/techniques specific to LIFT, e.g., context-aware learning via appropriate prompting, calibrated predictions, data generation, and two-stage fine-tuning. Our code is available at https://github.com/UW-Madison-Lee-Lab/LanguageInterfacedFineTuning. | ['Kangwook Lee', 'Dimitris Papailiopoulos', 'Jy-yong Sohn', 'Michael Gira', 'Shashank Rajput', 'Ziqian Lin', 'Ruisu Zhang', 'Yuchen Zeng', 'Tuan Dinh'] | 2022-06-14 | null | null | null | null | ['classification'] | ['methodology'] | [ 7.01055899e-02 -2.47361716e-02 -2.58062154e-01 -5.22975445e-01
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268ba5d4-f716-4c4c-8554-167f38582fea | skip-connected-3d-densenet-for-volumetric | null | null | https://www.sciencedirect.com/science/article/abs/pii/S1746809419301946 | https://www.sciencedirect.com/science/article/abs/pii/S1746809419301946 | Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation | Automatic 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI) is still less accurate owing to the low intensity contrast among tissues. To tackle the problem, we introduce an accurate segmentation method for volumetric infant brain MRI built upon a densely connected network that achieves state-of-the-art accuracy. Specifically, we carefully design a fully convolutional densely connected network with skip connections such that the information from different levels of dense blocks can be directly combined to achieve highly accurate segmentation results. The proposed network, called 3D-SkipDenseSeg, exploits the advantage of the recently DenseNet for classification task and extends this to segment the 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI). Experimental results demonstrate a competitive performance with regard to both segmentation accuracy and parameter efficiency of the proposed method over the existing methods; namely, the proposed 3D-SkipDenseSeg achieved the best dice similarity coefficient (DSC) of 90.37 ± 1.38% (WM), 92.27 ± 0.81% (GM), and 95.79 ± 0.54% (CSF) among the 21 participating teams in the 6-month infant brain dataset (iSeg-2017) and required only 10–30% of the parameters compared to similar deep learning-based methods. | ['Taesup Moon', 'Jitae Shin', 'Toan Duc Bui'] | 2019-09-01 | null | null | null | biomedical-signal-processing-and-control-2019-1 | ['infant-brain-mri-segmentation'] | ['medical'] | [-8.70863572e-02 2.06990063e-01 1.06081627e-01 -5.53100228e-01
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7bdeca64-80be-4d0c-a25a-0aa2e4e78964 | improving-cross-modal-alignment-for-text | 2301.11362 | null | https://arxiv.org/abs/2301.11362v1 | https://arxiv.org/pdf/2301.11362v1.pdf | Improving Cross-modal Alignment for Text-Guided Image Inpainting | Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-modal fusion model to integrate cross-modal features. However, these methods allocate most of the computation to visual encoding, while light computation on modeling modality interactions. Moreover, they take cross-modal fusion for depth features, which ignores a fine-grained alignment between text and image. Recently, vision-language pre-trained models (VLPM), encapsulating rich cross-modal alignment knowledge, have advanced in most multimodal tasks. In this work, we propose a novel model for TGII by improving cross-modal alignment (CMA). CMA model consists of a VLPM as a vision-language encoder, an image generator and global-local discriminators. To explore cross-modal alignment knowledge for image restoration, we introduce cross-modal alignment distillation and in-sample distribution distillation. In addition, we employ adversarial training to enhance the model to fill the missing region in complicated structures effectively. Experiments are conducted on two popular vision-language datasets. Results show that our model achieves state-of-the-art performance compared with other strong competitors. | ['Guodong Long', 'Yucheng Zhou'] | 2023-01-26 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.56433564e-01 -1.38249949e-01 -1.10249333e-01 -2.13345349e-01
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de7d241d-06b1-4b27-84ec-5a883ae56db1 | analysis-and-approximate-inference-of-large | 2306.08489 | null | https://arxiv.org/abs/2306.08489v1 | https://arxiv.org/pdf/2306.08489v1.pdf | Analysis and Approximate Inference of Large and Dense Random Kronecker Graphs | Random graph models are playing an increasingly important role in science and industry, and finds their applications in a variety of fields ranging from social and traffic networks, to recommendation systems and molecular genetics. In this paper, we perform an in-depth analysis of the random Kronecker graph model proposed in \cite{leskovec2010kronecker}, when the number of graph vertices $N$ is large. Built upon recent advances in random matrix theory, we show, in the dense regime, that the random Kronecker graph adjacency matrix follows approximately a signal-plus-noise model, with a small-rank (of order at most $\log N$) signal matrix that is linear in the graph parameters and a random noise matrix having a quarter-circle-form singular value distribution. This observation allows us to propose a ``denoise-and-solve'' meta algorithm to approximately infer the graph parameters, with reduced computational complexity and (asymptotic) performance guarantee. Numerical experiments of graph inference and graph classification on both synthetic and realistic graphs are provided to support the advantageous performance of the proposed approach. | ['Yong Xiao', 'Chengmei Niu', 'Yuanqian Xia', 'Zhenyu Liao'] | 2023-06-14 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.07604605e-01 3.96324664e-01 6.88477047e-03 2.11757086e-02
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10d73e62-44b5-4c3e-9d4e-4254b996dc6e | safer-data-efficient-and-safe-reinforcement-1 | 2202.04849 | null | https://arxiv.org/abs/2202.04849v2 | https://arxiv.org/pdf/2202.04849v2.pdf | SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition | Methods that extract policy primitives from offline demonstrations using deep generative models have shown promise at accelerating reinforcement learning(RL) for new tasks. Intuitively, these methods should also help to trainsafeRLagents because they enforce useful skills. However, we identify these techniques are not well equipped for safe policy learning because they ignore negative experiences(e.g., unsafe or unsuccessful), focusing only on positive experiences, which harms their ability to generalize to new tasks safely. Rather, we model the latentsafetycontextusing principled contrastive training on an offline dataset of demonstrations from many tasks, including both negative and positive experiences. Using this late variable, our RL framework, SAFEty skill pRiors (SAFER) extracts task-specific safe primitive skills to safely and successfully generalize to new tasks. In the inference stage, policies trained with SAFER learn to compose safe skills into successful policies. We theoretically characterize why SAFER can enforce safe policy learning and demonstrate its effectiveness on several complex safety-critical robotic grasping tasks inspired by the game Operation, in which SAFERoutperforms state-of-the-art primitive learning methods in success and safety. | ['Nevan Wichers', 'Bo Dai', 'Yinlam Chow', 'Dylan Slack'] | 2022-02-10 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-8.19077622e-03 1.89415902e-01 -4.25043434e-01 -2.20173135e-01
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ab2921a0-7ed4-4509-88f8-6f06d759d04f | parcorfull2-0-a-parallel-corpus-annotated | null | null | https://aclanthology.org/2022.lrec-1.85 | https://aclanthology.org/2022.lrec-1.85.pdf | ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference | In this paper, we describe ParCorFull2.0, a parallel corpus annotated with full coreference chains for multiple languages, which is an extension of the existing corpus ParCorFull (Lapshinova-Koltunski et al., 2018). Similar to the previous version, this corpus has been created to address translation of coreference across languages, a phenomenon still challenging for machine translation (MT) and other multilingual natural language processing (NLP) applications. The current version of the corpus that we present here contains not only parallel texts for the language pair English-German, but also for English-French and English-Portuguese, which are all major European languages. The new language pairs belong to the Romance languages. The addition of a new language group creates a need of extension not only in terms of texts added, but also in terms of the annotation guidelines. Both French and Portuguese contain structures not found in English and German. Moreover, Portuguese is a pro-drop language bringing even more systemic differences in the realisation of coreference into our cross-lingual resources. These differences cause problems for multilingual coreference resolution and machine translation. Our parallel corpus with full annotation of coreference will be a valuable resource with a variety of uses not only for NLP applications, but also for contrastive linguists and researchers in translation studies. | ['Christian Hardmeier', 'Elina Lartaud', 'Pedro Augusto Ferreira', 'Ekaterina Lapshinova-Koltunski'] | null | null | null | null | lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [-1.91749841e-01 3.02413166e-01 -5.84011018e-01 -1.76589880e-02
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6bc48046-8d8e-4945-a269-d24e34417d10 | rsfdm-net-real-time-spatial-and-frequency | 2302.12186 | null | https://arxiv.org/abs/2302.12186v1 | https://arxiv.org/pdf/2302.12186v1.pdf | RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement | Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Specifically, our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Convolutional Attention Module (MCAM) to generate vectors carrying low-frequency background information and high-frequency detail features, which effectively promote the network to model global background information and local texture details. To more precisely correct the color cast and low saturation of the image, we introduce a Three-branch Feature Extraction (TFE) block in the primary net that processes images pixel by pixel to integrate the color information extended by the same channel (R, G, or B). This block consists of three small branches, each of which has its own weights. Extensive experiments demonstrate that our network significantly outperforms over state-of-the-art methods in both visual quality and quantitative metrics. | ['ErKang Chen', 'Tian Ye', 'Sixiang Chen', 'Junjie Yin', 'Yun Liu', 'Jinbin Bai', 'Jingxia Jiang'] | 2023-02-23 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 4.72609609e-01 -2.92645931e-01 6.42608523e-01 -4.37243730e-01
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f51b97bc-c8c6-4afd-a7d2-6c565123666e | speech-denoising-by-parametric-resynthesis | 1904.01537 | null | http://arxiv.org/abs/1904.01537v1 | http://arxiv.org/pdf/1904.01537v1.pdf | Speech denoising by parametric resynthesis | This work proposes the use of clean speech vocoder parameters as the target
for a neural network performing speech enhancement. These parameters have been
designed for text-to-speech synthesis so that they both produce high-quality
resyntheses and also are straightforward to model with neural networks, but
have not been utilized in speech enhancement until now. In comparison to a
matched text-to-speech system that is given the ground truth transcripts of the
noisy speech, our model is able to produce more natural speech because it has
access to the true prosody in the noisy speech. In comparison to two denoising
systems, the oracle Wiener mask and a DNN-based mask predictor, our model
equals the oracle Wiener mask in subjective quality and intelligibility and
surpasses the realistic system. A vocoder-based upper bound shows that there is
still room for improvement with this approach beyond the oracle Wiener mask. We
test speaker-dependence with two speakers and show that a single model can be
used for multiple speakers. | ['Soumi Maiti', 'Michael I Mandel'] | 2019-04-02 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.51658934e-01 5.25213897e-01 3.80213588e-01 -3.33619535e-01
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8.64373326e-01 5.43480292e-02 -6.84146166e-01 2.64841914e-01] | [15.141275405883789, 6.093997001647949] |
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