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93373cea-05bd-4aed-bb8a-1b1cdee4f95a | ensemble-learning-of-colorectal-cancer | 1409.0788 | null | http://arxiv.org/abs/1409.0788v1 | http://arxiv.org/pdf/1409.0788v1.pdf | Ensemble Learning of Colorectal Cancer Survival Rates | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. We build on
existing research on clustering and machine learning facets of this data to
demonstrate a role for an ensemble approach to highlighting patients with
clearer prognosis parameters. Results for survival prediction using 3 different
approaches are shown for a subset of the data which is most difficult to model.
The performance of each model individually is compared with subsets of the data
where some agreement is reached for multiple models. Significant improvements
in model accuracy on an unseen test set can be achieved for patients where
agreement between models is achieved. | ['John Scholefield', 'Uwe Aickelin', 'Chris Roadknight', 'Lindy Durrant'] | 2014-09-02 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 1.88585445e-01 -2.05056090e-02 -3.39500040e-01 -2.54745752e-01
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a17f3bf7-cee8-4d19-9f10-b346b902b9b4 | a-comparison-of-temporal-encoders-for | 2301.09962 | null | https://arxiv.org/abs/2301.09962v1 | https://arxiv.org/pdf/2301.09962v1.pdf | A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons | With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition. One promising approach is the use of neuromorphic sensors and spiking neural networks (SNNs) implemented in neuromorphic processors for sparse event-driven sensing. However, this requires resource-efficient SNN mechanisms for temporal encoding, which need to consider that these systems process information in a streaming manner, with physical time being an intrinsic property of their operation. In this work, two candidate neurocomputational elements for temporal encoding and feature extraction in SNNs described in recent literature - the spiking time-difference encoder (TDE) and disynaptic excitatory-inhibitory (E-I) elements - are comparatively investigated in a keyword-spotting task on formants computed from spoken digits in the TIDIGITS dataset. While both encoders improve performance over direct classification of the formant features in the training data, enabling a complete binary classification with a logistic regression model, they show no clear improvements on the test set. Resource-efficient keyword spotting applications may benefit from the use of these encoders, but further work on methods for learning the time constants and weights is required to investigate their full potential. | ['Fredrik Sandin', 'Elisabetta Chicca', 'Foteini Liwicki', 'Lyes Khacef', 'Ton Juny Pina', 'Mattias Nilsson'] | 2023-01-24 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 5.16806483e-01 -2.84402996e-01 2.16270298e-01 -4.23839629e-01
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f173f153-0b4c-4875-9b1a-26badb75ca74 | simultaneity-in-binary-outcome-models-with-an | 2207.07343 | null | https://arxiv.org/abs/2207.07343v2 | https://arxiv.org/pdf/2207.07343v2.pdf | Simultaneity in Binary Outcome Models with an Application to Employment for Couples | Two of Peter Schmidt's many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this estimation strategy to a simple model for the intra-household relationship in employment. Our main conclusion is that the within-household dependence in employment differs significantly by the ethnicity composition of the couple even after one allows for unobserved household specific heterogeneity. | ['Martin Weidner', 'Ekaterini Kyriazidou', 'Luojia Hu', 'Bo E. Honoré'] | 2022-07-15 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-2.32558295e-01 -8.00718963e-02 -7.43976831e-01 -3.73410821e-01
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fbbb2c4f-1a39-4a68-8336-606c0bf86f64 | spectrogram-frame-linear-network-and | null | null | https://www.sciencedirect.com/science/article/abs/pii/S1574954119303206?via%3Dihub | https://www.sciencedirect.com/science/article/abs/pii/S1574954119303206?via%3Dihub | Spectrogram-frame linear network and continuous frame sequence for bird sound classification | Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN). In order to form a continuous frame sequence as the standard input for SFLN, a sliding window algorithm of short frame length is suitable for differentiate the Mel-spectrogram of bird sound. The vertical 3D filter in the linear layer moves linearly along the continuous frame and cover its full frequency band. The weight is initialized to a Gaussian distribution to attenuate the high-and low-frequency noise, thereby extracting the long-and short-term features of the continuous frame of the bird sound. Finally, the GRU network is connected and used as a classifier to directly output the prediction results. Four kinds of bird sound from the xeno-canto website are tested to evaluate the influences of different parameters of sliding window on the effect of SFLN-based classification. In the comparison experiment, the mean average precision (MAP) achieves the highest value of 0.97. | ['Xiaohu Qiang', 'Xibei Huang', 'Zhiqiang Zhang', 'Guoxiong Zhou', 'Aibin Chen', 'Xin Zhang'] | 2019-11-01 | null | null | null | ecological-informatics-2019-11 | ['sound-classification'] | ['audio'] | [-1.51303649e-01 -7.00131118e-01 -2.11847238e-02 -9.34217125e-02
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7b66ab57-d546-4081-940f-776187b4719d | bures-wasserstein-means-of-graphs | 2305.19738 | null | https://arxiv.org/abs/2305.19738v1 | https://arxiv.org/pdf/2305.19738v1.pdf | Bures-Wasserstein Means of Graphs | Finding the mean of sampled data is a fundamental task in machine learning and statistics. However, in cases where the data samples are graph objects, defining a mean is an inherently difficult task. We propose a novel framework for defining a graph mean via embeddings in the space of smooth graph signal distributions, where graph similarity can be measured using the Wasserstein metric. By finding a mean in this embedding space, we can recover a mean graph that preserves structural information. We establish the existence and uniqueness of the novel graph mean, and provide an iterative algorithm for computing it. To highlight the potential of our framework as a valuable tool for practical applications in machine learning, it is evaluated on various tasks, including k-means clustering of structured graphs, classification of functional brain networks, and semi-supervised node classification in multi-layer graphs. Our experimental results demonstrate that our approach achieves consistent performance, outperforms existing baseline approaches, and improves state-of-the-art methods. | ['Pascal Frossard', 'Isabel Haasler'] | 2023-05-31 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 3.46826315e-01 1.56756371e-01 -1.26411483e-01 -3.50112468e-01
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c12c1d9c-2373-45f1-9935-2d62158f02d5 | multi-channel-end-to-end-neural-network-for | 2206.09728 | null | https://arxiv.org/abs/2206.09728v1 | https://arxiv.org/pdf/2206.09728v1.pdf | Multi-channel end-to-end neural network for speech enhancement, source localization, and voice activity detection | Speech enhancement and source localization has been active research for several decades with a wide range of real-world applications. Recently, the Deep Complex Convolution Recurrent network (DCCRN) has yielded impressive enhancement performance for single-channel systems. In this study, a neural beamformer consisting of a beamformer and a novel multi-channel DCCRN is proposed for speech enhancement and source localization. Complex-valued filters estimated by the multi-channel DCCRN serve as the weights of beamformer. In addition, a one-stage learning-based procedure is employed for speech enhancement and source localization. The proposed network composed of the multi-channel DCCRN and the auxiliary network models the sound field, while minimizing the distortionless response loss function. Simulation results show that the proposed neural beamformer is effective in enhancing speech signals, with speech quality well preserved. The proposed neural beamformer also provides source localization and voice activity detection (VAD) functions. | ['Mingsian R. Bai', 'Yicheng Hsu', 'Yuan Chen'] | 2022-06-20 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.27248302e-01 -5.52160144e-01 1.74922422e-01 5.95439598e-02
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1826054b-e2ad-4378-bbe5-6715b507d618 | image-semantic-relation-generation | 2210.11253 | null | https://arxiv.org/abs/2210.11253v1 | https://arxiv.org/pdf/2210.11253v1.pdf | Image Semantic Relation Generation | Scene graphs provide structured semantic understanding beyond images. For downstream tasks, such as image retrieval, visual question answering, visual relationship detection, and even autonomous vehicle technology, scene graphs can not only distil complex image information but also correct the bias of visual models using semantic-level relations, which has broad application prospects. However, the heavy labour cost of constructing graph annotations may hinder the application of PSG in practical scenarios. Inspired by the observation that people usually identify the subject and object first and then determine the relationship between them, we proposed to decouple the scene graphs generation task into two sub-tasks: 1) an image segmentation task to pick up the qualified objects. 2) a restricted auto-regressive text generation task to generate the relation between given objects. Therefore, in this work, we introduce image semantic relation generation (ISRG), a simple but effective image-to-text model, which achieved 31 points on the OpenPSG dataset and outperforms strong baselines respectively by 16 points (ResNet-50) and 5 points (CLIP). | ['Mingzhe Du'] | 2022-10-19 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.20261765e-01 4.96140182e-01 5.99616319e-02 -3.35416406e-01
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c03c66f2-dc70-4bc9-a8f5-fe025e0f138a | getting-topology-and-point-cloud-generation | 1912.03787 | null | https://arxiv.org/abs/1912.03787v1 | https://arxiv.org/pdf/1912.03787v1.pdf | Getting Topology and Point Cloud Generation to Mesh | In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface, a discretized mesh, and a set of points sampled from the surface. This view motivates a generative model that works by progressively deforming a uniform sphere until it approximates the goal point cloud. We review the underlying concepts leading to this conclusion from computer graphics and topology in differential geometry, and model the generation process as deformation via deep neural network parameterization. Finally, we show that this view of the problem produces a model that can generate quality meshes efficiently. | ['Chun-Liang Li', 'Songwei Ge', 'Eunsu Kang', 'Austin Dill'] | 2019-12-08 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 1.42077237e-01 5.72089553e-01 4.35408622e-01 -1.78914577e-01
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1b22bf71-cab6-43f9-a695-830e62a6f705 | ankh-optimized-protein-language-model-unlocks | 2301.06568 | null | https://arxiv.org/abs/2301.06568v1 | https://arxiv.org/pdf/2301.06568v1.pdf | Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling | As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model size and the richness of its learned representations is validated, we prioritize accessibility and pursue a path of data-efficient, cost-reduced, and knowledge-guided optimization. Through over twenty experiments ranging from masking, architecture, and pre-training data, we derive insights from protein-specific experimentation into building a model that interprets the language of life, optimally. We present Ankh, the first general-purpose PLM trained on Google's TPU-v4 surpassing the state-of-the-art performance with fewer parameters (<10% for pre-training, <7% for inference, and <30% for the embedding dimension). We provide a representative range of structure and function benchmarks where Ankh excels. We further provide a protein variant generation analysis on High-N and One-N input data scales where Ankh succeeds in learning protein evolutionary conservation-mutation trends and introducing functional diversity while retaining key structural-functional characteristics. We dedicate our work to promoting accessibility to research innovation via attainable resources. | ['Burkhard Rost', 'Charlotte Rochereau', 'Mohamed Elkerdawy', 'Walid Moustafa', 'Wafaa Salah-Eldin', 'Hazem Essam', 'Ahmed Elnaggar'] | 2023-01-16 | null | null | null | null | ['protein-language-model', 'protein-function-prediction'] | ['medical', 'medical'] | [ 4.26698029e-01 2.83797771e-01 -2.23050773e-01 -3.54224592e-01
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5c6c4370-0091-4b2c-8912-a593e4283bd5 | query-dependent-video-representation-for | 2303.13874 | null | https://arxiv.org/abs/2303.13874v1 | https://arxiv.org/pdf/2303.13874v1.pdf | Query-Dependent Video Representation for Moment Retrieval and Highlight Detection | Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at github.com/wjun0830/QD-DETR. | ['Jae-Pil Heo', 'Dongchan Park', 'Sanguk Park', 'Sangeek Hyun', 'WonJun Moon'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['highlight-detection', 'moment-retrieval', 'video-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.32414803e-01 -3.74209702e-01 -4.11174566e-01 -2.27776945e-01
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57262b16-5e21-4d17-8577-27c5650f8eb6 | fraunhofer-sit-at-checkthat-2023-tackling | 2307.02377 | null | https://arxiv.org/abs/2307.02377v1 | https://arxiv.org/pdf/2307.02377v1.pdf | Fraunhofer SIT at CheckThat! 2023: Tackling Classification Uncertainty Using Model Souping on the Example of Check-Worthiness Classification | This paper describes the second-placed approach developed by the Fraunhofer SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text snippet from a political debate, the aim of this task is to determine whether it should be assessed for check-worthiness. Detecting check-worthy statements aims to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. It can also be considered as primary step of a fact-checking system. Our best-performing method took advantage of an ensemble classification scheme centered on Model Souping. When applied to the English data set, our submitted model achieved an overall F1 score of 0.878 and was ranked as the second-best model in the competition. | ['Jeong-Eun Choi', 'Inna Vogel', 'Raphael Frick'] | 2023-07-03 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 7.82422442e-03 5.82542300e-01 -3.06842953e-01 -1.82623044e-01
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e1f0618d-0bb3-4b3e-af53-f8bafb082432 | kochet-a-korean-cultural-heritage-corpus-for | 2209.00367 | null | https://arxiv.org/abs/2209.00367v2 | https://arxiv.org/pdf/2209.00367v2.pdf | KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks | As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. To achieve this, we propose KoCHET - a Korean cultural heritage corpus for the typical entity-related tasks, i.e., named entity recognition (NER), relation extraction (RE), and entity typing (ET). Advised by cultural heritage experts based on the data construction guidelines of government-affiliated organizations, KoCHET consists of respectively 112,362, 38,765, 113,198 examples for NER, RE, and ET tasks, covering all entity types related to Korean cultural heritage. Moreover, unlike the existing public corpora, modified redistribution can be allowed both domestic and foreign researchers. Our experimental results make the practical usability of KoCHET more valuable in terms of cultural heritage. We also provide practical insights of KoCHET in terms of statistical and linguistic analysis. Our corpus is freely available at https://github.com/Gyeongmin47/KoCHET. | ['Heuiseok Lim', 'Junyoung Son', 'Jinsung Kim', 'Gyeongmin Kim'] | 2022-09-01 | null | https://aclanthology.org/2022.coling-1.308 | https://aclanthology.org/2022.coling-1.308.pdf | coling-2022-10 | ['entity-typing'] | ['natural-language-processing'] | [-4.37729776e-01 2.04596650e-02 -2.25871116e-01 -8.56168568e-02
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6b2d2872-ed2d-4ae4-8f84-0568a8c246c0 | query-based-summarization-using-mdl-principle | null | null | https://aclanthology.org/W17-1004 | https://aclanthology.org/W17-1004.pdf | Query-based summarization using MDL principle | Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum description length (MDL) principle that employs Krimp compression algorithm (Vreeken et al., 2011). The key idea of our approach is to select frequent word sets related to a given query that compress document sentences better and therefore describe the document better. A summary is extracted by selecting sentences that best cover query-related frequent word sets. The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are specifically designed for query-based summarization (DUC, 2005 2006). It competes with the best results. | ['Marina Litvak', 'Natalia Vanetik'] | 2017-04-01 | null | null | null | ws-2017-4 | ['query-based-extractive-summarization'] | ['natural-language-processing'] | [ 5.96196234e-01 3.12726200e-01 -4.05812979e-01 -8.19441751e-02
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595342fa-e935-48f9-8424-d551595653d6 | an-efficient-mini-batch-method-via-partial | 2108.09645 | null | https://arxiv.org/abs/2108.09645v4 | https://arxiv.org/pdf/2108.09645v4.pdf | Improving Mini-batch Optimal Transport via Partial Transportation | Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications. Despite their practicality, m-OT suffers from misspecified mappings, namely, mappings that are optimal on the mini-batch level but are partially wrong in the comparison with the optimal transportation plan between the original measures. Motivated by the misspecified mappings issue, we propose a novel mini-batch method by using partial optimal transport (POT) between mini-batch empirical measures, which we refer to as mini-batch partial optimal transport (m-POT). Leveraging the insight from the partial transportation, we explain the source of misspecified mappings from the m-OT and motivate why limiting the amount of transported masses among mini-batches via POT can alleviate the incorrect mappings. Finally, we carry out extensive experiments on various applications such as deep domain adaptation, partial domain adaptation, deep generative model, color transfer, and gradient flow to demonstrate the favorable performance of m-POT compared to current mini-batch methods. | ['Tung Pham', 'The-Anh Vu-Le', 'Nhat Ho', 'Dang Nguyen', 'Khai Nguyen'] | 2021-08-22 | improving-mini-batch-optimal-transport-via | https://openreview.net/forum?id=9Sf8fbue1br | https://openreview.net/pdf?id=9Sf8fbue1br | null | ['partial-domain-adaptation'] | ['methodology'] | [-3.67042780e-01 -2.94451326e-01 3.66625888e-03 -3.43267530e-01
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b791aa47-efb9-400d-95fb-f0886616acc2 | detection-and-analysis-of-drive-by-download | null | null | https://sites.cs.ucsb.edu/~chris/research/doc/www10_jsand.pdf | https://sites.cs.ucsb.edu/~chris/research/doc/www10_jsand.pdf | Detection and Analysis of Drive-by-Download Attacks and Malicious JavaScript Code | JavaScript is a browser scripting language that allows developers
to create sophisticated client-side interfaces for web applications.
However, JavaScript code is also used to carry out attacks against
the user’s browser and its extensions. These attacks usually result
in the download of additional malware that takes complete con-
trol of the victim’s platform, and are, therefore, called “drive-by
downloads.” Unfortunately, the dynamic nature of the JavaScript
language and its tight integration with the browser make it difficult
to detect and block malicious JavaScript code.
This paper presents a novel approach to the detection and analy-
sis of malicious JavaScript code. Our approach combines anomaly
detection with emulation to automatically identify malicious Java-
Script code and to support its analysis. We developed a system that
uses a number of features and machine-learning techniques to es-
tablish the characteristics of normal JavaScript code. Then, during
detection, the system is able to identify anomalous JavaScript code
by emulating its behavior and comparing it to the established pro-
files. In addition to identifying malicious code, the system is able
to support the analysis of obfuscated code and to generate detection
signatures for signature-based systems. The system has been made
publicly available and has been used by thousands of analysts. | ['Giovanni Vigna', 'Christopher Kruegel', 'Marco Cova'] | 2010-04-26 | null | null | null | www-10-proceedings-of-the-19th-international | ['anomaly-detection'] | ['methodology'] | [-1.48135712e-02 -3.02353978e-01 -9.60926265e-02 -5.15152253e-02
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886be60b-1525-4ab0-b679-55a74b7fc24f | erasing-concepts-from-diffusion-models | 2303.07345 | null | https://arxiv.org/abs/2303.07345v3 | https://arxiv.org/pdf/2303.07345v3.pdf | Erasing Concepts from Diffusion Models | Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing explicit or realistic artwork, it has raised concerns regarding its potential for misuse. We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher. We benchmark our method against previous approaches that remove sexually explicit content and demonstrate its effectiveness, performing on par with Safe Latent Diffusion and censored training. To evaluate artistic style removal, we conduct experiments erasing five modern artists from the network and conduct a user study to assess the human perception of the removed styles. Unlike previous methods, our approach can remove concepts from a diffusion model permanently rather than modifying the output at the inference time, so it cannot be circumvented even if a user has access to model weights. Our code, data, and results are available at https://erasing.baulab.info/ | ['David Bau', 'Jaden Fiotto-Kaufman', 'Joanna Materzynska', 'Rohit Gandikota'] | 2023-03-13 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 7.08491266e-01 2.20222995e-01 5.61907515e-02 -2.58495271e-01
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8cb2f8b6-a8b3-43da-908b-45c2028a7577 | xpqa-cross-lingual-product-question-answering | 2305.09249 | null | https://arxiv.org/abs/2305.09249v1 | https://arxiv.org/pdf/2305.09249v1.pdf | xPQA: Cross-Lingual Product Question Answering across 12 Languages | Product Question Answering (PQA) systems are key in e-commerce applications to provide responses to customers' questions as they shop for products. While existing work on PQA focuses mainly on English, in practice there is need to support multiple customer languages while leveraging product information available in English. To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages across 9 branches, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate. We evaluate various approaches involving machine translation at runtime or offline, leveraging multilingual pre-trained LMs, and including or excluding xPQA training data. We find that (1) In-domain data is essential as cross-lingual rankers trained on other domains perform poorly on the PQA task; (2) Candidate ranking often prefers runtime-translation approaches while answer generation prefers multilingual approaches; (3) Translating offline to augment multilingual models helps candidate ranking mainly on languages with non-Latin scripts; and helps answer generation mainly on languages with Latin scripts. Still, there remains a significant performance gap between the English and the cross-lingual test sets. | ['Adrià De Gispert', 'Bill Byrne', 'Akari Asai', 'Xiaoyu Shen'] | 2023-05-16 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [ 7.36487582e-02 -1.30756408e-01 -1.82058096e-01 -5.62905312e-01
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17ad83c8-a0e0-427c-8a3d-7433ef5167fa | hade-hierarchical-affective-dialog-encoder | null | null | https://openreview.net/forum?id=cpMUiyD9EA | https://openreview.net/pdf?id=cpMUiyD9EA | HADE: Hierarchical Affective Dialog Encoder for Personality Recognition in Conversation | Personality recognition in conversation aims to determine the personality traits of speakers through the dialogue content, which is of great importance in designing personalized conversational AI. Existing methods that use only linguistic patterns in utterances limit their performance. To fill in the gap, we investigate the effectiveness of incorporating affective information and modeling the interactions among speakers in conversations for personality recognition. However, available corpus with personality and explicit affective annotations is rare. Besides, modeling the dialog flow with multiple speakers is difficult. Faced with the issues, we proposed Hierarchical Affective Dialog Encoder (HADE) for effective personality recognition in conversation. HADE utilizes manual annotated Valance-Arousal-Dominance (VAD) vectors of single words and implicitly extracts affective information from utterances. Then, it introduces a hierarchical architecture with the dialog state embeddings to identify the speakers and encode the whole dialog flow. Finally, the affective information is integrated by an auxiliary VAD regression task to enhance personality recognition. Extensive experiments on a well-known dataset, \textbf{FriendsPersona}, demonstrate the effectiveness of our method compared with state-of-the-art models. Besides, we conduct an ablation study to discuss different approaches for integrating affective information and dialog flow modeling; the design of both parts in HADE is also verified to be effective for personality recognition in conversation. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['personality-recognition-in-conversation'] | ['natural-language-processing'] | [-4.57682788e-01 3.11597735e-01 1.72598466e-01 -8.45582783e-01
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a00f438d-fc0e-45b9-8db5-0682f56ebf86 | event-based-stereo-depth-estimation-from-ego | 2210.08927 | null | https://arxiv.org/abs/2210.08927v1 | https://arxiv.org/pdf/2210.08927v1.pdf | Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion | Event cameras are bio-inspired sensors that mimic the human retina by responding to brightness changes in the scene. They generate asynchronous spike-based outputs at microsecond resolution, providing advantages over traditional cameras like high dynamic range, low motion blur and power efficiency. Most event-based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work investigates how to estimate depth from stereo event cameras without explicit data association by fusing back-projected ray densities, and demonstrates its effectiveness on head-mounted camera data, which is recorded in an egocentric fashion. Code and video are available at https://github.com/tub-rip/dvs_mcemvs | ['Guillermo Gallego', 'Suman Ghosh'] | 2022-10-17 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.50696486e-01 -5.62419116e-01 3.36911470e-01 -2.30249658e-01
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b46c9161-5528-4b40-932b-44b9b4ef0635 | content-based-medical-image-retrieval-with | 2211.15371 | null | https://arxiv.org/abs/2211.15371v1 | https://arxiv.org/pdf/2211.15371v1.pdf | Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss | Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearance to suspected cases can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large data repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on dense embedding vectors that represent image features to allow quantitative similarity assessments. Triplet learning has emerged as a powerful approach to recover embeddings in CBIR, albeit traditional loss functions ignore the dynamic relationship between opponent image classes. Here, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. OCAM uses a variable margin value that is updated continually during the course of training to maintain optimally discriminative representations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against baselines. | ['Tolga Cukur', 'Emin Celik', 'Şaban Öztürk'] | 2022-11-22 | null | null | null | null | ['medical-image-retrieval', 'content-based-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [ 2.89380282e-01 -3.52324754e-01 -3.69640142e-01 -4.98681307e-01
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e7774629-67ed-43a4-8ba1-620d9f4a2a7f | purpose-and-polarity-of-citation-towards-nlp | null | null | https://aclanthology.org/N13-1067 | https://aclanthology.org/N13-1067.pdf | Purpose and Polarity of Citation: Towards NLP-based Bibliometrics | null | ['Dragomir Radev', 'Amjad Abu-Jbara', 'Jefferson Ezra'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['citation-intent-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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51533747-2dba-4914-9015-41517d4bbe30 | what-the-language-you-tweet-says-about-your | 1701.06233 | null | http://arxiv.org/abs/1701.06233v1 | http://arxiv.org/pdf/1701.06233v1.pdf | What the Language You Tweet Says About Your Occupation | Many aspects of people's lives are proven to be deeply connected to their
jobs. In this paper, we first investigate the distinct characteristics of major
occupation categories based on tweets. From multiple social media platforms, we
gather several types of user information. From users' LinkedIn webpages, we
learn their proficiencies. To overcome the ambiguity of self-reported
information, a soft clustering approach is applied to extract occupations from
crowd-sourced data. Eight job categories are extracted, including Marketing,
Administrator, Start-up, Editor, Software Engineer, Public Relation, Office
Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for
understanding their linguistic styles, interests, and personalities. Our
results suggest that people of different jobs have unique tendencies in certain
language styles and interests. Our results also clearly reveal distinctive
levels in terms of Big Five Traits for different jobs. Finally, a classifier is
built to predict job types based on the features extracted from tweets. A high
accuracy indicates a strong discrimination power of language features for job
prediction task. | ['Thuy-vy Thi Nguyen', 'Haoyuan Xiao', 'Jiebo Luo', 'Tianran Hu'] | 2017-01-22 | null | null | null | null | ['job-prediction'] | ['natural-language-processing'] | [-1.70175388e-01 4.12228405e-02 -4.51786548e-01 -7.88506866e-01
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54b97711-ecaa-4d02-a75a-8316158fe598 | non-negative-bregman-divergence-minimization | 2006.06979 | null | https://arxiv.org/abs/2006.06979v3 | https://arxiv.org/pdf/2006.06979v3.pdf | Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation | Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation. In existing studies on DRE, methods based on Bregman divergence (BD) minimization have been extensively studied. However, BD minimization when applied with highly flexible models, such as deep neural networks, tends to suffer from what we call train-loss hacking, which is a source of overfitting caused by a typical characteristic of empirical BD estimators. In this paper, to mitigate train-loss hacking, we propose a non-negative correction for empirical BD estimators. Theoretically, we confirm the soundness of the proposed method through a generalization error bound. Through our experiments, the proposed methods show a favorable performance in inlier-based outlier detection. | ['Takeshi Teshima', 'Masahiro Kato'] | 2020-06-12 | null | https://openreview.net/forum?id=PGmqOzKEPZN | https://openreview.net/pdf?id=PGmqOzKEPZN | null | ['density-ratio-estimation'] | ['methodology'] | [-1.67406082e-01 -2.76047885e-01 1.10228874e-01 -5.36307275e-01
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200cca13-e13d-4d7a-b0bb-c7d649f13545 | ji-yu-ping-xing-jiao-hu-zhu-yi-li-wang-luo-de | null | null | https://aclanthology.org/2022.ccl-1.21 | https://aclanthology.org/2022.ccl-1.21.pdf | 基于平行交互注意力网络的中文电子病历实体及关系联合抽取(Parallel Interactive Attention Network for Joint Entity and Relation Extraction Based on Chinese Electronic Medical Record) | “基于电子病历构建医学知识图谱对医疗技术的发展具有重要意义,实体和关系抽取是构建知识图谱的关键技术。本文针对目前实体关系联合抽取中存在的特征交互不充分的问题,提出了一种平行交互注意力网络(PIAN)以充分挖掘实体与关系的相关性,在多个标准的医学和通用数据集上取得最优结果;当前中文医学实体及关系标注数据集较少,本文基于中文电子病历构建了实体和关系抽取数据集(CEMRIE),与医学专家共同制定了语料标注规范,并基于所提出的模型实验得出基准结果。” | ['Yuan Guanghui', 'Xueyang Qin', 'Zehao Wang', 'Lishuang Li'] | null | null | null | null | ccl-2022-10 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-8.11145782e-01 -7.50582039e-01 6.79321051e-01 3.93725753e-01
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ea44ca46-ff10-4924-a2b1-c8b92b867edb | hypernyms-under-siege-linguistically | 1612.04460 | null | http://arxiv.org/abs/1612.04460v2 | http://arxiv.org/pdf/1612.04460v2.pdf | Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection | The fundamental role of hypernymy in NLP has motivated the development of
many methods for the automatic identification of this relation, most of which
rely on word distribution. We investigate an extensive number of such
unsupervised measures, using several distributional semantic models that differ
by context type and feature weighting. We analyze the performance of the
different methods based on their linguistic motivation. Comparison to the
state-of-the-art supervised methods shows that while supervised methods
generally outperform the unsupervised ones, the former are sensitive to the
distribution of training instances, hurting their reliability. Being based on
general linguistic hypotheses and independent from training data, unsupervised
measures are more robust, and therefore are still useful artillery for
hypernymy detection. | ['Dominik Schlechtweg', 'Enrico Santus', 'Vered Shwartz'] | 2016-12-14 | hypernyms-under-siege-linguistically-1 | https://aclanthology.org/E17-1007 | https://aclanthology.org/E17-1007.pdf | eacl-2017-4 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 9.30759385e-02 1.69854477e-01 -4.32263017e-01 -4.00590748e-01
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d22af100-1b11-4985-a442-fdb77f22848f | taming-lagrangian-chaos-with-multi-objective | 2212.09612 | null | https://arxiv.org/abs/2212.09612v1 | https://arxiv.org/pdf/2212.09612v1.pdf | Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning | We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi Objective Reinforcement Learning (MORL), combining scalarization techniques together with a Q-learning algorithm, for Lagrangian drifters that have variable swimming velocity. We show that MORL is able to find a set of trade-off solutions forming an optimal Pareto frontier. As a benchmark, we show that a set of heuristic strategies are dominated by the MORL solutions. We consider the situation in which the agents cannot update their control variables continuously, but only after a discrete (decision) time, $\tau$. We show that there is a range of decision times, in between the Lyapunov time and the continuous updating limit, where Reinforcement Learning finds strategies that significantly improve over heuristics. In particular, we discuss how large decision times require enhanced knowledge of the flow, whereas for smaller $\tau$ all a priori heuristic strategies become Pareto optimal. | ['Massimo Cencini', 'Antonio Celani', 'Francesco Borra', 'Luca Biferale', 'Chiara Calascibetta'] | 2022-12-19 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.03984916e-01 8.29458889e-03 -8.77203643e-02 4.99405712e-01
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e5d939b5-b92a-4c44-95bd-2b4723cae64b | cardiac-segmentation-using-transfer-learning | 2209.09714 | null | https://arxiv.org/abs/2209.09714v1 | https://arxiv.org/pdf/2209.09714v1.pdf | Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts | Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement). | ['Kevin McGuinness', "Noel E. O'Connor", 'Kathleen M. Curran', 'Eric Arazo', 'Carles Garcia-Cabrera'] | 2022-09-20 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.71290898e-01 3.73643160e-01 3.01234394e-01 -3.98420483e-01
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24716363-74b1-40a6-9c90-b94f55160a3d | multimodal-analysis-informed-content | 2104.13276 | null | https://arxiv.org/abs/2104.13276v3 | https://arxiv.org/pdf/2104.13276v3.pdf | MULTIMODAL ANALYSIS: Informed content estimation and audio source separation | This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to music that can be used (e.g. reviews, metadata, or social network feedback), we concentrate on lyrics. The singing voice directly connects the audio signal and the text information in a unique way, combining melody and lyrics where a linguistic dimension complements the abstraction of musical instruments. Our study focuses on the audio and lyrics interaction for targeting source separation and informed content estimation. | ['Gabriel Meseguer-Brocal'] | 2021-04-27 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.54113686e-02 -2.34601930e-01 -4.30780381e-01 1.34071603e-01
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15b6a364-dcee-4d68-b4a0-940337b2c8fd | breaking-character-are-subwords-good-enough-1 | 2204.04748 | null | https://arxiv.org/abs/2204.04748v1 | https://arxiv.org/pdf/2204.04748v1.pdf | Breaking Character: Are Subwords Good Enough for MRLs After All? | Large pretrained language models (PLMs) typically tokenize the input string into contiguous subwords before any pretraining or inference. However, previous studies have claimed that this form of subword tokenization is inadequate for processing morphologically-rich languages (MRLs). We revisit this hypothesis by pretraining a BERT-style masked language model over character sequences instead of word-pieces. We compare the resulting model, dubbed TavBERT, against contemporary PLMs based on subwords for three highly complex and ambiguous MRLs (Hebrew, Turkish, and Arabic), testing them on both morphological and semantic tasks. Our results show, for all tested languages, that while TavBERT obtains mild improvements on surface-level tasks \`a la POS tagging and full morphological disambiguation, subword-based PLMs achieve significantly higher performance on semantic tasks, such as named entity recognition and extractive question answering. These results showcase and (re)confirm the potential of subword tokenization as a reasonable modeling assumption for many languages, including MRLs. | ['Omer Levy', 'Reut Tsarfaty', 'Tal Avinari', 'Omri Keren'] | 2022-04-10 | null | null | null | null | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.00839189e-02 2.49320358e-01 -1.01489507e-01 -3.75111073e-01
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14a1b62f-d85f-43db-a2f2-bab57cc2d9b6 | covost-a-diverse-multilingual-speech-to-text | 2002.01320 | null | https://arxiv.org/abs/2002.01320v2 | https://arxiv.org/pdf/2002.01320v2.pdf | CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus | Spoken language translation has recently witnessed a resurgence in popularity, thanks to the development of end-to-end models and the creation of new corpora, such as Augmented LibriSpeech and MuST-C. Existing datasets involve language pairs with English as a source language, involve very specific domains or are low resource. We introduce CoVoST, a multilingual speech-to-text translation corpus from 11 languages into English, diversified with over 11,000 speakers and over 60 accents. We describe the dataset creation methodology and provide empirical evidence of the quality of the data. We also provide initial benchmarks, including, to our knowledge, the first end-to-end many-to-one multilingual models for spoken language translation. CoVoST is released under CC0 license and free to use. We also provide additional evaluation data derived from Tatoeba under CC licenses. | ['Juan Pino', 'Jiatao Gu', 'Anne Wu', 'Changhan Wang'] | 2020-02-04 | covost-a-diverse-multilingual-speech-to-text-1 | https://aclanthology.org/2020.lrec-1.517 | https://aclanthology.org/2020.lrec-1.517.pdf | lrec-2020-5 | ['speech-to-text-translation'] | ['natural-language-processing'] | [-4.21478450e-01 -1.21868700e-01 -4.14082527e-01 -6.86062634e-01
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eebe3b3e-a859-466e-a278-914d2999d0d0 | left-ventricular-wall-motion-estimation-by | 2008.04615 | null | https://arxiv.org/abs/2008.04615v1 | https://arxiv.org/pdf/2008.04615v1.pdf | Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection | Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over the HMC-QU dataset show that the proposed approach can achieve high accuracy, sensitivity and precision in MI detection even though the echo quality is quite poor, and the temporal resolution is low. | ['Junaid Malik', 'Aysen Degerli', 'Serkan Kiranyaz', 'Ridha Hamila', 'Rashid Mazhar', 'Tahir Hamid', 'Rayyan Ahmed', 'Moncef Gabbouj', 'Rayaan Abouhasera', 'Morteza Zabihi'] | 2020-08-11 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [-1.83632135e-01 -4.13747221e-01 -1.24625929e-01 2.22588688e-01
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e438de8e-1796-4ec4-a5e1-116ee38accac | joint-activity-delay-detection-and-channel | 2305.12372 | null | https://arxiv.org/abs/2305.12372v1 | https://arxiv.org/pdf/2305.12372v1.pdf | Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access | Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice. Therefore, this paper considers asynchronous grant-free massive RA systems and develops novel algorithms for joint user activity detection, synchronization delay detection, and channel estimation. In particular, the framework of orthogonal approximate message passing (OAMP) is first utilized to deal with the non-independent and identically distributed (i.i.d.) pilot matrix in asynchronous grant-free massive RA systems, and an OAMP-based algorithm capable of leveraging the common sparsity among the received pilot signals from multiple base station antennas is developed. To reduce the computational complexity, a memory AMP (MAMP)based algorithm is further proposed that eliminates the matrix inversions in the OAMP-based algorithm. Simulation results demonstrate the effectiveness of the two proposed algorithms over the baseline methods. Besides, the MAMP-based algorithm reduces 37% of the computations while maintaining comparable detection/estimation accuracy, compared with the OAMP-based algorithm. | ['Jun Zhang', 'Yuyi Mao', 'Xinyu Bian'] | 2023-05-21 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.32463475e-02 -3.03740621e-01 -1.90881401e-01 3.08199823e-01
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bc49096f-0940-4864-bfa9-8ce4af5f3d4a | answer-mining-from-a-pool-of-images-towards | 2306.16713 | null | https://arxiv.org/abs/2306.16713v1 | https://arxiv.org/pdf/2306.16713v1.pdf | Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering | We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-based visual question answering (or RETVQA in short). The RETVQA is distinctively different and more challenging than the traditionally-studied Visual Question Answering (VQA), where a given question has to be answered with a single relevant image in context. Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. Further, we introduce the largest dataset in this space, namely RETVQA, which has the following salient features: multi-image and retrieval requirement for VQA, metadata-independent questions over a pool of heterogeneous images, expecting a mix of classification-oriented and open-ended generative answers. Our proposed framework achieves an accuracy of 76.5% and a fluency of 79.3% on the proposed dataset, namely RETVQA and also outperforms state-of-the-art methods by 4.9% and 11.8% on the image segment of the publicly available WebQA dataset on the accuracy and fluency metrics, respectively. | ['Anand Mishra', 'Mithun Das Gupta', 'Manish Gupta', 'Abhirama Subramanyam Penamakuri'] | 2023-06-29 | null | null | null | null | ['visual-question-answering-1', 'retrieval', 'question-answering', 'answer-generation'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.55945981e-01 1.44022062e-01 1.26300707e-01 -4.26557332e-01
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bb4f1612-2f02-4b4f-8161-eb410040c8d8 | on-implicit-bias-in-overparameterized-bilevel | 2212.14032 | null | https://arxiv.org/abs/2212.14032v1 | https://arxiv.org/pdf/2212.14032v1.pdf | On Implicit Bias in Overparameterized Bilevel Optimization | Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization. | ['Roger Grosse', 'David Duvenaud', 'Fabian Pedregosa', 'Jonathan Lorraine', 'Paul Vicol'] | 2022-12-28 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-3.29133004e-01 3.88433374e-02 -3.37563157e-01 -4.27868456e-01
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9af93b81-7733-4b12-9687-e9c4f6c060ce | time-conditioned-action-anticipation-in-one | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Ke_Time-Conditioned_Action_Anticipation_in_One_Shot_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ke_Time-Conditioned_Action_Anticipation_in_One_Shot_CVPR_2019_paper.pdf | Time-Conditioned Action Anticipation in One Shot | The goal of human action anticipation is to predict future actions. Ideally, in real-world applications such as video surveillance and self-driving systems, future actions should not only be predicted with high accuracy but also at arbitrary and variable time-horizons ranging from short- to long-term predictions. Current work mostly focuses on predicting the next action and thus long-term prediction is achieved by recursive prediction of each next action, which is both inefficient and accumulates errors. In this paper, we propose a novel time-conditioned method for efficient and effective long-term action anticipation. There are two key ingredients to our approach. First, by explicitly conditioning our anticipation network on time allows to efficiently anticipate also long-term actions. And second, we propose an attended temporal feature and a time-conditioned skip connection to extract relevant and useful information from observations for effective anticipation. We conduct extensive experiments on the large-scale Epic-Kitchen and the 50Salads Datasets. Experimental results show that the proposed method is capable of anticipating future actions at both short-term and long-term, and achieves state-of-the-art performance.
| [' Bernt Schiele', ' Mario Fritz', 'Qiuhong Ke'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['action-anticipation'] | ['computer-vision'] | [ 4.89604741e-01 7.53585547e-02 -3.49590212e-01 -7.15348840e-01
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69be3e07-36dd-4db1-976b-f0ed8b29e413 | open-world-pose-transfer-via-sequential-test | 2303.10945 | null | https://arxiv.org/abs/2303.10945v1 | https://arxiv.org/pdf/2303.10945v1.pdf | Open-World Pose Transfer via Sequential Test-Time Adaption | Pose transfer aims to transfer a given person into a specified posture, has recently attracted considerable attention. A typical pose transfer framework usually employs representative datasets to train a discriminative model, which is often violated by out-of-distribution (OOD) instances. Recently, test-time adaption (TTA) offers a feasible solution for OOD data by using a pre-trained model that learns essential features with self-supervision. However, those methods implicitly make an assumption that all test distributions have a unified signal that can be learned directly. In open-world conditions, the pose transfer task raises various independent signals: OOD appearance and skeleton, which need to be extracted and distributed in speciality. To address this point, we develop a SEquential Test-time Adaption (SETA). In the test-time phrase, SETA extracts and distributes external appearance texture by augmenting OOD data for self-supervised training. To make non-Euclidean similarity among different postures explicit, SETA uses the image representations derived from a person re-identification (Re-ID) model for similarity computation. By addressing implicit posture representation in the test-time sequentially, SETA greatly improves the generalization performance of current pose transfer models. In our experiment, we first show that pose transfer can be applied to open-world applications, including Tiktok reenactment and celebrity motion synthesis. | ['Liang Lin', 'Jinshan Pan', 'Yukai Shi', 'Yongyi Lu', 'Tianshui Chen', 'Zhijing Yang', 'Xiaoyu Xian', 'Junyang Chen'] | 2023-03-20 | null | null | null | null | ['person-re-identification', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 1.43198580e-01 -6.47033425e-03 -2.03298137e-01 -6.78838372e-01
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d042e807-93d7-41d9-9795-02ca02296c96 | resdiff-combining-cnn-and-diffusion-model-for | 2303.08714 | null | https://arxiv.org/abs/2303.08714v2 | https://arxiv.org/pdf/2303.08714v2.pdf | ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution | Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content. Therefore, we present ResDiff, a novel Diffusion Probabilistic Model based on Residual structure for Single Image Super-Resolution (SISR). ResDiff utilizes a combination of a CNN, which restores primary low-frequency components, and a DPM, which predicts the residual between the ground-truth image and the CNN-predicted image. In contrast to the common diffusion-based methods that directly use LR images to guide the noise towards HR space, ResDiff utilizes the CNN's initial prediction to direct the noise towards the residual space between HR space and CNN-predicted space, which not only accelerates the generation process but also acquires superior sample quality. Additionally, a frequency-domain-based loss function for CNN is introduced to facilitate its restoration, and a frequency-domain guided diffusion is designed for DPM on behalf of predicting high-frequency details. The extensive experiments on multiple benchmark datasets demonstrate that ResDiff outperforms previous diffusion-based methods in terms of shorter model convergence time, superior generation quality, and more diverse samples. | ['Jinglin Zhang', 'Guangxing Liu', 'Zhengyang Shan', 'Shuyao Shang'] | 2023-03-15 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 2.43188486e-01 -4.76207733e-02 -1.06538087e-01 5.52211069e-02
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e7a6de43-d678-4c6e-b614-936e9a25ea2d | towards-black-box-adversarial-example | 2306.02021 | null | https://arxiv.org/abs/2306.02021v1 | https://arxiv.org/pdf/2306.02021v1.pdf | Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method | Adversarial example detection is known to be an effective adversarial defense method. Black-box attack, which is a more realistic threat and has led to various black-box adversarial training-based defense methods, however, does not attract considerable attention in adversarial example detection. In this paper, we fill this gap by positioning the problem of black-box adversarial example detection (BAD). Data analysis under the introduced BAD settings demonstrates (1) the incapability of existing detectors in addressing the black-box scenario and (2) the potential of exploring BAD solutions from a data perspective. To tackle the BAD problem, we propose a data reconstruction-based adversarial example detection method. Specifically, we use variational auto-encoder (VAE) to capture both pixel and frequency representations of normal examples. Then we use reconstruction error to detect adversarial examples. Compared with existing detection methods, the proposed method achieves substantially better detection performance in BAD, which helps promote the deployment of adversarial example detection-based defense solutions in real-world models. | ['Jitao Sang', 'Yunfan Yang', 'Zhiyu Lin', 'YiFei Gao'] | 2023-06-03 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.14162397e-01 -1.03431717e-01 6.80979267e-02 2.69082487e-01
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ea534c45-f6a2-431e-b983-0df89faa38a9 | improving-image-clustering-through-sample | 2209.12621 | null | https://arxiv.org/abs/2209.12621v1 | https://arxiv.org/pdf/2209.12621v1.pdf | Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images | Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the State-of-the-Art image clustering models, achieving accuracy performance gains ranging from $2.1\%$ to $15.9\%$. Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote--sensing images. | ['Guoping Qiu', 'Qinglin Li'] | 2022-09-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.96202153e-01 -2.56963134e-01 -3.49654913e-01 -5.74899733e-01
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b92e9227-da0d-4950-aae5-411a1f1e60c1 | active-exploration-based-on-information-gain | 2211.10934 | null | https://arxiv.org/abs/2211.10934v2 | https://arxiv.org/pdf/2211.10934v2.pdf | Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation | Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation and rapid environmental coverage. To address this issue, we propose an active inference method, referred to as spatial concept formation with information gain-based active exploration (SpCoAE) that combines sequential Bayesian inference using particle filters and information gain-based destination determination in a probabilistic generative model. This study interprets the robot's action as a selection of destinations to ask the user, `What kind of place is this?' in the context of active inference. This study provides insights into the technical aspects of the proposed method, including active perception and exploration by the robot, and how the method can enable mobile robots to learn spatial concepts through active exploration. Our experiment demonstrated the effectiveness of the SpCoAE in efficiently determining a destination for learning appropriate spatial concepts in home environments. | ['Tadahiro Taniguchi', 'Yoshinobu Hagiwara', 'Lotfi El Hafi', 'Tomochika Ishikawa', 'Yoshiki Tabuchi', 'Akira Taniguchi'] | 2022-11-20 | null | null | null | null | ['language-acquisition', 'sequential-bayesian-inference'] | ['natural-language-processing', 'time-series'] | [ 1.37633562e-01 2.47697309e-01 -7.34052211e-02 -4.33329463e-01
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6e47c530-bf78-4d62-aced-4647a2c20be5 | experimental-results-on-the-native-language | null | null | https://aclanthology.org/W13-1710 | https://aclanthology.org/W13-1710.pdf | Experimental Results on the Native Language Identification Shared Task | null | ['Dragomir Radev', 'Amjad Abu-Jbara', 'Rahul Jha', 'Eric Morley'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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44588fc3-c4c0-4acb-b522-0095dc99a599 | prokd-an-unsupervised-prototypical-knowledge | 2301.08855 | null | https://arxiv.org/abs/2301.08855v2 | https://arxiv.org/pdf/2301.08855v2.pdf | ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition | For named entity recognition (NER) in zero-resource languages, utilizing knowledge distillation methods to transfer language-independent knowledge from the rich-resource source languages to zero-resource languages is an effective means. Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language. Despite the impressive performance achieved by these methods, we argue that they have two limitations. Firstly, the teacher network fails to effectively learn language-independent knowledge shared across languages due to the differences in the feature distribution between the source and target languages. Secondly, the student network acquires all of its knowledge from the teacher network and ignores the learning of target language-specific knowledge. Undesirably, these limitations would hinder the model's performance in the target language. This paper proposes an unsupervised prototype knowledge distillation network (ProKD) to address these issues. Specifically, ProKD presents a contrastive learning-based prototype alignment method to achieve class feature alignment by adjusting the distance among prototypes in the source and target languages, boosting the teacher network's capacity to acquire language-independent knowledge. In addition, ProKD introduces a prototypical self-training method to learn the intrinsic structure of the language by retraining the student network on the target data using samples' distance information from prototypes, thereby enhancing the student network's ability to acquire language-specific knowledge. Extensive experiments on three benchmark cross-lingual NER datasets demonstrate the effectiveness of our approach. | ['Chunming Hu', 'Jihong Liu', 'Hong Zhang', 'Guanghui Ma', 'Ling Ge'] | 2023-01-21 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.91169302e-02 4.23193090e-02 -4.06662643e-01 -4.61724311e-01
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a4b33c7b-ac7b-43ec-b520-411e28de76f2 | learning-instance-and-task-aware-dynamic | 2112.03494 | null | https://arxiv.org/abs/2112.03494v2 | https://arxiv.org/pdf/2112.03494v2.pdf | Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning | Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task. Dynamic networks have been shown capable of learning content-adaptive parameters efficiently, making them suitable for few-shot learning. In this paper, we propose to learn the dynamic kernels of a convolution network as a function of the task at hand, enabling faster generalization. To this end, we obtain our dynamic kernels based on the entire task and each sample and develop a mechanism further conditioning on each individual channel and position independently. This results in dynamic kernels that simultaneously attend to the global information whilst also considering minuscule details available. We empirically show that our model improves performance on few-shot classification and detection tasks, achieving a tangible improvement over several baseline models. This includes state-of-the-art results on 4 few-shot classification benchmarks: mini-ImageNet, tiered-ImageNet, CUB and FC100 and competitive results on a few-shot detection dataset: MS COCO-PASCAL-VOC. | ['Tom Drummond', 'Tianyu Zhu', 'Mehrtash Harandi', 'Yan Zuo', 'Gil Avraham', 'Pengfei Fang', 'Rongkai Ma'] | 2021-12-07 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.43482670e-01 -3.91008407e-01 -9.74003077e-02 -4.64055568e-01
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7e3bd489-b936-4e72-80bd-ef6e1f94e8b2 | stratified-transformer-for-3d-point-cloud | 2203.14508 | null | https://arxiv.org/abs/2203.14508v1 | https://arxiv.org/pdf/2203.14508v1.pdf | Stratified Transformer for 3D Point Cloud Segmentation | 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance. Specifically, we first put forward a novel key sampling strategy. For each query point, we sample nearby points densely and distant points sparsely as its keys in a stratified way, which enables the model to enlarge the effective receptive field and enjoy long-range contexts at a low computational cost. Also, to combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information, which facilitates convergence and boosts performance. Besides, we adopt contextual relative position encoding to adaptively capture position information. Finally, a memory-efficient implementation is introduced to overcome the issue of varying point numbers in each window. Extensive experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code is available at https://github.com/dvlab-research/Stratified-Transformer. | ['Jiaya Jia', 'Xiaojuan Qi', 'Shu Liu', 'Hengshuang Zhao', 'LiWei Wang', 'Li Jiang', 'Jianhui Liu', 'Xin Lai'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lai_Stratified_Transformer_for_3D_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [-2.62684107e-01 -5.57367563e-01 -2.42597654e-01 -4.19742018e-01
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d6c0c8ad-944e-4edb-9b56-24cb338c7b0d | simmc-simple-masked-contrastive-learning-of | 2204.09826 | null | https://arxiv.org/abs/2204.09826v4 | https://arxiv.org/pdf/2204.09826v4.pdf | SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification | Recent advances in skeleton-based person re-identification (re-ID) obtain impressive performance via either hand-crafted skeleton descriptors or skeleton representation learning with deep learning paradigms. However, they typically require skeletal pre-modeling and label information for training, which leads to limited applicability of these methods. In this paper, we focus on unsupervised skeleton-based person re-ID, and present a generic Simple Masked Contrastive learning (SimMC) framework to learn effective representations from unlabeled 3D skeletons for person re-ID. Specifically, to fully exploit skeleton features within each skeleton sequence, we first devise a masked prototype contrastive learning (MPC) scheme to cluster the most typical skeleton features (skeleton prototypes) from different subsequences randomly masked from raw sequences, and contrast the inherent similarity between skeleton features and different prototypes to learn discriminative skeleton representations without using any label. Then, considering that different subsequences within the same sequence usually enjoy strong correlations due to the nature of motion continuity, we propose the masked intra-sequence contrastive learning (MIC) to capture intra-sequence pattern consistency between subsequences, so as to encourage learning more effective skeleton representations for person re-ID. Extensive experiments validate that the proposed SimMC outperforms most state-of-the-art skeleton-based methods. We further show its scalability and efficiency in enhancing the performance of existing models. Our codes are available at https://github.com/Kali-Hac/SimMC. | ['Chunyan Miao', 'Haocong Rao'] | 2022-04-21 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.05684835e-01 -3.53188872e-01 -3.49614978e-01 -3.00424069e-01
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379cce60-62f8-4234-bf64-4b58d41f86af | adaptive-scene-category-discovery-with | 1502.00374 | null | http://arxiv.org/abs/1502.00374v1 | http://arxiv.org/pdf/1502.00374v1.pdf | Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling | This paper investigates a general framework to discover categories of
unlabeled scene images according to their appearances (i.e., textures and
structures). We jointly solve the two coupled tasks in an unsupervised manner:
(i) classifying images without pre-determining the number of categories, and
(ii) pursuing generative model for each category. In our method, each image is
represented by two types of image descriptors that are effective to capture
image appearances from different aspects. By treating each image as a graph
vertex, we build up an graph, and pose the image categorization as a graph
partition process. Specifically, a partitioned sub-graph can be regarded as a
category of scenes, and we define the probabilistic model of graph partition by
accumulating the generative models of all separated categories. For efficient
inference with the graph, we employ a stochastic cluster sampling algorithm,
which is designed based on the Metropolis-Hasting mechanism. During the
iterations of inference, the model of each category is analytically updated by
a generative learning algorithm. In the experiments, our approach is validated
on several challenging databases, and it outperforms other popular
state-of-the-art methods. The implementation details and empirical analysis are
presented as well. | ['Liang Lin', 'Xiaohua Duan', 'Ruimao Zhang'] | 2015-02-02 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.59834766e-01 4.30227024e-03 -1.67655021e-01 -3.87792438e-01
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ea494ad1-6893-4c93-86fb-492605a5bb40 | detection-and-classification-of-cardiac | null | null | https://doi.org/10.1016/j.isci.2020.100886 | https://www.cell.com/action/showPdf?pii=S2589-0042%2820%2930070-5 | Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model | Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations. | ['Ming-Jing Hwang', 'Yu-Feng Hu', 'Chih-Han Huang', 'Tsai-Min Chen', 'Edward S.C. Shih'] | 2020-02-04 | null | null | null | iscience-2020-2 | ['arrhythmia-detection'] | ['medical'] | [ 2.46421158e-01 -2.59466887e-01 1.18539371e-01 -3.27302456e-01
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f7943e0a-c319-464a-a167-eb3d8deed810 | testing-deep-neural-network-based-image | 1905.07831 | null | https://arxiv.org/abs/1905.07831v3 | https://arxiv.org/pdf/1905.07831v3.pdf | Testing DNN Image Classifiers for Confusion & Bias Errors | Image classifiers are an important component of today's software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is the key catalyst behind such wide-spread success. However, wide adoption comes with serious concerns about the robustness of software systems dependent on DNNs for image classification, as several severe erroneous behaviors have been reported under sensitive and critical circumstances. We argue that developers need to rigorously test their software's image classifiers and delay deployment until acceptable. We present an approach to testing image classifier robustness based on class property violations. We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others. These bugs usually violate some class properties of one or more of those classes. Most DNN testing techniques focus on per-image violations, so fail to detect class-level confusions or biases. We developed a testing technique to automatically detect class-based confusion and bias errors in DNN-driven image classification software. We evaluated our implementation, DeepInspect, on several popular image classifiers with precision up to 100% (avg.~72.6%) for confusion errors, and up to 84.3% (avg.~66.8%) for bias errors. DeepInspect found hundreds of classification mistakes in widely-used models, many exposing errors indicating confusion or bias. | ['Vicente Ordonez', 'Gail Kaiser', 'Baishakhi Ray', 'Ziyuan Zhong', 'Yuchi Tian'] | 2019-05-20 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 3.64086807e-01 -1.62370101e-01 -9.80428532e-02 -7.41037309e-01
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80fdafb0-9425-43f3-a583-e71a64370002 | reinforcement-causal-structure-learning-on | 2211.12151 | null | https://arxiv.org/abs/2211.12151v1 | https://arxiv.org/pdf/2211.12151v1.pdf | Reinforcement Causal Structure Learning on Order Graph | Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. RCL-OG first defines reinforcement learning with a new reward mechanism to approximate the posterior distribution of orderings in an efficacy way, and uses deep Q-learning to update and transfer rewards between nodes. Next, it obtains the probability transition model of nodes on order graph, and computes the posterior probability of different orderings. In this way, we can sample on this model to obtain the ordering with high probability. Experiments on synthetic and benchmark datasets show that RCL-OG provides accurate posterior probability approximation and achieves better results than competitive causal discovery algorithms. | ['Maozu Guo', 'Zhengtian Wu', 'Jun Wang', 'Guoxian Yu', 'Dezhi Yang'] | 2022-11-22 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-2.00112522e-01 1.71245918e-01 -5.01686633e-01 -3.91964585e-01
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78f1a494-edbb-4758-ba12-c25859f0b859 | talkclip-talking-head-generation-with-text | 2304.00334 | null | https://arxiv.org/abs/2304.00334v1 | https://arxiv.org/pdf/2304.00334v1.pdf | TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles | In order to produce facial-expression-specified talking head videos, previous audio-driven one-shot talking head methods need to use a reference video with a matching speaking style (i.e., facial expressions). However, finding videos with a desired style may not be easy, potentially restricting their application. In this work, we propose an expression-controllable one-shot talking head method, dubbed TalkCLIP, where the expression in a speech is specified by the natural language. This would significantly ease the difficulty of searching for a video with a desired speaking style. Here, we first construct a text-video paired talking head dataset, in which each video has alternative prompt-alike descriptions. Specifically, our descriptions involve coarse-level emotion annotations and facial action unit (AU) based fine-grained annotations. Then, we introduce a CLIP-based style encoder that first projects natural language descriptions to the CLIP text embedding space and then aligns the textual embeddings to the representations of speaking styles. As extensive textual knowledge has been encoded by CLIP, our method can even generalize to infer a speaking style whose description has not been seen during training. Extensive experiments demonstrate that our method achieves the advanced capability of generating photo-realistic talking heads with vivid facial expressions guided by text descriptions. | ['Xin Yu', 'Zhidong Deng', 'Zhipeng Hu', 'Changjie Fan', 'Tangjie Lv', 'Bowen Ma', 'Yu Ding', 'Suzhen Wang', 'Yifeng Ma'] | 2023-04-01 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 1.48175150e-01 -3.39710265e-02 -2.49879315e-01 -1.01680064e+00
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6fbec2f4-666b-4ec7-bd6d-a2a8446422fe | federated-learning-for-breast-density | 2009.01871 | null | https://arxiv.org/abs/2009.01871v3 | https://arxiv.org/pdf/2009.01871v3.pdf | Federated Learning for Breast Density Classification: A Real-World Implementation | Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data. | ['Jayashree Kalpathy-Cramer', 'Mona Flores', 'Ram C. Naidu', 'Evan Leibovitz', 'Behrooz Hashemian', 'Jesse Tetreault', 'Matheus Mendonça', 'Felipe Kitamura', 'Vikash Gupta', 'Yuhong Wen', 'Nir Neumark', 'Meesam Shah', 'Katharina V. Hoebel', 'Bryan Chen', 'B. Min Yun', 'Alvin Ihsani', 'Vitor Lavor', 'Thomas Schultz', 'Sean Ko', 'Richard White', 'Prerna Dogra', 'Praveer Singh', 'Laura Coombs', 'Keith J. Dreyer', 'Ittai Dayan', 'Holger R. Roth', 'Colin Compas', 'Adam McCarthy', 'Yan Cheng', 'Wenqi Li', 'Varun Buch', 'Sharut Gupta', 'Selnur Erdal', 'Miao Zhang', 'Elshaimaa Sharaf', 'Daniel Rubin', 'Daguang Xu', 'Bernardo C. Bizzo', 'Liangqiong Qu', 'Ken Chang', 'Jay B. Patel', 'Etta D. Pisano', 'Ahmed Harouni'] | 2020-09-03 | null | null | null | null | ['breast-density-classification'] | ['medical'] | [-1.60442770e-01 3.64763945e-01 -5.10021210e-01 -8.97696257e-01
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1c92af2a-3eb2-4f89-a6f7-c632d0642e15 | on-the-risk-of-misinformation-pollution-with | 2305.13661 | null | https://arxiv.org/abs/2305.13661v1 | https://arxiv.org/pdf/2305.13661v1.pdf | On the Risk of Misinformation Pollution with Large Language Models | In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems. We establish a threat model and simulate potential misuse scenarios, both unintentional and intentional, to assess the extent to which LLMs can be utilized to produce misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of ODQA systems. To mitigate the harm caused by LLM-generated misinformation, we explore three defense strategies: prompting, misinformation detection, and majority voting. While initial results show promising trends for these defensive strategies, much more work needs to be done to address the challenge of misinformation pollution. Our work highlights the need for further research and interdisciplinary collaboration to address LLM-generated misinformation and to promote responsible use of LLMs. | ['William Yang Wang', 'Min-Yen Kan', 'Preslav Nakov', 'Wenhu Chen', 'Liangming Pan', 'Yikang Pan'] | 2023-05-23 | null | null | null | null | ['misinformation', 'open-domain-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 8.61938149e-02 4.26205963e-01 3.81955169e-02 -1.19176500e-01
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14958994-ed50-4ff7-9a50-4a0e7903f1cf | actions-speak-louder-than-goalsvaluing-player | null | null | https://www.researchgate.net/publication/323302738_Actions_Speak_Louder_Than_Goals_Valuing_Player_Actions_in_Soccer | https://arxiv.org/pdf/1802.07127.pdf | Actions Speak Louder than Goals:Valuing Player Actions in Soccer | Assessing the impact of the individual actions performed by soccerplayers during games is a crucial aspect of the player recruitmentprocess. Unfortunately, most traditional metrics fall short in ad-dressing this task as they either focus on rare actions like shotsand goals alone or fail to account for the context in which theactions occurred. This paper introduces (1) a new language for de-scribing individual player actions on the pitch and (2) a frameworkfor valuing any type of player action based on its impact on thegame outcome while accounting for the context in which the actionhappened. By aggregating soccer players’ action values, their totaloffensive and defensive contributions to their team can be quan-tified. We show how our approach considers relevant contextualinformation that traditional player evaluation metrics ignore andpresent a number of use cases related to scouting and playing stylecharacterization in the 2016/2017 and 2017/2018 seasons in Europe’stop competitions. | ['Jesse Davis', 'Lotte Bransen', 'Tom Decroos', 'Jan Van Haaren'] | 2019-07-10 | null | null | null | published-in-kdd-2018-2019-7 | ['football-action-valuation'] | ['playing-games'] | [-2.19280422e-02 -1.18653968e-01 -3.65128756e-01 1.47393290e-02
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b8055988-f4cb-491a-ba6f-10929c1909e3 | brdf-estimation-of-complex-materials-with | 1811.09131 | null | http://arxiv.org/abs/1811.09131v1 | http://arxiv.org/pdf/1811.09131v1.pdf | BRDF Estimation of Complex Materials with Nested Learning | The estimation of the optical properties of a material from RGB-images is an
important but extremely ill-posed problem in Computer Graphics. While recent
works have successfully approached this problem even from just a single
photograph, significant simplifications of the material model are assumed,
limiting the usability of such methods. The detection of complex material
properties such as anisotropy or Fresnel effect remains an unsolved challenge.
We propose a novel method that predicts the model parameters of an
artist-friendly, physically-based BRDF, from only two low-resolution shots of
the material. Thanks to a novel combination of deep neural networks in a nested
architecture, we are able to handle the ambiguities given by the
non-orthogonality and non-convexity of the parameter space. To train the
network, we generate a novel dataset of physically-based synthetic images. We
prove that our model can recover new properties like anisotropy, index of
refraction and a second reflectance color, for materials that have tinted
specular reflections or whose albedo changes at glancing angles. | ['Jorge Lopez-Moreno', 'Dan Casas', 'Raquel Vidaurre', 'Elena Garces'] | 2018-11-22 | null | null | null | null | ['brdf-estimation'] | ['computer-vision'] | [ 6.57364011e-01 -6.19071536e-03 7.67895639e-01 -3.66322190e-01
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03fa0e15-d9c1-4f71-bc02-deea154408b1 | ersam-neural-architecture-search-for-energy | 2303.10727 | null | https://arxiv.org/abs/2303.10727v2 | https://arxiv.org/pdf/2303.10727v2.pdf | ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement | Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facilitate wide adoption of the aforementioned applications, the required computational complexity of state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at odds with the often constrained resources on mobile devices. Furthermore, only limited labeled data is available or practical when it comes to SAM under clinical settings due to various privacy constraints and the required human effort, further challenging the achievable accuracy of on-device SAM solutions. To this end, we propose a dedicated neural architecture search framework for Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework can automatically search for DNNs that push forward the achievable accuracy vs. hardware efficiency frontier of mobile SAM solutions. For example, ERSAM-delivered DNNs only consume 40 mW x 12 h energy and 0.05 seconds processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only achieving an error rate of 14.3% on a social ambiance dataset generated by LibriSpeech. We can expect that our ERSAM framework can pave the way for ubiquitous on-device SAM solutions which are in growing demand. | ['Yingyan Lin', 'Ashutosh Sabharwal', 'Jiayi Yuan', 'Wenwan Chen', 'Chaojian Li'] | 2023-03-19 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 4.01040643e-01 1.47214085e-02 -3.56909722e-01 -3.12936962e-01
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3b65f4d3-bfe5-40a4-a083-f2f6599ccf57 | graph-based-automatic-domain-term-extraction | null | null | https://aclanthology.org/2020.icon-termtraction.1 | https://aclanthology.org/2020.icon-termtraction.1.pdf | Graph Based Automatic Domain Term Extraction | We present a Graph Based Approach to automatically extract domain specific terms from technical domains like Biochemistry, Communication, Computer Science and Law. Our approach is similar to TextRank with an extra post-processing step to reduce the noise. We performed our experiments on the mentioned domains provided by ICON TermTraction - 2020 shared task. Presented precision, recall and f1-score for all experiments. Further, it is observed that our method gives promising results without much noise in domain terms. | ['Dipti Sharma', 'Hema Ala'] | null | null | null | null | icon-2020-12 | ['term-extraction'] | ['natural-language-processing'] | [ 8.73953328e-02 2.14262381e-01 -5.47367394e-01 -1.66326135e-01
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901a9d3c-0f3c-4f15-92b6-e200d02be010 | molcpt-molecule-continuous-prompt-tuning-to | 2212.10614 | null | https://arxiv.org/abs/2212.10614v1 | https://arxiv.org/pdf/2212.10614v1.pdf | MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning | Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their structure modeling capabilities. Since labeled data is often scarce and expensive to obtain, it is a great challenge for GNNs to generalize in the extensive molecular space. Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs. It uses self-supervised information to pre-train the GNN, and then performs fine-tuning to optimize the downstream task with just a few labels. However, pre-training does not always yield statistically significant improvement, especially for self-supervised learning with random structural masking. In fact, the molecular structure is characterized by motif subgraphs, which are frequently occurring and influence molecular properties. To leverage the task-related motifs, we propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. The prompt effectively augments the molecular graph with meaningful motifs in the continuous representation space; this provides more structural patterns to aid the downstream classifier in identifying molecular properties. Extensive experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction, with or without a few fine-tuning steps. | ['Xia Hu', 'Xiao Huang', 'Kaixiong Zhou', 'Cameron Diao'] | 2022-12-20 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.35636806e-01 -2.48541772e-01 -7.07540512e-01 -5.24660707e-01
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fac4f04d-7dfc-4569-8102-4e5f9bacc9b7 | polarstream-streaming-lidar-object-detection | 2106.07545 | null | https://arxiv.org/abs/2106.07545v2 | https://arxiv.org/pdf/2106.07545v2.pdf | PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars | Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud. However, due to use of cartesian coordinate systems these methods represent the sectors as rectangular regions, wasting memory and compute. In this work we propose using a polar coordinate system and make two key improvements on this design. First, we increase the spatial context by using multi-scale padding from neighboring sectors: preceding sector from the current scan and/or the following sector from the past scan. Second, we improve the core polar convolutional architecture by introducing feature undistortion and range stratified convolutions. Experimental results on the nuScenes dataset show significant improvements over other streaming based methods. We also achieve comparable results to existing non-streaming methods but with lower latencies. The code and pretrained models are available at \url{https://github.com/motional/polarstream}. | ['Oscar Beijbom', 'Sourabh Vora', 'Qi Chen'] | 2021-06-14 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.56894112e-01 -1.50411889e-01 -2.23993771e-02 -5.45979202e-01
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5ed8756c-096f-41e1-827b-984dca81f8f8 | space-partitioning-ransac | 2111.12385 | null | https://arxiv.org/abs/2111.12385v2 | https://arxiv.org/pdf/2111.12385v2.pdf | Space-Partitioning RANSAC | A new algorithm is proposed to accelerate RANSAC model quality calculations. The method is based on partitioning the joint correspondence space, e.g., 2D-2D point correspondences, into a pair of regular grids. The grid cells are mapped by minimal sample models, estimated within RANSAC, to reject correspondences that are inconsistent with the model parameters early. The proposed technique is general. It works with arbitrary transformations even if a point is mapped to a point set, e.g., as a fundamental matrix maps to epipolar lines. The method is tested on thousands of image pairs from publicly available datasets on fundamental and essential matrix, homography and radially distorted homography estimation. On average, it reduces the RANSAC run-time by 41% with provably no deterioration in the accuracy. It can be straightforwardly plugged into state-of-the-art RANSAC frameworks, e.g. VSAC. | ['Gabor Valasek', 'Daniel Barath'] | 2021-11-24 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 5.41892871e-02 -2.62778789e-01 3.21216397e-02 -1.65240526e-01
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ec3056c5-e5e5-4972-8f51-d2945988f7a2 | ghost-at-semeval-2021-task-5-is-explanation | null | null | https://aclanthology.org/2021.semeval-1.114 | https://aclanthology.org/2021.semeval-1.114.pdf | GHOST at SemEval-2021 Task 5: Is explanation all you need? | This paper discusses different approaches to the Toxic Spans Detection task. The problem posed by the task was to determine which words contribute mostly to recognising a document as toxic. As opposed to binary classification of entire texts, word-level assessment could be of great use during comment moderation, also allowing for a more in-depth comprehension of the model{'}s predictions. As the main goal was to ensure transparency and understanding, this paper focuses on the current state-of-the-art approaches based on the explainable AI concepts and compares them to a supervised learning solution with word-level labels. The work consists of two xAI approaches that automatically provide the explanation for models trained for binary classification of toxic documents: an LSTM model with attention as a model-specific approach and the Shapley values for interpreting BERT predictions as a model-agnostic method. The competing approach considers this problem as supervised token classification, where models like BERT and its modifications were tested. The paper aims to explore, compare and assess the quality of predictions for different methods on the task. The advantages of each approach and further research direction are also discussed. | ['Hanna Klimczak', "Kamil Pluci{\\'n}ski"] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 6.06237173e-01 6.18237793e-01 -4.09617990e-01 -3.83399785e-01
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c3484dae-51c8-4961-9bd7-6074d039723c | illumination-adaptive-person-re | 1905.04525 | null | https://arxiv.org/abs/1905.04525v2 | https://arxiv.org/pdf/1905.04525v2.pdf | Illumination-Adaptive Person Re-identification | Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common, and person images are often captured under different illumination conditions at different times across a day. In this situation, the performances of existing ReID models often degrade dramatically. This paper addresses the ReID problem with illumination variations and names it as {\em Illumination-Adaptive Person Re-identification (IA-ReID)}. We propose an Illumination-Identity Disentanglement (IID) network to dispel different scales of illuminations away while preserving individuals' identity information. To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations. Experimental results on the simulated datasets and real-world images demonstrate the effectiveness of the proposed framework. | ["Shin'ichi Satoh", 'Yung-Yu Chuang', 'Zheng Wang', 'Zelong Zeng', 'Yinqiang Zheng', 'Zhixiang Wang'] | 2019-05-11 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [ 1.14656463e-02 -8.79088998e-01 1.30671665e-01 -5.44964850e-01
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1b5489b8-c334-48a6-b817-ed1d96caef14 | explicit-occlusion-modeling-for-3d-object | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Zia_Explicit_Occlusion_Modeling_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Zia_Explicit_Occlusion_Modeling_2013_CVPR_paper.pdf | Explicit Occlusion Modeling for 3D Object Class Representations | Despite the success of current state-of-the-art object class detectors, severe occlusion remains a major challenge. This is particularly true for more geometrically expressive 3D object class representations. While these representations have attracted renewed interest for precise object pose estimation, the focus has mostly been on rather clean datasets, where occlusion is not an issue. In this paper, we tackle the challenge of modeling occlusion in the context of a 3D geometric object class model that is capable of fine-grained, part-level 3D object reconstruction. Following the intuition that 3D modeling should facilitate occlusion reasoning, we design an explicit representation of likely geometric occlusion patterns. Robustness is achieved by pooling image evidence from of a set of fixed part detectors as well as a non-parametric representation of part configurations in the spirit of poselets. We confirm the potential of our method on cars in a newly collected data set of inner-city street scenes with varying levels of occlusion, and demonstrate superior performance in occlusion estimation and part localization, compared to baselines that are unaware of occlusions. | ['Michael Stark', 'M. Zeeshan Zia', 'Konrad Schindler'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['occlusion-estimation', '3d-object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-4.00755592e-02 2.48119682e-01 -1.45835862e-01 -4.74958330e-01
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d6b73676-11bc-4057-ac0f-cb34d96f10ba | conversational-semantic-role-labeling-with | 2210.03037 | null | https://arxiv.org/abs/2210.03037v1 | https://arxiv.org/pdf/2210.03037v1.pdf | Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph | Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In this work, we investigate the integration of a latent graph for CSRL. We propose to automatically induce a predicate-oriented latent graph (POLar) with a predicate-centered Gaussian mechanism, by which the nearer and informative words to the predicate will be allocated with more attention. The POLar structure is then dynamically pruned and refined so as to best fit the task need. We additionally introduce an effective dialogue-level pre-trained language model, CoDiaBERT, for better supporting multiple utterance sentences and handling the speaker coreference issue in CSRL. Our system outperforms best-performing baselines on three benchmark CSRL datasets with big margins, especially achieving over 4% F1 score improvements on the cross-utterance argument detection. Further analyses are presented to better understand the effectiveness of our proposed methods. | ['Donghong Ji', 'Yafeng Ren', 'Meishan Zhang', 'Shengqiong Wu', 'Hao Fei'] | 2022-10-06 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.37921584e-01 9.53510821e-01 -3.54431540e-01 -6.67795956e-01
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3ce59a90-a96d-42d0-8896-e565893ebfd4 | last-layer-fairness-fine-tuning-is-simple-and | 2304.03935 | null | https://arxiv.org/abs/2304.03935v1 | https://arxiv.org/pdf/2304.03935v1.pdf | Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks | As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern and varieties of fairness criteria have been proposed. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of training method because they don't require accessing sensitive attributes during test time in contrast to post-processing methods. Although imposing fairness constraints have been studied extensively for classical machine learning models, the effect these techniques have on deep neural networks is still unclear. Recent research has shown that adding fairness constraints to the objective function leads to severe over-fitting to fairness criteria in large models, and how to solve this challenge is an important open question. To address this challenge, we leverage the wisdom and power of pre-training and fine-tuning and develop a simple but novel framework to train fair neural networks in an efficient and inexpensive way. We conduct comprehensive experiments on two popular image datasets with state-of-art architectures under different fairness notions to show that last-layer fine-tuning is sufficient for promoting fairness of the deep neural network. Our framework brings new insights into representation learning in training fair neural networks. | ['James Zou', 'Kenji Kawaguchi', 'Ting Ye', 'Huaxiu Yao', 'Zhun Deng', 'Yuzhen Mao'] | 2023-04-08 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-4.22145948e-02 -9.79141444e-02 -3.74691665e-01 -1.02578855e+00
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9cafe3ba-4836-4f9a-9927-3c1e8fad90a2 | dual-contrastive-attributed-graph-clustering | 2206.07897 | null | https://arxiv.org/abs/2206.07897v2 | https://arxiv.org/pdf/2206.07897v2.pdf | NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering | Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-structured data. However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated; 2) depend too much on data augmentation, which greatly limits the capability of contrastive learning; 3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results. In this paper, we propose a Neighborhood Contrast Framework for Attributed Graph Clustering, namely NCAGC, seeking for conquering the aforementioned limitations. Specifically, by leveraging the Neighborhood Contrast Module, the representation of neighbor nodes will be 'push closer' and become clustering-oriented with the neighborhood contrast loss. Moreover, a Contrastive Self-Expression Module is built by minimizing the node representation before and after the self-expression layer to constraint the learning of self-expression matrix. All the modules of NCAGC are optimized in a unified framework, so the learned node representation contains clustering-oriented messages. Extensive experimental results on four attributed graph datasets demonstrate the promising performance of NCAGC compared with 16 state-of-the-art clustering methods. The code is available at https://github.com/wangtong627/NCAGC. | ['Junhua Wu', 'Zhenquan Zhang', 'Qijia He', 'Guanyu Yang', 'Tong Wang'] | 2022-06-16 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 2.72478852e-02 1.88015759e-01 -4.40662205e-01 -3.02460015e-01
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5bdb9ef4-2f71-41c9-95b7-439aa1f0e5c4 | multi-components-system-for-automatic-arabic | null | null | https://link.springer.com/chapter/10.1007/978-3-030-45439-5_23 | https://link.springer.com/content/pdf/10.1007%2F978-3-030-45439-5_23.pdf | Multi-components System for Automatic Arabic Diacritization | In this paper, we propose an approach to tackle the problem of the automatic restoration of Arabic diacritics that includes three components stacked in a pipeline: a deep learning model which is a multi-layer recurrent neural network with LSTM and Dense layers, a character-level rule-based corrector which applies deterministic operations to prevent some errors, and a word-level statistical corrector which uses the context and the distance information to fix some diacritization issues. This approach is novel in a way that combines methods of different types and adds edit distance based corrections.
We used a large public dataset containing raw diacritized Arabic text (Tashkeela) for training and testing our system after cleaning and normalizing it. On a newly-released benchmark test set, our system outperformed all the tested systems by achieving DER of 3.39% and WER of 9.94% when taking all Arabic letters into account, DER of 2.61% and WER of 5.83% when ignoring the diacritization of the last letter of every word. | ['Shengwu Xiong', 'Hamza Abbad'] | 2020-04-08 | null | null | null | null | ['arabic-text-diacritization'] | ['natural-language-processing'] | [ 1.65806800e-01 -8.39455128e-02 6.43127561e-01 -4.80742902e-01
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91391434-d4fc-4037-ac89-089aa11be0c5 | neural-machine-translation-for-query | 1806.10478 | null | http://arxiv.org/abs/1806.10478v2 | http://arxiv.org/pdf/1806.10478v2.pdf | Neural Machine Translation for Query Construction and Composition | Research on question answering with knowledge base has recently seen an
increasing use of deep architectures. In this extended abstract, we study the
application of the neural machine translation paradigm for question parsing. We
employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph
query language and their compositions. Instead of inducing the programs through
question-answer pairs, we expect a semi-supervised approach, where alignments
between questions and queries are built through templates. We argue that the
coverage of language utterances can be expanded using late notable works in
natural language generation. | ['André Valdestilhas', 'Gustavo Publio', 'Diego Esteves', 'Edgard Marx', 'Tommaso Soru', 'Diego Moussallem'] | 2018-06-27 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 5.43187559e-01 1.13276565e+00 -2.93935500e-02 -6.67649746e-01
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4e6344b2-24a9-4501-91d6-cb5b520cfdc2 | real-time-pear-fruit-detection-and-counting | null | null | https://www.mdpi.com/1188352 | https://www.mdpi.com/1424-8220/21/14/4803/pdf | Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT | This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.50 of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8–14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected. | ['Tofael Ahamed', 'Addie Ira Borja Parico'] | 2021-07-14 | null | null | null | sensors-2021-7 | ['object-counting'] | ['computer-vision'] | [-7.69281611e-02 -3.21054369e-01 1.13420961e-02 2.70797640e-01
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f98e78cd-f5da-4b49-a3eb-52dadd91538d | relating-eeg-recordings-to-speech-using | 2303.06435 | null | https://arxiv.org/abs/2303.06435v1 | https://arxiv.org/pdf/2303.06435v1.pdf | Relating EEG recordings to speech using envelope tracking and the speech-FFR | During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding' Signal Processing Grand Challenge. A simple ensembling method is shown to considerably improve upon the baseline decoder performance. Even higher classification rates are achieved by jointly decoding the speech-evoked frequency-following response and responses to the temporal envelope of speech, as well as by fine-tuning the decoders to individual subjects. Our results could have applications in the diagnosis of hearing disorders or in cognitively steered hearing aids. | ['Tobias Reichenbach', 'Danilo Mandic', 'Mike Thornton'] | 2023-03-11 | null | null | null | null | ['eeg-decoding', 'eeg', 'eeg', 'eeg-decoding'] | ['medical', 'methodology', 'time-series', 'time-series'] | [ 4.01685238e-01 -1.17788631e-02 8.87584269e-01 -3.75941366e-01
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ea363674-e222-49ad-9836-ad073fddf5da | balancing-the-trade-off-between-cost-and | 2207.09089 | null | https://arxiv.org/abs/2207.09089v1 | https://arxiv.org/pdf/2207.09089v1.pdf | Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method | The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA. | ['Zhenzhou Tang', 'Qian Hu', 'Fangyi Xu', 'Yingying Xu', 'Long Chen'] | 2022-07-19 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-7.19565675e-02 -9.89276096e-02 -1.17376179e-01 4.59515043e-02
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e85b86fa-e633-4132-90be-aa9d73525876 | a-semi-supervised-deep-transfer-learning | null | null | https://ieeexplore.ieee.org/document/9552475 | https://ieeexplore.ieee.org/document/9552475 | A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction | Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the availability of labeled historical data but also on the careful samples selection. However, in real operating systems such as induction machines, which generally have a long reliable life, storing the entire operation history, including deterioration (i.e. bearings), will be very expensive and difficult to feed accurately into the training model. Other alternatives sequentially store samples that hold degradation patterns similar to real ones in damage behavior by imposing an accelerated deterioration. Labels lack and differences in distributions caused by the imposed deterioration will ultimately discriminate the training model and limit its knowledge capacity. In an attempt to overcome these drawbacks, a novel sequence-by-sequence deep learning algorithm able to expand the generalization capacity by transferring obtained knowledge from life cycles of similar systems is proposed. The new algorithm aims to determine health status by involving long short-term memory neural network as a primary component of adaptive learning to extract both health stage and health index inferences. Experimental validations are performed using the PRONOSTIA bearing degradation datasets. | ['Mohamed Benbouzid', 'Toufik Bentrcia', 'Leila-Hayet Mouss', 'Tarek Berghout'] | 2021-09-29 | null | null | null | ieee-transaction-on-energy-conversion-2021-9 | ['interpretability-techniques-for-deep-learning'] | ['miscellaneous'] | [ 2.16232911e-01 -3.94849181e-01 -6.54748902e-02 -3.27565700e-01
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273d5553-b506-45b9-97e2-f28a504931fe | unveiling-covid-19-from-chest-x-ray-with-deep | 2004.05405 | null | https://arxiv.org/abs/2004.05405v1 | https://arxiv.org/pdf/2004.05405v1.pdf | Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data | The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR. | ['Carlo Alberto Barbano', 'Enzo Tartaglione', 'Claudio Berzovini', 'Marco Grangetto', 'Marco Calandri'] | 2020-04-11 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.92551687e-01 -3.07582259e-01 6.22633621e-02 -3.73349130e-01
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0348bdfa-8fcb-4492-a362-eaa328d196b8 | evaluating-the-acquisition-of-semantic | null | null | https://aclanthology.org/2021.cmcl-1.24 | https://aclanthology.org/2021.cmcl-1.24.pdf | Evaluating the Acquisition of Semantic Knowledge from Cross-situational Learning in Artificial Neural Networks | When learning their native language, children acquire the meanings of words and sentences from highly ambiguous input without much explicit supervision. One possible learning mechanism is cross-situational learning, which has been successfully tested in laboratory experiments with children. Here we use Artificial Neural Networks to test if this mechanism scales up to more natural language and visual scenes using a large dataset of crowd-sourced images with corresponding descriptions. We evaluate learning using a series of tasks inspired by methods commonly used in laboratory studies of language acquisition. We show that the model acquires rich semantic knowledge both at the word- and sentence-level, mirroring the patterns and trajectory of learning in early childhood. Our work highlights the usefulness of low-level co-occurrence statistics across modalities in facilitating the early acquisition of higher-level semantic knowledge. | ['Abdellah Fourtassi', 'Mitja Nikolaus'] | null | null | null | null | naacl-cmcl-2021-6 | ['language-acquisition'] | ['natural-language-processing'] | [ 3.68491650e-01 -8.41199830e-02 1.93021953e-01 -6.95062101e-01
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5cfc75dd-69c0-4712-a4c8-4f86a6520680 | syreanet-a-physically-guided-underwater-image | 2302.08269 | null | https://arxiv.org/abs/2302.08269v2 | https://arxiv.org/pdf/2302.08269v2.pdf | SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images | Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework \textit{SyreaNet} for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://github.com/RockWenJJ/SyreaNet.git. | ['Ben M. Chen', 'Lihua Dou', 'Zhi Gao', 'Ruixin Yan', 'Zhenjun Zhao', 'Jinqiang Cui', 'Junjie Wen'] | 2023-02-16 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 3.44423562e-01 1.94057584e-01 6.86336577e-01 -2.48741686e-01
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5af035b2-c8f4-4eea-b06c-351adaa96dd9 | end-to-end-dense-video-captioning-as-sequence | null | null | https://openreview.net/forum?id=dWjknwjRtZ0 | https://openreview.net/pdf?id=dWjknwjRtZ0 | End-to-end Dense Video Captioning as Sequence Generation | Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage generative process, which first proposes a segment for each event, then renders a caption for each identified segment. Recent advances in large-scale sequence generation pretraining have seen great success in unifying task formulation for a great variety of tasks, but so far, more complex tasks such as dense video captioning are not able to fully utilize this powerful paradigm.In this work, we show how to model the two subtasks of dense video captioning jointly as one sequence generation task, and simultaneously predict the events and the corresponding descriptions. Experiments on YouCook2 and ViTT show encouraging results and indicate the feasibility of training complex tasks such as end-to-end dense video captioning integrated into large-scale pretrained models. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['dense-video-captioning'] | ['computer-vision'] | [ 5.32797217e-01 2.45082274e-01 -1.49616420e-01 -3.80518049e-01
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5aece3ac-b542-4807-95c6-08a80afb7d50 | a-comprehensive-study-of-cotton-price | 2212.01584 | null | https://arxiv.org/abs/2212.01584v2 | https://arxiv.org/pdf/2212.01584v2.pdf | A comprehensive study of cotton price fluctuations using multiple Econometric and LSTM neural network models | This paper proposes a new coherent model for a comprehensive study of the cotton price using econometrics and Long-Short term memory neural network (LSTM) methodologies. We call a simple cotton price trend and then assumed conjectures in structural method (ARMA), Markov switching dynamic regression, simultaneous equation system, GARCH families procedures, and Artificial Neural Networks that determine the characteristics of cotton price trend duration 1990-2020. It is established that in the structural method, the best procedure is AR (2) by Markov switching estimation. Based on the MS-AR procedure, it concludes that tending to regime change from decreasing trend to an increasing one is more significant than a reverse mode. The simultaneous equation system investigates three procedures based on the acreage cotton, value-added, and real cotton price. Finally, prediction with the GARCH families TARCH procedure is the best-fitting model, and in the LSTM neural network, the results show an accurate prediction by the training-testing method. | ['Mohammad Hemami', 'Mehdi Ghasemi Meymandi', 'Morteza Tahami Pour Zarandi'] | 2022-12-03 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-2.71029770e-01 -3.44431609e-01 -4.07434940e-01 -1.60694242e-01
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69fb63a5-fa3a-4cb1-8b73-93adeefa02bf | interpretable-deep-models-for-cardiac | 2006.13811 | null | https://arxiv.org/abs/2006.13811v2 | https://arxiv.org/pdf/2006.13811v2.pdf | Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction | Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical trust and facilitating clinical translation. Furthermore, for many problems in medicine there is a wealth of existing clinical knowledge to draw upon, which may be useful in generating explanations, but it is not obvious how this knowledge can be encoded into DL models - most models are learnt either from scratch or using transfer learning from a different domain. In this paper we address both of these issues. We propose a novel DL framework for image-based classification based on a variational autoencoder (VAE). The framework allows prediction of the output of interest from the latent space of the autoencoder, as well as visualisation (in the image domain) of the effects of crossing the decision boundary, thus enhancing the interpretability of the classifier. Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge. The framework can predict outputs as well as explanations for these outputs, and also raises the possibility of discovering new biomarkers that are separate (or disentangled) from the existing knowledge. We demonstrate our framework on the problem of predicting response of patients with cardiomyopathy to cardiac resynchronization therapy (CRT) from cine cardiac magnetic resonance images. The sensitivity and specificity of the proposed model on the task of CRT response prediction are 88.43% and 84.39% respectively, and we showcase the potential of our model in enhancing understanding of the factors contributing to CRT response. | ['Christopher A. Rinaldi', 'Baldeep S. Sidhu', 'Esther Puyol-Antón', 'Mark Elliott', 'James R. Clough', 'Daniel Rueckert', 'Chen Chen', 'Vishal Mehta', 'Justin Gould', 'Bram Ruijsink', 'Bradley Porter', 'Andrew P. King'] | 2020-06-24 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.44889930e-01 6.69346750e-01 -3.19675118e-01 -3.61783981e-01
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1dc8031d-99e9-42b8-814a-db9f36a34d45 | x-risawoz-high-quality-end-to-end | 2306.17674 | null | https://arxiv.org/abs/2306.17674v1 | https://arxiv.org/pdf/2306.17674v1.pdf | X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents | Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language. X-RiSAWOZ has more than 18,000 human-verified dialogue utterances for each language, and unlike most multilingual prior work, is an end-to-end dataset for building fully-functioning agents. The many difficulties we encountered in creating X-RiSAWOZ led us to develop a toolset to accelerate the post-editing of a new language dataset after translation. This toolset improves machine translation with a hybrid entity alignment technique that combines neural with dictionary-based methods, along with many automated and semi-automated validation checks. We establish strong baselines for X-RiSAWOZ by training dialogue agents in the zero- and few-shot settings where limited gold data is available in the target language. Our results suggest that our translation and post-editing methodology and toolset can be used to create new high-quality multilingual dialogue agents cost-effectively. Our dataset, code, and toolkit are released open-source. | ['Monica S. Lam', 'Deyi Xiong', 'Chaobin You', 'Aditya Yadavalli', 'Michael Sun', 'Manish Shrivastava', 'Vivek Seshadri', 'Jiwon Seo', 'Sina J. Semnani', 'Nasredine Semmar', 'Ponnurangam Kumaraguru', 'Prashant Kodali', 'Sungkyun Kim', 'Anmol Goel', 'Gaël de Chalendar', 'Monojit Choudhury', 'Kalika Bali', 'Tianhao Shen', 'Mehrad Moradshahi'] | 2023-06-30 | null | null | null | null | ['entity-alignment', 'machine-translation', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-2.57131517e-01 1.50467709e-01 -1.23590127e-01 -3.62976581e-01
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7aa2a951-7b4a-4d3d-9429-8e9f17dfd67b | k-arma-models-for-clustering-time-series-data | 2207.00039 | null | https://arxiv.org/abs/2207.00039v1 | https://arxiv.org/pdf/2207.00039v1.pdf | K-ARMA Models for Clustering Time Series Data | We present an approach to clustering time series data using a model-based generalization of the K-Means algorithm which we call K-Models. We prove the convergence of this general algorithm and relate it to the hard-EM algorithm for mixture modeling. We then apply our method first with an AR($p$) clustering example and show how the clustering algorithm can be made robust to outliers using a least-absolute deviations criteria. We then build our clustering algorithm up for ARMA($p,q$) models and extend this to ARIMA($p,d,q$) models. We develop a goodness of fit statistic for the models fitted to clusters based on the Ljung-Box statistic. We perform experiments with simulated data to show how the algorithm can be used for outlier detection, detecting distributional drift, and discuss the impact of initialization method on empty clusters. We also perform experiments on real data which show that our method is competitive with other existing methods for similar time series clustering tasks. | ['Martin T. Wells', 'David S. Matteson', 'Derek O. Hoare'] | 2022-06-30 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-4.03577298e-01 -3.34960073e-01 3.57793421e-01 -4.25820380e-01
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b54d0706-7ab4-4f11-9716-f83b1ca70b6c | affective-reasoning-at-utterance-level-in | 2305.02615 | null | https://arxiv.org/abs/2305.02615v1 | https://arxiv.org/pdf/2305.02615v1.pdf | Affective Reasoning at Utterance Level in Conversations: A Causal Discovery Approach | The affective reasoning task is a set of emerging affect-based tasks in conversation, including Emotion Recognition in Conversation (ERC),Emotion-Cause Pair Extraction (ECPE), and Emotion-Cause Span Recognition (ECSR). Existing methods make various assumptions on the apparent relationship while neglecting the essential causal model due to the nonuniqueness of skeletons and unobservability of implicit causes. This paper settled down the above two problems and further proposed Conversational Affective Causal Discovery (CACD). It is a novel causal discovery method showing how to discover causal relationships in a conversation via designing a common skeleton and generating a substitute for implicit causes. CACD contains two steps: (i) building a common centering one graph node causal skeleton for all utterances in variable-length conversations; (ii) Causal Auto-Encoder (CAE) correcting the skeleton to yield causal representation through generated implicit causes and known explicit causes. Comprehensive experiments demonstrate that our novel method significantly outperforms the SOTA baselines in six affect-related datasets on the three tasks. | ['Wenjing Zhu', 'Xinyu Yang', 'Jing Luo', 'Hang Chen'] | 2023-05-04 | null | null | null | null | ['causal-discovery', 'emotion-recognition-in-conversation', 'emotion-cause-pair-extraction'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 4.50558752e-01 7.14483857e-01 -8.46108422e-02 -7.21235454e-01
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2463a202-0299-4dc2-83f7-8b3bda2cd0fa | cooperative-tuning-of-multi-agent-optimal | 2209.12017 | null | https://arxiv.org/abs/2209.12017v1 | https://arxiv.org/pdf/2209.12017v1.pdf | Cooperative Tuning of Multi-Agent Optimal Control Systems | This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts parameters in their dynamics, objective functions, or controllers in a coordinated way to minimize the sum of their loss functions. Different from classical techniques for tuning parameters in a controller, we allow tunable parameters appearing in both the system dynamics and the objective functions of each agent. A framework is developed to allow all agents to reach a consensus on the tunable parameter, which minimizes team loss. The key idea of the proposed algorithm rests on the integration of consensus-based distributed optimization for a multi-agent system and a gradient generator capturing the optimal performance as a function of the parameter in the feedback loop tuning the parameter for each agent. Both theoretical results and simulations for a synchronous multi-agent rendezvous problem are provided to validate the proposed method for cooperative tuning of multi-agent optimal control. | ['Brian D. O. Anderson', 'Shaoshuai Mou', 'Wanxin Jin', 'Zehui Lu'] | 2022-09-24 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.55100775e-01 2.01802611e-01 -5.51437319e-04 9.82779935e-02
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46e97d2c-ad1d-41fe-a71b-3ebd95d05b22 | event-based-visual-odometry-with-full | 2306.01188 | null | https://arxiv.org/abs/2306.01188v1 | https://arxiv.org/pdf/2306.01188v1.pdf | Event-based Visual Odometry with Full Temporal Resolution via Continuous-time Gaussian Process Regression | Event-based cameras asynchronously capture individual visual changes in a scene. This makes them more robust than traditional frame-based cameras to highly dynamic motions and poor illumination. It also means that every measurement in a scene can occur at a unique time. Handling these different measurement times is a major challenge of using event-based cameras. It is often addressed in visual odometry (VO) pipelines by approximating temporally close measurements as occurring at one common time. This grouping simplifies the estimation problem but sacrifices the inherent temporal resolution of event-based cameras. This paper instead presents a complete stereo VO pipeline that estimates directly with individual event-measurement times without requiring any grouping or approximation. It uses continuous-time trajectory estimation to maintain the temporal fidelity and asynchronous nature of event-based cameras through Gaussian process regression with a physically motivated prior. Its performance is evaluated on the MVSEC dataset, where it achieves 7.9e-3 and 5.9e-3 RMS relative error on two independent sequences, outperforming the existing publicly available event-based stereo VO pipeline by two and four times, respectively. | ['Jonathan D. Gammell', 'Jianeng Wang'] | 2023-06-01 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 1.26480594e-01 -6.10713184e-01 2.34279186e-01 -1.59889936e-01
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caa0fa6f-6358-4365-ad29-f35a5e15b789 | item-silk-road-recommending-items-from | 1706.03205 | null | http://arxiv.org/abs/1706.03205v1 | http://arxiv.org/pdf/1706.03205v1.pdf | Item Silk Road: Recommending Items from Information Domains to Social Users | Online platforms can be divided into information-oriented and social-oriented
domains. The former refers to forums or E-commerce sites that emphasize
user-item interactions, like Trip.com and Amazon; whereas the latter refers to
social networking services (SNSs) that have rich user-user connections, such as
Facebook and Twitter. Despite their heterogeneity, these two domains can be
bridged by a few overlapping users, dubbed as bridge users. In this work, we
address the problem of cross-domain social recommendation, i.e., recommending
relevant items of information domains to potential users of social networks. To
our knowledge, this is a new problem that has rarely been studied before.
Existing cross-domain recommender systems are unsuitable for this task since
they have either focused on homogeneous information domains or assumed that
users are fully overlapped. Towards this end, we present a novel Neural Social
Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item
interactions in information domains and user-user connections in SNSs. In the
information domain part, the attributes of users and items are leveraged to
strengthen the embedding learning of users and items. In the SNS part, the
embeddings of bridge users are propagated to learn the embeddings of other
non-bridge users. Extensive experiments on two real-world datasets demonstrate
the effectiveness and rationality of our NSCR method. | ['Tat-Seng Chua', 'Xiang Wang', 'Liqiang Nie', 'Xiangnan He'] | 2017-06-10 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-3.07107538e-01 1.31420210e-01 -4.93411183e-01 -3.67837936e-01
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9e266e21-501a-437e-b81c-eab6e108d5db | adaptive-graph-signal-processing-algorithms | 1709.03726 | null | http://arxiv.org/abs/1709.03726v1 | http://arxiv.org/pdf/1709.03726v1.pdf | Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies | The goal of this paper is to propose novel strategies for adaptive learning
of signals defined over graphs, which are observed over a (randomly
time-varying) subset of vertices. We recast two classical adaptive algorithms
in the graph signal processing framework, namely, the least mean squares (LMS)
and the recursive least squares (RLS) adaptive estimation strategies. For both
methods, a detailed mean-square analysis illustrates the effect of random
sampling on the adaptive reconstruction capability and the steady-state
performance. Then, several probabilistic sampling strategies are proposed to
design the sampling probability at each node in the graph, with the aim of
optimizing the tradeoff between steady-state performance, graph sampling rate,
and convergence rate of the adaptive algorithms. Finally, a distributed RLS
strategy is derived and is shown to be convergent to its centralized
counterpart. Numerical simulations carried out over both synthetic and real
data illustrate the good performance of the proposed sampling and
reconstruction strategies for (possibly distributed) adaptive learning of
signals defined over graphs. | ['Paolo Di Lorenzo', 'Geert Leus', 'Sergio Barbarossa', 'Paolo Banelli', 'Elvin Isufi'] | 2017-09-12 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 5.09222388e-01 2.74773568e-01 1.18995808e-01 -2.60502193e-02
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34ac8f59-ecdd-4531-b234-d10dfacd6ed6 | compodiff-versatile-composed-image-retrieval | 2303.11916 | null | https://arxiv.org/abs/2303.11916v1 | https://arxiv.org/pdf/2303.11916v1.pdf | CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion | This paper proposes a novel diffusion-based model, CompoDiff, for solving Composed Image Retrieval (CIR) with latent diffusion and presents a newly created dataset of 18 million reference images, conditions, and corresponding target image triplets to train the model. CompoDiff not only achieves a new zero-shot state-of-the-art on a CIR benchmark such as FashionIQ but also enables a more versatile CIR by accepting various conditions, such as negative text and image mask conditions, which are unavailable with existing CIR methods. In addition, the CompoDiff features are on the intact CLIP embedding space so that they can be directly used for all existing models exploiting the CLIP space. The code and dataset used for the training, and the pre-trained weights are available at https://github.com/navervision/CompoDiff | ['Sangdoo Yun', 'Yoohoon Kang', 'HeeJae Jun', 'Wonjae Kim', 'Sanghyuk Chun', 'Geonmo Gu'] | 2023-03-21 | null | null | null | null | ['composed-image-retrieval'] | ['computer-vision'] | [ 7.88083393e-03 -7.48976707e-01 -4.85349387e-01 -1.22644745e-01
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d207a4e9-1801-4982-8f41-43463ade46f5 | pruning-the-search-space-of-the-wolof-lfg | null | null | https://aclanthology.org/L14-1497 | https://aclanthology.org/L14-1497.pdf | Pruning the Search Space of the Wolof LFG Grammar Using a Probabilistic and a Constraint Grammar Parser | This paper presents a method for greatly reducing parse times in LFG by integrating a Constraint Grammar parser into a probabilistic context-free grammar. The CG parser is used in the pre-processing phase to reduce morphological and lexical ambiguity. Similarly, the c-structure pruning mechanism of XLE is used in the parsing phase to discard low-probability c-structures, before f-annotations are solved. The experiment results show a considerable increase in parsing efficiency and robustness in the annotation of Wolof running text. The Wolof CG parser indicated an f-score of 90{\%} for morphological disambiguation and a speedup of ca. 40{\%}, while the c-structure pruning method increased the speed of the Wolof grammar by over 36{\%}. On a small amount of data, CG disambiguation and c-structure pruning allowed for a speedup of 58{\%}, however with a substantial drop in parse accuracy of 3.62. | ['Cheikh M. Bamba Dione'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.45537633e-01 6.58280432e-01 1.24441236e-01 -5.92490315e-01
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d00b3122-b3a7-4bc4-83c5-bbc7a1d22caa | in-field-high-throughput-grapevine | 2104.06945 | null | https://arxiv.org/abs/2104.06945v1 | https://arxiv.org/pdf/2104.06945v1.pdf | In-field high throughput grapevine phenotyping with a consumer-grade depth camera | Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted onboard an agricultural vehicle. | ['Giulio Reina', 'Antonio Petitti', 'Roberto Marani', 'Annalisa Milella'] | 2021-04-14 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 2.30697259e-01 -2.79809535e-01 -1.73522085e-01 2.71826804e-01
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3897179b-2a78-4e60-946b-cc45049b4f4d | vector-quantized-neural-networks-for-acoustic | 2005.09409 | null | https://arxiv.org/abs/2005.09409v2 | https://arxiv.org/pdf/2005.09409v2.pdf | Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge | In this paper, we explore vector quantization for acoustic unit discovery. Leveraging unlabelled data, we aim to learn discrete representations of speech that separate phonetic content from speaker-specific details. We propose two neural models to tackle this challenge - both use vector quantization to map continuous features to a finite set of codes. The first model is a type of vector-quantized variational autoencoder (VQ-VAE). The VQ-VAE encodes speech into a sequence of discrete units before reconstructing the audio waveform. Our second model combines vector quantization with contrastive predictive coding (VQ-CPC). The idea is to learn a representation of speech by predicting future acoustic units. We evaluate the models on English and Indonesian data for the ZeroSpeech 2020 challenge. In ABX phone discrimination tests, both models outperform all submissions to the 2019 and 2020 challenges, with a relative improvement of more than 30%. The models also perform competitively on a downstream voice conversion task. Of the two, VQ-CPC performs slightly better in general and is simpler and faster to train. Finally, probing experiments show that vector quantization is an effective bottleneck, forcing the models to discard speaker information. | ['Leanne Nortje', 'Herman Kamper', 'Benjamin van Niekerk'] | 2020-05-19 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 8.58911425e-02 3.41920197e-01 -1.49357975e-01 -4.60860580e-01
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fccab0ef-5774-456d-aaf4-d72e670ba74a | humanbench-towards-general-human-centric | 2303.05675 | null | https://arxiv.org/abs/2303.05675v1 | https://arxiv.org/pdf/2303.05675v1.pdf | HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining | Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is desirable to have a general pretrain model for versatile human-centric downstream tasks. This paper forges ahead along this path from the aspects of both benchmark and pretraining methods. Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting. To learn both coarse-grained and fine-grained knowledge in human bodies, we further propose a \textbf{P}rojector \textbf{A}ssis\textbf{T}ed \textbf{H}ierarchical pretraining method (\textbf{PATH}) to learn diverse knowledge at different granularity levels. Comprehensive evaluations on HumanBench show that our PATH achieves new state-of-the-art results on 17 downstream datasets and on-par results on the other 2 datasets. The code will be publicly at \href{https://github.com/OpenGVLab/HumanBench}{https://github.com/OpenGVLab/HumanBench}. | ['Wanli Ouyang', 'Rui Zhao', 'Li Yi', 'Haiyang Yang', 'Feng Zhu', 'Lei Bai', 'Yuanzheng Ci', 'Yizhou Wang', 'Meilin Chen', 'Qingsong Xie', 'Cheng Chen', 'Shixiang Tang'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_HumanBench_Towards_General_Human-Centric_Perception_With_Projector_Assisted_Pretraining_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pedestrian-attribute-recognition', 'pedestrian-detection', 'human-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.40786716e-01 1.00145871e-02 1.10240981e-01 -6.87545717e-01
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f5893faf-1e9c-4e7d-9e4b-16e8c83b6c80 | fedhil-heterogeneity-resilient-federated | 2307.01780 | null | https://arxiv.org/abs/2307.01780v1 | https://arxiv.org/pdf/2307.01780v1.pdf | FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices | Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called FedHIL. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy. FedHIL integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62x better localization accuracy on average than the best performing FL-based indoor localization framework from prior work. | ['Sudeep Pasricha', 'Danish Gufran'] | 2023-07-04 | null | null | null | null | ['indoor-localization', 'federated-learning', 'management'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-2.73335904e-01 -7.08693624e-01 -1.88541025e-01 -3.02295148e-01
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ecfbd9bb-ccf6-41cb-8625-0297374fb2ef | learning-without-forgetting-for-vision | 2305.19270 | null | https://arxiv.org/abs/2305.19270v1 | https://arxiv.org/pdf/2305.19270v1.pdf | Learning without Forgetting for Vision-Language Models | Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former ones. While traditional CIL methods focus on visual information to grasp core features, recent advances in Vision-Language Models (VLM) have shown promising capabilities in learning generalizable representations with the aid of textual information. However, when continually trained with new classes, VLMs often suffer from catastrophic forgetting of former knowledge. Applying VLMs to CIL poses two major challenges: 1) how to adapt the model without forgetting; and 2) how to make full use of the multi-modal information. To this end, we propose PROjectiOn Fusion (PROOF) that enables VLMs to learn without forgetting. To handle the first challenge, we propose training task-specific projections based on the frozen image/text encoders. When facing new tasks, new projections are expanded and former projections are fixed, alleviating the forgetting of old concepts. For the second challenge, we propose the fusion module to better utilize the cross-modality information. By jointly adjusting visual and textual features, the model can capture semantic information with stronger representation ability. Extensive experiments on nine benchmark datasets validate PROOF achieves state-of-the-art performance. | ['Ziwei Liu', 'De-Chuan Zhan', 'Han-Jia Ye', 'Jingyi Ning', 'Yuanhan Zhang', 'Da-Wei Zhou'] | 2023-05-30 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 2.63138175e-01 -2.39273861e-01 -1.63447872e-01 -2.58459032e-01
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44f44abd-19bf-4969-ab5b-f8531ff16c95 | quasi-balanced-self-training-on-noise-aware | 2203.03833 | null | https://arxiv.org/abs/2203.03833v2 | https://arxiv.org/pdf/2203.03833v2.pdf | Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap | Semantic analyses of object point clouds are largely driven by releasing of benchmarking datasets, including synthetic ones whose instances are sampled from object CAD models. However, learning from synthetic data may not generalize to practical scenarios, where point clouds are typically incomplete, non-uniformly distributed, and noisy. Such a challenge of Simulation-to-Reality (Sim2Real) domain gap could be mitigated via learning algorithms of domain adaptation; however, we argue that generation of synthetic point clouds via more physically realistic rendering is a powerful alternative, as systematic non-uniform noise patterns can be captured. To this end, we propose an integrated scheme consisting of physically realistic synthesis of object point clouds via rendering stereo images via projection of speckle patterns onto CAD models and a novel quasi-balanced self-training designed for more balanced data distribution by sparsity-driven selection of pseudo labeled samples for long tailed classes. Experiment results can verify the effectiveness of our method as well as both of its modules for unsupervised domain adaptation on point cloud classification, achieving the state-of-the-art performance. Source codes and the SpeckleNet synthetic dataset are available at https://github.com/Gorilla-Lab-SCUT/QS3. | ['Kui Jia', 'Ke Chen', 'Longkun Zou', 'ZiHao Wang', 'Yongwei Chen'] | 2022-03-08 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.89291954e-01 -2.04418480e-01 -1.77769363e-02 -5.34805059e-01
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6edad42b-706e-418d-a7d5-23229458db7e | deepsynth-program-synthesis-for-automatic | 1911.10244 | null | https://arxiv.org/abs/1911.10244v5 | https://arxiv.org/pdf/1911.10244v5.pdf | DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning | This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton so that the generation of a control policy by deep RL is guided by the discovered structure encoded in the automaton. The proposed approach is able to cope with both high-dimensional, low-level features and unknown sparse non-Markovian rewards. We have evaluated DeepSynth's performance in a set of experiments that includes the Atari game Montezuma's Revenge. Compared to existing approaches, we obtain a reduction of two orders of magnitude in the number of iterations required for policy synthesis, and also a significant improvement in scalability. | ['Mohammadhosein Hasanbeig', 'Alessandro Abate', 'Tom Melham', 'Daniel Kroening', 'Natasha Yogananda Jeppu'] | 2019-11-22 | null | null | null | null | ['probabilistic-deep-learning', 'montezumas-revenge'] | ['computer-vision', 'playing-games'] | [ 7.49474317e-02 3.12194586e-01 -2.60557801e-01 8.87816623e-02
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8098bbda-b577-4968-aab3-d9b2c3e9e0ab | mcf-mutual-correction-framework-for-semi | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_MCF_Mutual_Correction_Framework_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation | Semi-supervised learning is a promising method for medical image segmentation under limited annotation. However, the model cognitive bias impairs the segmentation performance, especially for edge regions. Furthermore, current mainstream semi-supervised medical image segmentation (SSMIS) methods lack designs to handle model bias. The neural network has a strong learning ability, but the cognitive bias will gradually deepen during the training, and it is difficult to correct itself. We propose a novel mutual correction framework (MCF) to explore network bias correction and improve the performance of SSMIS. Inspired by the plain contrast idea, MCF introduces two different subnets to explore and utilize the discrepancies between subnets to correct cognitive bias of the model. More concretely, a contrastive difference review (CDR) module is proposed to find out inconsistent prediction regions and perform a review training. Additionally, a dynamic competitive pseudo-label generation (DCPLG) module is proposed to evaluate the performance of subnets in real-time, dynamically selecting more reliable pseudo-labels. Experimental results on two medical image databases with different modalities (CT and MRI) show that our method achieves superior performance compared to several state-of-the-art methods. The code will be available at https://github.com/WYC-321/MCF. | ['Xinbo Gao', 'Weisheng Li', 'Xiuli Bi', 'Bin Xiao', 'Yongchao Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['semi-supervised-medical-image-segmentation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 5.07061362e-01 4.69340563e-01 -5.55321038e-01 -6.61961734e-01
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ab9a6253-335c-4245-b7c7-48a6ef12efe2 | data-driven-news-generation-for-automated | null | null | https://aclanthology.org/W17-3528 | https://aclanthology.org/W17-3528.pdf | Data-Driven News Generation for Automated Journalism | Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce. In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability. Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent. We illustrate its practical application in the production of news articles about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design. We then draw some lessons for future automated journalism. | ['Mark Granroth-Wilding', 'Leo Lepp{\\"a}nen', 'Myriam Munezero', 'Hannu Toivonen'] | 2017-09-01 | null | null | null | ws-2017-9 | ['news-generation'] | ['natural-language-processing'] | [ 8.10113847e-02 6.70507312e-01 -3.75845551e-01 -1.94536313e-01
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4ff086e3-dc1b-4b38-9973-05d5cf154efb | lsdm-long-short-diffeomorphic-motion-for | 2301.04748 | null | https://arxiv.org/abs/2301.04748v2 | https://arxiv.org/pdf/2301.04748v2.pdf | LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound Landmark Tracking | Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. Ultrasound imaging is a promising modality benefiting from low-cost and real-time acquisition. However, generating a precise landmark tracklet is very challenging, as attempts can be easily distorted by different interference such as landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prior to search for the plausible deformation of landmark. Specifically, we design a novel diffeomorphism representation in both long and short temporal domains for delineating motion margins and reducing long-term cumulative tracking errors. To further mitigate local anatomical ambiguity, we propose an expectation maximisation motion alignment module to iteratively optimize both long and short deformation, aligning to the same directional and spatial representation. The proposed multi-task system can be trained in a weakly-supervised manner, which only requires few landmark annotations for tracking and zero annotation for long-short deformation learning. We conduct extensive experiments on two ultrasound landmark tracking datasets. Experimental results show that our proposed method can achieve better or competitive landmark tracking performance compared with other state-of-the-art tracking methods, with a strong generalization capability across different scanner types and different ultrasound modalities. | ['Huiyu Zhou', 'Xuejun Ni', 'Yan Shen', 'Bin Yang', 'Zhihua Liu'] | 2023-01-11 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 1.06307484e-01 -3.02562467e-03 -3.75087947e-01 -2.32203305e-01
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1be615b0-ea69-4689-a320-b655dae79fea | audio-visual-face-reenactment | 2210.02755 | null | https://arxiv.org/abs/2210.02755v1 | https://arxiv.org/pdf/2210.02755v1.pdf | Audio-Visual Face Reenactment | This work proposes a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We improve the quality of lip sync using audio as an additional input, helping the network to attend to the mouth region. We use additional priors using face segmentation and face mesh to improve the structure of the reconstructed faces. Finally, we improve the visual quality of the generations by incorporating a carefully designed identity-aware generator module. The identity-aware generator takes the source image and the warped motion features as input to generate a high-quality output with fine-grained details. Our method produces state-of-the-art results and generalizes well to unseen faces, languages, and voices. We comprehensively evaluate our approach using multiple metrics and outperforming the current techniques both qualitative and quantitatively. Our work opens up several applications, including enabling low bandwidth video calls. We release a demo video and additional information at http://cvit.iiit.ac.in/research/projects/cvit-projects/avfr. | ['C V Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'Madhav Agarwal'] | 2022-10-06 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 5.32158390e-02 2.51449019e-01 5.80227235e-03 -4.83167440e-01
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1cd318b6-74c8-4433-aedd-57c1b23f8bf2 | ambisep-ambisonic-to-ambisonic-reverberant | 2206.06184 | null | https://arxiv.org/abs/2206.06184v1 | https://arxiv.org/pdf/2206.06184v1.pdf | AmbiSep: Ambisonic-to-Ambisonic Reverberant Speech Separation Using Transformer Networks | Consider a multichannel Ambisonic recording containing a mixture of several reverberant speech signals. Retreiving the reverberant Ambisonic signals corresponding to the individual speech sources blindly from the mixture is a challenging task as it requires to estimate multiple signal channels for each source. In this work, we propose AmbiSep, a deep neural network-based plane-wave domain masking approach to solve this task. The masking network uses learned feature representations and transformers in a triple-path processing configuration. We train and evaluate the proposed network architecture on a spatialized WSJ0-2mix dataset, and show that the method achieves a multichannel scale-invariant signal-to-distortion ratio improvement of 17.7 dB on the blind test set, while preserving the spatial characteristics of the separated sounds. | ['Emanuël A. P. Habets', 'Srikanth Raj Chetupalli', 'Adrian Herzog'] | 2022-06-13 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.07544416e-01 -7.45229006e-01 6.52697802e-01 -4.43698540e-02
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ca93de5d-dab2-48d2-af85-5ba5c6f30b6f | re-evaluating-adem-a-deeper-look-at-scoring | 1902.08832 | null | http://arxiv.org/abs/1902.08832v1 | http://arxiv.org/pdf/1902.08832v1.pdf | Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses | Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. ADEM(Lowe et al. 2017) formulated the
automatic evaluation of dialogue systems as a learning problem and showed that
such a model was able to predict responses which correlate significantly with
human judgements, both at utterance and system level. Their system was shown to
have beaten word-overlap metrics such as BLEU with large margins. We start with
the question of whether an adversary can game the ADEM model. We design a
battery of targeted attacks at the neural network based ADEM evaluation system
and show that automatic evaluation of dialogue systems still has a long way to
go. ADEM can get confused with a variation as simple as reversing the word
order in the text! We report experiments on several such adversarial scenarios
that draw out counterintuitive scores on the dialogue responses. We take a
systematic look at the scoring function proposed by ADEM and connect it to
linear system theory to predict the shortcomings evident in the system. We also
devise an attack that can fool such a system to rate a response generation
system as favorable. Finally, we allude to future research directions of using
the adversarial attacks to design a truly automated dialogue evaluation system. | ['Mithun Das Gupta', 'Mitesh M. Khapra', 'Ananya B. Sai', 'Mukundhan Srinivasan'] | 2019-02-23 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.40346074e-01 8.12496543e-01 4.12727892e-01 -6.18828177e-01
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