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1c38f3a6-3836-482a-9f89-db18e14f019c | model-error-propagation-via-learned | 2104.08695 | null | https://arxiv.org/abs/2104.08695v2 | https://arxiv.org/pdf/2104.08695v2.pdf | Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems | We present a method for contraction-based feedback motion planning of locally incrementally exponentially stabilizable systems with unknown dynamics that provides probabilistic safety and reachability guarantees. Given a dynamics dataset, our method learns a deep control-affine approximation of the dynamics. To find a trusted domain where this model can be used for planning, we obtain an estimate of the Lipschitz constant of the model error, which is valid with a given probability, in a region around the training data, providing a local, spatially-varying model error bound. We derive a trajectory tracking error bound for a contraction-based controller that is subjected to this model error, and then learn a controller that optimizes this tracking bound. With a given probability, we verify the correctness of the controller and tracking error bound in the trusted domain. We then use the trajectory error bound together with the trusted domain to guide a sampling-based planner to return trajectories that can be robustly tracked in execution. We show results on a 4D car, a 6D quadrotor, and a 22D deformable object manipulation task, showing our method plans safely with learned models of high-dimensional underactuated systems, while baselines that plan without considering the tracking error bound or the trusted domain can fail to stabilize the system and become unsafe. | ['Dmitry Berenson', 'Necmiye Ozay', 'Glen Chou'] | 2021-04-18 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-2.47409418e-01 8.18806827e-01 -3.76951426e-01 2.92128295e-01
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9db87700-3efe-48e3-bbd1-1d7d463ce358 | evaluating-the-evaluators-are-current-few | 2307.02732 | null | https://arxiv.org/abs/2307.02732v1 | https://arxiv.org/pdf/2307.02732v1.pdf | Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose? | Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first investigation into task-level evaluation -- a fundamental step when deploying a model. We measure the accuracy of performance estimators in the few-shot setting, consider strategies for model selection, and examine the reasons for the failure of evaluators usually thought of as being robust. We conclude that cross-validation with a low number of folds is the best choice for directly estimating the performance of a model, whereas using bootstrapping or cross validation with a large number of folds is better for model selection purposes. Overall, we find that existing benchmarks for few-shot learning are not designed in such a way that one can get a reliable picture of how effectively methods can be used on individual tasks. | ['Henry Gouk', 'Timothy Hospedales', 'Luísa Shimabucoro'] | 2023-07-06 | null | null | null | null | ['model-selection', 'few-shot-learning'] | ['methodology', 'methodology'] | [ 1.54871956e-01 -2.80728847e-01 -2.89415747e-01 -4.60254997e-01
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9b1feab8-d3df-460d-8b04-6a2998ae4f76 | data-efficient-weakly-supervised-learning-for-1 | 2012.14345 | null | https://arxiv.org/abs/2012.14345v2 | https://arxiv.org/pdf/2012.14345v2.pdf | From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach | Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodiment can be exploited to mitigate this burden by acquiring automatically annotated training data via a natural interaction with a human showing the object of interest, handheld. However, learning solely from this data may introduce biases (the so-called domain shift), and prevents adaptation to novel tasks. While Weakly-supervised Learning (WSL) offers a well-established set of techniques to cope with these problems in general-purpose Computer Vision, its adoption in challenging robotic domains is still at a preliminary stage. In this work, we target the scenario of a robot trained in a teacher-learner setting to detect handheld objects. The aim is to improve detection performance in different settings by letting the robot explore the environment with a limited human labeling budget. We compare several techniques for WSL in detection pipelines to reduce model re-training costs without compromising accuracy, proposing solutions which target the considered robotic scenario. We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning). We integrate our strategies into an on-line detection method, achieving efficient model update capabilities with few labels. We experimentally benchmark our method on challenging robotic object detection tasks under domain shift. | ['Raffaello Camoriano', 'Andrea Maracani', 'Lorenzo Natale', 'Lorenzo Rosasco', 'Vadim Tikhanoff', 'Giulia Pasquale', 'Elisa Maiettini'] | 2020-12-28 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 5.67810953e-01 7.91678011e-01 -7.00055435e-02 -3.29332739e-01
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340223a0-978a-4aa1-8cf3-75f7712162a1 | the-information-retrieval-experiment-platform | 2305.18932 | null | https://arxiv.org/abs/2305.18932v1 | https://arxiv.org/pdf/2305.18932v1.pdf | The Information Retrieval Experiment Platform | We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and even blinded retrieval experiments. Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible with ir_datasets and ir_measures. However, none of this is a must for reproducibility and scalability, as TIRA can run any dockerized software locally or remotely in a cloud-native execution environment. Version control and caching ensure efficient (re)execution. TIRA allows for blind evaluation when an experiment runs on a remote server or cloud not under the control of the experimenter. The test data and ground truth are then hidden from public access, and the retrieval software has to process them in a sandbox that prevents data leaks. We currently host an instance of TIREx with 15 corpora (1.9 billion documents) on which 32 shared retrieval tasks are based. Using Docker images of 50 standard retrieval approaches, we automatically evaluated all approaches on all tasks (50 $\cdot$ 32 = 1,600~runs) in less than a week on a midsize cluster (1,620 CPU cores and 24 GPUs). This instance of TIREx is open for submissions and will be integrated with the IR Anthology, as well as released open source. | ['Martin Potthast', 'Matthias Hagen', 'Benno Stein', 'Janek Bevendorff', 'Simon Reich', 'Niklas Deckers', 'Sean MacAvaney', 'Jan Heinrich Reimer', 'Maik Fröbe'] | 2023-05-30 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-4.05970037e-01 -6.84483647e-01 -4.44664480e-03 -2.26542965e-01
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9b9d3e81-3141-47b7-9f92-a8107293baed | a-new-cervical-cytology-dataset-for-nucleus | 1811.09651 | null | http://arxiv.org/abs/1811.09651v1 | http://arxiv.org/pdf/1811.09651v1.pdf | A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection | Analyzing Pap cytology slides is an important tasks in detecting and grading
precancerous and cancerous cervical cancer stages. Processing cytology images
usually involve segmenting nuclei and overlapping cells. We introduce a
cervical cytology dataset that can be used to evaluate nucleus detection, as
well as image classification methods in the cytology image processing area.
This dataset contains 93 real image stacks with their grade labels and manually
annotated nuclei within images. We also present two methods: a baseline method
based on a previously proposed approach, and a deep learning method, and
compare their results with other state-of-the-art methods. Both the baseline
method and the deep learning method outperform other state-of-the-art methods
by significant margins. Along with the dataset, we publicly make the evaluation
code and the baseline method available to download for further benchmarking. | ['Hady Ahmady Phoulady', 'Peter R. Mouton'] | 2018-11-23 | null | null | null | null | ['cervical-nucleus-detection'] | ['medical'] | [ 3.41377258e-01 2.10878104e-01 -2.58420318e-01 -2.70933151e-01
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e314afd7-8c48-4f12-9890-763f48264ecc | a-fully-dense-and-globally-consistent-3d-map | 1705.06524 | null | http://arxiv.org/abs/1705.06524v1 | http://arxiv.org/pdf/1705.06524v1.pdf | A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot | In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is emerging as a novel, minimally invasive diagnostic
technology for inspection of the GI tract and diagnosis of a wide range of
diseases and pathologies. Since the development of this technology, medical
device companies and many research groups have made substantial progress in
converting passive capsule endoscopes to robotic active capsule endoscopes with
most of the functionality of current active flexible endoscopes. However,
robotic capsule endoscopy still has some challenges. In particular, the use of
such devices to generate a precise three-dimensional (3D) mapping of the entire
inner organ remains an unsolved problem. Such global 3D maps of inner organs
would help doctors to detect the location and size of diseased areas more
accurately and intuitively, thus permitting more reliable diagnoses. To our
knowledge, this paper presents the first complete pipeline for a complete 3D
visual map reconstruction of the stomach. The proposed pipeline is modular and
includes a preprocessing module, an image registration module, and a final
shape-from-shading-based 3D reconstruction module; the 3D map is primarily
generated by a combination of image stitching and shape-from-shading
techniques, and is updated in a frame-by-frame iterative fashion via capsule
motion inside the stomach. A comprehensive quantitative analysis of the
proposed 3D reconstruction method is performed using an esophagus gastro
duodenoscopy simulator, three different endoscopic cameras, and a 3D optical
scanner. | ['Redhwan Jamiruddin', 'Metin Sitti', 'Yusuf Yigit Pilavci', 'Mehmet Turan', 'Helder Araujo', 'Ender Konukoglu'] | 2017-05-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [-3.66568595e-01 2.19830543e-01 2.15645581e-01 1.29366130e-01
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0e884671-99b6-46ed-8f70-750d795cad9b | restore-from-restored-video-restoration-with | 2003.04279 | null | https://arxiv.org/abs/2003.04279v3 | https://arxiv.org/pdf/2003.04279v3.pdf | Restore from Restored: Video Restoration with Pseudo Clean Video | In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional neural network (FCN) as the baseline, we can improve video denoising performance without accurate optical flow estimation and registration steps, in contrast to many conventional video restoration methods, due to the translation equivariant property of the FCN. Specifically, the proposed method can take advantage of plentiful similar patches existing across multiple consecutive frames (i.e., patch-recurrence); these patches can boost the performance of the baseline network by a large margin. We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames. In our experiments, we apply the proposed method to state-of-the-art denoisers and show that our fine-tuned networks achieve a considerable improvement in denoising performance. | ['Donghyeon Cho', 'Seunghwan Lee', 'Tae Hyun Kim', 'Jiwon Kim'] | 2020-03-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.73496023e-01 -3.58064026e-01 1.13333009e-01 -1.87864110e-01
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fdeeab70-ce4d-4858-9daf-899bb35b4537 | massive-feature-extraction-for-explaining-and | 2108.00846 | null | https://arxiv.org/abs/2108.00846v2 | https://arxiv.org/pdf/2108.00846v2.pdf | Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale | Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of -- mostly new for hydrology -- concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13 000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale... | ['Elena Volpi', 'Salvatore Grimaldi', 'Ilias G. Pechlivanidis', 'Hristos Tyralis', 'Georgia Papacharalampous'] | 2021-07-25 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.01520616e-01 -2.37871125e-01 -2.36373425e-01 -3.58045280e-01
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877d3290-b838-4ecb-a195-11d993ed5b3a | unified-analysis-of-sgd-type-methods | 2303.16502 | null | https://arxiv.org/abs/2303.16502v1 | https://arxiv.org/pdf/2303.16502v1.pdf | Unified analysis of SGD-type methods | This note focuses on a simple approach to the unified analysis of SGD-type methods from (Gorbunov et al., 2020) for strongly convex smooth optimization problems. The similarities in the analyses of different stochastic first-order methods are discussed along with the existing extensions of the framework. The limitations of the analysis and several alternative approaches are mentioned as well. | ['Eduard Gorbunov'] | 2023-03-29 | null | null | null | null | ['type'] | ['speech'] | [-2.84438580e-01 2.56655842e-01 5.44830449e-02 -3.35395545e-01
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8f752da2-776a-4771-8142-d22a4fe4f842 | reducing-spurious-correlations-for-aspect | 2303.02846 | null | https://arxiv.org/abs/2303.02846v2 | https://arxiv.org/pdf/2303.02846v2.pdf | Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning | Deep learning techniques have dominated the literature on aspect-based sentiment analysis (ABSA), yielding state-of-the-art results. However, these deep models generally suffer from spurious correlation problems between input features and output labels, which creates significant barriers to robustness and generalization capability. In this paper, we propose a novel Contrastive Variational Information Bottleneck framework (called CVIB) to reduce spurious correlations for ABSA. The proposed CVIB framework is composed of an original network and a self-pruned network, and these two networks are optimized simultaneously via contrastive learning. Concretely, we employ the Variational Information Bottleneck (VIB) principle to learn an informative and compressed network (self-pruned network) from the original network, which discards the superfluous patterns or spurious correlations between input features and prediction labels. Then, self-pruning contrastive learning is devised to pull together semantically similar positive pairs and push away dissimilar pairs, where the representations of the anchor learned by the original and self-pruned networks respectively are regarded as a positive pair while the representations of two different sentences within a mini-batch are treated as a negative pair. To verify the effectiveness of our CVIB method, we conduct extensive experiments on five benchmark ABSA datasets and the experimental results show that our approach achieves better performance than the strong competitors in terms of overall prediction performance, robustness, and generalization. | ['Ruifeng Xu', 'Qingshan Jiang', 'Min Yang', 'Mingshan Chang'] | 2023-03-06 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.20459148e-01 -8.37359130e-02 -1.58054277e-01 -5.05735457e-01
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fad6cff7-d7e7-4065-a0e9-00a6e9fd184e | jointly-learning-author-and-annotated | null | null | https://aclanthology.org/R19-1080 | https://aclanthology.org/R19-1080.pdf | Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text | An author{'}s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not. In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles. We train a neural character n-gram based language model using an external corpus of literary texts and transfer learned representations for use in downstream tasks. We show that augmenting the knowledge from external works of authors produces results competitive with other style-based methods for book likability prediction, genre classification, and authorship attribution. | ["Fabio A. Gonz{\\'a}lez", 'Deepthi Mave', 'Suraj Maharjan', 'Thamar Solorio', 'Prasha Shrestha', 'Manuel Montes'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['genre-classification'] | ['computer-vision'] | [ 8.87318235e-03 -1.59452930e-01 -5.29039145e-01 -3.52245301e-01
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18c7a862-1df4-4eb5-870d-85df5a4f6ff4 | local-latin-hypercube-refinement-for-multi | 2108.08890 | null | https://arxiv.org/abs/2108.08890v2 | https://arxiv.org/pdf/2108.08890v2.pdf | Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization | Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements. Surrogate models based on statistical and machine learning methods are used to increase the sample efficiency. However, for higher dimensional or multi-modal systems, surrogate models may also require a large amount of samples to achieve good results. We propose a sequential sampling strategy for the surrogate based solution of multi-objective reliability based robust design optimization problems. Proposed local Latin hypercube refinement (LoLHR) strategy is model-agnostic and can be combined with any surrogate model because there is no free lunch but possibly a budget one. The proposed method is compared to stationary sampling as well as other proposed strategies from the literature. Gaussian process and support vector regression are both used as surrogate models. Empirical evidence is presented, showing that LoLHR achieves on average better results compared to other surrogate based strategies on the tested examples. | ['Tamara Nestorović', 'Dirk Roos', 'Can Bogoclu'] | 2021-08-19 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 7.30624720e-02 -1.40498713e-01 -1.93997025e-01 -1.23800956e-01
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dce291f3-d546-4ffc-9a53-741487a2c1ac | check-your-facts-and-try-again-improving | 2302.12813 | null | https://arxiv.org/abs/2302.12813v3 | https://arxiv.org/pdf/2302.12813v3.pdf | Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback | Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It also iteratively revises LLM prompts to improve model responses using feedback generated by utility functions, e.g., the factuality score of a LLM-generated response. The effectiveness of LLM-Augmenter is empirically validated on two types of scenarios, task-oriented dialog and open-domain question answering. LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses. We make the source code and models publicly available. | ['Jianfeng Gao', 'Weizhu Chen', 'Zhou Yu', 'Lars Liden', 'Qiuyuan Huang', 'Yu Hu', 'Yujia Xie', 'Hao Cheng', 'Pengcheng He', 'Michel Galley', 'Baolin Peng'] | 2023-02-24 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-3.79754990e-01 6.23198688e-01 1.88811332e-01 -5.15100300e-01
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4f222796-a441-4bb9-a8a9-5f39943009a5 | rule-neural-symbolic-knowledge-graph | 2210.14905 | null | https://arxiv.org/abs/2210.14905v1 | https://arxiv.org/pdf/2210.14905v1.pdf | RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding | Knowledge graph (KG) reasoning is an important problem for knowledge graphs. It predicts missing links by reasoning on existing facts. Knowledge graph embedding (KGE) is one of the most popular methods to address this problem. It embeds entities and relations into low-dimensional vectors and uses the learned entity/relation embeddings to predict missing facts. However, KGE only uses zeroth-order (propositional) logic to encode existing triplets (e.g., ``Alice is Bob's wife."); it is unable to leverage first-order (predicate) logic to represent generally applicable logical \textbf{rules} (e.g., ``$\forall x,y \colon x ~\text{is}~ y\text{'s wife} \rightarrow y ~\text{is}~ x\text{'s husband}$''). On the other hand, traditional rule-based KG reasoning methods usually rely on hard logical rule inference, making it brittle and hardly competitive with KGE. In this paper, we propose RulE, a novel and principled framework to represent and model logical rules and triplets. RulE jointly represents entities, relations and logical rules in a unified embedding space. By learning an embedding for each logical rule, RulE can perform logical rule inference in a soft way and give a confidence score to each grounded rule, similar to how KGE gives each triplet a confidence score. Compared to KGE alone, RulE allows injecting prior logical rule information into the embedding space, which improves the generalization of knowledge graph embedding. Besides, the learned confidence scores of rules improve the logical rule inference process by softly controlling the contribution of each rule, which alleviates the brittleness of logic. We evaluate our method with link prediction tasks. Experimental results on multiple benchmark KGs demonstrate the effectiveness of RulE. | ['Muhan Zhang', 'Yitao Liang', 'Song-Chun Zhu', 'Xiaojuan Tang'] | 2022-10-24 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-1.41312525e-01 8.64790082e-01 -6.74541771e-01 -3.56477797e-01
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bcb66d31-1a4c-4674-8f32-33a55b9d9ebc | interpretable-and-scalable-graphical-models | 2301.06021 | null | https://arxiv.org/abs/2301.06021v1 | https://arxiv.org/pdf/2301.06021v1.pdf | Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes | This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternating linearized minimization method, which scales tensor-variate Gaussian graphical model estimations to modern big-data settings. Third, it connects Kronecker-structured (inverse) covariance models with spatio-temporal partial differential equations (PDEs) and introduces a new framework for ensemble Kalman filtering that is capable of tracking chaotic physical systems. Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods. Throughout, practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using EEG data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data. We show in each case that the graphical modeling framework introduced here leads to improved interpretability, accuracy, and scalability. | ['Yu Wang'] | 2023-01-15 | null | null | null | null | ['weather-forecasting', 'topic-models'] | ['miscellaneous', 'natural-language-processing'] | [-4.81367409e-01 -2.53691345e-01 6.03656590e-01 -4.19753343e-01
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0cd78773-900b-4d07-b41d-024eb3f2fbc6 | securing-voice-driven-interfaces-against-fake | 1902.06782 | null | http://arxiv.org/abs/1902.06782v1 | http://arxiv.org/pdf/1902.06782v1.pdf | Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks | Voice cloning technologies have found applications in a variety of areas
ranging from personalized speech interfaces to advertisement, robotics, and so
on. Existing voice cloning systems are capable of learning speaker
characteristics and use trained models to synthesize a person's voice from only
a few audio samples. Advances in cloned speech generation technologies are
capable of generating perceptually indistinguishable speech from a bona-fide
speech. These advances pose new security and privacy threats to voice-driven
interfaces and speech-based access control systems. The state-of-the-art speech
synthesis technologies use trained or tuned generative models for cloned speech
generation. Trained generative models rely on linear operations, learned
weights, and excitation source for cloned speech synthesis. These systems leave
characteristic artifacts in the synthesized speech. Higher-order spectral
analysis is used to capture differentiating attributes between bona-fide and
cloned audios. Specifically, quadrature phase coupling (QPC) in the estimated
bicoherence, Gaussianity test statistics, and linearity test statistics are
used to capture generative model artifacts. Performance of the proposed method
is evaluated on cloned audios generated using speaker adaptation- and speaker
encoding-based approaches. Experimental results for a dataset consisting of 126
cloned speech and 8 bona-fide speech samples indicate that the proposed method
is capable of detecting bona-fide and cloned audios with close to a perfect
detection rate. | [] | 2019-02-18 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 8.52366462e-02 9.46026444e-02 8.49100109e-03 -3.30745608e-01
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2a655d95-377c-45e9-98f5-5d61200b89f8 | histopathology-slide-indexing-and-search-are | 2306.17019 | null | https://arxiv.org/abs/2306.17019v1 | https://arxiv.org/pdf/2306.17019v1.pdf | Histopathology Slide Indexing and Search: Are We There Yet? | The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a qualitative assessment of each model's performance in providing retrieval results that are reliable and useful to pathologists. We found that all three image search engines fail to produce consistently reliable results and have difficulties in capturing granular and subtle features of malignancy, limiting their diagnostic accuracy. Based on our findings, we also propose a minimal set of requirements to further advance the development of accurate and reliable histopathology image search engines for successful clinical adoption. | ['Jacob M. Luber', 'Jitin Makker', 'Chace Moleta', 'Manfred Huber', 'Amir Hajighasemi', 'MD Jillur Rahman Saurav', 'Parisa Boodaghi Malidarreh', 'Jai Prakash Veerla', 'Mohammad Sadegh Nasr', 'Helen H. Shang'] | 2023-06-29 | null | null | null | null | ['image-retrieval', 'retrieval'] | ['computer-vision', 'methodology'] | [ 5.55075333e-02 -2.22593114e-01 -3.86035323e-01 -4.10045572e-02
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f65e50b2-3020-4beb-b264-ae0e0632633d | diva-a-dirichlet-process-based-incremental | 2305.14067 | null | https://arxiv.org/abs/2305.14067v2 | https://arxiv.org/pdf/2305.14067v2.pdf | DIVA: A Dirichlet Process Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder | Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a memoized online variational inference method that enables the "birth" and "merge" moves of clusters, allowing our framework to cluster data in a "dynamic-adaptive" manner, without requiring prior knowledge of the number of features. We name the framework as DIVA, a Dirichlet Process-based Incremental deep clustering framework via Variational Auto-Encoder. Our framework, which outperforms state-of-the-art baselines, exhibits superior performance in classifying complex data with dynamically changing features, particularly in the case of incremental features. We released our source code implementation at: https://github.com/Ghiara/diva | ['Alois Knoll', 'Kai Huang', 'Xiaojie Su', 'Hang Su', 'Yuqi Yun', 'Yuan Meng', 'Zhenshan Bing'] | 2023-05-23 | null | null | null | null | ['nonparametric-deep-clustering', 'deep-clustering', 'deep-clustering'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-6.65387928e-01 -4.05445367e-01 7.63276070e-02 -4.74283159e-01
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2c0bae22-b9f9-4f49-958d-869d3f0db144 | multi-layer-feature-aggregation-for-deep | 2011.02572 | null | https://arxiv.org/abs/2011.02572v1 | https://arxiv.org/pdf/2011.02572v1.pdf | Multi-layer Feature Aggregation for Deep Scene Parsing Models | Scene parsing from images is a fundamental yet challenging problem in visual content understanding. In this dense prediction task, the parsing model assigns every pixel to a categorical label, which requires the contextual information of adjacent image patches. So the challenge for this learning task is to simultaneously describe the geometric and semantic properties of objects or a scene. In this paper, we explore the effective use of multi-layer feature outputs of the deep parsing networks for spatial-semantic consistency by designing a novel feature aggregation module to generate the appropriate global representation prior, to improve the discriminative power of features. The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information. At the same time, the multiple skip connections form a strong supervision, making the deep parsing network easy to train. Extensive experiments on four public scene parsing datasets prove that the deep parsing network equipped with the proposed feature aggregation module can achieve very promising results. | ['Qiang Wu', 'Jian Zhang', 'Jun Zhou', 'Yongsheng Gao', 'Litao Yu'] | 2020-11-04 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 3.17685932e-01 -1.86371431e-02 -1.83751971e-01 -8.37430179e-01
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d1edc83d-0dd0-4107-a97a-4671dfbe0509 | mstream-fast-streaming-multi-aspect-group | 2009.08451 | null | https://arxiv.org/abs/2009.08451v4 | https://arxiv.org/pdf/2009.08451v4.pdf | MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams | Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MSTREAM which can detect unusual group anomalies as they occur, in a dynamic manner. MSTREAM has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MSTREAM is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines. | ['Bryan Hooi', 'Arjit Jain', 'Siddharth Bhatia', 'Ritesh Kumar', 'Pan Li'] | 2020-09-17 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-1.39387948e-02 -2.59673417e-01 -5.99862896e-02 -7.11803511e-02
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5d9a21e9-ea67-4006-a8c3-51f8303418ab | uncertainty-aware-adaptation-for-self | 2203.15293 | null | https://arxiv.org/abs/2203.15293v1 | https://arxiv.org/pdf/2203.15293v1.pdf | Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation | The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose learning as an unsupervised domain adaptation problem. We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations; a) model-free joint localization and b) model-based parametric regression. Such a design allows us to derive suitable measures to quantify prediction uncertainty at both pose and joint level granularity. While supervising only on labeled synthetic samples, the adaptation process aims to minimize the uncertainty for the unlabeled target images while maximizing the same for an extreme out-of-distribution dataset (backgrounds). Alongside synthetic-to-real 3D pose adaptation, the joint-uncertainties allow expanding the adaptation to work on in-the-wild images even in the presence of occlusion and truncation scenarios. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets. | ['R. Venkatesh Babu', 'Anirban Chakraborty', 'Varun Jampani', 'Pradyumna YM', 'Siddharth Seth', 'Jogendra Nath Kundu'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['monocular-3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.08615644e-01 4.87249643e-01 1.17590785e-01 -6.55034423e-01
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d2ac6b81-5e44-4415-bfb9-b25dd469c8f9 | heuristics-for-image-generation-from-scene | null | null | https://openreview.net/forum?id=r1eqsNXgdV | https://openreview.net/pdf?id=r1eqsNXgdV | Heuristics for Image Generation from Scene Graphs | Generating realistic images from scene graphs requires neural networks to be able to reason about object relationships and compositionality. Learning a sufficiently rich representation to facilitate this reasoning is challenging due to dataset limitations. Synthetic scene graphs from COCO only have basic geometric relationships, and Visual Genome scene graphs are replete with missing relations or mislabeled nodes. Existing scene graph to image models have two stages: (1) a scene composition stage, and an (2) image generation stage. In this paper, we propose two methods to improve the intermediate representation of these stages. First, we use visual heuristics to augment relationships between pairs of objects. Second, we introduce a graph convolution-based network to generate a scene graph context representation that enriches the image generation. These contributions significantly improve the scene composition (relation score of 59.8% compared to 51.2%) and image generation (74% versus 64% in mean relation opinion score). Introspection shows that these heuristics are particularly effective in learning differentiated representations for scenes with multiple instances of the same object category. Obtaining accurate and complete scene graph annotations is costly, and our use of heuristics and prior structure to enhance intermediate representations allows our model to compensate for limited or incomplete data. | ['Hanlin Tang', 'Alexei Bastidas', 'Anahita Bhiwandiwalla', 'Subarna Tripathi'] | 2019-03-20 | null | null | null | iclr-workshop-lld-2019 | ['image-generation-from-scene-graphs'] | ['computer-vision'] | [ 5.97272873e-01 5.66178679e-01 4.73037884e-02 -4.87354547e-01
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9af4beb2-d813-45da-b11a-1158ba257dc7 | uav-human-a-large-benchmark-for-human | 2104.00946 | null | https://arxiv.org/abs/2104.00946v4 | https://arxiv.org/pdf/2104.00946v4.pdf | UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles | Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark - UAVHuman - for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. Such a comprehensive and challenging benchmark shall be able to promote the research of UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Furthermore, we propose a fisheye-based action recognition method that mitigates the distortions in fisheye videos via learning unbounded transformations guided by flat RGB videos. Experiments show the efficacy of our method on the UAV-Human dataset. The project page: https://github.com/SUTDCV/UAV-Human | ['Zhiheng Li', 'Wenqian Wang', 'Yun Ni', 'Wei zhang', 'Jun Liu', 'Tianjiao Li'] | 2021-04-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.pdf | cvpr-2021-1 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-7.70947244e-03 -6.95812106e-01 5.73579744e-02 -4.01084185e-01
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2d01cbe4-0cbe-40e6-b00e-ef95a90f571b | neilf-inter-reflectable-light-fields-for | 2303.17147 | null | https://arxiv.org/abs/2303.17147v1 | https://arxiv.org/pdf/2303.17147v1.pdf | NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation | We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images. In contrast to previous methods which assume a simplified environment map or co-located flashlights, in this work, we formulate the lighting of a static scene as one neural incident light field (NeILF) and one outgoing neural radiance field (NeRF). The key insight of the proposed method is the union of the incident and outgoing light fields through physically-based rendering and inter-reflections between surfaces, making it possible to disentangle the scene geometry, material, and lighting from image observations in a physically-based manner. The proposed incident light and inter-reflection framework can be easily applied to other NeRF systems. We show that our method can not only decompose the outgoing radiance into incident lights and surface materials, but also serve as a surface refinement module that further improves the reconstruction detail of the neural surface. We demonstrate on several datasets that the proposed method is able to achieve state-of-the-art results in terms of geometry reconstruction quality, material estimation accuracy, and the fidelity of novel view rendering. | ['Long Quan', 'Yanghai Tsin', 'David McKinnon', 'Tian Fang', 'Jingbo Liu', 'Shiwei Li', 'Yao Yao', 'Jingyang Zhang'] | 2023-03-30 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 6.11363292e-01 -2.53195047e-01 8.09816718e-01 -4.15204763e-01
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7aa72e49-97d1-45b8-acef-14646f8b3e23 | apple-of-sodom-hidden-backdoors-in-superior | 2210.11082 | null | https://arxiv.org/abs/2210.11082v1 | https://arxiv.org/pdf/2210.11082v1.pdf | Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning | This paper finds that contrastive learning can produce superior sentence embeddings for pre-trained models but is also vulnerable to backdoor attacks. We present the first backdoor attack framework, BadCSE, for state-of-the-art sentence embeddings under supervised and unsupervised learning settings. The attack manipulates the construction of positive and negative pairs so that the backdoored samples have a similar embedding with the target sample (targeted attack) or the negative embedding of its clean version (non-targeted attack). By injecting the backdoor in sentence embeddings, BadCSE is resistant against downstream fine-tuning. We evaluate BadCSE on both STS tasks and other downstream tasks. The supervised non-targeted attack obtains a performance degradation of 194.86%, and the targeted attack maps the backdoored samples to the target embedding with a 97.70% success rate while maintaining the model utility. | ['Zhonghai Wu', 'Qingni Shen', 'Shiqing Ma', 'Shengfang Zhai', 'Baisong Xin', 'Xiaoyi Chen'] | 2022-10-20 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 1.52050018e-01 3.26503009e-01 -4.93614912e-01 -3.18650633e-01
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19d420bb-45be-4a50-b317-e9da6eac8674 | solution-existence-uniqueness-and-stability | 2305.03330 | null | https://arxiv.org/abs/2305.03330v1 | https://arxiv.org/pdf/2305.03330v1.pdf | Solution existence, uniqueness, and stability of discrete basis sinograms in multispectral CT | This work investigates conditions for quantitative image reconstruction in multispectral computed tomography (MSCT), which remains a topic of active research. In MSCT, one seeks to obtain from data the spatial distribution of linear attenuation coefficient, referred to as a virtual monochromatic image (VMI), at a given X-ray energy, within the subject imaged. As a VMI is decomposed often into a linear combination of basis images with known decomposition coefficients, the reconstruction of a VMI is thus tantamount to that of the basis images. An empirical, but highly effective, two-step data-domain-decomposition (DDD) method has been developed and used widely for quantitative image reconstruction in MSCT. In the two-step DDD method, step (1) estimates the so-called basis sinogram from data through solving a nonlinear transform, whereas step (2) reconstructs basis images from their basis sinograms estimated. Subsequently, a VMI can readily be obtained from the linear combination of basis images reconstructed. As step (2) involves the inversion of a straightforward linear system, step (1) is the key component of the DDD method in which a nonlinear system needs to be inverted for estimating the basis sinograms from data. In this work, we consider a {\it discrete} form of the nonlinear system in step (1), and then carry out theoretical and numerical analyses of conditions on the existence, uniqueness, and stability of a solution to the discrete nonlinear system for accurately estimating the discrete basis sinograms, leading to quantitative reconstruction of VMIs in MSCT. | ['Chong Chen', 'Xiaochuan Pan', 'Yu Gao'] | 2023-05-05 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.09500945e-01 -1.22750901e-01 1.23290338e-01 -8.77634529e-03
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98ae413b-7601-4497-85f4-dc1cf2a17990 | one-shot-transfer-learning-for-population | 2108.06228 | null | https://arxiv.org/abs/2108.06228v2 | https://arxiv.org/pdf/2108.06228v2.pdf | One-shot Transfer Learning for Population Mapping | Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due to the limitations of data collection, including infrastructure density, user privacy, and business security, such fine-grained data is hard to collect and usually, only coarse-grained data is available. Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem. To tackle this problem, existing methods mainly rely on sufficient fine-grained ground truth for training, which is not often available for the majority of cities. That limits the applications of these methods and brings the necessity to transfer knowledge between data-sufficient source cities to data-scarce target cities. In knowledge transfer scenario, we employ single reference fine-grained ground truth in target city, which is easy to obtain via remote sensing or questionnaire, as the ground truth to inform the large-scale urban structure and support the knowledge transfer in target city. By this approach, we transform the fine-grained population mapping problem into a one-shot transfer learning problem. In this paper, we propose a novel one-shot transfer learning framework PSRNet to transfer spatial-temporal knowledge across cities from the view of network structure, the view of data, and the view of optimization. Experiments on real-life datasets of 4 cities demonstrate that PSRNet has significant advantages over 8 state-of-the-art baselines by reducing RMSE and MAE by more than 25%. Our code and datasets are released in Github (https://github.com/erzhuoshao/PSRNet-CIKM). | ['Yong Li', 'Tong Xia', 'Yingheng Wang', 'Jie Feng', 'Erzhuo Shao'] | 2021-08-13 | null | null | null | null | ['population-mapping'] | ['computer-vision'] | [-3.17715883e-01 -1.08631708e-01 -3.64428759e-01 -1.08131453e-01
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00aebf75-58ee-4dad-8a90-33f8b276e36f | spatial-dual-modality-graph-reasoning-for-key | 2103.14470 | null | https://arxiv.org/abs/2103.14470v1 | https://arxiv.org/pdf/2103.14470v1.pdf | Spatial Dual-Modality Graph Reasoning for Key Information Extraction | Key information extraction from document images is of paramount importance in office automation. Conventional template matching based approaches fail to generalize well to document images of unseen templates, and are not robust against text recognition errors. In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. We model document images as dual-modality graphs, nodes of which encode both the visual and textual features of detected text regions, and edges of which represent the spatial relations between neighboring text regions. The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes. In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild. It contains 25 key information categories, a total of about 69000 text boxes, and is about 2 times larger than the existing public datasets. Extensive experiments validate that all information including visual features, textual features and spatial relations can benefit key information extraction. It has been shown that SDMG-R can effectively extract key information from document images of unseen templates, and obtain new state-of-the-art results on the recent popular benchmark SROIE and our WildReceipt. Our code and dataset will be publicly released. | ['Wayne Zhang', 'Chenhao Lin', 'Xiaoyu Yue', 'Zhanghui Kuang', 'Hongbin Sun'] | 2021-03-26 | null | null | null | null | ['template-matching', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 5.55299699e-01 -1.17896445e-01 4.14209329e-02 -1.16004176e-01
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70ad9fb3-e369-47bb-ba48-34410048d3ea | ensemble-transfer-learning-for-the-prediction | 2005.09572 | null | https://arxiv.org/abs/2005.09572v1 | https://arxiv.org/pdf/2005.09572v1.pdf | Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response | Transfer learning has been shown to be effective in many applications in which training data for the target problem are limited but data for a related (source) problem are abundant. In this paper, we apply transfer learning to the prediction of anti-cancer drug response. Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset, and extends the framework through ensemble. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures. Uniquely, we investigate its power for three application settings including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We test the proposed ensemble transfer learning on benchmark in vitro drug screening datasets, taking one dataset as the source domain and another dataset as the target domain. The analysis results demonstrate the benefit of applying ensemble transfer learning for predicting anti-cancer drug response in all three applications with both LightGBM and DNN models. Compared between the different prediction models, a DNN model with two subnetworks for the inputs of tumor features and drug features separately outperforms LightGBM and the other DNN model that concatenates tumor features and drug features for input in the drug repurposing and precision oncology applications. In the more challenging application of new drug development, LightGBM performs better than the other two DNN models. | ['Alexander Partin', 'Yvonne A. Evrard', 'Yitan Zhu', 'Thomas Brettin', 'Maulik Shukla', 'Fangfang Xia', 'Rick Stevens', 'James H. Doroshow', 'Hyunseung Yoo'] | 2020-05-13 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 4.01028395e-01 -2.53579468e-01 -6.03104174e-01 -2.24394768e-01
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100b74cb-64c2-48aa-b821-ab57fcd430a9 | multi-classification-using-one-versus-one | 2306.09668 | null | https://arxiv.org/abs/2306.09668v1 | https://arxiv.org/pdf/2306.09668v1.pdf | Multi-Classification using One-versus-One Deep Learning Strategy with Joint Probability Estimates | The One-versus-One (OvO) strategy is an approach of multi-classification models which focuses on training binary classifiers between each pair of classes. While the OvO strategy takes advantage of balanced training data, the classification accuracy is usually hindered by the voting mechanism to combine all binary classifiers. In this paper, a novel OvO multi-classification model incorporating a joint probability measure is proposed under the deep learning framework. In the proposed model, a two-stage algorithm is developed to estimate the class probability from the pairwise binary classifiers. Given the binary classifiers, the pairwise probability estimate is calibrated by a distance measure on the separating feature hyperplane. From that, the class probability of the subject is estimated by solving a joint probability-based distance minimization problem. Numerical experiments in different applications show that the proposed model achieves generally higher classification accuracy than other state-of-the-art models. | ['Lingjia Dai', 'Raymond HonFu Chan', 'Anthony Hei-Long Chan'] | 2023-06-16 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 1.56567842e-02 5.28331064e-02 -5.80942154e-01 -8.45835745e-01
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b4a8573e-f84e-4492-8d0c-f0ca0096d664 | responding-to-challenge-call-of-machine | 2111.14354 | null | https://arxiv.org/abs/2111.14354v1 | https://arxiv.org/pdf/2111.14354v1.pdf | Responding to Challenge Call of Machine Learning Model Development in Diagnosing Respiratory Disease Sounds | In this study, a machine learning model was developed for automatically detecting respiratory system sounds such as sneezing and coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the "Pfizer digital medicine challenge" on the "OSFHOME" open access platform. "Environmental sound classification" called ESC-50 and AudioSet sound files were used to prepare the dataset. In this dataset, which consisted of three parts, features that effectively showed coughing and sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. Three different classification techniques were considered to perform successful respiratory sound classification in the dataset containing more than 3800 different sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, ensemble aggregation and decision tree classification methods were used as classification techniques. In an attempt to classify coughing and sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success. | ['Negin Melek'] | 2021-11-29 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.36668827e-02 -5.86477697e-01 1.81339532e-01 1.59299240e-01
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1747dc6e-734a-442f-9485-4750853050f1 | widening-the-dialogue-workflow-modeling | 2011.08334 | null | https://arxiv.org/abs/2011.08334v1 | https://arxiv.org/pdf/2011.08334v1.pdf | Widening the Dialogue Workflow Modeling Bottleneck in Ontology-Based Personal Assistants | We present a new approach to dialogue specification for Virtual Personal Assistants (VPAs) based on so-called dialogue workflow graphs, with several demonstrated advantages over current ontology-based methods. Our new dialogue specification language (DSL) enables customers to more easily participate in the VPA modeling process due to a user-friendly modeling framework. Resulting models are also significantly more compact. VPAs can be developed much more rapidly. The DSL is a new modeling layer on top of our ontology-based Dialogue Management (DM) framework OntoVPA. We explain the rationale and benefits behind the new language and support our claims with concrete reduced Level-of-Effort (LOE) numbers from two recent OntoVPA projects. | ['Andreas Kathol', 'Girish Acharya', 'Edgar Kalns', 'Michael Wessel'] | 2020-11-16 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-2.10313842e-01 1.38459384e+00 -3.69150341e-02 -6.21499717e-01
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d13cc4b8-656d-4c73-92c4-5567253fb739 | representer-point-selection-for-explaining-1 | 2305.20002 | null | https://arxiv.org/abs/2305.20002v2 | https://arxiv.org/pdf/2305.20002v2.pdf | Representer Point Selection for Explaining Regularized High-dimensional Models | We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples. Our workhorse is a novel representer theorem for general regularized high-dimensional models, which decomposes the model prediction in terms of contributions from each of the training samples: with positive (negative) values corresponding to positive (negative) impact training samples to the model's prediction. We derive consequences for the canonical instances of $\ell_1$ regularized sparse models, and nuclear norm regularized low-rank models. As a case study, we further investigate the application of low-rank models in the context of collaborative filtering, where we instantiate high-dimensional representers for specific popular classes of models. Finally, we study the empirical performance of our proposed methods on three real-world binary classification datasets and two recommender system datasets. We also showcase the utility of high-dimensional representers in explaining model recommendations. | ['Pradeep Ravikumar', 'Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Eli Chien', 'Jiong Zhang', 'Che-Ping Tsai'] | 2023-05-31 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 3.44849974e-01 7.71351933e-01 -5.89664102e-01 -5.34305871e-01
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9ecebad4-0285-4573-9ff7-14aee44fd5c1 | hybrid-contrastive-learning-with-cluster | 2201.11995 | null | https://arxiv.org/abs/2201.11995v2 | https://arxiv.org/pdf/2201.11995v2.pdf | Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification | Unsupervised person re-identification (ReID) aims to match a query image of a pedestrian to the images in gallery set without supervision labels. The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo labels to train a deep neural network. However, the pseudo labels are noisy and sensitive to the hyper-parameter(s) in clustering algorithm. In this paper, we propose a Hybrid Contrastive Learning (HCL) approach for unsupervised person ReID, which is based on a hybrid between instance-level and cluster-level contrastive loss functions. Moreover, we present a Multi-Granularity Clustering Ensemble based Hybrid Contrastive Learning (MGCE-HCL) approach, which adopts a multi-granularity clustering ensemble strategy to mine priority information among the pseudo positive sample pairs and defines a priority-weighted hybrid contrastive loss for better tolerating the noises in the pseudo positive samples. We conduct extensive experiments on two benchmark datasets Market-1501 and DukeMTMC-reID. Experimental results validate the effectiveness of our proposals. | ['Chun-Guang Li', 'Mingkun Li', 'He Sun'] | 2022-01-28 | null | null | null | null | ['unsupervised-person-re-identification', 'clustering-ensemble'] | ['computer-vision', 'graphs'] | [-4.39089537e-02 -4.28350687e-01 1.44896761e-01 -6.32870793e-01
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ed14773f-0b2a-49c7-8a31-c7e5ae0cddf7 | calib-anything-zero-training-lidar-camera | 2306.02656 | null | https://arxiv.org/abs/2306.02656v1 | https://arxiv.org/pdf/2306.02656v1.pdf | Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything | The research on extrinsic calibration between Light Detection and Ranging(LiDAR) and camera are being promoted to a more accurate, automatic and generic manner. Since deep learning has been employed in calibration, the restrictions on the scene are greatly reduced. However, data driven method has the drawback of low transfer-ability. It cannot adapt to dataset variations unless additional training is taken. With the advent of foundation model, this problem can be significantly mitigated. By using the Segment Anything Model(SAM), we propose a novel LiDAR-camera calibration method, which requires zero extra training and adapts to common scenes. With an initial guess, we opimize the extrinsic parameter by maximizing the consistency of points that are projected inside each image mask. The consistency includes three properties of the point cloud: the intensity, normal vector and categories derived from some segmentation methods. The experiments on different dataset have demonstrated the generality and comparable accuracy of our method. The code is available at https://github.com/OpenCalib/CalibAnything. | ['Yikang Li', 'Guohang Yan', 'Zhaotong Luo'] | 2023-06-05 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-1.23552373e-03 -4.01164144e-01 -6.31744638e-02 -8.05168211e-01
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6eb342b5-3024-4cc2-aa76-56698bdf922a | marinevrs-marine-video-retrieval-system-with | 2306.04593 | null | https://arxiv.org/abs/2306.04593v1 | https://arxiv.org/pdf/2306.04593v1.pdf | MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding | Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we present MarineVRS, a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. In addition, unlike the conventional video retrieval system, which only permits users to index a collection of images or videos and search using a free-form natural language sentence, our retrieval system includes an additional Explainability module that outputs the segmentation masks of the objects that the input query referred to. This feature allows users to identify and isolate specific objects in the video footage, leading to more detailed analysis and understanding of their behavior and movements. Finally, with its adaptability, explainability, accuracy, and scalability, MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species. | ['Sai-Kit Yeung', 'Tuan-Anh Vu', 'Hai Nguyen-Truong', 'Tan-Sang Ha'] | 2023-06-07 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-2.22751766e-01 -7.16248035e-01 4.47538972e-01 -1.98501691e-01
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367044b7-5058-4329-9b13-c92baa664ac2 | privacy-protection-drone-patrol-system-based | 2005.14390 | null | https://arxiv.org/abs/2005.14390v1 | https://arxiv.org/pdf/2005.14390v1.pdf | Privacy-Protection Drone Patrol System based on Face Anonymization | The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies thanks to their mobility. These days, for security robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting their advantage: they can even go to any hazardous place that humans cannot access. For UAVs, Drone has been a representative model and has several merits to consist of various sensors such as high-resolution cameras. Therefore, Drone is the most suitable as a mobile surveillance robot. These attractive advantages such as high-resolution cameras and mobility can be a double-edged sword, i.e., privacy infringement. Surveillance drones take videos with high-resolution to fulfill their role, however, those contain a lot of privacy sensitive information. The indiscriminate shooting is a critical issue for those who are very reluctant to be exposed. To tackle the privacy infringement, this work proposes face-anonymizing drone patrol system. In this system, one person's face in a video is transformed into a different face with facial components maintained. To construct our privacy-preserving system, we have adopted the latest generative adversarial networks frameworks and have some modifications on losses of those frameworks. Our face-anonymzing approach is evaluated with various public face-image and video dataset. Moreover, our system is evaluated with a customized drone consisting of a high-resolution camera, a companion computer, and a drone control computer. Finally, we confirm that our system can protect privacy sensitive information with our face-anonymzing algorithm while preserving the performance of robot perception, i.e., simultaneous localization and mapping. | ['Hyun Jong Yang', 'Yeongjun Kim', 'Hyeonsu Lyu', 'Myeung Un Kim', 'Harim Lee'] | 2020-05-29 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 2.09714606e-01 2.00697348e-01 1.58169106e-01 -3.59654576e-01
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336cdd45-485f-4452-8236-c857dd0b4891 | a-federated-approach-for-hate-speech | 2302.09243 | null | https://arxiv.org/abs/2302.09243v1 | https://arxiv.org/pdf/2302.09243v1.pdf | A Federated Approach for Hate Speech Detection | Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied. The majority of research has focused on centralised machine learning infrastructures which risk leaking data. In this paper, we show that using federated machine learning can help address privacy the concerns that are inherent to hate speech detection while obtaining up to 6.81% improvement in terms of F1-score. | ['Zeerak Talat', 'Jash Mehta', 'Deep Gandhi', 'Jay Gala'] | 2023-02-18 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.32750215e-02 2.63942689e-01 1.25273198e-01 4.26758267e-03
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99deb7ee-795a-4b4a-be17-84d2bc5eaf79 | collective-opinion-target-extraction-in | null | null | https://aclanthology.org/D13-1189 | https://aclanthology.org/D13-1189.pdf | Collective Opinion Target Extraction in Chinese Microblogs | null | ['Xinjie Zhou', 'Xiaojun Wan', 'Jianguo Xiao'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['stock-market-prediction'] | ['time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7f0efaf9-2707-445f-8b16-0c3d091d468a | reconstruction-cognizant-graph-sampling-using | 1811.03206 | null | http://arxiv.org/abs/1811.03206v2 | http://arxiv.org/pdf/1811.03206v2.pdf | Reconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment | Graph sampling with noise is a fundamental problem in graph signal processing
(GSP). Previous works assume an unbiased least square (LS) signal
reconstruction scheme and select samples greedily via expensive extreme
eigenvector computation. A popular biased scheme using graph Laplacian
regularization (GLR) solves a system of linear equations for its
reconstruction. Assuming this GLR-based scheme, we propose a
reconstruction-cognizant sampling strategy to maximize the numerical stability
of the linear system---\textit{i.e.}, minimize the condition number of the
coefficient matrix. Specifically, we maximize the eigenvalue lower bounds of
the matrix, represented by left-ends of Gershgorin discs of the coefficient
matrix. To accomplish this efficiently, we propose an iterative algorithm to
traverse the graph nodes via Breadth First Search (BFS) and align the left-ends
of all corresponding Gershgorin discs at lower-bound threshold $T$ using two
basic operations: disc shifting and scaling. We then perform binary search to
maximize $T$ given a sample budget $K$. Experiments on real graph data show
that the proposed algorithm can effectively promote large eigenvalue lower
bounds, and the reconstruction MSE is the same or smaller than existing
sampling methods for different budget $K$ at much lower complexity. | [] | 2019-02-16 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.93022567e-01 2.07235068e-01 -1.87483028e-01 6.35353848e-02
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690d31cd-b15a-4c34-a266-2314e4c84ace | directional-deep-embedding-and-appearance | 2002.06736 | null | https://arxiv.org/abs/2002.06736v1 | https://arxiv.org/pdf/2002.06736v1.pdf | Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation | Most recent semi-supervised video object segmentation (VOS) methods rely on fine-tuning deep convolutional neural networks online using the given mask of the first frame or predicted masks of subsequent frames. However, the online fine-tuning process is usually time-consuming, limiting the practical use of such methods. We propose a directional deep embedding and appearance learning (DDEAL) method, which is free of the online fine-tuning process, for fast VOS. First, a global directional matching module, which can be efficiently implemented by parallel convolutional operations, is proposed to learn a semantic pixel-wise embedding as an internal guidance. Second, an effective directional appearance model based statistics is proposed to represent the target and background on a spherical embedding space for VOS. Equipped with the global directional matching module and the directional appearance model learning module, DDEAL learns static cues from the labeled first frame and dynamically updates cues of the subsequent frames for object segmentation. Our method exhibits state-of-the-art VOS performance without using online fine-tuning. Specifically, it achieves a J & F mean score of 74.8% on DAVIS 2017 dataset and an overall score G of 71.3% on the large-scale YouTube-VOS dataset, while retaining a speed of 25 fps with a single NVIDIA TITAN Xp GPU. Furthermore, our faster version runs 31 fps with only a little accuracy loss. Our code and trained networks are available at https://github.com/YingjieYin/Directional-Deep-Embedding-and-Appearance-Learning-for-Fast-Video-Object-Segmentation. | ['Yingjie Yin', 'Xingang Wang', 'Lei Zhang', 'De Xu'] | 2020-02-17 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.61447451e-01 -1.91744417e-01 -2.63707161e-01 -4.79712278e-01
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3f046458-96ef-4533-a983-ac9fdede076d | distant-reading-in-digital-humanities-case | null | null | https://aclanthology.org/2022.lrec-1.356 | https://aclanthology.org/2022.lrec-1.356.pdf | Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection | In this paper we present the Serbian part of the ELTeC multilingual corpus of novels written in the time period 1840-1920. The corpus is being built in order to test various distant reading methods and tools with the aim of re-thinking the European literary history. We present the various steps that led to the production of the Serbian sub-collection: the novel selection and retrieval, text preparation, structural annotation, POS-tagging, lemmatization and named entity recognition. The Serbian sub-collection was published on different platforms in order to make it freely available to various users. Several use examples show that this sub-collection is usefull for both close and distant reading approaches. | ['Milica Ikonić Nešić', 'Mihailo Skoric', 'Dusko Vitas', 'Branislava Šandrih Todorović', 'Cvetana Krstev', 'Ranka Stanković'] | null | null | null | null | lrec-2022-6 | ['lemmatization'] | ['natural-language-processing'] | [-1.04926765e-01 -1.12237498e-01 -5.79930423e-03 -2.59463638e-01
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8875951a-7a72-4ba3-9a0a-4f33908b58f9 | a-novel-neural-network-model-for-joint-pos | 1705.05952 | null | http://arxiv.org/abs/1705.05952v2 | http://arxiv.org/pdf/1705.05952v2.pdf | A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing | We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 languages from the Universal Dependencies project, show that
our model outperforms the state-of-the-art neural network-based
Stack-propagation model for joint POS tagging and transition-based dependency
parsing, resulting in a new state of the art. Our code is open-source and
available together with pre-trained models at:
https://github.com/datquocnguyen/jPTDP | ['Mark Johnson', 'Mark Dras', 'Dat Quoc Nguyen'] | 2017-05-16 | a-novel-neural-network-model-for-joint-pos-1 | https://aclanthology.org/K17-3014 | https://aclanthology.org/K17-3014.pdf | conll-2017-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-3.80874187e-01 5.18981934e-01 -6.49920046e-01 -7.14034736e-01
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a327b95d-e3d8-4556-93d5-0dafaed92682 | movie-recommendation-system-using-composite | 2212.00139 | null | https://arxiv.org/abs/2212.00139v2 | https://arxiv.org/pdf/2212.00139v2.pdf | Movie Recommendation System using Composite Ranking | In today's world, abundant digital content like e-books, movies, videos and articles are available for consumption. It is daunting to review everything accessible and decide what to watch next. Consequently, digital media providers want to capitalise on this confusion and tackle it to increase user engagement, eventually leading to higher revenues. Content providers often utilise recommendation systems as an efficacious approach for combating such information overload. This paper concentrates on developing a synthetic approach for recommending movies. Traditionally, movie recommendation systems use either collaborative filtering, which utilises user interaction with the media, or content-based filtering, which makes use of the movie's available metadata. Technological advancements have also introduced a hybrid technique that integrates both systems. However, our approach deals solely with content-based recommendations, further enhancing it with a ranking algorithm based on content similarity metrics. The three metrics contributing to the ranking are similarity in metadata, visual content, and user reviews of the movies. We use text vectorization followed by cosine similarity for metadata, feature extraction by a pre-trained VGG19 followed by K-means clustering for visual content, and a comparison of sentiments for user reviews. Such a system allows viewers to know movies that "feel" the same. | ['Aashal Kamdar', 'Irish Mehta'] | 2022-11-30 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.66891679e-01 -3.52397203e-01 -2.91556597e-01 -3.03387731e-01
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472d7fa3-f7d1-4ea6-a41b-5218a96ed3e9 | segmenting-medical-instruments-in-minimally | 2203.11358 | null | https://arxiv.org/abs/2203.11358v1 | https://arxiv.org/pdf/2203.11358v1.pdf | Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask | Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in medical image processing. However, image degradations, small instruments, and the generalization between different surgery types make medical instrument segmentation challenging. To cope with these challenges, we adapt the object proposal generation system AttentionMask and propose a dedicated post-processing to select promising proposals. The results on the ROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is a strong foundation for generating state-of-the-art performance. Our evaluation in an object proposal generation framework shows that our adapted AttentionMask system is robust to image degradations, generalizes well to unseen types of surgeries, and copes well with small instruments. | ['Simone Frintrop', 'Rüdiger Schmitz', 'Alexander Michael Gerlach', 'Christian Wilms'] | 2022-03-21 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 6.31136656e-01 2.66081840e-01 -3.94035816e-01 -7.07440674e-02
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f3fc0da1-a5ce-4740-b4d8-3f3591ff54aa | a-neural-network-based-on-device-learning | 1907.10147 | null | https://arxiv.org/abs/1907.10147v5 | https://arxiv.org/pdf/1907.10147v5.pdf | A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices | Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches have recently drawn attention because of their good generalization capability. In a typical situation, BP-NN-based models are iteratively optimized in server machines with input data gathered from edge devices. However, (1) the iterative optimization often requires significant efforts to follow changes in the distribution of normal data (i.e., concept drift), and (2) data transfers between edge and server impose additional latency and energy consumption. To address these issues, we propose ONLAD and its IP core, named ONLAD Core. ONLAD is highly optimized to perform fast sequential learning to follow concept drift in less than one millisecond. ONLAD Core realizes on-device learning for edge devices at low power consumption, which realizes standalone execution where data transfers between edge and server are not required. Experiments show that ONLAD has favorable anomaly detection capability in an environment that simulates concept drift. Evaluations of ONLAD Core confirm that the training latency is 1.95x~6.58x faster than the other software implementations. Also, the runtime power consumption of ONLAD Core implemented on PYNQ-Z1 board, a small FPGA/CPU SoC platform, is 5.0x~25.4x lower than them. | ['Hiroki Matsutani', 'Masaaki Kondo', 'Mineto Tsukada'] | 2019-07-23 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-2.72613466e-01 -5.15928626e-01 -9.20705050e-02 -4.20938909e-01
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545a2365-71a1-4751-b1da-a9626b87fc86 | g2mf-wa-geometric-multi-model-fitting-with | 2001.06965 | null | https://arxiv.org/abs/2001.06965v1 | https://arxiv.org/pdf/2001.06965v1.pdf | G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data | In this paper we attempt to address the problem of geometric multi-model fitting with resorting to a few weakly annotated (WA) data points, which has been sparsely studied so far. In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of the WA data to boost the multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that the WA data annotated with the same weak label has a high probability of being assigned to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lie on/near the latent model are likely to connect together and further form a subset/cluster for effective proposals generation. With the proposals generated, the $\alpha$-expansion is adopted for labeling, and our method in return updates the proposals. This works in an iterative way. Extensive experiments validate our method and show that the proposed method produces noticeably better results than state-of-the-art techniques in most cases. | ['Xi Yang', 'Katsuya Hotta', 'Xuequan Lu', 'Chao Zhang'] | 2020-01-20 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 3.26590449e-01 4.76337165e-01 -2.67546982e-01 -2.18958393e-01
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a4256ed9-4e01-49b6-8592-0cd5c55ec63e | video-reenactment-as-inductive-bias-for | 2102.00324 | null | https://arxiv.org/abs/2102.00324v3 | https://arxiv.org/pdf/2102.00324v3.pdf | Video Reenactment as Inductive Bias for Content-Motion Disentanglement | Independent components within low-dimensional representations are essential inputs in several downstream tasks, and provide explanations over the observed data. Video-based disentangled factors of variation provide low-dimensional representations that can be identified and used to feed task-specific models. We introduce MTC-VAE, a self-supervised motion-transfer VAE model to disentangle motion and content from videos. Unlike previous work on video content-motion disentanglement, we adopt a chunk-wise modeling approach and take advantage of the motion information contained in spatiotemporal neighborhoods. Our model yields independent per-chunk representations that preserve temporal consistency. Hence, we reconstruct whole videos in a single forward-pass. We extend the ELBO's log-likelihood term and include a Blind Reenactment Loss as an inductive bias to leverage motion disentanglement, under the assumption that swapping motion features yields reenactment between two videos. We evaluate our model with recently-proposed disentanglement metrics and show that it outperforms a variety of methods for video motion-content disentanglement. Experiments on video reenactment show the effectiveness of our disentanglement in the input space where our model outperforms the baselines in reconstruction quality and motion alignment. | ['Adín Ramírez Rivera', 'Juan F. Hernández Albarracín'] | 2021-01-30 | null | null | null | null | ['motion-disentanglement'] | ['computer-vision'] | [ 4.53468896e-02 -3.31073910e-01 -6.13058269e-01 -2.25846484e-01
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70256078-e9a9-44f5-b52a-da0156ef67e2 | unsupervised-machine-learning-for-networking | 1709.06599 | null | http://arxiv.org/abs/1709.06599v1 | http://arxiv.org/pdf/1709.06599v1.pdf | Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges | While machine learning and artificial intelligence have long been applied in
networking research, the bulk of such works has focused on supervised learning.
Recently there has been a rising trend of employing unsupervised machine
learning using unstructured raw network data to improve network performance and
provide services such as traffic engineering, anomaly detection, Internet
traffic classification, and quality of service optimization. The interest in
applying unsupervised learning techniques in networking emerges from their
great success in other fields such as computer vision, natural language
processing, speech recognition, and optimal control (e.g., for developing
autonomous self-driving cars). Unsupervised learning is interesting since it
can unconstrain us from the need of labeled data and manual handcrafted feature
engineering thereby facilitating flexible, general, and automated methods of
machine learning. The focus of this survey paper is to provide an overview of
the applications of unsupervised learning in the domain of networking. We
provide a comprehensive survey highlighting the recent advancements in
unsupervised learning techniques and describe their applications for various
learning tasks in the context of networking. We also provide a discussion on
future directions and open research issues, while also identifying potential
pitfalls. While a few survey papers focusing on the applications of machine
learning in networking have previously been published, a survey of similar
scope and breadth is missing in literature. Through this paper, we advance the
state of knowledge by carefully synthesizing the insights from these survey
papers while also providing contemporary coverage of recent advances. | ['Ala Al-Fuqaha', 'Kok-Lim Alvin Yau', 'Aunn Raza', 'Amir Hussain', 'Junaid Qadir', 'Yehia Elkhatib', 'Muhammad Usama', 'Hunain Arif'] | 2017-09-19 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.65283817e-01 1.45429760e-01 -8.78980517e-01 -6.01946831e-01
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cd86cefe-f73d-418e-924b-c3da1143abe8 | lance-stress-testing-visual-models-by | 2305.19164 | null | https://arxiv.org/abs/2305.19164v1 | https://arxiv.org/pdf/2305.19164v1.pdf | LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images | We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in large language modeling and text-based image editing to augment an IID test set with a suite of diverse, realistic, and challenging test images without altering model weights. We benchmark the performance of a diverse set of pretrained models on our generated data and observe significant and consistent performance drops. We further analyze model sensitivity across different types of edits, and demonstrate its applicability at surfacing previously unknown class-level model biases in ImageNet. | ['Judy Hoffman', 'Prithvijit Chattopadhyay', 'Sriram Yenamandra', 'Viraj Prabhu'] | 2023-05-30 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 6.90341830e-01 5.46346158e-02 -6.61406144e-02 -5.29654145e-01
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2bbb6631-fe4d-4202-8b11-6b09a043938d | discrete-and-continuous-action-representation | 1912.11077 | null | https://arxiv.org/abs/1912.11077v1 | https://arxiv.org/pdf/1912.11077v1.pdf | Discrete and Continuous Action Representation for Practical RL in Video Games | While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Actor-Critic algorithm able to handle discrete, continuous and parameterized actions in a principled way. We show that Hybrid SAC can successfully solve a highspeed driving task in one of our games, and is competitive with the state-of-the-art on parameterized actions benchmark tasks. We also explore the impact of using normalizing flows to enrich the expressiveness of the policy at minimal computational cost, and identify a potential undesired effect of SAC when used with normalizing flows, that may be addressed by optimizing a different objective. | ['Adrien Logut', 'Eloi Alonso', 'Olivier Delalleau', 'Maxim Peter'] | 2019-12-23 | null | null | null | null | ['control-with-prametrised-actions'] | ['playing-games'] | [-4.08400185e-02 3.21885884e-01 -3.11140478e-01 4.23961356e-02
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42838037-fce5-42f7-a35a-08057438cfed | memecap-a-dataset-for-captioning-and | 2305.13703 | null | https://arxiv.org/abs/2305.13703v1 | https://arxiv.org/pdf/2305.13703v1.pdf | MemeCap: A Dataset for Captioning and Interpreting Memes | Memes are a widely popular tool for web users to express their thoughts using visual metaphors. Understanding memes requires recognizing and interpreting visual metaphors with respect to the text inside or around the meme, often while employing background knowledge and reasoning abilities. We present the task of meme captioning and release a new dataset, MemeCap. Our dataset contains 6.3K memes along with the title of the post containing the meme, the meme captions, the literal image caption, and the visual metaphors. Despite the recent success of vision and language (VL) models on tasks such as image captioning and visual question answering, our extensive experiments using state-of-the-art VL models show that they still struggle with visual metaphors, and perform substantially worse than humans. | ['Vered Shwartz', 'EunJeong Hwang'] | 2023-05-23 | null | null | null | null | ['image-captioning', 'meme-captioning'] | ['computer-vision', 'natural-language-processing'] | [-1.11925565e-01 1.87243700e-01 1.20027684e-01 3.97718232e-03
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ddce3747-32b9-4298-8ef3-7f800f3c69c6 | deep-q-learning-versus-proximal-policy | 2306.01451 | null | https://arxiv.org/abs/2306.01451v1 | https://arxiv.org/pdf/2306.01451v1.pdf | Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task | This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated production system. We utilize a Petri Net (PN)-based simulation environment, which was previously proposed in related work. The performance of the two algorithms is compared based on several evaluation metrics, including average percentage of correctly assembled and sorted products, average episode length, and percentage of successful episodes. The results show that PPO outperforms DQN in terms of all evaluation metrics. The study highlights the advantages of policy-based algorithms in problems with high-dimensional state and action spaces. The study contributes to the field of deep RL in context of production systems by providing insights into the effectiveness of different algorithms and their suitability for different tasks. | ['Simon Hirländer', 'Stefan Wegenkittl', 'Reuf Kozlica'] | 2023-06-02 | null | null | null | null | ['q-learning'] | ['methodology'] | [-8.47847760e-02 5.38408011e-02 -2.39353597e-01 2.49263585e-01
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9ac78507-01d5-42d3-970b-f40c77a9b364 | improving-efficient-neural-ranking-models | 2010.02666 | null | https://arxiv.org/abs/2010.02666v2 | https://arxiv.org/pdf/2010.02666v2.pdf | Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation | Retrieval and ranking models are the backbone of many applications such as web search, open domain QA, or text-based recommender systems. The latency of neural ranking models at query time is largely dependent on the architecture and deliberate choices by their designers to trade-off effectiveness for higher efficiency. This focus on low query latency of a rising number of efficient ranking architectures make them feasible for production deployment. In machine learning an increasingly common approach to close the effectiveness gap of more efficient models is to apply knowledge distillation from a large teacher model to a smaller student model. We find that different ranking architectures tend to produce output scores in different magnitudes. Based on this finding, we propose a cross-architecture training procedure with a margin focused loss (Margin-MSE), that adapts knowledge distillation to the varying score output distributions of different BERT and non-BERT passage ranking architectures. We apply the teachable information as additional fine-grained labels to existing training triples of the MSMARCO-Passage collection. We evaluate our procedure of distilling knowledge from state-of-the-art concatenated BERT models to four different efficient architectures (TK, ColBERT, PreTT, and a BERT CLS dot product model). We show that across our evaluated architectures our Margin-MSE knowledge distillation significantly improves re-ranking effectiveness without compromising their efficiency. Additionally, we show our general distillation method to improve nearest neighbor based index retrieval with the BERT dot product model, offering competitive results with specialized and much more costly training methods. To benefit the community, we publish the teacher-score training files in a ready-to-use package. | ['Allan Hanbury', 'Mete Sertkan', 'Michael Schröder', 'Sophia Althammer', 'Sebastian Hofstätter'] | 2020-10-06 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-1.96503177e-02 -1.69172928e-01 -2.66234845e-01 -5.31778574e-01
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12b4e112-93d7-41fc-97a8-1856620cc74d | byel-bootstrap-on-your-emotion-latent | 2207.10003 | null | https://arxiv.org/abs/2207.10003v2 | https://arxiv.org/pdf/2207.10003v2.pdf | BYEL : Bootstrap Your Emotion Latent | With the improved performance of deep learning, the number of studies trying to apply deep learning to human emotion analysis is increasing rapidly. But even with this trend going on, it is still difficult to obtain high-quality images and annotations. For this reason, the Learning from Synthetic Data (LSD) Challenge, which learns from synthetic images and infers from real images, is one of the most interesting areas. In general, Domain Adaptation methods are widely used to address LSD challenges, but there is a limitation that target domains (real images) are still needed. Focusing on these limitations, we propose a framework Bootstrap Your Emotion Latent (BYEL), which uses only synthetic images in training. BYEL is implemented by adding Emotion Classifiers and Emotion Vector Subtraction to the BYOL framework that performs well in Self-Supervised Representation Learning. We train our framework using synthetic images generated from the Aff-wild2 dataset and evaluate it using real images from the Aff-wild2 dataset. The result shows that our framework (0.3084) performs 2.8% higher than the baseline (0.3) on the macro F1 score metric. | ['Sejoon Lim', 'Hwangyu Lim', 'Hyungjun Lee'] | 2022-07-20 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 7.34144822e-02 2.85615008e-02 9.50950831e-02 -6.90887034e-01
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de280eb0-29fc-4eb5-aeef-db4ea011dee5 | reasoning-over-public-and-private-data-in | 2203.11027 | null | https://arxiv.org/abs/2203.11027v1 | https://arxiv.org/pdf/2203.11027v1.pdf | Reasoning over Public and Private Data in Retrieval-Based Systems | Users and organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private data is important to personalize open-domain applications such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve relevant information to a user question from a background corpus before producing an answer. While today's retrieval systems assume the corpus is fully accessible, users are often unable or unwilling to expose their private data to entities hosting public data. We first define the PUBLIC-PRIVATE AUTOREGRESSIVE INFORMATION RETRIEVAL (PAIR) privacy framework for the novel retrieval setting over multiple privacy scopes. We then argue that an adequate benchmark is missing to study PAIR since existing textual benchmarks require retrieving from a single data distribution. However, public and private data intuitively reflect different distributions, motivating us to create ConcurrentQA, the first textual QA benchmark to require concurrent retrieval over multiple data-distributions. Finally, we show that existing systems face large privacy vs. performance tradeoffs when applied to our proposed retrieval setting and investigate how to mitigate these tradeoffs. | ['Christopher Ré', 'Jacob Kahn', 'Angela Fan', 'Patrick Lewis', 'Simran Arora'] | 2022-03-14 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.17906496e-01 3.45972419e-01 -1.12195231e-01 -6.83523893e-01
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1c499078-f487-4f1d-b543-c0231c721f6f | psiminer-a-tool-for-mining-rich-abstract | 2103.12778 | null | https://arxiv.org/abs/2103.12778v1 | https://arxiv.org/pdf/2103.12778v1.pdf | PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code | The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract syntax trees have been used for a long time in various software engineering domains, and in particular in IDEs. The API of modern IDEs allows to manipulate and traverse ASTs, resolve references between code elements, etc. Such algorithms can enrich ASTs with new data and therefore may be useful in ML-based code analysis. In this work, we present PSIMiner - a tool for processing PSI trees from the IntelliJ Platform. PSI trees contain code syntax trees as well as functions to work with them, and therefore can be used to enrich code representation using static analysis algorithms of modern IDEs. To showcase this idea, we use our tool to infer types of identifiers in Java ASTs and extend the code2seq model for the method name prediction problem. | ['Timofey Bryksin', 'Vladimir Kovalenko', 'Egor Bogomolov', 'Egor Spirin'] | 2021-03-23 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 6.54289871e-02 2.03680564e-02 -3.62236500e-01 -4.85697716e-01
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6fd2d158-6d09-4ec7-9255-8685b0c7dfd5 | an-exploratory-study-of-masked-face | 2306.08549 | null | https://arxiv.org/abs/2306.08549v1 | https://arxiv.org/pdf/2306.08549v1.pdf | An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms | Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. | ['Mustafa Atay', 'Megh Pudyel'] | 2023-06-14 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 6.75978303e-01 -3.25479060e-01 -1.63930416e-01 -2.69935519e-01
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172655de-c751-4084-8b14-2228b40a6724 | voxlingua107-a-dataset-for-spoken-language | null | null | https://arxiv.org/pdf/2011.12998.pdf | https://arxiv.org/abs/2011.12998 | VOXLINGUA107: A DATASET FOR SPOKEN LANGUAGE RECOGNITION | This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are used to extract segments from the videos that contain speech. Post-filtering is used to remove segments from the database that are likely not in the given language, increasing the proportion of correctly labeled segments to 98%, based on crowd-sourced verification. The size of the resulting training set (VoxLingua107) is 6628 hours (62 hours per language on the average) and it is accompanied by an evaluation set of 1609 verified utterances. We use the data to build language recognition models for several spoken language identification tasks. Experiments show that using the automatically retrieved training data gives competitive results to using hand-labeled proprietary datasets. The dataset is publicly available. | ['Tanel Alumae', 'Jorgen Valk'] | 2020-11-25 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 8.68403614e-02 5.54545373e-02 -3.23976696e-01 -5.46728373e-01
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9f6da0f4-8f76-4378-b681-6b30561c6b06 | can-gpt-4-perform-neural-architecture-search | 2304.10970 | null | https://arxiv.org/abs/2304.10970v3 | https://arxiv.org/pdf/2304.10970v3.pdf | Can GPT-4 Perform Neural Architecture Search? | We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textbf{G}PT-4 \textbf{E}nhanced \textbf{N}eural arch\textbf{I}tect\textbf{U}re \textbf{S}earch (GENIUS), leverages the generative capabilities of GPT-4 as a black-box optimiser to quickly navigate the architecture search space, pinpoint promising candidates, and iteratively refine these candidates to improve performance. We assess GENIUS across several benchmarks, comparing it with existing state-of-the-art NAS techniques to illustrate its effectiveness. Rather than targeting state-of-the-art performance, our objective is to highlight GPT-4's potential to assist research on a challenging technical problem through a simple prompting scheme that requires relatively limited domain expertise\footnote{Code available at \href{https://github.com/mingkai-zheng/GENIUS}{https://github.com/mingkai-zheng/GENIUS}.}. More broadly, we believe our preliminary results point to future research that harnesses general purpose language models for diverse optimisation tasks. We also highlight important limitations to our study, and note implications for AI safety. | ['Samuel Albanie', 'Chang Xu', 'Chen Qian', 'Fei Wang', 'Shan You', 'Xiu Su', 'Mingkai Zheng'] | 2023-04-21 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-2.37129480e-02 3.78735393e-01 -8.69495273e-02 -2.05835074e-01
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a18d956a-3526-41f0-aa5b-c370a719d934 | efficient-open-domain-multi-hop-question | 2305.13691 | null | https://arxiv.org/abs/2305.13691v1 | https://arxiv.org/pdf/2305.13691v1.pdf | Efficient Open Domain Multi-Hop Question Answering with Few-Shot Data Synthesis | Few-shot learning for open domain multi-hop question answering typically relies on large language models (LLMs). While powerful, LLMs are inefficient at the inference time. We propose a data synthesis framework for multi-hop question answering that allows for improving smaller language models with less than 10 human-annotated question answer pairs. The framework is built upon the data generation functions parameterized by LLMs and prompts, which requires minimal hand-crafted features. Empirically, we synthesize millions of multi-hop questions and claims. After finetuning language models on the synthetic data, we evaluate the models on popular benchmarks on multi-hop question answering and fact verification. Our experimental results show that finetuning on the synthetic data improves model performance significantly, allowing our finetuned models to be competitive with prior models while being almost one-third the size in terms of parameter counts. | ['Wen-tau Yih', 'Xilun Chen', 'Mingda Chen'] | 2023-05-23 | null | null | null | null | ['multi-hop-question-answering', 'fact-verification'] | ['knowledge-base', 'natural-language-processing'] | [-3.01281046e-02 4.88450170e-01 -4.63599205e-01 -2.58352250e-01
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9aaa27b5-cdb2-45b5-a387-3846fe566757 | ernie-vil-2-0-multi-view-contrastive-learning | 2209.15270 | null | https://arxiv.org/abs/2209.15270v1 | https://arxiv.org/pdf/2209.15270v1.pdf | ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training | Recent Vision-Language Pre-trained (VLP) models based on dual encoder have attracted extensive attention from academia and industry due to their superior performance on various cross-modal tasks and high computational efficiency. They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality. Actually, an image or a text contains various potential views, just as humans could capture a real-world scene via diverse descriptions or photos. In this paper, we propose ERNIE-ViL 2.0, a Multi-View Contrastive learning framework to build intra-modal and inter-modal correlations between diverse views simultaneously, aiming at learning a more robust cross-modal representation. Specifically, we construct multiple views within each modality to learn the intra-modal correlation for enhancing the single-modal representation. Besides the inherent visual/textual views, we construct sequences of object tags as a special textual view to narrow the cross-modal semantic gap on noisy image-text pairs. Pre-trained with 29M publicly available datasets, ERNIE-ViL 2.0 achieves competitive results on English cross-modal retrieval. Additionally, to generalize our method to Chinese cross-modal tasks, we train ERNIE-ViL 2.0 through scaling up the pre-training datasets to 1.5B Chinese image-text pairs, resulting in significant improvements compared to previous SOTA results on Chinese cross-modal retrieval. We release our pre-trained models in https://github.com/PaddlePaddle/ERNIE. | ['Haifeng Wang', 'Hua Wu', 'Hao Tian', 'Yu Sun', 'Weichong Yin', 'Bin Shan'] | 2022-09-30 | null | null | null | null | ['zero-shot-cross-modal-retrieval'] | ['miscellaneous'] | [-5.82028031e-02 -6.21166945e-01 -3.10514420e-01 -3.31596345e-01
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6f3f97f6-f2cc-4c51-826e-c1783b7d7e98 | subclass-balancing-contrastive-learning-for | 2306.15925 | null | https://arxiv.org/abs/2306.15925v1 | https://arxiv.org/pdf/2306.15925v1.pdf | Subclass-balancing Contrastive Learning for Long-tailed Recognition | Long-tailed recognition with imbalanced class distribution naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and supervised contrastive learning enforce the class balance with a price of introducing imbalance between instances of head class and tail class, which may ignore the underlying rich semantic substructures of the former and exaggerate the biases in the latter. We overcome these drawbacks by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that clusters each head class into multiple subclasses of similar sizes as the tail classes and enforce representations to capture the two-layer class hierarchy between the original classes and their subclasses. Since the clustering is conducted in the representation space and updated during the course of training, the subclass labels preserve the semantic substructures of head classes. Meanwhile, it does not overemphasize tail class samples, so each individual instance contribute to the representation learning equally. Hence, our method achieves both the instance- and subclass-balance, while the original class labels are also learned through contrastive learning among subclasses from different classes. We evaluate SBCL over a list of long-tailed benchmark datasets and it achieves the state-of-the-art performance. In addition, we present extensive analyses and ablation studies of SBCL to verify its advantages. | ['Tianyi Zhou', 'Haonan Wang', 'Jieyu Zhang', 'Chengkai Hou'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 1.08427428e-01 1.93954092e-02 -6.92171037e-01 -6.88551128e-01
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fc647f39-93e7-4a8a-a2e1-b345d07974e1 | pyretri-a-pytorch-based-library-for | 2005.02154 | null | https://arxiv.org/abs/2005.02154v2 | https://arxiv.org/pdf/2005.02154v2.pdf | PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks | Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner. In order to fill this gap, we introduce PyRetri, an open source library for deep learning based unsupervised image retrieval. The library encapsulates the retrieval process in several stages and provides functionality that covers various prominent methods for each stage. The idea underlying its design is to provide a unified platform for deep learning based image retrieval research, with high usability and extensibility. To the best of our knowledge, this is the first open-source library for unsupervised image retrieval by deep learning. | ['Xian-Sheng Hua', 'Xiu-Shen Wei', 'Ren-Jie Song', 'Yuehu Liu', 'Benyi Hu', 'Yazhou Yao'] | 2020-05-02 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-2.94693172e-01 -5.52046299e-01 -2.53704548e-01 -2.14659408e-01
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ae7d707e-7517-4a3d-ad13-ec30238b6dde | drive-deep-reinforced-accident-anticipation | 2107.10189 | null | https://arxiv.org/abs/2107.10189v2 | https://arxiv.org/pdf/2107.10189v2.pdf | DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation | Traffic accident anticipation aims to accurately and promptly predict the occurrence of a future accident from dashcam videos, which is vital for a safety-guaranteed self-driving system. To encourage an early and accurate decision, existing approaches typically focus on capturing the cues of spatial and temporal context before a future accident occurs. However, their decision-making lacks visual explanation and ignores the dynamic interaction with the environment. In this paper, we propose Deep ReInforced accident anticipation with Visual Explanation, named DRIVE. The method simulates both the bottom-up and top-down visual attention mechanism in a dashcam observation environment so that the decision from the proposed stochastic multi-task agent can be visually explained by attentive regions. Moreover, the proposed dense anticipation reward and sparse fixation reward are effective in training the DRIVE model with our improved reinforcement learning algorithm. Experimental results show that the DRIVE model achieves state-of-the-art performance on multiple real-world traffic accident datasets. Code and pre-trained model are available at \url{https://www.rit.edu/actionlab/drive}. | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2021-07-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bao_DRIVE_Deep_Reinforced_Accident_Anticipation_With_Visual_Explanation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bao_DRIVE_Deep_Reinforced_Accident_Anticipation_With_Visual_Explanation_ICCV_2021_paper.pdf | iccv-2021-1 | ['accident-anticipation'] | ['computer-vision'] | [-7.90434852e-02 3.33105996e-02 -1.02124847e-01 -3.46256405e-01
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22a8b769-27aa-40e7-9b9c-fdced91205b8 | towards-automatic-face-to-face-translation-1 | 2003.00418 | null | https://arxiv.org/abs/2003.00418v1 | https://arxiv.org/pdf/2003.00418v1.pdf | Towards Automatic Face-to-Face Translation | In light of the recent breakthroughs in automatic machine translation systems, we propose a novel approach that we term as "Face-to-Face Translation". As today's digital communication becomes increasingly visual, we argue that there is a need for systems that can automatically translate a video of a person speaking in language A into a target language B with realistic lip synchronization. In this work, we create an automatic pipeline for this problem and demonstrate its impact on multiple real-world applications. First, we build a working speech-to-speech translation system by bringing together multiple existing modules from speech and language. We then move towards "Face-to-Face Translation" by incorporating a novel visual module, LipGAN for generating realistic talking faces from the translated audio. Quantitative evaluation of LipGAN on the standard LRW test set shows that it significantly outperforms existing approaches across all standard metrics. We also subject our Face-to-Face Translation pipeline, to multiple human evaluations and show that it can significantly improve the overall user experience for consuming and interacting with multimodal content across languages. Code, models and demo video are made publicly available. Demo video: https://www.youtube.com/watch?v=aHG6Oei8jF0 Code and models: https://github.com/Rudrabha/LipGAN | ['Vinay Namboodiri', 'Jerin Philip', 'C. V. Jawahar', 'Abhishek Jha', 'Rudrabha Mukhopadhyay', 'Prajwal K R'] | 2020-03-01 | towards-automatic-face-to-face-translation | https://dl.acm.org/doi/10.1145/3343031.3351066 | https://dl.acm.org/doi/pdf/10.1145/3343031.3351066?download=true | acm-multimedia-2019-2019-10 | ['face-to-face-translation', 'lip-sync', 'speech-to-speech-translation'] | ['computer-vision', 'computer-vision', 'speech'] | [ 7.82987848e-02 4.24868539e-02 -2.76108757e-02 -4.36719805e-01
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2f3a6e5e-43da-48e2-9af7-1ff21156aaeb | residual-q-learning-offline-and-online-policy | 2306.09526 | null | https://arxiv.org/abs/2306.09526v1 | https://arxiv.org/pdf/2306.09526v1.pdf | Residual Q-Learning: Offline and Online Policy Customization without Value | Imitation Learning (IL) is a widely used framework for learning imitative behavior from demonstrations. It is especially appealing for solving complex real-world tasks where handcrafting reward function is difficult, or when the goal is to mimic human expert behavior. However, the learned imitative policy can only follow the behavior in the demonstration. When applying the imitative policy, we may need to customize the policy behavior to meet different requirements coming from diverse downstream tasks. Meanwhile, we still want the customized policy to maintain its imitative nature. To this end, we formulate a new problem setting called policy customization. It defines the learning task as training a policy that inherits the characteristics of the prior policy while satisfying some additional requirements imposed by a target downstream task. We propose a novel and principled approach to interpret and determine the trade-off between the two task objectives. Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task. We propose a novel framework, Residual Q-learning, which can solve the formulated MDP by leveraging the prior policy without knowing the inherent reward or value function of the prior policy. We derive a family of residual Q-learning algorithms that can realize offline and online policy customization, and show that the proposed algorithms can effectively accomplish policy customization tasks in various environments. | ['Wei Zhan', 'Masayoshi Tomizuka', 'Jean Mercat', 'Haruki Nishimura', 'Chen Tang', 'Chenran Li'] | 2023-06-15 | null | null | null | null | ['q-learning', 'imitation-learning'] | ['methodology', 'methodology'] | [ 7.40017295e-02 -2.43876074e-02 -4.05737042e-01 -5.17098978e-02
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96e205aa-5437-451a-8876-f9c4809ab377 | towards-diverse-temporal-grounding-under | 2303.06545 | null | https://arxiv.org/abs/2303.06545v1 | https://arxiv.org/pdf/2303.06545v1.pdf | Towards Diverse Temporal Grounding under Single Positive Labels | Temporal grounding aims to retrieve moments of the described event within an untrimmed video by a language query. Typically, existing methods assume annotations are precise and unique, yet one query may describe multiple moments in many cases. Hence, simply taking it as a one-vs-one mapping task and striving to match single-label annotations will inevitably introduce false negatives during optimization. In this study, we reformulate this task as a one-vs-many optimization problem under the condition of single positive labels. The unlabeled moments are considered unobserved rather than negative, and we explore mining potential positive moments to assist in multiple moment retrieval. In this setting, we propose a novel Diverse Temporal Grounding framework, termed DTG-SPL, which mainly consists of a positive moment estimation (PME) module and a diverse moment regression (DMR) module. PME leverages semantic reconstruction information and an expected positive regularization to uncover potential positive moments in an online fashion. Under the supervision of these pseudo positives, DMR is able to localize diverse moments in parallel that meet different users. The entire framework allows for end-to-end optimization as well as fast inference. Extensive experiments on Charades-STA and ActivityNet Captions show that our method achieves superior performance in terms of both single-label and multi-label metrics. | ['Chuanping Hu', 'Yanjun Chen', 'Chongyang Zhang', 'Hao Zhou'] | 2023-03-12 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-7.28627713e-03 -1.29362077e-01 -7.21985519e-01 -5.83239496e-01
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0d708b8f-8fb9-4f8f-81d3-855988aa7923 | end-to-end-temporal-relation-extraction-in | null | null | https://ceur-ws.org/Vol-3370/paper2.pdf | https://ceur-ws.org/Vol-3370/paper2.pdf | End-to-End Temporal Relation Extraction in the Clinical Domain | Temporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events. In this paper, we present an end-to end temporal relation extraction system for the clinical domain, using the i2b2 2012 Temporal Relation challenge as a benchmark. In our proposal, we fine-tune REBEL —a sequence-to-sequence model for general relation extraction — with temporal annotations and discharge summaries. Our proposal is then able to simultaneously extract relevant clinical entities, time expressions and the temporal relations between them. Our results demonstrate the efectiveness of this approach, achieving reasonable performance on the End-To-End track of the i2b2 2012 Challenge. | ['Begoña Altuna', 'José Javier Saiz'] | 2023-04-02 | null | null | null | proceedings-of-the-text2story-23-workshop | ['temporal-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.79136068e-02 3.21777374e-01 -5.92690766e-01 -2.99535632e-01
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d87d3ae1-0f10-4b10-b635-087a0ea712c7 | swigs-a-swift-guided-sampling-method | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Fragoso_SWIGS_A_Swift_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Fragoso_SWIGS_A_Swift_2013_CVPR_paper.pdf | SWIGS: A Swift Guided Sampling Method | We present SWIGS, a Swift and efficient Guided Sampling method for robust model estimation from image feature correspondences. Our method leverages the accuracy of our new confidence measure (MR-Rayleigh), which assigns a correctness-confidence to a putative correspondence in an online fashion. MR-Rayleigh is inspired by Meta-Recognition (MR), an algorithm that aims to predict when a classifier's outcome is correct. We demonstrate that by using a Rayleigh distribution, the prediction accuracy of MR can be improved considerably. Our experiments show that MR-Rayleigh tends to predict better than the often-used Lowe's ratio, Brown's ratio, and the standard MR under a range of imaging conditions. Furthermore, our homography estimation experiment demonstrates that SWIGS performs similarly or better than other guided sampling methods while requiring fewer iterations, leading to fast and accurate model estimates. | ['Victor Fragoso', 'Matthew Turk'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['homography-estimation'] | ['computer-vision'] | [ 3.92486125e-01 7.53919482e-02 -4.09362018e-01 -1.56065613e-01
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a1247da4-211d-4c87-b857-f35c3e8ee621 | shape-aware-masking-for-inpainting-in-medical | 2207.05787 | null | https://arxiv.org/abs/2207.05787v1 | https://arxiv.org/pdf/2207.05787v1.pdf | Shape-Aware Masking for Inpainting in Medical Imaging | Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on different given classes of anatomy. In this work, we introduce a method for generating shape-aware masks for inpainting, which aims at learning the statistical shape prior. We hypothesize that although the variation of masks improves the generalizability of inpainting models, the shape of the masks should follow the topology of the organs of interest. Hence, we propose an unsupervised guided masking approach based on an off-the-shelf inpainting model and a superpixel over-segmentation algorithm to generate a wide range of shape-dependent masks. Experimental results on abdominal MR image reconstruction show the superiority of our proposed masking method over standard methods using square-shaped or dataset of irregular shape masks. | ['Nassir Navab', 'Azade Farshad', 'Yousef Yeganeh'] | 2022-07-12 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 5.96454561e-01 4.00238991e-01 -6.80107996e-02 -5.40648401e-01
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cd1f62fe-b0fd-4f5e-9b12-82903d388270 | discovery-of-governing-equations-with | 2009.11500 | null | https://arxiv.org/abs/2009.11500v1 | https://arxiv.org/pdf/2009.11500v1.pdf | Discovery of Governing Equations with Recursive Deep Neural Networks | Model discovery based on existing data has been one of the major focuses of mathematical modelers for decades. Despite tremendous achievements of model identification from adequate data, how to unravel the models from limited data is less resolved. In this paper, we focus on the model discovery problem when the data is not efficiently sampled in time. This is common due to limited experimental accessibility and labor/resource constraints. Specifically, we introduce a recursive deep neural network (RDNN) for data-driven model discovery. This recursive approach can retrieve the governing equation in a simple and efficient manner, and it can significantly improve the approximation accuracy by increasing the recursive stages. In particular, our proposed approach shows superior power when the existing data are sampled with a large time lag, from which the traditional approach might not be able to recover the model well. Several widely used examples of dynamical systems are used to benchmark this newly proposed recursive approach. Numerical comparisons confirm the effectiveness of this recursive neural network for model discovery. | ['Jia Zhao', 'Jarrod Mau'] | 2020-09-24 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-8.80248845e-02 -4.21664715e-01 -1.22329526e-01 4.00290042e-02
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d9c05d18-714b-4101-8bcd-f7922012756c | revisiting-temporal-alignment-for-video | 2111.15288 | null | https://arxiv.org/abs/2111.15288v2 | https://arxiv.org/pdf/2111.15288v2.pdf | Revisiting Temporal Alignment for Video Restoration | Long-range temporal alignment is critical yet challenging for video restoration tasks. Recently, some works attempt to divide the long-range alignment into several sub-alignments and handle them progressively. Although this operation is helpful in modeling distant correspondences, error accumulation is inevitable due to the propagation mechanism. In this work, we present a novel, generic iterative alignment module which employs a gradual refinement scheme for sub-alignments, yielding more accurate motion compensation. To further enhance the alignment accuracy and temporal consistency, we develop a non-parametric re-weighting method, where the importance of each neighboring frame is adaptively evaluated in a spatial-wise way for aggregation. By virtue of the proposed strategies, our model achieves state-of-the-art performance on multiple benchmarks across a range of video restoration tasks including video super-resolution, denoising and deblurring. Our project is available in \url{https://github.com/redrock303/Revisiting-Temporal-Alignment-for-Video-Restoration.git}. | ['Jiangbo Lu', 'Xiaoguang Han', 'Liying Lu', 'Wenbo Li', 'Kun Zhou'] | 2021-11-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.63109124e-01 -6.08172178e-01 -7.22776949e-02 -3.39689106e-01
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10140519-bd8c-4b2b-a352-6b75691a13fd | normalising-non-standardised-orthography-in | null | null | https://aclanthology.org/D19-5518 | https://aclanthology.org/D19-5518.pdf | Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data | We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard. We use an end-to-end deep neural model designed to deal with context-dependent spelling correction and normalisation. Results indicate that a model with two CNN sub-network encoders and an LSTM decoder performs the best, and that word context matters. Additionally, pre-processing data token-by-token with an edit-distance based aligner significantly improves the performance. We get promising results for the spelling correction and normalisation, as a pre-processing step for downstream tasks, on detecting binary Semantic Textual Similarity. | ['Jean-Philippe Bernardy', 'Simon Dobnik', 'Wafia Adouane'] | 2019-11-01 | null | null | null | ws-2019-11 | ['spelling-correction'] | ['natural-language-processing'] | [ 6.11998022e-01 -5.33613339e-02 3.12971979e-01 -8.42732549e-01
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daf3fb80-9251-4210-99fc-9d173bcf7445 | simple-dataset-for-proof-method | 2004.10667 | null | https://arxiv.org/abs/2004.10667v3 | https://arxiv.org/pdf/2004.10667v3.pdf | Simple Dataset for Proof Method Recommendation in Isabelle/HOL (Dataset Description) | Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers. However, the expressive nature of underlying logics and esoteric structures of proof documents impede machine learning practitioners, who often do not have much expertise in formal logic, let alone Isabelle/HOL, from achieving a large scale success in this field. In this data description, we present a simple dataset that contains data on over 400k proof method applications along with over 100 extracted features for each in a format that can be processed easily without any knowledge about formal logic. Our simple data format allows machine learning practitioners to try machine learning tools to predict proof methods in Isabelle/HOL without requiring domain expertise in logic. | ['Yutaka Nagashima'] | 2020-04-21 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [-1.94705576e-02 3.44924182e-01 -5.52096009e-01 -2.06489086e-01
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06c004f7-8cd8-462b-a7c3-68d731c1894c | capdet-unifying-dense-captioning-and-open | 2303.02489 | null | https://arxiv.org/abs/2303.02489v3 | https://arxiv.org/pdf/2303.02489v3.pdf | CapDet: Unifying Dense Captioning and Open-World Detection Pretraining | Benefiting from large-scale vision-language pre-training on image-text pairs, open-world detection methods have shown superior generalization ability under the zero-shot or few-shot detection settings. However, a pre-defined category space is still required during the inference stage of existing methods and only the objects belonging to that space will be predicted. To introduce a "real" open-world detector, in this paper, we propose a novel method named CapDet to either predict under a given category list or directly generate the category of predicted bounding boxes. Specifically, we unify the open-world detection and dense caption tasks into a single yet effective framework by introducing an additional dense captioning head to generate the region-grounded captions. Besides, adding the captioning task will in turn benefit the generalization of detection performance since the captioning dataset covers more concepts. Experiment results show that by unifying the dense caption task, our CapDet has obtained significant performance improvements (e.g., +2.1% mAP on LVIS rare classes) over the baseline method on LVIS (1203 classes). Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e.g., 15.44% mAP on VG V1.2 and 13.98% on the VG-COCO dataset. | ['Xiaodan Liang', 'Shen Zhao', 'Wei zhang', 'Pengzhen Ren', 'Hang Xu', 'Jianhua Han', 'Youpeng Wen', 'Yanxin Long'] | 2023-03-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-captioning'] | ['computer-vision'] | [ 7.20597580e-02 3.66272628e-01 -1.28057867e-01 -3.23825121e-01
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47a6fc63-7ebe-492e-a029-ad18afc60fa1 | learning-semantic-sentence-representations | 1903.11393 | null | http://arxiv.org/abs/1903.11393v1 | http://arxiv.org/pdf/1903.11393v1.pdf | Learning semantic sentence representations from visually grounded language without lexical knowledge | Current approaches to learning semantic representations of sentences often
use prior word-level knowledge. The current study aims to leverage visual
information in order to capture sentence level semantics without the need for
word embeddings. We use a multimodal sentence encoder trained on a corpus of
images with matching text captions to produce visually grounded sentence
embeddings. Deep Neural Networks are trained to map the two modalities to a
common embedding space such that for an image the corresponding caption can be
retrieved and vice versa. We show that our model achieves results comparable to
the current state-of-the-art on two popular image-caption retrieval benchmark
data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the
resulting sentence embeddings using the data from the Semantic Textual
Similarity benchmark task and show that the multimodal embeddings correlate
well with human semantic similarity judgements. The system achieves
state-of-the-art results on several of these benchmarks, which shows that a
system trained solely on multimodal data, without assuming any word
representations, is able to capture sentence level semantics. Importantly, this
result shows that we do not need prior knowledge of lexical level semantics in
order to model sentence level semantics. These findings demonstrate the
importance of visual information in semantics. | ['Stefan Frank', 'Danny Merkx'] | 2019-03-27 | null | null | null | null | ['learning-semantic-representations', 'grounded-language-learning'] | ['methodology', 'natural-language-processing'] | [ 3.87786537e-01 5.89108430e-02 -9.93635207e-02 -4.59575444e-01
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8f315dda-f422-412f-abf7-f873e6549d6e | explaining-black-box-android-malware | 1803.03544 | null | http://arxiv.org/abs/1803.03544v2 | http://arxiv.org/pdf/1803.03544v2.pdf | Explaining Black-box Android Malware Detection | Machine-learning models have been recently used for detecting malicious
Android applications, reporting impressive performances on benchmark datasets,
even when trained only on features statically extracted from the application,
such as system calls and permissions. However, recent findings have highlighted
the fragility of such in-vitro evaluations with benchmark datasets, showing
that very few changes to the content of Android malware may suffice to evade
detection. How can we thus trust that a malware detector performing well on
benchmark data will continue to do so when deployed in an operating
environment? To mitigate this issue, the most popular Android malware detectors
use linear, explainable machine-learning models to easily identify the most
influential features contributing to each decision. In this work, we generalize
this approach to any black-box machine- learning model, by leveraging a
gradient-based approach to identify the most influential local features. This
enables using nonlinear models to potentially increase accuracy without
sacrificing interpretability of decisions. Our approach also highlights the
global characteristics learned by the model to discriminate between benign and
malware applications. Finally, as shown by our empirical analysis on a popular
Android malware detection task, it also helps identifying potential
vulnerabilities of linear and nonlinear models against adversarial
manipulations. | ['Battista Biggio', 'Giorgio Giacinto', 'Fabio Roli', 'Marco Melis', 'Davide Maiorca'] | 2018-03-09 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.19352537e-01 1.03727378e-01 -5.52516222e-01 -1.05762340e-01
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7e7aae77-4fa5-4107-8f33-73ab16417c61 | solar-sinkhorn-label-refinery-for-imbalanced | 2209.10365 | null | https://arxiv.org/abs/2209.10365v1 | https://arxiv.org/pdf/2209.10365v1.pdf | SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning | Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods. Code and data are available at: https://github.com/hbzju/SoLar . | ['Junbo Zhao', 'Gang Chen', 'Lei Feng', 'YUREN MAO', 'Yixuan Li', 'Mingxuan Xia', 'Haobo Wang'] | 2022-09-21 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.10327560e-01 5.74799627e-02 -7.33152390e-01 -6.57271028e-01
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098c0161-b732-4531-8ee5-1a80b9dee827 | pcred-zero-shot-relation-triplet-extraction | 2211.14477 | null | https://arxiv.org/abs/2211.14477v2 | https://arxiv.org/pdf/2211.14477v2.pdf | PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection | Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets from unstructured texts under the zero-shot setting, where the relation sets at the training and testing stages are disjoint. Previous state-of-the-art method handles this challenging task by leveraging pretrained language models to generate data as additional training samples, which increases the training cost and severely constrains the model performance. To address the above issues, we propose a novel method named PCRED for ZeroRTE with Potential Candidate Relation Selection and Entity Boundary Detection. The remarkable characteristic of PCRED is that it does not rely on additional data and still achieves promising performance. The model adopts a relation-first paradigm, recognizing unseen relations through candidate relation selection. With this approach, the semantics of relations are naturally infused in the context. Entities are extracted based on the context and the semantics of relations subsequently. We evaluate our model on two ZeroRTE datasets. The experiment results show that our method consistently outperforms previous works. Our code will be available at https://anonymous.4open.science/r/PCRED. | ['Hui Zhao', 'Yunqi Zhang', 'Gang Zhao', 'Dongxu Li', 'Yuquan Lan'] | 2022-11-26 | null | null | null | null | ['boundary-detection', 'zero-shot-relation-triplet-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 1.61703929e-01 5.69270551e-01 -4.71436262e-01 -1.47053435e-01
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b282b767-b635-4a13-bb53-43c581fa6b70 | exploratory-hidden-markov-factor-models-for | 2202.12819 | null | https://arxiv.org/abs/2202.12819v2 | https://arxiv.org/pdf/2202.12819v2.pdf | Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae | Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among veterans and millions of Americans after traumatic exposures, resulting in substantial burdens for trauma survivors and society. Despite numerous studies conducted on APNS over the past decades, there has been limited progress in understanding the underlying neurobiological mechanisms due to several unique challenges. One of these challenges is the reliance on subjective self-report measures to assess APNS, which can easily result in measurement errors and biases (e.g., recall bias). To mitigate this issue, in this paper, we investigate the potential of leveraging the objective longitudinal mobile device data to identify homogeneous APNS states and study the dynamic transitions and potential risk factors of APNS after trauma exposure. To handle specific challenges posed by longitudinal mobile device data, we developed exploratory hidden Markov factor models and designed a Stabilized Expectation-Maximization algorithm for parameter estimation. Simulation studies were conducted to evaluate the performance of parameter estimation and model selection. Finally, to demonstrate the practical utility of the method, we applied it to mobile device data collected from the Advancing Understanding of RecOvery afteR traumA (AURORA) study. | ['Rui Song', 'Ronald Kessler', 'Samuel McLean', 'Donglin Zeng', 'Xinming An', 'Lin Ge'] | 2022-02-25 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.37206721e-01 -4.42872196e-01 -5.26552260e-01 -1.89673617e-01
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c2c30ffa-f337-4da5-a0d1-5f41594ac9cb | weakly-supervised-instance-segmentation-by-1 | 2007.09397 | null | https://arxiv.org/abs/2007.09397v1 | https://arxiv.org/pdf/2007.09397v1.pdf | Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances | Recent approaches for weakly supervised instance segmentations depend on two components: (i) a pseudo label generation model that provides instances which are consistent with a given annotation; and (ii) an instance segmentation model, which is trained in a supervised manner using the pseudo labels as ground-truth. Unlike previous approaches, we explicitly model the uncertainty in the pseudo label generation process using a conditional distribution. The samples drawn from our conditional distribution provide accurate pseudo labels due to the use of semantic class aware unary terms, boundary aware pairwise smoothness terms, and annotation aware higher order terms. Furthermore, we represent the instance segmentation model as an annotation agnostic prediction distribution. In contrast to previous methods, our representation allows us to define a joint probabilistic learning objective that minimizes the dissimilarity between the two distributions. Our approach achieves state of the art results on the PASCAL VOC 2012 data set, outperforming the best baseline by 4.2% mAP@0.5 and 4.8% mAP@0.75. | ['M. Pawan Kumar', 'C. V. Jawahar', 'Aditya Arun'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6083_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730256.pdf | eccv-2020-8 | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.91090143e-01 7.52545774e-01 -3.55216324e-01 -8.73368025e-01
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b99037b2-930e-49eb-aa43-af5886cdde8c | digitalexposome-quantifying-the-urban | 2101.12615 | null | https://arxiv.org/abs/2101.12615v1 | https://arxiv.org/pdf/2101.12615v1.pdf | DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network | In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodel mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: PM1, PM2.5, PM10, Oxidised, Reduced, NH3 and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals' perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge devices. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and spatial visualisations to unravel the relationship between the variables. Results showed that EDA and Heart Rate Variability HRV are noticeably impacted by the level of Particulate Matters (PM) in the environment well with the environmental variables. Furthermore, we adopted Deep Belief Network to extract features from the multimodel data feed which outperformed Convolutional Neural Network and achieved up to (a=80.8%, {\sigma}=0.001) accuracy. | ['Kieran Woodward', 'Eiman Kanjo', 'Thomas Johnson'] | 2021-01-29 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 8.20299014e-02 -1.78181902e-01 1.06398299e-01 -1.31538495e-01
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91524e59-17f0-47aa-986d-2624aa92aff0 | learning-to-generate-questions-by-enhancing | 2212.12192 | null | https://arxiv.org/abs/2212.12192v1 | https://arxiv.org/pdf/2212.12192v1.pdf | Learning to Generate Questions by Enhancing Text Generation with Sentence Selection | We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567). | ['Minh-Tien Nguyen', 'Hung Le', 'Nguyen Hong Son', 'Do Hoang Thai Duong'] | 2022-12-23 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.15022862e-01 7.27158546e-01 -2.83072013e-02 -4.85554904e-01
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74512684-e4d5-4864-91c8-811770395b30 | multi-objective-reinforcement-learning-based | 2009.13854 | null | https://arxiv.org/abs/2009.13854v1 | https://arxiv.org/pdf/2009.13854v1.pdf | Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments | Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on power management systems for such optimization but often are not satisfied with the results. In this paper, we present a novel multi-objective reinforcement learning framework with two-fold objectives of minimizing power consumption and maximizing user satisfaction. The framework explores the trade-off between the two objectives and converges to a better power management policy when both objectives are considered while finding an optimal policy. We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches. We also show that the devices that are used regularly and have several fluctuations in device modes at regular intervals should be targeted for optimization, and the experiments on data from other smart homes fetch similar results, hence ensuring transfer-ability of the proposed framework. | ['Arun Balaji Buduru', 'Siddhant Bhambri', 'Saurabh Gupta', 'Ponnurangam Kumaraguru', 'Karan Dhingra'] | 2020-09-29 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.78121448e-01 6.47872090e-02 -4.59474951e-01 -2.59337515e-01
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a11b5bad-c2c6-48c4-a523-b57266045107 | from-the-one-judge-of-the-whole-typed | 2306.04170 | null | https://arxiv.org/abs/2306.04170v1 | https://arxiv.org/pdf/2306.04170v1.pdf | From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation | Entailment Graphs (EGs) have been constructed based on extracted corpora as a strong and explainable form to indicate context-independent entailment relations in natural languages. However, EGs built by previous methods often suffer from the severe sparsity issues, due to limited corpora available and the long-tail phenomenon of predicate distributions. In this paper, we propose a multi-stage method, Typed Predicate-Entailment Graph Generator (TP-EGG), to tackle this problem. Given several seed predicates, TP-EGG builds the graphs by generating new predicates and detecting entailment relations among them. The generative nature of TP-EGG helps us leverage the recent advances from large pretrained language models (PLMs), while avoiding the reliance on carefully prepared corpora. Experiments on benchmark datasets show that TP-EGG can generate high-quality and scale-controllable entailment graphs, achieving significant in-domain improvement over state-of-the-art EGs and boosting the performance of down-stream inference tasks. | ['Dongyan Zhao', 'Yansong Feng', 'Zhibin Chen'] | 2023-06-07 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 4.16749328e-01 4.53769356e-01 -3.57975394e-01 -4.85570133e-01
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84b85930-7103-45c0-8d8a-7e08ce5d93b4 | jointly-modeling-aspect-and-sentiment-with | 2004.06427 | null | https://arxiv.org/abs/2004.06427v1 | https://arxiv.org/pdf/2004.06427v1.pdf | Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks | Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them. Both the previous pipeline and integrated methods fail to precisely model the innate connection between these two objectives. In this paper, we propose a novel dynamic heterogeneous graph to jointly model the two objectives in an explicit way. Both the ordinary words and sentiment labels are treated as nodes in the heterogeneous graph, so that the aspect words can interact with the sentiment information. The graph is initialized with multiple types of dependencies, and dynamically modified during real-time prediction. Experiments on the benchmark datasets show that our model outperforms the state-of-the-art models. Further analysis demonstrates that our model obtains significant performance gain on the challenging instances under multiple-opinion aspects and no-opinion aspect situations. | ['Xu sun', 'Yunfang Wu', 'Shu Liu', 'Qi Su', 'Wei Li'] | 2020-04-14 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 1.15542440e-02 3.36535603e-01 -5.05708277e-01 -7.54199743e-01
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fe3c79e9-50d1-4d44-bd83-2bfdf3239a09 | adversarial-attacks-on-adversarial-bandits | 2301.12595 | null | https://arxiv.org/abs/2301.12595v1 | https://arxiv.org/pdf/2301.12595v1.pdf | Adversarial Attacks on Adversarial Bandits | We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs the loss or reward signal to control the behavior of the victim bandit player. We show that the attacker is able to mislead any no-regret adversarial bandit algorithm into selecting a suboptimal target arm in every but sublinear (T-o(T)) number of rounds, while incurring only sublinear (o(T)) cumulative attack cost. This result implies critical security concern in real-world bandit-based systems, e.g., in online recommendation, an attacker might be able to hijack the recommender system and promote a desired product. Our proposed attack algorithms require knowledge of only the regret rate, thus are agnostic to the concrete bandit algorithm employed by the victim player. We also derived a theoretical lower bound on the cumulative attack cost that any victim-agnostic attack algorithm must incur. The lower bound matches the upper bound achieved by our attack, which shows that our attack is asymptotically optimal. | ['Zhijin Zhou', 'Yuzhe ma'] | 2023-01-30 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.94745094e-01 3.05047095e-01 -2.76374161e-01 5.71614206e-02
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c61b305d-7af5-4281-af44-6fdec836011d | learning-an-image-based-motion-context-for | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Leal-Taixe_Learning_an_Image-based_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Leal-Taixe_Learning_an_Image-based_2014_CVPR_paper.pdf | Learning an Image-based Motion Context for Multiple People Tracking | We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals. At the core of our model lies a learned dictionary of interaction feature strings which capture relationships between the motions of targets. These feature strings, created from low-level image features, lead to a much richer representation of the physical interactions between targets compared to hand-specified social force models that previous works have introduced for tracking. One disadvantage of using social forces is that all pedestrians must be detected in order for the forces to be applied, while our method is able to encode the effect of undetected targets, making the tracker more robust to partial occlusions. The interaction feature strings are used in a Random Forest framework to track targets according to the features surrounding them. Results on six publicly available sequences show that our method outperforms state-of-the-art approaches in multiple people tracking. | ['Laura Leal-Taixe', 'Silvio Savarese', 'Michele Fenzi', 'Alina Kuznetsova', 'Bodo Rosenhahn'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['multiple-people-tracking'] | ['computer-vision'] | [-9.30703655e-02 -2.85447061e-01 -2.10481659e-02 -2.35599726e-01
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14ec015e-f213-40a9-bf68-af08fdb8e7bb | multilayer-hypergraph-clustering-using-the | 2301.11657 | null | https://arxiv.org/abs/2301.11657v2 | https://arxiv.org/pdf/2301.11657v2.pdf | Multilayer hypergraph clustering using the aggregate similarity matrix | We consider the community recovery problem on a multilayer variant of the hypergraph stochastic block model (HSBM). Each layer is associated with an independent realization of a d-uniform HSBM on N vertices. Given the similarity matrix containing the aggregated number of hyperedges incident to each pair of vertices, the goal is to obtain a partition of the N vertices into disjoint communities. In this work, we investigate a semidefinite programming (SDP) approach and obtain information-theoretic conditions on the model parameters that guarantee exact recovery both in the assortative and the disassortative cases. | ['Lasse Leskelä', 'B. R. Vinay Kumar', 'Konstantin Avrachenkov', 'Kalle Alaluusua'] | 2023-01-27 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.66908041e-01 4.32581216e-01 -4.04112756e-01 -1.29004493e-01
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7b538f9d-ddd4-4476-8369-cee0f2402d31 | gan-based-disentanglement-learning-for-chest | 2110.09134 | null | https://arxiv.org/abs/2110.09134v1 | https://arxiv.org/pdf/2110.09134v1.pdf | GAN-based disentanglement learning for chest X-ray rib suppression | Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis. However, previous approaches on generating rib-suppressed CXR face challenges in preserving details and eliminating rib residues. We hereby propose a GAN-based disentanglement learning framework called Rib Suppression GAN, or RSGAN, to perform rib suppression by utilizing the anatomical knowledge embedded in unpaired computed tomography (CT) images. In this approach, we employ a residual map to characterize the intensity difference between CXR and the corresponding rib-suppressed result. To predict the residual map in CXR domain, we disentangle the image into structure- and contrast-specific features and transfer the rib structural priors from digitally reconstructed radiographs (DRRs) computed by CT. Furthermore, we employ additional adaptive loss to suppress rib residue and preserve more details. We conduct extensive experiments based on 1,673 CT volumes, and four benchmarking CXR datasets, totaling over 120K images, to demonstrate that (i) our proposed RSGAN achieves superior image quality compared to the state-of-the-art rib suppression methods; (ii) combining CXR with our rib-suppressed result leads to better performance in lung disease classification and tuberculosis area detection. | ['S. Kevin Zhou', 'Cheng Peng', 'Yuanyuan Lyu', 'Luyi Han'] | 2021-10-18 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [ 6.62441790e-01 -8.31642002e-02 -3.41205746e-01 -2.38582268e-02
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248169c4-f062-4970-8847-daaeea65b19f | implicit-motion-compensated-network-for | 2204.02791 | null | https://arxiv.org/abs/2204.02791v1 | https://arxiv.org/pdf/2204.02791v1.pdf | Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation | Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visually similar surroundings (appearance-based) or suffer from deterioration in the quality of their predictions because of dynamic background and inaccurate flow (flow-based). To overcome the limitations, we propose an implicit motion-compensated network (IMCNet) combining complementary cues ($\textit{i.e.}$, appearance and motion) with aligned motion information from the adjacent frames to the current frame at the feature level without estimating optical flows. The proposed IMCNet consists of an affinity computing module (ACM), an attention propagation module (APM), and a motion compensation module (MCM). The light-weight ACM extracts commonality between neighboring input frames based on appearance features. The APM then transmits global correlation in a top-down manner. Through coarse-to-fine iterative inspiring, the APM will refine object regions from multiple resolutions so as to efficiently avoid losing details. Finally, the MCM aligns motion information from temporally adjacent frames to the current frame which achieves implicit motion compensation at the feature level. We perform extensive experiments on $\textit{DAVIS}_{\textit{16}}$ and $\textit{YouTube-Objects}$. Our network achieves favorable performance while running at a faster speed compared to the state-of-the-art methods. | ['Zhengguo Li', 'Zhong Liu', 'Xingming Wu', 'Weihai Chen', 'Lin Xi'] | 2022-04-06 | null | null | null | null | ['motion-compensation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.68551797e-01 -3.34220558e-01 -9.62110087e-02 -1.85059890e-01
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c76ffba7-e804-48bf-9c38-1eec19c90453 | in-search-for-a-generalizable-method-for | 2302.06658 | null | https://arxiv.org/abs/2302.06658v2 | https://arxiv.org/pdf/2302.06658v2.pdf | In Search for a Generalizable Method for Source Free Domain Adaptation | Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data. In this work, we apply existing SFDA techniques to a challenging set of naturally-occurring distribution shifts in bioacoustics, which are very different from the ones commonly studied in computer vision. We find existing methods perform differently relative to each other than observed in vision benchmarks, and sometimes perform worse than no adaptation at all. We propose a new simple method which outperforms the existing methods on our new shifts while exhibiting strong performance on a range of vision datasets. Our findings suggest that existing SFDA methods are not as generalizable as previously thought and that considering diverse modalities can be a useful avenue for designing more robust models. | ['Eleni Triantafillou', 'Vincent Dumoulin', 'Bart van Merriënboer', 'Tom Denton', 'Malik Boudiaf'] | 2023-02-13 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.54477650e-01 -4.19941872e-01 7.60027766e-02 -4.05488342e-01
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d9faa591-bd57-4c62-9c1e-10af7ad02353 | statistical-depth-functions-for-ranking | 2201.08105 | null | https://arxiv.org/abs/2201.08105v1 | https://arxiv.org/pdf/2201.08105v1.pdf | Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications | The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$ say. As it sheds light onto only one aspect of $\Sigma$'s distribution $P$, it may neglect other informative features. It is the purpose of this paper to define analogs of quantiles, ranks and statistical procedures based on such quantities for the analysis of ranking data by means of a metric-based notion of depth function on the symmetric group. Overcoming the absence of vector space structure on $\mathfrak{S}_n$, the latter defines a center-outward ordering of the permutations in the support of $P$ and extends the classic metric-based formulation of consensus ranking (medians corresponding then to the deepest permutations). The axiomatic properties that ranking depths should ideally possess are listed, while computational and generalization issues are studied at length. Beyond the theoretical analysis carried out, the relevance of the novel concepts and methods introduced for a wide variety of statistical tasks are also supported by numerous numerical experiments. | ['Pavlo Mozharovskyi', 'Ekhine Irurozki', 'Stéphan Clémençon', 'Morgane Goibert'] | 2022-01-20 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.35853001e-01 2.30604149e-02 -2.82774083e-02 -6.57669127e-01
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c29559be-6827-4b20-b4fb-13dd075a12f1 | sketch-an-anchor-sub-epoch-fast-model | 2303.16769 | null | https://arxiv.org/abs/2303.16769v1 | https://arxiv.org/pdf/2303.16769v1.pdf | Sketch-an-Anchor: Sub-epoch Fast Model Adaptation for Zero-shot Sketch-based Image Retrieval | Sketch-an-Anchor is a novel method to train state-of-the-art Zero-shot Sketch-based Image Retrieval (ZSSBIR) models in under an epoch. Most studies break down the problem of ZSSBIR into two parts: domain alignment between images and sketches, inherited from SBIR, and generalization to unseen data, inherent to the zero-shot protocol. We argue one of these problems can be considerably simplified and re-frame the ZSSBIR problem around the already-stellar yet underexplored Zero-shot Image-based Retrieval performance of off-the-shelf models. Our fast-converging model keeps the single-domain performance while learning to extract similar representations from sketches. To this end we introduce our Semantic Anchors -- guiding embeddings learned from word-based semantic spaces and features from off-the-shelf models -- and combine them with our novel Anchored Contrastive Loss. Empirical evidence shows we can achieve state-of-the-art performance on all benchmark datasets while training for 100x less iterations than other methods. | ['Moacir Antonelli Ponti', 'Leo Sampaio Ferraz Ribeiro'] | 2023-03-29 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.60976008e-01 -3.42723519e-01 -6.90428257e-01 -3.27300966e-01
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6ed55145-7676-4199-af2d-0a3da321060f | classification-attention-for-chinese-ner | null | null | https://openreview.net/forum?id=B1gUn24tPr | https://openreview.net/pdf?id=B1gUn24tPr | Classification Attention for Chinese NER | The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER). However, such model would likely miss the overall information of the entity words. In this paper, we propose to combine priori entity information with BERT. Instead of relying on additional lexicons or pre-trained word embeddings, our model has generated entity classification embeddings directly on the pre-trained BERT, having the merit of increasing model practicability and avoiding OOV problem. Experiments show that our model has achieved state-of-the-art results on 3 Chinese NER datasets. | ['PeiYang', 'FanYang', 'Yuchen Ge'] | 2019-09-25 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.50714672e-01 1.22013271e-01 3.70894186e-02 -3.57670754e-01
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998e5671-0c1d-4a35-8b8f-790cfa8be754 | connecting-the-dots-a-comprehensive | 2307.00455 | null | https://arxiv.org/abs/2307.00455v1 | https://arxiv.org/pdf/2307.00455v1.pdf | Connecting the Dots: A Comprehensive Literature Review on Low and Medium-Voltage Cables, Fault Types, and Digital Signal Processing Techniques for Fault Location | The review begins with an exploration of acceptable cable types guided by local standards. It then investigates typical cable faults, including insulation degradation, conductor faults, and ground faults, providing insights into their characteristics, causes, and detection methods. Furthermore, the manuscript surveys the latest publications and standards on DSP techniques in fault location spanning various algorithms used. This review provides a comprehensive understanding of low and medium-voltage cables, fault types, and DSP techniques. The findings contribute to improved fault diagnosis and localization methods, facilitating more accurate and efficient cable fault management strategies | ['Sanjay Bahadoorsingh', 'Shankar Ramharack'] | 2023-07-02 | null | null | null | null | ['management'] | ['miscellaneous'] | [-7.53123611e-02 -6.89259827e-01 -1.94863245e-01 -1.53882310e-01
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08d17758-9315-40d5-a648-ee89ccea5aee | disentangled-pre-training-for-image-matting | 2304.00784 | null | https://arxiv.org/abs/2304.00784v1 | https://arxiv.org/pdf/2304.00784v1.pdf | Disentangled Pre-training for Image Matting | Image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale, significantly holding back the development of the research. In this work, we make the first attempt towards addressing this problem, by proposing a self-supervised pre-training approach that can leverage infinite numbers of data to boost the matting performance. The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective. The pre-trained model is then used as an initialisation of the downstream matting task for fine-tuning. Extensive experimental evaluations show that the proposed approach outperforms both the state-of-the-art matting methods and other alternative self-supervised initialisation approaches by a large margin. We also show the robustness of the proposed approach over different backbone architectures. The code and models will be publicly available. | ['Jianbo Jiao', 'Yunchao Wei', 'Ling Chen', 'Gang Yu', 'Zilong Huang', 'Yanda Li'] | 2023-04-03 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 6.01739943e-01 4.49297488e-01 -5.56067675e-02 -5.34763098e-01
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796712f9-5c73-4e2c-be37-bc43e95e7f60 | pc-hmr-pose-calibration-for-3d-human-mesh | 2103.09009 | null | https://arxiv.org/abs/2103.09009v2 | https://arxiv.org/pdf/2103.09009v2.pdf | PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos | The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh parameters from images or videos, while lacking explicit guidance of 3D human pose in visual data. As a result, the generated mesh often exhibits incorrect pose for complex activities. To tackle this problem, we propose to exploit 3D pose to calibrate human mesh. Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module. Furthermore, since the calibration module is designed via non-rigid pose transformation, our PC-HMR frameworks can flexibly tackle bone length variations to alleviate misplacement in the calibrated mesh. Finally, our frameworks are based on generic and complementary integration of data-driven learning and geometrical modeling. Via plug-and-play modules, they can be efficiently adapted for both image/video-based human mesh recovery. Additionally, they have no requirement of extra 3D pose annotations in the testing phase, which releases inference difficulties in practice. We perform extensive experiments on the popular bench-marks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks achieve the SOTA results. | ['Yu Qiao', 'Zhipeng Zhou', 'Zhe Wang', 'Junhao Zhang', 'Yali Wang', 'Tianyu Luan'] | 2021-03-16 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-1.62510201e-01 2.38996029e-01 -1.15023822e-01 -1.10446764e-02
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2.41048053e-01 4.33778584e-01 -3.99842113e-01 -4.96997118e-01
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-7.13096112e-02 -1.10042191e+00 1.54570173e-02 2.06506476e-01] | [7.0012946128845215, -1.1393630504608154] |
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