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7bf95c81-81df-4624-a7f9-60fce2197ffc | linear-convergence-of-a-policy-gradient | 2203.11758 | null | https://arxiv.org/abs/2203.11758v3 | https://arxiv.org/pdf/2203.11758v3.pdf | Linear convergence of a policy gradient method for some finite horizon continuous time control problems | Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive. This paper proposes proximal gradient algorithms for feedback controls of finite-time horizon stochastic control problems. The state dynamics are nonlinear diffusions with control-affine drift, and the cost functions are nonconvex in the state and nonsmooth in the control. The system noise can degenerate, which allows for deterministic control problems as special cases. We prove under suitable conditions that the algorithm converges linearly to a stationary point of the control problem, and is stable with respect to policy updates by approximate gradient steps. The convergence result justifies the recent reinforcement learning heuristics that adding entropy regularization or a fictitious discount factor to the optimization objective accelerates the convergence of policy gradient methods. The proof exploits careful regularity estimates of backward stochastic differential equations. | ['Yufei Zhang', 'Wolfgang Stockinger', 'Christoph Reisinger'] | 2022-03-22 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.28907251e-01 2.05200240e-01 -5.19407988e-01 2.99470603e-01
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7489a2e6-8e97-43be-a3ed-c474cde2798f | reweighted-laplace-prior-based-hyperspectral | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Reweighted_Laplace_Prior_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Reweighted_Laplace_Prior_2015_CVPR_paper.pdf | Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity | Compressive sensing(CS) has been exploited for hypespectral image(HSI) compression in recent years. Though it can greatly reduce the costs of computation and storage, the reconstruction of HSI from a few linear measurements is challenging. The underlying sparsity of HSI is crucial to improve the reconstruction accuracy. However, the sparsity of HSI is unknown in reality and varied with different noise, which makes the sparsity estimation difficult. To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study. First, the reweighted Laplace prior is proposed to model the distribution of sparsity in HSI. Second, the latent variable Bayes model is employed to learn the optimal configuration of the reweighted Laplace prior from the measurements. The model unifies signal recovery, prior learning and noise estimation into a variational framework to infer the parameters automatically. The learned sparsity prior can represent the underlying structure of the sparse signal very well and is adaptive to the unknown noise, which improves the reconstruction accuracy of HSI. The experimental results on three hyperspectral datasets demonstrate the proposed method outperforms serveral state-of-the-art hyperspectral CS methods on the reconstruction accuracy. | ['Chunna Tian', 'Yanning Zhang', 'Wei Wei', 'Lei Zhang', 'Fei Li'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['noise-estimation'] | ['medical'] | [ 7.45344222e-01 -5.71565926e-01 -7.37633929e-02 -1.58874869e-01
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d44f2b4b-9613-43e9-ad5b-c55831a3ad85 | good-examples-make-a-faster-learner-simple-1 | null | null | https://openreview.net/forum?id=W4ZeajjH2u9 | https://openreview.net/pdf?id=W4ZeajjH2u9 | Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates.
Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model's performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-text-classification'] | ['natural-language-processing'] | [-1.88267939e-02 -4.90594395e-02 -3.88014689e-02 -6.18988514e-01
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fd42c01e-bb3b-4c95-bda5-e0c6c1db32db | towards-automating-codenames-spymasters-with | 2212.14104 | null | https://arxiv.org/abs/2212.14104v1 | https://arxiv.org/pdf/2212.14104v1.pdf | Towards automating Codenames spymasters with deep reinforcement learning | Although most reinforcement learning research has centered on competitive games, little work has been done on applying it to co-operative multiplayer games or text-based games. Codenames is a board game that involves both asymmetric co-operation and natural language processing, which makes it an excellent candidate for advancing RL research. To my knowledge, this work is the first to formulate Codenames as a Markov Decision Process and apply some well-known reinforcement learning algorithms such as SAC, PPO, and A2C to the environment. Although none of the above algorithms converge for the Codenames environment, neither do they converge for a simplified environment called ClickPixel, except when the board size is small. | ['Sherman Siu'] | 2022-12-28 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-4.12946969e-01 1.17224984e-01 -1.65072978e-01 7.29041472e-02
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fa51e24e-607a-4f5b-8344-bd35a1643fd8 | pointcmc-cross-modal-multi-scale | 2211.12032 | null | https://arxiv.org/abs/2211.12032v2 | https://arxiv.org/pdf/2211.12032v2.pdf | PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding | Some self-supervised cross-modal learning approaches have recently demonstrated the potential of image signals for enhancing point cloud representation. However, it remains a question on how to directly model cross-modal local and global correspondences in a self-supervised fashion. To solve it, we proposed PointCMC, a novel cross-modal method to model multi-scale correspondences across modalities for self-supervised point cloud representation learning. In particular, PointCMC is composed of: (1) a local-to-local (L2L) module that learns local correspondences through optimized cross-modal local geometric features, (2) a local-to-global (L2G) module that aims to learn the correspondences between local and global features across modalities via local-global discrimination, and (3) a global-to-global (G2G) module, which leverages auxiliary global contrastive loss between the point cloud and image to learn high-level semantic correspondences. Extensive experiment results show that our approach outperforms existing state-of-the-art methods in various downstream tasks such as 3D object classification and segmentation. Code will be made publicly available upon acceptance. | ['Ming Zeng', 'Zizhao Wu', 'Jiawei Mao', 'Xiaogang Peng', 'Honggu Zhou'] | 2022-11-22 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 2.99070217e-02 1.92919597e-02 -3.01002532e-01 -4.99390036e-01
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42a56cf9-e02e-40b6-8a42-4d4b58c8d57e | risk-management-via-anomaly-circumvent | 1908.01112 | null | https://arxiv.org/abs/1908.01112v1 | https://arxiv.org/pdf/1908.01112v1.pdf | Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction | Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm predictions encounters large cumulative errors because they cannot avoid anomalies. In this paper, we propose a novel deep neural network Mid-LSTM for midterm stock prediction, which incorporates the market trend as hidden states. First, based on the autoregressive moving average model (ARMA), a midterm ARMA is formulated by taking into consideration both hidden states and the capital asset pricing model. Then, a midterm LSTM-based deep neural network is designed, which consists of three components: LSTM, hidden Markov model and linear regression networks. The proposed Mid-LSTM can avoid anomalies to reduce large prediction errors, and has good explanatory effects on the factors affecting stock prices. Extensive experiments on S&P 500 stocks show that (i) the proposed Mid-LSTM achieves 2-4% improvement in prediction accuracy, and (ii) in portfolio allocation investment, we achieve up to 120.16% annual return and 2.99 average Sharpe ratio. | ['Xiao-Yang Liu', 'Xinyi Li', 'Yinchuan Li', 'Christina Dan Wang'] | 2019-08-03 | null | null | null | null | ['stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-6.17357671e-01 -3.06110412e-01 -3.25298727e-01 -2.52216429e-01
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e7ff606e-c8d1-4ff0-9019-c46d7622d484 | a-convolutional-neural-network-smartphone-app | null | null | https://ieeexplore.ieee.org/abstract/document/8278160 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8278160 | A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection | This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a switch for noise reduction in the signal processing pipelines of hearing devices, enabling noise estimation or classification to be conducted in noise-only parts of noisy speech signals. The developed smartphone app is compared with a previously developed voice activity detection app as well as with two highly cited voice activity detection algorithms. The experimental results indicate that the developed app using convolutional neural network outperforms the previously developed smartphone app. | ['Nasser Kehtarnavaz', 'Abhishek Sehgal'] | 2018-02-01 | null | null | null | ieee-access-2018-2 | ['audio-signal-recognition', 'noise-estimation'] | ['audio', 'medical'] | [ 2.34045550e-01 -2.52935793e-02 5.28717637e-01 -9.73765478e-02
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4fb892ec-c60b-464e-8d85-2c1faf8f4796 | gansynth-adversarial-neural-audio-synthesis | 1902.08710 | null | http://arxiv.org/abs/1902.08710v2 | http://arxiv.org/pdf/1902.08710v2.pdf | GANSynth: Adversarial Neural Audio Synthesis | Efficient audio synthesis is an inherently difficult machine learning task,
as human perception is sensitive to both global structure and fine-scale
waveform coherence. Autoregressive models, such as WaveNet, model local
structure at the expense of global latent structure and slow iterative
sampling, while Generative Adversarial Networks (GANs), have global latent
conditioning and efficient parallel sampling, but struggle to generate
locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact
generate high-fidelity and locally-coherent audio by modeling log magnitudes
and instantaneous frequencies with sufficient frequency resolution in the
spectral domain. Through extensive empirical investigations on the NSynth
dataset, we demonstrate that GANs are able to outperform strong WaveNet
baselines on automated and human evaluation metrics, and efficiently generate
audio several orders of magnitude faster than their autoregressive
counterparts. | ['Jesse Engel', 'Ishaan Gulrajani', 'Chris Donahue', 'Adam Roberts', 'Kumar Krishna Agrawal', 'Shuo Chen'] | 2019-02-23 | gansynth-adversarial-neural-audio-synthesis-1 | https://openreview.net/forum?id=H1xQVn09FX | https://openreview.net/pdf?id=H1xQVn09FX | iclr-2019-5 | ['audio-generation'] | ['audio'] | [ 3.02076787e-01 9.13436115e-02 2.19133049e-01 6.54148385e-02
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1b396001-a184-412e-b2bf-97ba0657573c | crossclr-cross-modal-contrastive-learning-for | 2109.14910 | null | https://arxiv.org/abs/2109.14910v1 | https://arxiv.org/pdf/2109.14910v1.pdf | CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations | Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn cross-modal embeddings for video and text, yet without exploiting its full potential. In particular, previous losses do not take the intra-modality similarities into account, which leads to inefficient embeddings, as the same content is mapped to multiple points in the embedding space. With CrossCLR, we present a contrastive loss that fixes this issue. Moreover, we define sets of highly related samples in terms of their input embeddings and exclude them from the negative samples to avoid issues with false negatives. We show that these principles consistently improve the quality of the learned embeddings. The joint embeddings learned with CrossCLR extend the state of the art in video-text retrieval on Youcook2 and LSMDC datasets and in video captioning on Youcook2 dataset by a large margin. We also demonstrate the generality of the concept by learning improved joint embeddings for other pairs of modalities. | ['Thomas Brox', 'Peter Gehler', 'Yi Zhu', 'Mohammadreza Zolfaghari'] | 2021-09-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-text-retrieval'] | ['computer-vision'] | [-6.65269867e-02 -1.30382761e-01 -4.41930503e-01 -3.25029492e-01
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ab430381-c3df-42fd-a6c8-048027502f6f | zemi-learning-zero-shot-semi-parametric | 2210.00185 | null | https://arxiv.org/abs/2210.00185v2 | https://arxiv.org/pdf/2210.00185v2.pdf | Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks | Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment a smaller language model with an external retriever, have demonstrated promising language modeling capabilities. However, it remains unclear whether such semi-parametric language models can perform competitively well as their fully-parametric counterparts on zero-shot generalization to downstream tasks. In this work, we introduce $\text{Zemi}$, a zero-shot semi-parametric language model. To our best knowledge, this is the first semi-parametric language model that can demonstrate strong zero-shot performance on a wide range of held-out unseen tasks. We train $\text{Zemi}$ with a novel semi-parametric multitask prompted training paradigm, which shows significant improvement compared with the parametric multitask training as proposed by T0. Specifically, we augment the multitask training and zero-shot evaluation with retrieval from a large-scale task-agnostic unlabeled corpus. In order to incorporate multiple potentially noisy retrieved augmentations, we further propose a novel $\text{augmentation fusion}$ module leveraging perceiver resampler and gated cross-attention. Notably, our proposed $\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation tasks while being 3.9x smaller in model size. | ['Heng Ji', 'Jianshu Chen', 'Dong Yu', 'Dian Yu', 'Xiaoman Pan', 'Zhenhailong Wang'] | 2022-10-01 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 1.11896142e-01 4.13730666e-02 -1.29558876e-01 -3.30996722e-01
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ccca0717-6d3f-46d3-8559-ad2b00e08a4f | basetransformers-attention-over-base-data | 2210.02476 | null | https://arxiv.org/abs/2210.02476v1 | https://arxiv.org/pdf/2210.02476v1.pdf | BaseTransformers: Attention over base data-points for One Shot Learning | Few shot classification aims to learn to recognize novel categories using only limited samples per category. Most current few shot methods use a base dataset rich in labeled examples to train an encoder that is used for obtaining representations of support instances for novel classes. Since the test instances are from a distribution different to the base distribution, their feature representations are of poor quality, degrading performance. In this paper we propose to make use of the well-trained feature representations of the base dataset that are closest to each support instance to improve its representation during meta-test time. To this end, we propose BaseTransformers, that attends to the most relevant regions of the base dataset feature space and improves support instance representations. Experiments on three benchmark data sets show that our method works well for several backbones and achieves state-of-the-art results in the inductive one shot setting. Code is available at github.com/mayug/BaseTransformers | ["Noel O'Connor", 'Kevin McGuinness', 'Mayug Maniparambil'] | 2022-10-05 | null | null | null | null | ['few-shot-image-classification', 'one-shot-learning'] | ['computer-vision', 'methodology'] | [ 4.73940074e-01 1.82511777e-01 -7.26683438e-01 -6.33119285e-01
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1b45f5b2-e1e5-4e82-a000-31bc11db1a83 | unsupervised-deep-clustering-for-source | 1811.01531 | null | http://arxiv.org/abs/1811.01531v2 | http://arxiv.org/pdf/1811.01531v2.pdf | Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information | We present a monophonic source separation system that is trained by only
observing mixtures with no ground truth separation information. We use a deep
clustering approach which trains on multi-channel mixtures and learns to
project spectrogram bins to source clusters that correlate with various spatial
features. We show that using such a training process we can obtain separation
performance that is as good as making use of ground truth separation
information. Once trained, this system is capable of performing sound
separation on monophonic inputs, despite having learned how to do so using
multi-channel recordings. | ['Shrikant Venkataramani', 'Paris Smaragdis', 'Efthymios Tzinis'] | 2018-11-05 | null | null | null | null | ['multi-speaker-source-separation'] | ['speech'] | [ 2.41409428e-02 -2.58292556e-01 2.99623698e-01 -1.60577595e-01
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4826b99d-3afc-43fe-82d2-256cb1799561 | sinogram-constrained-tv-minimization-for | 1404.6691 | null | http://arxiv.org/abs/1404.6691v1 | http://arxiv.org/pdf/1404.6691v1.pdf | Sinogram constrained TV-minimization for metal artifact reduction in CT | A new method for reducing metal artifacts in X-ray computed tomography (CT)
images is presented. It bases on the solution of a convex optimization problem
with inequality constraints on the sinogram, and total variation regularization
for the reconstructed image. The Chambolle-Pock algorithm is used to
numerically solve the discretized version of the optimization problem. As proof
of concept we present and discuss numerical results for synthetic data. | ['Kristian Bredies', 'Clemens Schiffer'] | 2014-04-26 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.22450638e-01 2.99267054e-01 2.10213497e-01 -2.27886796e-01
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9387e2df-bff1-415c-a4e9-d98fcd65e486 | discriminative-sampling-of-proposals-in-self | 2209.09209 | null | https://arxiv.org/abs/2209.09209v2 | https://arxiv.org/pdf/2209.09209v2.pdf | Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization | Drones are employed in a growing number of visual recognition applications. A recent development in cell tower inspection is drone-based asset surveillance, where the autonomous flight of a drone is guided by localizing objects of interest in successive aerial images. In this paper, we propose a method to train deep weakly-supervised object localization (WSOL) models based only on image-class labels to locate object with high confidence. To train our localizer, pseudo labels are efficiently harvested from a self-supervised vision transformers (SSTs). However, since SSTs decompose the scene into multiple maps containing various object parts, and do not rely on any explicit supervisory signal, they cannot distinguish between the object of interest and other objects, as required WSOL. To address this issue, we propose leveraging the multiple maps generated by the different transformer heads to acquire pseudo-labels for training a deep WSOL model. In particular, a new Discriminative Proposals Sampling (DiPS) method is introduced that relies on a CNN classifier to identify discriminative regions. Then, foreground and background pixels are sampled from these regions in order to train a WSOL model for generating activation maps that can accurately localize objects belonging to a specific class. Empirical results on the challenging TelDrone dataset indicate that our proposed approach can outperform state-of-art methods over a wide range of threshold values over produced maps. We also computed results on CUB dataset, showing that our method can be adapted for other tasks. | ['Eric Granger', 'Aydin Sarraf', 'Marco Pedersoli', 'Soufiane Belharbi', 'Shakeeb Murtaza'] | 2022-09-09 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 4.40238595e-01 -1.91793650e-01 1.40010277e-02 -3.39527905e-01
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66d414ae-2118-4711-8cc0-6e2f1eea65a5 | unetr-transformers-for-3d-medical-image | 2103.10504 | null | https://arxiv.org/abs/2103.10504v3 | https://arxiv.org/pdf/2103.10504v3.pdf | UNETR: Transformers for 3D Medical Image Segmentation | Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global and local features and contextual representations which can be utilized for semantic output prediction by the decoder. Despite their success, the locality of convolutional layers in FCNNs, limits the capability of learning long-range spatial dependencies. Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem. We introduce a novel architecture, dubbed as UNEt TRansformers (UNETR), that utilizes a transformer as the encoder to learn sequence representations of the input volume and effectively capture the global multi-scale information, while also following the successful "U-shaped" network design for the encoder and decoder. The transformer encoder is directly connected to a decoder via skip connections at different resolutions to compute the final semantic segmentation output. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for multi-organ segmentation and the Medical Segmentation Decathlon (MSD) dataset for brain tumor and spleen segmentation tasks. Our benchmarks demonstrate new state-of-the-art performance on the BTCV leaderboard. Code: https://monai.io/research/unetr | ['Bennett Landman', 'Andriy Myronenko', 'Dong Yang', 'Vishwesh Nath', 'Yucheng Tang', 'Daguang Xu', 'Holger Roth', 'Ali Hatamizadeh'] | 2021-03-18 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 3.48414838e-01 3.47028673e-01 -2.03151003e-01 -6.21640742e-01
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250e6138-55a1-4a5d-9189-7b099ae555fc | rcl-relation-contrastive-learning-for-zero | null | null | https://aclanthology.org/2022.findings-naacl.188 | https://aclanthology.org/2022.findings-naacl.188.pdf | RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction | Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage. However, it still faces some challenges since the unseen relations of instances are similar or the input sentences have similar entities, the unseen relation representations from different categories tend to overlap and lead to errors. In this paper, we propose a novel Relation Contrastive Learning framework (RCL) to mitigate above two types of similar problems: Similar Relations and Similar Entities. By jointly optimizing a contrastive instance loss with a relation classification loss on seen relations, RCL can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously. Especially in contrastive instance learning, the dropout noise as data augmentation is adopted to amplify the semantic difference between similar instances without breaking relation representation, so as to promote model to learn more effective representations. Experiments conducted on two well-known datasets show that RCL can significantly outperform previous state-of-the-art methods. Moreover, if the seen relations are insufficient, RCL can also obtain comparable results with the model trained on the full training set, showing the robustness of our approach. | ['Bo Xiao', 'Yanan Wu', 'Yajing Xu', 'Bosen Zhang', 'Shusen Wang'] | null | null | null | null | findings-naacl-2022-7 | ['relation-classification'] | ['natural-language-processing'] | [ 2.43627936e-01 6.30522609e-01 -4.01136845e-01 -4.02105004e-01
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99804fc7-5065-4240-92ed-660b45079349 | tablebank-table-benchmark-for-image-based | 1903.01949 | null | https://arxiv.org/abs/1903.01949v2 | https://arxiv.org/pdf/1903.01949v2.pdf | TableBank: A Benchmark Dataset for Table Detection and Recognition | We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models are available at \url{https://github.com/doc-analysis/TableBank}. | ['Ming Zhou', 'Furu Wei', 'Zhoujun Li', 'Shaohan Huang', 'Minghao Li', 'Lei Cui'] | 2019-03-05 | null | null | null | lrec-2020-5 | ['table-detection'] | ['miscellaneous'] | [-3.80657800e-02 -1.18611734e-02 -4.37870771e-01 -5.41612685e-01
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77434056-986c-4c65-988c-fdc21a5bf8b0 | adversarial-aware-deep-learning-system-based | 2306.00314 | null | https://arxiv.org/abs/2306.00314v1 | https://arxiv.org/pdf/2306.00314v1.pdf | Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach | Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs. This understanding makes us develop a hypothesis that most classical machine learning models, such as Random Forest (RF), are immune to adversarial attack models because they do not rely on neural network design at all. Our experimental study of classical machine learning models against popular adversarial attacks supports this hypothesis. Based on this hypothesis, we propose a new adversarial-aware deep learning system by using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. Although the secondary classical machine learning model has less accurate output, it is only used for verification purposes, which does not impact the output accuracy of the primary deep learning model, and at the same time, can effectively detect an adversarial attack when a clear mismatch occurs. Our experiments based on CIFAR-100 dataset show that our proposed approach outperforms current state-of-the-art adversarial defense systems. | ['Cliff Zou', 'Abdulmajeed Alghamdi', 'Mnassar Alyami', 'Hisham Kholidy', 'Mohammed Alkhowaiter'] | 2023-06-01 | null | null | null | null | ['adversarial-defense', 'adversarial-attack'] | ['adversarial', 'adversarial'] | [ 1.89862758e-01 1.27214402e-01 -4.73989695e-02 -1.93949163e-01
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d80d617c-359f-4ceb-81d6-aebf5cdb6471 | twitter-sentiment-analysis-system | 1807.07752 | null | http://arxiv.org/abs/1807.07752v1 | http://arxiv.org/pdf/1807.07752v1.pdf | Twitter Sentiment Analysis System | Social media is increasingly used by humans to express their feelings and
opinions in the form of short text messages. Detecting sentiments in the text
has a wide range of applications including identifying anxiety or depression of
individuals and measuring well-being or mood of a community. Sentiments can be
expressed in many ways that can be seen such as facial expression and gestures,
speech and by written text. Sentiment Analysis in text documents is essentially
a content-based classification problem involving concepts from the domains of
Natural Language Processing as well as Machine Learning. In this paper,
sentiment recognition based on textual data and the techniques used in
sentiment analysis are discussed. | ['Shaunak Joshi', 'Deepali Deshpande'] | 2018-07-20 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.21136442e-01 -1.78912029e-01 -3.85559916e-01 -1.03410697e+00
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cdd5c62c-a648-491d-9f72-c61e7120c2ae | light-source-guided-single-image-flare | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Qiao_Light_Source_Guided_Single-Image_Flare_Removal_From_Unpaired_Data_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Qiao_Light_Source_Guided_Single-Image_Flare_Removal_From_Unpaired_Data_ICCV_2021_paper.pdf | Light Source Guided Single-Image Flare Removal From Unpaired Data | Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefined intensity and geometry priors of flares, and may fail to distinguish the difference between light sources and flare artifacts. We observe that the conditions of the light source in the image play an important role in the resulting flares. In this paper, we present a deep framework with light source aware guidance for single-image flare removal (SIFR). In particular, we first detect the light source regions and the flare regions separately, and then remove the flare artifacts based on the light source aware guidance. By learning the underlying relationships between the two types of regions, our approach can remove different kinds of flares from the image. In addition, instead of using paired training data which are difficult to collect, we propose the first unpaired flare removal dataset and new cycle-consistency constraints to obtain more diverse examples and avoid manual annotations. Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively. We also show that our model can be applied to flare effect manipulation (e.g., adding or changing image flares). | ['Rynson W.H. Lau', 'Gerhard P. Hancke', 'Xiaotian Qiao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['flare-removal'] | ['computer-vision'] | [ 4.98039424e-01 -7.54650235e-01 2.44668886e-01 -2.04936936e-01
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491c8419-72ac-465a-88bb-d36a31ecb032 | manifold-learning-supported-estimation-of | 2110.02189 | null | https://arxiv.org/abs/2110.02189v1 | https://arxiv.org/pdf/2110.02189v1.pdf | Manifold learning-supported estimation of relative transfer functions for spatial filtering | Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse conditions or need prior knowledge on the properties of the interfering sources. While state-of-the-art RTF estimators ignore prior knowledge about the acoustic enclosure, audio signal processing algorithms for teleconferencing equipment are often operating in the same or at least a similar acoustic enclosure, e.g., a car or an office, such that training data can be collected. In this contribution, we use such data to train Variational Autoencoders (VAEs) in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF estimates. Furthermore, a hybrid between classic RTF estimation and the trained VAE is investigated. Comprehensive experiments with real-world data confirm the efficacy for the proposed method. | ['Walter Kellermann', 'Johannes Zeitler', 'Andreas Brendel'] | 2021-10-05 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 1.14670172e-01 -2.00179860e-01 5.58039486e-01 -2.01633260e-01
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126eeb2d-6c98-4091-bc2d-90096320a20f | what-is-the-machine-learning | 1709.10106 | null | http://arxiv.org/abs/1709.10106v2 | http://arxiv.org/pdf/1709.10106v2.pdf | What is the Machine Learning? | Applications of machine learning tools to problems of physical interest are
often criticized for producing sensitivity at the expense of transparency. To
address this concern, we explore a data planing procedure for identifying
combinations of variables -- aided by physical intuition -- that can
discriminate signal from background. Weights are introduced to smooth away the
features in a given variable(s). New networks are then trained on this modified
data. Observed decreases in sensitivity diagnose the variable's discriminating
power. Planing also allows the investigation of the linear versus non-linear
nature of the boundaries between signal and background. We demonstrate the
efficacy of this approach using a toy example, followed by an application to an
idealized heavy resonance scenario at the Large Hadron Collider. By unpacking
the information being utilized by these algorithms, this method puts in context
what it means for a machine to learn. | ['Timothy Cohen', 'Spencer Chang', 'Bryan Ostdiek'] | 2017-09-28 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 3.80973995e-01 5.58763325e-01 -5.79400957e-01 -5.36027014e-01
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76c837b3-d87d-4b2a-9221-4d0a2c0751d7 | sparse-skill-coding-learning-behavioral | null | null | https://openreview.net/forum?id=Hygv3xrtDr | https://openreview.net/pdf?id=Hygv3xrtDr | Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes | Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks. We consider an alternative perspective based on identifying behavioral `motifs'---repeated action sequences that can be compressed to yield a compact code of action trajectories. We present a method for iteratively compressing action trajectories to learn nested behavioral hierarchies of arbitrary depth, with actions of arbitrary length. The learned temporally extended actions provide new action primitives that can participate in deeper hierarchies as the agent learns. We demonstrate the relevance of this approach for tasks with non-trivial hierarchical structure and show that the approach can be used to accelerate learning in recursively more complex tasks through transfer. | ['Thomas Griffiths', 'Sergey Levine', 'Michael Chang', 'Sophia Sanborn'] | 2019-09-25 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.53014386e-01 3.52936566e-01 -4.54550683e-01 -1.26140788e-01
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1d39b624-59d6-4f2f-9e1a-48e301f95fd9 | mlphon-a-multifunctional-grapheme-phoneme | null | null | https://ieeexplore.ieee.org/document/9877808 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9877808 | Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers | In this article we present the design and the development of a knowledge based computational linguistic tool, Mlphon for Malayalam language. Mlphon computationally models linguistic rules using finite state transducers and performs multiple functions including grapheme to phoneme (g2p) and phoneme to grapheme (p2g) conversions, syllabification, phonetic feature analysis and script grammar check. This open source software tool, released under MIT license, is developed as a one-stop solution to handle different speech related text processing tasks for automatic speech recognition, text to speech synthesis and non-speech natural language processing tasks including syllable subword based language modeling, phoneme diversity analysis and text sanity check. The tool is evaluated on a manually crafted gold standard lexicon. Mlphon performs orthographic syllabification with 99% accuracy with a syllable error rate of 0.62% on the gold standard lexicon. For grapheme to phoneme conversion task, overall phoneme recognition accuracy of 99% with a phoneme error rate of 0.55% is obtained on gold standard lexicon. Additionally an extrinsic evaluation of Mlphon is performed by employing the pronunciation lexicon created using Mlphon, in Malayalam automatic speech recognition (ASR) task. Performance analysis in terms of the computation time of lexicon creation process and the word error rate (WER) on ASR task are presented along with a comparison over other automated tools for lexicon creation. Pronunciation lexicons with more than 100k commonly used Malayalam words in phonemised and syllabified forms is created and they are published as open language resources along with this work. We also demonstrate the usage of Mlphon on different natural language processing applications - syllable subword ASR, assisted pronunciation learning, phoneme diversity analysis and text sanity check. Being a knowledge based solution with open source code, Mlphon can be adapted to other languages of similar script nature. | ['Rajeev Rajan', 'A R jayan', 'Kavya Manohar'] | 2022-09-05 | null | null | null | ieee-access-2022-9 | ['pronunciation-dictionary-creation', 'text-to-speech-synthesis'] | ['natural-language-processing', 'speech'] | [ 3.23439181e-01 6.79150000e-02 3.29303592e-01 -1.78675771e-01
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0b375805-0d0c-4273-80e2-9e5c4bc8caad | implicit-representation-priors-meet | 2304.08805 | null | https://arxiv.org/abs/2304.08805v2 | https://arxiv.org/pdf/2304.08805v2.pdf | Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping | Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene. In particular, identifying good grasping poses with Bayesian inference becomes difficult due to two reasons: i) generating data from uninformative priors proves to be inefficient, and ii) the posterior often entails a complex distribution defined on a Riemannian manifold. In this study, we explore the use of implicit representations to construct scene-dependent priors, thereby enabling the application of efficient simulation-based Bayesian inference algorithms for determining successful grasp poses in unstructured environments. Results from both simulation and physical benchmarks showcase the high success rate and promising potential of this approach. | ['Olivier Brüls', 'Gilles Louppe', 'Julien Gustin', 'Norman Marlier'] | 2023-04-18 | null | null | null | null | ['bayesian-inference', 'robotic-grasping'] | ['methodology', 'robots'] | [ 1.51636675e-01 -1.02272525e-01 2.61779040e-01 -2.74986118e-01
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22b60509-9832-4561-b844-8c1672a6a868 | learning-model-blind-temporal-denoisers | 2007.03241 | null | https://arxiv.org/abs/2007.03241v2 | https://arxiv.org/pdf/2007.03241v2.pdf | Learning Model-Blind Temporal Denoisers without Ground Truths | Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises, giving way to methods that can adapt to existing noise without knowing its ground truth. Previous image-based method leads to noise overfitting if directly applied to video denoisers, and has inadequate temporal information management especially in terms of occlusion and lighting variation, which considerably hinders its denoising performance. In this paper, we propose a general framework for video denoising networks that successfully addresses these challenges. A novel twin sampler assembles training data by decoupling inputs from targets without altering semantics, which not only effectively solves the noise overfitting problem, but also generates better occlusion masks efficiently by checking optical flow consistency. An online denoising scheme and a warping loss regularizer are employed for better temporal alignment. Lighting variation is quantified based on the local similarity of aligned frames. Our method consistently outperforms the prior art by 0.6-3.2dB PSNR on multiple noises, datasets and network architectures. State-of-the-art results on reducing model-blind video noises are achieved. Extensive ablation studies are conducted to demonstrate the significance of each technical components. | ['Yuxing Han', 'Shan Liu', 'Zhen Xia', 'Yanghao Li', 'Bichuan Guo', 'Jiangtao Wen'] | 2020-07-07 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.62373143e-01 -5.30359089e-01 2.53051788e-01 -6.37088567e-02
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76a7576b-49dc-4488-8fab-8c956218db91 | corediff-contextual-error-modulated | 2304.01814 | null | https://arxiv.org/abs/2304.01814v1 | https://arxiv.org/pdf/2304.01814v1.pdf | CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization | Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion models suffer from long inference times due to the large number of sampling steps involved. Very recently, cold diffusion model generalizes classical diffusion models and has greater flexibility. Inspired by the cold diffusion, this paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process. Second, to alleviate the error accumulation problem caused by the imperfect restoration operator in the sampling process, we propose a novel ContextuaL Error-modulAted Restoration Network (CLEAR-Net), which can leverage contextual information to constrain the sampling process from structural distortion and modulate time step embedding features for better alignment with the input at the next time step. Third, to rapidly generalize to a new, unseen dose level with as few resources as possible, we devise a one-shot learning framework to make CoreDiff generalize faster and better using only a single LDCT image (un)paired with NDCT. Extensive experimental results on two datasets demonstrate that our CoreDiff outperforms competing methods in denoising and generalization performance, with a clinically acceptable inference time. | ['Hongming Shan', 'Yi Zhang', 'Junping Zhang', 'Zilong Li', 'Qi Gao'] | 2023-04-04 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.90050447e-01 -7.96533450e-02 5.58928028e-02 -3.22270870e-01
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37f829e1-fdfe-4177-a586-b9d9eef016d1 | combinatorial-optimization-enriched-machine | 2304.00789 | null | https://arxiv.org/abs/2304.00789v1 | https://arxiv.org/pdf/2304.00789v1.pdf | Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows | With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same day deliveries. Existing multi-stage stochastic optimization approaches that allow to solve the underlying dynamic vehicle routing problem are either computationally too expensive for an application in online settings, or -- in the case of reinforcement learning -- struggle to perform well on high-dimensional combinatorial problems. To mitigate these drawbacks, we propose a novel machine learning pipeline that incorporates a combinatorial optimization layer. We apply this general pipeline to a dynamic vehicle routing problem with dispatching waves, which was recently promoted in the EURO Meets NeurIPS Vehicle Routing Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem. With this work, we provide a comprehensive numerical study that further highlights the efficacy and benefits of the proposed pipeline beyond the results achieved in the competition, e.g., by showcasing the robustness of the encoded policy against unseen instances and scenarios. | ['Maximilian Schiffer', 'Axel Parmentier', 'Patrick S. Klein', 'Kai Jungel', 'Léo Baty'] | 2023-04-03 | null | null | null | null | ['combinatorial-optimization', 'stochastic-optimization'] | ['methodology', 'methodology'] | [-6.93726866e-03 1.22685730e-01 -2.41936520e-01 -4.57030773e-01
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56c735e2-d6d4-4eb9-9177-046efca20383 | open-world-object-detection-via | 2302.11757 | null | https://arxiv.org/abs/2302.11757v1 | https://arxiv.org/pdf/2302.11757v1.pdf | Open-World Object Detection via Discriminative Class Prototype Learning | Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, the OWOD setting is task to: 1) detect objects seen during training while identifying unseen classes, and 2) incrementally learn the knowledge of the identified unknown objects when the corresponding annotations is available. We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discriminative Class Prototype Learning, which consists of a Proposal Embedding Aggregator (PEA), an Embedding Space Compressor (ESC) and a Cosine Similarity-based Classifier (CSC). All our proposed modules aim to learn the discriminative embeddings of known classes in the feature space to minimize the overlapping distributions of known and unknown classes, which is beneficial to differentiate known and unknown classes. Extensive experiments performed on PASCAL VOC and MS-COCO benchmark demonstrate the effectiveness of our proposed method. | ['Shaorong Xie', 'Yan Peng', 'Zhenglin Li', 'Liyan Ma', 'Jinan Yu'] | 2023-02-23 | null | null | null | null | ['open-world-object-detection', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 7.66208544e-02 -1.14517258e-02 -1.17942885e-01 -2.19743729e-01
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24fbe57e-e0ce-4582-b565-d1bfd210d06c | importance-tempering-group-robustness-for | 2209.08745 | null | https://arxiv.org/abs/2209.08745v2 | https://arxiv.org/pdf/2209.08745v2.pdf | Importance Tempering: Group Robustness for Overparameterized Models | Although overparameterized models have shown their success on many machine learning tasks, the accuracy could drop on the testing distribution that is different from the training one. This accuracy drop still limits applying machine learning in the wild. At the same time, importance weighting, a traditional technique to handle distribution shifts, has been demonstrated to have less or even no effect on overparameterized models both empirically and theoretically. In this paper, we propose importance tempering to improve the decision boundary and achieve consistently better results for overparameterized models. Theoretically, we justify that the selection of group temperature can be different under label shift and spurious correlation setting. At the same time, we also prove that properly selected temperatures can extricate the minority collapse for imbalanced classification. Empirically, we achieve state-of-the-art results on worst group classification tasks using importance tempering. | ['Lexing Ying', 'Zachary Izzo', 'Wenlong Ji', 'Yiping Lu'] | 2022-09-19 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 2.28511766e-01 -3.61795984e-02 -4.70158607e-01 -4.61416364e-01
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25ab19c5-72bf-43f2-92f4-6dc73caf5155 | how-can-cross-lingual-knowledge-contribute | null | null | https://aclanthology.org/2022.findings-acl.243 | https://aclanthology.org/2022.findings-acl.243.pdf | How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? | Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages. In this paper, by utilizing multilingual transfer learning via the mixture-of-experts approach, our model dynamically capture the relationship between target language and each source language, and effectively generalize to predict types of unseen entities in new languages. Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. We questioned the relationship between language similarity and the performance of CLET. A series of experiments refute the commonsense that the more source the better, and suggest the Similarity Hypothesis for CLET. | ['Qu Yincen', 'Zelin Dai', 'Hui Chen', 'Juanzi Li', 'Lei Hou', 'Tiansi Dong', 'Hailong Jin'] | null | null | null | null | findings-acl-2022-5 | ['type-prediction', 'entity-typing'] | ['computer-code', 'natural-language-processing'] | [-3.20153803e-01 -7.35153556e-02 -6.60911143e-01 -4.89124954e-01
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0af5d748-0e9a-45a9-9083-af3e3889c278 | audio-visual-scene-aware-dialog-avsd | 1806.00525 | null | http://arxiv.org/abs/1806.00525v1 | http://arxiv.org/pdf/1806.00525v1.pdf | Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7 | Scene-aware dialog systems will be able to have conversations with users
about the objects and events around them. Progress on such systems can be made
by integrating state-of-the-art technologies from multiple research areas
including end-to-end dialog systems visual dialog, and video description. We
introduce the Audio Visual Scene Aware Dialog (AVSD) challenge and dataset. In
this challenge, which is one track of the 7th Dialog System Technology
Challenges (DSTC7) workshop1, the task is to build a system that generates
responses in a dialog about an input video | ['Chiori Hori', 'Tim K. Marks', 'Raphael Gontijo Lopes', 'Vincent Cartillier', 'Huda Alamri', 'Jue Wang', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Anoop Cherian', 'Abhishek Das'] | 2018-06-01 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.51189668e-02 1.82046846e-01 2.95457482e-01 -9.52585578e-01
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281b91ac-9370-4122-b735-6fa53bbbd0ca | on-provably-robust-meta-bayesian-optimization | 2206.06872 | null | https://arxiv.org/abs/2206.06872v2 | https://arxiv.org/pdf/2206.06872v2.pdf | On Provably Robust Meta-Bayesian Optimization | Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous evaluations of potentially related functions. This begs the question as to whether we can leverage these previous experiences to accelerate the current BO task through meta-learning (meta-BO), while ensuring robustness against potentially harmful dissimilar tasks that could sabotage the convergence of BO. This paper introduces two scalable and provably robust meta-BO algorithms: robust meta-Gaussian process-upper confidence bound (RM-GP-UCB) and RM-GP-Thompson sampling (RM-GP-TS). We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS. We also exploit the theoretical guarantees to optimize the weights assigned to individual previous tasks through regret minimization via online learning, which diminishes the impact of dissimilar tasks and hence further enhances the robustness. Empirical evaluations show that (a) RM-GP-UCB performs effectively and consistently across various applications, and (b) RM-GP-TS, despite being less robust than RM-GP-UCB both in theory and in practice, performs competitively in some scenarios with less dissimilar tasks and is more computationally efficient. | ['Patrick Jaillet', 'Bryan Kian Hsiang Low', 'Haibin Yu', 'Yizhou Chen', 'Zhongxiang Dai'] | 2022-06-14 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 8.07842389e-02 -1.49034992e-01 7.56616890e-02 8.13210458e-02
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9c146954-606e-4336-9586-04e7ef609b56 | robust-unsupervised-cross-lingual-word | 2210.03319 | null | https://arxiv.org/abs/2210.03319v1 | https://arxiv.org/pdf/2210.03319v1.pdf | Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation | This paper investigates an unsupervised approach towards deriving a universal, cross-lingual word embedding space, where words with similar semantics from different languages are close to one another. Previous adversarial approaches have shown promising results in inducing cross-lingual word embedding without parallel data. However, the training stage shows instability for distant language pairs. Instead of mapping the source language space directly to the target language space, we propose to make use of a sequence of intermediate spaces for smooth bridging. Each intermediate space may be conceived as a pseudo-language space and is introduced via simple linear interpolation. This approach is modeled after domain flow in computer vision, but with a modified objective function. Experiments on intrinsic Bilingual Dictionary Induction tasks show that the proposed approach can improve the robustness of adversarial models with comparable and even better precision. Further experiments on the downstream task of Cross-Lingual Natural Language Inference show that the proposed model achieves significant performance improvement for distant language pairs in downstream tasks compared to state-of-the-art adversarial and non-adversarial models. | ['Helen Meng', 'ZhiQuan Luo', 'Zhen Li', 'Liping Tang'] | 2022-10-07 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-6.32985830e-02 1.82873264e-01 -2.59173751e-01 -4.34942961e-01
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433831e8-e575-4810-99e9-a3d9d3f07ec8 | causal-inference-via-predictive-coding | 2306.15479 | null | https://arxiv.org/abs/2306.15479v1 | https://arxiv.org/pdf/2306.15479v1.pdf | Causal Inference via Predictive Coding | Bayesian and causal inference are fundamental processes for intelligence. Bayesian inference models observations: what can be inferred about y if we observe a related variable x? Causal inference models interventions: if we directly change x, how will y change? Predictive coding is a neuroscience-inspired method for performing Bayesian inference on continuous state variables using local information only. In this work, we go beyond Bayesian inference, and show how a simple change in the inference process of predictive coding enables interventional and counterfactual inference in scenarios where the causal graph is known. We then extend our results, and show how predictive coding can be generalized to cases where this graph is unknown, and has to be inferred from data, hence performing causal discovery. What results is a novel and straightforward technique that allows us to perform end-to-end causal inference on predictive-coding-based structural causal models, and demonstrate its utility for potential applications in machine learning. | ['Thomas Lukasiewicz', 'Beren Millidge', "Amine M'Charrak", 'Luca Pinchetti', 'Tommaso Salvatori'] | 2023-06-27 | null | null | null | null | ['causal-inference', 'causal-discovery', 'bayesian-inference', 'counterfactual-inference', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'methodology', 'miscellaneous', 'miscellaneous'] | [ 7.33529329e-01 6.98033810e-01 -7.69791722e-01 -3.65674466e-01
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45cc6c0d-b532-4647-ad15-8c984bf085da | semi-supervised-teacher-student-architecture | null | null | https://aclanthology.org/W19-1505 | https://aclanthology.org/W19-1505.pdf | Semi-Supervised Teacher-Student Architecture for Relation Extraction | Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. In this paper, we adapt Mean Teacher (Tarvainen and Valpola, 2017), a denoising SSL framework to extract semantic relations between pairs of entities. We explore the sweet spot of amount of supervision required for good performance on this binary relation extraction task. Additionally, different syntax representations are incorporated into our models to enhance the learned representation of sentences. We evaluate our approach on the Google-IISc Distant Supervision (GDS) dataset, which removes test data noise present in all previous distance supervision datasets, which makes it a reliable evaluation benchmark (Jat et al., 2017). Our results show that the SSL Mean Teacher approach nears the performance of fully-supervised approaches even with only 10{\%} of the labeled corpus. Further, the syntax-aware model outperforms other syntax-free approaches across all levels of supervision. | ['Mihai Surdeanu', 'Rebecca Sharp', 'Ajay Nagesh', 'Fan Luo'] | 2019-06-01 | null | null | null | ws-2019-6 | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 2.97757715e-01 7.23233759e-01 -4.26123083e-01 -6.84167206e-01
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7a55ae0b-3f15-46cc-9a87-a475f78c40ae | offline-rl-with-resource-constrained-online | 2110.03165 | null | https://arxiv.org/abs/2110.03165v2 | https://arxiv.org/pdf/2110.03165v2.pdf | Offline RL With Resource Constrained Online Deployment | Offline reinforcement learning is used to train policies in scenarios where real-time access to the environment is expensive or impossible. As a natural consequence of these harsh conditions, an agent may lack the resources to fully observe the online environment before taking an action. We dub this situation the resource-constrained setting. This leads to situations where the offline dataset (available for training) can contain fully processed features (using powerful language models, image models, complex sensors, etc.) which are not available when actions are actually taken online. This disconnect leads to an interesting and unexplored problem in offline RL: Is it possible to use a richly processed offline dataset to train a policy which has access to fewer features in the online environment? In this work, we introduce and formalize this novel resource-constrained problem setting. We highlight the performance gap between policies trained using the full offline dataset and policies trained using limited features. We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features. To better capture the challenge of this setting, we propose a data collection procedure: Resource Constrained-Datasets for RL (RC-D4RL). We evaluate our transfer algorithm on RC-D4RL and the popular D4RL benchmarks and observe consistent improvement over the baseline (TD3+BC without transfer). The code for the experiments is available at https://github.com/JayanthRR/RC-OfflineRL. | ['Urun Dogan', 'Abhishek Gupta', 'Young Hun Jung', 'Frank Cheng', 'Aniket Anand Deshmukh', 'Jayanth Reddy Regatti'] | 2021-10-07 | null | null | null | null | ['d4rl'] | ['robots'] | [ 7.32959434e-02 -3.77076603e-02 -3.51624817e-01 -2.16683537e-01
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d5e609ca-0345-4c33-9274-60faca04541a | deepblueai-at-semeval-2021-task-1-lexical | null | null | https://aclanthology.org/2021.semeval-1.72 | https://aclanthology.org/2021.semeval-1.72.pdf | DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach | Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks. We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1. | ['Zhipeng Luo', 'Shengguang Wang', 'Bingyan Song', 'Chunguang Pan'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-simplification', 'lexical-complexity-prediction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.27424794e-01 3.56857985e-01 -8.69414806e-02 -4.26978230e-01
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f7f4b61b-78b3-4337-bde6-605b31f0e8ee | unsupervised-3d-pose-transfer-with-cross | 2211.10278 | null | https://arxiv.org/abs/2211.10278v3 | https://arxiv.org/pdf/2211.10278v3.pdf | Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction | The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervision of the ground truth, whose availability is limited in real-world scenarios. In this work, we present X-DualNet, a simple yet effective approach that enables unsupervised 3D pose transfer. In X-DualNet, we introduce a generator $G$ which contains correspondence learning and pose transfer modules to achieve 3D pose transfer. We learn the shape correspondence by solving an optimal transport problem without any key point annotations and generate high-quality meshes with our elastic instance normalization (ElaIN) in the pose transfer module. With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision. Besides that, we also adopt an as-rigid-as-possible deformer in the training process to fine-tune the body shape of the generated results. Extensive experiments on human and animal data demonstrate that our framework can successfully achieve comparable performance as the state-of-the-art supervised approaches. | ['Guosheng Lin', 'Fayao Liu', 'Ruibo Li', 'Jiacheng Wei', 'Chaoyue Song'] | 2022-11-18 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [-8.53153598e-03 2.27351904e-01 5.47729284e-02 -4.57077563e-01
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9f610d30-f26d-493b-8c6b-eddbcd85d7f5 | a-hypergraph-neural-network-framework-for | 2212.14077 | null | https://arxiv.org/abs/2212.14077v1 | https://arxiv.org/pdf/2212.14077v1.pdf | A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings | In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively. | ['Nesreen Ahmed', 'Eunyee Koh', 'Gromit Chan', 'Chang Xiao', 'Nedim Lipka', 'Jane Hoffswell', 'Shunan Guo', 'Ryan A. Rossi', 'Ryan Aponte'] | 2022-12-28 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [-1.29112393e-01 7.96376467e-01 -6.29424393e-01 -2.10632801e-01
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7138301f-65d3-4e56-a1e4-df7cb5d142ff | image-colorization-using-u-net-with-skip | 2205.12867 | null | https://arxiv.org/abs/2205.12867v1 | https://arxiv.org/pdf/2205.12867v1.pdf | Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images | We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image patches with global priors of an entire image on each class, forming visually more compelling colorization results. Finally, we validate our approach with a user study evaluation and compare it against state-of-the-art, resulting in improvements. | ['Randy Cahya Wihandika', 'Novanto Yudistira', 'Muhammad Hisyam Zayd'] | 2022-05-25 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.23499148e-01 1.68923978e-02 9.78204757e-02 -3.21717858e-01
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121a21e7-5490-42bd-8b2f-531b2449366e | pseudo-lidar-accurate-depth-for-3d-object | 1906.06310 | null | https://arxiv.org/abs/1906.06310v3 | https://arxiv.org/pdf/1906.06310v3.pdf | Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving | Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images, there is still a notable performance gap. In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation. Concretely, we adapt the stereo network architecture and loss function to be more aligned with accurate depth estimation of faraway objects --- currently the primary weakness of pseudo-LiDAR. Further, we explore the idea to leverage cheaper but extremely sparse LiDAR sensors, which alone provide insufficient information for 3D detection, to de-bias our depth estimation. We propose a depth-propagation algorithm, guided by the initial depth estimates, to diffuse these few exact measurements across the entire depth map. We show on the KITTI object detection benchmark that our combined approach yields substantial improvements in depth estimation and stereo-based 3D object detection --- outperforming the previous state-of-the-art detection accuracy for faraway objects by 40%. Our code is available at https://github.com/mileyan/Pseudo_Lidar_V2. | ['Mark Campbell', 'Wei-Lun Chao', 'Divyansh Garg', 'Kilian Q. Weinberger', 'Yurong You', 'Yan Wang', 'Geoff Pleiss', 'Bharath Hariharan'] | 2019-06-14 | null | https://openreview.net/forum?id=BJedHRVtPB | https://openreview.net/pdf?id=BJedHRVtPB | iclr-2020-1 | ['stereo-depth-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [ 3.07476558e-02 -8.61497372e-02 -9.36857164e-02 -4.71506178e-01
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7d0a0996-c393-4a39-b9b3-6c3deedc8998 | depth-estimation-by-combining-binocular | 2203.10493 | null | https://arxiv.org/abs/2203.10493v1 | https://arxiv.org/pdf/2203.10493v1.pdf | Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light | It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsystem can provide external guidance for the stereo matching networks, which can improve the matching accuracy significantly. In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes. The evaluation results show that the Bad 2.0 error of the proposed system is 28.2% of the passive stereo system when the network RAFT is used. The dataset and trained models are available at https://github.com/YuhuaXu/MonoStereoFusion. | ['Yulan Guo', 'Zhaobi Chu', 'Wei Jia', 'Yushan Yu', 'Xiaoli Yang', 'Yuhua Xu'] | 2022-03-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 2.25623906e-01 -2.43818209e-01 2.37318844e-01 -3.66079718e-01
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909a0273-c749-46c7-be58-73b1b0ae2196 | semi-supervised-text-simplification-with-back | 2004.14693 | null | https://arxiv.org/abs/2004.14693v1 | https://arxiv.org/pdf/2004.14693v1.pdf | Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders | Text simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity and quality of parallel sentences, which limits their applicability in different languages and domains. This work investigates how to leverage large amounts of unpaired corpora in TS task. We adopt the back-translation architecture in unsupervised machine translation (NMT), including denoising autoencoders for language modeling and automatic generation of parallel data by iterative back-translation. However, it is non-trivial to generate appropriate complex-simple pair if we directly treat the set of simple and complex corpora as two different languages, since the two types of sentences are quite similar and it is hard for the model to capture the characteristics in different types of sentences. To tackle this problem, we propose asymmetric denoising methods for sentences with separate complexity. When modeling simple and complex sentences with autoencoders, we introduce different types of noise into the training process. Such a method can significantly improve the simplification performance. Our model can be trained in both unsupervised and semi-supervised manner. Automatic and human evaluations show that our unsupervised model outperforms the previous systems, and with limited supervision, our model can perform competitively with multiple state-of-the-art simplification systems. | ['Zhi Chen', 'Yanbin Zhao', 'Kai Yu', 'Lu Chen'] | 2020-04-30 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.32441926e-01 -8.73303935e-02 7.37772584e-02 -3.96393508e-01
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bf391331-97ef-48bf-89ac-350cf8b6b0a2 | decision-making-and-control-with-metasurface | 2212.11278 | null | https://arxiv.org/abs/2212.11278v2 | https://arxiv.org/pdf/2212.11278v2.pdf | Decision-making and control with metasurface-based diffractive neural networks | The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. All-optical diffractive neural networks provide a promising solution for implementing artificial intelligence with high-speed and low-power consumption. To date, most of the reported diffractive neural networks focus on single or multiple tasks that do not involve interaction with the environment, such as object recognition and image classification. In contrast, the networks that can perform decision-making and control, to our knowledge, have not been developed yet. Here, we propose using deep reinforcement learning to implement diffractive neural networks that imitate human-level decision-making and control capability. Such networks allow for finding optimal control policies through interaction with the environment and can be readily realized with the dielectric metasurfaces. The superior performances of these networks are verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros., and Car Racing, and achieving the same or even higher levels comparable to human players. Our work represents a solid step of advancement in diffractive neural networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence. It may find exciting applications in autonomous driving, intelligent robots, and intelligent manufacturing. | ['Shuyuan Xiao', 'Andrey Miroshnichenko', 'Lujun Huang', 'Tianbao Yu', 'Jumin Qiu'] | 2022-12-21 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 9.73678231e-02 3.83882731e-01 2.51435757e-01 4.96889167e-02
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62ef05bb-8a34-4229-9583-658528610543 | motrv2-bootstrapping-end-to-end-multi-object | 2211.09791 | null | https://arxiv.org/abs/2211.09791v2 | https://arxiv.org/pdf/2211.09791v2.pdf | MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors | In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, MOTR and TrackFormer are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve MOTR by elegantly incorporating an extra object detector. We first adopt the anchor formulation of queries and then use an extra object detector to generate proposals as anchors, providing detection prior to MOTR. The simple modification greatly eases the conflict between joint learning detection and association tasks in MOTR. MOTRv2 keeps the query propogation feature and scales well on large-scale benchmarks. MOTRv2 ranks the 1st place (73.4% HOTA on DanceTrack) in the 1st Multiple People Tracking in Group Dance Challenge. Moreover, MOTRv2 reaches state-of-the-art performance on the BDD100K dataset. We hope this simple and effective pipeline can provide some new insights to the end-to-end MOT community. Code is available at \url{https://github.com/megvii-research/MOTRv2}. | ['Xiangyu Zhang', 'Tiancai Wang', 'Yuang Zhang'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multiple-people-tracking'] | ['computer-vision'] | [-3.28286618e-01 -1.96062699e-01 -2.06338868e-01 -1.41925916e-01
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9357c4f7-9be9-4892-899f-90aced0d485d | text-guided-mask-free-local-image-retouching | 2212.07603 | null | https://arxiv.org/abs/2212.07603v2 | https://arxiv.org/pdf/2212.07603v2.pdf | Text-Guided Mask-free Local Image Retouching | In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to constrain the region that may be modified. This not only makes it more challenging to develop these algorithms, but it also limits how widely deep learning can be used for image retouching. In this paper, we offer a text-guided mask-free image retouching approach that yields consistent results to address this concern. In order to perform image retouching without mask supervision, our technique can construct plausible and edge-sharp masks based on the text for each object in the image. Extensive experiments have shown that our method can produce high-quality, accurate images based on spoken language. The source code will be released soon. | ['Lechao Cheng', 'Zhangye Wang', 'Jin Wang', 'Jingxuan He', 'Fan Zhang', 'Zerun Liu'] | 2022-12-15 | null | null | null | null | ['image-retouching'] | ['computer-vision'] | [ 5.89883924e-01 2.91072845e-01 -4.42316644e-02 -7.59986416e-02
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9e092429-2bf0-47a7-874a-3f872af2ec31 | comparing-the-accuracy-of-deep-neural | 2111.11063 | null | https://arxiv.org/abs/2111.11063v1 | https://arxiv.org/pdf/2111.11063v1.pdf | Comparing the Accuracy of Deep Neural Networks (DNN) and Convolutional Neural Network (CNN) in Music Genre Recognition (MGR): Experiments on Kurdish Music | Musicologists use various labels to classify similar music styles under a shared title. But, non-specialists may categorize music differently. That could be through finding patterns in harmony, instruments, and form of the music. People usually identify a music genre solely by listening, but now computers and Artificial Intelligence (AI) can automate this process. The work on applying AI in the classification of types of music has been growing recently, but there is no evidence of such research on the Kurdish music genres. In this research, we developed a dataset that contains 880 samples from eight different Kurdish music genres. We evaluated two machine learning approaches, a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN), to recognize the genres. The results showed that the CNN model outperformed the DNN by achieving 92% versus 90% accuracy. | ['Hossein Hassani', 'Aza Zuhair'] | 2021-11-22 | null | null | null | null | ['music-genre-recognition'] | ['music'] | [-5.34178503e-02 -5.03890038e-01 -1.57815412e-01 -9.16283652e-02
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72e18ff7-a3ef-4ae6-8d40-99a6774873a4 | automating-staged-rollout-with-reinforcement | 2204.02189 | null | https://arxiv.org/abs/2204.02189v2 | https://arxiv.org/pdf/2204.02189v2.pdf | Automating Staged Rollout with Reinforcement Learning | Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultaneously considering multiple product or process metrics explicitly. This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects. | ['Lance Fiondella', 'Vidhyashree Nagaraju', 'Shadow Pritchard'] | 2022-04-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.24692708e-01 1.95429832e-01 1.01062423e-02 -2.18610719e-01
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dc1f9e8e-e6b9-4cf3-9000-18ba07c975dd | improving-speaker-identification-for-shared | 2109.02576 | null | https://arxiv.org/abs/2109.02576v1 | https://arxiv.org/pdf/2109.02576v1.pdf | Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets | Speaker identification typically involves three stages. First, a front-end speaker embedding model is trained to embed utterance and speaker profiles. Second, a scoring function is applied between a runtime utterance and each speaker profile. Finally, the speaker is identified using nearest neighbor according to the scoring metric. To better distinguish speakers sharing a device within the same household, we propose a household-adapted nonlinear mapping to a low dimensional space to complement the global scoring metric. The combined scoring function is optimized on labeled or pseudo-labeled speaker utterances. With input dropout, the proposed scoring model reduces EER by 45-71% in simulated households with 2 to 7 hard-to-discriminate speakers per household. On real-world internal data, the EER reduction is 49.2%. From t-SNE visualization, we also show that clusters formed by household-adapted speaker embeddings are more compact and uniformly distributed, compared to clusters formed by global embeddings before adaptation. | ['Andreas Stolcke', 'Eunjung Han', 'Yuguang Yang', 'Zhenning Tan'] | 2021-09-06 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 7.03046992e-02 2.53878772e-01 1.15241803e-01 -9.05696213e-01
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967e2235-50a7-4d94-bc23-f6d267a95f83 | statistical-reproducibility-of-meta-analysis | 2301.09189 | null | https://arxiv.org/abs/2301.09189v1 | https://arxiv.org/pdf/2301.09189v1.pdf | Statistical reproducibility of meta-analysis research claims for medical mask use in community settings to prevent COVID infection | The coronavirus pandemic (COVID) has been an exceptional test of current scientific evidence that inform and shape policy. Many US states, cities, and counties implemented public orders for mask use on the notion that this intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. P-value plotting was used to evaluate statistical reproducibility of meta-analysis research claims of a benefit for medical (surgical) mask use in community settings to prevent COVID infection. Eight studies (seven meta-analyses, one systematic review) published between 1 January 2020 and 7 December 2022 were evaluated. Base studies were randomized control trials with outcomes of medical diagnosis or laboratory-confirmed diagnosis of viral (Influenza or COVID) illness. Self-reported viral illness outcomes were excluded because of awareness bias. No evidence was observed for a medical mask use benefit to prevent viral infections in six p-value plots (five meta-analyses and one systematic review). Research claims of no benefit in three meta-analyses and the systematic review were reproduced in p-value plots. Research claims of a benefit in two meta-analyses were not reproduced in p-value plots. Insufficient data were available to construct p-value plots for two meta-analyses because of overreliance on self-reported outcomes. These findings suggest a benefit for medical mask use in community settings to prevent viral, including COVID infection, is unproven. | ['Warren B. Kindzierski', 'S. Stanley Young'] | 2023-01-22 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 3.81622970e-01 -1.22054935e-01 -8.55342746e-01 1.94166452e-01
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c24b720a-226f-44b0-ad3f-66d4a14e2d90 | referring-image-segmentation-via-recurrent | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Referring_Image_Segmentation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Referring_Image_Segmentation_CVPR_2018_paper.pdf | Referring Image Segmentation via Recurrent Refinement Networks | We address the problem of image segmentation from natural language descriptions. Existing deep learning-based methods encode image representations based on the output of the last convolutional layer. One general issue is that the resulting image representation lacks multi-scale semantics, which are key components in advanced segmentation systems. In this paper, we utilize the feature pyramids inherently existing in convolutional neural networks to capture the semantics at different scales. To produce suitable information flow through the path of feature hierarchy, we propose Recurrent Refinement Network (RRN) that takes pyramidal features as input to refine the segmentation mask progressively. Experimental results on four available datasets show that our approach outperforms multiple baselines and state-of-the-art. | ['Yi-Chun Kuo', 'Xiaojuan Qi', 'Ruiyu Li', 'Xiaoyong Shen', 'Jiaya Jia', 'Kaican Li', 'Michelle Shu'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.30174708e-01 1.83616772e-01 -4.46040332e-01 -4.99565631e-01
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0494faeb-4c23-4320-9834-5c4d24f75c87 | learning-to-detect-objects-with-a-1-megapixel | 2009.13436 | null | https://arxiv.org/abs/2009.13436v2 | https://arxiv.org/pdf/2009.13436v2.pdf | Learning to Detect Objects with a 1 Megapixel Event Camera | Event cameras encode visual information with high temporal precision, low data-rate, and high-dynamic range. Thanks to these characteristics, event cameras are particularly suited for scenarios with high motion, challenging lighting conditions and requiring low latency. However, due to the novelty of the field, the performance of event-based systems on many vision tasks is still lower compared to conventional frame-based solutions. The main reasons for this performance gap are: the lower spatial resolution of event sensors, compared to frame cameras; the lack of large-scale training datasets; the absence of well established deep learning architectures for event-based processing. In this paper, we address all these problems in the context of an event-based object detection task. First, we publicly release the first high-resolution large-scale dataset for object detection. The dataset contains more than 14 hours recordings of a 1 megapixel event camera, in automotive scenarios, together with 25M bounding boxes of cars, pedestrians, and two-wheelers, labeled at high frequency. Second, we introduce a novel recurrent architecture for event-based detection and a temporal consistency loss for better-behaved training. The ability to compactly represent the sequence of events into the internal memory of the model is essential to achieve high accuracy. Our model outperforms by a large margin feed-forward event-based architectures. Moreover, our method does not require any reconstruction of intensity images from events, showing that training directly from raw events is possible, more efficient, and more accurate than passing through an intermediate intensity image. Experiments on the dataset introduced in this work, for which events and gray level images are available, show performance on par with that of highly tuned and studied frame-based detectors. | ['Davide Nitti', 'Pierre de Tournemire', 'Jonathan Masci', 'Etienne Perot', 'Amos Sironi'] | 2020-09-28 | null | http://proceedings.neurips.cc/paper/2020/hash/c213877427b46fa96cff6c39e837ccee-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c213877427b46fa96cff6c39e837ccee-Paper.pdf | neurips-2020-12 | ['event-based-vision'] | ['computer-vision'] | [ 2.95160145e-01 -4.88809049e-01 1.14887573e-01 -3.52007747e-01
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1178d811-f23c-4a8c-b4ab-14023dd5012b | frame-level-prediction-of-facial-expressions | 2203.13436 | null | https://arxiv.org/abs/2203.13436v2 | https://arxiv.org/pdf/2203.13436v2.pdf | Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices | In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. As a result, our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior Analysis in-the-wild (ABAW) Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.15-0.2 higher performance measures for validation sets in uni-task Expression Classification, Valence-Arousal Estimation and Expression Classification. Due to simplicity, our approach may be considered as a new baseline for all four sub-challenges. | ['Andrey V. Savchenko'] | 2022-03-25 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.91790655e-01 2.95920130e-02 -5.20582348e-02 -8.03265691e-01
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31d7dfc7-95eb-4189-936e-143ded5a8fbf | the-battle-of-information-representations | 2303.14221 | null | https://arxiv.org/abs/2303.14221v1 | https://arxiv.org/pdf/2303.14221v1.pdf | The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends | The study of the stock market with the attraction of machine learning approaches is a major direction for revealing hidden market regularities. This knowledge contributes to a profound understanding of financial market dynamics and getting behavioural insights, which could hardly be discovered with traditional analytical methods. Stock prices are inherently interrelated with world events and social perception. Thus, in constructing the model for stock price prediction, the critical stage is to incorporate such information on the outside world, reflected through news and social media posts. To accommodate this, researchers leverage the implicit or explicit knowledge representations: (1) sentiments extracted from the texts or (2) raw text embeddings. However, there is too little research attention to the direct comparison of these approaches in terms of the influence on the predictive power of financial models. In this paper, we aim to close this gap and figure out whether the semantic features in the form of contextual embeddings are more valuable than sentiment attributes for forecasting market trends. We consider the corpus of Twitter posts related to the largest companies by capitalization from NASDAQ and their close prices. To start, we demonstrate the connection of tweet sentiments with the volatility of companies' stock prices. Convinced of the existing relationship, we train Temporal Fusion Transformer models for price prediction supplemented with either tweet sentiments or tweet embeddings. Our results show that in the substantially prevailing number of cases, the use of sentiment features leads to higher metrics. Noteworthy, the conclusions are justifiable within the considered scenario involving Twitter posts and stocks of the biggest tech companies. | ['Semen Budennyy', 'Elizaveta Kovtun', 'Aleksei Kazakov', 'Andrei Zaichenko'] | 2023-03-24 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-4.85390157e-01 -1.15435813e-02 -4.25529093e-01 -2.15970036e-02
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cd70ba1a-9b67-408a-a871-647e3fa7d7a1 | crvi-convex-relaxation-for-variational | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=2175 | http://proceedings.mlr.press/v80/fazelnia18a/fazelnia18a.pdf | CRVI: Convex Relaxation for Variational Inference |
We present a new technique for solving non-convex variational inference optimization problems. Variational inference is a widely used method for posterior approximation in which the inference problem is transformed into an optimization problem. For most models, this optimization is highly non-convex and so hard to solve. In this paper, we introduce a new approach to solving the variational inference optimization based on convex relaxation and semidefinite programming. Our theoretical results guarantee very tight relaxation bounds that get nearer to the global optimal solution than traditional coordinate ascent. We evaluate the performance of our approach on regression and sparse coding.
| ['Ghazal Fazelnia', 'John Paisley'] | 2018-07-01 | null | null | null | icml-2018-7 | ['inference-optimization'] | ['audio'] | [-1.26570582e-01 1.56659424e-01 -5.03753424e-01 -4.68174756e-01
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1d1efd5e-e5f7-430a-bfda-7c1f7f6c32d5 | unbiased-mean-teacher-for-cross-domain-object | 2003.00707 | null | https://arxiv.org/abs/2003.00707v2 | https://arxiv.org/pdf/2003.00707v2.pdf | Unbiased Mean Teacher for Cross-domain Object Detection | Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a new Unbiased Mean Teacher (UMT) model for cross-domain object detection. We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies. In particular, for the teacher model, we propose a cross-domain distillation method for MT to maximally exploit the expertise of the teacher model. Moreover, for the student model, we alleviate its bias by augmenting training samples with pixel-level adaptation. Finally, for the teaching process, we employ an out-of-distribution estimation strategy to select samples that most fit the current model to further enhance the cross-domain distillation process. By tackling the model bias issue with these strategies, our UMT model achieves mAPs of 44.1%, 58.1%, 41.7%, and 43.1% on benchmark datasets Clipart1k, Watercolor2k, Foggy Cityscapes, and Cityscapes, respectively, which outperforms the existing state-of-the-art results in notable margins. Our implementation is available at https://github.com/kinredon/umt. | ['Yu-Hua Chen', 'Lixin Duan', 'Wen Li', 'Jinhong Deng'] | 2020-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['small-data'] | ['computer-vision'] | [ 5.62690720e-02 -1.81510225e-01 -1.53217629e-01 -1.11579113e-01
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a2ac0c77-d18e-4c42-8be2-41dfb993195a | object-centric-auto-encoders-and-dummy | 1812.04960 | null | http://arxiv.org/abs/1812.04960v2 | http://arxiv.org/pdf/1812.04960v2.pdf | Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video | Abnormal event detection in video is a challenging vision problem. Most
existing approaches formulate abnormal event detection as an outlier detection
task, due to the scarcity of anomalous data during training. Because of the
lack of prior information regarding abnormal events, these methods are not
fully-equipped to differentiate between normal and abnormal events. In this
work, we formalize abnormal event detection as a one-versus-rest binary
classification problem. Our contribution is two-fold. First, we introduce an
unsupervised feature learning framework based on object-centric convolutional
auto-encoders to encode both motion and appearance information. Second, we
propose a supervised classification approach based on clustering the training
samples into normality clusters. A one-versus-rest abnormal event classifier is
then employed to separate each normality cluster from the rest. For the purpose
of training the classifier, the other clusters act as dummy anomalies. During
inference, an object is labeled as abnormal if the highest classification score
assigned by the one-versus-rest classifiers is negative. Comprehensive
experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and
UMN. Our approach provides superior results on all four data sets. On the
large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4%
in terms of frame-level AUC compared to the state-of-the-art method [Sultani et
al., CVPR 2018]. | ['Mariana-Iuliana Georgescu', 'Fahad Shahbaz Khan', 'Radu Tudor Ionescu', 'Ling Shao'] | 2018-12-11 | object-centric-auto-encoders-and-dummy-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ionescu_Object-Centric_Auto-Encoders_and_Dummy_Anomalies_for_Abnormal_Event_Detection_in_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ionescu_Object-Centric_Auto-Encoders_and_Dummy_Anomalies_for_Abnormal_Event_Detection_in_CVPR_2019_paper.pdf | cvpr-2019-6 | ['abnormal-event-detection-in-video', 'abnormal-event-detection-in-video'] | ['computer-vision', 'methodology'] | [ 2.46226653e-01 -1.71297863e-01 -4.47315313e-02 -3.57600927e-01
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36bfac1c-7d54-4eb4-8f44-ac34a0f3e636 | source-free-domain-adaptation-for-rgb-d | 2305.14269 | null | https://arxiv.org/abs/2305.14269v1 | https://arxiv.org/pdf/2305.14269v1.pdf | Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers | With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are attracting increasing interest. In the challenging task of semantic segmentation, depth maps allow to distinguish between similarly colored objects at different depths and provide useful geometric cues. On the other side, ground truth data for semantic segmentation is burdensome to be provided and thus domain adaptation is another significant research area. Specifically, we address the challenging source-free domain adaptation setting where the adaptation is performed without reusing source data. We propose MISFIT: MultImodal Source-Free Information fusion Transformer, a depth-aware framework which injects depth information into a segmentation module based on vision transformers at multiple stages, namely at the input, feature and output levels. Color and depth style transfer helps early-stage domain alignment while re-wiring self-attention between modalities creates mixed features allowing the extraction of better semantic content. Furthermore, a depth-based entropy minimization strategy is also proposed to adaptively weight regions at different distances. Our framework, which is also the first approach using vision transformers for source-free semantic segmentation, shows noticeable performance improvements with respect to standard strategies. | ['Pietro Zanuttigh', 'Donald Shenaj', 'Giulia Rizzoli'] | 2023-05-23 | null | null | null | null | ['style-transfer', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 5.43142140e-01 -1.62784616e-03 -9.85589400e-02 -4.62997705e-01
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f73b6674-d1f1-4071-b677-1d7dd3a82675 | attention-based-graph-resnet-for-motor-intent | 2007.13484 | null | https://arxiv.org/abs/2007.13484v1 | https://arxiv.org/pdf/2007.13484v1.pdf | Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals | In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. However, the latest neuroscience has suggested brain network connectivity. Thus, the exhibited interaction between EEG channels might not be appropriately measured via Euclidean distance. To fill the gap, an attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph. Meanwhile, deep residual learning with a full-attention architecture was introduced to address the degradation problem concerning deeper networks in raw EEG motor imagery (MI) data. Individual variability, the critical and longstanding challenge underlying EEG signals, has been successfully handled with the state-of-the-art performance, 98.08% accuracy at the subject level, 94.28% for 20 subjects. Numerical results were promising that the implementation of the graph-structured topology was superior to decode raw EEG data. The innovative deep learning approach was expected to entail a universal method towards both neuroscience research and real-world EEG-based practical applications, e.g., seizure prediction. | ['Yang Li', 'Yimin Hou', 'Yan Shi', 'Shuyue Jia'] | 2020-06-25 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.68511733e-01 3.45339894e-01 6.15873635e-01 -1.23984925e-01
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92ef8525-6ac8-44be-aa6a-688e7c1e72c9 | benchmarking-bayesian-deep-learning-on | 2211.12717 | null | https://arxiv.org/abs/2211.12717v1 | https://arxiv.org/pdf/2211.12717v1.pdf | Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks | Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue to be evaluated on unrealistic test beds that do not reflect the complexities of downstream real-world tasks that would benefit most from reliable uncertainty quantification. We propose the RETINA Benchmark, a set of real-world tasks that accurately reflect such complexities and are designed to assess the reliability of predictive models in safety-critical scenarios. Specifically, we curate two publicly available datasets of high-resolution human retina images exhibiting varying degrees of diabetic retinopathy, a medical condition that can lead to blindness, and use them to design a suite of automated diagnosis tasks that require reliable predictive uncertainty quantification. We use these tasks to benchmark well-established and state-of-the-art Bayesian deep learning methods on task-specific evaluation metrics. We provide an easy-to-use codebase for fast and easy benchmarking following reproducibility and software design principles. We provide implementations of all methods included in the benchmark as well as results computed over 100 TPU days, 20 GPU days, 400 hyperparameter configurations, and evaluation on at least 6 random seeds each. | ['Yarin Gal', 'Dustin Tran', 'Ghassen Jerfel', 'Michael W. Dusenberry', 'Zachary Nado', 'Angelos Filos', 'Qixuan Feng', 'Tim G. J. Rudner', 'Neil Band'] | 2022-11-23 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-1.27925411e-01 -1.26343995e-01 1.09158158e-01 -7.07710505e-01
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5e802531-3552-4af1-b9a5-dbcce7e07495 | temporal-tagging-on-different-domains | null | null | https://aclanthology.org/L12-1219 | https://aclanthology.org/L12-1219.pdf | Temporal Tagging on Different Domains: Challenges, Strategies, and Gold Standards | In the last years, temporal tagging has received increasing attention in the area of natural language processing. However, most of the research so far concentrated on processing news documents. Only recently, two temporal annotated corpora of narrative-style documents were developed, and it was shown that a domain shift results in significant challenges for temporal tagging. Thus, a temporal tagger should be aware of the domain associated with documents that are to be processed and apply domain-specific strategies for extracting and normalizing temporal expressions. In this paper, we analyze the characteristics of temporal expressions in different domains. In addition to news- and narrative-style documents, we add two further document types, namely colloquial and scientific documents. After discussing the challenges of temporal tagging on the different domains, we describe some strategies to tackle these challenges and describe their integration into our publicly available temporal tagger HeidelTime. Our cross-domain evaluation validates the benefits of domain-sensitive temporal tagging. Furthermore, we make available two new temporally annotated corpora and a new version of HeidelTime, which now distinguishes between four document domain types. | ['Jannik Str{\\"o}tgen', 'Michael Gertz'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-1.07083388e-01 -3.27884518e-02 -5.82687676e-01 -5.85519910e-01
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975f47c0-a2f0-4bf8-b7be-0e0c31bc18c5 | text-driven-toponym-resolution-using-indirect | null | null | https://aclanthology.org/P13-1144 | https://aclanthology.org/P13-1144.pdf | Text-Driven Toponym Resolution using Indirect Supervision | null | ['Michael Speriosu', 'Jason Baldridge'] | 2013-08-01 | null | null | null | acl-2013-8 | ['toponym-resolution'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ac461542-190f-470a-ba75-0cc891b74774 | chainnet-neural-network-based-successive | 2105.03742 | null | https://arxiv.org/abs/2105.03742v3 | https://arxiv.org/pdf/2105.03742v3.pdf | ChainNet: Neural Network-Based Successive Spectral Analysis | We discuss a new neural network-based direction of arrival estimation scheme that tackles the estimation task as a multidimensional classification problem. The proposed estimator uses a classification chain with as many stages as the number of sources. Each stage is a multiclass classification network that estimates the position of one of the sources. This approach can be interpreted as the approximation of a successive evaluation of the maximum a posteriori estimator. By means of simulations for fully sampled antenna arrays and systems with subarray sampling, we show that it is able to outperform existing estimation techniques in terms of accuracy, while maintaining a very low computational complexity. | ['Wolfgang Utschick', 'Andreas Barthelme'] | 2021-05-08 | null | null | null | null | ['direction-of-arrival-estimation', 'classification'] | ['audio', 'methodology'] | [ 2.76752412e-01 -8.67714658e-02 -1.14298031e-01 -4.82617915e-01
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fa61dd9e-acd3-4297-ae25-6f26cbb1e69d | efficient-2d-to-3d-correspondence-filtering | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Hao_Efficient_2D-to-3D_Correspondence_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Hao_Efficient_2D-to-3D_Correspondence_2013_CVPR_paper.pdf | Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition | 3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalability bottleneck, we propose an efficient 2D-to-3D correspondence filtering approach, which combines a light-weight neighborhoodbased step with a finer-grained pairwise step to remove spurious correspondences based on 2D/3D geometric cues. On a dataset of 300 3D objects, our solution achieves ~10 times speed improvement over the baseline, with a comparable recognition accuracy. A parallel implementation on a quad-core CPU can run at ~3fps for 1280x720 images. | ['Zhiwei Li', 'Yong Rui', 'Rui Cai', 'Lei Zhang', 'Feng Wu', 'Yanwei Pang', 'Qiang Hao'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['3d-object-recognition'] | ['computer-vision'] | [-3.94441048e-03 -6.07722819e-01 -6.29319027e-02 -3.53899449e-01
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e1dc56e9-5585-49c9-b408-9777c6703b09 | bayesian-optimisation-of-functions-on-graphs | 2306.05304 | null | https://arxiv.org/abs/2306.05304v1 | https://arxiv.org/pdf/2306.05304v1.pdf | Bayesian Optimisation of Functions on Graphs | The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be applied in this case, but they may be sample-inefficient and do not make use of information about the function values; on the other hand, Bayesian optimisation is a class of promising black-box solvers with superior sample efficiency, but it has been scarcely been applied to such novel setups. To fill this gap, we propose a novel Bayesian optimisation framework that optimises over functions defined on generic, large-scale and potentially unknown graphs. Through the learning of suitable kernels on graphs, our framework has the advantage of adapting to the behaviour of the target function. The local modelling approach further guarantees the efficiency of our method. Extensive experiments on both synthetic and real-world graphs demonstrate the effectiveness of the proposed optimisation framework. | ['Xiaowen Dong', 'Michael A. Osborne', 'Binxin Ru', 'Henry Kenlay', 'Pierre Osselin', 'Xingchen Wan'] | 2023-06-08 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.66917139e-01 3.82515907e-01 1.71383936e-02 -1.16983540e-01
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1d09b948-e3e9-496f-953f-f80d940ade5d | mt-multi-perspective-feature-learning-network | 2105.05455 | null | https://arxiv.org/abs/2105.05455v1 | https://arxiv.org/pdf/2105.05455v1.pdf | MT: Multi-Perspective Feature Learning Network for Scene Text Detection | Text detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors with different advantages: detection-based models enjoy fast detection speed, and segmentation-based algorithms are not limited by text shapes. However, for most intelligent systems, the detector needs to detect arbitrary-shaped texts with high speed and accuracy simultaneously. Thus, in this study, we design an efficient pipeline named as MT, which can detect adhesive arbitrary-shaped texts with only a single binary mask in the inference stage. This paper presents the contributions on three aspects: (1) a light-weight detection framework is designed to speed up the inference process while keeping high detection accuracy; (2) a multi-perspective feature module is proposed to learn more discriminative representations to segment the mask accurately; (3) a multi-factor constraints IoU minimization loss is introduced for training the proposed model. The effectiveness of MT is evaluated on four real-world scene text datasets, and it surpasses all the state-of-the-art competitors to a large extent. | ['Qi Wang', 'Yuan Yuan', 'Mulin Chen', 'Chuang Yang'] | 2021-05-12 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 2.02675983e-01 -6.51943266e-01 -6.10336661e-02 -2.48646840e-01
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23466fa7-780a-4ebd-91bc-eac88d18321e | sr-forest-a-genetic-programming-based | null | null | https://ieeexplore.ieee.org/abstract/document/10040601 | https://ieeexplore.ieee.org/abstract/document/10040601 | SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method | Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance. With the development of genetic programming-based symbolic regression methods, many papers begin to choose a popular ensemble learning method, random forests, as the baseline competitor. Instead of considering them as competitors, an alternative idea might be to consider symbolic regression as an enhancement technique for random forest. Genetic programming-based symbolic regression methods which fit a smooth function are complementary to the piecewise nature of decision trees, as the smooth variation is common in regression problems. In this article, we propose to form an ensemble model with symbolic regression-based decision trees to address this issue. Furthermore, we design a guided mutation operator to speed up the search on high-dimensional problems, a multi-fidelity evaluation strategy to reduce the computational cost and an ensemble selection mechanism to improve predictive performance. Finally, experimental results on a regression benchmark with 120 datasets show that the proposed ensemble model outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, the proposed method can provide notable insights on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods. | ['Mengjie Zhang', 'Bing Xue', 'Qi Chen', 'Aimin Zhou', 'Hengzhe Zhang'] | 2023-02-07 | null | null | null | ieee-transactions-on-evolutionary-computation-1 | ['penn-machine-learning-benchmark'] | ['miscellaneous'] | [ 2.46696666e-01 -3.80301893e-01 -3.15069854e-01 -4.75731432e-01
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58d92820-7b06-405d-91bf-7ec351f75837 | unifying-vision-language-representation-space | 2211.11153 | null | https://arxiv.org/abs/2211.11153v1 | https://arxiv.org/pdf/2211.11153v1.pdf | Unifying Vision-Language Representation Space with Single-tower Transformer | Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. We first identify difficulties in learning a generic one-tower model for vision-language pretraining (VLP), and propose OneR as a simple yet effective framework for our goal. We discover intriguing properties that distinguish OneR from the previous works that learn modality-specific representation spaces such as zero-shot object localization, text-guided visual reasoning and multi-modal retrieval, and present analyses to provide insights into this new form of multi-modal representation learning. Thorough evaluations demonstrate the potential of a unified modality-agnostic VLP framework. | ['Nojun Kwak', 'Seonhoon Kim', 'Donghyeon Jeon', 'Chaerin Kong', 'Jiho Jang'] | 2022-11-21 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.04909223e-01 -1.52481854e-01 -5.36666691e-01 -4.97194022e-01
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0b6fa30b-b11e-4771-aa1e-46698abad83a | sentence-ordering-in-electronic-navigational | null | null | https://aclanthology.org/W15-4710 | https://aclanthology.org/W15-4710.pdf | Sentence Ordering in Electronic Navigational Chart Companion Text Generation | null | ['Julie Sauvage-Vincent', 'John Puentes', 'Yannis Haralambous'] | 2015-09-01 | null | null | null | ws-2015-9 | ['sentence-ordering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f2292da3-80d4-4a20-856c-42e95e1f0ccc | pcpl-predicate-correlation-perception | 2009.00893 | null | https://arxiv.org/abs/2009.00893v1 | https://arxiv.org/pdf/2009.00893v1.pdf | PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation | Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling the class imbalance trouble of SGG is critical and challenging. In this paper, we first discover that when predicate labels have strong correlation with each other, prevalent re-balancing strategies(e.g., re-sampling and re-weighting) will give rise to either over-fitting the tail data(e.g., bench sitting on sidewalk rather than on), or still suffering the adverse effect from the original uneven distribution(e.g., aggregating varied parked on/standing on/sitting on into on). We argue the principal reason is that re-balancing strategies are sensitive to the frequencies of predicates yet blind to their relatedness, which may play a more important role to promote the learning of predicate features. Therefore, we propose a novel Predicate-Correlation Perception Learning(PCPL for short) scheme to adaptively seek out appropriate loss weights by directly perceiving and utilizing the correlation among predicate classes. Moreover, our PCPL framework is further equipped with a graph encoder module to better extract context features. Extensive experiments on the benchmark VG150 dataset show that the proposed PCPL performs markedly better on tail classes while well-preserving the performance on head ones, which significantly outperforms previous state-of-the-art methods. | ['Xian-Sheng Hua', 'Rongxin Jiang', 'Chen Shen', 'Zhongming Jin', 'Yaowu Chen', 'Jianqiang Huang', 'Shaotian Yan'] | 2020-09-02 | null | null | null | null | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 5.36259532e-01 7.97169134e-02 -4.82137799e-01 -3.94462675e-01
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8cef2a39-a570-4ff4-b56d-4c1863ec468a | are-large-language-models-robust-zero-shot | 2305.14489 | null | https://arxiv.org/abs/2305.14489v1 | https://arxiv.org/pdf/2305.14489v1.pdf | Are Large Language Models Robust Zero-shot Coreference Resolvers? | Recent progress in domain adaptation for coreference resolution relies on continued training using annotated data from target domains. At the same time, pre-trained large language models (LMs) have exhibited strong zero- and few-shot learning abilities across a wide range of NLP tasks including pronoun resolution. While this demonstrates evidence of coreference ability, previous work has mostly studied this ability using simple sentence-level datasets such as the Winograd Schema Challenge. In this work, we assess the feasibility of zero-shot learning for coreference resolution by evaluating instruction-tuned language models on more difficult, linguistically-complex coreference benchmarks (e.g., CoNLL-2012). We demonstrate that zero-shot prompting outperforms current unsupervised coreference systems. Further investigations reveal the robust zero-shot generalization ability of instruction-tuned LMs across a wide range of domains, languages, and time periods, as well as a strong reliance on high-quality mention detection systems. | ['Alan Ritter', 'Nghia T. Le'] | 2023-05-23 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.94697306e-01 2.27768302e-01 -7.49475837e-01 -2.54451901e-01
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e42c2b6e-5e53-4e21-9785-96d06495f2b0 | continuous-expressive-speaking-styles | null | null | https://aclanthology.org/C16-1036 | https://aclanthology.org/C16-1036.pdf | Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM | This paper introduces a continuous system capable of automatically producing the most adequate speaking style to synthesize a desired target text. This is done thanks to a joint modeling of the acoustic and lexical parameters of the speaker models by adapting the CVSM projection of the training texts using MR-HMM techniques. As such, we consider that as long as sufficient variety in the training data is available, we should be able to model a continuous lexical space into a continuous acoustic space. The proposed continuous automatic text to speech system was evaluated by means of a perceptual evaluation in order to compare them with traditional approaches to the task. The system proved to be capable of conveying the correct expressiveness (average adequacy of 3.6) with an expressive strength comparable to oracle traditional expressive speech synthesis (average of 3.6) although with a drop in speech quality mainly due to the semi-continuous nature of the data (average quality of 2.9). This means that the proposed system is capable of improving traditional neutral systems without requiring any additional user interaction. | ['Junichi Yamagishi', 'Ascension Gallardo-Antolin', 'Roberto Barra-Chicote', 'Jaime Lorenzo-Trueba', 'Juan M. Montero'] | 2016-12-01 | continuous-expressive-speaking-styles-1 | https://aclanthology.org/C16-1036 | https://aclanthology.org/C16-1036.pdf | coling-2016-12 | ['expressive-speech-synthesis'] | ['speech'] | [ 2.78746516e-01 3.33836555e-01 3.15908939e-01 -4.86960113e-01
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09554446-2747-4361-95bc-6303ea72003b | bowfire-detection-of-fire-in-still-images-by | 1506.03495 | null | http://arxiv.org/abs/1506.03495v1 | http://arxiv.org/pdf/1506.03495v1.pdf | BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis | Emergency events involving fire are potentially harmful, demanding a fast and
precise decision making. The use of crowdsourcing image and videos on crisis
management systems can aid in these situations by providing more information
than verbal/textual descriptions. Due to the usual high volume of data,
automatic solutions need to discard non-relevant content without losing
relevant information. There are several methods for fire detection on video
using color-based models. However, they are not adequate for still image
processing, because they can suffer on high false-positive results. These
methods also suffer from parameters with little physical meaning, which makes
fine tuning a difficult task. In this context, we propose a novel fire
detection method for still images that uses classification based on color
features combined with texture classification on superpixel regions. Our method
uses a reduced number of parameters if compared to previous works, easing the
process of fine tuning the method. Results show the effectiveness of our method
of reducing false-positives while its precision remains compatible with the
state-of-the-art methods. | ['Jose F. Rodrigues Jr.', 'Agma J. M. Traina', 'Letricia P. S. Avalhais', 'Daniel Y. T. Chino'] | 2015-06-10 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 3.88652563e-01 -4.77044612e-01 3.61231297e-01 -5.74351400e-02
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01307380-65a6-4ea6-a782-46a08848241c | combining-multi-sequence-and-synthetic-images | 1909.01182 | null | https://arxiv.org/abs/1909.01182v2 | https://arxiv.org/pdf/1909.01182v2.pdf | Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI | Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences. | ['Miguel A. González Ballester', 'Cristian Izquierdo', 'Carlos Martín-Isla', 'Víctor M. Campello', 'Steffen E. Petersen', 'Karim Lekadir'] | 2019-09-03 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 7.48053968e-01 2.16145232e-01 4.46197510e-01 -2.96480417e-01
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04fc2370-cac6-4328-81f0-5ee1b8d9d1aa | knowledge-interactive-network-with-sentiment | null | null | https://aclanthology.org/2021.findings-emnlp.245 | https://aclanthology.org/2021.findings-emnlp.245.pdf | Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations | Emotion Recognition in Conversation (ERC) has gained much attention from the NLP community recently. Some models concentrate on leveraging commonsense knowledge or multi-task learning to help complicated emotional reasoning. However, these models neglect direct utterance-knowledge interaction. In addition, these models utilize emotion-indirect auxiliary tasks, which provide limited affective information for the ERC task. To address the above issues, we propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning, namely KI-Net, which leverages both commonsense knowledge and sentiment lexicon to augment semantic information. Specifically, we use a self-matching module for internal utterance-knowledge interaction. Considering correlations with the ERC task, a phrase-level Sentiment Polarity Intensity Prediction (SPIP) task is devised as an auxiliary task. Experiments show that all knowledge integration, self-matching and SPIP modules improve the model performance respectively on three datasets. Moreover, our KI-Net model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset. | ['Zhenzhou Ji', 'Bingquan Liu', 'Chengjie Sun', 'Kailai Yang', 'Yunhe Xie'] | null | null | null | null | findings-emnlp-2021-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-9.91290808e-03 2.76254147e-01 -3.41011286e-01 -7.16336250e-01
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ee498df1-87cb-48ae-b9bf-077ea20c5e62 | unsupervised-recycled-fpga-detection-using | 2303.01807 | null | https://arxiv.org/abs/2303.01807v1 | https://arxiv.org/pdf/2303.01807v1.pdf | Unsupervised Recycled FPGA Detection Using Symmetry Analysis | Recently, recycled field-programmable gate arrays (FPGAs) pose a significant hardware security problem due to the proliferation of the semiconductor supply chain. Ring oscillator (RO) based frequency analyzing technique is one of the popular methods, where most studies used the known fresh FPGAs (KFFs) in machine learning-based detection, which is not a realistic approach. In this paper, we present a novel recycled FPGA detection method by examining the symmetry information of the RO frequency using unsupervised anomaly detection method. Due to the symmetrical array structure of the FPGA, some adjacent logic blocks on an FPGA have comparable RO frequencies, hence our method simply analyzes the RO frequencies of those blocks to determine how similar they are. The proposed approach efficiently categorizes recycled FPGAs by utilizing direct density ratio estimation through outliers detection. Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method accurately classifies recycled FPGAs from 10 fresh FPGAs by x fewer computations compared with the conventional method. | ['Liakot Ali', 'Maksim Jenihhin', 'Foisal Ahmed', 'Tanvir Ahmad Tarique'] | 2023-03-03 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 7.49602988e-02 -4.79491204e-01 -1.40741035e-01 3.90572287e-02
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93673870-d4f9-476f-b6c6-67b41d565cfd | analysis-of-arrhythmia-classification-on-ecg | 2301.10174 | null | https://arxiv.org/abs/2301.10174v1 | https://arxiv.org/pdf/2301.10174v1.pdf | Analysis of Arrhythmia Classification on ECG Dataset | The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze the arrhythmia problem from the ECG signals because of its fewer difficulties and cheapness. The heart peaks shown in the ECG graph are used to detect heart diseases, and the R peak is used to analyze arrhythmia disease. Arrhythmia is grouped into two groups - Tachycardia and Bradycardia for detection. In this paper, we discussed many different techniques such as Deep CNNs, LSTM, SVM, NN classifier, Wavelet, TQWT, etc., that have been used for detecting arrhythmia using various datasets throughout the previous decade. This work shows the analysis of some arrhythmia classification on the ECG dataset. Here, Data preprocessing, feature extraction, classification processes were applied on most research work and achieved better performance for classifying ECG signals to detect arrhythmia. Automatic arrhythmia detection can help cardiologists make the right decisions immediately to save human life. In addition, this research presents various previous research limitations with some challenges in detecting arrhythmia that will help in future research. | ['Nazmul Islam Khan', 'Tanzim Ahmed', 'Arindom Kundu', 'Taminul Islam'] | 2023-01-10 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [-5.77956513e-02 -4.06242073e-01 5.77280074e-02 3.82389463e-02
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30a5c522-c844-4508-9fae-091536a6adcc | memegraphs-linking-memes-to-knowledge-graphs | 2305.18391 | null | https://arxiv.org/abs/2305.18391v2 | https://arxiv.org/pdf/2305.18391v2.pdf | MemeGraphs: Linking Memes to Knowledge Graphs | Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies on the understanding of visual elements, language, and background knowledge. Thus, it is important to meaningfully represent these sources and the interaction between them in order to classify a meme as a whole. In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture. We compare our approach with ImgBERT, a multimodal model that uses only learned (instead of structured) representations of the meme, and observe consistent improvements. We further provide a dataset with human graph annotations that we compare to automatically generated graphs and entity linking. Analysis shows that automatic methods link more entities than human annotators and that automatically generated graphs are better suited for hatefulness classification in memes. | ['Benjamin Roth', 'Sahand Sharifzadeh', 'Sina Moayed Baharlou', 'Erion Çano', 'Thomas Kirchmair', 'Simon Fetzel', 'Vasiliki Kougia'] | 2023-05-28 | null | null | null | null | ['knowledge-graphs', 'meme-classification', 'entity-linking'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-1.28939986e-01 2.52325624e-01 1.14867449e-01 8.43428150e-02
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e59b45e4-5927-45d3-b8b4-a8ddbbdd9987 | hnerv-a-hybrid-neural-representation-for | 2304.02633 | null | https://arxiv.org/abs/2304.02633v1 | https://arxiv.org/pdf/2304.02633v1.pdf | HNeRV: A Hybrid Neural Representation for Videos | Implicit neural representations store videos as neural networks and have performed well for various vision tasks such as video compression and denoising. With frame index or positional index as input, implicit representations (NeRV, E-NeRV, \etc) reconstruct video from fixed and content-agnostic embeddings. Such embedding largely limits the regression capacity and internal generalization for video interpolation. In this paper, we propose a Hybrid Neural Representation for Videos (HNeRV), where a learnable encoder generates content-adaptive embeddings, which act as the decoder input. Besides the input embedding, we introduce HNeRV blocks, which ensure model parameters are evenly distributed across the entire network, such that higher layers (layers near the output) can have more capacity to store high-resolution content and video details. With content-adaptive embeddings and re-designed architecture, HNeRV outperforms implicit methods in video regression tasks for both reconstruction quality ($+4.7$ PSNR) and convergence speed ($16\times$ faster), and shows better internal generalization. As a simple and efficient video representation, HNeRV also shows decoding advantages for speed, flexibility, and deployment, compared to traditional codecs~(H.264, H.265) and learning-based compression methods. Finally, we explore the effectiveness of HNeRV on downstream tasks such as video compression and video inpainting. We provide project page at https://haochen-rye.github.io/HNeRV, and Code at https://github.com/haochen-rye/HNeRV | ['Abhinav Shrivastava', 'Ser-Nam Lim', 'Matt Gwilliam', 'Hao Chen'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-inpainting'] | ['computer-vision'] | [-2.27871001e-01 4.52443324e-02 -3.80881935e-01 -2.28207856e-01
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57b17ca5-0903-4152-902e-54dc0466d394 | towards-unifying-medical-vision-and-language | 2302.08958 | null | https://arxiv.org/abs/2302.08958v1 | https://arxiv.org/pdf/2302.08958v1.pdf | Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts | Medical vision-and-language pre-training (Med-VLP) has shown promising improvements on many downstream medical tasks owing to its applicability to extracting generic representations from medical images and texts. Practically, there exist two typical types, \textit{i.e.}, the fusion-encoder type and the dual-encoder type, depending on whether a heavy fusion module is used. The former is superior at multi-modal tasks owing to the sufficient interaction between modalities; the latter is good at uni-modal and cross-modal tasks due to the single-modality encoding ability. To take advantage of these two types, we propose an effective yet straightforward scheme named PTUnifier to unify the two types. We first unify the input format by introducing visual and textual prompts, which serve as a feature bank that stores the most representative images/texts. By doing so, a single model could serve as a \textit{foundation model} that processes various tasks adopting different input formats (\textit{i.e.}, image-only, text-only, and image-text-pair). Furthermore, we construct a prompt pool (instead of static ones) to improve diversity and scalability. Experimental results show that our approach achieves state-of-the-art results on a broad range of tasks, spanning uni-modal tasks (\textit{i.e.}, image/text classification and text summarization), cross-modal tasks (\textit{i.e.}, image-to-text generation and image-text/text-image retrieval), and multi-modal tasks (\textit{i.e.}, visual question answering), demonstrating the effectiveness of our approach. Note that the adoption of prompts is orthogonal to most existing Med-VLP approaches and could be a beneficial and complementary extension to these approaches. | ['Xiang Wan', 'Guanbin Li', 'Benyou Wang', 'Shizhe Diao', 'Zhihong Chen'] | 2023-02-17 | null | null | null | null | ['image-text-classification'] | ['miscellaneous'] | [ 5.62219739e-01 6.94715828e-02 -1.57820120e-01 -1.59061238e-01
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c300b2ac-8e02-41c2-8d32-0637ee882cbd | application-of-machine-learning-in-rock | 1808.09856 | null | http://arxiv.org/abs/1808.09856v1 | http://arxiv.org/pdf/1808.09856v1.pdf | Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation | With recent progress in algorithms and the availability of massive amounts of
computation power, application of machine learning techniques is becoming a hot
topic in the oil and gas industry. One of the most promising aspects to apply
machine learning to the upstream field is the rock facies classification in
reservoir characterization, which is crucial in determining the net pay
thickness of reservoirs, thus a definitive factor in drilling decision making
process. For complex machine learning tasks like facies classification, feature
engineering is often critical. This paper shows the inclusion of
physics-motivated feature interaction in feature augmentation can further
improve the capability of machine learning in rock facies classification. We
demonstrate this approach with the SEG 2016 machine learning contest dataset
and the top winning algorithms. The improvement is roboust and can be $\sim5\%$
better than current existing best F-1 score, where F-1 is an evaluation metric
used to quantify average prediction accuracy. | ['Jie Chen', 'Yu Zeng'] | 2018-08-29 | null | null | null | null | ['facies-classification'] | ['miscellaneous'] | [-1.47921830e-01 -1.90241057e-02 -1.52016252e-01 -3.20346415e-01
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b7859e46-f113-4f83-86eb-8a8a9444ef39 | mast-multimodal-abstractive-summarization | 2010.08021 | null | https://arxiv.org/abs/2010.08021v1 | https://arxiv.org/pdf/2010.08021v1.pdf | MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention | This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities -- text, audio and video -- in a multimodal video. Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities. We examine the usefulness and challenges of deriving information from the audio modality and present a sequence-to-sequence trimodal hierarchical attention-based model that overcomes these challenges by letting the model pay more attention to the text modality. MAST outperforms the current state of the art model (video-text) by 2.51 points in terms of Content F1 score and 1.00 points in terms of Rouge-L score on the How2 dataset for multimodal language understanding. | ['Udit Arora', 'Aman Khullar'] | 2020-10-15 | null | https://aclanthology.org/2020.nlpbt-1.7 | https://aclanthology.org/2020.nlpbt-1.7.pdf | emnlp-nlpbt-2020-11 | ['multimodal-abstractive-text-summarization'] | ['natural-language-processing'] | [ 5.25578856e-01 9.19349566e-02 -9.06744823e-02 -1.43084645e-01
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80af1fc0-4982-4ff1-b3a2-cebdc908f54e | state-regularized-recurrent-neural-networks-1 | 2212.05178 | null | https://arxiv.org/abs/2212.05178v1 | https://arxiv.org/pdf/2212.05178v1.pdf | State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions | Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is difficult to understand what exactly they learn as well as how they arrive at a particular prediction. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) non-regular languages such as balanced parentheses and palindromes where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition and text categorisation. We show that state-regularization (a) simplifies the extraction of finite state automata that display an RNN's state transition dynamic; (b) forces RNNs to operate more like automata with external memory and less like finite state machines, which potentiality leads to a more structural memory; (c) leads to better interpretability and explainability of RNNs by leveraging the probabilistic finite state transition mechanism over time steps. | ['Mathias Niepert', 'Carolin Lawrence', 'Cheng Wang'] | 2022-12-10 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 6.31090403e-01 1.90982774e-01 -3.34490240e-01 -1.40480489e-01
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c6bbd246-9bd0-4eae-8b5c-cceb86c3aec6 | cloze-evaluation-for-deeper-understanding-of | null | null | https://openreview.net/forum?id=zPFbyqOX8Cx | https://openreview.net/pdf?id=zPFbyqOX8Cx | Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian | Story comprehension that involves complex causal and temporal relations is imperative in NLP, but previous studies have focused on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (2016) in evaluating story understanding in Indonesian, by constructing a four-sentence story with one correct ending and one incorrect ending. To investigate commonsense knowledge acquisition in language models, we experimented with: (1) a classification task to predict the correct ending; and (2) a generation task to complete the story with a single sentence. We investigate these tasks in two settings: (i) monolingual training and (ii) zero-shot cross-lingual transfer between Indonesian and English. | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['cloze-test'] | ['natural-language-processing'] | [ 5.96465707e-01 1.62992656e-01 6.46426678e-02 -2.70953268e-01
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98cc5be7-a64e-474a-9561-3d27b239024a | cascaded-neural-networks-with-selective | 1611.07136 | null | http://arxiv.org/abs/1611.07136v1 | http://arxiv.org/pdf/1611.07136v1.pdf | Cascaded Neural Networks with Selective Classifiers and its evaluation using Lung X-ray CT Images | Lung nodule detection is a class imbalanced problem because nodules are found
with much lower frequency than non-nodules. In the class imbalanced problem,
conventional classifiers tend to be overwhelmed by the majority class and
ignore the minority class. We therefore propose cascaded convolutional neural
networks to cope with the class imbalanced problem. In the proposed approach,
cascaded convolutional neural networks that perform as selective classifiers
filter out obvious non-nodules. Successively, a convolutional neural network
trained with a balanced data set calculates nodule probabilities. The proposed
method achieved the detection sensitivity of 85.3% and 90.7% at 1 and 4 false
positives per scan in FROC curve, respectively. | ['Hiroki Nakano', 'Masaharu Sakamoto'] | 2016-11-22 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 2.99705416e-01 4.71302509e-01 -5.91223657e-01 -2.40319431e-01
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d192b64a-14b4-4de4-a3ee-5e3fa39c65f7 | tone-mapping-based-on-multi-scale-histogram | 2102.00408 | null | https://arxiv.org/abs/2102.00408v1 | https://arxiv.org/pdf/2102.00408v1.pdf | Tone Mapping Based on Multi-scale Histogram Synthesis | In this paper, we present a novel tone mapping algorithm that can be used for displaying wide dynamic range (WDR) images on low dynamic range (LDR) devices. The proposed algorithm is mainly motivated by the logarithmic response and local adaptation features of the human visual system (HVS). HVS perceives luminance differently when under different adaptation levels, and therefore our algorithm uses functions built upon different scales to tone map pixels to different values. Functions of large scales are used to maintain image brightness consistency and functions of small scales are used to preserve local detail and contrast. An efficient method using local variance has been proposed to fuse the values of different scales and to remove artifacts. The algorithm utilizes integral images and integral histograms to reduce computation complexity and processing time. Experimental results show that the proposed algorithm can generate high brightness, good contrast, and appealing images that surpass the performance of many state-of-the-art tone mapping algorithms. This project is available at https://github.com/jieyang1987/ToneMapping-Based-on-Multi-scale-Histogram-Synthesis. | ['Orly Yadid-Pecht', 'Ulian Shahnovich', 'Ziyi Liu', 'Jie Yang'] | 2021-01-31 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.58343542e-01 -7.15542316e-01 2.43263673e-02 -2.60014504e-01
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82026f2c-f963-445f-a884-1e99d3c706f0 | adversarial-attacks-against-a-satellite-borne | 2112.01723 | null | https://arxiv.org/abs/2112.01723v1 | https://arxiv.org/pdf/2112.01723v1.pdf | Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector | Data collected by Earth-observing (EO) satellites are often afflicted by cloud cover. Detecting the presence of clouds -- which is increasingly done using deep learning -- is crucial preprocessing in EO applications. In fact, advanced EO satellites perform deep learning-based cloud detection on board the satellites and downlink only clear-sky data to save precious bandwidth. In this paper, we highlight the vulnerability of deep learning-based cloud detection towards adversarial attacks. By optimising an adversarial pattern and superimposing it into a cloudless scene, we bias the neural network into detecting clouds in the scene. Since the input spectra of cloud detectors include the non-visible bands, we generated our attacks in the multispectral domain. This opens up the potential of multi-objective attacks, specifically, adversarial biasing in the cloud-sensitive bands and visual camouflage in the visible bands. We also investigated mitigation strategies against the adversarial attacks. We hope our work further builds awareness of the potential of adversarial attacks in the EO community. | ['Tat-Jun Chin', 'Michael Brown', 'Ken Clarke', 'Bo Chen', 'Michele Sasdelli', 'Yee Wei Law', 'Andrew Du'] | 2021-12-03 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 1.83565766e-01 -3.33606660e-01 5.75176418e-01 6.47268370e-02
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a2c24993-7dfc-445d-b575-1ca6d98c884a | semi-supervised-semantic-segmentation-via-4 | 2301.07340 | null | https://arxiv.org/abs/2301.07340v1 | https://arxiv.org/pdf/2301.07340v1.pdf | Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant | Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data. To effectively leverage the unlabeled data, pseudo labeling, along with the teacher-student framework, is widely adopted in semi-supervised semantic segmentation. Though proved to be effective, this paradigm suffers from incorrect pseudo labels which inevitably exist and are taken as auxiliary training data. To alleviate the negative impact of incorrect pseudo labels, we delve into the current Semi-Supervised Semantic Segmentation frameworks. We argue that the unlabeled data with pseudo labels can facilitate the learning of representative features in the feature extractor, but it is unreliable to supervise the mask predictor. Motivated by this consideration, we propose a novel framework, Gentle Teaching Assistant (GTA-Seg) to disentangle the effects of pseudo labels on feature extractor and mask predictor of the student model. Specifically, in addition to the original teacher-student framework, our method introduces a teaching assistant network which directly learns from pseudo labels generated by the teacher network. The gentle teaching assistant (GTA) is coined gentle since it only transfers the beneficial feature representation knowledge in the feature extractor to the student model in an Exponential Moving Average (EMA) manner, protecting the student model from the negative influences caused by unreliable pseudo labels in the mask predictor. The student model is also supervised by reliable labeled data to train an accurate mask predictor, further facilitating feature representation. Extensive experiment results on benchmark datasets validate that our method shows competitive performance against previous methods. Code is available at https://github.com/Jin-Ying/GTA-Seg. | ['Dahua Lin', 'Jiaqi Wang', 'Ying Jin'] | 2023-01-18 | semi-supervised-semantic-segmentation-via-3 | https://openreview.net/forum?id=r70ZpWKiCW | https://openreview.net/pdf?id=r70ZpWKiCW | nips-2022-11 | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.89360756e-01 5.38509190e-01 -3.40701997e-01 -6.89360738e-01
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c40c072c-215b-40b6-bc36-5470cb73243d | grounding-graph-network-simulators-using | 2302.11864 | null | https://arxiv.org/abs/2302.11864v2 | https://arxiv.org/pdf/2302.11864v2.pdf | Grounding Graph Network Simulators using Physical Sensor Observations | Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional simulators. Yet, the resulting predictors are confined to learning from data generated by existing mesh-based simulators and thus cannot include real world sensory information such as point cloud data. As these predictors have to simulate complex physical systems from only an initial state, they exhibit a high error accumulation for long-term predictions. In this work, we integrate sensory information to ground Graph Network Simulators on real world observations. In particular, we predict the mesh state of deformable objects by utilizing point cloud data. The resulting model allows for accurate predictions over longer time horizons, even under uncertainties in the simulation, such as unknown material properties. Since point clouds are usually not available for every time step, especially in online settings, we employ an imputation-based model. The model can make use of such additional information only when provided, and resorts to a standard Graph Network Simulator, otherwise. We experimentally validate our approach on a suite of prediction tasks for mesh-based interactions between soft and rigid bodies. Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail. | ['Gerhard Neumann', 'Franziska Mathis-Ullrich', 'Paul Maria Scheikl', 'Niklas Freymuth', 'Jonas Linkerhägner'] | 2023-02-23 | null | null | null | null | ['physical-simulations', 'motion-planning'] | ['miscellaneous', 'robots'] | [-7.04833120e-03 1.51948899e-01 -9.47855189e-02 1.30068287e-01
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179b7938-e892-484e-af15-ff51dfc38161 | an-evaluation-and-ranking-of-different-voting | 2305.05705 | null | https://arxiv.org/abs/2305.05705v1 | https://arxiv.org/pdf/2305.05705v1.pdf | An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition | Visual Place Recognition has recently seen a surge of endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion or switching involve combining different VPR techniques together, utilizing different strategies. One major aspect often common to many of these strategies is voting. Voting is widely used in many ensemble methods, so it is potentially a relevant subject to explore in terms of its application and significance for improving VPR performance. This paper attempts to looks into detail and analyze a variety of voting schemes to evaluate which voting technique is optimal for an ensemble VPR set up. We take inspiration from a variety of voting schemes that exist and are widely employed in other research fields such as politics and sociology. The idea is inspired by an observation that different voting methods result in different outcomes for the same type of data and each voting scheme is utilized for specific cases in different academic fields. Some of these voting schemes include Condorcet voting, Broda Count and Plurality voting. Voting employed in any aspect requires that a fair system be established, that outputs the best and most favourable results which in our case would involve improving VPR performance. We evaluate some of these voting techniques in a standardized testing of different VPR techniques, using a variety of VPR data sets. We aim to determine whether a single optimal voting scheme exists or, much like in other fields of research, the selection of a voting technique is relative to its application and environment. We also aim to propose a ranking of these different voting methods from best to worst according to our results as this will allow for better selection of voting schemes. | ['Shoaib Ehsan', 'Klaus McDonald-Maier', 'Xiaojun Zhai', 'Michael Milford', 'Maria Waheed'] | 2023-05-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.94681715e-02 -4.41472411e-01 8.19448456e-02 -1.42006740e-01
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f51903c4-f581-4201-8943-670af7c85433 | quantitative-analysis-of-primary-attribution | 2306.04037 | null | https://arxiv.org/abs/2306.04037v1 | https://arxiv.org/pdf/2306.04037v1.pdf | Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification | We present a comprehensive analysis of quantitatively evaluating explainable artificial intelligence (XAI) techniques for remote sensing image classification. Our approach leverages state-of-the-art machine learning approaches to perform remote sensing image classification across multiple modalities. We investigate the results of the models qualitatively through XAI methods. Additionally, we compare the XAI methods quantitatively through various categories of desired properties. Through our analysis, we offer insights and recommendations for selecting the most appropriate XAI method(s) to gain a deeper understanding of the models' decision-making processes. The code for this work is publicly available. | ['Joshua Peeples', 'Akshatha Mohan'] | 2023-06-06 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.30400598e-01 4.30116542e-02 -5.47634065e-01 -6.46049678e-01
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4dc601a5-5f43-48ca-92fb-b59c691cf546 | a-novel-facial-emotion-recognition-model | null | null | https://link.springer.com/article/10.1007/s41870-023-01184-z | https://link.springer.com/article/10.1007/s41870-023-01184-z | A novel facial emotion recognition model using segmentation VGG-19 architecture | Facial Emotion Recognition (FER) has gained popularity in recent years due to its many applications, including biometrics, detection of mental illness, understanding of human behavior, and psychological profiling. However, developing an accurate and robust FER pipeline is still challenging because multiple factors make it difficult to generalize across different emotions. The factors that challenge a promising FER pipeline include pose variation, heterogeneity of the facial structure, illumination, occlusion, low resolution, and aging factors. Many approaches were developed to overcome the above problems, such as the Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) histogram. However, these methods require manual feature selection. Convolutional Neural Networks (CNN) overcame this manual feature selection problem. CNN has shown great potential in FER tasks due to its unique feature extraction strategy compared to regular FER models. In this paper, we propose a novel CNN architecture by interfacing U-Net segmentation layers in-between Visual Geometry Group (VGG) layers to allow the network to emphasize more critical features from the feature map, which also controls the flow of redundant information through the VGG layers. Our model achieves state-of-the-art (SOTA) single network accuracy compared with other well-known FER models on the FER-2013 dataset. | ['Rajeswari Sridhar', 'M. Sridevi', 'M. Savithadevi', 'S. Vignesh'] | 2023-03-24 | null | null | null | international-journal-of-information | ['facial-emotion-recognition', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [-2.44754236e-02 -3.59408885e-01 -3.90365310e-02 -7.02412903e-01
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3b18bf19-7db1-4bd2-8048-7b6bf4b10b50 | unsupervised-3d-out-of-distribution-detection | 2307.03777 | null | https://arxiv.org/abs/2307.03777v1 | https://arxiv.org/pdf/2307.03777v1.pdf | Unsupervised 3D out-of-distribution detection with latent diffusion models | Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not trivially scale to 3D data. In this work, we propose to use Latent Diffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution 3D medical data. We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs). Not only does the proposed LDM-based approach achieve statistically significant better performance, it also shows less sensitivity to the underlying latent representation, more favourable memory scaling, and produces better spatial anomaly maps. Code is available at https://github.com/marksgraham/ddpm-ood | ['M. Jorge Cardoso', 'Sebastien Ourselin', 'Parashkev Nachev', 'David Werring', 'H. Rolf Jäger', 'James T. Teo', 'Yee H. Mah', 'Petru-Daniel Tudosiu', 'Paul Wright', 'Walter Hugo Lopez Pinaya', 'Mark S. Graham'] | 2023-07-07 | null | null | null | null | ['out-of-distribution-detection', 'denoising', 'ood-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.52196237e-02 6.81953728e-02 1.19428717e-01 -1.57019988e-01
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7322d715-94b5-4f29-ae3a-05d7841bf414 | physically-based-editing-of-indoor-scene | 2205.09343 | null | https://arxiv.org/abs/2205.09343v2 | https://arxiv.org/pdf/2205.09343v2.pdf | Physically-Based Editing of Indoor Scene Lighting from a Single Image | We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that requires modeling complex light transport, and disentangling HDR lighting from material and geometry with only a partial LDR observation of the scene. We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions. We use physically-based indoor light representations that allow for intuitive editing, and infer both visible and invisible light sources. Our neural rendering framework combines physically-based direct illumination and shadow rendering with deep networks to approximate global illumination. It can capture challenging lighting effects, such as soft shadows, directional lighting, specular materials, and interreflections. Previous single image inverse rendering methods usually entangle scene lighting and geometry and only support applications like object insertion. Instead, by combining parametric 3D lighting estimation with neural scene rendering, we demonstrate the first automatic method to achieve full scene relighting, including light source insertion, removal, and replacement, from a single image. All source code and data will be publicly released. | ['Manmohan Chandraker', 'Ravi Ramamoorthi', 'Zexiang Xu', 'Miloš Hašan', 'Kalyan Sunkavalli', 'Rui Zhu', 'Sai Bi', 'Jia Shi', 'Zhengqin Li'] | 2022-05-19 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 8.03262413e-01 -1.91501692e-01 6.44478738e-01 -4.94503975e-01
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379b07a1-ff6c-4200-802a-527f1242a65a | faithful-chain-of-thought-reasoning | 2301.13379 | null | https://arxiv.org/abs/2301.13379v2 | https://arxiv.org/pdf/2301.13379v2.pdf | Faithful Chain-of-Thought Reasoning | While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a faithful-by-construction framework that decomposes a reasoning task into two stages: Translation (Natural Language query $\rightarrow$ symbolic reasoning chain) and Problem Solving (reasoning chain $\rightarrow$ answer), using an LM and a deterministic solver respectively. We demonstrate the efficacy of our approach on 10 reasoning datasets from 4 diverse domains. It outperforms traditional CoT prompting on 9 out of the 10 datasets, with an average accuracy gain of 4.4 on Math Word Problems, 1.9 on Planning, 4.0 on Multi-hop Question Answering (QA), and 18.1 on Logical Inference, under greedy decoding. Together with self-consistency decoding, we achieve new state-of-the-art few-shot performance on 7 out of the 10 datasets, showing a strong synergy between faithfulness and accuracy. | ['Chris Callison-Burch', 'Marianna Apidianaki', 'Eric Wong', 'Delip Rao', 'Li Zhang', 'Adam Stein', 'Shreya Havaldar', 'Qing Lyu'] | 2023-01-31 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 6.88385218e-02 6.87312782e-01 -1.60857886e-01 -5.36554813e-01
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96efcc3e-fcf6-409e-93ed-cee16cf8e5d2 | universal-spam-detection-using-transfer | 2202.03480 | null | https://arxiv.org/abs/2202.03480v1 | https://arxiv.org/pdf/2202.03480v1.pdf | Universal Spam Detection using Transfer Learning of BERT Model | Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with four datasets by efficiently classifying ham or spam emails in real-time scenarios. Different methods for Enron, Spamassain, Lingspam, and Spamtext message classification datasets, were used to train models individually in which a single model was obtained with acceptable performance on four datasets. The Universal Spam Detection Model (USDM) was trained with four datasets and leveraged hyperparameters from each model. The combined model was finetuned with the same hyperparameters from these four models separately. When each model using its corresponding dataset, an F1-score is at and above 0.9 in individual models. An overall accuracy reached 97%, with an F1 score of 0.96. Research results and implications were discussed. | ['Sonya Hsu', 'Vijay Srinivas Tida'] | 2022-02-07 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.37813732e-01 1.02101468e-01 -9.87397283e-02 -3.21598023e-01
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978f0caa-b22a-4bd5-af3b-79acd6a68354 | faster-exact-decoding-and-global-training-for | 1708.09403 | null | http://arxiv.org/abs/1708.09403v1 | http://arxiv.org/pdf/1708.09403v1.pdf | Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set | We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Kuhlmann et al. (2011) to produce the first implementation of
worst-case O(n^3) exact decoders for arc-hybrid and arc-eager transition
systems. With our minimal features, we also present O(n^3) global training
methods. Finally, using ensembles including our new parsers, we achieve the
best unlabeled attachment score reported (to our knowledge) on the Chinese
Treebank and the "second-best-in-class" result on the English Penn Treebank. | ['Tianze Shi', 'Liang Huang', 'Lillian Lee'] | 2017-08-30 | faster-exact-decoding-and-global-training-for-1 | https://aclanthology.org/D17-1002 | https://aclanthology.org/D17-1002.pdf | emnlp-2017-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.66065355e-02 7.56758571e-01 -7.33317360e-02 -5.84809124e-01
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d9ab6c57-23b6-4dde-a74c-a8a6f8a24f28 | auxiliary-learning-for-self-supervised-video | 2112.04011 | null | https://arxiv.org/abs/2112.04011v3 | https://arxiv.org/pdf/2112.04011v3.pdf | Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation | Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. To mitigate these issues, we propose a novel approach to complement self-supervised pretraining via an auxiliary pretraining phase, based on knowledge similarity distillation, auxSKD, for better generalisation with a significantly smaller amount of video data, e.g. Kinetics-100 rather than Kinetics-400. Our method deploys a teacher network that iteratively distills its knowledge to the student model by capturing the similarity information between segments of unlabelled video data. The student model meanwhile solves a pretext task by exploiting this prior knowledge. We also introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which requires our model to predict the playback speed of a randomly selected segment of the input video to provide more reliable self-supervised representations. Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when added as an extra pretraining phase to recent state of the art self-supervised methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101 and HMDB51. Our code is available at https://github.com/Plrbear/auxSKD. | ['Majid Mirmehdi', 'Alan Whone', 'Amirhossein Dadashzadeh'] | 2021-12-07 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 4.09196705e-01 1.26932142e-02 -6.93996668e-01 -4.28313196e-01
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fcb61ce4-ac65-47aa-8f2f-de2774f10173 | ask-question-with-double-hints-visual | null | null | https://openreview.net/forum?id=-WwaX9vKKt | https://openreview.net/pdf?id=-WwaX9vKKt | Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference | The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e.g. answer type or the answer itself). Despite promising results have been achieved, previous works on VQG either i) suffer from one image to many questions mapping problem rendering the failure of generating referential and meaningful questions from an image, or ii) ignore rich correlations among the visual objects in an image and potential interactions between the side information and image. To address these limitations, we first propose a novel learning paradigm to generate visual questions with answer-awareness and region-reference. In particular, we aim to ask the right visual questions with \emph{Double Hints - textual answers and visual regions of interests}, effectively mitigating the existing one-to-many mapping issue. To this end, we develop a simple methodology to self-learn the visual hints without introducing any additional human annotations. Furthermore, to capture these sophisticated relationships, we propose a new double-hints guided Graph-to-Sequence learning framework that first models them as a dynamic graph and learns the implicit topology end-to-end, and then utilize a graph-to-sequence model to generate the questions with double hints. Our experiments on VQA2.0 and COCO-QA datasets demonstrate that our proposed model on this new setting can significantly outperform existing state-of-the-art baselines by a large margin. | ['Yueting Zhuang', 'Yu Qiang', 'Zhu Zhang', 'Fangli Xu', 'Siliang Tang', 'Lingfei Wu', 'Shen Kai'] | 2021-01-01 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 2.70100713e-01 4.16645885e-01 7.72889033e-02 -3.72388303e-01
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