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abc9bd39-e9c1-40e4-a464-e1774635f6f0 | weakly-supervised-multi-object-tracking-and | 2101.00667 | null | https://arxiv.org/abs/2101.00667v1 | https://arxiv.org/pdf/2101.00667v1.pdf | Weakly Supervised Multi-Object Tracking and Segmentation | We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7% for cars and pedestrians, respectively. | ['Joan Serrat', 'Peter Kontschieder', 'Samuel Rota Bulò', 'Lorenzo Porzi', 'Idoia Ruiz'] | 2021-01-03 | null | null | null | null | ['weakly-supervised-instance-segmentation', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.56035495e-01 3.05095881e-01 -2.11583257e-01 -2.91525930e-01
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cec2e810-a2c2-4038-80d5-ffa0c5c1e3eb | adaptive-policy-learning-for-offline-to | 2303.07693 | null | https://arxiv.org/abs/2303.07693v1 | https://arxiv.org/pdf/2303.07693v1.pdf | Adaptive Policy Learning for Offline-to-Online Reinforcement Learning | Conventional reinforcement learning (RL) needs an environment to collect fresh data, which is impractical when online interactions are costly. Offline RL provides an alternative solution by directly learning from the previously collected dataset. However, it will yield unsatisfactory performance if the quality of the offline datasets is poor. In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data. Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, that is, a pessimistic update strategy for the offline dataset and an optimistic/greedy update scheme for the online dataset. Such a simple and effective method provides a way to mix the offline and online RL and achieve the best of both worlds. We further provide two detailed algorithms for implementing the framework through embedding value or policy-based RL algorithms into it. Finally, we conduct extensive experiments on popular continuous control tasks, and results show that our algorithm can learn the expert policy with high sample efficiency even when the quality of offline dataset is poor, e.g., random dataset. | ['Jing Jiang', 'Dongsheng Li', 'Xuan Song', 'Pengfei Wei', 'Xufang Luo', 'Han Zheng'] | 2023-03-14 | null | null | null | null | ['offline-rl', 'continuous-control'] | ['playing-games', 'playing-games'] | [-2.64360666e-01 1.41869038e-01 -6.34113491e-01 -2.18648732e-01
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494f3a91-ed24-4315-9b56-b4b00a3ec71f | trollmeta-dravidianlangtech-eacl2021-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.39 | https://aclanthology.org/2021.dravidianlangtech-1.39.pdf | TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning | Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture. | ['Chinmaya Hs', 'Manoj Balaji J'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-1.49813220e-01 -1.34291679e-01 2.35325396e-01 1.90537751e-01
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c8d33594-b3bf-4fd0-94b2-3324c5eb17a4 | conversational-knowledge-teaching-agent-that | null | null | https://aclanthology.org/W15-4618 | https://aclanthology.org/W15-4618.pdf | Conversational Knowledge Teaching Agent that uses a Knowledge Base | null | ['Kyusong Lee', 'Gary Geunbae Lee', 'Junhwi Choi', 'Sangjun Koo', 'Paul Hongsuck Seo'] | 2015-09-01 | null | null | null | ws-2015-9 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c33639b5-ef7d-4ea2-ab1a-cbf52e3062c1 | the-devil-is-in-the-details-on-the-pitfalls-1 | 2306.06918 | null | https://arxiv.org/abs/2306.06918v2 | https://arxiv.org/pdf/2306.06918v2.pdf | The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation | Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we check the reliability of EE evaluations and identify three major pitfalls: (1) The data preprocessing discrepancy makes the evaluation results on the same dataset not directly comparable, but the data preprocessing details are not widely noted and specified in papers. (2) The output space discrepancy of different model paradigms makes different-paradigm EE models lack grounds for comparison and also leads to unclear mapping issues between predictions and annotations. (3) The absence of pipeline evaluation of many EAE-only works makes them hard to be directly compared with EE works and may not well reflect the model performance in real-world pipeline scenarios. We demonstrate the significant influence of these pitfalls through comprehensive meta-analyses of recent papers and empirical experiments. To avoid these pitfalls, we suggest a series of remedies, including specifying data preprocessing, standardizing outputs, and providing pipeline evaluation results. To help implement these remedies, we develop a consistent evaluation framework OMNIEVENT, which can be obtained from https://github.com/THU-KEG/OmniEvent. | ['Weixing Shen', 'Zhiyuan Liu', 'Juanzi Li', 'Lei Hou', 'Kaisheng Zeng', 'Feng Yao', 'Xiaozhi Wang', 'Hao Peng'] | 2023-06-12 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.09013073e-01 7.54720941e-02 -1.47659719e-01 -4.02836680e-01
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f199e6e6-ceb6-4cb3-b5cb-e2ee57f415c6 | clustering-word-embeddings-with-self | 2101.04197 | null | https://arxiv.org/abs/2101.04197v1 | https://arxiv.org/pdf/2101.04197v1.pdf | Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set | Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from one of the largest Romanian e-commerce platforms. We employ two sentiment classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (bag-of-word-embeddings generated by clustering word embeddings with k-means). As an additional contribution, we replace the k-means clustering algorithm with self-organizing maps (SOMs), obtaining better results because the generated clusters of word embeddings are closer to the Zipf's law distribution, which is known to govern natural language. We also demonstrate the generalization capacity of using SOMs for the clustering of word embeddings on another recently-introduced Romanian data set, for text categorization by topic. | ['Radu Tudor Ionescu', 'Mihaela Gaman', 'Anca Maria Tache'] | 2021-01-11 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-3.35305780e-01 5.06790802e-02 -3.10571045e-01 -4.93797362e-01
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aa3f71ff-9032-4cc6-a5ea-83bed9b87654 | on-the-soft-subnetwork-for-few-shot-class | 2209.07529 | null | https://arxiv.org/abs/2209.07529v2 | https://arxiv.org/pdf/2209.07529v2.pdf | On the Soft-Subnetwork for Few-shot Class Incremental Learning | Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks (SoftNet)}. Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets. | ['Chang D. Yoo', 'Sung Ju Hwang', 'Sultan Rizky Hikmawan Madjid', 'Jaehong Yoon', 'Haeyong Kang'] | 2022-09-15 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 4.37965691e-01 5.43354690e-01 -3.32098126e-01 -3.01792383e-01
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84accb14-6d92-46e1-abbd-572cfeec502d | unity-in-diversity-learning-distributed | 1912.11688 | null | https://arxiv.org/abs/1912.11688v1 | https://arxiv.org/pdf/1912.11688v1.pdf | Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization | Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the document-dependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC-2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines. | ['Abhishek Kumar Singh', 'Vasudeva Varma', 'Manish Gupta'] | 2019-12-25 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.07419586e-01 1.08735219e-01 -5.82966387e-01 -6.23388290e-01
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25a1b327-787c-4848-b28f-9c23be40212b | a-formal-perspective-on-byte-pair-encoding | 2306.16837 | null | https://arxiv.org/abs/2306.16837v1 | https://arxiv.org/pdf/2306.16837v1.pdf | A Formal Perspective on Byte-Pair Encoding | Byte-Pair Encoding (BPE) is a popular algorithm used for tokenizing data in NLP, despite being devised initially as a compression method. BPE appears to be a greedy algorithm at face value, but the underlying optimization problem that BPE seeks to solve has not yet been laid down. We formalize BPE as a combinatorial optimization problem. Via submodular functions, we prove that the iterative greedy version is a $\frac{1}{{\sigma(\boldsymbol{\mu}^\star)}}(1-e^{-{\sigma(\boldsymbol{\mu}^\star)}})$-approximation of an optimal merge sequence, where ${\sigma(\boldsymbol{\mu}^\star)}$ is the total backward curvature with respect to the optimal merge sequence $\boldsymbol{\mu}^\star$. Empirically the lower bound of the approximation is $\approx 0.37$. We provide a faster implementation of BPE which improves the runtime complexity from $\mathcal{O}\left(N M\right)$ to $\mathcal{O}\left(N \log M\right)$, where $N$ is the sequence length and $M$ is the merge count. Finally, we optimize the brute-force algorithm for optimal BPE using memoization. | ['Ryan Cotterell', 'Mrinmaya Sachan', 'Tim Vieira', 'Li Du', 'Juan Luis Gastaldi', 'Clara Meister', 'Vilém Zouhar'] | 2023-06-29 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.59751993e-01 2.27251098e-01 -3.55193168e-01 -2.09997773e-01
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47e3f4a4-abad-46c9-ab2d-7996d52c1e66 | a-symmetric-encoder-decoder-with-residual | 1905.11447 | null | https://arxiv.org/abs/1905.11447v1 | https://arxiv.org/pdf/1905.11447v1.pdf | A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion | In computer vision and image processing tasks, image fusion has evolved into an attractive research field. However, recent existing image fusion methods are mostly built on pixel-level operations, which may produce unacceptable artifacts and are time-consuming. In this paper, a symmetric encoder-decoder with a residual block (SEDR) for infrared and visible image fusion is proposed. For the training stage, the SEDR network is trained with a new dataset to obtain a fixed feature extractor. For the fusion stage, first, the trained model is utilized to extract the intermediate features and compensation features of two source images. Then, extracted intermediate features are used to generate two attention maps, which are multiplied to the input features for refinement. In addition, the compensation features generated by the first two convolutional layers are merged and passed to the corresponding deconvolutional layers. At last, the refined features are fused for decoding to reconstruct the final fused image. Experimental results demonstrate that the proposed fusion method (named as SEDRFuse) outperforms the state-of-the-art fusion methods in terms of both subjective and objective evaluations. | ['Gwanggil Jeon', 'Zheng Liu', 'Xiaomin Yang', 'Lihua Jian', 'Mingliang Gao', 'David Chisholm'] | 2019-05-27 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 5.78275144e-01 -4.67875779e-01 2.87482381e-01 -4.34558868e-01
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5d51ce07-ba0c-4244-b479-cf9e12da7b7f | deeplens-interactive-out-of-distribution-data | 2303.01577 | null | https://arxiv.org/abs/2303.01577v1 | https://arxiv.org/pdf/2303.01577v1.pdf | DeepLens: Interactive Out-of-distribution Data Detection in NLP Models | Machine Learning (ML) has been widely used in Natural Language Processing (NLP) applications. A fundamental assumption in ML is that training data and real-world data should follow a similar distribution. However, a deployed ML model may suffer from out-of-distribution (OOD) issues due to distribution shifts in the real-world data. Though many algorithms have been proposed to detect OOD data from text corpora, there is still a lack of interactive tool support for ML developers. In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora. Users can efficiently explore different OOD types in DeepLens with the help of a text clustering method. Users can also dig into a specific text by inspecting salient words highlighted through neuron activation analysis. In a within-subjects user study with 24 participants, participants using DeepLens were able to find nearly twice more types of OOD issues accurately with 22% more confidence compared with a variant of DeepLens that has no interaction or visualization support. | ['Tianyi Zhang', 'Lei Ma', 'Yuheng Huang', 'Zhijie Wang', 'Da Song'] | 2023-03-02 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-4.08365458e-01 1.65505800e-02 2.24431634e-01 -3.92991841e-01
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24356d17-bf27-43b2-b760-7ed834d5d53e | multi-domain-norm-referenced-encoding-enables | 2304.02309 | null | https://arxiv.org/abs/2304.02309v1 | https://arxiv.org/pdf/2304.02309v1.pdf | Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition | People can innately recognize human facial expressions in unnatural forms, such as when depicted on the unusual faces drawn in cartoons or when applied to an animal's features. However, current machine learning algorithms struggle with out-of-domain transfer in facial expression recognition (FER). We propose a biologically-inspired mechanism for such transfer learning, which is based on norm-referenced encoding, where patterns are encoded in terms of difference vectors relative to a domain-specific reference vector. By incorporating domain-specific reference frames, we demonstrate high data efficiency in transfer learning across multiple domains. Our proposed architecture provides an explanation for how the human brain might innately recognize facial expressions on varying head shapes (humans, monkeys, and cartoon avatars) without extensive training. Norm-referenced encoding also allows the intensity of the expression to be read out directly from neural unit activity, similar to face-selective neurons in the brain. Our model achieves a classification accuracy of 92.15\% on the FERG dataset with extreme data efficiency. We train our proposed mechanism with only 12 images, including a single image of each class (facial expression) and one image per domain (avatar). In comparison, the authors of the FERG dataset achieved a classification accuracy of 89.02\% with their FaceExpr model, which was trained on 43,000 images. | ['Martin Giese', 'Nick Taubert', 'Alexander Lappe', 'Michael Stettler'] | 2023-04-05 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.24226141e-01 4.11798470e-02 6.56577274e-02 -8.00471902e-01
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da15e19c-567b-4bd8-9305-e69f63bb447f | copy-move-image-forgery-detection-based-on | 2109.04381 | null | https://arxiv.org/abs/2109.04381v3 | https://arxiv.org/pdf/2109.04381v3.pdf | Copy-Move Image Forgery Detection Based on Evolving Circular Domains Coverage | The aim of this paper is to improve the accuracy of copy-move forgery detection (CMFD) in image forensics by proposing a novel scheme and the main contribution is evolving circular domains coverage (ECDC) algorithm. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. Firstly, the speed-up robust feature (SURF) in log-polar space and the scale invariant feature transform (SIFT) are extracted from an entire image. Secondly, generalized 2 nearest neighbor (g2NN) is employed to get massive matched pairs. Then, random sample consensus (RANSAC) algorithm is employed to filter out mismatched pairs, thus allowing rough localization of counterfeit areas. To present these forgery areas more accurately, we propose the efficient and accurate ECDC algorithm to present them. This algorithm can find satisfactory threshold areas by extracting block features from jointly evolving circular domains, which are centered on matched pairs. Finally, morphological operation is applied to refine the detected forgery areas. Experimental results indicate that the proposed CMFD scheme can achieve better detection performance under various attacks compared with other state-of-the-art CMFD schemes. | ['Yuejia Han', 'Shulu Han', 'Lu Chen', 'Chengyou Wang', 'Xinghong Hu', 'Shilin Lu'] | 2021-09-09 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.59672493e-01 -8.15090239e-01 6.78810431e-03 2.54210651e-01
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31384a4e-2b3c-4775-a4fc-6603c8151474 | tell-me-what-happened-unifying-text-guided | 2211.12824 | null | https://arxiv.org/abs/2211.12824v2 | https://arxiv.org/pdf/2211.12824v2.pdf | Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation | Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC. | ['Sean Bell', 'William Yang Wang', 'Jong-Chyi Su', 'Cheng-Yang Fu', 'Ning Zhang', 'Licheng Yu', 'Tsu-Jui Fu'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'video-prediction', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.02633166e-01 1.82508811e-01 -2.08412409e-01 -2.09436953e-01
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1b093d8b-1681-4f71-b529-9aaaf7a4b666 | discriminative-extended-canonical-correlation | 1306.2100 | null | http://arxiv.org/abs/1306.2100v1 | http://arxiv.org/pdf/1306.2100v1.pdf | Discriminative extended canonical correlation analysis for pattern set matching | In this paper we address the problem of matching sets of vectors embedded in
the same input space. We propose an approach which is motivated by canonical
correlation analysis (CCA), a statistical technique which has proven successful
in a wide variety of pattern recognition problems. Like CCA when applied to the
matching of sets, our extended canonical correlation analysis (E-CCA) aims to
extract the most similar modes of variability within two sets. Our first major
contribution is the formulation of a principled framework for robust inference
of such modes from data in the presence of uncertainty associated with noise
and sampling randomness. E-CCA retains the efficiency and closed form
computability of CCA, but unlike it, does not possess free parameters which
cannot be inferred directly from data (inherent data dimensionality, and the
number of canonical correlations used for set similarity computation). Our
second major contribution is to show that in contrast to CCA, E-CCA is readily
adapted to match sets in a discriminative learning scheme which we call
discriminative extended canonical correlation analysis (DE-CCA). Theoretical
contributions of this paper are followed by an empirical evaluation of its
premises on the task of face recognition from sets of rasterized appearance
images. The results demonstrate that our approach, E-CCA, already outperforms
both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for
all values of their free parameters. An even greater improvement is achieved
with the discriminative variant, DE-CCA. | ['Ognjen Arandjelovic'] | 2013-06-10 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 4.19534087e-01 -5.35145044e-01 4.74659741e-01 -3.38218212e-01
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36ae33a4-d4ef-4ae3-ba5a-832c5dc7b165 | word-movers-embedding-from-word2vec-to | 1811.01713 | null | http://arxiv.org/abs/1811.01713v1 | http://arxiv.org/pdf/1811.01713v1.pdf | Word Mover's Embedding: From Word2Vec to Document Embedding | While the celebrated Word2Vec technique yields semantically rich
representations for individual words, there has been relatively less success in
extending to generate unsupervised sentences or documents embeddings. Recent
work has demonstrated that a distance measure between documents called
\emph{Word Mover's Distance} (WMD) that aligns semantically similar words,
yields unprecedented KNN classification accuracy. However, WMD is expensive to
compute, and it is hard to extend its use beyond a KNN classifier. In this
paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to
building an unsupervised document (sentence) embedding from pre-trained word
embeddings. In our experiments on 9 benchmark text classification datasets and
22 textual similarity tasks, the proposed technique consistently matches or
outperforms state-of-the-art techniques, with significantly higher accuracy on
problems of short length. | ['Michael J. Witbrock', 'Pin-Yu Chen', 'Pradeep Ravikumar', 'Lingfei Wu', 'Kun Xu', 'Ian E. H. Yen', 'Fangli Xu', 'Avinash Balakrishnan'] | 2018-10-30 | word-movers-embedding-from-word2vec-to-1 | https://aclanthology.org/D18-1482 | https://aclanthology.org/D18-1482.pdf | emnlp-2018-10 | ['document-embedding'] | ['methodology'] | [ 2.86466569e-01 -2.02994704e-01 -3.33680302e-01 -4.01598632e-01
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97faa926-5387-430c-bb66-2c989a27ac1c | 3d-room-layout-estimation-from-a-cubemap-of | 2207.09291 | null | https://arxiv.org/abs/2207.09291v1 | https://arxiv.org/pdf/2207.09291v1.pdf | 3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform | Significant geometric structures can be compactly described by global wireframes in the estimation of 3D room layout from a single panoramic image. Based on this observation, we present an alternative approach to estimate the walls in 3D space by modeling long-range geometric patterns in a learnable Hough Transform block. We transform the image feature from a cubemap tile to the Hough space of a Manhattan world and directly map the feature to the geometric output. The convolutional layers not only learn the local gradient-like line features, but also utilize the global information to successfully predict occluded walls with a simple network structure. Unlike most previous work, the predictions are performed individually on each cubemap tile, and then assembled to get the layout estimation. Experimental results show that we achieve comparable results with recent state-of-the-art in prediction accuracy and performance. Code is available at https://github.com/Starrah/DMH-Net. | ['Yue Gao', 'Zhou Xue', 'Chao Wen', 'Yining Zhao'] | 2022-07-19 | null | null | null | null | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [-2.38218699e-02 1.84691802e-01 3.11352134e-01 -6.69701278e-01
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ab3859ba-9b84-407e-8eb5-355aedb451e7 | data-efficient-large-scale-place-recognition | 2303.11739 | null | https://arxiv.org/abs/2303.11739v2 | https://arxiv.org/pdf/2303.11739v2.pdf | Data-efficient Large Scale Place Recognition with Graded Similarity Supervision | Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous nature of camera pose. The binary similarity induces a noisy supervision signal into the training of VPR methods, which stall in local minima and require expensive hard mining algorithms to guarantee convergence. Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets. We compute graded similarity labels for image pairs based on available localization metadata. Furthermore, we propose a new Generalized Contrastive Loss (GCL) that uses graded similarity labels for training contrastive networks. We demonstrate that the use of the new labels and GCL allow to dispense from hard-pair mining, and to train image descriptors that perform better in VPR by nearest neighbor search, obtaining superior or comparable results than methods that require expensive hard-pair mining and re-ranking techniques. Code and models available at: https://github.com/marialeyvallina/generalized_contrastive_loss | ['Nicolai Petkov', 'Nicola Strisciuglio', 'Maria Leyva-Vallina'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.09235731e-02 -3.40960711e-01 -3.36769938e-01 -5.06184280e-01
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c9fbf8aa-523d-4ccc-adf7-9b215dd37472 | data-augmentation-for-biomedical-factoid-1 | 2204.04711 | null | https://arxiv.org/abs/2204.04711v1 | https://arxiv.org/pdf/2204.04711v1.pdf | Data Augmentation for Biomedical Factoid Question Answering | We study the effect of seven data augmentation (da) methods in factoid question answering, focusing on the biomedical domain, where obtaining training instances is particularly difficult. We experiment with data from the BioASQ challenge, which we augment with training instances obtained from an artificial biomedical machine reading comprehension dataset, or via back-translation, information retrieval, word substitution based on word2vec embeddings, or masked language modeling, question generation, or extending the given passage with additional context. We show that da can lead to very significant performance gains, even when using large pre-trained Transformers, contributing to a broader discussion of if/when da benefits large pre-trained models. One of the simplest da methods, word2vec-based word substitution, performed best and is recommended. We release our artificial training instances and code. | ['Ion Androutsopoulos', 'Prodromos Malakasiotis', 'Dimitris Pappas'] | 2022-04-10 | null | https://aclanthology.org/2022.bionlp-1.6 | https://aclanthology.org/2022.bionlp-1.6.pdf | bionlp-acl-2022-5 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 8.58686328e-01 6.01382494e-01 -2.17410363e-02 -1.55172214e-01
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e42275fc-a7f7-4ecc-a9f9-a7518c8dd749 | read-bad-a-new-dataset-and-evaluation-scheme | 1705.03311 | null | http://arxiv.org/abs/1705.03311v2 | http://arxiv.org/pdf/1705.03311v2.pdf | READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents | Text line detection is crucial for any application associated with Automatic
Text Recognition or Keyword Spotting. Modern algorithms perform good on
well-established datasets since they either comprise clean data or
simple/homogeneous page layouts. We have collected and annotated 2036 archival
document images from different locations and time periods. The dataset contains
varying page layouts and degradations that challenge text line segmentation
methods. Well established text line segmentation evaluation schemes such as the
Detection Rate or Recognition Accuracy demand for binarized data that is
annotated on a pixel level. Producing ground truth by these means is laborious
and not needed to determine a method's quality. In this paper we propose a new
evaluation scheme that is based on baselines. The proposed scheme has no need
for binarization and it can handle skewed as well as rotated text lines. The
ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on
Layout Analysis for Challenging Medieval Manuscripts used this evaluation
scheme. Finally, we present results achieved by a recently published text line
detection algorithm. | ['Markus Diem', 'Tobias Grüning', 'Florian Kleber', 'Roger Labahn', 'Stefan Fiel'] | 2017-05-09 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.00308669e-01 -4.08303469e-01 7.94136897e-03 -2.96475232e-01
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47cb0850-486f-48e9-b583-fab955335248 | advances-in-apparent-conceptual-physics | 2303.17012 | null | https://arxiv.org/abs/2303.17012v3 | https://arxiv.org/pdf/2303.17012v3.pdf | Advances in apparent conceptual physics reasoning in GPT-4 | ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation. Despite lacking any explicit programming regarding the laws of physics, recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something close to a minimal understanding of Newtonian Mechanics on the Force Concept Inventory. This work replicates those results and also demonstrates that the latest version, GPT-4, has reached a much higher mark in the latter context. Indeed, its responses come quite close to perfectly demonstrating expert-level competence, with a few very notable exceptions and limitations. We briefly comment on the implications of this for the future of physics education and pedagogy. | ['Colin G. West'] | 2023-03-29 | null | null | null | null | ['conceptual-physics'] | ['miscellaneous'] | [-2.14970440e-01 6.64147079e-01 -5.23892371e-03 -3.10584128e-01
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87fbf2a2-c416-4bbd-8a78-81db8f1c5e10 | jampatoisnli-a-jamaican-patois-natural | 2212.03419 | null | https://arxiv.org/abs/2212.03419v1 | https://arxiv.org/pdf/2212.03419v1.pdf | JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset | JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP. | ['Christopher Manning', 'John Hewitt', 'Ruth-Ann Armstrong'] | 2022-12-07 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-3.01099747e-01 7.51464516e-02 -4.99244452e-01 -4.95838523e-01
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26ef6c2f-be11-4a7a-af01-f901e2cf917e | morpho-syntactic-lexicon-generation-using | 1512.05030 | null | http://arxiv.org/abs/1512.05030v3 | http://arxiv.org/pdf/1512.05030v3.pdf | Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning | Morpho-syntactic lexicons provide information about the morphological and
syntactic roles of words in a language. Such lexicons are not available for all
languages and even when available, their coverage can be limited. We present a
graph-based semi-supervised learning method that uses the morphological,
syntactic and semantic relations between words to automatically construct wide
coverage lexicons from small seed sets. Our method is language-independent, and
we show that we can expand a 1000 word seed lexicon to more than 100 times its
size with high quality for 11 languages. In addition, the automatically created
lexicons provide features that improve performance in two downstream tasks:
morphological tagging and dependency parsing. | ['Ryan Mcdonald', 'Manaal Faruqui', 'Radu Soricut'] | 2015-12-16 | morpho-syntactic-lexicon-generation-using-1 | https://aclanthology.org/Q16-1001 | https://aclanthology.org/Q16-1001.pdf | tacl-2016-1 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.09853916e-01 3.55390340e-01 -4.30031180e-01 -5.16094804e-01
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269ff6bd-e684-4035-9178-827bb08d0cd1 | high-order-correlation-preserved-incomplete | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9718038 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9718038 | High-order Correlation Preserved Incomplete Multi-view Subspace Clustering | Incomplete multi-view clustering aims to exploit theinformation of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and that of views. To address this issue, we propose a high-order correlation preserved incomplete multi-view subspace clustering (HCP-IMSC) method which effectively recovers the missing views of samples and the subspace structure of incomplete multiview data. Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation. Then, a unified affinity matrix can be obtained by fusing the view-specific affinity matrices in a self-weighted manner. A hypergraph is further constructed from the unified affinity matrix to preserve the high-order geometrical structure of the data with incomplete views. Then, the samples with missing views are restricted to be reconstructed by their neighbor samples under the hypergraph-induced hyper-Laplacian regularization. Furthermore, the learning of view-specific affinity matrices as well as the unified one, tensor factorization, and hyper-Laplacian regularization are integrated into a unified optimization framework. An iterative algorithm is designed to solve the resultant model. Experimental results on various benchmark datasets indicate the superiority of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library: https://github.com/ChangTang/HCP-IMSC | ['and En Zhu', 'Wei zhang', 'Senior Member', 'Xinwang Liu', 'Xiao Zheng', 'IEEE', 'Member', 'Chang Tang', 'Zhenglai Li'] | 2022-02-21 | null | null | null | ieee-transactions-on-image-processing-2022-2 | ['incomplete-multi-view-clustering', 'multi-view-subspace-clustering'] | ['computer-vision', 'computer-vision'] | [-4.72796738e-01 -3.89658600e-01 -2.27624699e-01 -2.36123487e-01
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20cd8ea1-191a-4b2d-9f4f-9f26f0b54463 | language-acquisition-through-intention | null | null | https://aclanthology.org/2022.coling-1.2 | https://aclanthology.org/2022.coling-1.2.pdf | Language Acquisition through Intention Reading and Pattern Finding | One of AI’s grand challenges consists in the development of autonomous agents with communication systems offering the robustness, flexibility and adaptivity found in human languages. While the processes through which children acquire language are by now relatively well understood, a faithful computational operationalisation of the underlying mechanisms is still lacking. Two main cognitive processes are involved in child language acquisition. First, children need to reconstruct the intended meaning of observed utterances, a process called intention reading. Then, they can gradually abstract away from concrete utterances in a process called pattern finding and acquire productive schemata that generalise over form and meaning. In this paper, we introduce a mechanistic model of the intention reading process and its integration with pattern finding capacities. Concretely, we present an agent-based simulation in which an agent learns a grammar that enables them to ask and answer questions about a scene. This involves the reconstruction of queries that correspond to observed questions based on the answer and scene alone, and the generalization of linguistic schemata based on these reconstructed question-query pairs. The result is a productive grammar which can be used to map between natural language questions and queries without ever having observed the queries. | ['Katrien Beuls', 'Paul Van Eecke', 'Jonas Doumen', 'Jens Nevens'] | null | null | null | null | coling-2022-10 | ['language-acquisition'] | ['natural-language-processing'] | [ 4.12108541e-01 6.31292045e-01 4.20259595e-01 -5.24061382e-01
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dc2932c7-d5ba-498b-9610-e23fa16fec75 | mdace-mimic-documents-annotated-with-code-1 | 2307.03859 | null | https://arxiv.org/abs/2307.03859v1 | https://arxiv.org/pdf/2307.03859v1.pdf | MDACE: MIMIC Documents Annotated with Code Evidence | We introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in recent years, thanks to advances in machine learning technologies. Yet simply predicting a set of final codes for a patient encounter is insufficient as CAC systems are required to provide supporting textual evidence to justify the billing codes. A model able to produce accurate and reliable supporting evidence for each code would be a tremendous benefit. However, a human annotated code evidence corpus is extremely difficult to create because it requires specialized knowledge. In this paper, we introduce MDACE, the first publicly available code evidence dataset, which is built on a subset of the MIMIC-III clinical records. The dataset -- annotated by professional medical coders -- consists of 302 Inpatient charts with 3,934 evidence spans and 52 Profee charts with 5,563 evidence spans. We implemented several evidence extraction methods based on the EffectiveCAN model (Liu et al., 2021) to establish baseline performance on this dataset. MDACE can be used to evaluate code evidence extraction methods for CAC systems, as well as the accuracy and interpretability of deep learning models for multi-label classification. We believe that the release of MDACE will greatly improve the understanding and application of deep learning technologies for medical coding and document classification. | ['Matthew R. Gormley', 'Benjamin Striner', 'Edmond Lu', 'Russell Klopfer', 'April Russell', 'Rana Jafari', 'Hua Cheng'] | 2023-07-07 | mdace-mimic-documents-annotated-with-code | https://aclanthology.org/2023.acl-long.416/ | https://aclanthology.org/2023.acl-long.416.pdf | acl-2023-7 | ['multi-label-classification', 'extreme-multi-label-classification', 'multi-label-classification', 'document-classification'] | ['computer-vision', 'methodology', 'methodology', 'natural-language-processing'] | [ 1.81137726e-01 3.36272418e-01 -6.73949301e-01 -5.80367088e-01
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4c17dd92-3da7-4998-947b-be0ca31a7599 | underwater-image-color-correction-by | 2010.10748 | null | https://arxiv.org/abs/2010.10748v1 | https://arxiv.org/pdf/2010.10748v1.pdf | Underwater Image Color Correction by Complementary Adaptation | In this paper, we propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space. It presents a new variational interpretation of the complementary adaptation theory in psychophysics, which establishes the connection between colorimetric notions and color constancy of the human visual system (HVS). Understood as a long-term adaptive process, our method effectively removes the underwater color cast and yields a balanced color distribution. For visualization purposes, we enhance the image contrast by properly rescaling both lightness and chroma without trespassing the CIELAB gamut. The magnitude of the enhancement is hue-selective and image-based, thus our method is robust for different underwater imaging environments. To improve the uniformity of CIELAB, we include an approximate hue-linearization as the pre-processing and an inverse transform of the Helmholtz-Kohlrausch effect as the post-processing. We analyze and validate the proposed model by various numerical experiments. Based on image quality metrics designed for underwater conditions, we compare with some state-of-art approaches to show that the proposed method has consistently superior performances. | ['Yuchen He'] | 2020-10-21 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.20093176e-01 -3.73486847e-01 8.87734830e-01 -3.33844423e-01
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22a7002d-2ee2-4fe0-a272-b7b5f5192d81 | robustness-evaluation-of-deep-unsupervised | 2207.03576 | null | https://arxiv.org/abs/2207.03576v1 | https://arxiv.org/pdf/2207.03576v1.pdf | Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems | Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based intrusion detection systems to build secure computer networks. Increasingly, ML approaches are widely adopted than heuristic approaches for cybersecurity because they learn directly from data. Data is critical for the development of ML systems, and becomes potential targets for attackers. Basically, data poisoning or contamination is one of the most common techniques used to fool ML models through data. This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data. Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation when developing novel models, especially for intrusion detection systems. | ['Froduald Kabanza', 'Pierre-Marting Tardif', 'Marc Frappier', 'Jean-Charles Verdier', 'Arian Soltani', "D'Jeff Kanda Nkashama"] | 2022-06-25 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.44145906e-02 -2.79793531e-01 -2.13310152e-01 2.33674459e-02
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4aa2d588-6e0d-49ba-8d04-847ab7aacbea | tracking-progress-in-multi-agent-path-finding | 2305.08446 | null | https://arxiv.org/abs/2305.08446v1 | https://arxiv.org/pdf/2305.08446v1.pdf | Tracking Progress in Multi-Agent Path Finding | Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications. Many works appear on this topic each year, and a large number of substantial advancements and performance improvements have been reported. Yet measuring overall progress in MAPF is difficult: there are many potential competitors, and the computational burden for comprehensive experimentation is prohibitively large. Moreover, detailed data from past experimentation is usually unavailable. In this work, we introduce a set of methodological and visualisation tools which can help the community establish clear indicators for state-of-the-art MAPF performance and which can facilitate large-scale comparisons between MAPF solvers. Our objectives are to lower the barrier of entry for new researchers and to further promote the study of MAPF, since progress in the area and the main challenges are made much clearer. | ['Peter J. Stuckey', 'Daniel D. Harabor', 'Muhammad Aamir Cheema', 'Zhe Chen', 'Bojie Shen'] | 2023-05-15 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-4.44241650e-02 -1.74397066e-01 -5.27255297e-01 5.45161963e-02
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b43d0539-da47-4005-9cad-cc08b678f9bd | explorekit-automatic-feature-generation-and | null | null | https://ieeexplore.ieee.org/document/7837936 | http://people.eecs.berkeley.edu/~dawnsong/papers/icdm-2016.pdf | ExploreKit: Automatic Feature Generation and Selection | Feature generation is one of the challenging aspects of machine learning. We present ExploreKit, a framework for automated feature generation. ExploreKit generates a large set of candidate features by combining information in the original features, with the aim of maximizing predictive performance according to user-selected criteria. To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features. This approach enables efficient identification of the new features and produces superior results compared to existing feature selection solutions. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 25 datasets and 3 different classification algorithms. We show that ExploreKit can achieve classification-error reduction of 20% overall. | ['Gilad Katz', 'Eui Chul Richard Shin', 'Dawn Song'] | 2016-01-01 | null | null | null | icdm-2016-2016-1 | ['automated-feature-engineering'] | ['methodology'] | [ 2.24515021e-01 -2.20295474e-01 -1.76939368e-01 -4.59150970e-01
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b14d14b6-f9ec-4459-9864-29a0ec364f35 | constrained-nonnegative-matrix-factorization | 2003.01041 | null | https://arxiv.org/abs/2003.01041v5 | https://arxiv.org/pdf/2003.01041v5.pdf | Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence | Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances. The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and be mixed in a macroscopic degree before arriving at the sensor element of the imaging spectrometer as mixed spectra. Over the past few decades, many attempts have focused on imposing auxiliary constraints on the conventional nonnegative matrix factorization (NMF) framework in order to effectively unmix these mixed spectra. As a promising step toward finding an optimum constraint to extract endmembers, this paper presents a novel blind HU algorithm, referred to as Kurtosis-based Smooth Nonnegative Matrix Factorization (KbSNMF) which incorporates a novel constraint based on the statistical independence of the probability density functions of endmember spectra. Imposing this constraint on the conventional NMF framework promotes the extraction of independent endmembers while further enhancing the parts-based representation of data. Experiments conducted on diverse synthetic HSI datasets (with numerous numbers of endmembers, spectral bands, pixels, and noise levels) and three standard real HSI datasets demonstrate the validity of the proposed KbSNMF algorithm compared to several state-of-the-art NMF-based HU baselines. The proposed algorithm exhibits superior performance especially in terms of extracting endmember spectra from hyperspectral data; therefore, it could uplift the performance of recent deep learning HU methods which utilize the endmember spectra as supervisory input data for abundance extraction. | ['H. M. V. R. Herath', 'G. M. R. I. Godaliyadda', 'B. Rathnayake', 'H. M. H. K. Weerasooriya', 'M. P. B. Ekanayake', 'S. Herath', 'D. Y. L. Ranasinghe', 'E. M. M. B. Ekanayake'] | 2020-03-02 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.59438694e-01 -6.58703864e-01 3.23486403e-02 -8.25980399e-03
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8e3669af-6d7c-4c4f-905f-de01400dd01a | using-image-extracted-features-to-determine | 1911.01333 | null | https://arxiv.org/abs/1911.01333v2 | https://arxiv.org/pdf/1911.01333v2.pdf | Using image-extracted features to determine heart rate and blink duration for driver sleepiness detection | Heart rate and blink duration are two vital physiological signals which give information about cardiac activity and consciousness. Monitoring these two signals is crucial for various applications such as driver drowsiness detection. As there are several problems posed by the conventional systems to be used for continuous, long-term monitoring, a remote blink and ECG monitoring system can be used as an alternative. For estimating the blink duration, two strategies are used. In the first approach, pictures of open and closed eyes are fed into an Artificial Neural Network (ANN) to decide whether the eyes are open or close. In the second approach, they are classified and labeled using Linear Discriminant Analysis (LDA). The labeled images are then be used to determine the blink duration. For heart rate variability, two strategies are used to evaluate the passing blood volume: Independent Component Analysis (ICA); and a chrominance based method. Eye recognition yielded 78-92% accuracy in classifying open/closed eyes with ANN and 71-91% accuracy with LDA. Heart rate evaluations had a mean loss of around 16 Beats Per Minute (BPM) for the ICA strategy and 13 BPM for the chrominance based technique. | ['Hamid Soltanian-Zadeh', 'Armin Mohammadie-Zand', 'Erfan Darzi'] | 2019-11-04 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.06832221e-01 -3.62707525e-01 -6.11411147e-02 -3.23122591e-01
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f64e1507-5a34-473a-ad8c-c5a668e90c8c | robust-spatiotemporal-traffic-forecasting | 2306.14126 | null | https://arxiv.org/abs/2306.14126v1 | https://arxiv.org/pdf/2306.14126v1.pdf | Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training | Machine learning-based forecasting models are commonly used in Intelligent Transportation Systems (ITS) to predict traffic patterns and provide city-wide services. However, most of the existing models are susceptible to adversarial attacks, which can lead to inaccurate predictions and negative consequences such as congestion and delays. Therefore, improving the adversarial robustness of these models is crucial for ITS. In this paper, we propose a novel framework for incorporating adversarial training into spatiotemporal traffic forecasting tasks. We demonstrate that traditional adversarial training methods designated for static domains cannot be directly applied to traffic forecasting tasks, as they fail to effectively defend against dynamic adversarial attacks. Then, we propose a reinforcement learning-based method to learn the optimal node selection strategy for adversarial examples, which simultaneously strengthens the dynamic attack defense capability and reduces the model overfitting. Additionally, we introduce a self-knowledge distillation regularization module to overcome the "forgetting issue" caused by continuously changing adversarial nodes during training. We evaluate our approach on two real-world traffic datasets and demonstrate its superiority over other baselines. Our method effectively enhances the adversarial robustness of spatiotemporal traffic forecasting models. The source code for our framework is available at https://github.com/usail-hkust/RDAT. | ['Hao liu', 'Weijia Zhang', 'Fan Liu'] | 2023-06-25 | null | null | null | null | ['adversarial-robustness', 'self-knowledge-distillation'] | ['adversarial', 'computer-vision'] | [-3.16611230e-02 -2.28782505e-01 -2.82988966e-01 -2.31333226e-01
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11950f48-8e6c-4c64-bbb2-8aada2087250 | a-dual-contrastive-framework-for-low-resource | 2204.00796 | null | https://arxiv.org/abs/2204.00796v1 | https://arxiv.org/pdf/2204.00796v1.pdf | A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition | Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can alleviate the data-hungry problem for low-resource languages. However, few researches have focused on the scenario where the source-language labeled data is also limited in some specific domains. A common approach for this scenario is to generate more training data through translation or generation-based data augmentation method. Unfortunately, we find that simply combining source-language data and the corresponding translation cannot fully exploit the translated data and the improvements obtained are somewhat limited. In this paper, we describe our novel dual-contrastive framework ConCNER for cross-lingual NER under the scenario of limited source-language labeled data. Specifically, based on the source-language samples and their translations, we design two contrastive objectives for cross-language NER at different grammatical levels, namely Translation Contrastive Learning (TCL) to close sentence representations between translated sentence pairs and Label Contrastive Learning (LCL) to close token representations within the same labels. Furthermore, we utilize knowledge distillation method where the NER model trained above is used as the teacher to train a student model on unlabeled target-language data to better fit the target language. We conduct extensive experiments on a wide variety of target languages, and the results demonstrate that ConCNER tends to outperform multiple baseline methods. For reproducibility, our code for this paper is available at https://github.com/GKLMIP/ConCNER. | ['Shengyi Jiang', 'Ziyu Yang', 'Nankai Lin', 'Yingwen Fu'] | 2022-04-02 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.89263467e-02 -2.09507033e-01 -3.43837172e-01 -5.40753543e-01
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43000d4e-cecf-4ddb-8ab3-7cb2c1df0837 | hatsuki-an-anime-character-like-robot-figure | 2003.14121 | null | https://arxiv.org/abs/2003.14121v1 | https://arxiv.org/pdf/2003.14121v1.pdf | HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation | Japanese character figurines are popular and have pivot position in Otaku culture. Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine. Therefore, we take the first steps to bridge this gap by developing Hatsuki, which is a humanoid robot platform with anime based design. Hatsuki's novelty lies in aesthetic design, 2D facial expressions, and anime-style behaviors that allows it to deliver rich interaction experiences resembling anime-characters. We explain our design implementation process of Hatsuki, followed by our evaluations. In order to explore user impressions and opinions towards Hatsuki, we conducted a questionnaire in the world's largest anime-figurine event. The results indicate that participants were generally very satisfied with Hatsuki's design, and proposed various use case scenarios and deployment contexts for Hatsuki. The second evaluation focused on imitation learning, as such method can provide better interaction ability in the real world and generate rich, context-adaptive behavior in different situations. We made Hatsuki learn 11 actions, combining voice, facial expressions and motions, through neuron network based policy model with our proposed interface. Results show our approach was successfully able to generate the actions through self-organized contexts, which shows the potential for generalizing our approach in further actions under different contexts. Lastly, we present our future research direction for Hatsuki, and provide our conclusion. | ['Tetsuya OGATA', 'Kuo-Hao Shu', 'Chang-Chieh Chiu', 'Mohammed Al-Sada', 'Pin-Chu Yang', 'Tito Pradhono Tomo', 'Kanata Suzuki', 'Nelson Yalta', 'Kevin Kuo'] | 2020-03-31 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [-4.74935919e-01 3.91331166e-01 3.36799681e-01 8.09035525e-02
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74646dcc-af55-44fb-90f3-07e127032e95 | songs-across-borders-singable-and | 2305.16816 | null | https://arxiv.org/abs/2305.16816v1 | https://arxiv.org/pdf/2305.16816v1.pdf | Songs Across Borders: Singable and Controllable Neural Lyric Translation | The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75% relative enhancement on overall quality, compared against naive fine-tuning (Code available at https://github.com/Sonata165/ControllableLyricTranslation). | ['Ye Wang', 'Min-Yen Kan', 'Xichu Ma', 'Longshen Ou'] | 2023-05-26 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.63823444e-01 2.53281165e-02 -4.68469441e-01 -1.63997084e-01
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bc63f955-606f-407f-97fe-d56bbe800516 | openel-an-annotated-corpus-for-entity-linking | null | null | https://aclanthology.org/2022.lrec-1.241 | https://aclanthology.org/2022.lrec-1.241.pdf | OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue | Entity linking in dialogue is the task of mapping entity mentions in utterances to a target knowledge base. Prior work on entity linking has mainly focused on well-written articles such as Wikipedia, annotated newswire, or domain-specific datasets. We extend the study of entity linking to open domain dialogue by presenting the OpenEL corpus: an annotated multi-domain corpus for linking entities in natural conversation to Wikidata. Each dialogic utterance in 179 dialogues over 12 topics from the EDINA dataset has been annotated for entities realized by definite referring expressions as well as anaphoric forms such as he, she, it and they. This dataset supports training and evaluation of entity linking in open-domain dialogue, as well as analysis of the effect of using dialogue context and anaphora resolution in model training. It could also be used for fine-tuning a coreference resolution algorithm. To the best of our knowledge, this is the first substantial entity linking corpus publicly available for open-domain dialogue. We also establish baselines for this task using several existing entity linking systems. We found that the Transformer-based system Flair + BLINK has the best performance with a 0.65 F1 score. Our results show that dialogue context is extremely beneficial for entity linking in conversations, with Flair + Blink achieving an F1 of 0.61 without discourse context. These results also demonstrate the remaining performance gap between the baselines and human performance, highlighting the challenges of entity linking in open-domain dialogue, and suggesting many avenues for future research using OpenEL. | ['Beth Ann Hockey', 'Marilyn Walker', 'Leanne Rolston', 'Wen Cui'] | null | null | null | null | lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [-2.32843623e-01 1.00651681e+00 -3.99525523e-01 -3.56053114e-01
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9924bfc7-d540-499f-ada9-5d435c0f937b | you-only-hear-once-a-yolo-like-algorithm-for | 2109.00962 | null | https://arxiv.org/abs/2109.00962v3 | https://arxiv.org/pdf/2109.00962v3.pdf | You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection | Audio segmentation and sound event detection are crucial topics in machine listening that aim to detect acoustic classes and their respective boundaries. It is useful for audio-content analysis, speech recognition, audio-indexing, and music information retrieval. In recent years, most research articles adopt segmentation-by-classification. This technique divides audio into small frames and individually performs classification on these frames. In this paper, we present a novel approach called You Only Hear Once (YOHO), which is inspired by the YOLO algorithm popularly adopted in Computer Vision. We convert the detection of acoustic boundaries into a regression problem instead of frame-based classification. This is done by having separate output neurons to detect the presence of an audio class and predict its start and end points. The relative improvement for F-measure of YOHO, compared to the state-of-the-art Convolutional Recurrent Neural Network, ranged from 1% to 6% across multiple datasets for audio segmentation and sound event detection. As the output of YOHO is more end-to-end and has fewer neurons to predict, the speed of inference is at least 6 times faster than segmentation-by-classification. In addition, as this approach predicts acoustic boundaries directly, the post-processing and smoothing is about 7 times faster. | ['Eduardo Reck Miranda', 'David Moffat', 'Satvik Venkatesh'] | 2021-09-01 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 3.17954570e-01 -2.04475984e-01 1.08853929e-01 -1.40185073e-01
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c8370b90-57a4-409e-b6a3-1933ab655f0b | thermal-infrared-image-inpainting-via-edge | 2210.16000 | null | https://arxiv.org/abs/2210.16000v1 | https://arxiv.org/pdf/2210.16000v1.pdf | Thermal Infrared Image Inpainting via Edge-Aware Guidance | Image inpainting has achieved fundamental advances with deep learning. However, almost all existing inpainting methods aim to process natural images, while few target Thermal Infrared (TIR) images, which have widespread applications. When applied to TIR images, conventional inpainting methods usually generate distorted or blurry content. In this paper, we propose a novel task -- Thermal Infrared Image Inpainting, which aims to reconstruct missing regions of TIR images. Crucially, we propose a novel deep-learning-based model TIR-Fill. We adopt the edge generator to complete the canny edges of broken TIR images. The completed edges are projected to the normalization weights and biases to enhance edge awareness of the model. In addition, a refinement network based on gated convolution is employed to improve TIR image consistency. The experiments demonstrate that our method outperforms state-of-the-art image inpainting approaches on FLIR thermal dataset. | ['Kejie Huang', 'Quan Sun', 'Changyou Men', 'Haibin Shen', 'Zeyu Wang'] | 2022-10-28 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 7.96866357e-01 -2.51415908e-01 9.36205089e-02 -2.42397636e-01
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8282dab6-70a4-43f3-81da-61e59ff9765a | uncertainty-guided-source-free-domain | 2208.07591 | null | https://arxiv.org/abs/2208.07591v1 | https://arxiv.org/pdf/2208.07591v1.pdf | Uncertainty-guided Source-free Domain Adaptation | Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliable. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation. For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions. Uncertainties are estimated by employing a Laplace approximation and incorporated to identify target data points that do not lie in the source manifold and to down-weight them when maximizing the mutual information on the target data. Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source training or changes of the model architecture. We show the advantages of uncertainty-guided SFDA over traditional SFDA in the closed-set and open-set settings and provide empirical evidence that our approach is more robust to strong domain shifts even without tuning. | ['Arno Solin', 'Elisa Ricci', 'Nicu Sebe', 'Juho Kannala', 'Andrea Pilzer', 'Martin Trapp', 'Subhankar Roy'] | 2022-08-16 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.34021503e-01 5.30534267e-01 -3.96249354e-01 -6.02054119e-01
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5d8f516f-3e1d-4b2e-876f-f93a0e28df74 | on-the-tour-towards-dpllmapf-and-beyond | 1907.07631 | null | https://arxiv.org/abs/1907.07631v1 | https://arxiv.org/pdf/1907.07631v1.pdf | On the Tour Towards DPLL(MAPF) and Beyond | We discuss milestones on the tour towards DPLL(MAPF), a multi-agent path finding (MAPF) solver fully integrated with the Davis-Putnam-Logemann-Loveland (DPLL) propositional satisfiability testing algorithm through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph in a non-colliding way so that each agent eventually reaches its unique goal vertex. At most one agent can reside in a vertex at a time. Agents can move instantaneously by traversing edges provided the movement does not result in a collision. Recently attempts to solve MAPF optimally w.r.t. the sum-of-costs or the makespan based on the reduction of MAPF to propositional satisfiability (SAT) have appeared. The most successful methods rely on building the propositional encoding for the given MAPF instance lazily by a process inspired in the SMT paradigm. The integration of satisfiability testing by the SAT solver and the high-level construction of the encoding is however relatively loose in existing methods. Therefore the ultimate goal of research in this direction is to build the DPLL(MAPF) algorithm, a MAPF solver where the construction of the encoding is fully integrated with the underlying SAT solver. We discuss the current state-of-the-art in MAPF solving and what steps need to be done to get DPLL(MAPF). The advantages of DPLL(MAPF) in terms of its potential to be alternatively parametrized with MAPF$^R$, a theory of continuous MAPF with geometric agents, are also discussed. | ['Pavel Surynek'] | 2019-07-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.90795749e-01 7.52866805e-01 7.82334432e-02 -1.05753914e-01
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f290727d-3642-44d0-b5f1-62af48c62da4 | deep-reinforcement-learning-based-mapless | 2304.03593 | null | https://arxiv.org/abs/2304.03593v1 | https://arxiv.org/pdf/2304.03593v1.pdf | Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots | Classical map-based navigation methods are commonly used for robot navigation, but they often struggle in crowded environments due to the Frozen Robot Problem (FRP). Deep reinforcement learning-based methods address the FRP problem, however, suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method that uses Collision Probability (CP) to help the robot navigate safely through crowds. The inclusion of CP in the observation space gives the robot a sense of the level of danger of the moving crowd. The robot will navigate through the crowd when it appears safe but will take a detour when the crowd is moving aggressively. By focusing on the most dangerous obstacle, the robot will not be confused when the crowd density is high, ensuring scalability of the model. Our approach was developed using deep reinforcement learning (DRL) and trained using the Gazebo simulator in a non cooperative crowd environment with obstacles moving at randomized speeds and directions. We then evaluated our model on four different crowd-behavior scenarios with varying densities of crowds. The results shown that our method achieved a 100% success rate in all test settings. We compared our approach with a current state-of-the-art DRLbased approach, and our approach has performed significantly better. Importantly, our method is highly generalizable and requires no fine-tuning after being trained once. We further demonstrated the crowd navigation capability of our model in real-world tests. | ['Owais Ahmed Malik', 'Ong Wee Hong', 'Hafiq Anas'] | 2023-04-07 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-5.95881045e-01 -4.17386880e-03 5.72116613e-01 1.40174210e-01
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b0e6634a-f8a9-4de5-b922-afd46db3ce3d | learning-not-to-reconstruct-anomalies | 2110.09742 | null | https://arxiv.org/abs/2110.09742v2 | https://arxiv.org/pdf/2110.09742v2.pdf | Learning Not to Reconstruct Anomalies | Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencoder (AE) is trained to reconstruct the input with training set consisting only of normal data. At test time, the AE is then expected to well reconstruct the normal data while poorly reconstructing the anomalous data. However, several studies have shown that, even with only normal data training, AEs can often start reconstructing anomalies as well which depletes the anomaly detection performance. To mitigate this problem, we propose a novel methodology to train AEs with the objective of reconstructing only normal data, regardless of the input (i.e., normal or abnormal). Since no real anomalies are available in the OCC settings, the training is assisted by pseudo anomalies that are generated by manipulating normal data to simulate the out-of-normal-data distribution. We additionally propose two ways to generate pseudo anomalies: patch and skip frame based. Extensive experiments on three challenging video anomaly datasets demonstrate the effectiveness of our method in improving conventional AEs, achieving state-of-the-art performance. | ['Seung-Ik Lee', 'Jae-Yeong Lee', 'Muhammad Zaigham Zaheer', 'Marcella Astrid'] | 2021-10-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 4.23841715e-01 -1.70287266e-01 2.62878358e-01 -8.81857574e-02
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fe25ff58-f2fc-4bc8-a71e-1a1b8316c73c | a-multi-task-learning-network-using-shared | null | null | https://proceedings-of-deim.github.io/DEIM2021/papers/D13-2.pdf | https://proceedings-of-deim.github.io/DEIM2021/papers/D13-2.pdf | A multi-task learning network using shared BERT models for aspect-based sentiment analysis | Abstract Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of specific aspect words occurring
in a text. ABSA includes aspect-category sentiment analysis (ACSA) and aspect-target sentiment analysis (ATSA). There have
been many previous studies addressing both tasks through RNNs and other neural models. With BERT's remarkable
performance on NLP tasks, several studies have enhanced aspect word extraction to solve ATSA by building new BERT-based
models. But such an approach is not directly applicable to the ACSA task, because aspect words in ACSA are often not
explicitly present in the text, so aspect word extraction becomes more difficult. In this paper, we propose a multi-task learning
(MTL) approach to solve these problems. Our approach is based on a shared BERT model to construct a multi-task learning
network, which is trained by strongly and weakly related tasks. We also use a multi-head self-attention layer to replace a linear
layer in traditional multi-task learning networks, to enhance the ability to capture global semantics. We also propose a new
fine-tuning strategy that can better improve the performance of the model. Experiments were conducted on four datasets from
the ATSA task and four datasets from the ACSA task: laptop, restaurant, restaurant-2014, and restaurant-large from
SemEval-2014. Our experimental results show that: In the ACSA task, our model outperformed all the baseline models,
achieving the current state-of-the-art performance on the multiple datasets. For the ATSA task, our model performs close to the
state-of-the-art, with much simpler architecture. | ['Mizuho Iwaihara', 'Quanzhen Liu'] | 2020-12-27 | null | null | null | deim-forum-2020-12 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-8.35995972e-02 -1.45628005e-01 -3.14425319e-01 -7.26397812e-01
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0d76e18a-a636-46e7-a3b6-e76e75497e08 | transfer-from-multiple-mdps | null | null | http://papers.nips.cc/paper/4435-transfer-from-multiple-mdps | http://papers.nips.cc/paper/4435-transfer-from-multiple-mdps.pdf | Transfer from Multiple MDPs | Transfer reinforcement learning (RL) methods leverage on the experience collected on a set of source tasks to speed-up RL algorithms. A simple and effective approach is to transfer samples from source tasks and include them in the training set used to solve a target task. In this paper, we investigate the theoretical properties of this transfer method and we introduce novel algorithms adapting the transfer process on the basis of the similarity between source and target tasks. Finally, we report illustrative experimental results in a continuous chain problem. | ['Marcello Restelli', 'Alessandro Lazaric'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['transfer-reinforcement-learning'] | ['methodology'] | [ 4.08029705e-01 -1.67798880e-03 -2.67745584e-01 -1.60458520e-01
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e73d16ed-21a2-4754-bf22-329bfc316436 | subject-based-non-contrastive-self-supervised | 2305.10347 | null | https://arxiv.org/abs/2305.10347v1 | https://arxiv.org/pdf/2305.10347v1.pdf | Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing | Extracting information from the electrocardiography (ECG) signal is an essential step in the design of digital health technologies in cardiology. In recent years, several machine learning (ML) algorithms for automatic extraction of information in ECG have been proposed. Supervised learning methods have successfully been used to identify specific aspects in the signal, like detection of rhythm disorders (arrhythmias). Self-supervised learning (SSL) methods, on the other hand, can be used to extract all the features contained in the data. The model is optimized without any specific goal and learns from the data itself. By adapting state-of-the-art computer vision methodologies to the signal processing domain, a few SSL approaches have been reported recently for ECG processing. However, such SSL methods require either data augmentation or negative pairs, which limits the method to only look for similarities between two ECG inputs, either two versions of the same signal or two signals from the same subject. This leads to models that are very effective at extracting characteristics that are stable in a subject, such as gender or age. But they are not successful at capturing changes within the ECG recording that can explain dynamic aspects, like different arrhythmias or different sleep stages. In this work, we introduce the first SSL method that uses neither data augmentation nor negative pairs for understanding ECG signals, and still, achieves comparable quality representations. As a result, it is possible to design a SSL method that not only captures similarities between two inputs, but also captures dissimilarities for a complete understanding of the data. In addition, a model based on transformer blocks is presented, which produces better results than a model based on convolutional layers (XResNet50) with almost the same number of parameters. | ['Sadasivan Puthusserypady', 'Jakob Bardram', 'Adrian Atienza'] | 2023-05-12 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 2.79741883e-01 1.76493838e-01 5.24480548e-03 -5.13540924e-01
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97e22201-f252-46a8-8e60-fa918254c184 | wearing-the-same-outfit-in-different-ways-a | 2211.16989 | null | https://arxiv.org/abs/2211.16989v1 | https://arxiv.org/pdf/2211.16989v1.pdf | Wearing the Same Outfit in Different Ways -- A Controllable Virtual Try-on Method | An outfit visualization method generates an image of a person wearing real garments from images of those garments. Current methods can produce images that look realistic and preserve garment identity, captured in details such as collar, cuffs, texture, hem, and sleeve length. However, no current method can both control how the garment is worn -- including tuck or untuck, opened or closed, high or low on the waist, etc.. -- and generate realistic images that accurately preserve the properties of the original garment. We describe an outfit visualization method that controls drape while preserving garment identity. Our system allows instance independent editing of garment drape, which means a user can construct an edit (e.g. tucking a shirt in a specific way) that can be applied to all shirts in a garment collection. Garment detail is preserved by relying on a warping procedure to place the garment on the body and a generator then supplies fine shading detail. To achieve instance independent control, we use control points with garment category-level semantics to guide the warp. The method produces state-of-the-art quality images, while allowing creative ways to style garments, including allowing tops to be tucked or untucked; jackets to be worn open or closed; skirts to be worn higher or lower on the waist; and so on. The method allows interactive control to correct errors in individual renderings too. Because the edits are instance independent, they can be applied to large pools of garments automatically and can be conditioned on garment metadata (e.g. all cropped jackets are worn closed or all bomber jackets are worn closed). | ['David Forsyth', 'Shao-Yu Chang', 'Jeffrey Zhang', 'Kedan Li'] | 2022-11-29 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 3.29671741e-01 -3.42568867e-02 1.65134236e-01 -1.93821304e-02
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cf7fa349-9122-4223-8698-5ee83de66819 | spacing-loss-for-discovering-novel-categories | 2204.10595 | null | https://arxiv.org/abs/2204.10595v1 | https://arxiv.org/pdf/2204.10595v1.pdf | Spacing Loss for Discovering Novel Categories | Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets. | ['Vineeth N Balasubramanian', 'Kai Han', 'Piyush Rai', 'Soma Biswas', 'Gaurav Aggarwal', 'Sujoy Paul', 'K J Joseph'] | 2022-04-22 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.75986046e-01 2.13614781e-03 -5.65568447e-01 -8.06181371e-01
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6279ab56-c8a8-4039-b16d-e62ba4af3977 | controlling-synthetic-characters-in | 2101.02231 | null | https://arxiv.org/abs/2101.02231v1 | https://arxiv.org/pdf/2101.02231v1.pdf | Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma | Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior in both natural and artificial systems, provide a conceptually valid common basis, as evidenced by the current efforts towards a standard model of the mind, to generate human-like intelligent behavior for these synthetic characters. Sigma is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis. Sigma leverages an extended form of factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory-of-Mind and that are perceptual, autonomous, interactive, affective, and adaptive. In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities: (1) Distributional reinforcement learning models in; (2) A pair of adaptive and interactive agent models that demonstrate rule-based, probabilistic, and social reasoning; and (3) A knowledge-free exploration model in which an agent leverages only architectural appraisal variables, namely attention and curiosity, to locate an item while building up a map in a Unity environment. | ['Jeremy Nuttal', 'Seyed Sajjadi', 'Paul S. Rosenbloom', 'Volkan Ustun'] | 2021-01-06 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.17893383e-01 6.33394301e-01 5.34894109e-01 -1.68770514e-02
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ac69d44f-c9a3-46d8-b3a2-22ff8f8f6155 | history-repeats-overcoming-catastrophic | 2305.18675 | null | https://arxiv.org/abs/2305.18675v1 | https://arxiv.org/pdf/2305.18675v1.pdf | History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion | Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received incrementally as events unfold, leading to a dynamic non-stationary data distribution over time. While one could incorporate fine-tuning to existing methods to allow them to adapt to evolving TKG data, this can lead to forgetting previously learned patterns. Alternatively, retraining the model with the entire updated TKG can mitigate forgetting but is computationally burdensome. To address these challenges, we propose a general continual training framework that is applicable to any TKG completion method, and leverages two key ideas: (i) a temporal regularization that encourages repurposing of less important model parameters for learning new knowledge, and (ii) a clustering-based experience replay that reinforces the past knowledge by selectively preserving only a small portion of the past data. Our experimental results on widely used event-centric TKG datasets demonstrate the effectiveness of our proposed continual training framework in adapting to new events while reducing catastrophic forgetting. Further, we perform ablation studies to show the effectiveness of each component of our proposed framework. Finally, we investigate the relation between the memory dedicated to experience replay and the benefit gained from our clustering-based sampling strategy. | ['Aram Galstyan', 'Mohammad Rostami', 'Mehrnoosh Mirtaheri'] | 2023-05-30 | null | null | null | null | ['knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['knowledge-base', 'knowledge-base'] | [-1.16518920e-03 6.15998171e-02 -2.16058969e-01 -3.17203030e-02
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6683be9e-1f2a-4841-8c8b-f7e3900b6425 | imgcl-revisiting-graph-contrastive-learning | 2205.11332 | null | https://arxiv.org/abs/2205.11332v2 | https://arxiv.org/pdf/2205.11332v2.pdf | ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification | Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, the underlying class distribution of unlabeled nodes for the given graph is usually imbalanced. This highly imbalanced class distribution inevitably deteriorates the quality of learned node representations in GCL. Indeed, we empirically find that most state-of-the-art GCL methods cannot obtain discriminative representations and exhibit poor performance on imbalanced node classification. Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels. Specifically, we first introduce the online clustering based progressively balanced sampling (PBS) method with theoretical rationale, which balances the training sets based on pseudo-labels obtained from learned representations in GCL. We then develop the node centrality based PBS method to better preserve the intrinsic structure of graphs, by upweighting the important nodes of the given graph. Extensive experiments on multiple imbalanced graph datasets and imbalanced settings demonstrate the effectiveness of our proposed framework, which significantly improves the performance of the recent state-of-the-art GCL methods. Further experimental ablations and analyses show that the ImGCL framework consistently improves the representation quality of nodes in under-represented (tail) classes. | ['Jian Li', 'Peilin Zhao', 'Ziqi Gao', 'Lanqing Li', 'Liang Zeng'] | 2022-05-23 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 1.63488865e-01 3.80176932e-01 -7.24758625e-01 -2.40351394e-01
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95fc80d9-6356-47a0-b8df-b27e3ae4fa64 | a-novel-online-action-detection-framework | 2003.07734 | null | https://arxiv.org/abs/2003.07734v1 | https://arxiv.org/pdf/2003.07734v1.pdf | A Novel Online Action Detection Framework from Untrimmed Video Streams | Online temporal action localization from an untrimmed video stream is a challenging problem in computer vision. It is challenging because of i) in an untrimmed video stream, more than one action instance may appear, including background scenes, and ii) in online settings, only past and current information is available. Therefore, temporal priors, such as the average action duration of training data, which have been exploited by previous action detection methods, are not suitable for this task because of the high intra-class variation in human actions. We propose a novel online action detection framework that considers actions as a set of temporally ordered subclasses and leverages a future frame generation network to cope with the limited information issue associated with the problem outlined above. Additionally, we augment our data by varying the lengths of videos to allow the proposed method to learn about the high intra-class variation in human actions. We evaluate our method using two benchmark datasets, THUMOS'14 and ActivityNet, for an online temporal action localization scenario and demonstrate that the performance is comparable to state-of-the-art methods that have been proposed for offline settings. | ['Seong-Whan Lee', 'Nam-Gyu Cho', 'Da-Hye Yoon'] | 2020-03-17 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 5.41651845e-01 -3.08719993e-01 -4.23355818e-01 -6.54581636e-02
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ab7182bd-5eda-4932-a1d9-1beb4237dc5f | container-few-shot-named-entity-recognition-1 | null | null | https://openreview.net/forum?id=nxEqWd4Ddth | https://openreview.net/pdf?id=nxEqWd4Ddth | CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning | Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances. To this end, we present CONTaiNER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. Instead of optimizing class-specific attributes, CONTaiNER optimizes a generalized objective of differentiating between token categories based on their Gaussian-distributed embeddings. This effectively alleviates overfitting issues originating from training domains. Our experiments in several traditional test domains (OntoNotes, CoNLL'03, WNUT '17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that on average, CONTaiNER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance. | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['few-shot-ner'] | ['natural-language-processing'] | [-3.10104400e-01 -9.93754566e-02 -7.62102827e-02 -4.62268412e-01
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cead63e9-4ca4-4045-998f-1881c1333017 | global-structure-aware-drum-transcription | 2105.05791 | null | https://arxiv.org/abs/2105.05791v1 | https://arxiv.org/pdf/2105.05791v1.pdf | Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms | This paper describes an automatic drum transcription (ADT) method that directly estimates a tatum-level drum score from a music signal, in contrast to most conventional ADT methods that estimate the frame-level onset probabilities of drums. To estimate a tatum-level score, we propose a deep transcription model that consists of a frame-level encoder for extracting the latent features from a music signal and a tatum-level decoder for estimating a drum score from the latent features pooled at the tatum level. To capture the global repetitive structure of drum scores, which is difficult to learn with a recurrent neural network (RNN), we introduce a self-attention mechanism with tatum-synchronous positional encoding into the decoder. To mitigate the difficulty of training the self-attention-based model from an insufficient amount of paired data and improve the musical naturalness of the estimated scores, we propose a regularized training method that uses a global structure-aware masked language (score) model with a self-attention mechanism pretrained from an extensive collection of drum scores. Experimental results showed that the proposed regularized model outperformed the conventional RNN-based model in terms of the tatum-level error rate and the frame-level F-measure, even when only a limited amount of paired data was available so that the non-regularized model underperformed the RNN-based model. | ['Kazuyoshi Yoshii', 'Ryo Nishikimi', 'Ryoto Ishizuka'] | 2021-05-12 | null | null | null | null | ['drum-transcription'] | ['music'] | [ 3.68582398e-01 -1.81272641e-01 2.15758085e-01 1.06324948e-01
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f733a69c-9074-490e-bef9-b03ee446c788 | learning-to-recover-causal-relationship-from | 2305.02640 | null | https://arxiv.org/abs/2305.02640v2 | https://arxiv.org/pdf/2305.02640v2.pdf | Learning to Recover Causal Relationship from Indefinite Data in the Presence of Latent Confounders | In Causal Discovery with latent variables, We define two data paradigms: definite data: a single-skeleton structure with observed nodes single-value, and indefinite data: a set of multi-skeleton structures with observed nodes multi-value. Multi,skeletons induce low sample utilization and multi values induce incapability of the distribution assumption, both leading that recovering causal relations from indefinite data is, as of yet, largely unexplored. We design the causal strength variational model to settle down these two problems. Specifically, we leverage the causal strength instead of independent noise as latent variable to mediate evidence lower bound. By this design ethos, The causal strength of different skeletons is regarded as a distribution and can be expressed as a single-valued causal graph matrix. Moreover, considering the latent confounders, we disentangle the causal graph G into two relatisubgraphs O and C. O contains pure relations between observed nodes, while C represents the relations from latent variables to observed nodes. We summarize the above designs as Confounding Disentanglement Causal Discovery (biCD), which is tailored to learn causal representation from indefinite data under the latent confounding. Finally, we conduct comprehensive experiments on synthetic and real-world data to demonstrate the effectiveness of our method. | ['Qing Yang', 'Xinyu Yang', 'Hang Chen'] | 2023-05-04 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.07122290e-01 4.78139609e-01 -7.38996267e-01 -3.71123761e-01
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f331ab1b-3d6c-4d05-92f1-23152ccc8520 | dkt-diverse-knowledge-transfer-transformer | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_DKT_Diverse_Knowledge_Transfer_Transformer_for_Class_Incremental_Learning_CVPR_2023_paper.pdf | DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning | Deep neural networks suffer from catastrophic forgetting in class incremental learning, where the classification accuracy of old classes drastically deteriorates when the networks learn the knowledge of new classes. Many works have been proposed to solve the class incremental learning problem. However, most of them either suffer from serious catastrophic forgetting and stability-plasticity dilemma or need too many extra parameters and computations. To meet the challenge, we propose a novel framework, Diverse Knowledge Transfer Transformer (DKT). which contains two novel knowledge transfers based on the attention mechanism to transfer the task-general knowledge and task-specific knowledge to the current task to alleviate catastrophic forgetting. Besides, we propose a duplex classifier to address the stability-plasticity dilemma, and a novel loss function to cluster the same categories in feature space and discriminate the features between old and new tasks to force the task specific knowledge to be more diverse. Our method needs only a few extra parameters, which are negligible, to tackle the increasing number of tasks. We conduct comprehensive experimental results on CIFAR100, ImageNet100/1000 datasets. The experiment results show that our method outperforms other competitive methods and achieves state-of-the-art performance. | ['Yihong Gong', 'Xing Wei', 'Jie Cheng', 'Songlin Dong', 'Yuhang He', 'Xinyuan Gao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'incremental-learning', 'general-knowledge'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 1.92684561e-01 -3.33995610e-01 1.92408010e-01 -3.20954859e-01
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5108b586-f1f8-4f91-b809-d97a9ca68a4c | gaitgci-generative-counterfactual-1 | 2306.03428 | null | https://arxiv.org/abs/2306.03428v1 | https://arxiv.org/pdf/2306.03428v1.pdf | GaitGCI: Generative Counterfactual Intervention for Gait Recognition | Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern; 2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost; 3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild). | ['Xi Li', 'Yining Lin', 'Yunlong Yu', 'Wei Su', 'Pengyi Zhang', 'Huanzhang Dou'] | 2023-06-06 | gaitgci-generative-counterfactual | http://openaccess.thecvf.com//content/CVPR2023/html/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dou_GaitGCI_Generative_Counterfactual_Intervention_for_Gait_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gait-recognition'] | ['computer-vision'] | [ 4.22015749e-02 -3.06528490e-02 -3.38061750e-01 -2.84010708e-01
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67ede698-154b-404f-a5c8-f2c7f74c93ec | a-modular-vision-language-navigation-and | 2101.07891 | null | https://arxiv.org/abs/2101.07891v1 | https://arxiv.org/pdf/2101.07891v1.pdf | A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment | In this paper we propose a new framework - MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household tasks. While several data-driven, end-to-end learning frameworks have been proposed for targeted navigation tasks based on the vision and language modalities, performance on recent benchmark data sets revealed the gap in developing comprehensive techniques for long horizon, compositional tasks (involving manipulation and navigation) with diverse object categories, realistic instructions and visual scenarios with non-reversible state changes. We propose a modular approach to deal with the combined navigation and object interaction problem without the need for strictly aligned vision and language training data (e.g., in the form of expert demonstrated trajectories). Such an approach is a significant departure from the traditional end-to-end techniques in this space and allows for a more tractable training process with separate vision and language data sets. Specifically, we propose a novel geometry-aware mapping technique for cluttered indoor environments, and a language understanding model generalized for household instruction following. We demonstrate a significant increase in success rates for long-horizon, compositional tasks over the baseline on the recently released benchmark data set-ALFRED. | ['Soumik Sarkar', 'Qisai Liu', 'Fateme Fotouhif', 'Homagni Saha'] | 2021-01-19 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 1.49366140e-01 -1.48592712e-02 2.59232193e-01 -4.59405094e-01
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2163a059-d1f0-4c6e-9eee-afc10c30655f | a-comprehensive-survey-of-artificial | 2307.03195 | null | https://arxiv.org/abs/2307.03195v1 | https://arxiv.org/pdf/2307.03195v1.pdf | A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics | In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics. | ['Hui Xiong', 'HengShu Zhu', 'Chen Zhu', 'Ying Sun', 'Qi Zhang', 'Dazhong Shen', 'Rui Zha', 'Le Zhang', 'Chuan Qin'] | 2023-07-03 | null | null | null | null | ['management', 'decision-making'] | ['miscellaneous', 'reasoning'] | [-1.91292688e-01 -3.43496948e-01 -5.91124237e-01 -2.87165374e-01
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34cc80dc-d558-42f3-b9ce-7289a783388e | diffusion-convolutional-recurrent-neural | 1707.01926 | null | http://arxiv.org/abs/1707.01926v3 | http://arxiv.org/pdf/1707.01926v3.pdf | Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting | Spatiotemporal forecasting has various applications in neuroscience, climate
and transportation domain. Traffic forecasting is one canonical example of such
learning task. The task is challenging due to (1) complex spatial dependency on
road networks, (2) non-linear temporal dynamics with changing road conditions
and (3) inherent difficulty of long-term forecasting. To address these
challenges, we propose to model the traffic flow as a diffusion process on a
directed graph and introduce Diffusion Convolutional Recurrent Neural Network
(DCRNN), a deep learning framework for traffic forecasting that incorporates
both spatial and temporal dependency in the traffic flow. Specifically, DCRNN
captures the spatial dependency using bidirectional random walks on the graph,
and the temporal dependency using the encoder-decoder architecture with
scheduled sampling. We evaluate the framework on two real-world large scale
road network traffic datasets and observe consistent improvement of 12% - 15%
over state-of-the-art baselines. | ['Rose Yu', 'Cyrus Shahabi', 'Yaguang Li', 'Yan Liu'] | 2017-07-06 | diffusion-convolutional-recurrent-neural-1 | https://openreview.net/forum?id=SJiHXGWAZ | https://openreview.net/pdf?id=SJiHXGWAZ | iclr-2018-1 | ['spatio-temporal-forecasting'] | ['time-series'] | [ 2.02358402e-02 -1.61261529e-01 -4.22545314e-01 -5.30832171e-01
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c2157453-703b-4a4b-b212-442df4f90155 | sequential-knockoffs-for-variable-selection | 2303.14281 | null | https://arxiv.org/abs/2303.14281v1 | https://arxiv.org/pdf/2303.14281v1.pdf | Sequential Knockoffs for Variable Selection in Reinforcement Learning | In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state which is larger than necessary, e.g., by concatenating measurements over contiguous time points. However, needlessly increasing the dimension of the state can slow learning and obfuscate the learned policy. We introduce the notion of a minimal sufficient state in a Markov decision process (MDP) as the smallest subvector of the original state under which the process remains an MDP and shares the same optimal policy as the original process. We propose a novel sequential knockoffs (SEEK) algorithm that estimates the minimal sufficient state in a system with high-dimensional complex nonlinear dynamics. In large samples, the proposed method controls the false discovery rate, and selects all sufficient variables with probability approaching one. As the method is agnostic to the reinforcement learning algorithm being applied, it benefits downstream tasks such as policy optimization. Empirical experiments verify theoretical results and show the proposed approach outperforms several competing methods in terms of variable selection accuracy and regret. | ['Eric B. Laber', 'Chengchun Shi', 'Zhengling Qi', 'Hengrui Cai', 'Tao Ma'] | 2023-03-24 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.23482846e-01 2.91983318e-02 -6.54588580e-01 2.42607638e-01
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b0805d65-791d-468b-b6e6-469f2fc9a0c7 | ebola-optimization-search-algorithm-eosa-a | 2106.01416 | null | https://arxiv.org/abs/2106.01416v2 | https://arxiv.org/pdf/2106.01416v2.pdf | Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease | The Ebola virus and the disease in effect tend to randomly move individuals in the population around susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population. Motivated by the effectiveness in propagating the disease through the virus, a new bio-inspired and population-based optimization algorithm is proposed. This paper presents a novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To correctly achieve this, this study models the propagation mechanism of the Ebola virus disease, emphasising all consistent states of the propagation. The model was further represented using a mathematical model based on first-order differential equations. After that, the combined propagation and mathematical models were adapted for developing the new metaheuristic algorithm. To evaluate the proposed method's performance and capability compared with other optimization methods, the underlying propagation and mathematical models were first investigated to determine how they successfully simulate the EVD. Furthermore, two sets of benchmark functions consisting of forty-seven (47) classical and over thirty (30) constrained IEEE CEC-2017 benchmark functions are investigated numerically. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability analysis, convergence analysis, and sensitivity analysis. Extensive simulation results indicate that the EOSA outperforms other state-of-the-art popular metaheuristic optimization algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC) on some shifted, high dimensional and large search range problems. | ['Absalom E. Ezugwu', 'Olaide N. Oyelade'] | 2021-06-02 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.87621653e-01 -6.26964688e-01 1.57974977e-02 4.97570217e-01
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c780d5b8-191a-4fa6-b049-6a485ab4462e | a-splitting-based-iterative-algorithm-for-gpu | 1905.00934 | null | https://arxiv.org/abs/1905.00934v1 | https://arxiv.org/pdf/1905.00934v1.pdf | A Splitting-Based Iterative Algorithm for GPU-Accelerated Statistical Dual-Energy X-Ray CT Reconstruction | When dealing with material classification in baggage at airports, Dual-Energy Computed Tomography (DECT) allows characterization of any given material with coefficients based on two attenuative effects: Compton scattering and photoelectric absorption. However, straightforward projection-domain decomposition methods for this characterization often yield poor reconstructions due to the high dynamic range of material properties encountered in an actual luggage scan. Hence, for better reconstruction quality under a timing constraint, we propose a splitting-based, GPU-accelerated, statistical DECT reconstruction algorithm. Compared to prior art, our main contribution lies in the significant acceleration made possible by separating reconstruction and decomposition within an ADMM framework. Experimental results, on both synthetic and real-world baggage phantoms, demonstrate a significant reduction in time required for convergence. | ['Avinash Kak', 'Tanmay Prakash', 'Ankit Manerikar', 'Fangda Li'] | 2019-05-02 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 2.76034683e-01 -3.29240561e-01 3.68578792e-01 8.10837001e-02
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b69317d7-7d44-429d-a114-da69ef3ed80c | ner-to-mrc-named-entity-recognition | 2305.03970 | null | https://arxiv.org/abs/2305.03970v1 | https://arxiv.org/pdf/2305.03970v1.pdf | NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension | Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpora and incorporating search engines. However, these methods suffer from high costs associated with data collection and pre-training, and additional training process of the retrieved data from search engines. To address the above challenges, we completely frame NER as a machine reading comprehension (MRC) problem, called NER-to-MRC, by leveraging MRC with its ability to exploit existing data efficiently. Several prior works have been dedicated to employing MRC-based solutions for tackling the NER problem, several challenges persist: i) the reliance on manually designed prompts; ii) the limited MRC approaches to data reconstruction, which fails to achieve performance on par with methods utilizing extensive additional data. Thus, our NER-to-MRC conversion consists of two components: i) transform the NER task into a form suitable for the model to solve with MRC in a efficient manner; ii) apply the MRC reasoning strategy to the model. We experiment on 6 benchmark datasets from three domains and achieve state-of-the-art performance without external data, up to 11.24% improvement on the WNUT-16 dataset. | ['Hayato Yamana', 'Tetsuya Sakai', 'Xinyu Zhu', 'Junjie Wang', 'Yuxiang Zhang'] | 2023-05-06 | null | null | null | null | ['named-entity-recognition-ner', 'reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.13852930e-01 3.09991628e-01 1.88837443e-02 -2.85478294e-01
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62932547-1322-4546-ac2b-c6bbf60b9ab6 | ssn-dibertsity-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.12 | https://aclanthology.org/2021.ltedi-1.12.pdf | ssn_diBERTsity@LT-EDI-EACL2021:Hope Speech Detection on multilingual YouTube comments via transformer based approach | In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach. The task was aimed at classifying texts into ‘Hope_speech’, ‘Non_hope_speech’, and ‘Not in language’. The datasets were provided by the LT-EDI organisers in English, Tamil, and Malayalam language with texts sourced from YouTube comments. We trained our data using transformer models, specifically mBERT for Tamil and Malayalam and BERT for English, and achieved weighted average F1-scores of 0.46, 0.81, 0.92 for Tamil, Malayalam, and English respectively. | ['Senthil Kumar B', 'Thenmozhi D.', 'Avantika Balaji', 'Akshay Ramakrishnan', 'Arunima S'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-2.45802224e-01 2.92093962e-01 -6.08960032e-01 -2.12625653e-01
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529cceb4-3a30-4f86-b8ef-6bb3f362e32a | towards-a-standard-evaluation-method-for | null | null | https://aclanthology.info/papers/N15-1060/n15-1060 | https://www.aclweb.org/anthology/N15-1060 | Towards a standard evaluation method for grammatical error detection and correction | null | ['Ted Briscoe', 'Mariano Felice'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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74c4c829-1271-4a8a-9872-6050be196b9e | enhancing-patent-retrieval-using-text-and | 2211.01976 | null | https://arxiv.org/abs/2211.01976v1 | https://arxiv.org/pdf/2211.01976v1.pdf | Enhancing Patent Retrieval using Text and Knowledge Graph Embeddings: A Technical Note | Patent retrieval influences several applications within engineering design research, education, and practice as well as applications that concern innovation, intellectual property, and knowledge management etc. In this article, we propose a method to retrieve patents relevant to an initial set of patents, by synthesizing state-of-the-art techniques among natural language processing and knowledge graph embedding. Our method involves a patent embedding that captures text, citation, and inventor information, which individually represent different facets of knowledge communicated through a patent document. We obtain text embeddings using Sentence-BERT applied to titles and abstracts. We obtain citation and inventor embeddings through TransE that is trained using the corresponding knowledge graphs. We identify using a classification task that the concatenation of text, citation, and inventor embeddings offers a plausible representation of a patent. While the proposed patent embedding could be used to associate a pair of patents, we observe using a recall task that multiple initial patents could be associated with a target patent using mean cosine similarity, which could then be utilized to rank all target patents and retrieve the most relevant ones. We apply the proposed patent retrieval method to a set of patents corresponding to a product family and an inventor's portfolio. | ['Jianxi Luo', 'Guangtong Li', 'L Siddharth'] | 2022-11-03 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [ 2.28460148e-01 1.06963858e-01 -4.36125875e-01 2.99750775e-01
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c28952ec-9034-414e-8e3e-a0609bbda3b0 | a-robust-likelihood-model-for-novelty | 2306.03331 | null | https://arxiv.org/abs/2306.03331v1 | https://arxiv.org/pdf/2306.03331v1.pdf | A Robust Likelihood Model for Novelty Detection | Current approaches to novelty or anomaly detection are based on deep neural networks. Despite their effectiveness, neural networks are also vulnerable to imperceptible deformations of the input data. This is a serious issue in critical applications, or when data alterations are generated by an adversarial attack. While this is a known problem that has been studied in recent years for the case of supervised learning, the case of novelty detection has received very limited attention. Indeed, in this latter setting the learning is typically unsupervised because outlier data is not available during training, and new approaches for this case need to be investigated. We propose a new prior that aims at learning a robust likelihood for the novelty test, as a defense against attacks. We also integrate the same prior with a state-of-the-art novelty detection approach. Because of the geometric properties of that approach, the resulting robust training is computationally very efficient. An initial evaluation of the method indicates that it is effective at improving performance with respect to the standard models in the absence and presence of attacks. | ['Gianfranco Doretto', 'Donald A. Adjeroh', 'Shivang Patel', 'Ranya Almohsen'] | 2023-06-06 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 3.86146426e-01 -1.25231752e-02 2.50503570e-01 -6.30194023e-02
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22b0a7c4-77f3-4819-9e5d-93cb462cb3af | l1-regularized-reconstruction-error-as-alpha | 1702.02744 | null | http://arxiv.org/abs/1702.02744v1 | http://arxiv.org/pdf/1702.02744v1.pdf | L1-regularized Reconstruction Error as Alpha Matte | Sampling-based alpha matting methods have traditionally followed the
compositing equation to estimate the alpha value at a pixel from a pair of
foreground (F) and background (B) samples. The (F,B) pair that produces the
least reconstruction error is selected, followed by alpha estimation. The
significance of that residual error has been left unexamined. In this letter,
we propose a video matting algorithm that uses L1-regularized reconstruction
error of F and B samples as a measure of the alpha matte. A multi-frame
non-local means framework using coherency sensitive hashing is utilized to
ensure temporal coherency in the video mattes. Qualitative and quantitative
evaluations on a dataset exclusively for video matting demonstrate the
effectiveness of the proposed matting algorithm. | ['Jubin Johnson', 'Hisham Cholakkal', 'Deepu Rajan'] | 2017-02-09 | null | null | null | null | ['video-matting'] | ['computer-vision'] | [ 3.02949309e-01 -4.75396514e-01 -9.73303616e-02 -2.95944214e-01
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dc5943fd-60c5-4d1c-b0c3-20377244103d | chinese-characters-mapping-table-of-japanese | null | null | https://aclanthology.org/L12-1140 | https://aclanthology.org/L12-1140.pdf | Chinese Characters Mapping Table of Japanese, Traditional Chinese and Simplified Chinese | Chinese characters are used both in Japanese and Chinese, which are called Kanji and Hanzi respectively. Chinese characters contain significant semantic information, a mapping table between Kanji and Hanzi can be very useful for many Japanese-Chinese bilingual applications, such as machine translation and cross-lingual information retrieval. Because Kanji characters are originated from ancient China, most Kanji have corresponding Chinese characters in Hanzi. However, the relation between Kanji and Hanzi is quite complicated. In this paper, we propose a method of making a Chinese characters mapping table of Japanese, Traditional Chinese and Simplified Chinese automatically by means of freely available resources. We define seven categories for Kanji based on the relation between Kanji and Hanzi, and classify mappings of Chinese characters into these categories. We use a resource from Wiktionary to show the completeness of the mapping table we made. Statistical comparison shows that our proposed method makes a more complete mapping table than the current version of Wiktionary. | ['Sadao Kurohashi', 'Toshiaki Nakazawa', 'Chenhui Chu'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.62726760e-01 -3.81342620e-01 -1.41031936e-01 -9.63100493e-02
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1037d104-daaa-4c71-a41d-e1a14d0a3704 | 6d-object-pose-estimation-from-approximate-3d | 2303.13241 | null | https://arxiv.org/abs/2303.13241v3 | https://arxiv.org/pdf/2303.13241v3.pdf | 6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics | We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model. To achieve this, we employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel. In addition to the 3D coordinates, our model also estimates the pixel-wise coordinate error to discard correspondences that are likely wrong. This allows us to generate multiple 6D pose hypotheses of the object, which we then refine iteratively using a highly efficient region-based approach. We also introduce a novel pixel-wise posterior formulation by which we can estimate the probability for each hypothesis and select the most likely one. As we show in experiments, our approach is capable of dealing with extreme visual conditions including overexposure, high contrast, or low signal-to-noise ratio. This makes it a powerful technique for the particularly challenging task of estimating the pose of tumbling satellites for in-orbit robotic applications. Our method achieves state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021 post-mortem competition. | ['Rudolph Triebel', 'Manuel Stoiber', 'Martin Sundermeyer', 'Maximilian Durner', 'Maximilian Ulmer'] | 2023-03-23 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-1.03390977e-01 1.01423189e-01 6.15807204e-03 -4.07030821e-01
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6c69634f-2a84-481c-8fe0-532614a3daba | inverse-category-frequency-based-supervised | 1012.2609 | null | http://arxiv.org/abs/1012.2609v4 | http://arxiv.org/pdf/1012.2609v4.pdf | Inverse-Category-Frequency based supervised term weighting scheme for text categorization | Term weighting schemes often dominate the performance of many classifiers,
such as kNN, centroid-based classifier and SVMs. The widely used term weighting
scheme in text categorization, i.e., tf.idf, is originated from information
retrieval (IR) field. The intuition behind idf for text categorization seems
less reasonable than IR. In this paper, we introduce inverse category frequency
(icf) into term weighting scheme and propose two novel approaches, i.e., tf.icf
and icf-based supervised term weighting schemes. The tf.icf adopts icf to
substitute idf factor and favors terms occurring in fewer categories, rather
than fewer documents. And the icf-based approach combines icf and relevance
frequency (rf) to weight terms in a supervised way. Our cross-classifier and
cross-corpus experiments have shown that our proposed approaches are superior
or comparable to six supervised term weighting schemes and three traditional
schemes in terms of macro-F1 and micro-F1. | ['HUI ZHANG', 'Deqing Wang'] | 2010-12-13 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 1.52249590e-01 -4.02267933e-01 -6.74005330e-01 -5.63075244e-01
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0e6700c3-fe2d-4105-8756-6634e0d001e4 | anomaly-detection-with-conditioned-denoising | 2305.15956 | null | https://arxiv.org/abs/2305.15956v1 | https://arxiv.org/pdf/2305.15956v1.pdf | Anomaly Detection with Conditioned Denoising Diffusion Models | Reconstruction-based methods have struggled to achieve competitive performance on anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD). We propose a novel denoising process for image reconstruction conditioned on a target image. This results in a coherent restoration that closely resembles the target image. Subsequently, our anomaly detection framework leverages this conditioning where the target image is set as the input image to guide the denoising process, leading to defectless reconstruction while maintaining nominal patterns. We localise anomalies via a pixel-wise and feature-wise comparison of the input and reconstructed image. Finally, to enhance the effectiveness of feature comparison, we introduce a domain adaptation method that utilises generated examples from our conditioned denoising process to fine-tune the feature extractor. The veracity of the approach is demonstrated on various datasets including MVTec and VisA benchmarks, achieving state-of-the-art results of 99.5% and 99.3% image-level AUROC respectively. | ['Jawad Tayyub', 'Thomas Brox', 'Arian Mousakhan'] | 2023-05-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 7.02876687e-01 -2.79281318e-01 6.15498126e-01 -3.29286128e-01
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6fe26bc5-6ed3-4311-b0f4-134cd15be126 | gnn-based-android-malware-detection-with | 2201.07537 | null | https://arxiv.org/abs/2201.07537v9 | https://arxiv.org/pdf/2201.07537v9.pdf | Graph Neural Network-based Android Malware Classification with Jumping Knowledge | This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification. | ['Marius Portmann', 'Marcus Gallagher', 'Mohanad Sarhan', 'Siamak Layeghy', 'Wai Weng Lo'] | 2022-01-19 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.94791132e-01 -2.34569505e-01 -6.55168772e-01 -4.12552692e-02
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836ddf65-a7bc-4ae3-aab4-8c1f68b69003 | self-supervised-learning-for-video | 1905.00875 | null | https://arxiv.org/abs/1905.00875v5 | https://arxiv.org/pdf/1905.00875v5.pdf | Self-supervised Learning for Video Correspondence Flow | The objective of this paper is self-supervised learning of feature embeddings that are suitable for matching correspondences along the videos, which we term correspondence flow. By leveraging the natural spatial-temporal coherence in videos, we propose to train a ``pointer'' that reconstructs a target frame by copying pixels from a reference frame. We make the following contributions: First, we introduce a simple information bottleneck that forces the model to learn robust features for correspondence matching, and prevent it from learning trivial solutions, \eg matching based on low-level colour information. Second, to tackle the challenges from tracker drifting, due to complex object deformations, illumination changes and occlusions, we propose to train a recursive model over long temporal windows with scheduled sampling and cycle consistency. Third, we achieve state-of-the-art performance on DAVIS 2017 video segmentation and JHMDB keypoint tracking tasks, outperforming all previous self-supervised learning approaches by a significant margin. Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation. | ['Weidi Xie', 'Zihang Lai'] | 2019-05-02 | null | null | null | null | ['video-correspondence-flow', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.24480791e-01 -2.59810518e-02 -4.83446062e-01 -1.59719020e-01
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5345cb4a-57b9-4a17-bb72-cc0b7c607968 | motiontrack-learning-motion-predictor-for | 2306.02585 | null | https://arxiv.org/abs/2306.02585v1 | https://arxiv.org/pdf/2306.02585v1.pdf | MotionTrack: Learning Motion Predictor for Multiple Object Tracking | Significant advancements have been made in multi-object tracking (MOT) with the development of detection and re-identification (ReID) techniques. Despite these developments, the task of accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains challenging due to the insufficient discriminability of ReID features and the predominant use of linear motion models in MOT. In this context, we present a novel learnable motion predictor, named MotionTrack, which comprehensively incorporates two levels of granularity of motion features to enhance the modeling of temporal dynamics and facilitate accurate future motion prediction of individual objects. Specifically, the proposed approach adopts a self-attention mechanism to capture token-level information and a Dynamic MLP layer to model channel-level features. MotionTrack is a simple, online tracking approach. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on demanding datasets such as SportsMOT and Dancetrack, which feature highly nonlinear object motion. Notably, without fine-tuning on target datasets, MotionTrack also exhibits competitive performance on conventional benchmarks including MOT17 and MOT20. | ['DaCheng Tao', 'Zhigang Luo', 'Huayue Cai', 'Xiang Zhang', 'Long Lan', 'Yujie Zhong', 'Qiong Cao', 'Changcheng Xiao'] | 2023-06-05 | null | null | null | null | ['motion-prediction', 'object-tracking', 'multiple-object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.03271842e-01 -7.11854935e-01 -5.68859518e-01 3.97181185e-03
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e608730a-a700-45a4-b03c-40b9b69a85c4 | empowering-graph-representation-learning-with | null | null | https://openreview.net/forum?id=BJeRykBKDH | https://openreview.net/pdf?id=BJeRykBKDH | Empowering Graph Representation Learning with Paired Training and Graph Co-Attention | Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions. The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.
Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs. Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them. We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark. Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs. | ['Jian Tang', 'Pietro Lio', 'Petar Velickovic', 'Yu-Hsiang Huang', 'Andreea Deac'] | 2019-09-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 7.61071384e-01 5.42336285e-01 -4.86087352e-01 -1.43393859e-01
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77f67343-65e2-445f-af0c-8e1faf047294 | temporally-consistent-video-transformer-for | 2210.02396 | null | https://arxiv.org/abs/2210.02396v2 | https://arxiv.org/pdf/2210.02396v2.pdf | Temporally Consistent Transformers for Video Generation | To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world. Current algorithms enable accurate predictions over short horizons but tend to suffer from temporal inconsistencies. When generated content goes out of view and is later revisited, the model invents different content instead. Despite this severe limitation, no established benchmarks on complex data exist for rigorously evaluating video generation with long temporal dependencies. In this paper, we curate 3 challenging video datasets with long-range dependencies by rendering walks through 3D scenes of procedural mazes, Minecraft worlds, and indoor scans. We perform a comprehensive evaluation of current models and observe their limitations in temporal consistency. Moreover, we introduce the Temporally Consistent Transformer (TECO), a generative model that substantially improves long-term consistency while also reducing sampling time. By compressing its input sequence into fewer embeddings, applying a temporal transformer, and expanding back using a spatial MaskGit, TECO outperforms existing models across many metrics. Videos are available on the website: https://wilson1yan.github.io/teco | ['Pieter Abbeel', 'Stephen James', 'Danijar Hafner', 'Wilson Yan'] | 2022-10-05 | null | null | null | null | ['video-generation', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [ 6.31984770e-02 -1.88114345e-01 -2.94188827e-01 -2.67379224e-01
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0e2f2836-6425-4819-a25f-a2d8dc90565b | revisiting-the-centroid-based-method-a-strong | 1708.07690 | null | http://arxiv.org/abs/1708.07690v1 | http://arxiv.org/pdf/1708.07690v1.pdf | Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization | The centroid-based model for extractive document summarization is a simple
and fast baseline that ranks sentences based on their similarity to a centroid
vector. In this paper, we apply this ranking to possible summaries instead of
sentences and use a simple greedy algorithm to find the best summary.
Furthermore, we show possi- bilities to scale up to larger input docu- ment
collections by selecting a small num- ber of sentences from each document prior
to constructing the summary. Experiments were done on the DUC2004 dataset for
multi-document summarization. We ob- serve a higher performance over the orig-
inal model, on par with more complex state-of-the-art methods. | ['Demian Gholipour Ghalandari'] | 2017-08-25 | null | null | null | ws-2017-9 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.56750107e-01 1.74517259e-01 -2.01917171e-01 -4.17329341e-01
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d2286d4e-202c-4207-938f-4913d719c7d6 | precision-isnt-everything-a-hybrid-approach | null | null | https://aclanthology.org/W12-2027 | https://aclanthology.org/W12-2027.pdf | Precision Isn't Everything: A Hybrid Approach to Grammatical Error Detection | null | ['Joel Tetreault', 'Aoife Cahill', 'Michael Heilman'] | 2012-06-01 | null | null | null | ws-2012-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f3f92f4e-16d7-4824-81d9-31abc58afdae | the-mi-motion-dataset-and-benchmark-for-3d | 2306.13566 | null | https://arxiv.org/abs/2306.13566v2 | https://arxiv.org/pdf/2306.13566v2.pdf | The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction | 3D multi-person motion prediction is a challenging task that involves modeling individual behaviors and interactions between people. Despite the emergence of approaches for this task, comparing them is difficult due to the lack of standardized training settings and benchmark datasets. In this paper, we introduce the Multi-Person Interaction Motion (MI-Motion) Dataset, which includes skeleton sequences of multiple individuals collected by motion capture systems and refined and synthesized using a game engine. The dataset contains 167k frames of interacting people's skeleton poses and is categorized into 5 different activity scenes. To facilitate research in multi-person motion prediction, we also provide benchmarks to evaluate the performance of prediction methods in three settings: short-term, long-term, and ultra-long-term prediction. Additionally, we introduce a novel baseline approach that leverages graph and temporal convolutional networks, which has demonstrated competitive results in multi-person motion prediction. We believe that the proposed MI-Motion benchmark dataset and baseline will facilitate future research in this area, ultimately leading to better understanding and modeling of multi-person interactions. | ['Zizhao Wu', 'Yu Ding', 'Hao Wen', 'Yikai Luo', 'Xiao Zhou', 'Xiaogang Peng'] | 2023-06-23 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 3.22550312e-02 -4.66145247e-01 -2.91317612e-01 -2.05603153e-01
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a857edfb-76c5-4e29-bc64-5c6499bb0f9f | extrapolation-to-complete-basis-set-limit-in | 2303.14760 | null | https://arxiv.org/abs/2303.14760v3 | https://arxiv.org/pdf/2303.14760v3.pdf | Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models | The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached with an infinitely large basis set, i.e., in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, exciting and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model is used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions. | ['Claudia Draxl', 'Matthias Scheffler', 'Sven Lubeck', 'Luca Ghiringhelli', 'Christian Carbogno', 'Daniel T. Speckhard'] | 2023-03-26 | null | null | null | null | ['prediction-intervals', 'total-energy'] | ['miscellaneous', 'miscellaneous'] | [ 1.54099194e-02 -1.96872488e-01 -2.23115399e-01 -3.00446302e-01
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02a6bc88-f8bf-4d03-88dc-53f626184b4d | circuitnet-an-open-source-dataset-for-machine | 2208.01040 | null | https://arxiv.org/abs/2208.01040v4 | https://arxiv.org/pdf/2208.01040v4.pdf | CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA) | The electronic design automation (EDA) community has been actively exploring machine learning (ML) for very large-scale integrated computer-aided design (VLSI CAD). Many studies explored learning-based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. Although building ML models usually requires a large amount of data, most studies can only generate small internal datasets for validation because of the lack of large public datasets. In this essay, we present the first open-source dataset called CircuitNet for ML tasks in VLSI CAD. | ['Ru Huang', 'Runsheng Wang', 'Wei Liu', 'Yibo Lin', 'Yuxiang Zhao', 'Zhuomin Chai'] | 2022-08-01 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-3.13301623e-01 -3.12959291e-02 -1.01596546e+00 -6.97396934e-01
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73854762-43d4-4b3a-a060-8e527490fffe | designing-stable-neural-networks-using-convex | 2306.17332 | null | https://arxiv.org/abs/2306.17332v1 | https://arxiv.org/pdf/2306.17332v1.pdf | Designing Stable Neural Networks using Convex Analysis and ODEs | Motivated by classical work on the numerical integration of ordinary differential equations we present a ResNet-styled neural network architecture that encodes non-expansive (1-Lipschitz) operators, as long as the spectral norms of the weights are appropriately constrained. This is to be contrasted with the ordinary ResNet architecture which, even if the spectral norms of the weights are constrained, has a Lipschitz constant that, in the worst case, grows exponentially with the depth of the network. Further analysis of the proposed architecture shows that the spectral norms of the weights can be further constrained to ensure that the network is an averaged operator, making it a natural candidate for a learned denoiser in Plug-and-Play algorithms. Using a novel adaptive way of enforcing the spectral norm constraints, we show that, even with these constraints, it is possible to train performant networks. The proposed architecture is applied to the problem of adversarially robust image classification, to image denoising, and finally to the inverse problem of deblurring. | ['Carola-Bibiane Schönlieb', 'Brynjulf Owren', 'Davide Murari', 'Matthias J. Ehrhardt', 'Elena Celledoni', 'Ferdia Sherry'] | 2023-06-29 | null | null | null | null | ['deblurring', 'image-denoising', 'numerical-integration'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.58995587e-01 4.11618203e-01 4.77918953e-01 -5.16321957e-02
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bb19b657-35b9-4301-b678-5a2deff35041 | leveraging-cross-utterance-context-for-asr | 2306.16903 | null | https://arxiv.org/abs/2306.16903v1 | https://arxiv.org/pdf/2306.16903v1.pdf | Leveraging Cross-Utterance Context For ASR Decoding | While external language models (LMs) are often incorporated into the decoding stage of automated speech recognition systems, these models usually operate with limited context. Cross utterance information has been shown to be beneficial during second pass re-scoring, however this limits the hypothesis space based on the local information available to the first pass LM. In this work, we investigate the incorporation of long-context transformer LMs for cross-utterance decoding of acoustic models via beam search, and compare against results from n-best rescoring. Results demonstrate that beam search allows for an improved use of cross-utterance context. When evaluating on the long-format dataset AMI, results show a 0.7\% and 0.3\% absolute reduction on dev and test sets compared to the single-utterance setting, with improvements when including up to 500 tokens of prior context. Evaluations are also provided for Tedlium-1 with less significant improvements of around 0.1\% absolute. | ['Anton Ragni', 'Robert Flynn'] | 2023-06-29 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 5.20409286e-01 2.20754728e-01 7.50756562e-02 -6.39542758e-01
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37ee7c6f-3259-4eaa-a939-d01280222052 | neural-academic-paper-generation | 1912.01982 | null | https://arxiv.org/abs/1912.01982v1 | https://arxiv.org/pdf/1912.01982v1.pdf | Neural Academic Paper Generation | In this work, we tackle the problem of structured text generation, specifically academic paper generation in $\LaTeX{}$, inspired by the surprisingly good results of basic character-level language models. Our motivation is using more recent and advanced methods of language modeling on a more complex dataset of $\LaTeX{}$ source files to generate realistic academic papers. Our first contribution is preparing a dataset with $\LaTeX{}$ source files on recent open-source computer vision papers. Our second contribution is experimenting with recent methods of language modeling and text generation such as Transformer and Transformer-XL to generate consistent $\LaTeX{}$ code. We report cross-entropy and bits-per-character (BPC) results of the trained models, and we also discuss interesting points on some examples of the generated $\LaTeX{}$ code. | ['Özgur Özdemir', 'Uras Mutlu', 'Samet Demir'] | 2019-12-02 | null | null | null | null | ['paper-generation'] | ['natural-language-processing'] | [ 3.09887409e-01 2.66676784e-01 2.63380527e-01 -1.64274529e-01
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1a7707a4-a7d4-4064-b63b-89170a009dd4 | a-model-data-driven-network-embedding | 2211.15002 | null | https://arxiv.org/abs/2211.15002v1 | https://arxiv.org/pdf/2211.15002v1.pdf | A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging | Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The correlation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimensional deep unfolding imaging network is constructed. On the basis of it, we add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Meanwhile, to train the proposed multifeature-based imaging network, we construct a tomoSAR simulation dataset consisting entirely of simulation data of buildings. Experiments verify the effectiveness of the model. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy. | ['Tianjiao Zeng', 'Shunjun Wei', 'Jun Shi', 'Xu Zhan', 'Xiaoling Zhang', 'Yu Ren'] | 2022-11-28 | null | null | null | null | ['compressive-sensing', 'network-embedding'] | ['computer-vision', 'methodology'] | [ 3.33376288e-01 -2.28419155e-01 4.45985228e-01 -5.84337294e-01
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2775cccb-841b-4909-a268-2abcf9a4f7ad | amrize-then-parse-enhancing-amr-parsing-with | null | null | https://openreview.net/forum?id=5Q-ihWzhSi | https://openreview.net/pdf?id=5Q-ihWzhSi | AMRize, then Parse! Enhancing AMR Parsing with PseudoAMR Data | As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. With carefully designed control experiments, we find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring much more significant performance gain than unrelated tasks in the text-to-AMR transition. 2) To make a better fit for AMR, data from auxiliary tasks should be properly "AMRized'' to PseudoAMR before training. 3) Intermediate-task training paradigm outperforms multitask learning when introducing auxiliary tasks to AMR parsing. From an empirical perspective, we propose a principled method to choose, reform, and train auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on in-distribution, out-of-distribution, and few-shots benchmarks of AMR parsing. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.08783865e-01 6.59512639e-01 -4.90673155e-01 -6.68426454e-01
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edc8674f-b149-4edc-915d-f045ca93c3f6 | deep-deterministic-independent-component | 2202.02951 | null | https://arxiv.org/abs/2202.02951v2 | https://arxiv.org/pdf/2202.02951v2.pdf | Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing | We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components. Using the matrix-based R{\'e}nyi's $\alpha$-order entropy functional, our network can be directly optimized by stochastic gradient descent (SGD), without any variational approximation or adversarial training. As a solid application, we evaluate our ICA in the problem of hyperspectral unmixing (HU) and refute a statement that "\emph{ICA does not play a role in unmixing hyperspectral data}", which was initially suggested by \cite{nascimento2005does}. Code and additional remarks of our DDICA is available at https://github.com/hongmingli1995/DDICA. | ['Jose C. Principe', 'Shujian Yu', 'Hongming Li'] | 2022-02-07 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.41137886e-01 -1.08674034e-01 3.93538794e-04 -2.21443132e-01
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4ce1e892-25d2-4d4a-91cd-b53712a60488 | isotropic-gaussian-processes-on-finite-spaces | 2211.01689 | null | https://arxiv.org/abs/2211.01689v3 | https://arxiv.org/pdf/2211.01689v3.pdf | Isotropic Gaussian Processes on Finite Spaces of Graphs | We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops. We endow each of these sets with a geometric structure, inducing the notions of closeness and symmetries, by turning them into a vertex set of an appropriate metagraph. Building on this, we describe the class of priors that respect this structure and are analogous to the Euclidean isotropic processes, like squared exponential or Mat\'ern. We propose an efficient computational technique for the ostensibly intractable problem of evaluating these priors' kernels, making such Gaussian processes usable within the usual toolboxes and downstream applications. We go further to consider sets of equivalence classes of unweighted graphs and define the appropriate versions of priors thereon. We prove a hardness result, showing that in this case, exact kernel computation cannot be performed efficiently. However, we propose a simple Monte Carlo approximation for handling moderately sized cases. Inspired by applications in chemistry, we illustrate the proposed techniques on a real molecular property prediction task in the small data regime. | ['Andreas Krause', 'Vignesh Ram Somnath', 'Mohammad Reza Karimi', 'Viacheslav Borovitskiy'] | 2022-11-03 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.99014878e-01 5.10199070e-01 1.77391663e-01 -1.89230919e-01
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a98fde42-e27e-4737-a3f9-c94a0bcc3265 | an-asynchronous-kalman-filter-for-hybrid | 2012.05590 | null | https://arxiv.org/abs/2012.05590v4 | https://arxiv.org/pdf/2012.05590v4.pdf | An Asynchronous Kalman Filter for Hybrid Event Cameras | Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fused using a novel asynchronous Kalman filter under a unifying uncertainty model for both sensors. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions and our new dataset with HDR reference. The proposed algorithm outperforms state-of-the-art methods in both absolute intensity error (48% reduction) and image similarity indexes (average 11% improvement). | ['Robert Mahony', 'Cedric Scheerlinck', 'Yonhon Ng', 'Ziwei Wang'] | 2020-12-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_An_Asynchronous_Kalman_Filter_for_Hybrid_Event_Cameras_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 4.87342864e-01 -6.50795639e-01 2.40249634e-01 -3.00678641e-01
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69a39251-ded7-4e31-a651-b90e05a66a03 | predicting-patient-outcomes-with-graph | 2101.03940 | null | https://arxiv.org/abs/2101.03940v1 | https://arxiv.org/pdf/2101.03940v1.pdf | Predicting Patient Outcomes with Graph Representation Learning | Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnoses and medications. When they are included, they are usually concatenated in the late stages of a model, which may struggle to learn from rarer disease patterns. Instead, we propose a strategy to exploit diagnoses as relational information by connecting similar patients in a graph. To this end, we propose LSTM-GNN for patient outcome prediction tasks: a hybrid model combining Long Short-Term Memory networks (LSTMs) for extracting temporal features and Graph Neural Networks (GNNs) for extracting the patient neighbourhood information. We demonstrate that LSTM-GNNs outperform the LSTM-only baseline on length of stay prediction tasks on the eICU database. More generally, our results indicate that exploiting information from neighbouring patient cases using graph neural networks is a promising research direction, yielding tangible returns in supervised learning performance on Electronic Health Records. | ['Pietro Liò', 'Nicholas Lane', 'Petar Veličković', 'Catherine Tong', 'Emma Rocheteau'] | 2021-01-11 | null | null | null | null | ['length-of-stay-prediction', 'predicting-patient-outcomes'] | ['medical', 'medical'] | [ 2.80582577e-01 4.41855431e-01 -4.43143189e-01 -2.36101851e-01
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5a2a3d5a-81c6-47bd-9632-e2a3faff208a | type-aware-decomposed-framework-for-few-shot | 2302.06397 | null | https://arxiv.org/abs/2302.06397v1 | https://arxiv.org/pdf/2302.06397v1.pdf | Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition | Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance. | ['Tieyun Qian', 'Yongqi Li'] | 2023-02-13 | null | null | null | null | ['few-shot-ner', 'type'] | ['natural-language-processing', 'speech'] | [-1.12989478e-01 -2.05091730e-01 -3.56233120e-01 -3.65429789e-01
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ed1e75e6-f711-44e3-b662-231d49351bdb | a-graph-transduction-game-for-multi-target | 1806.07227 | null | http://arxiv.org/abs/1806.07227v2 | http://arxiv.org/pdf/1806.07227v2.pdf | A Graph Transduction Game for Multi-target Tracking | Semi-supervised learning is a popular class of techniques to learn from
labeled and unlabeled data. The paper proposes an application of a recently
proposed approach of graph transduction that exploits game theoretic notions to
the problem of multiple people tracking. Within the proposed framework, targets
are considered as players of a multi-player non-cooperative game. The
equilibria of the game is considered as a consistent labeling solution and thus
an estimation of the target association in the sequence of frames. Patches of
persons are extracted from the video frames using a HOG based detector and
their similarity is modeled using distances among their covariance matrices.
The solution we propose achieves satisfactory results on video surveillance
datasets. The experiments show the robustness of the method even with a heavy
unbalance between the number of labeled and unlabeled input patches. | ['Rita Cucchiara', 'Tewodros Mulugeta Dagnew', 'Marcello Pelillo', 'Dalia Coppi'] | 2018-06-12 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [ 3.79821025e-02 3.20468843e-01 -1.12971887e-01 -1.78400561e-01
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b4d4938b-b88f-4cf3-ad5c-417e1df6e81f | optimal-inference-in-contextual-stochastic | 2306.07948 | null | https://arxiv.org/abs/2306.07948v1 | https://arxiv.org/pdf/2306.07948v1.pdf | Optimal Inference in Contextual Stochastic Block Models | The contextual stochastic block model (cSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the cSBM has been widely used as a synthetic dataset for evaluating the performance of graph-neural networks (GNNs) for semi-supervised node classification. We consider a probabilistic Bayes-optimal formulation of the inference problem and we derive a belief-propagation-based algorithm for the semi-supervised cSBM; we conjecture it is optimal in the considered setting and we provide its implementation. We show that there can be a considerable gap between the accuracy reached by this algorithm and the performance of the GNN architectures proposed in the literature. This suggests that the cSBM, along with the comparison to the performance of the optimal algorithm, readily accessible via our implementation, can be instrumental in the development of more performant GNN architectures. | ['L. Zdeborová', 'O. Duranthon'] | 2023-06-06 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 4.11691546e-01 4.98100966e-01 -1.76565021e-01 -1.70839787e-01
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301f0131-46fd-46ce-9a7d-01aaf583cb7a | rssi-based-outdoor-localization-with-single | 2004.10083 | null | https://arxiv.org/abs/2004.10083v1 | https://arxiv.org/pdf/2004.10083v1.pdf | RSSI-based Outdoor Localization with Single Unmanned Aerial Vehicle | Localization of a target object has been performed conventionally using multiple terrestrial reference nodes. This paradigm is recently shifted towards utilization of unmanned aerial vehicles (UAVs) for locating target objects. Since locating of a target using simultaneous multiple UAVs is costly and impractical, achieving this task by utilizing single UAV becomes desirable. Hence, in this paper, we propose an RSSI-based localization method that utilizes only a single UAV. The proposed approach is based on clustering method along with the Singular Value Decomposition (SVD). The performance of the proposed method is verified by the experimental measurements collected by a UAV that we have designed and computer simulations. The results show that the proposed method can achieve location accuracy as low as 7m depending on the number of iterations. | ['Yakup Genc', 'Hasari Celebi', 'Seyma Yucer', 'Mesih Veysi Kilinc', 'Furkan Tektas', 'Yusuf Sinan Akgul', 'Ilyas Kandemir'] | 2020-04-20 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [ 1.31533686e-02 -4.33929652e-01 1.38271123e-01 3.84099483e-02
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3edcf6b1-59e2-4a25-b414-c9df6ea3cbe7 | exponential-utility-maximization-in-small | 2208.06549 | null | https://arxiv.org/abs/2208.06549v1 | https://arxiv.org/pdf/2208.06549v1.pdf | Exponential utility maximization in small/large financial markets | Obtaining utility maximizing optimal portfolios in closed form is a challenging issue when the return vector follows a more general distribution than the normal one. In this note, we give closed form expressions, in markets based on finitely many assets, for optimal portfolios that maximize the expected exponential utility when the return vector follows normal mean-variance mixture models. We then consider large financial markets based on normal mean-variance mixture models also and show that, under exponential utility, the optimal utilities based on small markets converge to the optimal utility in the large financial market. This result shows, in particular, that to reach optimal utility level investors need to diversify their portfolios to include infinitely many assets into their portfolio and with portfolios based on any set of only finitely many assets, they never be able to reach optimum level of utility. In this paper, we also consider portfolio optimization problems with more general class of utility functions and provide an easy-to-implement numerical procedure for locating optimal portfolios. Especially, our approach in this part of the paper reduces a high dimensional problem in locating optimal portfolio into a three dimensional problem for a general class of utility functions. | ['Hasanjan Sayit', 'Miklós Rásonyi'] | 2022-08-13 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.58633989e-01 1.93406031e-01 -9.59674492e-02 -1.39214080e-02
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fed7cdeb-f7fc-4e2a-b1f3-59f550ab4222 | tedb-system-description-to-a-shared-task-on | 2301.06602 | null | https://arxiv.org/abs/2301.06602v1 | https://arxiv.org/pdf/2301.06602v1.pdf | TEDB System Description to a Shared Task on Euphemism Detection 2022 | In this report, we describe our Transformers for euphemism detection baseline (TEDB) submissions to a shared task on euphemism detection 2022. We cast the task of predicting euphemism as text classification. We considered Transformer-based models which are the current state-of-the-art methods for text classification. We explored different training schemes, pretrained models, and model architectures. Our best result of 0.816 F1-score (0.818 precision and 0.814 recall) consists of a euphemism-detection-finetuned TweetEval/TimeLMs-pretrained RoBERTa model as a feature extractor frontend with a KimCNN classifier backend trained end-to-end using a cosine annealing scheduler. We observed pretrained models on sentiment analysis and offensiveness detection to correlate with more F1-score while pretraining on other tasks, such as sarcasm detection, produces less F1-scores. Also, putting more word vector channels does not improve the performance in our experiments. | ['Peratham Wiriyathammabhum'] | 2023-01-16 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-1.26373813e-01 2.13755623e-01 -2.46292144e-01 -4.05993551e-01
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ba2a1ac3-18ef-4543-808f-87ddc12a1efe | identity-aware-textual-visual-matching-with | 1708.01988 | null | http://arxiv.org/abs/1708.01988v1 | http://arxiv.org/pdf/1708.01988v1.pdf | Identity-Aware Textual-Visual Matching with Latent Co-attention | Textual-visual matching aims at measuring similarities between sentence
descriptions and images. Most existing methods tackle this problem without
effectively utilizing identity-level annotations. In this paper, we propose an
identity-aware two-stage framework for the textual-visual matching problem. Our
stage-1 CNN-LSTM network learns to embed cross-modal features with a novel
Cross-Modal Cross-Entropy (CMCE) loss. The stage-1 network is able to
efficiently screen easy incorrect matchings and also provide initial training
point for the stage-2 training. The stage-2 CNN-LSTM network refines the
matching results with a latent co-attention mechanism. The spatial attention
relates each word with corresponding image regions while the latent semantic
attention aligns different sentence structures to make the matching results
more robust to sentence structure variations. Extensive experiments on three
datasets with identity-level annotations show that our framework outperforms
state-of-the-art approaches by large margins. | ['Shuang Li', 'Wei Yang', 'Tong Xiao', 'Hongsheng Li', 'Xiaogang Wang'] | 2017-08-07 | identity-aware-textual-visual-matching-with-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Identity-Aware_Textual-Visual_Matching_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Identity-Aware_Textual-Visual_Matching_ICCV_2017_paper.pdf | iccv-2017-10 | ['nlp-based-person-retrival'] | ['computer-vision'] | [ 4.01983440e-01 -1.42329633e-01 -2.79606879e-01 -5.84951580e-01
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9e551e60-c2b4-46d8-8e15-b10fec907e2d | segmentation-free-vehicle-license-plate | 1701.06439 | null | http://arxiv.org/abs/1701.06439v1 | http://arxiv.org/pdf/1701.06439v1.pdf | Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN | While vehicle license plate recognition (VLPR) is usually done with a sliding
window approach, it can have limited performance on datasets with characters
that are of variable width. This can be solved by hand-crafting algorithms to
prescale the characters. While this approach can work fairly well, the
recognizer is only aware of the pixels within each detector window, and fails
to account for other contextual information that might be present in other
parts of the image. A sliding window approach also requires training data in
the form of presegmented characters, which can be more difficult to obtain. In
this paper, we propose a unified ConvNet-RNN model to recognize real-world
captured license plate photographs. By using a Convolutional Neural Network
(ConvNet) to perform feature extraction and using a Recurrent Neural Network
(RNN) for sequencing, we address the problem of sliding window approaches being
unable to access the context of the entire image by feeding the entire image as
input to the ConvNet. This has the added benefit of being able to perform
end-to-end training of the entire model on labelled, full license plate images.
Experimental results comparing the ConvNet-RNN architecture to a sliding
window-based approach shows that the ConvNet-RNN architecture performs
significantly better. | ['Teik Koon Cheang', 'Yong Haur Tay', 'Yong Shean Chong'] | 2017-01-23 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 5.61022103e-01 -3.54275525e-01 2.95716920e-03 -3.23127449e-01
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