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d3c65d4c-a853-4767-a1e2-a27eae5bde5b | select-and-trade-towards-unified-pair-trading | 2301.10724 | null | https://arxiv.org/abs/2301.10724v2 | https://arxiv.org/pdf/2301.10724v2.pdf | Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning | Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods. | ['Jimin Huang', 'Yanzhao Lai', 'Min Peng', 'Qianqian Xie', 'Boyi Zhang', 'Weiguang Han'] | 2023-01-25 | null | null | null | null | ['hierarchical-reinforcement-learning', 'pair-trading'] | ['methodology', 'time-series'] | [-2.28773504e-01 8.87054503e-02 -2.79130310e-01 -3.12552243e-01
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6af29234-a960-4bf6-9947-fbb89a09c3f9 | multiple-object-forecasting-predicting-future | 1909.11944 | null | https://arxiv.org/abs/1909.11944v2 | https://arxiv.org/pdf/1909.11944v2.pdf | Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments | This paper introduces the problem of multiple object forecasting (MOF), in which the goal is to predict future bounding boxes of tracked objects. In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-level perspective and call for the prediction of full object bounding boxes, rather than trajectories alone. Towards solving this task, we introduce the Citywalks dataset, which consists of over 200k high-resolution video frames. Citywalks comprises of footage recorded in 21 cities from 10 European countries in a variety of weather conditions and over 3.5k unique pedestrian trajectories. For evaluation, we adapt existing trajectory forecasting methods for MOF and confirm cross-dataset generalizability on the MOT-17 dataset without fine-tuning. Finally, we present STED, a novel encoder-decoder architecture for MOF. STED combines visual and temporal features to model both object-motion and ego-motion, and outperforms existing approaches for MOF. Code & dataset link: https://github.com/olly-styles/Multiple-Object-Forecasting | ['Victor Sanchez', 'Tanaya Guha', 'Olly Styles'] | 2019-09-26 | null | null | null | null | ['multiple-object-forecasting'] | ['computer-vision'] | [-2.34453291e-01 -4.12058890e-01 -2.63288051e-01 -5.22249281e-01
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7119f3ac-e306-410c-a5d0-1b4fbd19ec18 | bridging-the-gap-between-multi-step-and-one | 2306.03367 | null | https://arxiv.org/abs/2306.03367v1 | https://arxiv.org/pdf/2306.03367v1.pdf | Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision | Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supervision schemes that can be constructed by chaining multiple time-steps. We address this issue by proposing a middle-ground where multiple trajectory segments are chained together. Our proposed Multi-Branch Self-Supervised Predictor receives additional training on new predictions starting at intermediate future segments. In addition, the model 'imagines' the latent context and 'predicts the past' while combining multi-modal trajectories in a tree-like manner. We deliberately keep aspects such as interaction and environment modeling simplistic and nevertheless achieve competitive results on the INTERACTION dataset. Furthermore, we investigate the sparsely explored uncertainty estimation of deterministic predictors. We find positive correlations between the prediction error and two proposed metrics, which might pave way for determining prediction confidence. | ['J. Marius Zöllner', 'Maxim Dolgov', 'Max Keller', 'Faris Janjoš'] | 2023-06-06 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 4.63373326e-02 3.56652707e-01 -8.53305399e-01 -1.01951289e+00
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49237467-5a6f-4676-beca-2349f29ff8ea | rethinking-movie-genre-classification-with | 2012.02639 | null | https://arxiv.org/abs/2012.02639v3 | https://arxiv.org/pdf/2012.02639v3.pdf | Rethinking movie genre classification with fine-grained semantic clustering | Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies within a single genre definition. We expand these 'coarse' genre labels by identifying 'fine-grained' semantic information within the multi-modal content of movies. By leveraging pre-trained 'expert' networks, we learn the influence of different combinations of modes for multi-label genre classification. Using a contrastive loss, we continue to fine-tune this 'coarse' genre classification network to identify high-level intertextual similarities between the movies across all genre labels. This leads to a more 'fine-grained' and detailed clustering, based on semantic similarities while still retaining some genre information. Our approach is demonstrated on a newly introduced multi-modal 37,866,450 frame, 8,800 movie trailer dataset, MMX-Trailer-20, which includes pre-computed audio, location, motion, and image embeddings. | ['Andrew Gilbert', 'Jon Weinbren', 'Edward Fish'] | 2020-12-04 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.19052508e-01 -5.65384090e-01 -2.64971793e-01 -4.53448057e-01
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8db49586-efcb-4a0d-bf7e-140338ba50cd | learning-unsupervised-word-translations | null | null | https://aclanthology.org/D18-1063 | https://aclanthology.org/D18-1063.pdf | Learning Unsupervised Word Translations Without Adversaries | Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised {--} no seed dictionary or parallel corpora required; and introduces no adversary {--} therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods. | ['Timothy Hospedales', 'Makoto Yamada', 'Tanmoy Mukherjee'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['multilingual-word-embeddings'] | ['methodology'] | [ 2.61147946e-01 -4.45792004e-02 -5.66568553e-01 -1.07596509e-01
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e218b185-faa0-47a9-8892-0382e8eb0d56 | greed-is-good-near-optimal-submodular | 1704.01652 | null | http://arxiv.org/abs/1704.01652v1 | http://arxiv.org/pdf/1704.01652v1.pdf | Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization | It is known that greedy methods perform well for maximizing monotone
submodular functions. At the same time, such methods perform poorly in the face
of non-monotonicity. In this paper, we show - arguably, surprisingly - that
invoking the classical greedy algorithm $O(\sqrt{k})$-times leads to the
(currently) fastest deterministic algorithm, called Repeated Greedy, for
maximizing a general submodular function subject to $k$-independent system
constraints. Repeated Greedy achieves $(1 + O(1/\sqrt{k}))k$ approximation
using $O(nr\sqrt{k})$ function evaluations (here, $n$ and $r$ denote the size
of the ground set and the maximum size of a feasible solution, respectively).
We then show that by a careful sampling procedure, we can run the greedy
algorithm only once and obtain the (currently) fastest randomized algorithm,
called Sample Greedy, for maximizing a submodular function subject to
$k$-extendible system constraints (a subclass of $k$-independent system
constrains). Sample Greedy achieves $(k + 3)$-approximation with only $O(nr/k)$
function evaluations. Finally, we derive an almost matching lower bound, and
show that no polynomial time algorithm can have an approximation ratio smaller
than $ k + 1/2 - \varepsilon$. To further support our theoretical results, we
compare the performance of Repeated Greedy and Sample Greedy with prior art in
a concrete application (movie recommendation). We consistently observe that
while Sample Greedy achieves practically the same utility as the best baseline,
it performs at least two orders of magnitude faster. | ['Amin Karbasi', 'Moran Feldman', 'Christopher Harshaw'] | 2017-04-05 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.00072220e-01 2.61297584e-01 -3.49805325e-01 -1.42996192e-01
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20765d8f-2652-43f9-9a37-1b64a15150e2 | boosting-multi-modal-e-commerce-attribute | 2207.07278 | null | https://arxiv.org/abs/2207.07278v2 | https://arxiv.org/pdf/2207.07278v2.pdf | Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization | With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such diversified data, especially via extracting the attribute-value pairs in text sequences with the aid of helpful image regions. Although a series of previous works have been dedicated to this task, there remain seldomly investigated obstacles that hinder further improvements: 1) Parameters from up-stream single-modal pretraining are inadequately applied, without proper jointly fine-tuning in a down-stream multi-modal task. 2) To select descriptive parts of images, a simple late fusion is widely applied, regardless of priori knowledge that language-related information should be encoded into a common linguistic embedding space by stronger encoders. 3) Due to diversity across products, their attribute sets tend to vary greatly, but current approaches predict with an unnecessary maximal range and lead to more potential false positives. To address these issues, we propose in this paper a novel approach to boost multi-modal e-commerce attribute value extraction via unified learning scheme and dynamic range minimization: 1) Firstly, a unified scheme is designed to jointly train a multi-modal task with pretrained single-modal parameters. 2) Secondly, a text-guided information range minimization method is proposed to adaptively encode descriptive parts of each modality into an identical space with a powerful pretrained linguistic model. 3) Moreover, a prototype-guided attribute range minimization method is proposed to first determine the proper attribute set of the current product, and then select prototypes to guide the prediction of the chosen attributes. Experiments on the popular multi-modal e-commerce benchmarks show that our approach achieves superior performance over the other state-of-the-art techniques. | ['Xu-Cheng Yin', 'Wei Liu', 'Hongfa Wang', 'Weibo Gu', 'Hongyu Gao', 'Chao Zhu', 'Mengyin Liu'] | 2022-07-15 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 3.71736139e-01 -3.08747947e-01 -5.67200363e-01 -6.85390651e-01
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f7689f2c-36fc-4d48-863a-802e830b01f7 | self-aware-and-cross-sample-prototypical | 2305.16214 | null | https://arxiv.org/abs/2305.16214v1 | https://arxiv.org/pdf/2305.16214v1.pdf | Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation | Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs. Furthermore, we introduce a self-aware consistency learning method that exploits unlabeled data to improve the compactness of pseudo labels within each class. Moreover, a dual loss re-weighting method is integrated into the cross-sample prototypical consistency learning method to improve the reliability and stability of our model. Extensive experiments on ACDC dataset and PROMISE12 dataset validate that SCP-Net outperforms other state-of-the-art semi-supervised segmentation methods and achieves significant performance gains compared to the limited supervised training. Our code will come soon. | ['Zhicheng Jiao', 'Fan Yang', 'Xin Li', 'Heng Zhou', 'Chunna Tian', 'Ran Ran', 'Zhenxi Zhang'] | 2023-05-25 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.02305852e-01 2.83429702e-03 -8.13707709e-01 -6.83764219e-01
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751ad82e-42a4-4434-8786-fa634a9b80ae | a-knowledge-distillation-framework-for | 2211.02638 | null | https://arxiv.org/abs/2211.02638v1 | https://arxiv.org/pdf/2211.02638v1.pdf | A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data | Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038. | ['Anjula C. De Silva', 'Chamira U. S. Edussooriya', 'Simon L. Kappel', 'Jathurshan Pradeepkumar', 'Mithunjha Anandakumar'] | 2022-10-27 | null | null | null | null | ['sleep-quality-prediction', 'sleep-staging', 'eeg-based-sleep-staging'] | ['medical', 'medical', 'time-series'] | [-3.45721513e-01 2.70818900e-02 -6.95984438e-02 -3.73906523e-01
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751cc477-a687-4c4f-a568-b42380302e82 | automatic-acute-ischemic-stroke-lesion | 1908.03735 | null | https://arxiv.org/abs/1908.03735v3 | https://arxiv.org/pdf/1908.03735v3.pdf | Automatic acute ischemic stroke lesion segmentation using semi-supervised learning | Ischemic stroke is a common disease in the elderly population, which can cause long-term disability and even death. However, the time window for treatment of ischemic stroke in its acute stage is very short. To fast localize and quantitively evaluate the acute ischemic stroke (AIS) lesions, many deep-learning-based lesion segmentation methods have been proposed in the literature, where a deep convolutional neural network (CNN) was trained on hundreds of fully labeled subjects with accurate annotations of AIS lesions. Despite that high segmentation accuracy can be achieved, the accurate labels should be annotated by experienced clinicians, and it is therefore very time-consuming to obtain a large number of fully labeled subjects. In this paper, we propose a semi-supervised method to automatically segment AIS lesions in diffusion weighted images and apparent diffusion coefficient maps. By using a large number of weakly labeled subjects and a small number of fully labeled subjects, our proposed method is able to accurately detect and segment the AIS lesions. In particular, our proposed method consists of three parts: 1) a double-path classification net (DPC-Net) trained in a weakly-supervised way is used to detect the suspicious regions of AIS lesions; 2) a pixel-level K-Means clustering algorithm is used to identify the hyperintensive regions on the DWIs; and 3) a region-growing algorithm combines the outputs of the DPC-Net and the K-Means to obtain the final precise lesion segmentation. In our experiment, we use 460 weakly labeled subjects and 15 fully labeled subjects to train and fine-tune the proposed method. By evaluating on a clinical dataset with 150 fully labeled subjects, our proposed method achieves a mean dice coefficient of 0.642, and a lesion-wise F1 score of 0.822. | ['Shuxue Ding', 'Hong Wu', 'Chen Cao', 'Bin Zhao', 'Zhiyang Liu', 'Song Jin', 'Guohua Liu'] | 2019-08-10 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [-2.24578064e-02 -1.34767637e-01 -2.65261233e-01 -5.14467120e-01
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c8c792d0-5f17-425f-91ba-12e99b379428 | coresets-for-time-series-clustering | 2110.15263 | null | https://arxiv.org/abs/2110.15263v1 | https://arxiv.org/pdf/2110.15263v1.pdf | Coresets for Time Series Clustering | We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and economics due to the proliferation of sensors facilitating real-time measurement and rapid drop in storage costs. In particular, we consider the setting where the time series data on $N$ entities is generated from a Gaussian mixture model with autocorrelations over $k$ clusters in $\mathbb{R}^d$. Our main contribution is an algorithm to construct coresets for the maximum likelihood objective for this mixture model. Our algorithm is efficient, and under a mild boundedness assumption on the covariance matrices of the underlying Gaussians, the size of the coreset is independent of the number of entities $N$ and the number of observations for each entity, and depends only polynomially on $k$, $d$ and $1/\varepsilon$, where $\varepsilon$ is the error parameter. We empirically assess the performance of our coreset with synthetic data. | ['Nisheeth K. Vishnoi', 'K. Sudhir', 'Lingxiao Huang'] | 2021-10-28 | null | http://proceedings.neurips.cc/paper/2021/hash/c115ba9e04ab27fbbb664f932112246d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c115ba9e04ab27fbbb664f932112246d-Paper.pdf | neurips-2021-12 | ['time-series-clustering'] | ['time-series'] | [-2.11689830e-01 -9.69615802e-02 8.77529010e-02 -1.09681942e-01
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6f681d2c-7c4c-4c94-a3db-e4a4fd00c5ef | speech-separation-based-on-contrastive | 2305.10652 | null | https://arxiv.org/abs/2305.10652v1 | https://arxiv.org/pdf/2305.10652v1.pdf | Speech Separation based on Contrastive Learning and Deep Modularization | The current monaural state of the art tools for speech separation relies on supervised learning. This means that they must deal with permutation problem, they are impacted by the mismatch on the number of speakers used in training and inference. Moreover, their performance heavily relies on the presence of high-quality labelled data. These problems can be effectively addressed by employing a fully unsupervised technique for speech separation. In this paper, we use contrastive learning to establish the representations of frames then use the learned representations in the downstream deep modularization task. Concretely, we demonstrate experimentally that in speech separation, different frames of a speaker can be viewed as augmentations of a given hidden standard frame of that speaker. The frames of a speaker contain enough prosodic information overlap which is key in speech separation. Based on this, we implement a self-supervised learning to learn to minimize the distance between frames belonging to a given speaker. The learned representations are used in a downstream deep modularization task to cluster frames based on speaker identity. Evaluation of the developed technique on WSJ0-2mix and WSJ0-3mix shows that the technique attains SI-SNRi and SDRi of 20.8 and 21.0 respectively in WSJ0-2mix. In WSJ0-3mix, it attains SI-SNRi and SDRi of 20.7 and 20.7 respectively in WSJ0-2mix. Its greatest strength being that as the number of speakers increase, its performance does not degrade significantly. | ['Peter Ochieng'] | 2023-05-18 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.98073864e-01 2.09427476e-01 1.60946518e-01 -1.02051072e-01
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c82f8f7b-2870-43a6-85dc-57c07676f70d | definition-modelling-for-appropriate | null | null | https://aclanthology.org/2021.emnlp-main.194 | https://aclanthology.org/2021.emnlp-main.194.pdf | Definition Modelling for Appropriate Specificity | Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems. | ['Yuki Arase', 'Tomoyuki Kajiwara', 'Han Huang'] | null | null | null | null | emnlp-2021-11 | ['definition-modelling'] | ['natural-language-processing'] | [ 9.17511344e-01 1.72340423e-01 -3.49148065e-01 -3.92554939e-01
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36a59909-5736-49a9-89d8-daced6b71c1e | contextual-causal-bayesian-optimisation | 2301.12412 | null | https://arxiv.org/abs/2301.12412v1 | https://arxiv.org/pdf/2301.12412v1.pdf | Contextual Causal Bayesian Optimisation | Causal Bayesian optimisation (CaBO) combines causality with Bayesian optimisation (BO) and shows that there are situations where the optimal reward is not achievable if causal knowledge is ignored. While CaBO exploits causal relations to determine the set of controllable variables to intervene on, it does not exploit purely observational variables and marginalises them. We show that, in general, utilising a subset of observational variables as a context to choose the values of interventional variables leads to lower cumulative regrets. We propose a general framework of contextual causal Bayesian optimisation that efficiently searches through combinations of controlled and contextual variables, known as policy scopes, and identifies the one yielding the optimum. We highlight the difficulties arising from the application of the causal acquisition function currently used in CaBO to select the policy scope in contextual settings and propose a multi-armed bandits based selection mechanism. We analytically show that well-established methods, such as contextual BO (CoBO) or CaBO, are not able to achieve the optimum in some cases, and empirically show that the proposed method achieves sub-linear regret in various environments and under different configurations. | ['Haitham Bou-Ammar', 'Antoine Grosnit', 'Vahan Arsenyan'] | 2023-01-29 | null | null | null | null | ['bayesian-optimisation', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 5.10659337e-01 3.62241596e-01 -6.72624052e-01 -1.39087349e-01
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85943013-0c43-4717-b706-a069733453e8 | the-segment-anything-model-sam-for-remote | 2306.16623 | null | https://arxiv.org/abs/2306.16623v1 | https://arxiv.org/pdf/2306.16623v1.pdf | The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot | Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images from diverse geographical contexts. Our exploration involved testing SAM across multi-scale datasets using various input prompts, such as bounding boxes, individual points, and text descriptors. To enhance the model's performance, we implemented a novel automated technique that combines a text-prompt-derived general example with one-shot training. This adjustment resulted in an improvement in accuracy, underscoring SAM's potential for deployment in remote sensing imagery and reducing the need for manual annotation. Despite the limitations encountered with lower spatial resolution images, SAM exhibits promising adaptability to remote sensing data analysis. We recommend future research to enhance the model's proficiency through integration with supplementary fine-tuning techniques and other networks. Furthermore, we provide the open-source code of our modifications on online repositories, encouraging further and broader adaptations of SAM to the remote sensing domain. | ['José Marcato Junior', 'Jonathan Li', 'Ana Paula Marques Ramos', 'Wesley Nunes Gonçalves', 'Eduardo Lopes de Lemos', 'Qiusheng Wu', 'Lucas Prado Osco'] | 2023-06-29 | null | null | null | null | ['zero-shot-learning'] | ['methodology'] | [ 6.59010470e-01 1.56968459e-02 -1.06609315e-01 -4.42472368e-01
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16492d4e-0531-4a4a-8460-a9590be9daa7 | va-gcn-a-vector-attention-graph-convolution | 2106.00227 | null | https://arxiv.org/abs/2106.00227v1 | https://arxiv.org/pdf/2106.00227v1.pdf | VA-GCN: A Vector Attention Graph Convolution Network for learning on Point Clouds | Owing to the development of research on local aggregation operators, dramatic breakthrough has been made in point cloud analysis models. However, existing local aggregation operators in the current literature fail to attach decent importance to the local information of the point cloud, which limits the power of the models. To fit this gap, we propose an efficient Vector Attention Convolution module (VAConv), which utilizes K-Nearest Neighbor (KNN) to extract the neighbor points of each input point, and then uses the elevation and azimuth relationship of the vectors between the center point and its neighbors to construct an attention weight matrix for edge features. Afterwards, the VAConv adopts a dual-channel structure to fuse weighted edge features and global features. To verify the efficiency of the VAConv, we connect the VAConvs with different receptive fields in parallel to obtain a Multi-scale graph convolutional network, VA-GCN. The proposed VA-GCN achieves state-of-the-art performance on standard benchmarks including ModelNet40, S3DIS and ShapeNet. Remarkably, on the ModelNet40 dataset for 3D classification, VA-GCN increased by 2.4% compared to the baseline. | ['Huixiao Le', 'Fanyi Wang', 'Haotian Hu'] | 2021-06-01 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-3.63421291e-01 -3.65916580e-01 2.05782324e-01 -2.17245266e-01
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ebf76e8a-c693-4a9f-83e8-449340ce71a6 | learnable-weight-initialization-for | 2306.09320 | null | https://arxiv.org/abs/2306.09320v3 | https://arxiv.org/pdf/2306.09320v3.pdf | Learnable Weight Initialization for Volumetric Medical Image Segmentation | Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention. While mainly focusing on architectural modifications, most existing hybrid approaches still use conventional data-independent weight initialization schemes which restrict their performance due to ignoring the inherent volumetric nature of the medical data. To address this issue, we propose a learnable weight initialization approach that utilizes the available medical training data to effectively learn the contextual and structural cues via the proposed self-supervised objectives. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach, leading to state-of-the-art segmentation performance. Our proposed data-dependent initialization approach performs favorably as compared to the Swin-UNETR model pretrained using large-scale datasets on multi-organ segmentation task. Our source code and models are available at: https://github.com/ShahinaKK/LWI-VMS. | ['Fahad Shahbaz Khan', 'Salman Khan', 'Muzammal Naseer', 'Abdelrahman Shaker', 'Shahina Kunhimon'] | 2023-06-15 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 7.65864775e-02 1.62124887e-01 -4.01502281e-01 -4.94638741e-01
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8fd70fc9-a7cc-46a4-8253-dde1b0685804 | probabilistic-robustness-analysis-for-dnns | 2101.10102 | null | https://arxiv.org/abs/2101.10102v2 | https://arxiv.org/pdf/2101.10102v2.pdf | Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning | To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. From the learned model, we can infer the corresponding PAC-model robustness property. The innovation of our work is the integration of model learning into PAC robustness analysis: that is, we construct a PAC guarantee on the model level instead of sample distribution, which induces a more faithful and accurate robustness evaluation. This is in contrast to existing statistical methods without model learning. We implement our method in a prototypical tool named DeepPAC. As a black-box method, DeepPAC is scalable and efficient, especially when DNNs have complex structures or high-dimensional inputs. We extensively evaluate DeepPAC, with 4 baselines (using formal verification, statistical methods, testing and adversarial attack) and 20 DNN models across 3 datasets, including MNIST, CIFAR-10, and ImageNet. It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN. Also, its results are consistent with existing DNN testing work like DeepGini. | ['Lijun Zhang', 'Youcheng Sun', 'Bai Xue', 'Cheng-Chao Huang', 'Pengfei Yang', 'Renjue Li'] | 2021-01-25 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 7.95211494e-02 3.06161195e-01 -3.71364564e-01 -1.47622988e-01
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63a0b016-718d-4dd3-9fbb-d61677365196 | a-rule-based-approach-to-aspect-extraction | null | null | https://aclanthology.org/W14-5905 | https://aclanthology.org/W14-5905.pdf | A Rule-Based Approach to Aspect Extraction from Product Reviews | null | ['er', 'Lun-Wei Ku', 'Soujanya Poria', 'Chen Gui', 'Alex Gelbukh', 'Erik Cambria'] | 2014-08-01 | null | null | null | ws-2014-8 | ['aspect-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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3fa750c5-427c-4539-88f4-7a2482a8f1c4 | quality-assessment-of-image-matchers-for-dsm | 2108.08369 | null | https://arxiv.org/abs/2108.08369v1 | https://arxiv.org/pdf/2108.08369v1.pdf | Quality assessment of image matchers for DSM generation -- a comparative study based on UAV images | Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pix4UAV and an SGM implementation from DLR. | ['Cive Fraser', 'Armin Gruen', 'Rongjun Qin'] | 2021-08-18 | null | null | null | null | ['3d-surface-generation'] | ['computer-vision'] | [ 6.12405717e-01 -3.25347751e-01 4.27127957e-01 -4.87320930e-01
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7e4efed4-d271-4120-87b1-bc22707860d5 | unified-loss-of-pair-similarity-optimization | 2209.13869 | null | https://arxiv.org/abs/2209.13869v2 | https://arxiv.org/pdf/2209.13869v2.pdf | Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval | There are two popular loss functions used for vision-language retrieval, i.e., triplet loss and contrastive learning loss, both of them essentially minimize the difference between the similarities of negative pairs and positive pairs. More specifically, Triplet loss with Hard Negative mining (Triplet-HN), which is widely used in existing retrieval models to improve the discriminative ability, is easy to fall into local minima in training. On the other hand, Vision-Language Contrastive learning loss (VLC), which is widely used in the vision-language pre-training, has been shown to achieve significant performance gains on vision-language retrieval, but the performance of fine-tuning with VLC on small datasets is not satisfactory. This paper proposes a unified loss of pair similarity optimization for vision-language retrieval, providing a powerful tool for understanding existing loss functions. Our unified loss includes the hard sample mining strategy of VLC and introduces the margin used by the triplet loss for better similarity separation. It is shown that both Triplet-HN and VLC are special forms of our unified loss. Compared with the Triplet-HN, our unified loss has a fast convergence speed. Compared with the VLC, our unified loss is more discriminative and can provide better generalization in downstream fine-tuning tasks. Experiments on image-text and video-text retrieval benchmarks show that our unified loss can significantly improve the performance of the state-of-the-art retrieval models. | ['Zhongtian Du', 'Jenq-Neng Hwang', 'Zerun Feng', 'Xin Wang', 'Caili Guo', 'Zheng Li'] | 2022-09-28 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-2.78102398e-01 -7.41065502e-01 -6.24774098e-01 -3.25829327e-01
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b82a7bfa-2bbb-41f9-97f9-179020cf3c34 | multiple-hankel-matrix-rank-minimization-for | 2303.18023 | null | https://arxiv.org/abs/2303.18023v1 | https://arxiv.org/pdf/2303.18023v1.pdf | Multiple Hankel matrix rank minimization for audio inpainting | Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structured matrices constructed from time-domain signal blocks. We adapt their approach to solve the audio inpainting problem, where samples are missing in the signal. We analyze the algorithm and provide modifications, some of them leading to an improved performance. Overall, it turns out that the new algorithms perform reasonably well for speech signals but they are not competitive in the case of music signals. | ['Ondřej Mokrý', 'Pavel Rajmic', 'Pavel Záviška'] | 2023-03-31 | null | null | null | null | ['audio-declipping', 'audio-inpainting'] | ['audio', 'audio'] | [ 5.10805011e-01 2.70190328e-01 4.80817296e-02 1.46484703e-01
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e33d113e-2c2a-45aa-871b-3c7fb72b4cec | ae-red-a-hyperspectral-unmixing-framework | 2307.00269 | null | https://arxiv.org/abs/2307.00269v1 | https://arxiv.org/pdf/2307.00269v1.pdf | AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising | Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information. In particular, autoencoder based architectures are elaborately designed to solve blind unmixing and model complex nonlinear mixtures. Nevertheless, these methods perform unmixing task as blackboxes and lack of interpretability. On the other hand, conventional unmixing methods carefully design the regularizer to add explicit information, in which algorithms such as plug-and-play (PnP) strategies utilize off-the-shelf denoisers to plug powerful priors. In this paper, we propose a generic unmixing framework to integrate the autoencoder network with regularization by denoising (RED), named AE-RED. More specially, we decompose the unmixing optimized problem into two subproblems. The first one is solved using deep autoencoders to implicitly regularize the estimates and model the mixture mechanism. The second one leverages the denoiser to bring in the explicit information. In this way, both the characteristics of the deep autoencoder based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance. Experiment results on both synthetic and real data sets show the superiority of our proposed framework compared with state-of-the-art unmixing approaches. | ['Nicolas Dobigeon', 'Jie Chen', 'Min Zhao'] | 2023-07-01 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [-1.55942261e-01 -3.02332729e-01 -3.03410720e-02 -3.20542119e-02
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4dd26f0b-b71a-4135-9837-f62080ec19bd | tram-a-token-level-retrieval-augmented | 2305.11074 | null | https://arxiv.org/abs/2305.11074v1 | https://arxiv.org/pdf/2305.11074v1.pdf | Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization | Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although Neural Language Models achieve significant performance in this field, an emerging trend is combining neural models with external knowledge. Most previous approaches rely on the sentence-level retrieval and combination paradigm (retrieval of similar code snippets and use of the corresponding code and summary pairs) on the encoder side. However, this paradigm is coarse-grained and cannot directly take advantage of the high-quality retrieved summary tokens on the decoder side. In this paper, we explore a fine-grained token-level retrieval-augmented mechanism on the decoder side to help the vanilla neural model generate a better code summary. Furthermore, to mitigate the limitation of token-level retrieval on capturing contextual code semantics, we propose to integrate code semantics into summary tokens. Extensive experiments and human evaluation reveal that our token-level retrieval-augmented approach significantly improves performance and is more interpretive. | ['Shouling Ji', 'Wenhai Wang', 'Peiyu Liu', 'Yangkai Du', 'Xuhong Zhang', 'Tengfei Ma', 'Lingfei Wu', 'Tong Ye'] | 2023-05-18 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.58216822e-01 1.97745889e-01 -4.15600985e-01 -2.10808694e-01
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5d8f72ec-4b79-4748-a330-583217501508 | residual-pattern-learning-for-pixel-wise-out | 2211.14512 | null | https://arxiv.org/abs/2211.14512v1 | https://arxiv.org/pdf/2211.14512v1.pdf | Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation | Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on their ability not only to classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., country surroundings) outside the training set (e.g., city surroundings). In this paper, we mitigate these issues with: (i) a new residual pattern learning (RPL) module that assists the segmentation model to detect OoD pixels without affecting the inlier segmentation performance; and (ii) a novel context-robust contrastive learning (CoroCL) that enforces RPL to robustly detect OoD pixels among various contexts. Our approach improves by around 10\% FPR and 7\% AuPRC the previous state-of-the-art in Fishyscapes, Segment-Me-If-You-Can, and RoadAnomaly datasets. Our code is available at: https://github.com/yyliu01/RPL. | ['Gustavo Carneiro', 'Ian Reid', 'Vasileios Belagiannis', 'Guansong Pang', 'Yu Tian', 'Choubo Ding', 'Yuyuan Liu'] | 2022-11-26 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.84716448e-01 1.51346549e-01 -2.42144298e-02 -2.09636062e-01
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bb9b9391-05ec-41d9-86e3-f6ed37f562d2 | neto-neural-reconstruction-of-transparent | 2303.11219 | null | https://arxiv.org/abs/2303.11219v1 | https://arxiv.org/pdf/2303.11219v1.pdf | NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing | We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for general-purpose reconstruction techniques due to the specular light transport phenomena. Although existing refraction-tracing based methods, designed specially for this task, achieve impressive results, they still suffer from unstable optimization and loss of fine details, since the explicit surface representation they adopted is difficult to be optimized, and the self-occlusion problem is ignored for refraction-tracing. In this paper, we propose to leverage implicit Signed Distance Function (SDF) as surface representation, and optimize the SDF field via volume rendering with a self-occlusion aware refractive ray tracing. The implicit representation enables our method to be capable of reconstructing high-quality reconstruction even with a limited set of images, and the self-occlusion aware strategy makes it possible for our method to accurately reconstruct the self-occluded regions. Experiments show that our method achieves faithful reconstruction results and outperforms prior works by a large margin. Visit our project page at \url{https://www.xxlong.site/NeTO/} | ['Chunxia Xiao', 'Fei Luo', 'Wenping Wang', 'Tuo Cao', 'Yusen Wang', 'Xiaoxiao Long', 'Zongcheng Li'] | 2023-03-20 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 2.90305138e-01 -5.13966680e-02 2.38965943e-01 -1.70498371e-01
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c1d2719b-eb8d-40e7-922d-8810a968002a | epvt-environment-aware-prompt-vision | 2304.01508 | null | https://arxiv.org/abs/2304.01508v3 | https://arxiv.org/pdf/2304.01508v3.pdf | EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition | Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent research has revealed that deep neural networks for skin lesion recognition may overly depend on disease-irrelevant image artifacts (i.e., dark corners, dense hairs), leading to poor generalization in unseen environments. To address this issue, we propose a novel domain generalization method called EPVT, which involves embedding prompts into the vision transformer to collaboratively learn knowledge from diverse domains. Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset. To facilitate knowledge sharing and the interaction of different prompts, we introduce a domain prompt generator that enables low-rank multiplicative updates between domain prompts and the shared prompt. A domain mixup strategy is additionally devised to reduce the co-occurring artifacts in each domain, which allows for more flexible decision margins and mitigates the issue of incorrectly assigned domain labels. Experiments on four out-of-distribution datasets and six different biased ISIC datasets demonstrate the superior generalization ability of EPVT in skin lesion recognition across various environments. Code is avaliable at https://github.com/SiyuanYan1/EPVT. | ['ZongYuan Ge', 'Peter Soyer', 'Monika Janda', 'Victoria Mar', 'Dwarikanath Mahapatrainst', 'Lie Ju', 'Zhen Yu', 'Chi Liu', 'Siyuan Yan'] | 2023-04-04 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 3.82077396e-01 -2.11523607e-01 -2.32241571e-01 -4.21827316e-01
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11f2d94f-bee8-4ddf-b98c-c91f1779b4a0 | snider-single-noisy-image-denoising-and | 1910.03876 | null | https://arxiv.org/abs/1910.03876v1 | https://arxiv.org/pdf/1910.03876v1.pdf | SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition | In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rectification, and each task is conducted by Convolutional Neural Networks. Existing denoising and rectification have been treated separately as a single network in previous research. In contrast to the previous work, we here propose an end-to-end trainable network for image recovery, Single Noisy Image DEnoising and Rectification (SNIDER), which focuses on solving both the problems jointly. It overcomes those obstacles by designing a novel network to address the denoising and rectification jointly. Moreover, we propose a way to leverage optimization with the auxiliary tasks for multi-task fitting and novel training losses. Extensive experiments on two challenging LPR datasets demonstrate the effectiveness of our proposed method in recovering the high-quality license plate image from the low-quality one and show that the the proposed method outperforms other state-of-the-art methods. | ['Younkwan Lee', 'Moongu Jeon', 'Juhyun Lee', 'Hoyeon Ahn'] | 2019-10-09 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.24522668e-01 -4.15399909e-01 1.70440748e-01 -3.80760521e-01
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3016b9df-0bf8-4f2c-9e0a-40b5189c4bd8 | a-deep-complex-network-with-multi-frame | 2202.01630 | null | https://arxiv.org/abs/2202.01630v2 | https://arxiv.org/pdf/2202.01630v2.pdf | A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation | In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, various types of noise in communication environments further reduce speech quality and intelligibility. It is difficult to extract the near-end signal from the microphone signal within one step, especially in low signal-to-noise ratio scenarios. In this paper, we propose a deep complex network approach to address this issue. Specially, we decompose the stereophonic acoustic echo cancellation into two stages, including linear stereophonic acoustic echo cancellation module and residual echo suppression module, where both modules are based on deep learning architectures. A multi-frame filtering strategy is introduced to benefit the estimation of linear echo by capturing more inter-frame information. Moreover, we decouple the complex spectral mapping into magnitude estimation and complex spectrum refinement. Experimental results demonstrate that our proposed approach achieves stage-of-the-art performance over previous advanced algorithms under various conditions. | ['Renhua Peng', 'Yuquan Wu', 'XiaoDong Li', 'Andong Li', 'Chengshi Zheng', 'Linjuan Cheng'] | 2022-02-03 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.54903704e-01 -4.14203346e-01 6.06796026e-01 -7.64354914e-02
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f3ba9f51-7cd5-4a2d-9b8f-d8a506ee2009 | fractal-measures-of-image-local-features-an | 2108.12491 | null | https://arxiv.org/abs/2108.12491v1 | https://arxiv.org/pdf/2108.12491v1.pdf | Fractal measures of image local features: an application to texture recognition | Here we propose a new method for the classification of texture images combining fractal measures (fractal dimension, multifractal spectrum and lacunarity) with local binary patterns. More specifically we compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector. The proposal is assessed in the classification of three benchmark databases: KTHTIPS-2b, UMD and UIUC as well as in a real-world problem, namely the identification of Brazilian plant species (database 1200Tex) using scanned images of their leaves. The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature. Such results confirmed the potential of combining a powerful local coding description with the multiscale information captured by the fractal dimension for texture classification. | ['Joao B. Florindo', 'Pedro M. Silva'] | 2021-08-27 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.53633559e-01 -4.01087373e-01 -1.10852025e-01 3.17519642e-02
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94ce66a6-f7db-4840-bda4-960e1f23fcfe | dymslam4d-dynamic-scene-reconstruction-based | 2003.04569 | null | https://arxiv.org/abs/2003.04569v1 | https://arxiv.org/pdf/2003.04569v1.pdf | DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation | Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects. We at first detect and match the interesting points between successive frames by using traditional SLAM methods. Then the interesting points belonging to different motion models (including ego-motion and motion models of rigid moving objects) are segmented by a multi-model fitting approach. Based on the interesting points belonging to the ego-motion, we are able to estimate the trajectory of the camera and reconstruct the static background. The interesting points belonging to the motion models of rigid moving objects are then used to estimate their relative motion models to the camera and reconstruct the 3D models of the objects. We then transform the relative motion to the trajectories of the moving objects in the global reference frame. Finally, we then fuse the 3D models of the moving objects into the 3D map of the environment by considering their motion trajectories to obtain a 4D (3D+time) sequence. DymSLAM obtains information about the dynamic objects instead of ignoring them and is suitable for unknown rigid objects. Hence, the proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects. We conducted experiments in a real-world environment where both the camera and the objects were moving in a wide range. | ['Xin Su', 'Lu Yin', 'Chengyuan Li', 'Yajun Wang', 'Bin Luo', 'Yun Zhang', 'Qing Zhao', 'Wei Wang', 'Chenjie Wang'] | 2020-03-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-1.00132428e-01 -4.63601023e-01 1.55003816e-01 -5.52322119e-02
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c438285d-33dd-472d-a6d8-3a37d3c959be | online-clustering-based-multi-camera-vehicle | 2102.04091 | null | https://arxiv.org/abs/2102.04091v1 | https://arxiv.org/pdf/2102.04091v1.pdf | Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs | Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high latency and post-processing requirements. In this paper, we present a new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view (FOVs), such as road intersections. Firstly, the proposed approach detects vehicles at each camera. Then, the detections are merged between cameras by applying cross-camera clustering based on appearance and location. Lastly, the clusters containing different detections of the same vehicle are temporally associated to compute the tracks on a frame-by-frame basis. The experiments show promising low-latency results while addressing real-world challenges such as the a priori unknown and time-varying number of targets and the continuous state estimation of them without performing any post-processing of the trajectories. | ['Marcos Escudero-Viñolo', 'Jose M. Martínez', 'Juan C. SanMiguel', 'Elena Luna'] | 2021-02-08 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 7.27279037e-02 -7.24181235e-01 -8.32941234e-02 -2.25410610e-01
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e987888c-443e-4287-b6c9-b00f368dc24d | learning-from-extreme-bandit-feedback | 2009.12947 | null | https://arxiv.org/abs/2009.12947v2 | https://arxiv.org/pdf/2009.12947v2.pdf | Learning from eXtreme Bandit Feedback | We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions are made over sets consisting of millions of choices in a single day, yielding massive observational data. In these large-scale real-world applications, supervised learning frameworks such as eXtreme Multi-label Classification (XMC) are widely used despite the fact that they incur significant biases due to the mismatch between bandit feedback and supervised labels. Such biases can be mitigated by importance sampling techniques, but these techniques suffer from impractical variance when dealing with a large number of actions. In this paper, we introduce a selective importance sampling estimator (sIS) that operates in a significantly more favorable bias-variance regime. The sIS estimator is obtained by performing importance sampling on the conditional expectation of the reward with respect to a small subset of actions for each instance (a form of Rao-Blackwellization). We employ this estimator in a novel algorithmic procedure -- named Policy Optimization for eXtreme Models (POXM) -- for learning from bandit feedback on XMC tasks. In POXM, the selected actions for the sIS estimator are the top-p actions of the logging policy, where p is adjusted from the data and is significantly smaller than the size of the action space. We use a supervised-to-bandit conversion on three XMC datasets to benchmark our POXM method against three competing methods: BanditNet, a previously applied partial matching pruning strategy, and a supervised learning baseline. Whereas BanditNet sometimes improves marginally over the logging policy, our experiments show that POXM systematically and significantly improves over all baselines. | ['Inderjit S. Dhillon', 'Michael. I. Jordan', 'Romain Lopez'] | 2020-09-27 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.92412072e-01 -7.49948844e-02 -8.91571403e-01 -3.96982789e-01
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e1909332-b23e-4acb-9050-7e1f6d01f42c | metal-inpainting-in-cbct-projections-using | 2209.09733 | null | https://arxiv.org/abs/2209.09733v1 | https://arxiv.org/pdf/2209.09733v1.pdf | Metal Inpainting in CBCT Projections Using Score-based Generative Model | During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems. Due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality greatly. To reduce the artifacts, many metal artifact reduction algorithms have been developed and metal inpainting in projection domain is an essential step. In this work, a score-based generative model is trained on simulated knee projections and the inpainted image is obtained by removing the noise in conditional resampling process. The result implies that the inpainted images by score-based generative model have more detailed information and achieve the lowest mean absolute error and the highest peak-signal-to-noise-ratio compared with interpolation and CNN based method. Besides, the score-based model can also recover projections with big circlar and rectangular masks, showing its generalization in inpainting task. | ['Andreas Maier', 'Fuxin Fan', 'Siyuan Mei'] | 2022-09-20 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.51235545e-01 1.30531833e-01 3.74907196e-01 1.30346283e-01
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58cc08d6-46b1-4665-bc06-a714fc35c8cb | a-two-stage-deep-network-for-high-dynamic | 2104.09386 | null | https://arxiv.org/abs/2104.09386v1 | https://arxiv.org/pdf/2104.09386v1.pdf | A Two-stage Deep Network for High Dynamic Range Image Reconstruction | Mapping a single exposure low dynamic range (LDR) image into a high dynamic range (HDR) is considered among the most strenuous image to image translation tasks due to exposure-related missing information. This study tackles the challenges of single-shot LDR to HDR mapping by proposing a novel two-stage deep network. Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings. Therefore, we aim to perform image enhancement task like denoising, exposure correction, etc., in the first stage. Additionally, the second stage of our deep network learns tone mapping and bit-expansion from a convex set of data samples. The qualitative and quantitative comparisons demonstrate that the proposed method can outperform the existing LDR to HDR works with a marginal difference. Apart from that, we collected an LDR image dataset incorporating different camera systems. The evaluation with our collected real-world LDR images illustrates that the proposed method can reconstruct plausible HDR images without presenting any visual artefacts. Code available: https://github. com/sharif-apu/twostageHDR_NTIRE21. | ['Kim Sungjun', 'Mithun Biswas', 'Rizwan Ali Naqvi', 'SMA Sharif'] | 2021-04-19 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.51110512e-01 -2.94970930e-01 2.79722452e-01 -3.25038821e-01
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a8837677-8157-4f35-be69-bb6dc11a4012 | patched-diffusion-models-for-unsupervised | 2303.03758 | null | https://arxiv.org/abs/2303.03758v1 | https://arxiv.org/pdf/2303.03758v1.pdf | Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI | The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines. | ['Alexander Schlaefer', 'Roland Opfer', 'Julia Krüger', 'Debayan Bhattacharya', 'Finn Behrendt'] | 2023-03-07 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 5.50520182e-01 3.99010092e-01 4.04958278e-01 -6.07266724e-01
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7710169b-101f-444f-acdb-4bd68c76f7c2 | neural-quality-estimation-with-multiple | 2105.04443 | null | https://arxiv.org/abs/2105.04443v1 | https://arxiv.org/pdf/2105.04443v1.pdf | Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction | Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC evidence from different hypotheses. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. VERNet establishes interactions among hypotheses with a reasoning graph and conducts two kinds of attention mechanisms to propagate GEC evidence to verify the quality of generated hypotheses. Our experiments on four GEC datasets show that VERNet achieves state-of-the-art grammatical error detection performance, achieves the best quality estimation results, and significantly improves GEC performance by reranking hypotheses. All data and source codes are available at https://github.com/thunlp/VERNet. | ['Tat-Seng Chua', 'Liner Yang', 'Maosong Sun', 'Xiaoyuan Yi', 'Zhenghao Liu'] | 2021-05-10 | null | https://aclanthology.org/2021.naacl-main.429 | https://aclanthology.org/2021.naacl-main.429.pdf | naacl-2021-4 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-5.32774962e-02 1.84717163e-01 -3.98347937e-02 -5.83899200e-01
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c646694c-250c-4aa8-9487-9124abd1a92e | first-order-penalty-methods-for-bilevel | 2301.01716 | null | https://arxiv.org/abs/2301.01716v1 | https://arxiv.org/pdf/2301.01716v1.pdf | First-order penalty methods for bilevel optimization | In this paper we study a class of unconstrained and constrained bilevel optimization problems in which the lower-level part is a convex optimization problem, while the upper-level part is possibly a nonconvex optimization problem. In particular, we propose penalty methods for solving them, whose subproblems turn out to be a structured minimax problem and are suitably solved by a first-order method developed in this paper. Under some suitable assumptions, an \emph{operation complexity} of ${\cal O}(\varepsilon^{-4}\log\varepsilon^{-1})$ and ${\cal O}(\varepsilon^{-7}\log\varepsilon^{-1})$, measured by their fundamental operations, is established for the proposed penalty methods for finding an $\varepsilon$-KKT solution of the unconstrained and constrained bilevel optimization problems, respectively. To the best of our knowledge, the methodology and results in this paper are new. | ['Sanyou Mei', 'Zhaosong Lu'] | 2023-01-04 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 2.48622820e-01 3.33480984e-01 -1.66141525e-01 -9.95688662e-02
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a370bc7a-a68f-4648-ae34-5c386979717a | multiearth-2022-multimodal-learning-for-earth | 2204.07649 | null | https://arxiv.org/abs/2204.07649v3 | https://arxiv.org/pdf/2204.07649v3.pdf | MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge | The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022) will be the first competition aimed at the monitoring and analysis of deforestation in the Amazon rainforest at any time and in any weather conditions. The goal of the Challenge is to provide a common benchmark for multimodal information processing and to bring together the earth and environmental science communities as well as multimodal representation learning communities to compare the relative merits of the various multimodal learning methods to deforestation estimation under well-defined and strictly comparable conditions. MultiEarth 2022 will have three sub-challenges: 1) matrix completion, 2) deforestation estimation, and 3) image-to-image translation. This paper presents the challenge guidelines, datasets, and evaluation metrics for the three sub-challenges. Our challenge website is available at https://sites.google.com/view/rainforest-challenge. | ['Jean Piou', 'Brandon Swenson', 'Yen-Chen Lin', 'Bill Freeman', 'Taylor Perron', 'Phillip Isola', 'Armando Cabrera', 'Sam Goldberg', 'Mark Hamilton', 'Gregory Angelides', 'Morgan Schmidt', 'Kuan Wei Huang', 'Miriam Cha'] | 2022-04-15 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.40845704e-01 -4.38197821e-01 -1.71443418e-01 -3.61574113e-01
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12eb7105-3812-4775-aabb-c3c206370276 | flow-based-density-of-states-for-complex | 2203.01243 | null | https://arxiv.org/abs/2203.01243v1 | https://arxiv.org/pdf/2203.01243v1.pdf | Flow-based density of states for complex actions | Emerging sampling algorithms based on normalizing flows have the potential to solve ergodicity problems in lattice calculations. Furthermore, it has been noted that flows can be used to compute thermodynamic quantities which are difficult to access with traditional methods. This suggests that they are also applicable to the density-of-states approach to complex action problems. In particular, flow-based sampling may be used to compute the density directly, in contradistinction to the conventional strategy of reconstructing it via measuring and integrating the derivative of its logarithm. By circumventing this procedure, the accumulation of errors from the numerical integration is avoided completely and the overall normalization factor can be determined explicitly. In this proof-of-principle study, we demonstrate our method in the context of two-component scalar field theory where the $O(2)$ symmetry is explicitly broken by an imaginary external field. First, we concentrate on the zero-dimensional case which can be solved exactly. We show that with our method, the Lee-Yang zeroes of the associated partition function can be successfully located. Subsequently, we confirm that the flow-based approach correctly reproduces the density computed with conventional methods in one- and two-dimensional models. | ['Julian M. Urban', 'Jan M. Pawlowski'] | 2022-03-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 3.71570766e-01 -5.23882434e-02 -4.92640175e-02 8.05139989e-02
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a9893f01-8a69-49ac-9e19-9edc3c0fa7ed | low-resource-compositional-semantic-parsing | 2301.09809 | null | https://arxiv.org/abs/2301.09809v2 | https://arxiv.org/pdf/2301.09809v2.pdf | Low-Resource Compositional Semantic Parsing with Concept Pretraining | Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve the capabilities of a voice assistant by adding a new domain, the underlying semantic parsing model needs to be retrained using thousands of annotated examples from the new domain, which is time-consuming and expensive. In this work, we present an architecture to perform such domain adaptation automatically, with only a small amount of metadata about the new domain and without any new training data (zero-shot) or with very few examples (few-shot). We use a base seq2seq (sequence-to-sequence) architecture and augment it with a concept encoder that encodes intent and slot tags from the new domain. We also introduce a novel decoder-focused approach to pretrain seq2seq models to be concept aware using Wikidata and use it to help our model learn important concepts and perform well in low-resource settings. We report few-shot and zero-shot results for compositional semantic parsing on the TOPv2 dataset and show that our model outperforms prior approaches in few-shot settings for the TOPv2 and SNIPS datasets. | ['Mukund Sridhar', 'Andrew McCallum', 'Wael Hamza', 'Konstantine Arkoudas', 'Haidar Khan', 'Subendhu Rongali'] | 2023-01-24 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.47994143e-01 6.43818676e-01 -1.18528329e-01 -7.56179452e-01
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5ddb6b02-8953-4dd6-aefa-c29c511bab6b | end-to-end-out-of-distribution-detection-with | 2307.00519 | null | https://arxiv.org/abs/2307.00519v1 | https://arxiv.org/pdf/2307.00519v1.pdf | End-to-End Out-of-distribution Detection with Self-supervised Sampling | Out-of-distribution (OOD) detection empowers the model trained on the closed set to identify unknown data in the open world. Though many prior techniques have yielded considerable improvements, two crucial obstacles still remain. Firstly, a unified perspective has yet to be presented to view the developed arts with individual designs, which is vital for providing insights into the related directions. Secondly, most research focuses on the post-processing schemes of the pre-trained features while disregarding the superiority of end-to-end training, dramatically limiting the upper bound of OOD detection. To tackle these issues, we propose a general probabilistic framework to interpret many existing methods and an OOD-data-free model, namely Self-supervised Sampling for OOD Detection (SSOD), to unfold the potential of end-to-end learning. SSOD efficiently exploits natural OOD signals from the in-distribution (ID) data based on the local property of convolution. With these supervisions, it jointly optimizes the OOD detection and conventional ID classification. Extensive experiments reveal that SSOD establishes competitive state-of-the-art performance on many large-scale benchmarks, where it outperforms the most recent approaches, such as KNN, by a large margin, e.g., 48.99% to 35.52% on SUN at FPR95. | ['Xun Wang', 'Xinglong Wu', 'Qi Chen', 'Peng Qin', 'Jiaxi Sun', 'Sen Pei'] | 2023-07-02 | null | null | null | null | ['out-of-distribution-detection', 'ood-detection'] | ['computer-vision', 'computer-vision'] | [ 4.22173031e-02 -1.21993862e-01 -5.28750360e-01 -1.90498292e-01
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cc3c3447-7e03-420d-b97c-84381391c054 | exploiting-unlabeled-data-for-target-oriented | 2208.08280 | null | https://arxiv.org/abs/2208.08280v1 | https://arxiv.org/pdf/2208.08280v1.pdf | Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction | Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the scarcity of training data due to the expensive data annotation process. Limited labeled data increase the risk of distribution shift between test data and training data. In this paper, we propose exploiting massive unlabeled data to reduce the risk by increasing the exposure of the model to varying distribution shifts. Specifically, we propose a novel Multi-Grained Consistency Regularization (MGCR) method to make use of unlabeled data and design two filters specifically for TOWE to filter noisy data at different granularity. Extensive experimental results on four TOWE benchmark datasets indicate the superiority of MGCR compared with current state-of-the-art methods. The in-depth analysis also demonstrates the effectiveness of the different-granularity filters. Our codes are available at https://github.com/TOWESSL/TOWESSL. | ['Yue Zhang', 'Manabu Okumura', 'Takahiro Shinozaki', 'Jindong Wang', 'Zhen Wu', 'Wenxin Hou', 'Ao Liu', 'Hao Wu', 'Yidong Wang'] | 2022-08-17 | null | https://aclanthology.org/2022.coling-1.617 | https://aclanthology.org/2022.coling-1.617.pdf | coling-2022-10 | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [-7.16685876e-02 -3.72247517e-01 -2.27746256e-02 -7.42685795e-01
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44419536-0f9e-4090-811d-e0965573e73d | artiboost-boosting-articulated-3d-hand-object | 2109.05488 | null | https://arxiv.org/abs/2109.05488v2 | https://arxiv.org/pdf/2109.05488v2.pdf | ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis | Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, we propose ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training. We apply ArtiBoost on a simple learning baseline network and witness the performance boost on several hand-object benchmarks. Our models and code are available at https://github.com/lixiny/ArtiBoost. | ['Cewu Lu', 'Jiefeng Li', 'Wenqiang Xu', 'Jun Lv', 'Xinyu Zhan', 'Lixin Yang', 'Kailin Li'] | 2021-09-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['hand-object-pose'] | ['computer-vision'] | [-1.64089158e-01 -3.12162697e-01 -3.04691523e-01 -3.15395117e-01
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009cb4a3-1f55-46fc-806e-1297763765e4 | active-speakers-in-context | 2005.09812 | null | https://arxiv.org/abs/2005.09812v1 | https://arxiv.org/pdf/2005.09812v1.pdf | Active Speakers in Context | Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker. Although this strategy can be enough for addressing single-speaker scenarios, it prevents accurate detection when the task is to identify who of many candidate speakers are talking. This paper introduces the Active Speaker Context, a novel representation that models relationships between multiple speakers over long time horizons. Our Active Speaker Context is designed to learn pairwise and temporal relations from an structured ensemble of audio-visual observations. Our experiments show that a structured feature ensemble already benefits the active speaker detection performance. Moreover, we find that the proposed Active Speaker Context improves the state-of-the-art on the AVA-ActiveSpeaker dataset achieving a mAP of 87.1%. We present ablation studies that verify that this result is a direct consequence of our long-term multi-speaker analysis. | ['Pablo Arbelaez', 'Joon-Young Lee', 'Long Mai', 'Juan Leon Alcazar', 'Federico Perazzi', 'Fabian Caba Heilbron', 'Bernard Ghanem'] | 2020-05-20 | active-speakers-in-context-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.pdf | cvpr-2020-6 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.95349425e-01 -7.21989712e-03 -9.03693810e-02 -5.02005935e-01
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c466df3f-7b5e-4c7e-b70c-028c4f9acfda | 3dqd-generalized-deep-3d-shape-prior-via-part | 2303.10406 | null | https://arxiv.org/abs/2303.10406v1 | https://arxiv.org/pdf/2303.10406v1.pdf | 3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process | We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE) is utilized to index local geometry from a compactly learned codebook based on a broad set of task training data. On the other hand, a discrete diffusion generator is introduced to model the inherent structural dependencies among different tokens. In the meantime, a multi-frequency fusion module (MFM) is developed to suppress high-frequency shape feature fluctuations, guided by multi-frequency contextual information. The above designs jointly equip our proposed 3D shape prior model with high-fidelity, diverse features as well as the capability of cross-modality alignment, and extensive experiments have demonstrated superior performances on various 3D shape generation tasks. | ['Fuzhen Wang', 'Yutian Liu', 'Yilin Sun', 'Bingbing Ni', 'Xuanhong Chen', 'Yishun Dou', 'Yuhan Li'] | 2023-03-18 | null | null | null | null | ['point-cloud-completion', '3d-shape-generation'] | ['computer-vision', 'computer-vision'] | [-1.25313312e-01 -2.43634842e-02 2.27856353e-01 -1.63396716e-01
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46d60808-7b33-4be9-aaf1-1b05893f120d | gafar-graph-attention-feature-augmentation | 2307.02339 | null | https://arxiv.org/abs/2307.02339v1 | https://arxiv.org/pdf/2307.02339v1.pdf | GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm | Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://github.com/mordecaimalignatius/GAFAR/. | ['Friedrich Fraundorfer', 'Ismail Geles', 'Ludwig Mohr'] | 2023-07-05 | null | null | null | null | ['3d-scene-reconstruction', 'graph-attention'] | ['computer-vision', 'graphs'] | [ 4.40518185e-02 -1.59352094e-01 1.40362322e-01 -3.79040480e-01
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6508760b-96cf-4a22-9e9f-ae9f35d5abc7 | multi-channel-time-series-person-and-soft | 2304.01585 | null | https://arxiv.org/abs/2304.01585v1 | https://arxiv.org/pdf/2304.01585v1.pdf | Multi-Channel Time-Series Person and Soft-Biometric Identification | Multi-channel time-series datasets are popular in the context of human activity recognition (HAR). On-body device (OBD) recordings of human movements are often preferred for HAR applications not only for their reliability but as an approach for identity protection, e.g., in industrial settings. Contradictory, the gait activity is a biometric, as the cyclic movement is distinctive and collectable. In addition, the gait cycle has proven to contain soft-biometric information of human groups, such as age and height. Though general human movements have not been considered a biometric, they might contain identity information. This work investigates person and soft-biometrics identification from OBD recordings of humans performing different activities using deep architectures. Furthermore, we propose the use of attribute representation for soft-biometric identification. We evaluate the method on four datasets of multi-channel time-series HAR, measuring the performance of a person and soft-biometrics identification and its relation concerning performed activities. We find that person identification is not limited to gait activity. The impact of activities on the identification performance was found to be training and dataset specific. Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets. | ['Gernot A. Fink', 'Christopher Reining', 'Fernando Moya Rueda', 'Nilah Ravi Nair'] | 2023-04-04 | null | null | null | null | ['person-identification', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 2.87063211e-01 -2.49759004e-01 -1.53588101e-01 -3.26903105e-01
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8d092b71-3231-475a-a2cd-366771b075ec | a-general-mobile-manipulator-automation | 2302.04486 | null | https://arxiv.org/abs/2302.04486v1 | https://arxiv.org/pdf/2302.04486v1.pdf | A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments | To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose's bias to modify the end-effector's path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the harsh industrial conditions and parking precision. For the released code, please contact marketing@amigaga.com | ['Robert B. Fisher', 'Jinnian Pu', 'Chuanyu Yang', 'Can Pu'] | 2023-02-09 | null | null | null | null | ['point-cloud-registration', 'marketing'] | ['computer-vision', 'miscellaneous'] | [-1.38938636e-01 1.16401829e-01 2.62705714e-01 -5.90039864e-02
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2a04758d-c797-4818-bbe1-2f698e03e43b | optimizing-transformer-for-low-resource | 2011.02266 | null | https://arxiv.org/abs/2011.02266v1 | https://arxiv.org/pdf/2011.02266v1.pdf | Optimizing Transformer for Low-Resource Neural Machine Translation | Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achieved significant improvements for many language pairs and has become the de facto mainstream architecture, its capability under low-resource conditions has not been fully investigated yet. Our experiments on different subsets of the IWSLT14 training data show that the effectiveness of Transformer under low-resource conditions is highly dependent on the hyper-parameter settings. Our experiments show that using an optimized Transformer for low-resource conditions improves the translation quality up to 7.3 BLEU points compared to using the Transformer default settings. | ['Christof Monz', 'Ali Araabi'] | 2020-11-04 | null | https://aclanthology.org/2020.coling-main.304 | https://aclanthology.org/2020.coling-main.304.pdf | coling-2020-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 5.91751095e-03 -5.24560928e-01 -6.19499624e-01 -2.24812716e-01
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b72f6f06-7c92-40ff-9425-9cdc2e1975c2 | mafw-a-large-scale-multi-modal-compound | 2208.00847 | null | https://arxiv.org/abs/2208.00847v1 | https://arxiv.org/pdf/2208.00847v1.pdf | MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild | Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accurately imitate human emotions and fulfill applications in the real world. In this paper, we propose MAFW, a large-scale multi-modal compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment. To ensure high quality of the labels, we filter out the unreliable annotations by an Expectation Maximization (EM) algorithm, and then obtain 11 single-label emotion categories and 32 multi-label emotion categories. To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions. Additionally, we also propose a novel Transformer-based expression snippet feature learning method to recognize the compound emotions leveraging the expression-change relations among different emotions and modalities. Extensive experiments on MAFW database show the advantages of the proposed method over other state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database is publicly available from https://mafw-database.github.io/MAFW. | ['Shiguang Shan', 'Jiabei Zeng', 'Guanghao Yin', 'Wenbin Wang', 'Chuanxu Feng', 'Wei Dai', 'Yuanyuan Liu'] | 2022-08-01 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-1.06267877e-01 -3.04106176e-01 -1.48275092e-01 -7.06221163e-01
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443d8689-612d-4ac9-8d94-2b517981dd62 | dub-discrete-unit-back-translation-for-speech | 2305.11411 | null | https://arxiv.org/abs/2305.11411v1 | https://arxiv.org/pdf/2305.11411v1.pdf | DUB: Discrete Unit Back-translation for Speech Translation | How can speech-to-text translation (ST) perform as well as machine translation (MT)? The key point is to bridge the modality gap between speech and text so that useful MT techniques can be applied to ST. Recently, the approach of representing speech with unsupervised discrete units yields a new way to ease the modality problem. This motivates us to propose Discrete Unit Back-translation (DUB) to answer two questions: (1) Is it better to represent speech with discrete units than with continuous features in direct ST? (2) How much benefit can useful MT techniques bring to ST? With DUB, the back-translation technique can successfully be applied on direct ST and obtains an average boost of 5.5 BLEU on MuST-C En-De/Fr/Es. In the low-resource language scenario, our method achieves comparable performance to existing methods that rely on large-scale external data. Code and models are available at https://github.com/0nutation/DUB. | ['Yaqian Zhou', 'Mingxuan Wang', 'Tom Ko', 'Rong Ye', 'Dong Zhang'] | 2023-05-19 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.89292747e-01 3.63208503e-01 -2.52238184e-01 -2.50674814e-01
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e70c0655-9157-4549-9eb7-4f9081c8a37d | amt-all-pairs-multi-field-transforms-for | 2304.09790 | null | https://arxiv.org/abs/2304.09790v1 | https://arxiv.org/pdf/2304.09790v1.pdf | AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation | We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://github.com/MCG-NKU/AMT. | ['Ming-Ming Cheng', 'Chun-Le Guo', 'Qibin Hou', 'Ling-Hao Han', 'Zuo-Liang Zhu', 'Zhen Li'] | 2023-04-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-frame-interpolation'] | ['computer-vision'] | [-2.73126394e-01 -4.67037797e-01 -2.29145408e-01 -2.51106918e-01
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f94823a1-8515-4206-b90d-2571f7a4b46c | lemmas-generation-selection-application | 2303.05854 | null | https://arxiv.org/abs/2303.05854v1 | https://arxiv.org/pdf/2303.05854v1.pdf | Lemmas: Generation, Selection, Application | Noting that lemmas are a key feature of mathematics, we engage in an investigation of the role of lemmas in automated theorem proving. The paper describes experiments with a combined system involving learning technology that generates useful lemmas for automated theorem provers, demonstrating improvement for several representative systems and solving a hard problem not solved by any system for twenty years. By focusing on condensed detachment problems we simplify the setting considerably, allowing us to get at the essence of lemmas and their role in proof search. | ['Wolfgang Bibel', 'Zsolt Zombori', 'Christoph Wernhard', 'Michael Rawson'] | 2023-03-10 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.53033934e-03 6.00561559e-01 -3.00789356e-01 1.39541805e-01
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b57372d8-c3ab-40fc-b303-ad072552a7a3 | collaborative-diffusion-for-multi-modal-face | 2304.10530 | null | https://arxiv.org/abs/2304.10530v1 | https://arxiv.org/pdf/2304.10530v1.pdf | Collaborative Diffusion for Multi-Modal Face Generation and Editing | Diffusion models arise as a powerful generative tool recently. Despite the great progress, existing diffusion models mainly focus on uni-modal control, i.e., the diffusion process is driven by only one modality of condition. To further unleash the users' creativity, it is desirable for the model to be controllable by multiple modalities simultaneously, e.g., generating and editing faces by describing the age (text-driven) while drawing the face shape (mask-driven). In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Our key insight is that diffusion models driven by different modalities are inherently complementary regarding the latent denoising steps, where bilateral connections can be established upon. Specifically, we propose dynamic diffuser, a meta-network that adaptively hallucinates multi-modal denoising steps by predicting the spatial-temporal influence functions for each pre-trained uni-modal model. Collaborative Diffusion not only collaborates generation capabilities from uni-modal diffusion models, but also integrates multiple uni-modal manipulations to perform multi-modal editing. Extensive qualitative and quantitative experiments demonstrate the superiority of our framework in both image quality and condition consistency. | ['Ziwei Liu', 'Yuming Jiang', 'Kelvin C. K. Chan', 'Ziqi Huang'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-generation'] | ['computer-vision'] | [ 5.83866760e-02 8.87862369e-02 8.04547444e-02 -2.19707116e-01
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162e0bbe-7777-4572-a9ff-781fe3c86dd2 | asking-clarification-questions-to-handle | 2305.13808 | null | https://arxiv.org/abs/2305.13808v1 | https://arxiv.org/pdf/2305.13808v1.pdf | Asking Clarification Questions to Handle Ambiguity in Open-Domain QA | Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have tackled this issue by generating disambiguated questions for all possible interpretations of the ambiguous question. This can be effective, but not ideal for providing an answer to the user. Instead, we propose to ask a clarification question, where the user's response will help identify the interpretation that best aligns with the user's intention. We first present CAMBIGNQ, a dataset consisting of 5,654 ambiguous questions, each with relevant passages, possible answers, and a clarification question. The clarification questions were efficiently created by generating them using InstructGPT and manually revising them as necessary. We then define a pipeline of tasks and design appropriate evaluation metrics. Lastly, we achieve 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA, providing strong baselines for future work. | ['Kyomin Jung', 'Sang-Woo Lee', 'Joonsuk Park', 'Hwanhee Lee', 'Minwoo Lee', 'Segwang Kim', 'Dongryeol Lee'] | 2023-05-23 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 3.38939726e-01 5.85290670e-01 2.44072109e-01 -5.82078755e-01
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4f8eedca-4572-4875-805e-7f58f565c642 | latent-coincidence-analysis-a-hidden-variable | null | null | http://papers.nips.cc/paper/4634-latent-coincidence-analysis-a-hidden-variable-model-for-distance-metric-learning | http://papers.nips.cc/paper/4634-latent-coincidence-analysis-a-hidden-variable-model-for-distance-metric-learning.pdf | Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning | We describe a latent variable model for supervised dimensionality reduction and distance metric learning. The model discovers linear projections of high dimensional data that shrink the distance between similarly labeled inputs and expand the distance between differently labeled ones. The model’s continuous latent variables locate pairs of examples in a latent space of lower dimensionality. The model differs significantly from classical factor analysis in that the posterior distribution over these latent variables is not always multivariate Gaussian. Nevertheless we show that inference is completely tractable and derive an Expectation-Maximization (EM) algorithm for parameter estimation. We also compare the model to other approaches in distance metric learning. The model’s main advantage is its simplicity: at each iteration of the EM algorithm, the distance metric is re-estimated by solving an unconstrained least-squares problem. Experiments show that these simple updates are highly effective. | ['Matthew Der', 'Lawrence K. Saul'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 8.84717703e-03 1.71780944e-01 -2.80932099e-01 -4.82029766e-01
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24eb539f-31b7-4e4d-a633-9027b533f238 | a-study-of-semantic-augmentation-of-word | null | null | https://aclanthology.org/W19-8909 | https://aclanthology.org/W19-8909.pdf | A study of semantic augmentation of word embeddings for extractive summarization | In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to sentence-level representations produced by aggregated deep neural word embeddings. Multiple dimensionality reduction techniques and combination strategies are examined via feature transformation and clustering methods. An experimental evaluation on the MultiLing 2015 MSS dataset illustrates that semantic information can introduce benefits to the extractive summarization process in terms of F1, ROUGE-1 and ROUGE-2 scores, with LSA-based post-processing introducing the largest improvements. | ['Vangelis Karkaletsis', 'Nikiforos Pittaras'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.45288604e-01 5.80512404e-01 -2.79363215e-01 -1.69213474e-01
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dc5a1b03-6453-461c-8ae4-76f6e6581809 | on-the-expected-size-of-conformal-prediction | 2306.07254 | null | https://arxiv.org/abs/2306.07254v1 | https://arxiv.org/pdf/2306.07254v1.pdf | On the Expected Size of Conformal Prediction Sets | While conformal predictors reap the benefits of rigorous statistical guarantees for their error frequency, the size of their corresponding prediction sets is critical to their practical utility. Unfortunately, there is currently a lack of finite-sample analysis and guarantees for their prediction set sizes. To address this shortfall, we theoretically quantify the expected size of the prediction set under the split conformal prediction framework. As this precise formulation cannot usually be calculated directly, we further derive point estimates and high probability intervals that can be easily computed, providing a practical method for characterizing the expected prediction set size across different possible realizations of the test and calibration data. Additionally, we corroborate the efficacy of our results with experiments on real-world datasets, for both regression and classification problems. | ['Tom Rainforth', 'George Deligiannidis', 'Guneet S. Dhillon'] | 2023-06-12 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [ 3.48774850e-01 2.28058800e-01 -3.83931786e-01 -4.26678687e-01
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194426b8-6927-4ca4-b091-10e01394343b | on-stopwords-filtering-and-data-sparsity-for | null | null | https://aclanthology.org/L14-1265 | https://aclanthology.org/L14-1265.pdf | On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter | Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier{'}s feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and shrinking the feature space. | ['Fern', 'Yulan He', 'Miriam ez', 'Hassan Saif', 'Harith Alani'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.97650373e-01 -2.74543256e-01 -9.13269669e-02 -3.45421463e-01
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b7cfedd7-8300-4f9a-b787-c4d57436b18a | unsupervised-person-re-identification-by-deep | 1901.10177 | null | http://arxiv.org/abs/1901.10177v1 | http://arxiv.org/pdf/1901.10177v1.pdf | Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding | Person re-identification (Re-ID) aims to match identities across
non-overlapping camera views. Researchers have proposed many supervised Re-ID
models which require quantities of cross-view pairwise labelled data. This
limits their scalabilities to many applications where a large amount of data
from multiple disjoint camera views is available but unlabelled. Although some
unsupervised Re-ID models have been proposed to address the scalability
problem, they often suffer from the view-specific bias problem which is caused
by dramatic variances across different camera views, e.g., different
illumination, viewpoints and occlusion. The dramatic variances induce specific
feature distortions in different camera views, which can be very disturbing in
finding cross-view discriminative information for Re-ID in the unsupervised
scenarios, since no label information is available to help alleviate the bias.
We propose to explicitly address this problem by learning an unsupervised
asymmetric distance metric based on cross-view clustering. The asymmetric
distance metric allows specific feature transformations for each camera view to
tackle the specific feature distortions. We then design a novel unsupervised
loss function to embed the asymmetric metric into a deep neural network, and
therefore develop a novel unsupervised deep framework named the DEep
Clustering-based Asymmetric MEtric Learning (DECAMEL). In such a way, DECAMEL
jointly learns the feature representation and the unsupervised asymmetric
metric. DECAMEL learns a compact cross-view cluster structure of Re-ID data,
and thus help alleviate the view-specific bias and facilitate mining the
potential cross-view discriminative information for unsupervised Re-ID.
Extensive experiments on seven benchmark datasets whose sizes span several
orders show the effectiveness of our framework. | ['An-Cong Wu', 'Wei-Shi Zheng', 'Hong-Xing Yu'] | 2019-01-29 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.17050670e-01 -4.45294768e-01 -3.26509848e-02 -7.40067780e-01
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d4c8df18-9ab9-418c-a676-e49f86a3e2cc | a-survey-of-decision-making-in-adversarial | 2207.07971 | null | https://arxiv.org/abs/2207.07971v1 | https://arxiv.org/pdf/2207.07971v1.pdf | A Survey of Decision Making in Adversarial Games | Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or cooperative manner, but without obvious malice to other players. However, in many practical applications, such as poker, chess, evader pursuing, drug interdiction, coast guard, cyber-security, and national defense, players often have apparently adversarial stances, that is, selfish actions of each player inevitably or intentionally inflict loss or wreak havoc on other players. Along this line, this paper provides a systematic survey on three main game models widely employed in adversarial games, i.e., zero-sum normal-form and extensive-form games, Stackelberg (security) games, zero-sum differential games, from an array of perspectives, including basic knowledge of game models, (approximate) equilibrium concepts, problem classifications, research frontiers, (approximate) optimal strategy seeking techniques, prevailing algorithms, and practical applications. Finally, promising future research directions are also discussed for relevant adversarial games. | ['Jie Chen', 'Yiguang Hong', 'Min Meng', 'Xiuxian Li'] | 2022-07-16 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 2.19099354e-02 3.66172940e-01 1.17126003e-01 5.56690395e-01
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0bcdec03-02c9-47dd-a2a7-2afb8009c506 | can-the-state-of-relevant-neurons-in-a-deep | 2010.15974 | null | https://arxiv.org/abs/2010.15974v1 | https://arxiv.org/pdf/2010.15974v1.pdf | Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks? | We present a method for adversarial attack detection based on the inspection of a sparse set of neurons. We follow the hypothesis that adversarial attacks introduce imperceptible perturbations in the input and that these perturbations change the state of neurons relevant for the concepts modelled by the attacked model. Therefore, monitoring the status of these neurons would enable the detection of adversarial attacks. Focusing on the image classification task, our method identifies neurons that are relevant for the classes predicted by the model. A deeper qualitative inspection of these sparse set of neurons indicates that their state changes in the presence of adversarial samples. Moreover, quantitative results from our empirical evaluation indicate that our method is capable of recognizing adversarial samples, produced by state-of-the-art attack methods, with comparable accuracy to that of state-of-the-art detectors. | ['Jose Oramas', 'Tinne Tuytelaars', 'Roger Granda'] | 2020-10-29 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 4.06482279e-01 2.09574506e-01 2.75868475e-01 1.33862972e-01
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0406259e-a618-419a-90e9-cb8efdbbdf25 | few-shot-dialogue-generation-without | 1908.05854 | null | https://arxiv.org/abs/1908.05854v1 | https://arxiv.org/pdf/1908.05854v1.pdf | Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach | Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an ever-growing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable. In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data. We do this by leveraging background knowledge from a larger, more highly represented dialogue source --- namely, the MetaLWOz dataset. We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains. We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient by not requiring any data annotation. | ['Sungjin Lee', 'Arash Eshghi', 'Igor Shalyminov', 'Oliver Lemon'] | 2019-08-16 | few-shot-dialogue-generation-without-1 | https://aclanthology.org/W19-5904 | https://aclanthology.org/W19-5904.pdf | ws-2019-9 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.76873747e-02 5.17043889e-01 -6.02988377e-02 -5.13905287e-01
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29432de9-4fa1-446a-aa41-27056226fb52 | semeval-2023-task-2-fine-grained-multilingual | 2305.06586 | null | https://arxiv.org/abs/2305.06586v2 | https://arxiv.org/pdf/2305.06586v2.pdf | SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2) | We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. MultiCoNER 2 was one of the most popular tasks of SemEval-2023. It attracted 842 submissions from 47 teams, and 34 teams submitted system papers. Results showed that complex entity types such as media titles and product names were the most challenging. Methods fusing external knowledge into transformer models achieved the best performance, and the largest gains were on the Creative Work and Group classes, which are still challenging even with external knowledge. Some fine-grained classes proved to be more challenging than others, such as SCIENTIST, ARTWORK, and PRIVATECORP. We also observed that noisy data has a significant impact on model performance, with an average drop of 10% on the noisy subset. The task highlights the need for future research on improving NER robustness on noisy data containing complex entities. | ['Shervin Malmasi', 'Oleg Rokhlenko', 'Zhiyu Chen', 'Sudipta Kar', 'Besnik Fetahu'] | 2023-05-11 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-6.72664165e-01 -2.31089592e-01 -2.00479664e-02 -1.31063670e-01
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111cad2d-af90-464f-82f3-ac173383b144 | good-is-bad-causality-inspired-cloth | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.pdf | Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification | Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM, is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods. | ['Zheng Wang', 'Yu Wu', 'Xian Zhong', 'Meng Lin', 'Zhengwei Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification'] | ['computer-vision'] | [ 1.95781395e-01 -1.86958313e-01 -4.19284739e-02 -4.33218747e-01
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d61a56c0-57bd-4fd3-8531-b4c85989fb8d | position-wise-optimizer-a-nature-inspired | 2204.05312 | null | https://arxiv.org/abs/2204.05312v1 | https://arxiv.org/pdf/2204.05312v1.pdf | Position-wise optimizer: A nature-inspired optimization algorithm | The human nervous system utilizes synaptic plasticity to solve optimization problems. Previous studies have tried to add the plasticity factor to the training process of artificial neural networks, but most of those models require complex external control over the network or complex novel rules. In this manuscript, a novel nature-inspired optimization algorithm is introduced that imitates biological neural plasticity. Furthermore, the model is tested on three datasets and the results are compared with gradient descent optimization. | ['Amir Valizadeh'] | 2022-04-11 | null | null | null | null | ['nature-inspired-optimization-algorithm'] | ['computer-code'] | [ 1.46028146e-01 -2.91792750e-01 -1.01865031e-01 -2.08653122e-01
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34ff4d45-bc56-4e9b-bb35-ca00f7854aed | dermoscopic-image-classification-with-neural | 2105.07592 | null | https://arxiv.org/abs/2105.07592v2 | https://arxiv.org/pdf/2105.07592v2.pdf | Dermoscopic Image Classification with Neural Style Transfer | Skin cancer, the most commonly found human malignancy, is primarily diagnosed visually via dermoscopic analysis, biopsy, and histopathological examination. However, unlike other types of cancer, automated image classification of skin lesions is deemed more challenging due to the irregularity and variability in the lesions' appearances. In this work, we propose an adaptation of the Neural Style Transfer (NST) as a novel image pre-processing step for skin lesion classification problems. We represent each dermoscopic image as the style image and transfer the style of the lesion onto a homogeneous content image. This transfers the main variability of each lesion onto the same localized region, which allows us to integrate the generated images together and extract latent, low-rank style features via tensor decomposition. We train and cross-validate our model on a dermoscopic data set collected and preprocessed from the International Skin Imaging Collaboration (ISIC) database. We show that the classification performance based on the extracted tensor features using the style-transferred images significantly outperforms that of the raw images by more than 10%, and is also competitive with well-studied, pre-trained CNN models through transfer learning. Additionally, the tensor decomposition further identifies latent style clusters, which may provide clinical interpretation and insights. | ['Mike Yeh', 'Annie Qu', 'Ruoqing Zhu', 'Yutong Li'] | 2021-05-17 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.68817163e-01 -9.10587683e-02 -1.91591591e-01 -1.59319028e-01
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617ac35c-5fcd-475d-9922-614847ebc75b | ghost-handwritten-digit-recognition-based-on | 2004.02068 | null | https://arxiv.org/abs/2004.02068v2 | https://arxiv.org/pdf/2004.02068v2.pdf | Ghost Handwritten Digit Recognition based on Deep Learning | We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the classification is designed as the output of the DNN. The results show that the proposed scheme has a higher recognition accuracy (as high as 98.14% for the simulations, and 92.9% for the experiments ) with a smaller sampling ratio (say 12.76%). With the increase of the sampling ratio, the recognition accuracy is enhanced greatly. Compared with the traditional recognition scheme using the same DNN structure, the proposed scheme has a little better performance with a lower complexity and non-locality property. The proposed scheme provides a promising way for remote sensing. | ['Le Wang', 'Shengmei Zhao', 'Xing He'] | 2020-04-05 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-4.84165363e-03 -5.68109155e-01 2.43829459e-01 -1.96367994e-01
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14d7fae2-b0c6-4eeb-9ec2-605b7bc26d14 | reinforcement-learning-based-graph-to | 1908.04942 | null | https://arxiv.org/abs/1908.04942v4 | https://arxiv.org/pdf/1908.04942v4.pdf | Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation | Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail to fully exploit the answer information. To address these limitations, in this paper, we propose a reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator with a novel Bidirectional Gated Graph Neural Network based encoder to embed the passage, and a hybrid evaluator with a mixed objective combining both cross-entropy and RL losses to ensure the generation of syntactically and semantically valid text. We also introduce an effective Deep Alignment Network for incorporating the answer information into the passage at both the word and contextual levels. Our model is end-to-end trainable and achieves new state-of-the-art scores, outperforming existing methods by a significant margin on the standard SQuAD benchmark. | ['Lingfei Wu', 'Yu Chen', 'Mohammed J. Zaki'] | 2019-08-14 | null | https://openreview.net/forum?id=HygnDhEtvr | https://openreview.net/pdf?id=HygnDhEtvr | iclr-2020-1 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 3.98335546e-01 4.75799829e-01 2.76583582e-01 -2.30785578e-01
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6c4b45c5-41b8-44ce-ab62-c7ca0a70caa7 | efficient-nonlinear-manifold-reduced-order | 2011.07727 | null | https://arxiv.org/abs/2011.07727v1 | https://arxiv.org/pdf/2011.07727v1.pdf | Efficient nonlinear manifold reduced order model | Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, such as advection-dominated flow phenomena, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed an efficient nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models (FOMs). The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 2D Burgers' equations with a high Reynolds number. A speed-up of up to 11.7 for 2D Burgers' equations is achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. | ['Tarek Zohdi', 'David Widemann', 'Youngsoo Choi', 'Youngkyu Kim'] | 2020-11-13 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-2.29757950e-01 -2.62547642e-01 2.36010566e-01 3.15311283e-01
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866c0afb-3b4c-4222-adb8-e5c1168423e8 | video-text-modeling-with-zero-shot-transfer | 2212.04979 | null | https://arxiv.org/abs/2212.04979v3 | https://arxiv.org/pdf/2212.04979v3.pdf | VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners | We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. While previous works adapt image-text models with various cross-frame fusion modules, we find that the generative attentional pooling and contrastive attentional pooling layers in CoCa are instantly adaptable to flattened frame embeddings, yielding state-of-the-art results on zero-shot video classification and zero-shot text-to-video retrieval. Furthermore, we explore lightweight finetuning on top of VideoCoCa, and achieve strong results on video question-answering and video captioning. | ['Jiahui Yu', 'Yonghui Wu', 'Soham Ghosh', 'Mi Zhang', 'Yuan Cao', 'ZiRui Wang', 'Tao Zhu', 'Shen Yan'] | 2022-12-09 | null | null | null | null | ['zero-shot-action-recognition', 'video-classification', 'video-question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.67811829e-01 -4.73496430e-02 -2.48699814e-01 -2.86867112e-01
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ded27bc6-5176-4463-a0bc-1829071598b8 | separating-flows-in-encrypted-tunnel-traffic | null | null | https://doi.org/10.1109/ICMLA55696.2022.00094 | https://alexhartl.eu/papers/separating.pdf | Separating Flows in Encrypted Tunnel Traffic | In many scenarios like wireless Internet access or encrypted VPN tunnels, encryption is performed on a per-packet basis. While this encryption approach effectively protects the confidentiality of the transmitted payload, it leaves traffic patterns involving inter-arrival times and packet lengths observable, e.g., to eavesdroppers on the air interface. It is a widespread belief that by only observing interleaved packets of different parallel flows, analysis and classification of the corresponding traffic by an eavesdropper is very difficult or close to impossible.
In this paper, we show that it is indeed possible to separate packets belonging to different flows purely from patterns observed in the interleaved packet sequence. We devise a novel deep recurrent neural network architecture that allows us to detect individual anomalous packets in a flow. Based on this anomaly detector, we develop an algorithm to find a separation into flows that minimizes the anomaly score indicated by our model. Our experimental results obtained with synthetically crafted flows and real-world network traces indicate that our approach is indeed able to separate flows successfully with high accuracy.
Being able to recover a flow's packet sequence from multiple interleaved flows, we show with this paper that the common packetlevel encryption might be insufficient in scenarios where high levels of privacy have to be achieved. On the defender's side, our approach constitutes a valuable tool in encrypted traffic analysis, but also contributes a novel neural network architecture in the field of network intrusion detection in general. | ['Tanja Zseby', 'Joachim Fabini', 'Alexander Hartl'] | 2022-12-12 | null | null | null | ieee-international-conference-on-machine | ['network-intrusion-detection'] | ['miscellaneous'] | [ 4.15672421e-01 -2.15562388e-01 1.27427563e-01 -1.56460822e-01
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71c6bcfb-b2e2-4186-ba30-844bf0727de2 | evaluating-neural-model-robustness-for | null | null | https://aclanthology.org/2021.eacl-main.210 | https://aclanthology.org/2021.eacl-main.210.pdf | Evaluating Neural Model Robustness for Machine Comprehension | We evaluate neural model robustness to adversarial attacks using different types of linguistic unit perturbations {--} character and word, and propose a new method for strategic sentence-level perturbations. We experiment with different amounts of perturbations to examine model confidence and misclassification rate, and contrast model performance with different embeddings BERT and ELMo on two benchmark datasets SQuAD and TriviaQA. We demonstrate how to improve model performance during an adversarial attack by using ensembles. Finally, we analyze factors that effect model behavior under adversarial attack, and develop a new model to predict errors during attacks. Our novel findings reveal that (a) unlike BERT, models that use ELMo embeddings are more susceptible to adversarial attacks, (b) unlike word and paraphrase, character perturbations affect the model the most but are most easily compensated for by adversarial training, (c) word perturbations lead to more high-confidence misclassifications compared to sentence- and character-level perturbations, (d) the type of question and model answer length (the longer the answer the more likely it is to be incorrect) is the most predictive of model errors in adversarial setting, and (e) conclusions about model behavior are dataset-specific. | ['Svitlana Volkova', 'Dustin Arendt', 'Winston Wu'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['triviaqa'] | ['miscellaneous'] | [ 1.28151208e-01 -9.30742994e-02 2.93102980e-01 -3.07169735e-01
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5dd5bec7-8542-4cc0-8ea5-c557663d4c0c | efficient-and-flexible-topic-modeling-using | 2302.03106 | null | https://arxiv.org/abs/2302.03106v2 | https://arxiv.org/pdf/2302.03106v2.pdf | Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences | Pre-trained language models have led to a new state-of-the-art in many NLP tasks. However, for topic modeling, statistical generative models such as LDA are still prevalent, which do not easily allow incorporating contextual word vectors. They might yield topics that do not align very well with human judgment. In this work, we propose a novel topic modeling and inference algorithm. We suggest a bag of sentences (BoS) approach using sentences as the unit of analysis. We leverage pre-trained sentence embeddings by combining generative process models with clustering. We derive a fast inference algorithm based on expectation maximization, hard assignments, and an annealing process. Our evaluation shows that our method yields state-of-the art results with relatively little computational demands. Our methods is more flexible compared to prior works leveraging word embeddings, since it provides the possibility to customize topic-document distributions using priors. Code is at \url{https://github.com/JohnTailor/BertSenClu}. | ['Johannes Schneider'] | 2023-02-06 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.94071436e-01 1.82151690e-01 -3.34385842e-01 -4.89321738e-01
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a8df547a-978a-4625-b298-ddfe277c584a | anomaly-detection-in-networks-via-score-based | 2306.15324 | null | https://arxiv.org/abs/2306.15324v1 | https://arxiv.org/pdf/2306.15324v1.pdf | Anomaly Detection in Networks via Score-Based Generative Models | Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it. | ['Evgeny Burnaev', 'Dmitrii Gavrilev'] | 2023-06-27 | null | null | null | null | ['anomaly-detection', 'outlier-detection'] | ['methodology', 'methodology'] | [-1.66922271e-01 4.27964032e-01 2.46280823e-02 -2.95315772e-01
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f316922d-b2f3-402f-9b8b-0c051cd82f74 | mipi-2022-challenge-on-rgb-tof-depth | 2209.07057 | null | https://arxiv.org/abs/2209.07057v1 | https://arxiv.org/pdf/2209.07057v1.pdf | MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report | Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGB+ToF Depth Completion, one of the five tracks, working on the fusion of RGB sensor and ToF sensor (with spot illumination) is introduced. The participants were provided with a new dataset called TetrasRGBD, which contains 18k pairs of high-quality synthetic RGB+Depth training data and 2.3k pairs of testing data from mixed sources. All the data are collected in an indoor scenario. We require that the running time of all methods should be real-time on desktop GPUs. The final results are evaluated using objective metrics and Mean Opinion Score (MOS) subjectively. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022. | ['Jinwei Gu', 'Chen Change Loy', 'Qingyu Yang', 'Jun Jiang', 'Shangchen Zhou', 'Ruicheng Feng', 'Chongyi Li', 'Qingpeng Zhu', 'Wenxiu Sun'] | 2022-09-15 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 5.37985981e-01 -2.83620358e-01 1.66521430e-01 -6.21267080e-01
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bcdb9f92-57ec-4db8-902e-1deed371c729 | deep-learning-enabled-sleep-staging-from | 2306.03711 | null | https://arxiv.org/abs/2306.03711v1 | https://arxiv.org/pdf/2306.03711v1.pdf | Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera | Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging. | ['Lionel Tarassenko', 'Oliver Gibson', 'Bindia Venugopal', 'João Jorge', 'Jonathan Carter'] | 2023-06-06 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 3.39999139e-01 -3.12803276e-02 -3.56634319e-01 -5.77040553e-01
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eab04586-ffeb-443d-85e9-57e6eb75bf57 | application-of-particle-swarm-optimization-to | 1403.2842 | null | http://arxiv.org/abs/1403.2842v1 | http://arxiv.org/pdf/1403.2842v1.pdf | Application of Particle Swarm Optimization to Microwave Tapered Microstrip Lines | Application of metaheuristic algorithms has been of continued interest in the
field of electrical engineering because of their powerful features. In this
work special design is done for a tapered transmission line used for matching
an arbitrary real load to a 50{\Omega} line. The problem at hand is to match
this arbitrary load to 50 {\Omega} line using three section tapered
transmission line with impedances in decreasing order from the load. So the
problem becomes optimizing an equation with three unknowns with various
conditions. The optimized values are obtained using Particle Swarm
Optimization. It can easily be shown that PSO is very strong in solving this
kind of multiobjective optimization problems. | ['Ezgi Deniz Ulker', 'Sadik Ulker'] | 2014-03-12 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.37180710e-01 -1.32263750e-01 2.77717143e-01 -9.51612219e-02
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8a46316e-4989-4207-a840-b39836bf448d | delexicalised-multilingual-discourse | null | null | https://aclanthology.org/2021.disrpt-1.4 | https://aclanthology.org/2021.disrpt-1.4.pdf | Delexicalised Multilingual Discourse Segmentation for DISRPT 2021 and Tense, Mood, Voice and Modality Tagging for 11 Languages | This paper describes our participating system for the Shared Task on Discourse Segmentation and Connective Identification across Formalisms and Languages. Key features of the presented approach are the formulation as a clause-level classification task, a language-independent feature inventory based on Universal Dependencies grammar, and composite-verb-form analysis. The achieved F1 is 92% for German and English and lower for other languages. The paper also presents a clause-level tagger for grammatical tense, aspect, mood, voice and modality in 11 languages. | ['Tillmann Dönicke'] | null | null | null | null | emnlp-disrpt-2021-11 | ['discourse-segmentation'] | ['natural-language-processing'] | [-1.73210219e-01 3.74706596e-01 -5.19911110e-01 -5.55727720e-01
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96923aac-e0bd-4969-bf7f-9f3ff79a3039 | an-offline-technique-for-localization-of | 1003.1072 | null | http://arxiv.org/abs/1003.1072v2 | http://arxiv.org/pdf/1003.1072v2.pdf | An Offline Technique for Localization of License Plates for Indian Commercial Vehicles | Automatic License Plate Recognition (ALPR) is a challenging area of research
due to its importance to variety of commercial applications. The overall
problem may be subdivided into two key modules, firstly, localization of
license plates from vehicle images, and secondly, optical character recognition
of extracted license plates. In the current work, we have concentrated on the
first part of the problem, i.e., localization of license plate regions from
Indian commercial vehicles as a significant step towards development of a
complete ALPR system for Indian vehicles. The technique is based on color based
segmentation of vehicle images and identification of potential license plate
regions. True license plates are finally localized based on four spatial and
horizontal contrast features. The technique successfully localizes the actual
license plates in 73.4% images. | ['Subhadip Basu', 'Mita Nasipuri', 'Dipak Kumar Basu', 'Satadal Saha'] | 2010-03-04 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 1.58323735e-01 -6.48647070e-01 9.20419097e-02 -1.20053440e-01
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1f1e74ad-ccd7-4806-8df0-410d457c14e0 | siamese-labels-auxiliary-network-silanet | 2103.00200 | null | https://arxiv.org/abs/2103.00200v3 | https://arxiv.org/pdf/2103.00200v3.pdf | Siamese Labels Auxiliary Learning | In deep learning, auxiliary training has been widely used to assist the training of models. During the training phase, using auxiliary modules to assist training can improve the performance of the model. During the testing phase, auxiliary modules can be removed, so the test parameters are not increased. In this paper, we propose a novel auxiliary training method, Siamese Labels Auxiliary Learning (SiLa). Unlike Deep Mutual Learning (DML), SiLa emphasizes auxiliary learning and can be easily combined with DML. In general, the main work of this paper include: (1) propose SiLa Learning, which improves the performance of common models without increasing test parameters; (2) compares SiLa with DML and proves that SiLa can improve the generalization of the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa can be used for various types of network structures. | ['Tong Zhang', 'C. L. Philip Chen', 'Zhulin Liu', 'Wenrui Gan'] | 2021-02-27 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-1.96115971e-01 -5.37317917e-02 -3.44284266e-01 -2.80504435e-01
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47d0d808-1b37-4eb7-9084-87bd2a73be2f | deep-learning-applied-to-chest-x-rays | 2009.10132 | null | https://arxiv.org/abs/2009.10132v1 | https://arxiv.org/pdf/2009.10132v1.pdf | Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts | While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest. For instance, clinical protocols might lead to a dataset in which patients with pacemakers are disproportionately likely to have congestive heart failure. This skew can lead to models that take shortcuts by heavily relying on the biased attribute. We explore this problem across a number of attributes in the context of diagnosing the cause of acute hypoxemic respiratory failure. Applied to chest X-rays, we show that i) deep nets can accurately identify many patient attributes including sex (AUROC = 0.96) and age (AUROC >= 0.90), ii) they tend to exploit correlations between such attributes and the outcome label when learning to predict a diagnosis, leading to poor performance when such correlations do not hold in the test population (e.g., everyone in the test set is male), and iii) a simple transfer learning approach is surprisingly effective at preventing the shortcut and promoting good generalization performance. On the task of diagnosing congestive heart failure based on a set of chest X-rays skewed towards older patients (age >= 63), the proposed approach improves generalization over standard training from 0.66 (95% CI: 0.54-0.77) to 0.84 (95% CI: 0.73-0.92) AUROC. While simple, the proposed approach has the potential to improve the performance of models across populations by encouraging reliance on clinically relevant manifestations of disease, i.e., those that a clinician would use to make a diagnosis. | ['Ella Kazerooni', 'Sarah Jabbour', 'Jenna Wiens', 'Michael W. Sjoding', 'David Fouhey'] | 2020-09-21 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 2.97200769e-01 4.95055318e-01 -4.79297131e-01 -7.98657477e-01
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185f5cbe-db2f-4103-9491-49eba47ae248 | towards-comprehensive-representation | null | null | https://link.springer.com/chapter/10.1007/978-3-031-19769-7_18 | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610299.pdf | Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation | Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics. However, since depth estimation and
semantic segmentation are fundamentally two types of tasks: one is regression while the other is classification, the distribution of depth feature
and semantic feature are naturally different. Previous works that leverage semantic information in depth estimation mostly neglect such representational discrimination, which leads to insufficient representation
enhancement of depth feature. In this work, we propose an attentionbased module to enhance task-specific feature by addressing their feature
uniqueness within instances. Additionally, we propose a metric learning based approach to accomplish comprehensive enhancement on depth
feature by creating a separation between instances in feature space. Extensive experiments and analysis demonstrate the effectiveness of our
proposed method. In the end, our method achieves the state-of-the-art
performance on KITTI dataset. | ['ShiLiang Pu', 'Xian Zhao', 'Nan Liu', 'Xiangyu Lei', 'Jingyuan Ma'] | 2022-10-23 | null | null | null | eccv-2022-10 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.03560457e-01 6.13014549e-02 -4.86465633e-01 -8.42187524e-01
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2bb4be69-3f37-4097-b281-ec4858417f8c | efficient-joint-noise-removal-and-multi | 2112.03701 | null | https://arxiv.org/abs/2112.03701v1 | https://arxiv.org/pdf/2112.03701v1.pdf | Efficient joint noise removal and multi exposure fusion | Multi-exposure fusion (MEF) is a technique for combining different images of the same scene acquired with different exposure settings into a single image. All the proposed MEF algorithms combine the set of images, somehow choosing from each one the part with better exposure. We propose a novel multi-exposure image fusion chain taking into account noise removal. The novel method takes advantage of DCT processing and the multi-image nature of the MEF problem. We propose a joint fusion and denoising strategy taking advantage of spatio-temporal patch selection and collaborative 3D thresholding. The overall strategy permits to denoise and fuse the set of images without the need of recovering each denoised exposure image, leading to a very efficient procedure. | ['O. Martorell', 'J. L Lisani', 'A. Buades'] | 2021-12-04 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 5.92348814e-01 -6.09729946e-01 5.79170763e-01 -1.52945906e-01
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dddc918d-d5f9-4b84-9f36-6f965b4b95ae | idol-net-an-interactive-dual-domain-parallel | 2104.01405 | null | https://arxiv.org/abs/2104.01405v1 | https://arxiv.org/pdf/2104.01405v1.pdf | IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction | Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network models have been proposed for metal artifact reduction (MAR). Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods. However,current dual-domain methods usually operate on both domains in a specific order, which implicitly imposes a certain priority prior into MAR and may ignore the latent information interaction between both domains. To address this problem, in this paper, we propose a novel interactive dualdomain parallel network for CT MAR, dubbed as IDOLNet. Different from existing dual-domain methods, the proposed IDOL-Net is composed of two modules. The disentanglement module is utilized to generate high-quality prior sinogram and image as the complementary inputs. The follow-up refinement module consists of two parallel and interactive branches that simultaneously operate on image and sinogram domain, fully exploiting the latent information interaction between both domains. The simulated and clinical results demonstrate that the proposed IDOL-Net outperforms several state-of-the-art models in both qualitative and quantitative aspects. | ['Yi Zhang', 'Jiliu Zhou', 'Hu Chen', 'Yan Liu', 'Huaiqiang Sun', 'Zexin Lu', 'Wenjun Xia', 'Tao Wang'] | 2021-04-03 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.29561731e-01 1.29115984e-01 -3.18977162e-02 -1.09960361e-04
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8060c064-b088-4418-a94e-69378fee2b68 | roadnet-rt-high-throughput-cnn-architecture | 2006.07644 | null | https://arxiv.org/abs/2006.07644v2 | https://arxiv.org/pdf/2006.07644v2.pdf | RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation | In recent years, convolutional neural network has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, often more complex structures and advanced operations are incorporated into the neural networks, which results very long inference time. For time-critical tasks such as autonomous driving and virtual reality, real-time processing is fundamental. In order to reach real-time process speed, a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this paper. It achieves 90.33% MaxF score on test set of KITTI road segmentation task and 8 ms per frame when running on GTX 1080 GPU. Comparing to the state-of-the-art network, RoadNet-RT speeds up the inference time by a factor of 20 at the cost of only 6.2% accuracy loss. For hardware design optimization, several techniques such as depthwise separable convolution and non-uniformed kernel size convolution are customized designed to further reduce the processing time. The proposed CNN architecture has been successfully implemented on an FPGA ZCU102 MPSoC platform that achieves the computation capability of 83.05 GOPS. The system throughput reaches 327.9 frames per second with image size 1216x176. | ['Yecheng Lyu', 'Xinming Huang', 'Lin Bai'] | 2020-06-13 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.40592247e-01 -4.50348519e-02 6.84982985e-02 -4.93623435e-01
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89517fca-67f5-4440-848d-94cc86961c03 | kvasir-seg-a-segmented-polyp-dataset | 1911.07069 | null | https://arxiv.org/abs/1911.07069v1 | https://arxiv.org/pdf/1911.07069v1.pdf | Kvasir-SEG: A Segmented Polyp Dataset | Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist. Moreover, we also generated the bounding boxes of the polyp regions with the help of segmentation masks. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep-learning based Convolutional Neural Network (CNN) approach. The dataset will be of value for researchers to reproduce results and compare methods. By adding segmentation masks to the Kvasir dataset, which only provide frame-wise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy images. | ['Håvard D. Johansen', 'Pål Halvorsen', 'Thomas de Lange', 'Michael A. Riegler', 'Dag Johansen', 'Debesh Jha', 'Pia H. Smedsrud'] | 2019-11-16 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [ 4.67630357e-01 3.48343670e-01 1.23335958e-01 -2.11397603e-01
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57d3f6f0-8e73-4892-bed7-967340a5be6b | few-shot-classification-in-unseen-domains-by | 2112.13539 | null | https://arxiv.org/abs/2112.13539v1 | https://arxiv.org/pdf/2112.13539v1.pdf | Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains | Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-class data in an unseen domain, this becomes an even more challenging task of domain generalized few-shot classification. In this paper, we present a unique learning framework for domain-generalized few-shot classification, where base classes are from homogeneous multiple source domains, while novel classes to be recognized are from target domains which are not seen during training. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features, with FSL ability introduced by metric-learning based mechanisms across support and query data. We conduct extensive experiments to verify the effectiveness of our proposed learning framework and show learning from small yet homogeneous source data is able to perform preferably against learning from large-scale one. Moreover, we provide insights into choices of backbone models for domain-generalized few-shot classification. | ['Yu-Chiang Frank Wang', 'Fu-En Yang', 'Ci-Siang Lin', 'Yuan-Chia Cheng'] | 2021-12-27 | null | null | null | null | ['generalized-few-shot-classification'] | ['computer-vision'] | [ 4.77162987e-01 5.04786009e-03 -6.00247502e-01 -6.26462400e-01
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f5588c49-f990-428e-af39-82ac15bfb99d | rid-noise-towards-robust-inverse-design-under | 2112.03912 | null | https://arxiv.org/abs/2112.03912v1 | https://arxiv.org/pdf/2112.03912v1.pdf | RID-Noise: Towards Robust Inverse Design under Noisy Environments | From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. However, classic robust design requires a lot of evaluations for a single design target, while the results of these evaluations could not be reused for a new target. To achieve a data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy data to train a conditional invertible neural network (cINN). Specifically, we estimate the robustness of a design parameter by its predictability, measured by the prediction error of a forward neural network. We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data. Further experiments on several real-world benchmark tasks with noises confirm that our method is more effective than other state-of-the-art inverse design methods. Code and supplementary is publicly available at https://github.com/ThyrixYang/rid-noise-aaai22 | ['De-Chuan Zhan', 'Hao Ma', 'Ke-Bin Fan', 'Jia-Qi Yang'] | 2021-12-07 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.73265234e-01 -2.35311702e-01 -2.10891776e-02 -3.28632265e-01
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c18ada7d-ed00-4ff0-bf7d-91f0a7a1469e | segmentation-and-classification-of-emg-time | 2104.09627 | null | https://arxiv.org/abs/2104.09627v3 | https://arxiv.org/pdf/2104.09627v3.pdf | Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement | Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge of the real-time detection of human grasp intent is the identification of dynamic EMG from hand movements. Previous studies mainly implemented steady-state EMG classification with a small number of grasp patterns on dynamic situations, which are insufficient to generate differentiated control regarding the muscular activity variation in practice. In order to better detect dynamic movements, more EMG variability could be integrated into the model. However, only limited research were concentrated on such detection of dynamic grasp motions, and most existing assessments on non-static EMG classification either require supervised ground-truth timestamps of the movement status, or only contain limited kinematic variations. In this study, we propose a framework for classifying dynamic EMG signals into gestures, and examine the impact of different movement phases, using an unsupervised method to segment and label the action transitions. We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on common sequences of the grasp movements. The classifier for identifying the gesture label was constructed afterwards based on the dynamic EMG signal, with no supervised annotation of kinematic movements required. Finally, we evaluated the performances of several training strategies using EMG data from different movement phases, and explored the information revealed from each phase. All experiments were evaluated in a real-time style with the performance transitions over time presented. | ['Sezen Yagmur Gunay', 'Deniz Erdogmus', 'Gunar Schirner', 'Mehrshad Zandigohar', 'Mo Han'] | 2021-04-19 | null | null | null | null | ['motion-detection', 'electromyography-emg'] | ['computer-vision', 'medical'] | [ 4.75902706e-01 -3.50463957e-01 -5.79221070e-01 -2.29371175e-01
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d26eb40b-c6c7-4f76-baa5-27b7fb089182 | proximal-causal-learning-of-heterogeneous | 2301.10913 | null | https://arxiv.org/abs/2301.10913v2 | https://arxiv.org/pdf/2301.10913v2.pdf | Proximal Causal Learning of Conditional Average Treatment Effects | Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available. These, however, typically rely on the assumption that the measured covariates are enough to justify conditional exchangeability. We propose the P-learner, motivated by the R- and DR-learner, a tailored two-stage loss function for learning heterogeneous treatment effects in settings where exchangeability given observed covariates is an implausible assumption, and we wish to rely on proxy variables for causal inference. Our proposed estimator can be implemented by off-the-shelf loss-minimizing machine learning methods, which in the case of kernel regression satisfies an oracle bound on the estimated error as long as the nuisance components are estimated reasonably well. | ['Yifan Cui', 'Erik Sverdrup'] | 2023-01-26 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.90200293e-01 2.95380592e-01 -1.05644715e+00 -5.66768169e-01
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59f240d5-bd77-4639-9a95-311000aadff0 | automatic-interaction-and-activity | 2304.09789 | null | https://arxiv.org/abs/2304.09789v2 | https://arxiv.org/pdf/2304.09789v2.pdf | Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection | This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects. | ['Arash Ajoudani', 'Edoardo Lamon', 'Marta Lagomarsino', 'Elena Merlo'] | 2023-04-19 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 4.67298120e-01 -5.98004580e-01 -2.34520987e-01 -9.89290550e-02
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167f9cf9-5dc4-4bb9-aaa8-33b4fb2e349a | the-challenge-of-non-technical-loss-detection | 1606.00626 | null | http://arxiv.org/abs/1606.00626v3 | http://arxiv.org/pdf/1606.00626v3.pdf | The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey | Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future. | ['Radu State', 'Jorge Augusto Meira', 'Petko Valtchev', 'Patrick Glauner', 'Franck Bettinger'] | 2016-06-02 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-2.46147588e-01 -1.29351139e-01 5.63108884e-02 -2.58319259e-01
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18b46fbf-3bb3-46f1-9dc6-d39d4923cfae | fiba-frequency-injection-based-backdoor | 2112.01148 | null | https://arxiv.org/abs/2112.01148v2 | https://arxiv.org/pdf/2112.01148v2.pdf | FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis | In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e.g., X-Ray, CT, and MRI) and analysis tasks (e.g., classification, detection, and segmentation). Most existing BA methods are designed to attack natural image classification models, which apply spatial triggers to training images and inevitably corrupt the semantics of poisoned pixels, leading to the failures of attacking dense prediction models. To address this issue, we propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images. Since it preserves the semantics of the poisoned image pixels, FIBA can perform attacks on both classification and dense prediction models. Experiments on three benchmarks in MIA (i.e., ISIC-2019 for skin lesion classification, KiTS-19 for kidney tumor segmentation, and EAD-2019 for endoscopic artifact detection), validate the effectiveness of FIBA and its superiority over state-of-the-art methods in attacking MIA models as well as bypassing backdoor defense. Source code will be available at https://github.com/HazardFY/FIBA. | ['DaCheng Tao', 'Yong Xia', 'Shanshan Zhao', 'Jing Zhang', 'Benteng Ma', 'Yu Feng'] | 2021-12-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.pdf | cvpr-2022-1 | ['skin-lesion-classification'] | ['medical'] | [ 4.09708589e-01 -1.99018463e-01 -1.14578173e-01 1.88160688e-01
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580fa100-f1f1-4b43-8d04-5b8d6f070e61 | siamthn-siamese-target-highlight-network-for | 2303.12304 | null | https://arxiv.org/abs/2303.12304v1 | https://arxiv.org/pdf/2303.12304v1.pdf | SiamTHN: Siamese Target Highlight Network for Visual Tracking | Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region. Additionally, there are no structural links between the classification and regression branches in these trackers, and the two branches are optimized separately during training. Therefore, there is a misalignment between the classification and regression branches, which results in less accurate tracking results. In this paper, a Target Highlight Module is proposed to help the generated similarity response maps to be more focused on the target region. To reduce the misalignment and produce more precise tracking results, we propose a corrective loss to train the model. The two branches of the model are jointly tuned with the use of corrective loss to produce more reliable prediction results. Experiments on 5 challenging benchmark datasets reveal that the method outperforms current models in terms of performance, and runs at 38 fps, proving its effectiveness and efficiency. | ['Menglong Yan', 'Wenhui Diao', 'Liangjin Zhao', 'Xian Sun', 'Kaiqiang Chen', 'Jiahao Bao'] | 2023-03-22 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.69574484e-01 -1.94522247e-01 -3.42479318e-01 -1.44717664e-01
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6e6bfe41-c2a8-4f7e-ac33-619886b73d9a | robust-variable-speed-variable-pitch-power | 2210.08928 | null | https://arxiv.org/abs/2210.08928v4 | https://arxiv.org/pdf/2210.08928v4.pdf | Variable-Pitch Power Regulation of Tethered-Wing Systems Based on Robust Gain-Scheduling H-infinity Control | In this paper, we deal with the power regulation of tethered-wing systems and demonstrate advantages of variable-pitch control in mitigating the dynamic mechanical loads and power fluctuations. The proposed scheme is based on a strategy that maximizes the energy capture during low-speed wind and prevents overloads during the high-speed wind. To realize this strategy, we use a tether reeling-speed controller to track the optimal generator speed during low-speed wind and a MIMO speed-force controller for power limitation during high-speed wind. The controllers are synthesized using H-infinity method and are based on a linear parameter varying (LPV) system that expresses the flexible dynamics of system as a function of the tether's length and force. Using this method, the controllers are made robust with respect to dynamic and parametric uncertainties and the wing's pitch angle activity is minimized during high-speed wind. We carry out extensive simulations to demonstrate the controllers' performance. These include the implementation of the scheme in a detailed 3-dimensional tethered-wing system simulator with a realistic turbulent wind field. | ['Amin Nikoobin', 'Mani Kakavand'] | 2022-10-17 | null | null | null | null | ['pitch-control'] | ['audio'] | [-1.38817504e-01 2.35624969e-01 -1.11996472e-01 5.85768342e-01
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d23dce2b-c9f1-4f8f-9554-d448b94026d8 | clover-towards-a-unified-video-language | 2207.07885 | null | https://arxiv.org/abs/2207.07885v3 | https://arxiv.org/pdf/2207.07885v3.pdf | Clover: Towards A Unified Video-Language Alignment and Fusion Model | Building a universal Video-Language model for solving various video understanding tasks (\emph{e.g.}, text-video retrieval, video question answering) is an open challenge to the machine learning field. Towards this goal, most recent works build the model by stacking uni-modal and cross-modal feature encoders and train it with pair-wise contrastive pre-text tasks. Though offering attractive generality, the resulted models have to compromise between efficiency and performance. They mostly adopt different architectures to deal with different downstream tasks. We find this is because the pair-wise training cannot well \emph{align} and \emph{fuse} features from different modalities. We then introduce \textbf{Clover}\textemdash a Correlated Video-Language pre-training method\textemdash towards a universal Video-Language model for solving multiple video understanding tasks with neither performance nor efficiency compromise. It improves cross-modal feature alignment and fusion via a novel tri-modal alignment pre-training task. Additionally, we propose to enhance the tri-modal alignment via incorporating learning from semantic masked samples and a new pair-wise ranking loss. Clover establishes new state-of-the-arts on multiple downstream tasks, including three retrieval tasks for both zero-shot and fine-tuning settings, and eight video question answering tasks. Codes and pre-trained models will be released at \url{https://github.com/LeeYN-43/Clover}. | ['Rongrong Ji', 'Xiaoshuai Sun', 'Xinglong Wu', 'Jiashi Feng', 'Yinan Li', 'Jingjia Huang'] | 2022-07-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 3.44806641e-01 -2.40769044e-01 -1.56151071e-01 -4.88381565e-01
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3c6fa0ef-a9dc-42d9-8d25-d310a4fb85d7 | citesum-citation-text-guided-scientific | 2205.06207 | null | https://arxiv.org/abs/2205.06207v2 | https://arxiv.org/pdf/2205.06207v2.pdf | CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision | Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the heavy human annotation and domain expertise required. In this paper, we propose a simple yet effective approach to automatically extracting TLDR summaries for scientific papers from their citation texts. Based on the proposed approach, we create a new benchmark CiteSum without human annotation, which is around 30 times larger than the previous human-curated dataset SciTLDR. We conduct a comprehensive analysis of CiteSum, examining its data characteristics and establishing strong baselines. We further demonstrate the usefulness of CiteSum by adapting models pre-trained on CiteSum (named CITES) to new tasks and domains with limited supervision. For scientific extreme summarization, CITES outperforms most fully-supervised methods on SciTLDR without any fine-tuning and obtains state-of-the-art results with only 128 examples. For news extreme summarization, CITES achieves significant gains on XSum over its base model (not pre-trained on CiteSum), e.g., +7.2 ROUGE-1 zero-shot performance and state-of-the-art few-shot performance. For news headline generation, CITES performs the best among unsupervised and zero-shot methods on Gigaword. Our dataset and code can be found at https://github.com/morningmoni/CiteSum. | ['Jiawei Han', 'Ming Zhong', 'Yuning Mao'] | 2022-05-12 | null | null | null | null | ['headline-generation', 'extreme-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.17269969e-02 2.40043312e-01 -4.36325341e-01 1.02372617e-01
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d59e830f-6ac0-4a39-bbab-f5d2b7b6d193 | density-aware-feature-embedding-for-face | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.pdf | Density-Aware Feature Embedding for Face Clustering | Clustering has many applications in research and industry. However, traditional clustering methods, such as K-means, DBSCAN and HAC, impose oversimplifying assumptions and thus are not well-suited to face clustering. To adapt to the distribution of realistic problems, a natural approach is to use Graph Convolutional Networks (GCNs) to enhance features for clustering. However, GCNs can only utilize local information, which ignores the overall characterisitcs of the clusters. In this paper, we propose a Density-Aware Feature Embedding Network (DA-Net) for the task of face clustering, which utilizes both local and non-local information, to learn a robust feature embedding. Specifically, DA-Net uses GCNs to aggregate features locally, and then incorporates non-local information using a density chain, which is a chain of faces from low density to high density. This density chain exploits the non-uniform distribution of face images in the dataset. Then, an LSTM takes the density chain as input to generate the final feature embedding. Once this embedding is generated, traditional clustering methods, such as density-based clustering, can be used to obtain the final clustering results. Extensive experiments verify the effectiveness of the proposed feature embedding method, which can achieve state-of-the-art performance on public benchmarks.
| [' Rui Zhao', ' Xiaogang Wang', ' Chao Zhang', ' Dapeng Chen', ' Jing Xu', 'Senhui Guo'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['face-clustering'] | ['computer-vision'] | [-4.11292553e-01 -3.02911997e-01 -6.45217970e-02 -6.49902523e-01
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1.55969813e-01 -7.89683461e-01 -5.51694930e-01 -1.21974587e-01] | [13.365599632263184, 1.1177064180374146] |
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