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72535554-217f-41fa-ac01-24f3b89c1224 | learning-selective-mutual-attention-and | 2010.05537 | null | https://arxiv.org/abs/2010.05537v1 | https://arxiv.org/pdf/2010.05537v1.pdf | Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection | How to effectively fuse cross-modal information is the key problem for RGB-D salient object detection. Early fusion and the result fusion schemes fuse RGB and depth information at the input and output stages, respectively, hence incur the problem of distribution gap or information loss. Many models use the feature fusion strategy but are limited by the low-order point-to-point fusion methods. In this paper, we propose a novel mutual attention model by fusing attention and contexts from different modalities. We use the non-local attention of one modality to propagate long-range contextual dependencies for the other modality, thus leveraging complementary attention cues to perform high-order and trilinear cross-modal interaction. We also propose to induce contrast inference from the mutual attention and obtain a unified model. Considering low-quality depth data may detriment the model performance, we further propose selective attention to reweight the added depth cues. We embed the proposed modules in a two-stream CNN for RGB-D SOD. Experimental results have demonstrated the effectiveness of our proposed model. Moreover, we also construct a new challenging large-scale RGB-D SOD dataset with high-quality, thus can both promote the training and evaluation of deep models. | ['Junwei Han', 'Ling Shao', 'Ni Zhang', 'Nian Liu'] | 2020-10-12 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.06031619e-01 -1.28098115e-01 1.48521513e-01 -4.38916057e-01
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3d07e5ce-20ca-4049-9974-6af811104c0f | consrec-learning-consensus-behind | 2302.03555 | null | https://arxiv.org/abs/2302.03555v1 | https://arxiv.org/pdf/2302.03555v1.pdf | ConsRec: Learning Consensus Behind Interactions for Group Recommendation | Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling. | ['Philip S. Yu', 'Yangyong Zhu', 'Jiawei Zhang', 'Yizhu Jiao', 'Yao Zhang', 'Yun Xiong', 'Xixi Wu'] | 2023-02-07 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.25187764e-02 -1.67247802e-02 -4.69513714e-01 -6.31256342e-01
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6d7dcf96-fece-4968-ae84-455cef0bed5b | thompson-sampling-achieves-tilde-o-sqrt-t | 2206.08520 | null | https://arxiv.org/abs/2206.08520v1 | https://arxiv.org/pdf/2206.08520v1.pdf | Thompson Sampling Achieves $\tilde O(\sqrt{T})$ Regret in Linear Quadratic Control | Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on the observed data. In this work, we study the problem of adaptive control of stabilizable linear-quadratic regulators (LQRs) using TS, where the system dynamics are unknown. Previous works have established that $\tilde O(\sqrt{T})$ frequentist regret is optimal for the adaptive control of LQRs. However, the existing methods either work only in restrictive settings, require a priori known stabilizing controllers, or utilize computationally intractable approaches. We propose an efficient TS algorithm for the adaptive control of LQRs, TS-based Adaptive Control, TSAC, that attains $\tilde O(\sqrt{T})$ regret, even for multidimensional systems, thereby solving the open problem posed in Abeille and Lazaric (2018). TSAC does not require a priori known stabilizing controller and achieves fast stabilization of the underlying system by effectively exploring the environment in the early stages. Our result hinges on developing a novel lower bound on the probability that the TS provides an optimistic sample. By carefully prescribing an early exploration strategy and a policy update rule, we show that TS achieves order-optimal regret in adaptive control of multidimensional stabilizable LQRs. We empirically demonstrate the performance and the efficiency of TSAC in several adaptive control tasks. | ['Babak Hassibi', 'Anima Anandkumar', 'Kamyar Azizzadenesheli', 'Sahin Lale', 'Taylan Kargin'] | 2022-06-17 | null | null | null | null | ['decision-making-under-uncertainty', 'thompson-sampling', 'decision-making-under-uncertainty'] | ['medical', 'methodology', 'reasoning'] | [ 2.01998949e-01 2.59794891e-01 -3.06583047e-01 3.66502881e-01
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ed7c645f-5b42-4692-9dd9-ec7f61c8a280 | open-data-for-moroccan-license-plates-for-ocr | 2104.08244 | null | https://arxiv.org/abs/2104.08244v1 | https://arxiv.org/pdf/2104.08244v1.pdf | Open data for Moroccan license plates for OCR applications : data collection, labeling, and model construction | Significant number of researches have been developed recently around intelligent system for traffic management, especially, OCR based license plate recognition, as it is considered as a main step for any automatic traffic management system. Good quality data sets are increasingly needed and produced by the research community to improve the performance of those algorithms. Furthermore, a special need of data is noted for countries having special characters on their licence plates, like Morocco, where Arabic Alphabet is used. In this work, we present a labeled open data set of circulation plates taken in Morocco, for different type of vehicles, namely cars, trucks and motorcycles. This data was collected manually and consists of 705 unique and different images. Furthermore this data was labeled for plate segmentation and for matriculation number OCR. Also, As we show in this paper, the data can be enriched using data augmentation techniques to create training sets with few thousands of images for different machine leaning and AI applications. We present and compare a set of models built on this data. Also, we publish this data as an open access data to encourage innovation and applications in the field of OCR and image processing for traffic control and other applications for transportation and heterogeneous vehicle management. | ['Ikram Chairi', 'Saad Benjelloun', 'Mohamed El Fakir', 'Abdelkrim Alahyane'] | 2021-04-16 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 1.72899678e-01 -3.41959685e-01 -6.51096255e-02 -2.63882399e-01
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688a37b4-30d8-4aef-8b3c-4d199481967b | a-critical-reassessment-of-evolutionary | 1407.1993 | null | http://arxiv.org/abs/1407.1993v1 | http://arxiv.org/pdf/1407.1993v1.pdf | A Critical Reassessment of Evolutionary Algorithms on the cryptanalysis of the simplified data encryption standard algorithm | In this paper we analyze the cryptanalysis of the simplified data encryption
standard algorithm using meta-heuristics and in particular genetic algorithms.
The classic fitness function when using such an algorithm is to compare n-gram
statistics of a the decrypted message with those of the target message. We show
that using such a function is irrelevant in case of Genetic Algorithm, simply
because there is no correlation between the distance to the real key (the
optimum) and the value of the fitness, in other words, there is no hidden
gradient. In order to emphasize this assumption we experimentally show that a
genetic algorithm perform worse than a random search on the cryptanalysis of
the simplified data encryption standard algorithm. | ['Cyril Fonlupt', 'Fabien Teytaud'] | 2014-07-08 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 4.43290323e-01 1.38343245e-01 2.08336800e-01 -8.97926744e-03
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f8b64185-3e05-48b5-b196-a263096337c1 | human-body-shape-classification-based-on-a | 2305.18480 | null | https://arxiv.org/abs/2305.18480v1 | https://arxiv.org/pdf/2305.18480v1.pdf | Human Body Shape Classification Based on a Single Image | There is high demand for online fashion recommender systems that incorporate the needs of the consumer's body shape. As such, we present a methodology to classify human body shape from a single image. This is achieved through the use of instance segmentation and keypoint estimation models, trained only on open-source benchmarking datasets. The system is capable of performing in noisy environments owing to to robust background subtraction. The proposed methodology does not require 3D body recreation as a result of classification based on estimated keypoints, nor requires historical information about a user to operate - calculating all required measurements at the point of use. We evaluate our methodology both qualitatively against existing body shape classifiers and quantitatively against a novel dataset of images, which we provide for use to the community. The resultant body shape classification can be utilised in a variety of downstream tasks, such as input to size and fit recommendation or virtual try-on systems. | ['Alberto de Santos', 'Dario Dotti', 'Filipa Peleja', 'Cameron Trotter'] | 2023-05-29 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 2.34731033e-01 1.14333056e-01 -3.47202197e-02 -3.80355239e-01
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da425478-1ee1-44b8-9a42-91f59fd54d53 | entertaining-and-opinionated-but-too | 1908.04832 | null | https://arxiv.org/abs/1908.04832v1 | https://arxiv.org/pdf/1908.04832v1.pdf | Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System | Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support casual open-domain social inter-action. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Over-all users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this. | ['Steve Whittaker', 'Kevin K. Bowden', 'Nicholas Santer', 'Marilyn Walker', 'Juraj Juraska', 'Wen Cui', 'Brian Schwarzmann', 'Vrindavan Harrison', 'Jiaqi Wu'] | 2019-08-13 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [-3.82137388e-01 8.28372359e-01 1.25385225e-01 -3.56497794e-01
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704b9ae6-62e5-4957-a955-5632bf5c47d2 | improved-projection-free-online-continuous | 2305.18442 | null | https://arxiv.org/abs/2305.18442v1 | https://arxiv.org/pdf/2305.18442v1.pdf | Improved Projection-free Online Continuous Submodular Maximization | We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently. To efficiently handle this problem, especially in the case with complicated decision sets, previous studies have proposed an efficient projection-free algorithm called Mono-Frank-Wolfe (Mono-FW) using $O(T)$ gradient evaluations and linear optimization steps in total. However, it only attains a $(1-1/e)$-regret bound of $O(T^{4/5})$. In this paper, we propose an improved projection-free algorithm, namely POBGA, which reduces the regret bound to $O(T^{3/4})$ while keeping the same computational complexity as Mono-FW. Instead of modifying Mono-FW, our key idea is to make a novel combination of a projection-based algorithm called online boosting gradient ascent, an infeasible projection technique, and a blocking technique. Furthermore, we consider the decentralized setting and develop a variant of POBGA, which not only reduces the current best regret bound of efficient projection-free algorithms for this setting from $O(T^{4/5})$ to $O(T^{3/4})$, but also reduces the total communication complexity from $O(T)$ to $O(\sqrt{T})$. | ['Mingli Song', 'Chang Yao', 'Yuanyu Wan', 'Yucheng Liao'] | 2023-05-29 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.21835180e-01 9.21300985e-03 -1.90573037e-01 -4.16821748e-01
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68107ab9-fce7-4783-aa0d-bad4baf320f0 | backflipping-with-miniature-quadcopters-by | 2209.14652 | null | https://arxiv.org/abs/2209.14652v2 | https://arxiv.org/pdf/2209.14652v2.pdf | Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning | The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian Process model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a Gaussian Process that is trained by using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver by using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters. | ['Roland Tóth', 'Tamás Péni', 'Péter Antal'] | 2022-09-29 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 1.02065645e-01 2.15111867e-01 -5.96506409e-02 2.45790422e-01
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c8d6f087-08dc-4073-b7f6-b9f9476ce24d | pali-nlp-at-semeval-2022-task-4 | 2203.04616 | null | https://arxiv.org/abs/2203.04616v2 | https://arxiv.org/pdf/2203.04616v2.pdf | PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection | Patronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems. At SemEval-2022 Task 4, we propose a novel Transformer-based model and its ensembles to accurately understand such language context for PCL detection. To facilitate comprehension of the subtle and subjective nature of PCL, two fine-tuning strategies are applied to capture discriminative features from diverse linguistic behaviour and categorical distribution. The system achieves remarkable results on the official ranking, including 1st in Subtask 1 and 5th in Subtask 2. Extensive experiments on the task demonstrate the effectiveness of our system and its strategies. | ['Xiaofeng Shi', 'Yang Mo', 'Lianxin Jiang', 'Meizhi Jin', 'Mengfei Yuan', 'Xiyang Du', 'Mengyuan Zhou', 'Dou Hu'] | 2022-03-09 | pali-nlp-at-semeval-2022-task-4-1 | https://aclanthology.org/2022.semeval-1.43 | https://aclanthology.org/2022.semeval-1.43.pdf | semeval-naacl-2022-7 | ['semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection'] | ['miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing'] | [-9.89113301e-02 -4.08370495e-01 -4.75562394e-01 -3.85587126e-01
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f79e4cbf-870c-43cd-94ae-f706b19fd776 | transweather-transformer-based-restoration-of | 2111.14813 | null | https://arxiv.org/abs/2111.14813v2 | https://arxiv.org/pdf/2111.14813v2.pdf | TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions | Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to deal with just removing one type of degradation. Recently, a CNN-based method using neural architecture search (All-in-One) was proposed to remove all the weather conditions at once. However, it has a large number of parameters as it uses multiple encoders to cater to each weather removal task and still has scope for improvement in its performance. In this work, we focus on developing an efficient solution for the all adverse weather removal problem. To this end, we propose TransWeather, a transformer-based end-to-end model with just a single encoder and a decoder that can restore an image degraded by any weather condition. Specifically, we utilize a novel transformer encoder using intra-patch transformer blocks to enhance attention inside the patches to effectively remove smaller weather degradations. We also introduce a transformer decoder with learnable weather type embeddings to adjust to the weather degradation at hand. TransWeather achieves improvements across multiple test datasets over both All-in-One network as well as methods fine-tuned for specific tasks. TransWeather is also validated on real world test images and found to be more effective than previous methods. Implementation code can be accessed at https://github.com/jeya-maria-jose/TransWeather . | ['Vishal M. Patel', 'Rajeev Yasarla', 'Jeya Maria Jose Valanarasu'] | 2021-11-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Valanarasu_TransWeather_Transformer-Based_Restoration_of_Images_Degraded_by_Adverse_Weather_Conditions_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Valanarasu_TransWeather_Transformer-Based_Restoration_of_Images_Degraded_by_Adverse_Weather_Conditions_CVPR_2022_paper.pdf | cvpr-2022-1 | ['single-image-desnowing', 'single-image-deraining'] | ['computer-vision', 'computer-vision'] | [-5.04270829e-02 -4.36469942e-01 5.10377407e-01 -5.39753139e-01
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57869c0e-84b9-47f1-92c2-823e94cc1a24 | a-novel-application-for-real-time-arrhythmia | 2305.16727 | null | https://arxiv.org/abs/2305.16727v1 | https://arxiv.org/pdf/2305.16727v1.pdf | A novel application for real-time arrhythmia detection using YOLOv8 | In recent years, there has been an increasing need to reduce healthcare costs in remote monitoring of cardiovascular health. Detecting and classifying cardiac arrhythmia is critical to diagnosing patients with cardiac abnormalities. This paper shows that complex systems such as electrocardiograms (ECG) can be applicable for at-home monitoring. This paper proposes a novel application for arrhythmia detection using the state-of-the-art You-Only-Look-Once (YOLO)v8 algorithm to classify single-lead ECG signals. A custom YOLOv8 model was fine-tuned on the MIT-BIH dataset to detect arrhythmia in real-time to allow continuous monitoring. Results show that our model can detect heartbeats with a mAP@50 of 0.961 with a detection time of 0.002s on an NVIDIA Tesla V100. Our study demonstrated the potential of real-time arrhythmia detection, where the model output can be visually interpreted for at-home users. Furthermore, this study could be extended into a real-time XAI model, deployed in the healthcare industry, and significantly advancing healthcare needs. | ['B. Shen', 'Z. T. Woon', 'T. Jason', 'R. B. A. Mustaffa', 'X. C. Lee', 'H. Chan', 'A. K. Goil', 'G. J. N. Ang'] | 2023-05-26 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 2.38067042e-02 -4.47111070e-01 3.68095785e-01 -1.98866367e-01
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d9865d27-160a-4f65-9eca-f8f235010f22 | futures-quantitative-investment-with | 2303.16532 | null | https://arxiv.org/abs/2303.16532v1 | https://arxiv.org/pdf/2303.16532v1.pdf | Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network | It is a challenging problem to predict trends of futures prices with traditional econometric models as one needs to consider not only futures' historical data but also correlations among different futures. Spatial-temporal graph neural networks (STGNNs) have great advantages in dealing with such kind of spatial-temporal data. However, we cannot directly apply STGNNs to high-frequency future data because future investors have to consider both the long-term and short-term characteristics when doing decision-making. To capture both the long-term and short-term features, we exploit more label information by designing four heterogeneous tasks: price regression, price moving average regression, price gap regression (within a short interval), and change-point detection, which involve both long-term and short-term scenes. To make full use of these labels, we train our model in a continual manner. Traditional continual GNNs define the gradient of prices as the parameter important to overcome catastrophic forgetting (CF). Unfortunately, the losses of the four heterogeneous tasks lie in different spaces. Hence it is improper to calculate the parameter importance with their losses. We propose to calculate parameter importance with mutual information between original observations and the extracted features. The empirical results based on 49 commodity futures demonstrate that our model has higher prediction performance on capturing long-term or short-term dynamic change. | ['Bin Liu', 'Lu Wei', 'YiXuan Wang', 'Min Hu', 'Zhizhong Tan'] | 2023-03-29 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [-3.32647860e-01 -3.34170491e-01 -1.31235108e-01 -3.57555360e-01
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58fbb027-d671-42c1-8a0f-5c728e59ca6c | on-formal-feature-attribution-and-its | 2307.03380 | null | https://arxiv.org/abs/2307.03380v1 | https://arxiv.org/pdf/2307.03380v1.pdf | On Formal Feature Attribution and Its Approximation | Recent years have witnessed the widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models. Despite their tremendous success, a number of vital problems like ML model brittleness, their fairness, and the lack of interpretability warrant the need for the active developments in explainable artificial intelligence (XAI) and formal ML model verification. The two major lines of work in XAI include feature selection methods, e.g. Anchors, and feature attribution techniques, e.g. LIME and SHAP. Despite their promise, most of the existing feature selection and attribution approaches are susceptible to a range of critical issues, including explanation unsoundness and out-of-distribution sampling. A recent formal approach to XAI (FXAI) although serving as an alternative to the above and free of these issues suffers from a few other limitations. For instance and besides the scalability limitation, the formal approach is unable to tackle the feature attribution problem. Additionally, a formal explanation despite being formally sound is typically quite large, which hampers its applicability in practical settings. Motivated by the above, this paper proposes a way to apply the apparatus of formal XAI to the case of feature attribution based on formal explanation enumeration. Formal feature attribution (FFA) is argued to be advantageous over the existing methods, both formal and non-formal. Given the practical complexity of the problem, the paper then proposes an efficient technique for approximating exact FFA. Finally, it offers experimental evidence of the effectiveness of the proposed approximate FFA in comparison to the existing feature attribution algorithms not only in terms of feature importance and but also in terms of their relative order. | ['Peter J. Stuckey', 'Alexey Ignatiev', 'Jinqiang Yu'] | 2023-07-07 | null | null | null | null | ['explainable-artificial-intelligence', 'fairness', 'feature-importance', 'fairness'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 4.10679221e-01 5.85524738e-01 -4.35270160e-01 -3.74095023e-01
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135774ff-abea-46c1-b7cc-5d10ded6e821 | dynamic-facial-expression-generation-on | 1907.10087 | null | https://arxiv.org/abs/1907.10087v2 | https://arxiv.org/pdf/1907.10087v2.pdf | Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets | In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphere. By proposing a conditional version of manifold-valued Wasserstein generative adversarial network (GAN) for motion generation on the hypersphere, we learn the distribution of facial expression dynamics of different classes, from which we synthesize new facial expression motions. The resulting motions can be transformed to sequences of landmarks and then to images sequences by editing the texture information using another conditional Generative Adversarial Network. To the best of our knowledge, this is the first work that explores manifold-valued representations with GAN to address the problem of dynamic facial expression generation. We evaluate our proposed approach both quantitatively and qualitatively on two public datasets; Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the effectiveness of our approach in generating realistic videos with continuous motion, realistic appearance and identity preservation. We also show the efficiency of our framework for dynamic facial expressions generation, dynamic facial expression transfer and data augmentation for training improved emotion recognition models. | ['Mohamed Daoudi', 'Lahoucine Ballihi', 'Naima Otberdout', 'Stefano Berretti', 'Anis Kacem'] | 2019-07-23 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 2.57145554e-01 3.93348485e-01 1.89034760e-01 -4.42631751e-01
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8ce05976-d4d0-47c0-840c-b9b63dd48799 | coastal-aquaculture-extraction-using-gf-3 | null | null | https://www.mdpi.com/2072-4292/15/9/2246 | https://www.mdpi.com/2072-4292/15/9/2246 | Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation | Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management.
However, the dense distribution and various sizes of aquacultures make it challenging to accurately
extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework
that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric
decomposition algorithms were applied to extract 13 polarimetric scattering features. Together
with the nine other polarisation and texture features, a total of 22 polarimetric features were then
extracted, among which four were optimised according to the separability index. Subsequently, to
reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual
aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground
functions, the results of which were then used in the marker-controlled watershed algorithm to obtain
refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers
into three categories (aquaculture, background and dikes) was applied to the marker function. In
addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks
to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a
morphological operation that was conducted for label augmentation. An experimental investigation
performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the
crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and
the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were
introduced, our proposed method performed better than standard UNet++ in aquaculture extraction,
achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The
experimental results indicate that the proposed method can handle the adhesion of both adjacent
objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries. | ['Jia Xu and Jiacheng Xiong', 'Mahdi Motagh', 'Peng Yang', 'Xiufeng He', 'Juanjuan Yu'] | 2023-04-19 | null | null | null | remote-sensing-2023-4 | ['culture'] | ['speech'] | [ 3.22779387e-01 -1.38336167e-01 7.41916656e-01 5.57721108e-02
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badef1f4-892f-4b15-970b-8804fe8289a2 | formal-specifications-from-natural-language | 2206.01962 | null | https://arxiv.org/abs/2206.01962v2 | https://arxiv.org/pdf/2206.01962v2.pdf | Formal Specifications from Natural Language | We study the generalization abilities of language models when translating natural language into formal specifications with complex semantics. In particular, we fine-tune language models on three datasets consisting of English sentences and their corresponding formal representation: 1) regular expressions (regex), frequently used in programming and search; 2) First-order logic (FOL), commonly used in software verification and theorem proving; and 3) linear-time temporal logic (LTL), which forms the basis for industrial hardware specification languages. Our experiments show that, in these diverse domains, the language models maintain their generalization capabilities from pre-trained knowledge of natural language to generalize, e.g., to new variable names or operator descriptions. Additionally, they achieve competitive performance, and even outperform the state-of-the-art for translating into regular expressions, with the benefits of being easy to access, efficient to fine-tune, and without a particular need for domain-specific reasoning. | ['Bernd Finkbeiner', 'Julian Siber', 'Niklas Metzger', 'Julia J. Tillman', 'Frederik Schmitt', 'Christopher Hahn'] | 2022-06-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.18752301e-01 2.00942203e-01 -7.90165901e-01 -3.11276317e-01
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2971a1c4-4625-43d2-911c-e617ef6928fb | iperceive-applying-common-sense-reasoning-to-1 | 2011.07735 | null | https://arxiv.org/abs/2011.07735v1 | https://arxiv.org/pdf/2011.07735v1.pdf | iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Most prior art in visual understanding relies solely on analyzing the "what" (e.g., event recognition) and "where" (e.g., event localization), which in some cases, fails to describe correct contextual relationships between events or leads to incorrect underlying visual attention. Part of what defines us as human and fundamentally different from machines is our instinct to seek causality behind any association, say an event Y that happened as a direct result of event X. To this end, we propose iPerceive, a framework capable of understanding the "why" between events in a video by building a common-sense knowledge base using contextual cues to infer causal relationships between objects in the video. We demonstrate the effectiveness of our technique using the dense video captioning (DVC) and video question answering (VideoQA) tasks. Furthermore, while most prior work in DVC and VideoQA relies solely on visual information, other modalities such as audio and speech are vital for a human observer's perception of an environment. We formulate DVC and VideoQA tasks as machine translation problems that utilize multiple modalities. By evaluating the performance of iPerceive DVC and iPerceive VideoQA on the ActivityNet Captions and TVQA datasets respectively, we show that our approach furthers the state-of-the-art. Code and samples are available at: iperceive.amanchadha.com. | ['Navpreet Kaloty', 'Gurneet Arora', 'Aman Chadha'] | 2020-11-16 | iperceive-applying-common-sense-reasoning-to | https://arxiv.org/abs/2011.07735 | https://arxiv.org/pdf/2011.07735 | null | ['dense-video-captioning'] | ['computer-vision'] | [ 3.33118081e-01 -1.54634237e-01 1.83748808e-02 -4.38865036e-01
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64adc95a-7981-40ba-a5b7-3a5ee578e5e2 | occlusion-guided-self-supervised-scene-flow | 2104.04724 | null | https://arxiv.org/abs/2104.04724v2 | https://arxiv.org/pdf/2104.04724v2.pdf | Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds | Understanding the flow in 3D space of sparsely sampled points between two consecutive time frames is the core stone of modern geometric-driven systems such as VR/AR, Robotics, and Autonomous driving. The lack of real, non-simulated, labeled data for this task emphasizes the importance of self- or un-supervised deep architectures. This work presents a new self-supervised training method and an architecture for the 3D scene flow estimation under occlusions. Here we show that smart multi-layer fusion between flow prediction and occlusion detection outperforms traditional architectures by a large margin for occluded and non-occluded scenarios. We report state-of-the-art results on Flyingthings3D and KITTI datasets for both the supervised and self-supervised training. | ['Dan Raviv', 'Bojun Ouyang'] | 2021-04-10 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.90995598e-01 -2.03775629e-01 -1.41119033e-01 -6.67753458e-01
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b3e86567-fc6b-4408-9f04-e1ff5accd861 | minimizing-age-of-information-for-mobile-edge | 2307.01366 | null | https://arxiv.org/abs/2307.01366v1 | https://arxiv.org/pdf/2307.01366v1.pdf | Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach | Exploiting the computational heterogeneity of mobile devices and edge nodes, mobile edge computation (MEC) provides an efficient approach to achieving real-time applications that are sensitive to information freshness, by offloading tasks from mobile devices to edge nodes. We use the metric Age-of-Information (AoI) to evaluate information freshness. An efficient solution to minimize the AoI for the MEC system with multiple users is non-trivial to obtain due to the random computing time. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB) problem and establish a hierarchical Markov Decision Process (MDP) to characterize the updating of AoI for the MEC system. Based on the hierarchical MDP, we propose a nested index framework and design a nested index policy with provably asymptotic optimality. Finally, the closed form of the nested index is obtained, which enables the performance tradeoffs between computation complexity and accuracy. Our algorithm leads to an optimality gap reduction of up to 40%, compared to benchmarks. Our algorithm asymptotically approximates the lower bound as the system scalar gets large enough. | ['Jun Wang', 'Meng Zhang', 'Ning Yang', 'Shuo Chen'] | 2023-07-03 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 9.81577579e-03 -1.09011188e-01 -8.76636326e-01 2.14018196e-01
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2f6daa9c-d015-448a-8087-4a813715accf | algorithms-for-generating-ordered-solutions | 1401.5852 | null | http://arxiv.org/abs/1401.5852v1 | http://arxiv.org/pdf/1401.5852v1.pdf | Algorithms for Generating Ordered Solutions for Explicit AND/OR Structures | We present algorithms for generating alternative solutions for explicit
acyclic AND/OR structures in non-decreasing order of cost. The proposed
algorithms use a best first search technique and report the solutions using an
implicit representation ordered by cost. In this paper, we present two versions
of the search algorithm -- (a) an initial version of the best first search
algorithm, ASG, which may present one solution more than once while generating
the ordered solutions, and (b) another version, LASG, which avoids the
construction of the duplicate solutions. The actual solutions can be
reconstructed quickly from the implicit compact representation used. We have
applied the methods on a few test domains, some of them are synthetic while the
others are based on well known problems including the search space of the 5-peg
Tower of Hanoi problem, the matrix-chain multiplication problem and the problem
of finding secondary structure of RNA. Experimental results show the efficacy
of the proposed algorithms over the existing approach. Our proposed algorithms
have potential use in various domains ranging from knowledge based frameworks
to service composition, where the AND/OR structure is widely used for
representing problems. | ['Pallab Dasgupta', 'Amit Sharma', 'P. P. Chakrabarti', 'Priyankar Ghosh'] | 2014-01-23 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 6.42332613e-01 1.80321902e-01 1.39246002e-01 -1.22822285e-01
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cf6aed0f-a1cb-4211-aa8e-c096772392c9 | entropic-descent-archetypal-analysis-for | 2209.11002 | null | https://arxiv.org/abs/2209.11002v2 | https://arxiv.org/pdf/2209.11002v2.pdf | Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing | In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural formulation for this task. This method does not require the presence of pure pixels (i.e., pixels containing a single material) but instead represents endmembers as convex combinations of a few pixels present in the original hyperspectral image. Our approach leverages an entropic gradient descent strategy, which (i) provides better solutions for hyperspectral unmixing than traditional archetypal analysis algorithms, and (ii) leads to efficient GPU implementations. Since running a single instance of our algorithm is fast, we also propose an ensembling mechanism along with an appropriate model selection procedure that make our method robust to hyper-parameter choices while keeping the computational complexity reasonable. By using six standard real datasets, we show that our approach outperforms state-of-the-art matrix factorization and recent deep learning methods. We also provide an open-source PyTorch implementation: https://github.com/inria-thoth/EDAA. | ['Julien Mairal', 'Jocelyn Chanussot', 'Behnood Rasti', 'Gedeon Muhawenayo', 'Alexandre Zouaoui'] | 2022-09-22 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 2.80458510e-01 -6.40727341e-01 3.20190564e-02 1.43874988e-01
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b311f6c2-79f3-4b53-b319-0eef34dfff8a | few-shot-3d-lidar-semantic-segmentation-for | 2302.08785 | null | https://arxiv.org/abs/2302.08785v2 | https://arxiv.org/pdf/2302.08785v2.pdf | Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving | In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the novel classes without considering the base classes. This setting cannot be directly applied to autonomous driving due to safety concerns. Thus, we propose a few-shot 3D LiDAR semantic segmentation method that predicts both novel classes and base classes simultaneously. Our method tries to solve the background ambiguity problem in generalized few-shot semantic segmentation. We first review the original cross-entropy and knowledge distillation losses, then propose a new loss function that incorporates the background information to achieve 3D LiDAR few-shot semantic segmentation. Extensive experiments on SemanticKITTI demonstrate the effectiveness of our method. | ['Yu Hu', 'Junbao Zhou', 'Jilin Mei'] | 2023-02-17 | null | null | null | null | ['generalized-few-shot-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.74530432e-01 9.80223194e-02 -3.91966373e-01 -7.22207308e-01
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a6070d56-74ba-4ceb-9141-265ac8570a89 | conditional-image-to-video-generation-with | 2303.13744 | null | https://arxiv.org/abs/2303.13744v1 | https://arxiv.org/pdf/2303.13744v1.pdf | Conditional Image-to-Video Generation with Latent Flow Diffusion Models | Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given image and condition. In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image. Compared to previous direct-synthesis-based works, our proposed LFDM can better synthesize spatial details and temporal motion by fully utilizing the spatial content of the given image and warping it in the latent space according to the generated temporally-coherent flow. The training of LFDM consists of two separate stages: (1) an unsupervised learning stage to train a latent flow auto-encoder for spatial content generation, including a flow predictor to estimate latent flow between pairs of video frames, and (2) a conditional learning stage to train a 3D-UNet-based diffusion model (DM) for temporal latent flow generation. Unlike previous DMs operating in pixel space or latent feature space that couples spatial and temporal information, the DM in our LFDM only needs to learn a low-dimensional latent flow space for motion generation, thus being more computationally efficient. We conduct comprehensive experiments on multiple datasets, where LFDM consistently outperforms prior arts. Furthermore, we show that LFDM can be easily adapted to new domains by simply finetuning the image decoder. Our code is available at https://github.com/nihaomiao/CVPR23_LFDM. | ['Martin Renqiang Min', 'Sharon X. Huang', 'Kai Li', 'Changhao Shi', 'Haomiao Ni'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'image-to-video'] | ['computer-vision', 'computer-vision'] | [ 4.20923293e-01 -4.46356786e-03 -2.87623793e-01 -7.45055731e-03
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5314b4f3-9d40-49d7-9660-510581871552 | mfrfnn-multi-functional-recurrent-fuzzy | null | null | https://www.sciencedirect.com/science/article/pii/S0925231222010074 | https://www.sciencedirect.com/science/article/pii/S0925231222010074 | MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction | Chaotic time series prediction, a challenging research topic in dynamic system modeling, has drawn great attention from researchers around the world. In recent years extensive researches have been done on developing chaotic time series prediction methods, and various models have been proposed. Among them, recurrent fuzzy neural networks (RFNNs) have shown significant potential in this area. Most of the proposed RFNNs learn a single function, but when dealing with chaotic time series, different outputs may be generated for a specific input based on the system’s state. So, a network is required that can learn multiple functions simultaneously. Based on this concept, a novel multi-functional recurrent fuzzy neural network (MFRFNN) is proposed in this paper. MFRFNN consists of two fuzzy neural networks with Takagi-Sugeno-Kang fuzzy rules, one is used to produce the output, and the other to determine the system’s state. There is a feedback loop between these two networks, which makes MFRFNN capable of learning and memorizing historical information of past observations. Employing the states allows the proposed network to learn multiple functions simultaneously. Moreover, a new learning algorithm, which employs the particle swarm optimization algorithm, is developed to train the networks’ weights. The effectiveness of MFRFNN is validated using the Lorenz and Rossler chaotic time series and four real-world datasets, including Box-Jenkins gas furnace, wind speed prediction, Google stock price prediction, and air quality index prediction. Based on the root mean square error, the proposed method shows a decrease of 35.12%, 13.95%, and 49.62% from the second best methods in the Lorenz time series, Box-Jenkins gas furnace, and wind speed prediction dataset, respectively. | ['Mohammad Mehdi Ebadzadeh', 'Hamid Nasiri'] | 2022-08-01 | null | null | null | neurocomputing-2022-8 | ['time-series-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-3.80400181e-01 -8.62452090e-01 1.16377302e-01 -2.35633366e-02
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576176ed-7733-4796-965d-b7b7c836aa6d | crossmatch-improving-semi-supervised-object | null | null | https://openreview.net/forum?id=rFUwBW8qgIZ | https://openreview.net/pdf?id=rFUwBW8qgIZ | CrossMatch: Improving Semi-Supervised Object Detection via Multi-Scale Consistency | We present a novel method, CrossMatch, for semi-supervised object detection. Inspired by the fact that teacher/student pseudo-labeling approaches result in a weak and sparse gradient signal due to the difficulty of confidence-thresholding, CrossMatch leverages \textit{multi-scale feature extraction} in object detection. Specifically, we enforce consistency between different scales across the student and teacher networks. To the best of our knowledge, this is the first work to use multi-scale consistency in semi-supervised object detection. Furthermore, unlike prior work that mostly uses hard pseudo-labeling methods, CrossMatch further densifies the gradient signal by enforcing multi-scale consistency through both hard and soft labels. This combination effectively strengthens the weak supervision signal from potentially noisy pseudo-labels. We evaluate our method on MS COCO and Pascal VOC under different experiment protocols, and our method significantly improves on previous state of the arts. Specifically, CrossMatch achieves 17.33 and 21.53 mAP with only 0.5\% and 1\% labeled data respectively on MS COCO, outperforming other state-of-the-art methods by $\sim$3 mAP. | ['Zsolt Kira', 'Chih-Yao Ma', 'Yen-Cheng Liu', 'Zhuoran Yu'] | 2021-09-29 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.27067327e-01 1.70528144e-01 -2.53969401e-01 -5.68897486e-01
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6ca801a0-2cf0-4a85-830e-b225f1cfaa20 | learning-semantic-aware-disentangled | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.pdf | Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing | 3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. Specifically, we establish a correspondence between latent vectors and geometric measures of body parts by designing a part-aware skeleton-separated decoupling strategy, which facilitates controllable editing of human bodies by modifying the corresponding latent codes. With the help of a bone-guided auto-encoder and an orientation-adaptive weighting strategy, our representation can be trained in an unsupervised manner. With the geometrically meaningful latent space, it can be applied to a wide range of applications, from human body editing to latent code interpolation and shape style transfer. Experimental results on public datasets demonstrate the accurate reconstruction and flexible editing abilities of the proposed method. The code will be available at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman. | ['Kun Li', 'Jingyu Yang', 'Yu-Kun Lai', 'Jinsong Zhang', 'Xiongzheng Li', 'Qiao Feng', 'Xiaokun Sun'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['style-transfer'] | ['computer-vision'] | [ 3.41053009e-01 1.13500558e-01 -3.06776375e-01 -3.42874616e-01
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75d6f1cc-cc05-4b68-901d-bc1f840cef31 | 3d-gated-recurrent-fusion-for-semantic-scene | 2002.07269 | null | https://arxiv.org/abs/2002.07269v1 | https://arxiv.org/pdf/2002.07269v1.pdf | 3D Gated Recurrent Fusion for Semantic Scene Completion | This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images contain texture details of the object(s) which are vital for semantic scene understanding. Meanwhile, depth images capture geometric clues of high relevance for shape completion. Using both RGB and depth images can further boost the accuracy of SSC over employing one modality in isolation. We propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network. Extensive experiments on two benchmark datasets demonstrate the superior performance and the effectiveness of the proposed GRFNet for data fusion in SSC. Code will be made available. | ['Cesar Cadena', 'Chunxia Zhao', 'Yu Liu', 'Xia Yuan', 'Qingsen Yan', 'Jie Li', 'Ian Reid'] | 2020-02-17 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 5.53866386e-01 -4.18977290e-02 1.58239052e-01 -4.36768353e-01
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e8e41042-fa8f-4bd9-879e-8a875661e5a1 | fast-segment-anything | 2306.12156 | null | https://arxiv.org/abs/2306.12156v1 | https://arxiv.org/pdf/2306.12156v1.pdf | Fast Segment Anything | The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and image editing. However, its huge computation costs prevent it from wider applications in industry scenarios. The computation mainly comes from the Transformer architecture at high-resolution inputs. In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance. By reformulating the task as segments-generation and prompting, we find that a regular CNN detector with an instance segmentation branch can also accomplish this task well. Specifically, we convert this task to the well-studied instance segmentation task and directly train the existing instance segmentation method using only 1/50 of the SA-1B dataset published by SAM authors. With our method, we achieve a comparable performance with the SAM method at 50 times higher run-time speed. We give sufficient experimental results to demonstrate its effectiveness. The codes and demos will be released at https://github.com/CASIA-IVA-Lab/FastSAM. | ['Jinqiao Wang', 'Ming Tang', 'Min Li', 'Tao Yu', 'Yinglong Du', 'Yongqi An', 'Wenchao Ding', 'Xu Zhao'] | 2023-06-21 | null | null | null | null | ['visual-prompting', 'object-proposal-generation', 'edge-detection', 'instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.23255730e-01 1.94847420e-01 -2.86693186e-01 -4.54136282e-01
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ecd93807-0b2d-488a-ad7c-282d3d49d080 | image-patch-matching-using-convolutional | 1710.11359 | null | http://arxiv.org/abs/1710.11359v1 | http://arxiv.org/pdf/1710.11359v1.pdf | Image Patch Matching Using Convolutional Descriptors with Euclidean Distance | In this work we propose a neural network based image descriptor suitable for
image patch matching, which is an important task in many computer vision
applications. Our approach is influenced by recent success of deep
convolutional neural networks (CNNs) in object detection and classification
tasks. We develop a model which maps the raw input patch to a low dimensional
feature vector so that the distance between representations is small for
similar patches and large otherwise. As a distance metric we utilize L2 norm,
i.e. Euclidean distance, which is fast to evaluate and used in most popular
hand-crafted descriptors, such as SIFT. According to the results, our approach
outperforms state-of-the-art L2-based descriptors and can be considered as a
direct replacement of SIFT. In addition, we conducted experiments with batch
normalization and histogram equalization as a preprocessing method of the input
data. The results confirm that these techniques further improve the performance
of the proposed descriptor. Finally, we show promising preliminary results by
appending our CNNs with recently proposed spatial transformer networks and
provide a visualisation and interpretation of their impact. | ['Esa Rahtu', 'Juho Kannala', 'Iaroslav Melekhov'] | 2017-10-31 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.13791548e-01 -3.36529851e-01 -1.03366464e-01 -4.93948013e-01
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c9d0a56b-2ca8-468e-8713-0b2e587696a7 | selfie-video-stabilization | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiyang_Yu_Selfie_Video_Stabilization_ECCV_2018_paper.pdf | Selfie Video Stabilization | We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that has optimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfie videos can be analyzed using a 3D face model, and background motion can be analyzed using optical flow. We use second derivative of temporal trajectory of selected pixels as the measure of smoothness. Our algorithm stabilizes selfie videos by minimizing the smoothness measure of the back-ground, regularized by the motion of the foreground. Experiments show that our method outperforms state-of-the-art general video stabilization techniques in selfie videos. | ['Ravi Ramamoorthi', 'Jiyang Yu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-stabilization'] | ['computer-vision'] | [-1.83085315e-02 3.73298163e-03 -1.55953482e-01 1.33758456e-01
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a334dc63-5812-4c77-b895-be1579c165a7 | autoattention-automatic-field-pair-selection | 2210.15154 | null | https://arxiv.org/abs/2210.15154v1 | https://arxiv.org/pdf/2210.15154v1.pdf | AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling | In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are proposed to learn an attentive weight for each user behavior and conduct weighted sum pooling. However, these methods only manually select several fields from the target item side as the query to interact with the behaviors, neglecting the other target item fields, as well as user and context fields. Directly including all these fields in the attention may introduce noise and deteriorate the performance. In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields. Pruning on these field pairs via these learnable weights lead to automatic field pair selection, so as to identify and remove noisy field pairs. Though including more fields, the computation cost of AutoAttention is still low due to using a simple attention function and field pair selection. Extensive experiments on the public dataset and Tencent's production dataset demonstrate the effectiveness of the proposed approach. | ['Jie Jiang', 'Dapeng Liu', 'Guihai Chen', 'Qi Luo', 'Junwei Pan', 'Xiaofeng Gao', 'Zuowu Zheng'] | 2022-10-27 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 2.84752369e-01 -4.64583278e-01 -3.11640203e-01 -6.01056159e-01
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5a4e6f68-2723-4008-8aeb-ce34e85668c0 | idea-increasing-text-diversity-via-online | 2207.05333 | null | https://arxiv.org/abs/2207.05333v2 | https://arxiv.org/pdf/2207.05333v2.pdf | IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training | Vision-Language Pre-training (VLP) with large-scale image-text pairs has demonstrated superior performance in various fields. However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP. Existing methods proposed to adopt an off-the-shelf object detector to utilize additional image tag information. However, the object detector is time-consuming and can only identify the pre-defined object categories, limiting the model capacity. Inspired by the observation that the texts incorporate incomplete fine-grained image information, we introduce IDEA, which stands for increasing text diversity via online multi-label recognition for VLP. IDEA shows that multi-label learning with image tags extracted from the texts can be jointly optimized during VLP. Moreover, IDEA can identify valuable image tags online to provide more explicit textual supervision. Comprehensive experiments demonstrate that IDEA can significantly boost the performance on multiple downstream datasets with a small extra computational cost. | ['Xiaobo Zhang', 'Yandong Guo', 'Yaqian Li', 'Yuejie Zhang', 'Rui Feng', 'RuiWei Zhao', 'Weiwei Tian', 'Ying Cheng', 'Youcai Zhang', 'Xinyu Huang'] | 2022-07-12 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.45742217e-01 -2.69212276e-01 -5.31978011e-01 -4.05795962e-01
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32a60976-649e-40da-983c-54822ac86ee2 | graph-based-label-propagation-for-semi | 2106.08207 | null | https://arxiv.org/abs/2106.08207v1 | https://arxiv.org/pdf/2106.08207v1.pdf | Graph-based Label Propagation for Semi-Supervised Speaker Identification | Speaker identification in the household scenario (e.g., for smart speakers) is typically based on only a few enrollment utterances but a much larger set of unlabeled data, suggesting semisupervised learning to improve speaker profiles. We propose a graph-based semi-supervised learning approach for speaker identification in the household scenario, to leverage the unlabeled speech samples. In contrast to most of the works in speaker recognition that focus on speaker-discriminative embeddings, this work focuses on speaker label inference (scoring). Given a pre-trained embedding extractor, graph-based learning allows us to integrate information about both labeled and unlabeled utterances. Considering each utterance as a graph node, we represent pairwise utterance similarity scores as edge weights. Graphs are constructed per household, and speaker identities are propagated to unlabeled nodes to optimize a global consistency criterion. We show in experiments on the VoxCeleb dataset that this approach makes effective use of unlabeled data and improves speaker identification accuracy compared to two state-of-the-art scoring methods as well as their semi-supervised variants based on pseudo-labels. | ['Andreas Stolcke', 'Venkatesh Ravichandran', 'Long Chen'] | 2021-06-15 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.63376290e-01 5.77513635e-01 -3.48997921e-01 -1.04415500e+00
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f9c60b5b-134c-4a84-8b0a-a4b6ec977fab | distribution-matching-for-heterogeneous-multi | 2105.03790 | null | https://arxiv.org/abs/2105.03790v1 | https://arxiv.org/pdf/2105.03790v1.pdf | Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study | Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly learned by a shared learning algorithm, such as a DNN. MTL is based on the assumption that the tasks under consideration are related; therefore it exploits shared knowledge for improving performance on each individual task. Tasks are generally considered to be homogeneous, i.e., to refer to the same type of problem. Moreover, MTL is usually based on ground truth annotations with full, or partial overlap across tasks. In this work, we deal with heterogeneous MTL, simultaneously addressing detection, classification & regression problems. We explore task-relatedness as a means for co-training, in a weakly-supervised way, tasks that contain little, or even non-overlapping annotations. Task-relatedness is introduced in MTL, either explicitly through prior expert knowledge, or through data-driven studies. We propose a novel distribution matching approach, in which knowledge exchange is enabled between tasks, via matching of their predictions' distributions. Based on this approach, we build FaceBehaviorNet, the first framework for large-scale face analysis, by jointly learning all facial behavior tasks. We develop case studies for: i) continuous affect estimation, action unit detection, basic emotion recognition; ii) attribute detection, face identification. We illustrate that co-training via task relatedness alleviates negative transfer. Since FaceBehaviorNet learns features that encapsulate all aspects of facial behavior, we conduct zero-/few-shot learning to perform tasks beyond the ones that it has been trained for, such as compound emotion recognition. By conducting a very large experimental study, utilizing 10 databases, we illustrate that our approach outperforms, by large margins, the state-of-the-art in all tasks and in all databases, even in these which have not been used in its training. | ['Stefanos Zafeiriou', 'Viktoriia Sharmanska', 'Dimitrios Kollias'] | 2021-05-08 | null | null | null | null | ['continuous-affect-estimation', 'action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.35980773e-01 1.18209578e-01 -1.88535228e-01 -4.53008324e-01
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c719db4d-6887-4ce9-852b-4362a866f9dd | scalable-regularization-of-scene-graph | 2209.02749 | null | https://arxiv.org/abs/2209.02749v1 | https://arxiv.org/pdf/2209.02749v1.pdf | Scalable Regularization of Scene Graph Generation Models using Symbolic Theories | Several techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art techniques can be divided into two families: one where the background knowledge is incorporated into the model in a subsymbolic fashion, and another in which the background knowledge is maintained in symbolic form. Despite promising results, both families of techniques face several shortcomings: the first one requires ad-hoc, more complex neural architectures increasing the training or inference cost; the second one suffers from limited scalability w.r.t. the size of the background knowledge. Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art. Our technique is model-agnostic, does not incur any cost at inference time, and scales to previously unmanageable background knowledge sizes. We demonstrate that our technique can improve the accuracy of state-of-the-art SGG models, by up to 33%. | ['Efthymia Tsamoura', 'Davide Buffelli'] | 2022-09-06 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.73200405e-01 5.30678272e-01 -9.40312669e-02 -1.30586773e-01
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649990b9-697d-4ba5-a68c-5bb4e59c0a36 | deep-learning-on-graphs-for-natural-language | null | null | https://aclanthology.org/2021.naacl-tutorials.3 | https://aclanthology.org/2021.naacl-tutorials.3.pdf | Deep Learning on Graphs for Natural Language Processing | Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of interests in applying deep learning on graph techniques to NLP, and has achieved considerable success in many NLP tasks, ranging from classification tasks like sentence classification, semantic role labeling and relation extraction, to generation tasks like machine translation, question generation and summarization. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming original text sequence data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges. This tutorial will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing). In addition, hands-on demonstration sessions will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library {--} Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. | ['Yunyao Li', 'Heng Ji', 'Yu Chen', 'Lingfei Wu'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 3.15770626e-01 6.46602392e-01 -1.35017812e-01 -3.12055886e-01
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35cd8abd-8769-4f20-adfb-db0ceb78a143 | depth-structure-preserving-scene-image | 1706.00212 | null | http://arxiv.org/abs/1706.00212v2 | http://arxiv.org/pdf/1706.00212v2.pdf | Depth Structure Preserving Scene Image Generation | Key to automatically generate natural scene images is to properly arrange
among various spatial elements, especially in the depth direction. To this end,
we introduce a novel depth structure preserving scene image generation network
(DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the
purpose of depth structure preserving scene generation. The main trunk of the
proposed infrastructure is built on a Hawkes point process that models the
spatial dependency between different depth layers. Within each layer generative
adversarial sub-networks are trained collaboratively to generate realistic
scene components, conditioned on the layer information produced by the point
process. We experiment our model on a sub-set of SUNdataset with annotated
scene images and demonstrate that our models are capable of generating
depth-realistic natural scene image. | ['Bingbing Ni', 'Yichao Yan', 'Jingwei Xu', 'Xiaokang Yang', 'Wendong Zhang'] | 2017-06-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 6.97237313e-01 4.78087723e-01 4.89242345e-01 -2.50565290e-01
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f489ea81-9b7b-4f1b-82a1-d2558892e2e0 | translating-a-math-word-problem-to-an | 1811.05632 | null | http://arxiv.org/abs/1811.05632v2 | http://arxiv.org/pdf/1811.05632v2.pdf | Translating a Math Word Problem to an Expression Tree | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to
automatic math word problem solving. Despite its simplicity, a drawback still
remains: a math word problem can be correctly solved by more than one
equations. This non-deterministic transduction harms the performance of maximum
likelihood estimation. In this paper, by considering the uniqueness of
expression tree, we propose an equation normalization method to normalize the
duplicated equations. Moreover, we analyze the performance of three popular
SEQ2SEQ models on the math word problem solving. We find that each model has
its own specialty in solving problems, consequently an ensemble model is then
proposed to combine their advantages. Experiments on dataset Math23K show that
the ensemble model with equation normalization significantly outperforms the
previous state-of-the-art methods. | ['Dongxiang Zhang', 'Xiaojiang Liu', 'Deng Cai', 'Yan Wang', 'Lei Wang'] | 2018-11-14 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.88303494e-01 -3.03975642e-01 1.16205327e-01 -4.14614379e-01
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a830dec3-8c6c-462e-8287-0731dd1f530c | astock-a-new-dataset-and-automated-stock | 2206.06606 | null | https://arxiv.org/abs/2206.06606v1 | https://arxiv.org/pdf/2206.06606v1.pdf | Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model | Natural Language Processing(NLP) demonstrates a great potential to support financial decision-making by analyzing the text from social media or news outlets. In this work, we build a platform to study the NLP-aided stock auto-trading algorithms systematically. In contrast to the previous work, our platform is characterized by three features: (1) We provide financial news for each specific stock. (2) We provide various stock factors for each stock. (3) We evaluate performance from more financial-relevant metrics. Such a design allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a more realistic setting. In addition to designing an evaluation platform and dataset collection, we also made a technical contribution by proposing a system to automatically learn a good feature representation from various input information. The key to our algorithm is a method called semantic role labeling Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a compact representation of each news paragraph. Based on SRLP, we further incorporate other stock factors to make the final prediction. In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system. Through our experimental study, we show that the proposed method achieves better performance and outperforms all the baselines' annualized rate of return as well as the maximum drawdown of the CSI300 index and XIN9 index on real trading. Our Astock dataset and code are available at https://github.com/JinanZou/Astock. | ['Javen Qinfeng Shi', 'Ehsan Abbasnejad', 'YuHao Lin', 'Lingqiao Liu', 'Haiyao Cao', 'Jinan Zou'] | 2022-06-14 | null | null | null | null | ['text-based-stock-prediction', 'news-classification', 'semantic-role-labeling', 'stock-trend-prediction', 'stock-market-prediction', 'stock-price-prediction', 'stock-prediction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'time-series', 'time-series', 'time-series', 'time-series'] | [-6.17304385e-01 -2.70674974e-01 -5.60716331e-01 -4.93611068e-01
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b0940aa8-36ce-4d23-8191-c956e8e215ed | pku-mmd-a-large-scale-benchmark-for | 1703.07475 | null | http://arxiv.org/abs/1703.07475v2 | http://arxiv.org/pdf/1703.07475v2.pdf | PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding | Despite the fact that many 3D human activity benchmarks being proposed, most
existing action datasets focus on the action recognition tasks for the
segmented videos. There is a lack of standard large-scale benchmarks,
especially for current popular data-hungry deep learning based methods. In this
paper, we introduce a new large scale benchmark (PKU-MMD) for continuous
multi-modality 3D human action understanding and cover a wide range of complex
human activities with well annotated information. PKU-MMD contains 1076 long
video sequences in 51 action categories, performed by 66 subjects in three
camera views. It contains almost 20,000 action instances and 5.4 million frames
in total. Our dataset also provides multi-modality data sources, including RGB,
depth, Infrared Radiation and Skeleton. With different modalities, we conduct
extensive experiments on our dataset in terms of two scenarios and evaluate
different methods by various metrics, including a new proposed evaluation
protocol 2D-AP. We believe this large-scale dataset will benefit future
researches on action detection for the community. | ['Yueyu Hu', 'Sijie Song', 'Jiaying Liu', 'Chunhui Liu', 'Yanghao Li'] | 2017-03-22 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 3.95357758e-01 -4.09378886e-01 -4.61441696e-01 -1.66153312e-01
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6e708809-4f48-4230-b249-0243b0d2483d | sample-hardness-based-gradient-loss-for-long | 2208.03779 | null | https://arxiv.org/abs/2208.03779v1 | https://arxiv.org/pdf/2208.03779v1.pdf | Sample hardness based gradient loss for long-tailed cervical cell detection | Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing state-of-the-art long-tailed learning methods in object detection focus on category distribution statistics to solve the problem in the long-tailed scenario without considering the "hardness" of each sample. To address this problem, in this work we propose a Grad-Libra Loss that leverages the gradients to dynamically calibrate the degree of hardness of each sample for different categories, and re-balance the gradients of positive and negative samples. Our loss can thus help the detector to put more emphasis on those hard samples in both head and tail categories. Extensive experiments on a long-tailed TCT WSI image dataset show that the mainstream detectors, e.g. RepPoints, FCOS, ATSS, YOLOF, etc. trained using our proposed Gradient-Libra Loss, achieved much higher (7.8%) mAP than that trained using cross-entropy classification loss. | ['Huisi Wu', 'Linlin Shen', 'Junliang Chen', 'Xiangbo Gao', 'Xuechen Li', 'Minmin Liu'] | 2022-08-07 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.38970241e-01 1.18941315e-01 -6.66332245e-01 -3.41555536e-01
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9c0acb36-bdd2-4203-8857-3952e23f7c29 | leveraging-neo4j-and-deep-learning-for | 2304.00192 | null | https://arxiv.org/abs/2304.00192v1 | https://arxiv.org/pdf/2304.00192v1.pdf | Leveraging Neo4j and deep learning for traffic congestion simulation & optimization | Traffic congestion has been a major challenge in many urban road networks. Extensive research studies have been conducted to highlight traffic-related congestion and address the issue using data-driven approaches. Currently, most traffic congestion analyses are done using simulation software that offers limited insight due to the limitations in the tools and utilities being used to render various traffic congestion scenarios. All that impacts the formulation of custom business problems which vary from place to place and country to country. By exploiting the power of the knowledge graph, we model a traffic congestion problem into the Neo4j graph and then use the load balancing, optimization algorithm to identify congestion-free road networks. We also show how traffic propagates backward in case of congestion or accident scenarios and its overall impact on other segments of the roads. We also train a sequential RNN-LSTM (Long Short-Term Memory) deep learning model on the real-time traffic data to assess the accuracy of simulation results based on a road-specific congestion. Our results show that graph-based traffic simulation, supplemented by AI ML-based traffic prediction can be more effective in estimating the congestion level in a road network. | ['Syed Adnan Yusuf', 'Riad Souissi', 'Arshad Ali Khan', 'Shyam Pratap Singh'] | 2023-04-01 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-3.21464986e-01 -7.14289099e-02 -2.41007864e-01 -2.82832861e-01
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2d91c3cc-8471-4647-bc5c-8e5e63550237 | lifelong-pretraining-continually-adapting | 2110.08534 | null | https://arxiv.org/abs/2110.08534v3 | https://arxiv.org/pdf/2110.08534v3.pdf | Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora | Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks. However, when deployed in the real world, a PTLM-based model must deal with data distributions that deviate from what the PTLM was initially trained on. In this paper, we study a lifelong language model pretraining challenge where a PTLM is continually updated so as to adapt to emerging data. Over a domain-incremental research paper stream and a chronologically-ordered tweet stream, we incrementally pretrain a PTLM with different continual learning algorithms, and keep track of the downstream task performance (after fine-tuning). We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora. Our experiments show distillation-based approaches to be most effective in retaining downstream performance in earlier domains. The algorithms also improve knowledge transfer, allowing models to achieve better downstream performance over the latest data, and improve temporal generalization when distribution gaps exist between training and evaluation because of time. We believe our problem formulation, methods, and analysis will inspire future studies towards continual pretraining of language models. | ['Xiang Ren', 'Andrew Arnold', 'Xiaokai Wei', 'Shang-Wen Li', 'Wei Xiao', 'Henghui Zhu', 'Dejiao Zhang', 'Xisen Jin'] | 2021-10-16 | null | https://aclanthology.org/2022.naacl-main.351 | https://aclanthology.org/2022.naacl-main.351.pdf | naacl-2022-7 | ['continual-pretraining'] | ['methodology'] | [-3.73068207e-04 -1.77059665e-01 -3.74222964e-01 -4.71313298e-01
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a3879dfa-7a0f-4b65-b8dc-baba9f8d1920 | social-fabric-tubelet-compositions-for-video | 2108.08363 | null | https://arxiv.org/abs/2108.08363v1 | https://arxiv.org/pdf/2108.08363v1.pdf | Social Fabric: Tubelet Compositions for Video Relation Detection | This paper strives to classify and detect the relationship between object tubelets appearing within a video as a <subject-predicate-object> triplet. Where existing works treat object proposals or tubelets as single entities and model their relations a posteriori, we propose to classify and detect predicates for pairs of object tubelets a priori. We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives. These primitives are learned over all relations, resulting in a compact representation able to localize and classify relations from the pool of co-occurring object tubelets across all timespans in a video. The encoding enables our two-stage network. In the first stage, we train Social Fabric to suggest proposals that are likely interacting. We use the Social Fabric in the second stage to simultaneously fine-tune and predict predicate labels for the tubelets. Experiments demonstrate the benefit of early video relation modeling, our encoding and the two-stage architecture, leading to a new state-of-the-art on two benchmarks. We also show how the encoding enables query-by-primitive-example to search for spatio-temporal video relations. Code: https://github.com/shanshuo/Social-Fabric. | ['Cees G. M. Snoek', 'Pascal Mettes', 'Zenglin Shi', 'Shuo Chen'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_Social_Fabric_Tubelet_Compositions_for_Video_Relation_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_Social_Fabric_Tubelet_Compositions_for_Video_Relation_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-visual-relation-detection'] | ['computer-vision'] | [ 9.37156472e-03 2.52465516e-01 -3.04765433e-01 -6.09494865e-01
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529196d2-6f41-471d-a87f-001fdf3761ce | analysis-by-synthesis-3d-object-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Hejrati_Analysis_by_Synthesis_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Hejrati_Analysis_by_Synthesis_2014_CVPR_paper.pdf | Analysis by Synthesis: 3D Object Recognition by Object Reconstruction | We introduce a new approach for recognizing and reconstructing 3D objects in images. Our approach is based on an analysis by synthesis strategy. A forward synthesis model constructs possible geometric interpretations of the world, and then selects the interpretation that best agrees with the measured visual evidence. The forward model synthesizes visual templates defined on invariant (HOG) features. These visual templates are discriminatively trained to be accurate for inverse estimation. We introduce an efficient "brute-force" approach to inference that searches through a large number of candidate reconstructions, returning the optimal one. One benefit of such an approach is that recognition is inherently (re)constructive. We show state of the art performance for detection and reconstruction on two challenging 3D object recognition datasets of cars and cuboids. | ['Deva Ramanan', 'Mohsen Hejrati'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.69812495e-01 5.33160046e-02 -1.11645930e-01 -4.81279850e-01
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ef2b1a9f-7048-41f8-82b1-7c5f8597fb04 | ssn-nlp-mlrg-at-semeval-2022-task-4-ensemble | null | null | https://aclanthology.org/2022.semeval-1.53 | https://aclanthology.org/2022.semeval-1.53.pdf | SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language | In this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which is to identify and categorize PCL language towards vulnerable communities. The shared task consists of two subtasks: Patronizing and Condescending language detection (Subtask A) which is the binary task classification and identifying the PCL categories that express the condescension (Subtask B) which is the multi-label text classification. For PCL language detection, We proposed the ensemble strategies of a system combination of BERT, Roberta, Distilbert, Roberta large, Albert achieved the official results for Subtask A with a macro f1 score of 0.5172 on the test set which is improved by baseline score. For PCL Category identification, We proposed a multi-label classification model to ensemble the various Bert-based models and the official results for Subtask B with a macro f1 score of 0.2117 on the test set which is improved by baseline score. | ['Thenmozhi Durairaj', 'Kalaivani Adaikkan'] | null | null | null | null | semeval-naacl-2022-7 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-1.60875707e-03 -3.00519675e-01 -2.28492007e-01 7.15406612e-02
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3b4dcb82-dc6f-4399-8837-b86178669aeb | openslu-a-unified-modularized-and-extensible | 2305.10231 | null | https://arxiv.org/abs/2305.10231v1 | https://arxiv.org/pdf/2305.10231v1.pdf | OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding | Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots). In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding. Specifically, OpenSLU unifies 10 SLU models for both single-intent and multi-intent scenarios, which support both non-pretrained and pretrained models simultaneously. Additionally, OpenSLU is highly modularized and extensible by decomposing the model architecture, inference, and learning process into reusable modules, which allows researchers to quickly set up SLU experiments with highly flexible configurations. OpenSLU is implemented based on PyTorch, and released at \url{https://github.com/LightChen233/OpenSLU}. | ['Wanxiang Che', 'Yunlong Feng', 'Xiao Xu', 'Qiguang Chen', 'Libo Qin'] | 2023-05-17 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-2.96841294e-01 1.95148230e-01 -1.86623797e-01 -7.28698313e-01
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36188a54-7609-491e-bc1c-b0762e990e21 | learning-node-representations-from-noisy | 2012.02434 | null | https://arxiv.org/abs/2012.02434v1 | https://arxiv.org/pdf/2012.02434v1.pdf | Learning Node Representations from Noisy Graph Structures | Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks to a large extent in that edges in networks propagate noises through the whole network instead of only the node itself. While existing methods tend to focus on preserving structural properties, the robustness of the learned representations against noises is generally ignored. In this paper, we propose a novel framework to learn noise-free node representations and eliminate noises simultaneously. Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting. On the one hand, the graph generator serves as a unified scheme to incorporate any useful graph prior knowledge to generate normal structures. We illustrate the generative process with community structures and power-law degree distributions as examples. On the other hand, the noise generator generates graph noises not only satisfying some fundamental properties but also in an adaptive way. Thus, real noises with arbitrary distributions can be handled successfully. Finally, in order to eliminate noises and obtain noise-free node representations, two generators need to be optimized jointly, and through maximum likelihood estimation, we equivalently convert the model into imposing different regularization constraints on the true graph and noises respectively. Our model is evaluated on both real-world and synthetic data. It outperforms other strong baselines for node classification and graph reconstruction tasks, demonstrating its ability to eliminate graph noises. | ['Chuan Shi', 'Guojie Song', 'Weiyu Zhang', 'Qingqing Long', 'Ziyao Li', 'Junshan Wang'] | 2020-12-04 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 1.27511024e-01 2.54997760e-01 9.20735598e-02 -6.05115928e-02
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ba3f53a3-567c-4776-9d10-6d7c919c4dae | interpreting-and-generating-gestures-with | null | null | https://openreview.net/forum?id=bAVVYLysfJ | https://openreview.net/pdf?id=bAVVYLysfJ | Interpreting and Generating Gestures with Embodied Human Computer Interactions | In this paper, we discuss the role that gesture plays for an embodied intelligent virtual agent (IVA) in the context of multimodal task-oriented dialogues with a human. We have developed a
simulation platform, VoxWorld, for modeling and building {\it Embodied Human-Computer Interactions (EHCI)}, where communication is facilitated through language, gesture, action, facial expressions, and gaze tracking. We believe that EHCI is a fruitful approach for studying and enabling robust interaction and communication between humans and intelligent agents and robots.
Gesture, language, and action are generated and interpreted by an IVA in a {\it situated meaning context}, which facilitates grounded and contextualized interpretations of communicative expressions in a dialogue.
The framework enables multiple methods for performing evaluation of gesture generation and recognition.
We discuss four separate scenarios involving the generation of non-verbal behavior in dialogue: (1)
deixis (pointing) gestures, generated to request information regarding an object, a location, or a direction when performing a specific action; (2) iconic action gestures, generated to clarify how (what manner of action) to perform a specific task; (3) affordance-denoting gestures, generated to describe how the IVA can interact with an object, even when it does not know what it is or what it might be used for; and (4) direct situated actions, where the IVA responds to a command or request by acting in the environment directly.
| ['Anonymous'] | 2020-09-18 | null | null | null | acm-iva-workshop-genea-2020-10 | ['gesture-generation'] | ['robots'] | [ 2.86585569e-01 3.82701188e-01 3.38319331e-01 -2.23974034e-01
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3aa75331-8b15-4d72-9322-56cc6c43c760 | detection-and-annotation-of-plant-organs-from | 2007.13106 | null | https://arxiv.org/abs/2007.13106v2 | https://arxiv.org/pdf/2007.13106v2.pdf | Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning | As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilize such information. In our study we use deep learning to detect plant organs on digitized herbarium specimens with Faster R-CNN. For our experiment we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but not equally well recognized. | ['Claus Weiland', 'Marco Schmidt', 'Thomas Hickler', 'Stefan Dressler', 'Sohaib Younis', 'Bernhard Seeger'] | 2020-07-26 | null | null | null | null | ['organ-detection'] | ['medical'] | [ 9.61589813e-02 1.31200448e-01 -1.45569906e-01 -9.04035196e-03
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cd27ac1b-1ae9-45c5-adf1-a24d2bc2f7ac | 190600884 | 1906.00884 | null | https://arxiv.org/abs/1906.00884v2 | https://arxiv.org/pdf/1906.00884v2.pdf | Fashion Editing with Adversarial Parsing Learning | Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods on image manipulation. | ['Jian Yin', 'Ziqi Zhang', 'Yixuan Zhang', 'Xiaohui Shen', 'Xiaodan Liang', 'Haoye Dong', 'Zhenyu Xie', 'Xujie Zhang', 'Bowen Wu'] | 2019-06-03 | fashion-editing-with-adversarial-parsing | http://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 5.71965456e-01 4.12780009e-02 -5.64660393e-02 -4.12354439e-01
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88bf5170-9fc7-4bea-a229-2bbbe792ed20 | vilio-state-of-the-art-visio-linguistic | 2012.07788 | null | https://arxiv.org/abs/2012.07788v1 | https://arxiv.org/pdf/2012.07788v1.pdf | Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes | This work presents Vilio, an implementation of state-of-the-art visio-linguistic models and their application to the Hateful Memes Dataset. The implemented models have been fitted into a uniform code-base and altered to yield better performance. The goal of Vilio is to provide a user-friendly starting point for any visio-linguistic problem. An ensemble of 5 different V+L models implemented in Vilio achieves 2nd place in the Hateful Memes Challenge out of 3,300 participants. The code is available at https://github.com/Muennighoff/vilio. | ['Niklas Muennighoff'] | 2020-12-14 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-7.44177163e-01 -7.27899894e-02 -2.92677313e-01 8.67312625e-02
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8517e516-1acd-43ed-a2a2-cb36fb10681f | textgail-generative-adversarial-imitation | 2004.13796 | null | https://arxiv.org/abs/2004.13796v4 | https://arxiv.org/pdf/2004.13796v4.pdf | TextGAIL: Generative Adversarial Imitation Learning for Text Generation | Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in their discriminators. To address this problem, we propose a generative adversarial imitation learning framework for text generation that uses large pre-trained language models to provide more reliable reward guidance. Our approach uses contrastive discriminator, and proximal policy optimization (PPO) to stabilize and improve text generation performance. For evaluation, we conduct experiments on a diverse set of unconditional and conditional text generation tasks. Experimental results show that TextGAIL achieves better performance in terms of both quality and diversity than the MLE baseline. We also validate our intuition that TextGAIL's discriminator demonstrates the capability of providing reasonable rewards with an additional task. | ['Lei LI', 'Qingyang Wu', 'Zhou Yu'] | 2020-04-07 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 2.22925439e-01 3.21383476e-01 -1.20986335e-01 -1.30872354e-01
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53b96ef7-3a91-46f3-8dee-6dc71aae839a | academic-expert-finding-via-k-mathcal-p-core | null | null | https://openreview.net/forum?id=BY5V3w4bWRU | https://openreview.net/pdf?id=BY5V3w4bWRU | Academic Expert Finding via $(k,\mathcal{P})$-Core based Embedding over Heterogeneous Graphs | Finding relevant experts in specified areas is often crucial for a wide range of applications in both academia and industry. Given a user input query and a large amount of academic knowledge (e.g., academic papers), expert finding aims to find and rank the experts who are most relevant to the given query, from the academic knowledge. Existing studies mainly focus on the embedding-based solutions that (1) consider academic papers' textual semantic similarities to a given query through document representation models and (2) extract the top-$n$ experts with the greatest similarities. Beyond the implicit textual semantics of papers, however, the papers’ explicit relationships (e.g., co-authorship, citation, and same-topic relationship) in a heterogeneous academic graph (e.g., DBLP) are critical for document representation, insofar as they help improve the expert finding quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this paper, we study the academic expert finding on heterogeneous graphs by considering the explicit relationships besides the implicit textual semantics of papers in one representation model. Specifically, we first define the $(k,\mathcal{P})$-core to denote a cohesive community of papers that are closely connected via a meta-path $\mathcal{P}$ ($\mathcal{P}$ indicates the different relationships of papers). We then propose an offline $(k,\mathcal{P})$-core based document embedding model to capture papers' various explicit relationships for representation. Moreover, by using papers' embeddings, we present an online threshold algorithm (TA)-based method to efficiently return top-$n$ experts via a carefully designed proximity graph-based index (PG-Index). We extend our approach to support multiple relationships simultaneously for representation. Extensive experiments over real-world datasets demonstrate the effectiveness and efficiency of our approach. | ['Anonymous'] | 2021-07-26 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-3.27247530e-01 -6.68661892e-02 -2.64092475e-01 2.02578995e-02
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284f5fd6-535e-469c-a79b-51a96a107c54 | distinguishing-representational-geometries | 2211.15053 | null | https://arxiv.org/abs/2211.15053v1 | https://arxiv.org/pdf/2211.15053v1.pdf | Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments | Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitatively distinct neural network models often predict similar representational geometries of typical stimulus sets. We propose a Bayesian experimental design approach to synthesizing stimulus sets for adjudicating among representational models efficiently. We apply our method to discriminate among candidate neural network models of behavioral face dissimilarity judgments. Our results indicate that a neural network trained to invert a 3D-face-model graphics renderer is more human-aligned than the same architecture trained on identification, classification, or autoencoding. Our proposed stimulus synthesis objective is generally applicable to designing experiments to be analyzed by representational similarity analysis for model comparison. | ['Nikolaus Kriegeskorte', 'Heiko H. Schütt', 'Wenxuan Guo', 'Tal Golan'] | 2022-11-28 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 7.74750412e-01 1.19157895e-01 1.16604775e-01 -7.96190441e-01
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78027481-b7df-40e4-a77e-edb75c2a4f9a | learning-treatment-plan-representations-for | 2206.02912 | null | https://arxiv.org/abs/2206.02912v2 | https://arxiv.org/pdf/2206.02912v2.pdf | Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans | Objective: Knowledge based planning (KBP) typically involves training an end-to-end deep learning model to predict dose distributions. However, training end-to-end methods may be associated with practical limitations due to the limited size of medical datasets that are often used. To address these limitations, we propose a content based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Approach: Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github. Main Results: The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available plans and clinical plans from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network. Significance: Applying CBIR to inform subsequent treatment planning potentially addresses many limitations associated with end-to-end KBP. Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works, potentially through methods like the MetaPlanner framework. | ['Lei Xing', 'Yong Yang', 'Joseph B. Schulz', 'Jen-Yeu Wang', 'Piotr Dubrowski', 'Yusuke Nomura', 'Md Tauhidul Islam', 'Oscar Pastor-Serrano', 'Varun Vasudevan', 'Charles Huang'] | 2022-06-06 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.84487239e-01 2.73090184e-01 -3.90194833e-01 -3.97985637e-01
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e1641d97-fb4e-404e-8b06-62c4998966d0 | pseudo-labels-regularization-for-imbalanced | 2303.03946 | null | https://arxiv.org/abs/2303.03946v1 | https://arxiv.org/pdf/2303.03946v1.pdf | Pseudo Labels Regularization for Imbalanced Partial-Label Learning | Partial-label learning (PLL) is an important branch of weakly supervised learning where the single ground truth resides in a set of candidate labels, while the research rarely considers the label imbalance. A recent study for imbalanced partial-Label learning proposed that the combinatorial challenge of partial-label learning and long-tail learning lies in matching between a decent marginal prior distribution with drawing the pseudo labels. However, we believe that even if the pseudo label matches the prior distribution, the tail classes will still be difficult to learn because the total weight is too small. Therefore, we propose a pseudo-label regularization technique specially designed for PLL. By punishing the pseudo labels of head classes, our method implements state-of-art under the standardized benchmarks compared to the previous PLL methods. | ['Zheng Lian', 'Mingyu Xu'] | 2023-03-06 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.22149296e-01 3.49554271e-01 -9.43864942e-01 -6.06362760e-01
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d61d4adf-6373-45e9-a116-ccda026958cf | relational-embeddings-for-language | 2210.05715 | null | https://arxiv.org/abs/2210.05715v1 | https://arxiv.org/pdf/2210.05715v1.pdf | Relational Embeddings for Language Independent Stance Detection | The large majority of the research performed on stance detection has been focused on developing more or less sophisticated text classification systems, even when many benchmarks are based on social network data such as Twitter. This paper aims to take on the stance detection task by placing the emphasis not so much on the text itself but on the interaction data available on social networks. More specifically, we propose a new method to leverage social information such as friends and retweets by generating relational embeddings, namely, dense vector representations of interaction pairs. Our method can be applied to any language and target without any manual tuning. Our experiments on seven publicly available datasets and four different languages show that combining our relational embeddings with textual methods helps to substantially improve performance, obtaining best results for six out of seven evaluation settings, outperforming strong baselines based on large pre-trained language models. | ['Rodrigo Agerri', 'Joseba Fernandez de Landa'] | 2022-10-11 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-1.35841966e-02 2.20152229e-01 -8.02136123e-01 -3.27619433e-01
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ebfc4dbd-0b72-4c10-8002-c3552ae9ac46 | restoring-ancient-text-using-deep-learning-a | 1910.06262 | null | https://arxiv.org/abs/1910.06262v1 | https://arxiv.org/pdf/1910.06262v1.pdf | Restoring ancient text using deep learning: a case study on Greek epigraphy | Ancient history relies on disciplines such as epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents Pythia, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle long-term context information, and deal efficiently with missing or corrupted character and word representations. To train it, we wrote a non-trivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML. On PHI-ML, Pythia's predictions achieve a 30.1% character error rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases the ground-truth sequence was among the Top-20 hypotheses of Pythia, which effectively demonstrates the impact of this assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration. | ['Jonathan Prag', 'Yannis Assael', 'Thea Sommerschield'] | 2019-10-14 | restoring-ancient-text-using-deep-learning-a-1 | https://aclanthology.org/D19-1668 | https://aclanthology.org/D19-1668.pdf | ijcnlp-2019-11 | ['ancient-tex-restoration'] | ['miscellaneous'] | [ 4.25866455e-01 3.92201692e-01 4.87329029e-02 -1.03685103e-01
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5167bd4d-3b5f-46fd-975c-c1a6cba57b0d | policy-based-inference-in-trick-taking-card | 1905.10911 | null | https://arxiv.org/abs/1905.10911v1 | https://arxiv.org/pdf/1905.10911v1.pdf | Policy Based Inference in Trick-Taking Card Games | Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions. This makes the number of histories exponentially large in the action sequence length, as well as creating extremely large information sets. As a result, these games become too large to solve. To deal with these issues many algorithms employ inference, the estimation of the probability of states within an information set. In this paper, we demonstrate a Policy Based Inference (PI) algorithm that uses player modelling to infer the probability we are in a given state. We perform experiments in the German trick-taking card game Skat, in which we show that this method vastly improves the inference as compared to previous work, and increases the performance of the state-of-the-art Skat AI system Kermit when it is employed into its determinized search algorithm. | ['Douglas Rebstock', 'Christopher Solinas', 'Nathan R. Sturtevant', 'Michael Buro'] | 2019-05-27 | null | null | null | null | ['card-games'] | ['playing-games'] | [ 3.46821696e-02 1.14048421e-01 -3.56997907e-01 2.54030228e-01
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22ea8c8c-73bb-47ba-80fa-4d412e3cb5ae | warping-residual-based-image-stitching-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_Warping_Residual_Based_Image_Stitching_for_Large_Parallax_CVPR_2020_paper.pdf | Warping Residual Based Image Stitching for Large Parallax | Image stitching techniques align two images captured at different viewing positions onto a single wider image. When the captured 3D scene is not planar and the camera baseline is large, two images exhibit parallax where the relative positions of scene structures are quite different from each view. The existing image stitching methods often fail to work on the images with large parallax. In this paper, we propose an image stitching algorithm robust to large parallax based on the novel concept of warping residuals. We first estimate multiple homographies and find their inlier feature matches between two images. Then we evaluate warping residual for each feature match with respect to the multiple homographies. To alleviate the parallax artifacts, we partition input images into superpixels and warp each superpixel adaptively according to an optimal homography which is computed by minimizing the error of feature matches weighted by the warping residuals. Experimental results demonstrate that the proposed algorithm provides accurate stitching results for images with large parallax, and outperforms the existing methods qualitatively and quantitatively.
| [' Jae-Young Sim', 'Kyu-Yul Lee'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-stitching'] | ['computer-vision'] | [ 4.74469244e-01 -3.95111263e-01 2.91571952e-02 2.15477228e-01
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fbdcca12-9dfa-4b99-bc53-47bb8a3700b5 | attentive-task-agnostic-meta-learning-for-few | null | null | https://openreview.net/forum?id=SyxMWh09KX | https://openreview.net/pdf?id=SyxMWh09KX | Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification | Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding. Inspired by the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification. The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms. We provide evidence to show that the attention mechanism in ATAML has a synergistic effect on learning performance. Our experimental results reveal that, for few-shot text classification tasks, gradient-based meta-learning approaches ourperform popular transfer learning methods. In comparisons with models trained from random initialization, pretrained models and meta trained MAML, our proposed ATAML method generalizes better on single-label and multi-label classification tasks in miniRCV1 and miniReuters-21578 datasets. | ['Stan Matwin', 'Nicolas Chapados', 'Xiang Jiang', 'Mohammad Havaei', 'Andrew Jesson', 'Thomas Vincent', 'Hassan Chouaib', 'Gabriel Chartrand'] | null | null | null | null | iclr-2019-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.33053520e-01 8.36401060e-02 -5.55302501e-01 -6.15044951e-01
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8eaa93e6-0bcf-418f-bc35-d499bf335181 | spade-semi-supervised-anomaly-detection-under | 2212.00173 | null | https://arxiv.org/abs/2212.00173v1 | https://arxiv.org/pdf/2212.00173v1.pdf | SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch | Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that isn't limited by the assumption that labeled and unlabeled data come from the same distribution. Indeed, the assumption is often violated in many applications - for example, the labeled data may contain only anomalies unlike unlabeled data, or unlabeled data may contain different types of anomalies, or labeled data may contain only 'easy-to-label' samples. SPADE utilizes an ensemble of one class classifiers as the pseudo-labeler to improve the robustness of pseudo-labeling with distribution mismatch. Partial matching is proposed to automatically select the critical hyper-parameters for pseudo-labeling without validation data, which is crucial with limited labeled data. SPADE shows state-of-the-art semi-supervised anomaly detection performance across a wide range of scenarios with distribution mismatch in both tabular and image domains. In some common real-world settings such as model facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art alternatives by 5% AUC in average. | ['Tomas Pfister', 'Sercan O. Arik', 'Chun-Liang Li', 'Kihyuk Sohn', 'Jinsung Yoon'] | 2022-11-30 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-4.43635583e-02 -8.64899065e-03 8.34970102e-02 -6.36394143e-01
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9f9da09d-d751-4034-ad6d-dc107fdcc189 | explainable-empirical-risk-minimization | 2009.01492 | null | https://arxiv.org/abs/2009.01492v3 | https://arxiv.org/pdf/2009.01492v3.pdf | Explainable Empirical Risk Minimization | The successful application of machine learning (ML) methods becomes increasingly dependent on their interpretability or explainability. Designing explainable ML systems is instrumental to ensuring transparency of automated decision-making that targets humans. The explainability of ML methods is also an essential ingredient for trustworthy artificial intelligence. A key challenge in ensuring explainability is its dependence on the specific human user ("explainee"). The users of machine learning methods might have vastly different background knowledge about machine learning principles. One user might have a university degree in machine learning or related fields, while another user might have never received formal training in high-school mathematics. This paper applies information-theoretic concepts to develop a novel measure for the subjective explainability of the predictions delivered by a ML method. We construct this measure via the conditional entropy of predictions, given a user feedback. The user feedback might be obtained from user surveys or biophysical measurements. Our main contribution is the explainable empirical risk minimization (EERM) principle of learning a hypothesis that optimally balances between the subjective explainability and risk. The EERM principle is flexible and can be combined with arbitrary machine learning models. We present several practical implementations of EERM for linear models and decision trees. Numerical experiments demonstrate the application of EERM to detecting the use of inappropriate language on social media. | ['A. Jung', 'L. Dogruel', 'A. Odnoblyudova', 'G. Karakasidis', 'L. Zhang'] | 2020-09-03 | null | null | null | null | ['high-school-mathematics'] | ['reasoning'] | [ 2.42079005e-01 7.55710781e-01 -3.27712864e-01 -7.14257061e-01
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8a5faeb2-4c29-430a-a928-081698690be3 | neural-prior-stochastic-block-model | 2303.09995 | null | https://arxiv.org/abs/2303.09995v1 | https://arxiv.org/pdf/2303.09995v1.pdf | Neural-prior stochastic block model | The stochastic block model (SBM) is widely studied as a benchmark for graph clustering aka community detection. In practice, graph data often come with node attributes that bear additional information about the communities. Previous works modeled such data by considering that the node attributes are generated from the node community memberships. In this work, motivated by a recent surge of works in signal processing using deep neural networks as priors, we propose to model the communities as being determined by the node attributes rather than the opposite. We define the corresponding model; we call it the neural-prior SBM. We propose an algorithm, stemming from statistical physics, based on a combination of belief propagation and approximate message passing. We analyze the performance of the algorithm as well as the Bayes-optimal performance. We identify detectability and exact recovery phase transitions, as well as an algorithmically hard region. The proposed model and algorithm can be used as a benchmark for both theory and algorithms. To illustrate this, we compare the optimal performances to the performance of simple graph neural networks. | ['L. Zdeborovà', 'O. Duranthon'] | 2023-03-17 | null | null | null | null | ['graph-clustering', 'stochastic-block-model', 'community-detection'] | ['graphs', 'graphs', 'graphs'] | [ 1.63368449e-01 1.71520770e-01 5.50405495e-02 -2.73648232e-01
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7ec9a0ae-c84f-43cd-a1ea-76097a3c3082 | multi-paradigm-analysis-of-thai-capital | null | null | https://www.researchgate.net/publication/368721017_Multi-Paradigm_Analysis_of_Thai_Capital_Market_Linkages_BivariateVine_Copulas_Granger_Causality_Network_Centrality_and_Graph_Neural_NetworkGraph_Embedding_Approaches | https://www.researchgate.net/publication/368721017_Multi-Paradigm_Analysis_of_Thai_Capital_Market_Linkages_BivariateVine_Copulas_Granger_Causality_Network_Centrality_and_Graph_Neural_NetworkGraph_Embedding_Approaches#fullTextFileContent | Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding Approaches | Analytically thorough understanding of causal, probabilistic, and informational linkages amongst modern, highly-interconnected capital markets is fundamental to the promotion of capital-market innovation, efficiency, and resilience; whereupon innovative, efficient, and resilient capital markets are fundamental to the sustainable economic development of any nation and the robust financial stability of her economy. Whereas analytics derived essentially from some form of linear correlation matrix remains to this day the archetype model representation of return co-movements within a single stock exchange, analysis of financial random multi-variates, especially across domestic/international/regional/global/ domains, warrants a generalised multi-paradigm investigation. In lieu of employing a particular quantitative technique to test a specific theoretically-motivated hypothesis, our aim here is to apply a multitude of analytical tools from semi-/non-parametric statistics, from time-series econometrics, from graph-theoretic network analysis, and from Graph Neural Network (GNN)/Geometric Deep Learning (GDL) paradigms to investigate quantifiable linkages amongst 14 selected variables representing Thai/Asia/Emerging Market/US foreign-exchange, fixed-income, and equity market movements, as well as foreign portfolio-investment flows into Thailand’s domestic equity and bond markets. In particular, Bivariate/Vine Copulas, Granger Causality, Network Centrality, and GNN/Graph Embedding together enable us to tease out subtle quantitative relations amongst said linked capital-market variables, which could in turn be exploited, for instance, in the form of better specified Monte Carlo Value-at-Risk/Expected Shortfall estimation for hypothetical portfolios comprising multi-national assets. Our experiments thus far delivered incremental, rather than game-changing, improvements w.r.t. market insights and accuracy gains. Nonetheless, our multi-paradigm framework, along with the publicly shared codebase, can be applied to any other “linkage” phenomena of future research interest and real-world application. | ['Isariyaporn Sukcharoenchaikul', 'Puvarith Veerabulyarith', 'Pitikorn Khlaisamniang', 'Kongkan Kalakan', 'Poomjai Nacaskul'] | 2023-02-23 | null | null | null | www-researchgate-net-2023-2 | ['econometrics'] | ['miscellaneous'] | [-4.87208277e-01 -1.76079378e-01 -2.62731999e-01 4.66440320e-01
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b46c7719-405a-4065-9baf-e3926a8e4063 | fleet-prognosis-with-physics-informed | 1901.05512 | null | http://arxiv.org/abs/1901.05512v1 | http://arxiv.org/pdf/1901.05512v1.pdf | Fleet Prognosis with Physics-informed Recurrent Neural Networks | Services and warranties of large fleets of engineering assets is a very
profitable business. The success of companies in that area is often related to
predictive maintenance driven by advanced analytics. Therefore, accurate
modeling, as a way to understand how the complex interactions between operating
conditions and component capability define useful life, is key for services
profitability. Unfortunately, building prognosis models for large fleets is a
daunting task as factors such as duty cycle variation, harsh environments,
inadequate maintenance, and problems with mass production can lead to large
discrepancies between designed and observed useful lives. This paper introduces
a novel physics-informed neural network approach to prognosis by extending
recurrent neural networks to cumulative damage models. We propose a new
recurrent neural network cell designed to merge physics-informed and
data-driven layers. With that, engineers and scientists have the chance to use
physics-informed layers to model parts that are well understood (e.g., fatigue
crack growth) and use data-driven layers to model parts that are poorly
characterized (e.g., internal loads). A simple numerical experiment is used to
present the main features of the proposed physics-informed recurrent neural
network for damage accumulation. The test problem consist of predicting fatigue
crack length for a synthetic fleet of airplanes subject to different mission
mixes. The model is trained using full observation inputs (far-field loads) and
very limited observation of outputs (crack length at inspection for only a
portion of the fleet). The results demonstrate that our proposed hybrid
physics-informed recurrent neural network is able to accurately model fatigue
crack growth even when the observed distribution of crack length does not match
with the (unobservable) fleet distribution. | ['Felipe A. C. Viana', 'Renato Giorgiani Nascimento'] | 2019-01-16 | null | null | null | null | ['physics-informed-machine-learning', 'graph-regression', 'graph-to-sequence'] | ['graphs', 'graphs', 'natural-language-processing'] | [-1.02159627e-01 -2.80801058e-01 1.82810128e-01 -2.75583501e-04
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d69fbb44-c46a-4945-aa89-4eee32beab45 | lightface-a-hybrid-deep-face-recognition | null | null | https://ieeexplore.ieee.org/document/9259802 | https://ieeexplore.ieee.org/document/9259802 | LightFace: A Hybrid Deep Face Recognition Framework | Face recognition constitutes a relatively a popular area which has emerged from the rulers of the social media to top universities in the world. Those frontiers and rule makers recently designed deep learning based custom face recognition models. A modern face recognition pipeline consists of four common stages: detecting, alignment, representation and verification. However, face recognition studies mainly mention the representation stage of a pipeline. In this paper, first of all a review face recognition has been done and then the description of the developed lightweight hybrid high performance face recognition framework has been made. Its hybrid feature enables to switch face recognition models among state-of-the-art ones. | ['Alper Ozpinar', 'Sefik Ilkin Serengil'] | 2020-11-23 | null | null | null | 2020-innovations-in-intelligent-systems-and | ['face-swapping'] | ['computer-vision'] | [ 9.24668014e-02 8.28406028e-03 -6.51157554e-03 -7.93727934e-01
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ca92a998-0d59-40a5-a1e4-fb974c779ee6 | ecgnet-learning-where-to-attend-for-detection-1 | 1812.07422 | null | http://arxiv.org/abs/1812.07422v2 | http://arxiv.org/pdf/1812.07422v2.pdf | ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention | The complexity of the patterns associated with Atrial Fibrillation (AF) and
the high level of noise affecting these patterns have significantly limited the
current signal processing and shallow machine learning approaches to get
accurate AF detection results. Deep neural networks have shown to be very
powerful to learn the non-linear patterns in the data. While a deep learning
approach attempts to learn complex pattern related to the presence of AF in the
ECG, they can benefit from knowing which parts of the signal is more important
to focus during learning. In this paper, we introduce a two-channel deep neural
network to more accurately detect AF presented in the ECG signal. The first
channel takes in a preprocessed ECG signal and automatically learns where to
attend for detection of AF. The second channel simultaneously takes in the
preprocessed ECG signal to consider all features of entire signals. The model
shows via visualization that what parts of the given ECG signal are important
to attend while trying to detect atrial fibrillation. In addition, this
combination significantly improves the performance of the atrial fibrillation
detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy
of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.) | [] | 2019-02-15 | ecgnet-learning-where-to-attend-for-detection | https://arxiv.org/abs/1812.07422 | https://arxiv.org/pdf/1812.07422 | arxiv181207422-2018-12 | ['atrial-fibrillation-detection'] | ['medical'] | [ 3.42710376e-01 -2.97622114e-01 2.21139446e-01 -3.79788399e-01
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9c58b35e-f5d1-482d-a350-a822da60850e | clinical-note-owns-its-hierarchy-multi-level | 2305.09756 | null | https://arxiv.org/abs/2305.09756v1 | https://arxiv.org/pdf/2305.09756v1.pdf | Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning | Leveraging knowledge from electronic health records (EHRs) to predict a patient's condition is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underused due to their difficult contents and complex hierarchies. Recently, hypergraph-based methods have been proposed for document classifications. Directly adopting existing hypergraph methods on clinical notes cannot sufficiently utilize the hierarchy information of the patient, which can degrade clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution. Thus, we propose a taxonomy-aware multi-level hypergraph neural network (TM-HGNN), where multi-level hypergraphs assemble useful neutral words with rare keywords via note and taxonomy level hyperedges to retain the clinical semantic information. The constructed patient hypergraphs are fed into hierarchical message passing layers for learning more balanced multi-level knowledge at the note and taxonomy levels. We validate the effectiveness of TM-HGNN by conducting extensive experiments with MIMIC-III dataset on benchmark in-hospital-mortality prediction. | ['Sun Kim', 'Yinhua Piao', 'Nayeon Kim'] | 2023-05-16 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 5.97786456e-02 4.31931406e-01 -4.05761510e-01 -2.04785481e-01
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3874891f-3094-4555-a062-d7a6f7a8a9e1 | model-rubik-s-cube-twisting-resolution-depth | 2010.14819 | null | https://arxiv.org/abs/2010.14819v2 | https://arxiv.org/pdf/2010.14819v2.pdf | Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets | To obtain excellent deep neural architectures, a series of techniques are carefully designed in EfficientNets. The giant formula for simultaneously enlarging the resolution, depth and width provides us a Rubik's cube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions. This paper aims to explore the twisting rules for obtaining deep neural networks with minimum model sizes and computational costs. Different from the network enlarging, we observe that resolution and depth are more important than width for tiny networks. Therefore, the original method, i.e., the compound scaling in EfficientNet is no longer suitable. To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint. Experimental results on the ImageNet benchmark illustrate that our TinyNet performs much better than the smaller version of EfficientNets using the inversed giant formula. For instance, our TinyNet-E achieves a 59.9% Top-1 accuracy with only 24M FLOPs, which is about 1.9% higher than that of the previous best MobileNetV3 with similar computational cost. Code will be available at https://github.com/huawei-noah/ghostnet/tree/master/tinynet_pytorch, and https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/tinynet. | ['Tong Zhang', 'Chunjing Xu', 'Wei zhang', 'Qiulin Zhang', 'Yunhe Wang', 'Kai Han'] | 2020-10-28 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [-5.32871962e-01 6.56127557e-02 -1.66666508e-01 -2.73870111e-01
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2a0dc300-aa7b-476e-86c4-11f535814e1e | structured-q-learning-for-antibody-design | 2209.04698 | null | https://arxiv.org/abs/2209.04698v2 | https://arxiv.org/pdf/2209.04698v2.pdf | Structured Q-learning For Antibody Design | Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences. For example, one of the crucial steps involved in antibody design is to find an arrangement of amino acids in a protein sequence that improves its binding with a pathogen. Combinatorial optimization of antibodies is difficult due to extremely large search spaces and non-linear objectives. Even for modest antibody design problems, where proteins have a sequence length of eleven, we are faced with searching over 2.05 x 10^14 structures. Applying traditional Reinforcement Learning algorithms such as Q-learning to combinatorial optimization results in poor performance. We propose Structured Q-learning (SQL), an extension of Q-learning that incorporates structural priors for combinatorial optimization. Using a molecular docking simulator, we demonstrate that SQL finds high binding energy sequences and performs favourably against baselines on eight challenging antibody design tasks, including designing antibodies for SARS-COV. | ['Haitham Bou Ammar', 'Jan Peters', 'Jun Wang', 'Liu Furui', 'Asif Khan', 'Aivar Sootla', 'Philip John Gorinski', 'Alexander I. Cowen-Rivers'] | 2022-09-10 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 3.52131993e-01 -2.68093497e-01 -1.85768500e-01 -2.45414019e-01
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23213d83-7655-42d8-b666-853cdc44acb5 | graphtts-graph-to-sequence-modelling-in | 2003.01924 | null | https://arxiv.org/abs/2003.01924v1 | https://arxiv.org/pdf/2003.01924v1.pdf | GraphTTS: graph-to-sequence modelling in neural text-to-speech | This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs consist of node and edge representations constructed from input texts. The encoding of these graphical inputs incorporates syntax information by a GNN encoder module. Besides, applying the encoder of GraphTTS as a graph auxiliary encoder (GAE) can analyse prosody information from the semantic structure of texts. This can remove the manual selection of reference audios process and makes prosody modelling an end-to-end procedure. Experimental analysis shows that GraphTTS outperforms the state-of-the-art sequence-to-sequence models by 0.24 in Mean Opinion Score (MOS). GAE can adjust the pause, ventilation and tones of synthesised audios automatically. This experimental conclusion may give some inspiration to researchers working on improving speech synthesis prosody. | ['Jing Xiao', 'Zhen Zeng', 'Huayi Peng', 'Aolan Sun', 'Jianzong Wang', 'Ning Cheng'] | 2020-03-04 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 4.91036981e-01 5.60519814e-01 -2.24449132e-02 -2.54299760e-01
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421362cf-6ab7-4060-bcf5-817b83c25641 | unsupervised-deep-shape-descriptor-with-point | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_Unsupervised_Deep_Shape_Descriptor_With_Point_Distribution_Learning_CVPR_2020_paper.pdf | Unsupervised Deep Shape Descriptor With Point Distribution Learning | Deep learning models have achieved great success in supervised shape descriptor learning for 3D shape retrieval, classification, and correspondence. However, the unsupervised shape descriptor calculated via deep learning is less studied than that of supervised ones due to the design challenges of unsupervised neural network architecture. This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning. In our approach, we firstly associate each point with a Gaussian, and the point clouds are modeled as the distribution of the points. We then use deep neural networks (DNNs) to model a maximum likelihood estimation process that is traditionally solved with an iterative Expectation-Maximization (EM) process. Our key novelty is that "training" these DNNs with unsupervised self-correspondence L2 distance loss will elegantly reveal the statically significant deep shape descriptor representation for the distribution of the point clouds. We have conducted experiments over various 3D datasets. Qualitative and quantitative comparisons demonstrate that our proposed method achieves superior classification performance over existing unsupervised 3D shape descriptors. In addition, we verified the following attractive properties of our shape descriptor through experiments: multi-scale shape representation, robustness to shape rotation, and robustness to noise.
| [' Yi Fang', ' Shuaihang Yuan', ' Mengchen Xu', 'Yi Shi'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.35950637e-01 -3.23868990e-01 -4.76155467e-02 -5.92809677e-01
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5830ca68-c269-4f06-bc1b-71b9672d8d03 | learning-saliency-propagation-for-semi | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Learning_Saliency_Propagation_for_Semi-Supervised_Instance_Segmentation_CVPR_2020_paper.pdf | Learning Saliency Propagation for Semi-Supervised Instance Segmentation | Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. We aim to improve the accuracy of the existing instance segmentation models by utilizing a large amount of detection supervision. We propose ShapeProp, which learns to activate the salient regions within the object detection and propagate the areas to the whole instance through an iterative learnable message passing module. ShapeProp can benefit from more bounding box supervision to locate the instances more accurately and utilize the feature activations from the larger number of instances to achieve more accurate segmentation. We extensively evaluate ShapeProp on three datasets (MS COCO, PASCAL VOC, and BDD100k) with different supervision setups based on both two-stage (Mask R-CNN) and single-stage (RetinaMask) models. The results show our method establishes new states of the art for semi-supervised instance segmentation.
| [' Fisher Yu', ' Trevor Darrell', ' Jianbin Jiao', ' Xin Wang', 'Yanzhao Zhou'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 3.78110528e-01 5.60022473e-01 -4.02075022e-01 -6.22290909e-01
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42940781-1826-4cac-899b-d7c2f0c23007 | 190412640 | 1904.12640 | null | http://arxiv.org/abs/1904.12640v2 | http://arxiv.org/pdf/1904.12640v2.pdf | TextCohesion: Detecting Text for Arbitrary Shapes | In this paper, we propose a pixel-wise method named TextCohesion for scene
text detection, which splits a text instance into five key components: a Text
Skeleton and four Directional Pixel Regions. These components are easier to
handle than the entire text instance. A confidence scoring mechanism is
designed to filter characters that are similar to text. Our method can
integrate text contexts intensively when backgrounds are complex. Experiments
on two curved challenging benchmarks demonstrate that TextCohesion outperforms
state-of-the-art methods, achieving the F-measure of 84.6% on Total-Text and
bfseries86.3% on SCUT-CTW1500. | ['Jici Xing', 'Hong Zhou', 'Weijia Wu'] | 2019-04-22 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 4.51645732e-01 -3.68200302e-01 -3.17113608e-01 -6.63699880e-02
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7fd7b31e-1bf3-4191-994f-10b0b0ddbd8f | center-focusing-network-for-real-time-lidar | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Center_Focusing_Network_for_Real-Time_LiDAR_Panoptic_Segmentation_CVPR_2023_paper.pdf | Center Focusing Network for Real-Time LiDAR Panoptic Segmentation | LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent proposal-free methods accelerate the algorithm, but their effectiveness and efficiency are still limited owing to the difficulty of modeling non-existent instance centers and the costly center-based clustering modules. To achieve accurate and real-time LiDAR panoptic segmentation, a novel center focusing network (CFNet) is introduced. Specifically, the center focusing feature encoding (CFFE) is proposed to explicitly understand the relationships between the original LiDAR points and virtual instance centers by shifting the LiDAR points and filling in the center points. Moreover, to leverage the redundantly detected centers, a fast center deduplication module (CDM) is proposed to select only one center for each instance. Experiments on the SemanticKITTI and nuScenes panoptic segmentation benchmarks demonstrate that our CFNet outperforms all existing methods by a large margin and is 1.6 times faster than the most efficient method. | ['BaoCai Yin', 'Yongli Hu', 'Boyue Wang', 'Gang Zhang', 'Xiaoyan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [-1.01747975e-01 -4.45302367e-01 -3.82852376e-01 -4.10427332e-01
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0229c8d9-3563-48a1-86a6-48f4a3f04998 | robustfusion-robust-volumetric-performance | 2104.14837 | null | https://arxiv.org/abs/2104.14837v1 | https://arxiv.org/pdf/2104.14837v1.pdf | RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream | High-quality 4D reconstruction of human performance with complex interactions to various objects is essential in real-world scenarios, which enables numerous immersive VR/AR applications. However, recent advances still fail to provide reliable performance reconstruction, suffering from challenging interaction patterns and severe occlusions, especially for the monocular setting. To fill this gap, in this paper, we propose RobustFusion, a robust volumetric performance reconstruction system for human-object interaction scenarios using only a single RGBD sensor, which combines various data-driven visual and interaction cues to handle the complex interaction patterns and severe occlusions. We propose a semantic-aware scene decoupling scheme to model the occlusions explicitly, with a segmentation refinement and robust object tracking to prevent disentanglement uncertainty and maintain temporal consistency. We further introduce a robust performance capture scheme with the aid of various data-driven cues, which not only enables re-initialization ability, but also models the complex human-object interaction patterns in a data-driven manner. To this end, we introduce a spatial relation prior to prevent implausible intersections, as well as data-driven interaction cues to maintain natural motions, especially for those regions under severe human-object occlusions. We also adopt an adaptive fusion scheme for temporally coherent human-object reconstruction with occlusion analysis and human parsing cue. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality 4D human performance reconstruction under complex human-object interactions whilst still maintaining the lightweight monocular setting. | ['Lu Fang', 'Shuxue Quan', 'Fan Deng', 'Zhong Li', 'Dawei Zhong', 'Lan Xu', 'Zhuo Su'] | 2021-04-30 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 2.54975390e-02 -2.03024745e-01 1.92511424e-01 -2.38038450e-01
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6f9c29f8-c41d-448f-a707-a70ac41b530c | inverting-adversarially-robust-networks-for | 2106.06927 | null | https://arxiv.org/abs/2106.06927v5 | https://arxiv.org/pdf/2106.06927v5.pdf | Inverting Adversarially Robust Networks for Image Synthesis | Despite unconditional feature inversion being the foundation of many image synthesis applications, training an inverter demands a high computational budget, large decoding capacity and imposing conditions such as autoregressive priors. To address these limitations, we propose the use of adversarially robust representations as a perceptual primitive for feature inversion. We train an adversarially robust encoder to extract disentangled and perceptually-aligned image representations, making them easily invertible. By training a simple generator with the mirror architecture of the encoder, we achieve superior reconstruction quality and generalization over standard models. Based on this, we propose an adversarially robust autoencoder and demonstrate its improved performance on style transfer, image denoising and anomaly detection tasks. Compared to recent ImageNet feature inversion methods, our model attains improved performance with significantly less complexity. | ['Anh Nguyen', 'Minh N. Do', 'Raymond A. Yeh', 'Renan A. Rojas-Gomez'] | 2021-06-13 | null | null | null | null | ['deep-feature-inversion'] | ['computer-vision'] | [ 6.24973834e-01 2.86749572e-01 2.58539338e-02 -2.77460963e-01
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8b4fa6d1-53d7-4baa-982d-9ca93f58cda9 | predictive-coding-for-animation-based-video | 2307.04187 | null | https://arxiv.org/abs/2307.04187v1 | https://arxiv.org/pdf/2307.04187v1.pdf | Predictive Coding For Animation-Based Video Compression | We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e. each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a datasetof talking-head videos | ['Giuseppe Valenzise', 'Stéphane Lathuilière', 'Goluck Konuko'] | 2023-07-09 | null | null | null | null | ['video-compression', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 3.57771903e-01 2.13793516e-01 -3.09196085e-01 -1.12057276e-01
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0fe76a2c-17ee-44d7-9ae6-8bdbe6f6449b | device-robust-acoustic-scene-classification-1 | 2305.07499 | null | https://arxiv.org/abs/2305.07499v2 | https://arxiv.org/pdf/2305.07499v2.pdf | Device-Robust Acoustic Scene Classification via Impulse Response Augmentation | The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphones introduce distributional shifts in the digitized audio signals due to their varying frequency responses. If this domain shift is not taken into account during training, the model's performance could degrade severely when it is applied to signals recorded by unseen devices. In particular, training a model on audio signals recorded with a small number of different microphones can make generalization to unseen devices difficult. To tackle this problem, we convolve audio signals in the training set with pre-recorded device impulse responses (DIRs) to artificially increase the diversity of recording devices. We systematically study the effect of DIR augmentation on the task of Acoustic Scene Classification using CNNs and Audio Spectrogram Transformers. The results show that DIR augmentation in isolation performs similarly to the state-of-the-art method Freq-MixStyle. However, we also show that DIR augmentation and Freq-MixStyle are complementary, achieving a new state-of-the-art performance on signals recorded by devices unseen during training. | ['Gerhard Widmer', 'Khaled Koutini', 'Florian Schmid', 'Tobias Morocutti'] | 2023-05-12 | null | null | null | null | ['acoustic-scene-classification', 'audio-classification', 'scene-classification'] | ['audio', 'audio', 'computer-vision'] | [ 4.80019242e-01 -3.03611010e-01 5.08482099e-01 -2.50057817e-01
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4ced243b-45c7-4470-a9e4-7387d0b8fc13 | sentence-embedder-guided-utterance-encoder | 2305.12301 | null | https://arxiv.org/abs/2305.12301v1 | https://arxiv.org/pdf/2305.12301v1.pdf | Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding | The pre-trained speech encoder wav2vec 2.0 performs very well on various spoken language understanding (SLU) tasks. However, on many tasks, it trails behind text encoders with textual input. To improve the understanding capability of SLU encoders, various studies have used knowledge distillation to transfer knowledge from natural language understanding (NLU) encoders. We use a very simple method of distilling from a textual sentence embedder directly into wav2vec 2.0 as pre-training, utilizing paired audio-text datasets. We observed that this method is indeed capable of improving SLU task performance in fine-tuned settings, as well as full-data and few-shot transfer on a frozen encoder. However, the model performs worse on certain tasks highlighting the strengths and weaknesses of our approach. | ['Soujanya Poria', 'Navonil Majumder', 'Yi Xuan Tan'] | 2023-05-20 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 1.91878363e-01 5.24168491e-01 -3.28770326e-03 -3.87781322e-01
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dee8a47e-d88c-4f86-960f-2fbed7ab3235 | explainable-artificial-intelligence-and-1 | 2211.10595 | null | https://arxiv.org/abs/2211.10595v1 | https://arxiv.org/pdf/2211.10595v1.pdf | Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection | Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various analyses. | ['Laveti Ramesh Naidu', 'Abhay Anand Mane', 'Vadlamani Ravi', 'Yelleti Vivek'] | 2022-11-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 9.73714516e-02 -1.86849222e-01 -1.60976619e-01 -2.71606326e-01
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be1df11f-4ab0-4466-a5db-c43e315b5293 | a-maximal-inequality-for-local-empirical | 2307.01328 | null | https://arxiv.org/abs/2307.01328v1 | https://arxiv.org/pdf/2307.01328v1.pdf | A maximal inequality for local empirical processes under weak dependence | We introduce a maximal inequality for a local empirical process under strongly mixing data. Local empirical processes are defined as the (local) averages $\frac{1}{nh}\sum_{i=1}^n \mathbf{1}\{x - h \leq X_i \leq x+h\}f(Z_i)$, where $f$ belongs to a class of functions, $x \in \mathbb{R}$ and $h > 0$ is a bandwidth. Our nonasymptotic bounds control estimation error uniformly over the function class, evaluation point $x$ and bandwidth $h$. They are also general enough to accomodate function classes whose complexity increases with $n$. As an application, we apply our bounds to function classes that exhibit polynomial decay in their uniform covering numbers. When specialized to the problem of kernel density estimation, our bounds reveal that, under weak dependence with exponential decay, these estimators achieve the same (up to a logarithmic factor) sharp uniform-in-bandwidth rates derived in the iid setting by \cite{Einmahl2005}. | ['Cristine Pinto', 'Luis Alvarez'] | 2023-07-03 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-5.62479161e-02 2.62199193e-01 -1.53177738e-01 1.94127616e-02
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ebd2b5ee-3e16-4354-9554-db8dbc10df1e | closed-loop-acas-xu-nncs-is-unsafe-quantized | 2201.06626 | null | https://arxiv.org/abs/2201.06626v3 | https://arxiv.org/pdf/2201.06626v3.pdf | Neural Network Compression of ACAS Xu Early Prototype is Unsafe: Closed-Loop Verification through Quantized State Backreachability | ACAS Xu is an air-to-air collision avoidance system designed for unmanned aircraft that issues horizontal turn advisories to avoid an intruder aircraft. Due the use of a large lookup table in the design, a neural network compression of the policy was proposed. Analysis of this system has spurred a significant body of research in the formal methods community on neural network verification. While many powerful methods have been developed, most work focuses on open-loop properties of the networks, rather than the main point of the system -- collision avoidance -- which requires closed-loop analysis. In this work, we develop a technique to verify a closed-loop approximation of the system using state quantization and backreachability. We use favorable assumptions for the analysis -- perfect sensor information, instant following of advisories, ideal aircraft maneuvers and an intruder that only flies straight. When the method fails to prove the system is safe, we refine the quantization parameters until generating counterexamples where the original (non-quantized) system also has collisions. | ['Hoang-Dung Tran', 'Stanley Bak'] | 2022-01-17 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 4.44248289e-01 6.67737544e-01 -3.48617554e-01 -6.27140328e-02
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3f7a1a35-9c83-4349-a149-1c0af5985416 | hearing-lips-in-noise-universal-viseme | 2306.10563 | null | https://arxiv.org/abs/2306.10563v1 | https://arxiv.org/pdf/2306.10563v1.pdf | Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition | Audio-visual speech recognition (AVSR) provides a promising solution to ameliorate the noise-robustness of audio-only speech recognition with visual information. However, most existing efforts still focus on audio modality to improve robustness considering its dominance in AVSR task, with noise adaptation techniques such as front-end denoise processing. Though effective, these methods are usually faced with two practical challenges: 1) lack of sufficient labeled noisy audio-visual training data in some real-world scenarios and 2) less optimal model generality to unseen testing noises. In this work, we investigate the noise-invariant visual modality to strengthen robustness of AVSR, which can adapt to any testing noises while without dependence on noisy training data, a.k.a., unsupervised noise adaptation. Inspired by human perception mechanism, we propose a universal viseme-phoneme mapping (UniVPM) approach to implement modality transfer, which can restore clean audio from visual signals to enable speech recognition under any noisy conditions. Extensive experiments on public benchmarks LRS3 and LRS2 show that our approach achieves the state-of-the-art under various noisy as well as clean conditions. In addition, we also outperform previous state-of-the-arts on visual speech recognition task. | ['Eng Siong Chng', 'Qiushi Zhu', 'Chengwei Qin', 'Chen Chen', 'Ruizhe Li', 'Yuchen Hu'] | 2023-06-18 | null | null | null | null | ['visual-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 4.54087704e-01 -3.99641931e-01 3.11514586e-01 -6.62736148e-02
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42f245d1-ac9f-4dca-b46c-56f039076342 | action-sensitivity-learning-for-temporal | 2305.15701 | null | https://arxiv.org/abs/2305.15701v1 | https://arxiv.org/pdf/2305.15701v1.pdf | Action Sensitivity Learning for Temporal Action Localization | Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an Action Sensitivity Learning framework (ASL) to tackle this task, which aims to assess the value of each frame and then leverage the generated action sensitivity to recalibrate the training procedure. We first introduce a lightweight Action Sensitivity Evaluator to learn the action sensitivity at the class level and instance level, respectively. The outputs of the two branches are combined to reweight the gradient of the two sub-tasks. Moreover, based on the action sensitivity of each frame, we design an Action Sensitive Contrastive Loss to enhance features, where the action-aware frames are sampled as positive pairs to push away the action-irrelevant frames. The extensive studies on various action localization benchmarks (i.e., MultiThumos, Charades, Ego4D-Moment Queries v1.0, Epic-Kitchens 100, Thumos14 and ActivityNet1.3) show that ASL surpasses the state-of-the-art in terms of average-mAP under multiple types of scenarios, e.g., single-labeled, densely-labeled and egocentric. | ['Yi Yang', 'Jiang Yang', 'Junjun Zheng', 'Ruijie Quan', 'Xiaohan Wang', 'Jiayi Shao'] | 2023-05-25 | null | null | null | null | ['video-understanding', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.69423217e-01 -3.41136307e-01 -4.92599219e-01 -3.59506547e-01
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661f70e9-492f-4fee-98f1-f17170313d9b | modeling-hierarchical-reasoning-chains-by-1 | 2306.12069 | null | https://arxiv.org/abs/2306.12069v1 | https://arxiv.org/pdf/2306.12069v1.pdf | Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension | Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical relations exist at different granularity levels. However, most existing methods of logical reasoning individually focus on either entity-aware or discourse-based information but ignore the hierarchical relations that may even have mutual effects. In this paper, we propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning, to provide a more fine-grained relation extraction. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism to improve the interpretation of MRC systems. Experimental results on logical reasoning QA datasets (ReClor and LogiQA) and natural language inference datasets (SNLI and ANLI) show the effectiveness and generalization of our method, and in-depth analysis verifies its capability to understand complex logical relations. | ['Hai Zhao', 'Zhuosheng Zhang', 'Jialin Chen'] | 2023-06-21 | modeling-hierarchical-reasoning-chains-by | https://aclanthology.org/2022.coling-1.126 | https://aclanthology.org/2022.coling-1.126.pdf | coling-2022-10 | ['natural-language-inference', 'relation-extraction', 'reading-comprehension', 'machine-reading-comprehension', 'logical-reasoning'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [ 1.46657348e-01 6.93214536e-01 -2.42919743e-01 -5.16765535e-01
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05d4740c-ea58-430f-9db0-13805196a8b4 | coupled-attention-networks-for-multivariate | 2306.07114 | null | https://arxiv.org/abs/2306.07114v1 | https://arxiv.org/pdf/2306.07114v1.pdf | Coupled Attention Networks for Multivariate Time Series Anomaly Detection | Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as smart surveillance and risk management with unprecedented capabilities. Nevertheless, MTAD is facing critical challenges deriving from the dependencies among sensors and variables, which often change over time. To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships. We combine adaptive graph learning methods with graph attention to generate a global-local graph that can represent both global correlations and dynamic local correlations among sensors. To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module to construct a coupled attention module. In addition, we develop a multilevel encoder-decoder architecture that accommodates reconstruction and prediction tasks to better characterize multivariate time series data. Extensive experiments on real-world datasets have been conducted to evaluate the performance of the proposed CAN approach, and the results show that CAN significantly outperforms state-of-the-art baselines. | ['Linlin You', 'Mujie Liu', 'Mingliang Hou', 'Shuo Yu', 'Xin Chen', 'Feng Xia'] | 2023-06-12 | null | null | null | null | ['graph-attention', 'anomaly-detection', 'time-series-anomaly-detection'] | ['graphs', 'methodology', 'time-series'] | [ 1.04838423e-02 -3.05204123e-01 8.55720937e-02 -2.25792438e-01
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0fa4e8f3-ed7f-4006-9446-c13b36c0c31a | domain-adaptive-deep-network-compression | 1709.01041 | null | http://arxiv.org/abs/1709.01041v2 | http://arxiv.org/pdf/1709.01041v2.pdf | Domain-adaptive deep network compression | Deep Neural Networks trained on large datasets can be easily transferred to
new domains with far fewer labeled examples by a process called fine-tuning.
This has the advantage that representations learned in the large source domain
can be exploited on smaller target domains. However, networks designed to be
optimal for the source task are often prohibitively large for the target task.
In this work we address the compression of networks after domain transfer.
We focus on compression algorithms based on low-rank matrix decomposition.
Existing methods base compression solely on learned network weights and ignore
the statistics of network activations. We show that domain transfer leads to
large shifts in network activations and that it is desirable to take this into
account when compressing. We demonstrate that considering activation statistics
when compressing weights leads to a rank-constrained regression problem with a
closed-form solution. Because our method takes into account the target domain,
it can more optimally remove the redundancy in the weights. Experiments show
that our Domain Adaptive Low Rank (DALR) method significantly outperforms
existing low-rank compression techniques. With our approach, the fc6 layer of
VGG19 can be compressed more than 4x more than using truncated SVD alone --
with only a minor or no loss in accuracy. When applied to domain-transferred
networks it allows for compression down to only 5-20% of the original number of
parameters with only a minor drop in performance. | ['Jose M. Alvarez', 'Joost Van de Weijer', 'Marc Masana', 'Andrew D. Bagdanov', 'Luis Herranz'] | 2017-09-04 | domain-adaptive-deep-network-compression-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Masana_Domain-Adaptive_Deep_Network_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Masana_Domain-Adaptive_Deep_Network_ICCV_2017_paper.pdf | iccv-2017-10 | ['low-rank-compression'] | ['computer-code'] | [ 5.36753118e-01 2.62512326e-01 -2.60877818e-01 -4.50783104e-01
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2429d414-f696-4256-a545-24b35728df03 | biff-bi-level-future-fusion-with-polyline | 2306.14161 | null | https://arxiv.org/abs/2306.14161v1 | https://arxiv.org/pdf/2306.14161v1.pdf | BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction | Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset. | ['Shaojie Shen', 'Di Luan', 'Yiyao Zhu'] | 2023-06-25 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-4.09473449e-01 3.11829168e-02 -4.00568157e-01 -4.46591318e-01
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de4d02c5-354b-4d5c-8bab-9d1427401498 | sesame-street-to-mount-sinai-bert-constrained | null | null | https://aclanthology.org/2021.wnut-1.52 | https://aclanthology.org/2021.wnut-1.52.pdf | Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization | This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token classification preprocessing step, where for each token the type of the necessary transformation is predicted (none, uppercase, lowercase, capitalize, modify), and a character-level SMT step where the text is translated from original to normalized given the BERT-predicted transformation constraints. For some languages, depending on the results on development data, the training data was extended by back-translating OpenSubtitles data. In the final ordering of the ten participating teams, the HEL-LJU team has taken the second place, scoring better than the previous state-of-the-art. | ['Nikola Ljubešić', 'Yves Scherrer'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 4.70604599e-02 1.21645972e-01 -3.85253161e-01 -5.80414712e-01
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e34bb439-0767-4a93-878b-989455402376 | multimodal-representation-for-neural-code | 2107.00992 | null | https://arxiv.org/abs/2107.00992v3 | https://arxiv.org/pdf/2107.00992v3.pdf | Multimodal Representation for Neural Code Search | Semantic code search is about finding semantically relevant code snippets for a given natural language query. In the state-of-the-art approaches, the semantic similarity between code and query is quantified as the distance of their representation in the shared vector space. In this paper, to improve the vector space, we introduce tree-serialization methods on a simplified form of AST and build the multimodal representation for the code data. We conduct extensive experiments using a single corpus that is large-scale and multi-language: CodeSearchNet. Our results show that both our tree-serialized representations and multimodal learning model improve the performance of code search. Last, we define intuitive quantification metrics oriented to the completeness of semantic and syntactic information of the code data, to help understand the experimental findings. | ['Martin Monperrus', 'Zimin Chen', 'Jian Gu'] | 2021-07-02 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.96865672e-01 -3.97476584e-01 -4.30135310e-01 -3.45281184e-01
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2bb5940c-fd5f-4843-83c8-e163824581e2 | hybrid-parallel-imaging-and-compressed | 2209.08807 | null | https://arxiv.org/abs/2209.08807v2 | https://arxiv.org/pdf/2209.08807v2.pdf | A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction | Parallel imaging accelerates MRI data acquisition by acquiring additional sensitivity information with an array of receiver coils, resulting in fewer phase encoding steps. Because of fewer data requirements than parallel imaging, compressed sensing magnetic resonance imaging (CS-MRI) has gained popularity in the field of medical imaging. Parallel imaging and compressed sensing (CS) both reduce the amount of data captured in the k-space, which speeds up traditional MRI acquisition. As acquisition time is inversely proportional to sample count, forming an image from reduced k-space samples results in faster acquisition but with aliasing artifacts. For de-aliasing the reconstructed image, this paper proposes a novel Generative Adversarial Network (GAN) called RECGAN-GR that is supervised with multi-modal losses. In comparison to existing GAN networks, our proposed method introduces a novel generator network, RemU-Net, which is integrated with dual-domain loss functions such as weighted magnitude and phase loss functions, as well as parallel imaging-based loss, GRAPPA consistency loss. As refinement learning, a k-space correction block is proposed to make the GAN network self-resistant to generating unnecessary data, which speeds up the reconstruction process. Comprehensive results show that the proposed RECGAN-GR not only improves the PSNR by 4 dB over GAN-based methods but also by 2 dB over conventional state-of-the-art CNN methods available in the literature for single-coil data. The proposed work significantly improves image quality for low-retained data, resulting in five to ten times faster acquisition. | ['Md. Kamrul Hasan', 'Farhan Sadik'] | 2022-09-19 | null | null | null | null | ['de-aliasing', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.29619586e-01 1.66978285e-01 9.97092351e-02 -3.64503592e-01
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9f46f42b-f38d-4ebd-aad7-b507f200e76e | pixel-pair-occlusion-relationship-map-p2orm | 2007.12088 | null | https://arxiv.org/abs/2007.12088v1 | https://arxiv.org/pdf/2007.12088v1.pdf | Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application | We formalize concepts around geometric occlusion in 2D images (i.e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation. The former provides a way to generate large-scale accurate occlusion datasets while, based on the latter, we propose a novel method for task-independent pixel-level occlusion relationship estimation from single images. Experiments on a variety of datasets demonstrate that our method outperforms existing ones on this task. To further illustrate the value of our formulation, we also propose a new depth map refinement method that consistently improve the performance of state-of-the-art monocular depth estimation methods. Our code and data are available at http://imagine.enpc.fr/~qiux/P2ORM/. | ['Xuchong Qiu', 'Renaud Marlet', 'Chaohui Wang', 'Yang Xiao'] | 2020-07-23 | null | null | null | null | ['occlusion-estimation'] | ['computer-vision'] | [ 1.54215232e-01 1.85138751e-02 -2.80926883e-01 -2.69229084e-01
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3d985e4c-07a0-4a25-bd24-86a07614eff3 | coder-an-efficient-framework-for-improving | 2112.08766 | null | https://arxiv.org/abs/2112.08766v3 | https://arxiv.org/pdf/2112.08766v3.pdf | CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking | Contrastive learning has been the dominant approach to training dense retrieval models. In this work, we investigate the impact of ranking context - an often overlooked aspect of learning dense retrieval models. In particular, we examine the effect of its constituent parts: jointly scoring a large number of negatives per query, using retrieved (query-specific) instead of random negatives, and a fully list-wise loss. To incorporate these factors into training, we introduce Contextual Document Embedding Reranking (CODER), a highly efficient retrieval framework. When reranking, it incurs only a negligible computational overhead on top of a first-stage method at run time (delay per query in the order of milliseconds), allowing it to be easily combined with any state-of-the-art dual encoder method. After fine-tuning through CODER, which is a lightweight and fast process, models can also be used as stand-alone retrievers. Evaluating CODER in a large set of experiments on the MS~MARCO and TripClick collections, we show that the contextual reranking of precomputed document embeddings leads to a significant improvement in retrieval performance. This improvement becomes even more pronounced when more relevance information per query is available, shown in the TripClick collection, where we establish new state-of-the-art results by a large margin. | ['Carsten Eickhoff', 'Daniel Cohen', 'Navid Rekabsaz', 'George Zerveas'] | 2021-12-16 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-5.27187064e-02 -5.23966074e-01 -3.56926680e-01 -9.99946147e-02
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0fc16826-4ccd-48f3-a6ab-2628ac76cf67 | eigenlanes-data-driven-lane-descriptors-for | 2203.15302 | null | https://arxiv.org/abs/2203.15302v1 | https://arxiv.org/pdf/2203.15302v1.pdf | Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes | A novel algorithm to detect road lanes in the eigenlane space is proposed in this paper. First, we introduce the notion of eigenlanes, which are data-driven descriptors for structurally diverse lanes, including curved, as well as straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation of a lane matrix containing all lanes in a training set. Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space. Third, using the lane candidates, we determine an optimal set of lanes by developing an anchor-based detection network, called SIIC-Net. Experimental results demonstrate that the proposed algorithm provides excellent detection performance for structurally diverse lanes. Our codes are available at https://github.com/dongkwonjin/Eigenlanes. | ['Chang-Su Kim', 'Heeyeon Kwon', 'Seong-Gyun Jeong', 'Wonhui Park', 'Dongkwon Jin'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jin_Eigenlanes_Data-Driven_Lane_Descriptors_for_Structurally_Diverse_Lanes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lane-detection'] | ['computer-vision'] | [-1.64182261e-01 -1.26506373e-01 -3.01556766e-01 -2.81243205e-01
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41f49e2d-1a97-4db6-aad6-2dd24a7fec4d | end-to-end-chinese-speaker-identification | null | null | https://aclanthology.org/2022.naacl-main.165 | https://aclanthology.org/2022.naacl-main.165.pdf | End-to-End Chinese Speaker Identification | Speaker identification (SI) in texts aims to identify the speaker(s) for each utterance in texts. Previous studies divide SI into several sub-tasks (e.g., quote extraction, named entity recognition, gender identification, and coreference resolution). However, we are still far from solving these sub-tasks, making SI systems that rely on them seriously suffer from error propagation. End-to-end SI systems, on the other hand, are not limited by individual modules, but suffer from insufficient training data from the existing small-scale datasets. To make large end-to-end models possible, we design a new annotation guideline that regards SI as span extraction from the local context, and we annotate by far the largest SI dataset for Chinese named CSI based on eighteen novels. Viewing SI as a span selection task also introduces the possibility of applying existing storng extractive machine reading comprehension (MRC) baselines. Surprisingly, simply using such a baseline without human-annotated character names and carefully designed rules, we can already achieve performance comparable or better than those of previous state-of-the-art SI methods on all public SI datasets for Chinese. Furthermore, we show that our dataset can serve as additional training data for existing benchmarks, which leads to further gains (up to 6.5% in accuracy). Finally, using CSI as a clean source, we design an effective self-training paradigm to continuously leverage hundreds of unlabeled novels. | ['Dong Yu', 'Ben Zhou', 'Dian Yu'] | null | null | null | null | naacl-2022-7 | ['coreference-resolution', 'machine-reading-comprehension', 'speaker-identification'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.77525598e-01 3.47841680e-01 -2.16476917e-01 -5.12564778e-01
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7458ecf7-223b-473b-a35f-03b15d69ff06 | deep-attentive-ranking-networks-for-learning | 2001.00056 | null | https://arxiv.org/abs/2001.00056v1 | https://arxiv.org/pdf/2001.00056v1.pdf | Deep Attentive Ranking Networks for Learning to Order Sentences | We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning. | ['Dhanajit Brahma', 'Pawan Kumar', 'Piyush Rai', 'Harish Karnick'] | 2019-12-31 | deep-attentive-ranking-networks-for-learning-1 | null | null | aaai-2020-2019-12 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.02049598e-01 1.70159656e-05 -5.07638335e-01 -9.26970661e-01
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6.71183884e-01 -3.72966379e-01 -4.69522655e-01 -2.51150161e-01] | [11.190410614013672, 8.77800464630127] |
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