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e6c43d56-ed4c-4773-bba7-3749b3237e94 | a-simple-approach-to-adversarial-robustness-1 | 2204.05432 | null | https://arxiv.org/abs/2204.05432v1 | https://arxiv.org/pdf/2204.05432v1.pdf | A Simple Approach to Adversarial Robustness in Few-shot Image Classification | Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning approaches with adversarial training to improve the robustness of few-shot classifiers. We show that a simple transfer-learning based approach can be used to train adversarially robust few-shot classifiers. We also present a method for novel classification task based on calibrating the centroid of the few-shot category towards the base classes. We show that standard adversarial training on base categories along with calibrated centroid-based classifier in the novel categories, outperforms or is on-par with state-of-the-art advanced methods on standard benchmarks for few-shot learning. Our method is simple, easy to scale, and with little effort can lead to robust few-shot classifiers. Code is available here: \url{https://github.com/UCDvision/Simple_few_shot.git} | ['Hamed Pirsiavash', 'Akshayvarun Subramanya'] | 2022-04-11 | a-simple-approach-to-adversarial-robustness | https://openreview.net/forum?id=__ObYt4753c | https://openreview.net/pdf?id=__ObYt4753c | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.64509758e-01 4.78114747e-02 -5.08134067e-02 -3.75611216e-01
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6faee8dc-7142-46e3-8fd3-27a8aed05b94 | a-fused-gromov-wasserstein-framework-for | 2305.06574 | null | https://arxiv.org/abs/2305.06574v1 | https://arxiv.org/pdf/2305.06574v1.pdf | A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment | Entity alignment is the task of identifying corresponding entities across different knowledge graphs (KGs). Although recent embedding-based entity alignment methods have shown significant advancements, they still struggle to fully utilize KG structural information. In this paper, we introduce FGWEA, an unsupervised entity alignment framework that leverages the Fused Gromov-Wasserstein (FGW) distance, allowing for a comprehensive comparison of entity semantics and KG structures within a joint optimization framework. To address the computational challenges associated with optimizing FGW, we devise a three-stage progressive optimization algorithm. It starts with a basic semantic embedding matching, proceeds to approximate cross-KG structural and relational similarity matching based on iterative updates of high-confidence entity links, and ultimately culminates in a global structural comparison between KGs. We perform extensive experiments on four entity alignment datasets covering 14 distinct KGs across five languages. Without any supervision or hyper-parameter tuning, FGWEA surpasses 21 competitive baselines, including cutting-edge supervised entity alignment methods. Our code is available at https://github.com/squareRoot3/FusedGW-Entity-Alignment. | ['Jia Li', 'Kangfei Zhao', 'Jianheng Tang'] | 2023-05-11 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-3.93517278e-02 2.60736376e-01 -6.33382320e-01 -4.04900730e-01
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d3ed9783-cf93-4b24-a0dd-08d745a9dd64 | srtr-self-reasoning-transformer-with-visual | 2212.09329 | null | https://arxiv.org/abs/2212.09329v1 | https://arxiv.org/pdf/2212.09329v1.pdf | SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation | Objects in a scene are not always related. The execution efficiency of the one-stage scene graph generation approaches are quite high, which infer the effective relation between entity pairs using sparse proposal sets and a few queries. However, they only focus on the relation between subject and object in triplet set subject entity, predicate entity, object entity, ignoring the relation between subject and predicate or predicate and object, and the model lacks self-reasoning ability. In addition, linguistic modality has been neglected in the one-stage method. It is necessary to mine linguistic modality knowledge to improve model reasoning ability. To address the above-mentioned shortcomings, a Self-reasoning Transformer with Visual-linguistic Knowledge (SrTR) is proposed to add flexible self-reasoning ability to the model. An encoder-decoder architecture is adopted in SrTR, and a self-reasoning decoder is developed to complete three inferences of the triplet set, s+o-p, s+p-o and p+o-s. Inspired by the large-scale pre-training image-text foundation models, visual-linguistic prior knowledge is introduced and a visual-linguistic alignment strategy is designed to project visual representations into semantic spaces with prior knowledge to aid relational reasoning. Experiments on the Visual Genome dataset demonstrate the superiority and fast inference ability of the proposed method. | ['Shuai Wang', 'Zhenbo Liu', 'Yuxiang Zhang'] | 2022-12-19 | null | null | null | null | ['scene-graph-generation', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 2.50132740e-01 5.13594031e-01 -3.01926672e-01 -5.60280502e-01
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72528dc1-f483-4bd8-ba6d-56b38dd313d1 | spatiality-guided-transformer-for-3d-dense | 2204.10688 | null | https://arxiv.org/abs/2204.10688v1 | https://arxiv.org/pdf/2204.10688v1.pdf | Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds | Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natural language description on visual appearance and spatial relations for each scene object of interest. To detect and describe objects in a scene, following the spirit of neural machine translation, we propose a transformer-based encoder-decoder architecture, namely SpaCap3D, to transform objects into descriptions, where we especially investigate the relative spatiality of objects in 3D scenes and design a spatiality-guided encoder via a token-to-token spatial relation learning objective and an object-centric decoder for precise and spatiality-enhanced object caption generation. Evaluated on two benchmark datasets, ScanRefer and ReferIt3D, our proposed SpaCap3D outperforms the baseline method Scan2Cap by 4.94% and 9.61% in CIDEr@0.5IoU, respectively. Our project page with source code and supplementary files is available at https://SpaCap3D.github.io/ . | ['Weidong Cai', 'Jianhui Yu', 'Chaoyi Zhang', 'Heng Wang'] | 2022-04-22 | null | null | null | null | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [ 5.20374253e-02 2.95116335e-01 -1.71286345e-01 -7.15550184e-01
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97318698-cca9-41bf-bc66-0987108a9f95 | 190600823 | 1906.00823 | null | https://arxiv.org/abs/1906.00823v3 | https://arxiv.org/pdf/1906.00823v3.pdf | Data-driven Estimation of Sinusoid Frequencies | Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to estimate the frequency of each component in a multisinusoidal signal from a finite number of noisy samples. A recent machine-learning approach uses a neural network to output a learned representation with local maxima at the position of the frequency estimates. In this work, we propose a novel neural-network architecture that produces a significantly more accurate representation, and combine it with an additional neural-network module trained to detect the number of frequencies. This yields a fast, fully-automatic method for frequency estimation that achieves state-of-the-art results. In particular, it outperforms existing techniques by a substantial margin at medium-to-high noise levels. | ['Carlos Fernandez-Granda', 'Gautier Izacard', 'Sreyas Mohan'] | 2019-06-03 | data-driven-estimation-of-sinusoid | http://papers.nips.cc/paper/8756-data-driven-estimation-of-sinusoid-frequencies | http://papers.nips.cc/paper/8756-data-driven-estimation-of-sinusoid-frequencies.pdf | neurips-2019-12 | ['seismic-imaging'] | ['miscellaneous'] | [ 4.96187299e-01 -8.30664784e-02 6.03050962e-02 -2.62792617e-01
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75558096-eebf-4510-b367-7edc88e8a2ae | start-small-think-big-on-hyperparameter | 2207.04979 | null | https://arxiv.org/abs/2207.04979v1 | https://arxiv.org/pdf/2207.04979v1.pdf | Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings | Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensitive to hyperparameter settings, however, and that suitable choices are dataset-dependent. In this paper, we explore hyperparameter optimization (HPO) for very large knowledge graphs, where the cost of evaluating individual hyperparameter configurations is excessive. Prior studies often avoided this cost by using various heuristics; e.g., by training on a subgraph or by using fewer epochs. We systematically discuss and evaluate the quality and cost savings of such heuristics and other low-cost approximation techniques. Based on our findings, we introduce GraSH, an efficient multi-fidelity HPO algorithm for large-scale KGEs that combines both graph and epoch reduction techniques and runs in multiple rounds of increasing fidelities. We conducted an experimental study and found that GraSH obtains state-of-the-art results on large graphs at a low cost (three complete training runs in total). | ['Rainer Gemulla', 'Fritz Niesel', 'Adrian Kochsiek'] | 2022-07-11 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.22616470e-01 2.16001943e-01 -2.61402994e-01 -7.16862381e-02
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92d1a7ec-a916-4026-96a9-32afbc290c03 | prognet-a-transferable-deep-network-for | null | null | https://www.mdpi.com/2226-4310/10/1/10 | https://www.mdpi.com/2226-4310/10/1/10 | ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions | Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust feature engineering. Initially, data engineering encompassing: (i) principal component analysis (PCA) dimensionality reduction; (ii) feature selection using correlation analysis; (iii) denoising with empirical Bayesian Cauchy prior wavelets; and (iv) feature scaling is used to obtain the required learning representations. Next, an adaptive deep learning model, namely ProgNet, is trained on a source domain with sufficient degradation trajectories generated from PrognosEase, a run-to-fail data generator for health deterioration analysis. Then, ProgNet is transferred to the target domain of obtained degradation features for fine-tuning. The primary goal is to achieve a higher-level generalization while reducing algorithmic complexity, making experiments reproducible on available commercial computers with quad-core microprocessors. ProgNet is tested on the popular New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset describing real flight scenarios. To the extent we can report, this is the first time that all N-CMAPSS subsets have been fully screened in such an experiment. ProgNet evaluations with numerous metrics, including the well-known CMAPSS scoring function, demonstrate promising performance levels, reaching 234.61 for the entire test set. This is approximately four times better than the results obtained with the compared conventional deep learning models. | ['Mohamed Benbouzid', 'Leïla-Hayet Mouss', 'Mohamed-Djamel Mouss', 'Tarek Berghout'] | 2022-12-23 | null | null | null | mdpi-aerospace-2022-12 | ['feature-engineering'] | ['methodology'] | [-1.94906116e-01 -4.35131073e-01 -6.97324798e-03 -6.97663650e-02
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d264838f-5349-4f71-8129-45b2380d1070 | open-set-automatic-target-recognition | 2211.05883 | null | https://arxiv.org/abs/2211.05883v1 | https://arxiv.org/pdf/2211.05883v1.pdf | Open-Set Automatic Target Recognition | Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such as military and surveillance applications. Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution. Thus, these algorithms have not been robust to unknown classes not seen during the training phase, limiting their utility in real-world applications. To this end, we propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms. In addition, we introduce a plugin Category-aware Binary Classifier (CBC) module to effectively tackle unknown classes seen during inference. The proposed CBC module can be easily integrated with any existing ATR algorithms and can be trained in an end-to-end manner. Experimental results show that the proposed approach outperforms many open-set methods on the DSIAC and CIFAR-10 datasets. To the best of our knowledge, this is the first work to address the open-set classification problem for ATR algorithms. Source code is available at: https://github.com/bardisafa/Open-set-ATR. | ['Vishal M. Patel', 'Shuowen Hu', 'Celso M. de Melo', 'Vibashan VS', 'Bardia Safaei'] | 2022-11-10 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.68120933e-01 -2.61968315e-01 -1.60621136e-01 -5.63391805e-01
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b521b223-23e6-403c-bf1f-774209860bf9 | integrating-generative-artificial | 2305.17137 | null | https://arxiv.org/abs/2305.17137v1 | https://arxiv.org/pdf/2305.17137v1.pdf | Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems | This paper aims to serve as a comprehensive guide for researchers and practitioners, offering insights into the current state, potential applications, and future research directions for generative artificial intelligence and foundation models within the context of intelligent vehicles. As the automotive industry progressively integrates AI, generative artificial intelligence technologies hold the potential to revolutionize user interactions, delivering more immersive, intuitive, and personalised in-car experiences. We provide an overview of current applications of generative artificial intelligence in the automotive domain, emphasizing speech, audio, vision, and multimodal interactions. We subsequently outline critical future research areas, including domain adaptability, alignment, multimodal integration and others, as well as, address the challenges and risks associated with ethics. By fostering collaboration and addressing these research areas, generative artificial intelligence can unlock its full potential, transforming the driving experience and shaping the future of intelligent vehicles. | ['Björn W. Schuller', 'Nicolas Flores-Herr', 'Hans-Jörg Vögel', 'Serena Striegel', 'Jeremy Dillmann', 'Lukas Stappen'] | 2023-05-15 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 7.88525939e-02 6.58057034e-01 -1.95084378e-01 -2.59639263e-01
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80579f6a-6fb5-4cf6-b9c8-3ea690594b03 | an-analysis-of-mixed-initiative-and | 2005.12340 | null | https://arxiv.org/abs/2005.12340v1 | https://arxiv.org/pdf/2005.12340v1.pdf | An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues | The ability to engage in mixed-initiative interaction is one of the core requirements for a conversational search system. How to achieve this is poorly understood. We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types. Using ConversationShape as a lens, we take a closer look at several conversational search datasets and compare them with other dialogue datasets to better understand the types of dialogue interaction they represent, either driven by the information seeker or the assistant. We discover that deviations from the ConversationShape of a human-human dialogue of the same type is predictive of the quality of a human-machine dialogue. | ['Svitlana Vakulenko', 'Maarten de Rijke', 'Evangelos Kanoulas'] | 2020-05-25 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 7.35500902e-02 5.30456960e-01 -3.10302079e-01 -5.73753834e-01
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2b4c89a0-d2e8-4ef1-b6b5-9f12d27cba86 | streaming-min-max-hypergraph-partitioning | null | null | http://papers.nips.cc/paper/5897-streaming-min-max-hypergraph-partitioning | http://papers.nips.cc/paper/5897-streaming-min-max-hypergraph-partitioning.pdf | Streaming Min-max Hypergraph Partitioning | In many applications, the data is of rich structure that can be represented by a hypergraph, where the data items are represented by vertices and the associations among items are represented by hyperedges. Equivalently, we are given an input bipartite graph with two types of vertices: items, and associations (which we refer to as topics). We consider the problem of partitioning the set of items into a given number of parts such that the maximum number of topics covered by a part of the partition is minimized. This is a natural clustering problem, with various applications, e.g. partitioning of a set of information objects such as documents, images, and videos, and load balancing in the context of computation platforms.In this paper, we focus on the streaming computation model for this problem, in which items arrive online one at a time and each item must be assigned irrevocably to a part of the partition at its arrival time. Motivated by scalability requirements, we focus on the class of streaming computation algorithms with memory limited to be at most linear in the number of the parts of the partition. We show that a greedy assignment strategy is able to recover a hidden co-clustering of items under a natural set of recovery conditions. We also report results of an extensive empirical evaluation, which demonstrate that this greedy strategy yields superior performance when compared with alternative approaches. | ['Dan Alistarh', 'Milan Vojnovic', 'Jennifer Iglesias'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['hypergraph-partitioning'] | ['graphs'] | [ 2.26587459e-01 1.95876192e-02 -3.59324932e-01 -5.33552803e-02
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7b67eb44-779c-4567-8694-c97fcc991d0f | dense-and-low-rank-gaussian-crfs-using-deep | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Chandra_Dense_and_Low-Rank_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Chandra_Dense_and_Low-Rank_ICCV_2017_paper.pdf | Dense and Low-Rank Gaussian CRFs Using Deep Embeddings | In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. The G-CRF system matrix is therefore low-rank, allowing us to solve the resulting system in a few milliseconds on the GPU by using conjugate gradients. As in G-CRF, inference is exact, the unary and pairwise terms are jointly trained end-to-end by using analytic expressions for the gradients, while we also develop even faster, Potts-type variants of our embeddings. We show that the learned embeddings capture pixel-to-pixel affinities in a task-specific manner, while our approach achieves state of the art results on three challenging benchmarks, namely semantic segmentation, human part segmentation, and saliency estimation. Our implementation is fully GPU based, built on top of the Caffe library, and is available at https://github.com/siddharthachandra/gcrf-v2.0
| ['Siddhartha Chandra', 'Nicolas Usunier', 'Iasonas Kokkinos'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['human-part-segmentation'] | ['computer-vision'] | [-1.10847965e-01 2.60635376e-01 -5.40382005e-02 -4.47096765e-01
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bdfb7837-0314-4262-b2e4-7e6146473049 | complex-knowledge-base-question-answering-a | 2108.06688 | null | https://arxiv.org/abs/2108.06688v5 | https://arxiv.org/pdf/2108.06688v5.pdf | Complex Knowledge Base Question Answering: A Survey | Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we describe benchmark datasets for complex KBQA task and introduce the construction process of these datasets. Next, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their major differences and similarities. After that, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions and techniques used in existing work. Finally, we conclude and discuss several promising directions related to complex KBQA for future research. | ['Ji-Rong Wen', 'Wayne Xin Zhao', 'Jing Jiang', 'Jinhao Jiang', 'Gaole He', 'Yunshi Lan'] | 2021-08-15 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.03567946e-01 2.82393724e-01 -6.35067895e-02 -5.95333695e-01
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e692d9f9-b166-476d-a255-40eb2a0c6e38 | procedural-content-generation-better | 2105.14780 | null | https://arxiv.org/abs/2105.14780v1 | https://arxiv.org/pdf/2105.14780v1.pdf | Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning | The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. This promises quicker learning and learning more complex methods. To gain an insight into the field and to detect emerging trends, we performed a database search. We note a surprisingly late adoption of deep learning that starts in 2018. The introduction of deep learning has not yet solved the greatest challenge of TRL: generalization. Transfer between different domains works well when domains have strong similarities (e.g. MountainCar to Cartpole), and most TRL publications focus on different tasks within the same domain that have few differences. Most TRL applications we encountered compare their improvements against self-defined baselines, and the field is still missing unified benchmarks. We consider this to be a disappointing situation. For the future, we note that: (1) A clear measure of task similarity is needed. (2) Generalization needs to improve. Promising approaches merge deep learning with planning via MCTS or introduce memory through LSTMs. (3) The lack of benchmarking tools will be remedied to enable meaningful comparison and measure progress. Already Alchemy and Meta-World are emerging as interesting benchmark suites. We note that another development, the increase in procedural content generation (PCG), can improve both benchmarking and generalization in TRL. | ['Aske Plaat', 'Mike Preuss', 'Matthias Müller-Brockhausen'] | 2021-05-31 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-8.71377364e-02 -2.19991252e-01 -3.05411190e-01 -1.89386800e-01
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13ada109-c880-416e-b388-e56f765e57f8 | an-electra-model-for-latin-token-tagging | null | null | https://aclanthology.org/2022.lt4hala-1.30 | https://aclanthology.org/2022.lt4hala-1.30.pdf | An ELECTRA Model for Latin Token Tagging Tasks | This report describes the KU Leuven / Brepols-CTLO submission to EvaLatin 2022. We present the results of our current small Latin ELECTRA model, which will be expanded to a larger model in the future. For the lemmatization task, we combine a neural token-tagging approach with the in-house rule-based lemma lists from Brepols’ ReFlex software. The results are decent, but suffer from inconsistencies between Brepols’ and EvaLatin’s definitions of a lemma. For POS-tagging, the results come up just short from the first place in this competition, mainly struggling with proper nouns. For morphological tagging, there is much more room for improvement. Here, the constraints added to our Multiclass Multilabel model were often not tight enough, causing missing morphological features. We will further investigate why the combination of the different morphological features, which perform fine on their own, leads to issues. | ['Alek Keersmaekers', 'Wouter Mercelis'] | null | null | null | null | lt4hala-lrec-2022-6 | ['lemmatization', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-1.82261273e-01 3.08676600e-01 -1.35978594e-01 -4.37623858e-01
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408f9c2d-a55b-49fb-bcde-5a9cb680f16b | high-resolution-synthesis-of-high-density | 2209.09809 | null | https://arxiv.org/abs/2209.09809v2 | https://arxiv.org/pdf/2209.09809v2.pdf | High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection | Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in highdensity breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in highresolution mammograms. The training images were split by breast density BIRADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist. | ['Karim Lekadir', 'Laura Igual', 'Fredrik Strand', 'Maciej Bobowicz', 'Javier del Riego', 'Alessandro Catanese', 'Oliver Diaz', 'Richard Osuala', 'Kaisar Kushibar', 'Lidia Garrucho'] | 2022-09-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.20787501e-01 9.31640744e-01 -4.88568723e-01 -4.16738361e-01
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33b6022f-c7aa-4e61-9721-0ab45c5dc600 | enlarging-instance-specific-and-class | 2303.15467 | null | https://arxiv.org/abs/2303.15467v1 | https://arxiv.org/pdf/2303.15467v1.pdf | Enlarging Instance-specific and Class-specific Information for Open-set Action Recognition | Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better uncertainty scores but dismiss the importance of feature representations. We find that features with richer semantic diversity can significantly improve the open-set performance under the same uncertainty scores. In this paper, we begin with analyzing the feature representation behavior in the open-set action recognition (OSAR) problem based on the information bottleneck (IB) theory, and propose to enlarge the instance-specific (IS) and class-specific (CS) information contained in the feature for better performance. To this end, a novel Prototypical Similarity Learning (PSL) framework is proposed to keep the instance variance within the same class to retain more IS information. Besides, we notice that unknown samples sharing similar appearances to known samples are easily misclassified as known classes. To alleviate this issue, video shuffling is further introduced in our PSL to learn distinct temporal information between original and shuffled samples, which we find enlarges the CS information. Extensive experiments demonstrate that the proposed PSL can significantly boost both the open-set and closed-set performance and achieves state-of-the-art results on multiple benchmarks. Code is available at https://github.com/Jun-CEN/PSL. | ['Qifeng Chen', 'Yingya Zhang', 'Zhiwu Qing', 'Yixuan Pei', 'Xiang Wang', 'Shiwei Zhang', 'Jun Cen'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-set-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 4.69240308e-01 -1.27874002e-01 -5.15836477e-01 -5.30774474e-01
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a3f5cc44-41de-4aa4-a008-877bcfad1b02 | license-plate-recognition-lpr-a-review-with | 1401.5559 | null | http://arxiv.org/abs/1401.5559v1 | http://arxiv.org/pdf/1401.5559v1.pdf | License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study | Most vehicle license plate recognition use neural network techniques to
enhance its computing capability. The image of the vehicle license plate is
captured and processed to produce a textual output for further processing. This
paper reviews image processing and neural network techniques applied at
different stages which are preprocessing, filtering, feature extraction,
segmentation and recognition in such way to remove the noise of the image, to
enhance the image quality and to expedite the computing process by converting
the characters in the image into respective text. An exemplar experiment has
been done in MATLAB to show the basic process of the image processing
especially for license plate in Malaysia case study. An algorithm is adapted
into the solution for parking management system. The solution then is
implemented as proof of concept to the algorithm. | ['Mohd Adili Norasikin', 'Norazira A Jalil', 'Emaliana Kasmuri', 'Nuzulha Khilwani Ibrahim', 'Sazilah Salam', 'Mohamad Riduwan Md Nawawi'] | 2014-01-22 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.10753518e-01 -5.55018783e-01 1.39599487e-01 -4.12511647e-01
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cf50d4f7-efa4-4dee-8cd7-30e9b8b216a3 | uncertainty-quantification-in-synthetic | 2210.05026 | null | https://arxiv.org/abs/2210.05026v2 | https://arxiv.org/pdf/2210.05026v2.pdf | Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption | We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions or estimators in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call `causal predictands', allowing for multiple treated units with treatment adoption at possibly different points in time. From a theoretical perspective, our uncertainty quantification methods improve on prior literature by (i) covering a large class of causal predictands in staggered adoption settings, (ii) allowing for synthetic control methods with possibly nonlinear constraints, (iii) proposing scalable robust conic optimization methods and principled data-driven tuning parameter selection, and (iv) offering valid uniform inference across post-treatment periods. We illustrate our methodology with an empirical application studying the effects of economic liberalization in the 1990s on GDP for emerging European countries. Companion general-purpose software packages are provided in Python, R and Stata. | ['Rocio Titiunik', 'Filippo Palomba', 'Yingjie Feng', 'Matias D. Cattaneo'] | 2022-10-10 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.96971279e-01 4.38295603e-01 -1.13479638e+00 -3.83482367e-01
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71e0234e-2e77-4633-9ac0-f69a24d6a31a | relational-temporal-graph-reasoning-for-dual | 2306.09114 | null | https://arxiv.org/abs/2306.09114v1 | https://arxiv.org/pdf/2306.09114v1.pdf | Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding | Dual-task dialog language understanding aims to tackle two correlative dialog language understanding tasks simultaneously via leveraging their inherent correlations. In this paper, we put forward a new framework, whose core is relational temporal graph reasoning.We propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to facilitate relational temporal modeling in dialog understanding and dual-task reasoning. Besides, different from previous works that only achieve implicit semantics-level interactions, we propose to model the explicit dependencies via integrating prediction-level interactions. To implement our framework, we first propose a novel model Dual-tAsk temporal Relational rEcurrent Reasoning network (DARER), which first generates the context-, speaker- and temporal-sensitive utterance representations through relational temporal modeling of SATG, then conducts recurrent dual-task relational temporal graph reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions. And the relational temporal modeling in DARER is achieved by relational convolutional networks (RGCNs). Then we further propose Relational Temporal Transformer (ReTeFormer), which achieves fine-grained relational temporal modeling via Relation- and Structure-aware Disentangled Multi-head Attention. Accordingly, we propose DARER with ReTeFormer (DARER2), which adopts two variants of ReTeFormer to achieve the relational temporal modeling of SATG and DTRG, respectively. The extensive experiments on different scenarios verify that our models outperform state-of-the-art models by a large margin. Remarkably, on the dialog sentiment classification task in the Mastodon dataset, DARER and DARER2 gain relative improvements of about 28% and 34% over the previous best model in terms of F1. | ['Ivor W. Tsang', 'Bowen Xing'] | 2023-06-15 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.27834806e-01 4.98458654e-01 -2.05045536e-01 -6.65555358e-01
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26a24a96-7bd5-4ea7-80d2-9fdb18b38505 | ice-hockey-player-identification-via | 2111.11535 | null | https://arxiv.org/abs/2111.11535v2 | https://arxiv.org/pdf/2111.11535v2.pdf | Ice hockey player identification via transformers and weakly supervised learning | Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in Natural Language Processing (NLP) and are swiftly gaining traction in computer vision. Motivated by the increasing success of transformers in computer vision, in this paper, we introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The transformer takes temporal sequences of player frames (also called player tracklets) as input and outputs the probabilities of jersey numbers present in the frames. The proposed network performs better than the previous benchmark on the dataset used. We implement a weakly-supervised training approach by generating approximate frame-level labels for jersey number presence and use the frame-level labels for faster training. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time. Using player shifts improved the player identification accuracy by 6%. | ['John S. Zelek', 'David A. Clausi', 'Pascale Walters', 'William McNally', 'Kanav Vats'] | 2021-11-22 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 2.51145095e-01 -3.48844886e-01 -2.83978164e-01 -1.62625149e-01
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5517ce5b-0604-4255-8e21-3633bae9191c | on-the-safety-of-vulnerable-road-users-by | 2004.11909 | null | https://arxiv.org/abs/2004.11909v1 | https://arxiv.org/pdf/2004.11909v1.pdf | On the safety of vulnerable road users by cyclist orientation detection using Deep Learning | In this work, orientation detection using Deep Learning is acknowledged for a particularly vulnerable class of road users,the cyclists. Knowing the cyclists' orientation is of great relevance since it provides a good notion about their future trajectory, which is crucial to avoid accidents in the context of intelligent transportation systems. Using Transfer Learning with pre-trained models and TensorFlow, we present a performance comparison between the main algorithms reported in the literature for object detection,such as SSD, Faster R-CNN and R-FCN along with MobilenetV2, InceptionV2, ResNet50, ResNet101 feature extractors. Moreover, we propose multi-class detection with eight different classes according to orientations. To do so, we introduce a new dataset called "Detect-Bike", containing 20,229 cyclist instances over 11,103 images, which has been labeled based on cyclist's orientation. Then, the same Deep Learning methods used for detection are trained to determine the target's heading. Our experimental results and vast evaluation showed satisfactory performance of all of the studied methods for the cyclists and their orientation detection, especially using Faster R-CNN with ResNet50 proved to be precise but significantly slower. Meanwhile, SSD using InceptionV2 provided good trade-off between precision and execution time, and is to be preferred for real-time embedded applications. | ['Carlos A. Carballo-Monsivais', 'Diego A. Mercado-Ravell', 'Marichelo Garcia-Venegas'] | 2020-04-25 | null | null | null | null | ['2d-cyclist-detection'] | ['computer-vision'] | [-2.60897487e-01 -1.29889429e-01 -3.56880993e-01 -2.98627585e-01
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2b3554cc-5490-427a-b04d-fdc45d2c93d8 | robust-training-under-label-noise-by-over | 2202.14026 | null | https://arxiv.org/abs/2202.14026v2 | https://arxiv.org/pdf/2202.14026v2.pdf | Robust Training under Label Noise by Over-parameterization | Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this work, we propose a principled approach for robust training of over-parameterized deep networks in classification tasks where a proportion of training labels are corrupted. The main idea is yet very simple: label noise is sparse and incoherent with the network learned from clean data, so we model the noise and learn to separate it from the data. Specifically, we model the label noise via another sparse over-parameterization term, and exploit implicit algorithmic regularizations to recover and separate the underlying corruptions. Remarkably, when trained using such a simple method in practice, we demonstrate state-of-the-art test accuracy against label noise on a variety of real datasets. Furthermore, our experimental results are corroborated by theory on simplified linear models, showing that exact separation between sparse noise and low-rank data can be achieved under incoherent conditions. The work opens many interesting directions for improving over-parameterized models by using sparse over-parameterization and implicit regularization. | ['Chong You', 'Qing Qu', 'Zhihui Zhu', 'Sheng Liu'] | 2022-02-28 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.55249619e-01 2.29847953e-02 -1.30397335e-01 -4.39977914e-01
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34f4aacf-723d-4147-85ce-dbaff4aee3e4 | a-graph-constrained-changepoint-learning | 2102.01319 | null | https://arxiv.org/abs/2102.01319v2 | https://arxiv.org/pdf/2102.01319v2.pdf | A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection | This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to other state-of-the-art approaches. The proposed method achieves the overall sensitivity of Sen = 99.64%, positive predictivity of PPR = 99.71%, and detection error rate of DER = 0.19. | ['Fatemeh Afghah', 'Toby Hocking', 'Atiyeh Fotoohinasab'] | 2021-02-02 | null | null | null | null | ['qrs-complex-detection'] | ['medical'] | [ 2.46692851e-01 2.32331961e-01 -2.29442671e-01 -3.65328223e-01
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9e42578c-0c5d-411e-8273-e935103319e3 | graph-embedding-augmented-skill-rating-system | 2304.08257 | null | https://arxiv.org/abs/2304.08257v1 | https://arxiv.org/pdf/2304.08257v1.pdf | Graph Embedding Augmented Skill Rating System | This paper presents a framework for learning player embeddings in competitive games and events. Players and their win-loss relationships are modeled as a skill gap graph, which is an undirected weighted graph. The player embeddings are learned from the graph using a random walk-based graph embedding method and can reflect the relative skill levels among players. Embeddings are low-dimensional vector representations that can be conveniently applied to subsequent tasks while still preserving the topological relationships in a graph. In the latter part of this paper, Graphical Elo (GElo) is introduced as an application of player embeddings when rating player skills. GElo is an extension of the classic Elo rating system. It constructs a skill gap graph based on player match histories and learns player embeddings from it. Afterward, the rating scores that were calculated by Elo are adjusted according to player activeness and cosine similarities among player embeddings. GElo can be executed offline and in parallel, and it is non-intrusive to existing rating systems. Experiments on public datasets show that GElo makes a more reliable evaluation of player skill levels than vanilla Elo. The experimental results suggest potential applications of player embeddings in competitive games and events. | ['Jiasheng Wang'] | 2023-04-17 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [-6.30359709e-01 4.73259091e-02 -3.29554349e-01 5.88904060e-02
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51894445-ce2a-4069-8b57-1b23fbce73bd | trecvid-2020-a-comprehensive-campaign-for | 2104.13473 | null | https://arxiv.org/abs/2104.13473v1 | https://arxiv.org/pdf/2104.13473v1.pdf | TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains | The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last twenty years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2020 represented a continuation of four tasks and the addition of two new tasks. In total, 29 teams from various research organizations worldwide completed one or more of the following six tasks: 1. Ad-hoc Video Search (AVS), 2. Instance Search (INS), 3. Disaster Scene Description and Indexing (DSDI), 4. Video to Text Description (VTT), 5. Activities in Extended Video (ActEV), 6. Video Summarization (VSUM). This paper is an introduction to the evaluation framework, tasks, data, and measures used in the evaluation campaign. | ['Georges Quenot', 'Wessel Kraaij', 'Gareth J. F. Jones', 'Yvette Graham', 'Alan F. Smeaton', 'Jeffrey Liu', 'Lukas Diduch', 'Baptiste Chocot', 'Eliot Godard', 'Jesse Zhang', 'Andrew Delgado', 'Yooyoung Lee', 'Afzal Godil', 'Jonathan Fiscus', 'Keith Curtis', 'Asad A. Butt', 'George Awad'] | 2021-04-27 | null | null | null | null | ['instance-search', 'ad-hoc-video-search'] | ['computer-vision', 'computer-vision'] | [ 3.73945296e-01 -6.44843578e-01 -4.59812954e-02 -3.44733953e-01
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51d603df-29fb-4c9a-8e2c-d381d8aa0537 | subword-based-cross-lingual-transfer-of-1 | null | null | https://aclanthology.org/2022.sigmorphon-1.7 | https://aclanthology.org/2022.sigmorphon-1.7.pdf | Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi and Nepali | Word embeddings are growing to be a crucial resource in the field of NLP for any language. This work introduces a novel technique for static subword embeddings transfer for Indic languages from a relatively higher resource language to a genealogically related low resource language. We primarily work with HindiMarathi, simulating a low-resource scenario for Marathi, and confirm observed trends on Nepali. We demonstrate the consistent benefits of unsupervised morphemic segmentation on both source and target sides over the treatment performed by fastText. Our best-performing approach uses an EM-style approach to learning bilingual subword embeddings; we also show, for the first time, that a trivial “copyand-paste” embeddings transfer based on even perfect bilingual lexicons is inadequate in capturing language-specific relationships. We find that our approach substantially outperforms the fastText baselines for both Marathi and Nepali on the Word Similarity task as well as WordNetBased Synonymy Tests; on the former task, its performance for Marathi is close to that of pretrained fastText embeddings that use three orders of magnitude more Marathi data. | ['Zdeněk Žabokrtský', 'Niyata Bafna'] | null | null | null | null | naacl-sigmorphon-2022-7 | ['word-similarity'] | ['natural-language-processing'] | [-2.37770051e-01 -1.92037627e-01 -3.98078561e-01 -3.01656812e-01
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5fb29603-96e1-40b3-80ac-4f3d7b19f324 | an-enhanced-conv-tasnet-model-for-speech | 2205.13657 | null | https://arxiv.org/abs/2205.13657v3 | https://arxiv.org/pdf/2205.13657v3.pdf | An enhanced Conv-TasNet model for speech separation using a speaker distance-based loss function | This work addresses the problem of speech separation in the Spanish Language using pre-trained deep learning models. As with many speech processing tasks, large databases in other languages different from English are scarce. Therefore this work explores different training strategies using the Conv-TasNet model as a benchmark. A scale-invariant signal distortion ratio (SI-SDR) metric value of 9.9 dB was achieved for the best training strategy. Then, experimentally, we identified an inverse relationship between the speakers' similarity and the model's performance, so an improved ConvTasNet architecture was proposed. The enhanced Conv-TasNet model uses pre-trained speech embeddings to add a between-speakers cosine similarity term in the cost function, yielding an SI-SDR of 10.6 dB. Lastly, final experiments regarding real-time deployment show some drawbacks in the speakers' channel synchronization due to the need to process small speech segments where only one of the speakers appears. | ['Julián D. Arias-Londoño', 'Jose A. Arango-Sánchez'] | 2022-05-26 | null | null | null | null | ['speech-separation'] | ['speech'] | [-9.75318253e-02 1.93739370e-01 2.11457372e-01 -3.58195841e-01
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28aa3de7-6559-4609-ba43-ce38b8f29294 | dexdeform-dexterous-deformable-object | 2304.03223 | null | https://arxiv.org/abs/2304.03223v1 | https://arxiv.org/pdf/2304.03223v1.pdf | DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics | In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects. At the same time, previous trajectory optimization approaches with differentiable physics for deformable manipulation would suffer from local optima caused by the explosion of contact modes from hand-object interactions. To address these challenges, we propose DexDeform, a principled framework that abstracts dexterous manipulation skills from human demonstration and refines the learned skills with differentiable physics. Concretely, we first collect a small set of human demonstrations using teleoperation. And we then train a skill model using demonstrations for planning over action abstractions in imagination. To explore the goal space, we further apply augmentations to the existing deformable shapes in demonstrations and use a gradient optimizer to refine the actions planned by the skill model. Finally, we adopt the refined trajectories as new demonstrations for finetuning the skill model. To evaluate the effectiveness of our approach, we introduce a suite of six challenging dexterous deformable object manipulation tasks. Compared with baselines, DexDeform is able to better explore and generalize across novel goals unseen in the initial human demonstrations. | ['Chuang Gan', 'Joshua B. Tenenbaum', 'Hao Su', 'Tao Du', 'Tao Chen', 'Zhiao Huang', 'Sizhe Li'] | 2023-03-27 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-6.59875050e-02 1.54537439e-01 2.65955031e-01 -6.84012920e-02
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2894bce0-1a1e-4279-a4ef-ee11c838d2a6 | rosko-row-skipping-outer-products-for-sparse | 2307.03930 | null | https://arxiv.org/abs/2307.03930v1 | https://arxiv.org/pdf/2307.03930v1.pdf | Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels | We propose Rosko -- row skipping outer products -- for deriving sparse matrix multiplication (SpMM) kernels in reducing computation and memory access requirements of deep neural networks (DNNs). Rosko allows skipping of entire row computations during program execution with low sparsity-management overheads. We analytically derive sparse CPU kernels that adapt to given hardware characteristics to effectively utilize processor cores and minimize data movement without the need for auto-tuning or search space exploration. Rosko can be integrated with other outer product scheduling methods, allowing them to leverage row skipping by using Rosko's packing format to skip unnecessary computation. Rosko kernels outperform existing auto-tuning and search-based solutions as well as state-of-the-art vendor-optimized libraries on real hardware across a variety of neural network workloads. For matrices with sparsities ranging from 65% to 99.8% typically found in machine learning, Rosko kernels achieve up to a 6.5x runtime reduction on Intel and ARM CPUs. | ['Mark Ting', 'H. T. Kung', 'Andrew Sabot', 'Vikas Natesh'] | 2023-07-08 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.45535424e-01 -1.01522617e-01 -3.40148509e-01 -4.68172371e-01
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9b1ef057-a774-4fbf-9c18-c2036bd1e4e9 | building-an-invisible-shield-for-your | 2305.12881 | null | https://arxiv.org/abs/2305.12881v1 | https://arxiv.org/pdf/2305.12881v1.pdf | Building an Invisible Shield for Your Portrait against Deepfakes | The issue of detecting deepfakes has garnered significant attention in the research community, with the goal of identifying facial manipulations for abuse prevention. Although recent studies have focused on developing generalized models that can detect various types of deepfakes, their performance is not always be reliable and stable, which poses limitations in real-world applications. Instead of learning a forgery detector, in this paper, we propose a novel framework - Integrity Encryptor, aiming to protect portraits in a proactive strategy. Our methodology involves covertly encoding messages that are closely associated with key facial attributes into authentic images prior to their public release. Unlike authentic images, where the hidden messages can be extracted with precision, manipulating the facial attributes through deepfake techniques can disrupt the decoding process. Consequently, the modified facial attributes serve as a mean of detecting manipulated images through a comparison of the decoded messages. Our encryption approach is characterized by its brevity and efficiency, and the resulting method exhibits a good robustness against typical image processing traces, such as image degradation and noise. When compared to baselines that struggle to detect deepfakes in a black-box setting, our method utilizing conditional encryption showcases superior performance when presented with a range of different types of forgeries. In experiments conducted on our protected data, our approach outperforms existing state-of-the-art methods by a significant margin. | ['Youjian Zhao', 'Errui Ding', 'Chengbin Quan', 'Lirui Deng', 'Zhizhi Guo', 'Hang Zhou', 'Tianshu Hu', 'Jiazhi Guan'] | 2023-05-22 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 5.72601438e-01 -1.81426182e-01 -1.40740871e-01 -2.39783436e-01
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5e11b42c-ee28-45f2-8c0e-2990e3db6614 | panoptic-scene-graph-generation | 2207.11247 | null | https://arxiv.org/abs/2207.11247v1 | https://arxiv.org/pdf/2207.11247v1.pdf | Panoptic Scene Graph Generation | Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected using bounding boxes followed by prediction of their pairwise relationships. We argue that such a paradigm causes several problems that impede the progress of the field. For instance, bounding box-based labels in current datasets usually contain redundant classes like hairs, and leave out background information that is crucial to the understanding of context. In this work, we introduce panoptic scene graph generation (PSG), a new problem task that requires the model to generate a more comprehensive scene graph representation based on panoptic segmentations rather than rigid bounding boxes. A high-quality PSG dataset, which contains 49k well-annotated overlapping images from COCO and Visual Genome, is created for the community to keep track of its progress. For benchmarking, we build four two-stage baselines, which are modified from classic methods in SGG, and two one-stage baselines called PSGTR and PSGFormer, which are based on the efficient Transformer-based detector, i.e., DETR. While PSGTR uses a set of queries to directly learn triplets, PSGFormer separately models the objects and relations in the form of queries from two Transformer decoders, followed by a prompting-like relation-object matching mechanism. In the end, we share insights on open challenges and future directions. | ['Ziwei Liu', 'Wayne Zhang', 'Kaiyang Zhou', 'Zujin Guo', 'Yi Zhe Ang', 'Jingkang Yang'] | 2022-07-22 | null | null | null | null | ['scene-graph-generation', 'panoptic-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.64546525e-01 2.47832865e-01 -3.05441972e-02 -5.41026056e-01
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5df84b88-6ad3-4633-8ff8-b7074d350220 | rewarded-soups-towards-pareto-optimal | 2306.04488 | null | https://arxiv.org/abs/2306.04488v1 | https://arxiv.org/pdf/2306.04488v1.pdf | Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards | Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; the diversity of objectives in real-world tasks and human opinions exacerbate the issue. This paper proposes embracing the heterogeneity of diverse rewards by following a multi-policy strategy. Rather than focusing on a single a priori reward, we aim for Pareto-optimal generalization across the entire space of preferences. To this end, we propose rewarded soup, first specializing multiple networks independently (one for each proxy reward) and then interpolating their weights linearly. This succeeds empirically because we show that the weights remain linearly connected when fine-tuned on diverse rewards from a shared pre-trained initialization. We demonstrate the effectiveness of our approach for text-to-text (summarization, Q&A, helpful assistant, review), text-image (image captioning, text-to-image generation, visual grounding, VQA), and control (locomotion) tasks. We hope to enhance the alignment of deep models, and how they interact with the world in all its diversity. | ['Matthieu Cord', 'Laure Soulier', 'Jean-Baptiste Gaya', 'Corentin Dancette', 'Mustafa Shukor', 'Guillaume Couairon', 'Alexandre Rame'] | 2023-06-07 | null | null | null | null | ['visual-grounding', 'image-captioning', 'text-summarization'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.16999251e-01 4.64630485e-01 -3.29384625e-01 -3.99878830e-01
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782d02c6-eb83-4461-8375-1e0e3f916faa | sadi-a-self-adaptive-decomposed-interpretable | 2306.08299 | null | https://arxiv.org/abs/2306.08299v1 | https://arxiv.org/pdf/2306.08299v1.pdf | SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events | Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of training samples under extreme conditions. Also load usually changes dramatically in these extreme conditions, which calls for interpretable model to make better decisions. In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components. The external variable triggered loss is proposed for the imbalanced learning under extreme events. Furthermore, Generalized Additive Model (GAM) is employed in the framework for desirable interpretability. The experiments on both Central China electric load and public energy meters from buildings show that the proposed SaDI framework achieves average 22.14% improvement compared with the current state-of-the-art algorithms in forecasting under extreme events in terms of daily mean of normalized RMSE. Code, Public datasets, and Appendix are available at: https://doi.org/10.24433/CO.9696980.v1 . | ['Liang Sun', 'Qingsong Wen', 'Yi Wang', 'Rui Xia', 'Tian Zhou', 'Linxiao Yang', 'Ziqing Ma', 'Hengbo Liu'] | 2023-06-14 | null | null | null | null | ['load-forecasting', 'management'] | ['miscellaneous', 'miscellaneous'] | [-2.84106821e-01 -5.40595353e-01 -9.70127881e-02 -7.09277809e-01
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3da313ce-cbc2-413e-ad3e-c44c20891e81 | from-real-to-synthetic-and-back-synthesizing | 2006.02110 | null | https://arxiv.org/abs/2006.02110v1 | https://arxiv.org/pdf/2006.02110v1.pdf | From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding | We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: being fully controllable and fully annotated with any type of standard or custom-defined ground truth. To reduce the synthetic-to-real domain gap, we introduce a pipeline consisting of the following steps: 1) we render scenes in a context modeled after the real world, 2) we train a human parsing model on the synthetic images, 3) we use the model to estimate segmentation maps for real images, 4) we train a conditional generative adversarial network (cGAN) to learn the inverse mapping -- from a segmentation map to a real image, and 5) given new synthetic segmentation maps, we use the cGAN to generate realistic images. An illustration of our pipeline is presented in Figure 2. We use the generated data to train a multi-task model on the challenging tasks of UV mapping and dense depth estimation. We demonstrate the value of the data generation and the trained model, both quantitatively and qualitatively on the CMU Panoptic Dataset. | ['Nadav Bhonker', 'Igor Kviatkovsky', 'Gerard Medioni'] | 2020-06-03 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 8.87451708e-01 6.26150787e-01 6.08412027e-01 -6.28171325e-01
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0368b205-dd27-45f8-9174-73833300a285 | end-to-end-video-matting-with-trimap | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_End-to-End_Video_Matting_With_Trimap_Propagation_CVPR_2023_paper.pdf | End-to-End Video Matting With Trimap Propagation | The research of video matting mainly focuses on temporal coherence and has gained significant improvement via neural networks. However, matting usually relies on user-annotated trimaps to estimate alpha values, which is a labor-intensive issue. Although recent studies exploit video object segmentation methods to propagate the given trimaps, they suffer inconsistent results. Here we present a more robust and faster end-to-end video matting model equipped with trimap propagation called FTP-VM (Fast Trimap Propagation - Video Matting). The FTP-VM combines trimap propagation and video matting in one model, where the additional backbone in memory matching is replaced with the proposed lightweight trimap fusion module. The segmentation consistency loss is adopted from automotive segmentation to fit trimap segmentation with the collaboration of RNN (Recurrent Neural Network) to improve the temporal coherence. The experimental results demonstrate that the FTP-VM performs competitively both in composited and real videos only with few given trimaps. The efficiency is eight times higher than the state-of-the-art methods, which confirms its robustness and applicability in real-time scenarios. The code is available at https://github.com/csvt32745/FTP-VM | ['Ming-Sui Lee', 'Wei-Lun Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-matting', 'video-matting', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-8.62127170e-02 -2.60526806e-01 -2.14081883e-01 -2.09047690e-01
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ba57e31e-31d4-412e-87e5-ab6dc34c90bb | a-heat-diffusion-perspective-on-geodesic | 2305.19043 | null | https://arxiv.org/abs/2305.19043v1 | https://arxiv.org/pdf/2305.19043v1.pdf | A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction | Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE). | ['Smita Krishnaswamy', 'Ian Adelstein', 'Guy Wolf', 'Yanlei Zhang', 'Edward De Brouwer', 'Alexander Tong', 'Guillaume Huguet'] | 2023-05-30 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-2.47389957e-01 3.48283239e-02 1.64715722e-01 -1.54390410e-01
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2c8b3901-3716-4e59-98c3-8bc07a98b502 | universal-domain-adaptation-via-compressive | 2304.11862 | null | https://arxiv.org/abs/2304.11862v2 | https://arxiv.org/pdf/2304.11862v2.pdf | Universal Domain Adaptation via Compressive Attention Matching | Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set. The challenge lies in how to determine whether the target samples belong to common categories. The mainstream methods make judgments based on the sample features, which overemphasizes global information while ignoring the most crucial local objects in the image, resulting in limited accuracy. To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information. The proposed framework introduces a novel Compressive Attention Matching (CAM) approach to explore the core information by compressively representing attentions. Furthermore, CAM incorporates a residual-based measurement to determine the sample commonness. By utilizing the measurement, UniAM achieves domain-wise and category-wise Common Feature Alignment (CFA) and Target Class Separation (TCS). Notably, UniAM is the first method utilizing the attention in vision transformer directly to perform classification tasks. Extensive experiments show that UniAM outperforms the current state-of-the-art methods on various benchmark datasets. | ['Chao Wu', 'Kun Kuang', 'Yunfeng Shao', 'Zexi Li', 'Junkun Yuan', 'Yincuan Li', 'Didi Zhu'] | 2023-04-24 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 5.38198948e-01 -4.23379362e-01 -3.18698227e-01 -2.24183902e-01
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b7848b79-fd0f-4fe9-92ab-f4c28ebb3e6a | robust-and-accurate-depth-estimation-by | 2207.06139 | null | https://arxiv.org/abs/2207.06139v1 | https://arxiv.org/pdf/2207.06139v1.pdf | Robust and accurate depth estimation by fusing LiDAR and Stereo | Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the performance of the sensor. Therefore, a precision and robust method for fusing the LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo camera, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods. | ['Rui Guo', 'Xiaoyu Long', 'En Li', 'Junfeng Fan', 'Guangyao Xu'] | 2022-07-13 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.15924411e-01 -6.22034252e-01 -1.56962778e-02 -4.13529336e-01
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73179ba7-f9fe-477b-a766-9a648469b698 | feasibility-of-universal-anomaly-detection | 2307.00750 | null | https://arxiv.org/abs/2307.00750v1 | https://arxiv.org/pdf/2307.00750v1.pdf | Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images | Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were oftenly employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956). | ['Yuankai Huo', 'Bennett A. Landman', 'Keith T. Wilson', 'Lori A. Coburn', 'Qi Liu', 'Ken S. Lau', 'Joseph T. Roland', 'Zuhayr Asad', 'Lucas W. Remedios', 'Ruining Deng', 'Yucheng Tang', 'Shunxing Bao', 'Yaohong Wang', 'Can Cui'] | 2023-07-03 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 3.01757008e-01 -1.01235524e-01 2.56339937e-01 -2.30951875e-01
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1f8ad830-6142-4350-8676-f32e96bda8be | scanpath-prediction-on-information | 2112.02340 | null | https://arxiv.org/abs/2112.02340v2 | https://arxiv.org/pdf/2112.02340v2.pdf | Scanpath Prediction on Information Visualisations | We propose Unified Model of Saliency and Scanpaths (UMSS) -- a model that learns to predict visual saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has been limited to predicting aggregated attention statistics, such as visual saliency. We present in-depth analyses of gaze behaviour for different information visualisation elements (e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while, overall, gaze patterns are surprisingly consistent across visualisations and viewers, there are also structural differences in gaze dynamics for different elements. Informed by our analyses, UMSS first predicts multi-duration element-level saliency maps, then probabilistically samples scanpaths from them. Extensive experiments on MASSVIS show that our method consistently outperforms state-of-the-art methods with respect to several, widely used scanpath and saliency evaluation metrics. Our method achieves a relative improvement in sequence score of 11.5% for scanpath prediction, and a relative improvement in Pearson correlation coefficient of up to 23.6% for saliency prediction. These results are auspicious and point towards richer user models and simulations of visual attention on visualisations without the need for any eye tracking equipment. | ['Andreas Bulling', 'Mihai Bâce', 'Yao Wang'] | 2021-12-04 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 3.60391289e-01 8.52208138e-02 -2.27713004e-01 -3.26745629e-01
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dd41e1bf-88a9-4176-acff-203683c54adb | demonstration-of-interactive-teaching-for-end | null | null | https://aclanthology.org/W17-5511 | https://aclanthology.org/W17-5511.pdf | Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks | This is a demonstration of interactive teaching for practical end-to-end dialog systems driven by a recurrent neural network. In this approach, a developer teaches the network by interacting with the system and providing on-the-spot corrections. Once a system is deployed, a developer can also correct mistakes in logged dialogs. This demonstration shows both of these teaching methods applied to dialog systems in three domains: pizza ordering, restaurant information, and weather forecasts. | ['Lars Liden', 'Jason D. Williams'] | 2017-08-01 | null | null | null | ws-2017-8 | ['dialog-learning'] | ['natural-language-processing'] | [-3.68727326e-01 5.47297597e-01 2.84526587e-01 -8.76389325e-01
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59dbf956-c227-4476-a21f-d93d0e81ca4a | an-efficient-and-scalable-collection-of-fly | 2109.10986 | null | https://arxiv.org/abs/2109.10986v1 | https://arxiv.org/pdf/2109.10986v1.pdf | An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments | State-of-the-art visual place recognition performance is currently being achieved utilizing deep learning based approaches. Despite the recent efforts in designing lightweight convolutional neural network based models, these can still be too expensive for the most hardware restricted robot applications. Low-overhead VPR techniques would not only enable platforms equipped with low-end, cheap hardware but also reduce computation on more powerful systems, allowing these resources to be allocated for other navigation tasks. In this work, our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations. Our first contribution is DrosoNet, an exceptionally compact model inspired by the odor processing abilities of the fruit fly, Drosophyla melanogaster. Our second and main contribution is a voting mechanism that leverages multiple small and efficient classifiers to achieve more robust and consistent VPR compared to a single one. We use DrosoNet as the baseline classifier for the voting mechanism and evaluate our models on five benchmark datasets, assessing moderate to extreme appearance changes and small to moderate viewpoint variations. We then compare the proposed algorithms to state-of-the-art methods, both in terms of precision-recall AUC results and computational efficiency. | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Bruno Arcanjo'] | 2021-09-22 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-7.66701698e-02 -2.74497867e-01 -8.91090259e-02 -3.23483676e-01
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14dd93e4-d9bb-43dc-a12f-290adec9b241 | drivingstereo-a-large-scale-dataset-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.pdf | DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios | Great progress has been made on estimating disparity maps from stereo images. However, with the limited stereo data available in the existing datasets and unstable ranging precision of current stereo methods, industry-level stereo matching in autonomous driving remains challenging. In this paper, we construct a novel large-scale stereo dataset named DrivingStereo. It contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points. For better evaluations, we present two new metrics for stereo matching in the driving scenes, i.e. a distance-aware metric and a semantic-aware metric. Extensive experiments show that compared with the models trained on FlyingThings3D or Cityscapes, the models trained on our DrivingStereo achieve higher generalization accuracy in real-world driving scenes, while the proposed metrics better evaluate the stereo methods on all-range distances and across different classes. Our dataset and code are available at https://drivingstereo-dataset.github.io.
| [' Bolei Zhou', ' Jianping Shi', ' Zhidong Deng', ' Chaoqin Huang', ' Xiao Song', 'Guorun Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [-4.29364257e-02 -3.47012192e-01 -4.92416061e-02 -9.44189847e-01
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ec6cb8ea-2103-4165-8e71-b9251ba4eb9a | selective-transfer-learning-with-adversarial | null | null | https://www.tandfonline.com/doi/full/10.1080/09540091.2021.2021143 | https://www.tandfonline.com/doi/full/10.1080/09540091.2021.2021143 | Selective transfer learning with adversarial training for stock movement prediction | Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield satisfactory prediction result. This paper contributes a new stock movement prediction model, Selective Transfer Learning with Adversarial Training (STLAT). Our STLAT method advances existing solutions in two major aspects: (i) tailoring the pre-trained and fine-tuned method for stock movement prediction and (ii) introducing the data selector module to select the more relevant training samples. More specifically, we pre-train the shared base model using three different tasks. The predictor task is constructed to measure the performance of the shared base model with source domain data and target domain data. The adversarial training task is constructed to improve the generalisation of the shared base model. The data selector task is introduced to select the most relevant and high-quality training samples from stocks in source domain. All three tasks are jointly trained with a loss function. As a result, the pre-trained shared base model can be fine-tuned with the stock data in target domain. To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset. Extensive experiments demonstrate the superiority of our STLAT method. It outperforms state-of-the-art stock prediction solutions on ACC evaluation of 3.76%, 4.12%, 4.89% on ACL18, KDD17 and CN50, respectively. | ['Zibin Zheng', 'Hong-Ning Dai', 'Yang Li'] | 2021-09-30 | null | null | null | taylor-francis-online-2021-9 | ['stock-prediction'] | ['time-series'] | [-3.67810786e-01 -4.51523334e-01 -3.10855120e-01 -3.19449753e-01
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1ce768fd-da86-401f-b221-6c0d1179de25 | selective-communication-for-cooperative | 2305.17181 | null | https://arxiv.org/abs/2305.17181v1 | https://arxiv.org/pdf/2305.17181v1.pdf | Selective Communication for Cooperative Perception in End-to-End Autonomous Driving | The reliability of current autonomous driving systems is often jeopardized in situations when the vehicle's field-of-view is limited by nearby occluding objects. To mitigate this problem, vehicle-to-vehicle communication to share sensor information among multiple autonomous driving vehicles has been proposed. However, to enable timely processing and use of shared sensor data, it is necessary to constrain communication bandwidth, and prior work has done so by restricting the number of other cooperative vehicles and randomly selecting the subset of vehicles to exchange information with from all those that are within communication range. Although simple and cost effective from a communication perspective, this selection approach suffers from its susceptibility to missing those vehicles that possess the perception information most critical to navigation planning. Inspired by recent multi-agent path finding research, we propose a novel selective communication algorithm for cooperative perception to address this shortcoming. Implemented with a lightweight perception network and a previously developed control network, our algorithm is shown to produce higher success rates than a random selection approach on previously studied safety-critical driving scenario simulations, with minimal additional communication overhead. | ['Stephen F. Smith', 'Hsu-kuang Chiu'] | 2023-05-26 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.33104363e-01 5.36935449e-01 -9.11255479e-02 -4.68021840e-01
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6cd822f3-dfa2-4ed8-9717-566823c7f06f | understanding-augmentation-based-self | 2306.00788 | null | https://arxiv.org/abs/2306.00788v1 | https://arxiv.org/pdf/2306.00788v1.pdf | Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation | Good data augmentation is one of the key factors that lead to the empirical success of self-supervised representation learning such as contrastive learning and masked language modeling, yet theoretical understanding of its role in learning good representations remains limited. Recent work has built the connection between self-supervised learning and approximating the top eigenspace of a graph Laplacian operator. Learning a linear probe on top of such features can naturally be connected to RKHS regression. In this work, we use this insight to perform a statistical analysis of augmentation-based pretraining. We start from the isometry property, a key geometric characterization of the target function given by the augmentation. Our first main theorem provides, for an arbitrary encoder, near tight bounds for both the estimation error incurred by fitting the linear probe on top of the encoder, and the approximation error entailed by the fitness of the RKHS the encoder learns. Our second main theorem specifically addresses the case where the encoder extracts the top-d eigenspace of a Monte-Carlo approximation of the underlying kernel with the finite pretraining samples. Our analysis completely disentangles the effects of the model and the augmentation. A key ingredient in our analysis is the augmentation complexity, which we use to quantitatively compare different augmentations and analyze their impact on downstream performance on synthetic and real datasets. | ['Pradeep Ravikumar', 'Zico Kolter', 'Andrej Risteski', 'Bingbin Liu', 'Runtian Zhai'] | 2023-06-01 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.82613695e-01 6.34398341e-01 -2.35882789e-01 -9.47957337e-02
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eac268d7-742c-4bdc-836f-8a8044a11140 | diffava-personalized-text-to-audio-generation | 2305.12903 | null | https://arxiv.org/abs/2305.12903v1 | https://arxiv.org/pdf/2305.12903v1.pdf | DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment | Text-to-audio (TTA) generation is a recent popular problem that aims to synthesize general audio given text descriptions. Previous methods utilized latent diffusion models to learn audio embedding in a latent space with text embedding as the condition. However, they ignored the synchronization between audio and visual content in the video, and tended to generate audio mismatching from video frames. In this work, we propose a novel and personalized text-to-sound generation approach with visual alignment based on latent diffusion models, namely DiffAVA, that can simply fine-tune lightweight visual-text alignment modules with frozen modality-specific encoders to update visual-aligned text embeddings as the condition. Specifically, our DiffAVA leverages a multi-head attention transformer to aggregate temporal information from video features, and a dual multi-modal residual network to fuse temporal visual representations with text embeddings. Then, a contrastive learning objective is applied to match visual-aligned text embeddings with audio features. Experimental results on the AudioCaps dataset demonstrate that the proposed DiffAVA can achieve competitive performance on visual-aligned text-to-audio generation. | ['Yapeng Tian', 'Jing Shi', 'Shentong Mo'] | 2023-05-22 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.09976463e-01 -1.17422044e-01 -1.45302102e-01 -1.98572978e-01
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871c286f-397f-4137-8d2a-cb75657b34c4 | pdvn-a-patch-based-dual-view-network-for-face | null | null | https://ieeexplore.ieee.org/document/10007998 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10007998 | PDVN: A Patch-based Dual-view Network for Face Liveness Detection using Light Field Focal Stack | Light Field Focal Stack (LFFS) can be efficiently rendered from a light field (LF) image captured by plenoptic cameras. Differences in the 3D surface and texture of biometric samples are internally reflected in the defocus blur and local patterns between the rendered slices of LFFS. This unique property makes LFFS quite appropriate to differentiate presentation attack instruments (PAIs) from bona fide samples. A patch-based dual-view network (PDVN) is proposed in this paper to leverage the merits of LFFS for face presentation attack detection (PAD). First, original LFFS data are divided into various local patches along spatial dimensions, which distracts the model from learning the useless facial semantics and greatly relieve the problem of insufficient samples. The strategy of dual-view branches is innovatively proposed, wherein the original view and microscopic view can simultaneously contribute to liveness detection. Separable 3D convolution on the focal dimension is verified to be more effective than vanilla 3D convolution for extracting discriminative features from LFFS data. The voting mechanism on predictions of patch LFFS samples further strengthens the robustness of the proposed framework. PDVN is compared with other face PAD methods on IST LLFFSD dataset and achieves perfect performance, i.e., ACER drops to 0. | ['Yunlong Wang; Mupei Li; Zhengquan Luo; Zhenan Sun'] | 2023-01-17 | null | null | null | 2022-ieee-international-joint-conference-on | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.27716833e-01 -2.20088363e-01 -1.86310038e-01 -2.09728241e-01
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70cba9e5-3759-4e79-83d4-b26293a7ac54 | domain-constraints-in-feature-space | 2205.15128 | null | https://arxiv.org/abs/2205.15128v3 | https://arxiv.org/pdf/2205.15128v3.pdf | Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to Strengthen Robustness of Android Malware Detection | The vulnerability to adversarial examples remains one major obstacle for Machine Learning (ML)-based Android malware detection. Realistic attacks in the Android malware domain create Realizable Adversarial Examples (RealAEs), i.e., AEs that satisfy the domain constraints of Android malware. Recent studies have shown that using such RealAEs in Adversarial Training (AT) is more effective in defending against realistic attacks than using unrealizable AEs (unRealAEs). This is because RealAEs allow defenders to explore certain pockets in the feature space that are vulnerable to realistic attacks. However, existing defenses commonly generate RealAEs in the problem space, which is known to be time-consuming and impractical for AT. In this paper, we propose to generate RealAEs in the feature space, leading to a simpler and more efficient solution. Our approach is driven by a novel interpretation of Android domain constraints in the feature space. More concretely, our defense first learns feature-space domain constraints by extracting meaningful feature dependencies from data and then applies them to generating feature-space RealAEs during AT. Extensive experiments on DREBIN, a well-known Android malware detector, demonstrate that our new defense outperforms not only unRealAE-based AT but also the state-of-the-art defense that relies on non-uniform perturbations. We further validate the ability of our learned feature-space domain constraints in representing Android malware properties by showing that our feature-space domain constraints can help distinguish RealAEs from unRealAEs. | ['Zhuoran Liu', 'Veelasha Moonsamy', 'Zhengyu Zhao', 'Hamid Bostani'] | 2022-05-30 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.47132683e-01 8.07975128e-04 -2.31840715e-01 -3.51080857e-02
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e8825a11-96ca-4c8a-b3b8-24931c509a2b | rewritenet-realistic-scene-text-image | 2107.11041 | null | https://arxiv.org/abs/2107.11041v2 | https://arxiv.org/pdf/2107.11041v2.pdf | RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles | Scene text editing (STE), which converts a text in a scene image into the desired text while preserving an original style, is a challenging task due to a complex intervention between text and style. In this paper, we propose a novel STE model, referred to as RewriteNet, that decomposes text images into content and style features and re-writes a text in the original image. Specifically, RewriteNet implicitly distinguishes the content from the style by introducing scene text recognition. Additionally, independent of the exact supervisions with synthetic examples, we propose a self-supervised training scheme for unlabeled real-world images, which bridges the domain gap between synthetic and real data. Our experiments present that RewriteNet achieves better generation performances than other comparisons. Further analysis proves the feature decomposition of RewriteNet and demonstrates the reliability and robustness through diverse experiments. Our implementation is publicly available at \url{https://github.com/clovaai/rewritenet} | ['Sungrae Park', 'Gayoung Lee', 'Seung Shin', 'Moonbin Yim', 'Seonghyeon Kim', 'Yoonsik Kim', 'Junyeop Lee'] | 2021-07-23 | null | null | null | null | ['scene-text-recognition', 'scene-text-editing'] | ['computer-vision', 'computer-vision'] | [ 7.21111774e-01 -2.08904833e-01 6.94423243e-02 -5.07913053e-01
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e78dce3b-9ea3-4c4b-8e99-9c51cda58849 | randomized-schur-complement-views-for-graph | 2306.04004 | null | https://arxiv.org/abs/2306.04004v1 | https://arxiv.org/pdf/2306.04004v1.pdf | Randomized Schur Complement Views for Graph Contrastive Learning | We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the technique generates unbiased approximations of its Schur complements and treats the corresponding graphs as augmented views. We discuss the benefits of our approach, provide theoretical justifications and present connections with graph diffusion. Unlike previous efforts, we study the empirical effectiveness of the augmentor in a controlled fashion by varying the design choices for subsequent GCL phases, such as encoding and contrasting. Extensive experiments on node and graph classification benchmarks demonstrate that our technique consistently outperforms pre-defined and adaptive augmentation approaches to achieve state-of-the-art results. | ['Vignesh Kothapalli'] | 2023-06-06 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.19457263e-01 6.12617016e-01 -5.29092431e-01 -1.30942157e-02
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7165a35e-b559-48c2-afb6-1a52d7229f12 | an-overlapping-free-leaf-segmentation-method | 1908.04018 | null | https://arxiv.org/abs/1908.04018v1 | https://arxiv.org/pdf/1908.04018v1.pdf | An overlapping-free leaf segmentation method for plant point clouds | Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering. | ['Siyuan Yan', 'Yang Chen', 'Yan Cao', 'Sifan Wang', 'Guoliang Shi', 'Xin Cai', 'Dawei Li'] | 2019-08-12 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [-3.10642682e-02 -2.03839079e-01 2.27706656e-02 1.09390847e-01
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400429be-1cd5-4456-a849-4ffe2be65dda | dynamic-context-guided-capsule-network-for | 2009.02016 | null | https://arxiv.org/abs/2009.02016v1 | https://arxiv.org/pdf/2009.02016v1.pdf | Dynamic Context-guided Capsule Network for Multimodal Machine Translation | Multimodal machine translation (MMT), which mainly focuses on enhancing text-only translation with visual features, has attracted considerable attention from both computer vision and natural language processing communities. Most current MMT models resort to attention mechanism, global context modeling or multimodal joint representation learning to utilize visual features. However, the attention mechanism lacks sufficient semantic interactions between modalities while the other two provide fixed visual context, which is unsuitable for modeling the observed variability when generating translation. To address the above issues, in this paper, we propose a novel Dynamic Context-guided Capsule Network (DCCN) for MMT. Specifically, at each timestep of decoding, we first employ the conventional source-target attention to produce a timestep-specific source-side context vector. Next, DCCN takes this vector as input and uses it to guide the iterative extraction of related visual features via a context-guided dynamic routing mechanism. Particularly, we represent the input image with global and regional visual features, we introduce two parallel DCCNs to model multimodal context vectors with visual features at different granularities. Finally, we obtain two multimodal context vectors, which are fused and incorporated into the decoder for the prediction of the target word. Experimental results on the Multi30K dataset of English-to-German and English-to-French translation demonstrate the superiority of DCCN. Our code is available on https://github.com/DeepLearnXMU/MM-DCCN. | ['Jiebo Luo', 'Jie zhou', 'Yubin Ge', 'Yongjing Yin', 'Jinsong Su', 'Fandong Meng', 'Zhengyuan Yang', 'Huan Lin'] | 2020-09-04 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.42548794e-01 -5.50829351e-01 -3.17311734e-01 -2.75402993e-01
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69761244-41a3-4e20-b712-5070796c3178 | correlative-preference-transfer-with | 2211.11191 | null | https://arxiv.org/abs/2211.11191v4 | https://arxiv.org/pdf/2211.11191v4.pdf | Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation | Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains. The former fails to leverage users' cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users' domain-specific preferences. In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. $\mathsf{H^3Trans}$ incorporates two hyperedge-based modules, namely dynamic item transfer (Hyper-I) and adaptive user aggregation (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users' scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections to improve user representations. Experiments on both public and production datasets verify the superiority of $\mathsf{H^3Trans}$ for MDR. | ['Liang Wang', 'Weimin Zhang', 'Bo Zheng', 'Shaoguo Liu', 'Penghui Wei', 'Zixuan Xu'] | 2022-11-21 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.23043917e-01 -1.42660871e-01 -5.42637944e-01 -5.68646491e-01
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9e5efa49-8c81-408d-8989-112530ef9f7d | egocentric-scene-context-for-human-centric | 2207.11365 | null | https://arxiv.org/abs/2207.11365v2 | https://arxiv.org/pdf/2207.11365v2.pdf | EgoEnv: Human-centric environment representations from egocentric video | First-person video highlights a camera-wearer's activities in the context of their persistent environment. However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and capture only what is immediately visible. We present an approach that links egocentric video and the environment by learning representations that are predictive of the camera-wearer's (potentially unseen) local surroundings to facilitate human-centric environment understanding. We train such models using videos from agents in simulated 3D environments where the environment is fully observable, and test them on human-captured real-world videos from unseen environments. On two human-centric video tasks, we show that state-of-the-art video models equipped with our environment-aware features consistently outperform their counterparts with traditional clip features. Moreover, despite being trained exclusively on simulated videos, our approach successfully handles real-world videos from HouseTours and Ego4D. Project page: https://vision.cs.utexas.edu/projects/ego-env/ | ['Kristen Grauman', 'James Hillis', 'Ruta Desai', 'Santhosh Kumar Ramakrishnan', 'Tushar Nagarajan'] | 2022-07-22 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [-2.55755633e-02 -2.68927306e-01 -4.23534252e-02 -1.13601297e-01
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cf293d68-71f1-4788-9c49-0014b1bbabb8 | drug-drug-interaction-extraction-from | 1701.08303 | null | http://arxiv.org/abs/1701.08303v2 | http://arxiv.org/pdf/1701.08303v2.pdf | Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network | Simultaneous administration of multiple drugs can have synergistic or
antagonistic effects as one drug can affect activities of other drugs.
Synergistic effects lead to improved therapeutic outcomes, whereas,
antagonistic effects can be life-threatening, may lead to increased healthcare
cost, or may even cause death. Thus identification of unknown drug-drug
interaction (DDI) is an important concern for efficient and effective
healthcare. Although multiple resources for DDI exist, they are often unable to
keep pace with rich amount of information available in fast growing biomedical
texts. Most existing methods model DDI extraction from text as a classification
problem and mainly rely on handcrafted features. Some of these features further
depend on domain specific tools. Recently neural network models using latent
features have been shown to give similar or better performance than the other
existing models dependent on handcrafted features. In this paper, we present
three models namely, {\it B-LSTM}, {\it AB-LSTM} and {\it Joint AB-LSTM} based
on long short-term memory (LSTM) network. All three models utilize word and
position embedding as latent features and thus do not rely on explicit feature
engineering. Further use of bidirectional long short-term memory (Bi-LSTM)
networks allow implicit feature extraction from the whole sentence. The two
models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the
output of Bi-LSTM layer to assign weights to features. Our experimental results
on the SemEval-2013 DDI extraction dataset show that the {\it Joint AB-LSTM}
model outperforms all the existing methods, including those relying on
handcrafted features. The other two proposed LSTM models also perform
competitively with state-of-the-art methods. | ['Sunil Kumar Sahu', 'Ashish Anand'] | 2017-01-28 | null | null | null | null | ['medical-relation-extraction', 'drug-drug-interaction-extraction'] | ['medical', 'natural-language-processing'] | [ 2.90771425e-01 -2.12255865e-01 -6.25366688e-01 -7.24799857e-02
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f4f4ac5e-ad84-4812-b907-e0e0f66f05f4 | illuminant-chromaticity-estimation-from | 1906.05526 | null | https://arxiv.org/abs/1906.05526v1 | https://arxiv.org/pdf/1906.05526v1.pdf | Illuminant Chromaticity Estimation from Interreflections | Reliable estimation of illuminant chromaticity is crucial for simulating color constancy and for white balancing digital images. However, estimating illuminant chromaticity from a single image is an ill-posed task, in general, and existing solutions typically employ a variety of assumptions and heuristics. In this paper, we present a new, physically-based, approach for estimating illuminant chromaticity from interreflections of light between diffuse surfaces. Our approach assumes that all of the direct illumination in the scene has the same chromaticity, and that at least two areas where interreflections between Lambertian surfaces occur may be detected in the image. No further assumptions or restrictions on the illuminant chromaticty or the shading in the scene are necessary. Our approach is based on representing interreflections as lines in a special 2D color space, and the chromaticity of the illuminant is estimated from the approximate intersection between two or more such lines. Experimental results are reported on a dataset of illumination and surface reflectance spectra, as well as on real images we captured. The results indicate that our approach can yield state-of-the-art results when the interreflections are significant enough to be captured by the camera. | ['Eytan Lifshitz', 'Dani Lischinski'] | 2019-06-13 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 6.14941657e-01 -5.75740218e-01 3.84080261e-01 -1.30891293e-01
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fe6b5786-e8b9-4c1f-aa9a-967f413ed5d1 | wave-physics-as-an-analog-recurrent-neural | 1904.12831 | null | https://arxiv.org/abs/1904.12831v2 | https://arxiv.org/pdf/1904.12831v2.pdf | Wave Physics as an Analog Recurrent Neural Network | Analog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here we identify a mapping between the dynamics of wave physics, and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain. | ['Momchil Minkov', 'Shanhui Fan', 'Tyler W. Hughes', 'Ian A. D. Williamson'] | 2019-04-29 | null | null | null | null | ['vowel-classification'] | ['audio'] | [ 1.87216416e-01 -3.43180984e-01 4.30753767e-01 -9.13917366e-03
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d316ff6a-05c8-427a-9ef4-eda9298746b8 | synthesis-of-sparse-linear-arrays-via-low | 2209.04577 | null | https://arxiv.org/abs/2209.04577v1 | https://arxiv.org/pdf/2209.04577v1.pdf | Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion | In the process of realizing the synthesis of the antenna array synthesis, it is of practical significance to arrange the nonuniform array and reduce the number of elements. According to the Matrix Pencil Method (MPM), we proposed an improved nonuniform array algorithm for reducing the number of array elements. We design the Hankel matrix by low-rank completion via log-det heuristic, and impose relatively loose constraints on the pattern, which may increase the degree of freedom of the radar array, so as to achieve the purpose of antenna array synthesis and non-uniform array. | ['Xuejing Zhang'] | 2022-09-10 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.41198683e-01 -1.14791706e-01 5.78362763e-01 4.11890820e-02
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09a00120-1432-4751-90e7-e0d3a7b537ec | analysis-of-machine-learning-for-link-quality | 1812.08856 | null | https://arxiv.org/abs/1812.08856v9 | https://arxiv.org/pdf/1812.08856v9.pdf | Machine Learning for Wireless Link Quality Estimation: A Survey | Since the emergence of wireless communication networks, a plethora of research papers focus their attention on the quality aspects of wireless links. The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling link quality estimation are becoming increasingly sophisticated. A number of recent estimators leverage machine learning (ML) techniques that require a sophisticated design and development process, each of which has a great potential to significantly affect the overall model performance. In this paper, we provide a comprehensive survey on link quality estimators developed from empirical data and then focus on the subset that use ML algorithms. We analyze ML-based link quality estimation (LQE) models from two perspectives using performance data. Firstly, we focus on how they address quality requirements that are important from the perspective of the applications they serve. Secondly, we analyze how they approach the standard design steps commonly used in the ML community. Having analyzed the scientific body of the survey, we review existing open source datasets suitable for LQE research. Finally, we round up our survey with the lessons learned and design guidelines for ML-based LQE development and dataset collection. | ['Mihael Mohorčič', 'Gregor Cerar', 'Carolina Fortuna', 'Halil Yetgin'] | 2018-12-07 | null | null | null | null | ['link-quality-estimation'] | ['miscellaneous'] | [-2.50140995e-01 -2.99810860e-02 -8.70627999e-01 -3.21332306e-01
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a16cd49c-8dd7-4081-b19b-c187fb9b7e19 | a-review-of-machine-learning-applications-in-1 | 2003.00646 | null | https://arxiv.org/abs/2003.00646v2 | https://arxiv.org/pdf/2003.00646v2.pdf | A review of machine learning applications in wildfire science and management | Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods. | ['Sean C P Coogan', 'Piyush Jain', 'Mike D Flannigan', 'Mark Crowley', 'Steve Taylor', 'Sriram Ganapathi Subramanian'] | 2020-03-02 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.01102185e-01 -5.58409631e-01 -7.02278852e-01 2.18811035e-01
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32577114-2afe-4eb9-8b4d-abad63068a5e | mdldroid-a-chainsgd-reduce-approach-to-mobile | 2002.02897 | null | https://arxiv.org/abs/2002.02897v2 | https://arxiv.org/pdf/2002.02897v2.pdf | MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing | Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model inference and update. Since data collection is costly in reality, Google's Federated Learning offers not only complete data privacy but also better model robustness based on multiple user data. However, personal mobile sensing applications are mostly user-specific and highly affected by environment. As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning. In addition, deploying Federated Learning on a local server, e.g., edge server, may quickly reach the bottleneck due to resource constraint and serious failure by attacks. Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, we propose a ChainSGD-reduce approach which includes a novel chain-directed Synchronous Stochastic Gradient Descent algorithm to effectively reduce overhead among multiple devices. We also design an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Our evaluations show that our model training on off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device training, and 1.5x faster than the master-slave approach. | ['Tao Gu', 'Yu Zhang', 'Xi Zhang'] | 2020-02-07 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-8.54005292e-02 -8.92357081e-02 -5.20660818e-01 -2.96069801e-01
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fb10b42c-7e03-4d92-b9a6-1831844c1a5f | modeling-empathic-similarity-in-personal | 2305.14246 | null | https://arxiv.org/abs/2305.14246v1 | https://arxiv.org/pdf/2305.14246v1.pdf | Modeling Empathic Similarity in Personal Narratives | The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives. We introduce a new task of identifying similarity in personal stories based on empathic resonance, i.e., the extent to which two people empathize with each others' experiences, as opposed to raw semantic or lexical similarity, as has predominantly been studied in NLP. Using insights from social psychology, we craft a framework that operationalizes empathic similarity in terms of three key features of stories: main events, emotional trajectories, and overall morals or takeaways. We create EmpathicStories, a dataset of 1,500 personal stories annotated with our empathic similarity features, and 2,000 pairs of stories annotated with empathic similarity scores. Using our dataset, we fine-tune a model to compute empathic similarity of story pairs, and show that this outperforms semantic similarity models on automated correlation and retrieval metrics. Through a user study with 150 participants, we also assess the effect our model has on retrieving stories that users empathize with, compared to naive semantic similarity-based retrieval, and find that participants empathized significantly more with stories retrieved by our model. Our work has strong implications for the use of empathy-aware models to foster human connection and empathy between people. | ['Cynthia Breazeal', 'Hae Won Park', 'Pedro Colon-Hernandez', 'Maarten Sap', 'Jocelyn Shen'] | 2023-05-23 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-5.81532657e-01 2.47495294e-01 -1.30499646e-01 -2.96582818e-01
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cf56750f-9084-4d39-9ecf-54f19aa9538a | a-policy-gradient-framework-for-stochastic | 2302.05816 | null | https://arxiv.org/abs/2302.05816v2 | https://arxiv.org/pdf/2302.05816v2.pdf | A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee | We consider policy gradient methods for stochastic optimal control problem in continuous time. In particular, we analyze the gradient flow for the control, viewed as a continuous time limit of the policy gradient method. We prove the global convergence of the gradient flow and establish a convergence rate under some regularity assumptions. The main novelty in the analysis is the notion of local optimal control function, which is introduced to characterize the local optimality of the iterate. | ['Jianfeng Lu', 'Mo Zhou'] | 2023-02-11 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.53215903e-01 1.88418955e-01 -5.07384479e-01 2.69676894e-01
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38ee4299-fad3-42cf-b9ac-ba35169e51a1 | model-agnostic-counterfactual-reasoning-for | 2010.15363 | null | https://arxiv.org/abs/2010.15363v2 | https://arxiv.org/pdf/2010.15363v2.pdf | Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System | The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. However, the normal training paradigm, i.e., fitting a recommender model to recover the user behavior data with pointwise or pairwise loss, makes the model biased towards popular items. This results in the terrible Matthew effect, making popular items be more frequently recommended and become even more popular. Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items. Although theoretically sound, IPW methods are highly sensitive to the weighting strategy, which is notoriously difficult to tune. In this work, we explore the popularity bias issue from a novel and fundamental perspective -- cause-effect. We identify that popularity bias lies in the direct effect from the item node to the ranking score, such that an item's intrinsic property is the cause of mistakenly assigning it a higher ranking score. To eliminate popularity bias, it is essential to answer the counterfactual question that what the ranking score would be if the model only uses item property. To this end, we formulate a causal graph to describe the important cause-effect relations in the recommendation process. During training, we perform multi-task learning to achieve the contribution of each cause; during testing, we perform counterfactual inference to remove the effect of item popularity. Remarkably, our solution amends the learning process of recommendation which is agnostic to a wide range of models -- it can be easily implemented in existing methods. We demonstrate it on Matrix Factorization (MF) and LightGCN [20]. Experiments on five real-world datasets demonstrate the effectiveness of our method. | ['Xiangnan He', 'JinFeng Yi', 'Ziwei Wu', 'Jiawei Chen', 'Fuli Feng', 'Tianxin Wei'] | 2020-10-29 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 1.71636860e-03 8.26437026e-02 -6.60597444e-01 -1.97413921e-01
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02ba376e-bfd7-470c-b53e-4bdad32f112c | an-empirical-analysis-of-formality-in-online | null | null | https://aclanthology.org/Q16-1005 | https://aclanthology.org/Q16-1005.pdf | An Empirical Analysis of Formality in Online Communication | This paper presents an empirical study of linguistic formality. We perform an analysis of humans{'} perceptions of formality in four different genres. These findings are used to develop a statistical model for predicting formality, which is evaluated under different feature settings and genres. We apply our model to an investigation of formality in online discussion forums, and present findings consistent with theories of formality and linguistic coordination. | ['Ellie Pavlick', 'Joel Tetreault'] | 2016-01-01 | null | null | null | tacl-2016-1 | ['automatic-writing'] | ['natural-language-processing'] | [-1.71066299e-01 4.24131513e-01 -2.58156747e-01 -4.08327550e-01
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bd36f7b8-e04b-40e3-9878-2ae85625ea3a | eye-tracked-virtual-reality-a-comprehensive | 2305.14080 | null | https://arxiv.org/abs/2305.14080v1 | https://arxiv.org/pdf/2305.14080v1.pdf | Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges | Latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despite these possibilities, eye-tracking data also reveal privacy-sensitive attributes of users when it is combined with the information about the presented stimulus. To address these possibilities and potential privacy issues, in this survey, we first cover major works in eye tracking, VR, and privacy areas between the years 2012 and 2022. While eye tracking in the VR part covers the complete pipeline of eye-tracking methodology from pupil detection and gaze estimation to offline use and analyses, as for privacy and security, we focus on eye-based authentication as well as computational methods to preserve the privacy of individuals and their eye-tracking data in VR. Later, taking all into consideration, we draw three main directions for the research community by mainly focusing on privacy challenges. In summary, this survey provides an extensive literature review of the utmost possibilities with eye tracking in VR and the privacy implications of those possibilities. | ['Enkelejda Kasneci', 'Eakta Jain', 'Kevin Butler', 'Hong Gao', 'Brendan David-John', 'Mengdi Wang', 'Süleyman Özdel', 'Efe Bozkir'] | 2023-05-23 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.69250622e-01 8.82942751e-02 -1.76554639e-03 -2.45215043e-01
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fb3f8ddb-0c6d-4201-9c47-e96e472476b4 | ethicist-targeted-training-data-extraction | 2307.04401 | null | https://arxiv.org/abs/2307.04401v1 | https://arxiv.org/pdf/2307.04401v1.pdf | Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation | Large pre-trained language models achieve impressive results across many tasks. However, recent works point out that pre-trained language models may memorize a considerable fraction of their training data, leading to the privacy risk of information leakage. In this paper, we propose a method named Ethicist for targeted training data extraction through loss smoothed soft prompting and calibrated confidence estimation, investigating how to recover the suffix in the training data when given a prefix. To elicit memorization in the attacked model, we tune soft prompt embeddings while keeping the model fixed. We further propose a smoothing loss that smooths the loss distribution of the suffix tokens to make it easier to sample the correct suffix. In order to select the most probable suffix from a collection of sampled suffixes and estimate the prediction confidence, we propose a calibrated confidence estimation method, which normalizes the confidence of the generated suffixes with a local estimation. We show that Ethicist significantly improves the extraction performance on a recently proposed public benchmark. We also investigate several factors influencing the data extraction performance, including decoding strategy, model scale, prefix length, and suffix length. Our code is available at https://github.com/thu-coai/Targeted-Data-Extraction. | ['Minlie Huang', 'Jiaxin Wen', 'Zhexin Zhang'] | 2023-07-10 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.75961429e-01 9.79542267e-03 -5.75793207e-01 -4.12646800e-01
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548d5970-2f41-46d4-9bf8-40fe5d3cb1f6 | fortunately-discourse-markers-can-enhance | 2201.02026 | null | https://arxiv.org/abs/2201.02026v2 | https://arxiv.org/pdf/2201.02026v2.pdf | Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis | In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks. However, in many cases, these models do not perform well when labeled data is scarce and the model is expected to perform in the zero or few shot setting. Recently, several works have shown that continual pretraining or performing a second phase of pretraining (inter-training) which is better aligned with the downstream task, can lead to improved results, especially in the scarce data setting. Here, we propose to leverage sentiment-carrying discourse markers to generate large-scale weakly-labeled data, which in turn can be used to adapt language models for sentiment analysis. Extensive experimental results show the value of our approach on various benchmark datasets, including the finance domain. Code, models and data are available at https://github.com/ibm/tslm-discourse-markers. | ['Noam Slonim', 'Ranit Aharonov', 'Lena Dankin', 'Artem Spector', 'Ilya Shnayderman', 'Liat Ein-Dor'] | 2022-01-06 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 1.67184293e-01 1.45742223e-01 -4.64082837e-01 -4.86741632e-01
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da7089e7-26b3-43a7-8357-a1ea159fdb57 | tan-ntm-topic-attention-networks-for-neural | 2012.01524 | null | https://arxiv.org/abs/2012.01524v2 | https://arxiv.org/pdf/2012.01524v2.pdf | TAN-NTM: Topic Attention Networks for Neural Topic Modeling | Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distribution. However, leveraging topic-word distribution for learning better features during document encoding has not been explored much. To this end, we develop a framework TAN-NTM, which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues. The output of topic attention module is then used to carry out variational inference. We perform extensive ablations and experiments resulting in ~9-15 percentage improvement over score of existing SOTA topic models in NPMI coherence on several benchmark datasets - 20Newsgroups, Yelp Review Polarity and AGNews. Further, we show that our method learns better latent document-topic features compared to existing topic models through improvement on two downstream tasks: document classification and topic guided keyphrase generation. | ['Balaji Krishnamurthy', 'Milan Aggarwal', 'Shashank Shailabh', 'Madhur Panwar'] | 2020-12-02 | null | https://aclanthology.org/2021.acl-long.299 | https://aclanthology.org/2021.acl-long.299.pdf | acl-2021-5 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 9.52693969e-02 3.67994010e-01 -7.35981226e-01 -5.98277926e-01
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c6e5fdb3-d3e8-4f3f-bb90-44d7d5b55d36 | a-critical-re-evaluation-of-neural-methods | null | null | https://dl.acm.org/doi/abs/10.14778/3529337.3529355 | https://www.vldb.org/pvldb/vol15/p1712-arora.pdf | A Critical Re-evaluation of Neural Methods for Entity Alignment | Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since 2017. However, surprisingly, an analysis of the differences between neural and non-neural EA methods has been lacking. We bridge this gap by performing an in-depth comparison among five carefully chosen representative state-of-the-art methods from the pre-neural and neural era. We unravel, and consequently mitigate, the inherent deficiencies in the experimental setup utilized for evaluating neural EA methods. To ensure fairness in evaluation, we homogenize the entity matching modules of neural and non-neural methods. Additionally, for the first time, we draw a parallel between EA and record linkage (RL) by empirically showcasing the ability of RL methods to perform EA. Our results indicate that Paris, the state-of-the-art non-neural method, statistically significantly outperforms all the representative state-of-the-art neural methods in terms of both efficacy and efficiency across a wide variety of dataset types and scenarios, and is second only to BERT-INT for a specific scenario of cross-lingual EA. Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks. Overall, our work results in two overarching conclusions: (1) Paris should be used as a baseline in every follow-up work on EA, and (2) neural methods need to be positioned better to showcase their true potential, for which we provide multiple recommendations. | ['Robert West', 'Alberto García-Durán', 'Akhil Arora', 'Stefano Huber', 'Manuel Leone'] | 2022-04-01 | null | null | null | pvldb-2022-4 | ['entity-resolution'] | ['natural-language-processing'] | [ 1.08611874e-01 1.20195009e-01 -3.29993278e-01 -3.91168118e-01
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d14734d9-66ce-4fde-ae7c-d1da5f053c68 | deepcapture-image-spam-detection-using-deep | 2006.08885 | null | https://arxiv.org/abs/2006.08885v1 | https://arxiv.org/pdf/2006.08885v1.pdf | DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation | Image spam emails are often used to evade text-based spam filters that detect spam emails with their frequently used keywords. In this paper, we propose a new image spam email detection tool called DeepCapture using a convolutional neural network (CNN) model. There have been many efforts to detect image spam emails, but there is a significant performance degrade against entirely new and unseen image spam emails due to overfitting during the training phase. To address this challenging issue, we mainly focus on developing a more robust model to address the overfitting problem. Our key idea is to build a CNN-XGBoost framework consisting of eight layers only with a large number of training samples using data augmentation techniques tailored towards the image spam detection task. To show the feasibility of DeepCapture, we evaluate its performance with publicly available datasets consisting of 6,000 spam and 2,313 non-spam image samples. The experimental results show that DeepCapture is capable of achieving an F1-score of 88%, which has a 6% improvement over the best existing spam detection model CNN-SVM with an F1-score of 82%. Moreover, DeepCapture outperformed existing image spam detection solutions against new and unseen image datasets. | ['Hyoungshick Kim', 'Bedeuro Kim', 'Sharif Abuadbba'] | 2020-06-16 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.29209653e-01 -4.82138246e-01 2.84474313e-01 -3.61221135e-01
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bbe33703-4a68-4ed8-957c-5acbe5d951f8 | reconnet-non-iterative-reconstruction-of | 1601.06892 | null | http://arxiv.org/abs/1601.06892v2 | http://arxiv.org/pdf/1601.06892v2.pdf | ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements | The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs an intermediate reconstruction. We call this network, ReconNet. The
intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the
final reconstructed image. On a standard dataset of images we show significant
improvements in reconstruction results (both in terms of PSNR and time
complexity) over state-of-the-art iterative CS reconstruction algorithms at
various measurement rates. Further, through qualitative experiments on real
data collected using our block single pixel camera (SPC), we show that our
network is highly robust to sensor noise and can recover visually better
quality images than competitive algorithms at extremely low sensing rates of
0.1 and 0.04. To demonstrate that our algorithm can recover semantically
informative images even at a low measurement rate of 0.01, we present a very
robust proof of concept real-time visual tracking application. | ['Suhas Lohit', 'Amit Ashok', 'Pavan Turaga', 'Ronan Kerviche', 'Kuldeep Kulkarni'] | 2016-01-26 | null | null | null | cvpr-2016 | ['real-time-visual-tracking'] | ['computer-vision'] | [ 9.35007036e-01 -1.32976443e-01 1.85582578e-01 -1.46057770e-01
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d768a83d-42b4-4144-a1cf-6d7852fc094d | stylemask-disentangling-the-style-space-of | 2209.13375 | null | https://arxiv.org/abs/2209.13375v1 | https://arxiv.org/pdf/2209.13375v1.pdf | StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment | In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e.g., facial shape, hair style, etc), even in the challenging case where the source and the target faces belong to different identities. In doing so, we address some of the limitations of the state-of-the-art works, namely, a) that they depend on paired training data (i.e., source and target faces have the same identity), b) that they rely on labeled data during inference, and c) that they do not preserve identity in large head pose changes. More specifically, we propose a framework that, using unpaired randomly generated facial images, learns to disentangle the identity characteristics of the face from its pose by incorporating the recently introduced style space $\mathcal{S}$ of StyleGAN2, a latent representation space that exhibits remarkable disentanglement properties. By capitalizing on this, we learn to successfully mix a pair of source and target style codes using supervision from a 3D model. The resulting latent code, that is subsequently used for reenactment, consists of latent units corresponding to the facial pose of the target only and of units corresponding to the identity of the source only, leading to notable improvement in the reenactment performance compared to recent state-of-the-art methods. In comparison to state of the art, we quantitatively and qualitatively show that the proposed method produces higher quality results even on extreme pose variations. Finally, we report results on real images by first embedding them on the latent space of the pretrained generator. We make the code and pretrained models publicly available at: https://github.com/StelaBou/StyleMask | ['Georgios Tzimiropoulos', 'Ioannis Patras', 'Vasileios Argyriou', 'Christos Tzelepis', 'Stella Bounareli'] | 2022-09-27 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 3.51777554e-01 2.39578754e-01 6.42588288e-02 -3.33941787e-01
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b457c285-97c3-4a5c-8e2a-6d5d63cb694f | ask-to-understand-question-generation-for | 2203.09073 | null | https://arxiv.org/abs/2203.09073v1 | https://arxiv.org/pdf/2203.09073v1.pdf | Ask to Understand: Question Generation for Multi-hop Question Answering | Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the "black-box" reasoning process to capture the potential relationship between entities and sentences, thus achieving good performance. At the same time, the latter provides a clear reasoning logical route by decomposing multi-hop questions into simple single-hop sub-questions. In this paper, we propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of our proposed QG module, human evaluation further clarifies its interpretability quantitatively, and thorough analysis shows that the QG module could generate better sub-questions than QD methods in terms of fluency, consistency, and diversity. | ['Yizhe Yang', 'Yang Gao', 'Mucheng Ren', 'Jiawei Li'] | 2022-03-17 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.57385215e-01 7.49620318e-01 4.53615457e-01 -3.30077082e-01
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aa1931a1-4c4a-4f30-9058-150492b7cd32 | few-shot-nlu-with-vector-projection-distance | 2112.04999 | null | https://arxiv.org/abs/2112.04999v1 | https://arxiv.org/pdf/2112.04999v1.pdf | Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF | Data sparsity problem is a key challenge of Natural Language Understanding (NLU), especially for a new target domain. By training an NLU model in source domains and applying the model to an arbitrary target domain directly (even without fine-tuning), few-shot NLU becomes crucial to mitigate the data scarcity issue. In this paper, we propose to improve prototypical networks with vector projection distance and abstract triangular Conditional Random Field (CRF) for the few-shot NLU. The vector projection distance exploits projections of contextual word embeddings on label vectors as word-label similarities, which is equivalent to a normalized linear model. The abstract triangular CRF learns domain-agnostic label transitions for joint intent classification and slot filling tasks. Extensive experiments demonstrate that our proposed methods can significantly surpass strong baselines. Specifically, our approach can achieve a new state-of-the-art on two few-shot NLU benchmarks (Few-Joint and SNIPS) in Chinese and English without fine-tuning on target domains. | ['Kai Yu', 'Qingliang Miao', 'Zhi Chen', 'Ruisheng Cao', 'Lu Chen', 'Su Zhu'] | 2021-12-09 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 4.68482405e-01 1.18022725e-01 -8.56756270e-01 -6.54139519e-01
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b3c9f973-3f0d-4832-9211-9b1fd6c66252 | deterministic-point-cloud-registration-via | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.pdf | Deterministic Point Cloud Registration via Novel Transformation Decomposition | Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF). Simultaneously estimating these 6 DOF is time-consuming due to high-dimensional parameter space. To solve this problem, it is common to decompose 6 DOF, i.e. independently compute 3-DOF rotation and 3-DOF translation. However, high non-linearity of 3-DOF rotation still limits the algorithm efficiency, especially when the number of correspondences is large. In contrast, we propose to decompose 6 DOF into (2+1) and (1+2) DOF. Specifically, (2+1) DOF represent 2-DOF rotation axis and 1-DOF displacement along this rotation axis. (1+2) DOF indicate 1-DOF rotation angle and 2-DOF displacement orthogonal to the above rotation axis. To compute these DOF, we design a novel two-stage strategy based on inlier set maximization. By leveraging branch and bound, we first search for (2+1) DOF, and then the remaining (1+2) DOF. Thanks to the proposed transformation decomposition and two-stage search strategy, our method is deterministic and leads to low computational complexity. We extensively compare our method with state-of-the-art approaches. Our method is more accurate and robust than the approaches that provide similar efficiency to ours. Our method is more efficient than the approaches whose accuracy and robustness are comparable to ours. | ['Yun-hui Liu', 'Qiang Nie', 'Haoang Li', 'Wen Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-registration'] | ['computer-vision'] | [-2.18612775e-01 -1.21948369e-01 -3.44265372e-01 2.37926379e-01
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503dce1d-154b-4fd4-b89a-bc853fdd5f6d | aiparsing-anchor-free-instance-level-human | 2207.06854 | null | https://arxiv.org/abs/2207.06854v1 | https://arxiv.org/pdf/2207.06854v1.pdf | AIParsing: Anchor-free Instance-level Human Parsing | Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level. To address these two issues, we have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. The anchor-free detector head inherits the pixel-like merits and effectively avoids the sensitivity of hyper-parameters as proved in object detection applications. By introducing the part-aware boundary clue, the edge-guided parsing head is capable to distinguish adjacent human parts from among each other up to 58 parts in a single human instance, even overlapping instances. Meanwhile, a refinement head integrating box-level score and part-level parsing quality is exploited to improve the quality of the parsing results. Experiments on two multiple human parsing datasets (i.e., CIHP and LV-MHP-v2.0) and one video instance-level human parsing dataset (i.e., VIP) show that our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives. | ['Jie zhou', 'Zhanjie Song', 'Guo-Jun Qi', 'Xiaochun Cao', 'Sanyi Zhang'] | 2022-07-14 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 3.67176682e-01 7.12068796e-01 -1.24841563e-01 -4.31919783e-01
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9d54954f-185b-4f16-a3e6-f3cb2a9a8942 | diverse-sample-generation-pushing-the-limit | 2109.00212 | null | https://arxiv.org/abs/2109.00212v3 | https://arxiv.org/pdf/2109.00212v3.pdf | Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization | Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach generates data utilizing batch normalization (BN) statistics of the full-precision networks to quantize the networks. However, it always faces the serious challenges of accuracy degradation in practice. We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels. This paper presents a generic Diverse Sample Generation (DSG) scheme for the generative data-free quantization, to mitigate detrimental homogenization. We first slack the statistics alignment for features in the BN layer to relax the distribution constraint. Then, we strengthen the loss impact of the specific BN layers for different samples and inhibit the correlation among samples in the generation process, to diversify samples from the statistical and spatial perspectives, respectively. Comprehensive experiments show that for large-scale image classification tasks, our DSG can consistently quantization performance on different neural architectures, especially under ultra-low bit-width. And data diversification caused by our DSG brings a general gain to various quantization-aware training and post-training quantization approaches, demonstrating its generality and effectiveness. | ['Jiakai Wang', 'Jiwen Lu', 'Xianglong Liu', 'Xiangguo Zhang', 'Yifu Ding', 'Haotong Qin'] | 2021-09-01 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.24081242e-01 -3.14922661e-01 -1.97637945e-01 -5.39438307e-01
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98ba119b-a7b9-40d5-8a9e-017553e098b1 | learning-the-dynamics-of-physical-systems-1 | 2203.08852 | null | https://arxiv.org/abs/2203.08852v1 | https://arxiv.org/pdf/2203.08852v1.pdf | Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks | We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we derive a continuous-time model for the dynamics of the data via the finite element method. The resulting graph neural network estimates the instantaneous effects of the unknown dynamics on each cell in a meshing of the spatial domain. Our model can incorporate prior knowledge via assumptions on the form of the unknown PDE, which induce a structural bias towards learning specific processes. Through this mechanism, we derive a transport variant of our model from the convection equation and show that it improves the transfer performance to higher-resolution meshes on sea surface temperature and gas flow forecasting against baseline models representing a selection of spatio-temporal forecasting methods. A qualitative analysis shows that our model disentangles the data dynamics into their constituent parts, which makes it uniquely interpretable. | ['Stephan Günnemann', 'Marten Lienen'] | 2022-03-16 | learning-the-dynamics-of-physical-systems | https://openreview.net/forum?id=HFmAukZ-k-2 | https://openreview.net/pdf?id=HFmAukZ-k-2 | iclr-2022-4 | ['interpretability-techniques-for-deep-learning', 'spatio-temporal-forecasting'] | ['miscellaneous', 'time-series'] | [ 2.84228344e-02 1.77365229e-01 4.77181040e-02 2.14072186e-02
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ff31e5a5-ed22-4a47-827d-791e8b9e0907 | improving-energy-management-of-hybrid | 2302.13157 | null | https://arxiv.org/abs/2302.13157v1 | https://arxiv.org/pdf/2302.13157v1.pdf | Improving Energy Management of Hybrid Electric Vehicles by Considering Battery Electric-Thermal Model | This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate. | ['Arash Mousaei'] | 2023-02-25 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.80126143e-01 -2.79557735e-01 -3.18442225e-01 7.34413341e-02
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13338419-13c6-4e8e-95d3-0dcf8b71350d | gotta-catch-em-all-using-concealed-trapdoors | 1904.08554 | null | https://arxiv.org/abs/1904.08554v6 | https://arxiv.org/pdf/1904.08554v6.pdf | Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks | Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new "honeypot" approach to protect DNN models. We intentionally inject trapdoors, honeypot weaknesses in the classification manifold that attract attackers searching for adversarial examples. Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space. Our defense then identifies attacks by comparing neuron activation signatures of inputs to those of trapdoors. In this paper, we introduce trapdoors and describe an implementation of a trapdoor-enabled defense. First, we analytically prove that trapdoors shape the computation of adversarial attacks so that attack inputs will have feature representations very similar to those of trapdoors. Second, we experimentally show that trapdoor-protected models can detect, with high accuracy, adversarial examples generated by state-of-the-art attacks (PGD, optimization-based CW, Elastic Net, BPDA), with negligible impact on normal classification. These results generalize across classification domains, including image, facial, and traffic-sign recognition. We also present significant results measuring trapdoors' robustness against customized adaptive attacks (countermeasures). | ['Hai-Tao Zheng', 'Emily Wenger', 'Bo Li', 'Ben Y. Zhao', 'Bolun Wang', 'Shawn Shan'] | 2019-04-18 | null | null | null | null | ['traffic-sign-recognition', 'adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'computer-vision', 'knowledge-base'] | [ 2.77553737e-01 2.91662682e-02 7.91134834e-02 -7.63825476e-02
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80c45b11-b230-452d-b8a2-deb2c3fd6ca0 | taxocom-topic-taxonomy-completion-with | 2201.06771 | null | https://arxiv.org/abs/2201.06771v2 | https://arxiv.org/pdf/2201.06771v2.pdf | TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters | Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with each other: (i) locally discriminative embedding optimizes the text embedding space to be discriminative among known (i.e., given) sub-topics, and (ii) novelty adaptive clustering assigns terms into either one of the known sub-topics or novel sub-topics. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage but also outperforms all other baselines for a downstream task. | ['Hwanjo Yu', 'Jiawei Han', 'Susik Yoon', 'SeongKu Kang', 'Jiaming Shen', 'Dongha Lee'] | 2022-01-18 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [-5.45483120e-02 1.86408296e-01 -4.51148897e-01 -4.10029739e-01
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d0a78022-efdf-4ca4-aa64-a0591db20bf1 | pre-training-multi-modal-dense-retrievers-for | 2306.16478 | null | https://arxiv.org/abs/2306.16478v1 | https://arxiv.org/pdf/2306.16478v1.pdf | Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering | This paper studies a category of visual question answering tasks, in which accessing external knowledge is necessary for answering the questions. This category is called outside-knowledge visual question answering (OK-VQA). A major step in developing OK-VQA systems is to retrieve relevant documents for the given multi-modal query. Current state-of-the-art asymmetric dense retrieval model for this task uses an architecture with a multi-modal query encoder and a uni-modal document encoder. Such an architecture requires a large amount of training data for effective performance. We propose an automatic data generation pipeline for pre-training passage retrieval models for OK-VQA tasks. The proposed approach leads to 26.9% Precision@5 improvements compared to the current state-of-the-art asymmetric architecture. Additionally, the proposed pre-training approach exhibits a good ability in zero-shot retrieval scenarios. | ['Hamed Zamani', 'Mahta Rafiee', 'Alireza Salemi'] | 2023-06-28 | null | null | null | null | ['visual-question-answering-1', 'retrieval', 'question-answering', 'passage-retrieval'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.81186527e-01 -6.01704232e-02 -7.80982804e-03 -1.46466702e-01
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0dd93087-d04e-47ad-9698-757a4d243e64 | kimera-multi-robust-distributed-dense-metric | 2106.14386 | null | https://arxiv.org/abs/2106.14386v2 | https://arxiv.org/pdf/2106.14386v2.pdf | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems | This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping, and (iii) builds a globally consistent metric-semantic 3D mesh model of the environment in real-time, where faces of the mesh are annotated with semantic labels. Kimera-Multi is implemented by a team of robots equipped with visual-inertial sensors. Each robot builds a local trajectory estimate and a local mesh using Kimera. When communication is available, robots initiate a distributed place recognition and robust pose graph optimization protocol based on a novel distributed graduated non-convexity algorithm. The proposed protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures while being robust to outliers. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots. Both real and simulated experiments involve long trajectories (e.g., up to 800 meters per robot). The experiments show that Kimera-Multi (i) outperforms the state of the art in terms of robustness and accuracy, (ii) achieves estimation errors comparable to a centralized SLAM system while being fully distributed, (iii) is parsimonious in terms of communication bandwidth, (iv) produces accurate metric-semantic 3D meshes, and (v) is modular and can be also used for standard 3D reconstruction (i.e., without semantic labels) or for trajectory estimation (i.e., without reconstructing a 3D mesh). | ['Luca Carlone', 'Jonathan P. How', 'Carlos Nieto-Granda', 'Fernando Herrera Arias', 'Yun Chang', 'Yulun Tian'] | 2021-06-28 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-2.72775084e-01 1.12044379e-01 2.38762289e-01 -4.02936079e-02
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6380fcc8-d386-4258-87e0-81356bbc361f | effective-convolutional-attention-network-for | null | null | https://aclanthology.org/2021.emnlp-main.481 | https://aclanthology.org/2021.emnlp-main.481.pdf | Effective Convolutional Attention Network for Multi-label Clinical Document Classification | Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. Our innovations are three-fold: (1) we utilize a deep convolution-based encoder with the squeeze-and-excitation networks and residual networks to aggregate the information across the document and learn meaningful document representations that cover different ranges of texts; (2) we explore multi-layer and sum-pooling attention to extract the most informative features from these multi-scale representations; (3) we combine binary cross entropy loss and focal loss to improve performance for rare labels. We focus our evaluation study on MIMIC-III, a widely used dataset in the medical domain. Our models outperform prior work on medical coding and achieve new state-of-the-art results on multiple metrics. We also demonstrate the language independent nature of our approach by applying it to two non-English datasets. Our model outperforms prior best model and a multilingual Transformer model by a substantial margin. | ['Thomas Schaaf', 'Matthew R. Gormley', 'Russell Klopfer', 'Hua Cheng', 'Yang Liu'] | null | null | null | null | emnlp-2021-11 | ['medical-code-prediction'] | ['medical'] | [ 5.44090390e-01 1.60633802e-01 -2.98096091e-01 -6.20051444e-01
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837c60c9-97e6-4915-8680-7823b84180fb | energy-efficient-task-adaptation-for-nlp-edge | 2303.16100 | null | https://arxiv.org/abs/2303.16100v2 | https://arxiv.org/pdf/2303.16100v2.pdf | Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures | Executing machine learning inference tasks on resource-constrained edge devices requires careful hardware-software co-design optimizations. Recent examples have shown how transformer-based deep neural network models such as ALBERT can be used to enable the execution of natural language processing (NLP) inference on mobile systems-on-chip housing custom hardware accelerators. However, while these existing solutions are effective in alleviating the latency, energy, and area costs of running single NLP tasks, achieving multi-task inference requires running computations over multiple variants of the model parameters, which are tailored to each of the targeted tasks. This approach leads to either prohibitive on-chip memory requirements or paying the cost of off-chip memory access. This paper proposes adapter-ALBERT, an efficient model optimization for maximal data reuse across different tasks. The proposed model's performance and robustness to data compression methods are evaluated across several language tasks from the GLUE benchmark. Additionally, we demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator to extrapolate performance, power, and area improvements over the execution of a traditional ALBERT model on the same hardware platform. | ['Marco Donato', 'Aleksandre Avaliani', 'Zirui Fu'] | 2023-03-25 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.76275516e-01 2.30575904e-01 -3.87066096e-01 -4.29035395e-01
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b43ef6cb-2e7f-48f9-9339-3a0c847c8987 | learning-garment-densepose-for-robust-warping | 2303.17688 | null | https://arxiv.org/abs/2303.17688v1 | https://arxiv.org/pdf/2303.17688v1.pdf | Learning Garment DensePose for Robust Warping in Virtual Try-On | Virtual try-on, i.e making people virtually try new garments, is an active research area in computer vision with great commercial applications. Current virtual try-on methods usually work in a two-stage pipeline. First, the garment image is warped on the person's pose using a flow estimation network. Then in the second stage, the warped garment is fused with the person image to render a new try-on image. Unfortunately, such methods are heavily dependent on the quality of the garment warping which often fails when dealing with hard poses (e.g., a person lifting or crossing arms). In this work, we propose a robust warping method for virtual try-on based on a learned garment DensePose which has a direct correspondence with the person's DensePose. Due to the lack of annotated data, we show how to leverage an off-the-shelf person DensePose model and a pretrained flow model to learn the garment DensePose in a weakly supervised manner. The garment DensePose allows a robust warping to any person's pose without any additional computation. Our method achieves the state-of-the-art equivalent on virtual try-on benchmarks and shows warping robustness on in-the-wild person images with hard poses, making it more suited for real-world virtual try-on applications. | ['Antoine Toisoul', 'Tao Xiang', 'Sen He', 'Aiyu Cui'] | 2023-03-30 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 8.58020596e-03 3.43720466e-02 -9.44284722e-02 -1.45810530e-01
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a3fa8d50-54a3-4eea-b2a8-99cdca921ce6 | spatio-temporal-relation-learning-for-video | 2209.13116 | null | https://arxiv.org/abs/2209.13116v1 | https://arxiv.org/pdf/2209.13116v1.pdf | Spatio-Temporal Relation Learning for Video Anomaly Detection | Anomaly identification is highly dependent on the relationship between the object and the scene, as different/same object actions in same/different scenes may lead to various degrees of normality and anomaly. Therefore, object-scene relation actually plays a crucial role in anomaly detection but is inadequately explored in previous works. In this paper, we propose a Spatial-Temporal Relation Learning (STRL) framework to tackle the video anomaly detection task. First, considering dynamic characteristics of the objects as well as scene areas, we construct a Spatio-Temporal Auto-Encoder (STAE) to jointly exploit spatial and temporal evolution patterns for representation learning. For better pattern extraction, two decoding branches are designed in the STAE module, i.e. an appearance branch capturing spatial cues by directly predicting the next frame, and a motion branch focusing on modeling the dynamics via optical flow prediction. Then, to well concretize the object-scene relation, a Relation Learning (RL) module is devised to analyze and summarize the normal relations by introducing the Knowledge Graph Embedding methodology. Specifically in this process, the plausibility of object-scene relation is measured by jointly modeling object/scene features and optimizable object-scene relation maps. Extensive experiments are conducted on three public datasets, and the superior performance over the state-of-the-art methods demonstrates the effectiveness of our method. | ['Jian Yang', 'Biao Wang', 'Zhen Cui', 'Hui Lv'] | 2022-09-27 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [ 0.06569105 -0.38036066 0.0948521 -0.20471267 0.19425543 -0.05244778
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b5929123-8ae5-41e7-8c06-7baec30d0bc3 | abpn-adaptive-blend-pyramid-network-for-real | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.pdf | ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo | Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid network, which aims to achieve fast local retouching on ultra high-resolution photos. The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL). The LRL is designed to implement local retouching on low-resolution images, giving full consideration of the global context and local texture information, and the BPL is then developed to progressively expand the low-resolution results to the higher ones, with the help of the proposed adaptive blend module and refining module. Our method outperforms the existing methods by a large margin on two local photo retouching tasks and exhibits excellent performance in terms of running speed, achieving real-time inference on 4K images with a single NVIDIA Tesla P100 GPU. Moreover, we introduce the first high-definition cloth retouching dataset CRHD-3K to promote the research on local photo retouching. The dataset is available at https://github.com/youngLBW/CRHD-3K. | ['Di Huang', 'Xuansong Xie', 'Miaomiao Cui', 'Hongyu Yang', 'Xiefan Guo', 'Biwen Lei'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['photo-retouching'] | ['computer-vision'] | [ 2.15427756e-01 -3.92309636e-01 -1.82211041e-01 3.47737968e-02
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84b4db99-8d02-4c0f-bbb3-074ee34b58ba | glimpse-clouds-human-activity-recognition | 1802.07898 | null | http://arxiv.org/abs/1802.07898v4 | http://arxiv.org/pdf/1802.07898v4.pdf | Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points | We propose a method for human activity recognition from RGB data that does
not rely on any pose information during test time and does not explicitly
calculate pose information internally. Instead, a visual attention module
learns to predict glimpse sequences in each frame. These glimpses correspond to
interest points in the scene that are relevant to the classified activities. No
spatial coherence is forced on the glimpse locations, which gives the module
liberty to explore different points at each frame and better optimize the
process of scrutinizing visual information. Tracking and sequentially
integrating this kind of unstructured data is a challenge, which we address by
separating the set of glimpses from a set of recurrent tracking/recognition
workers. These workers receive glimpses, jointly performing subsequent motion
tracking and activity prediction. The glimpses are soft-assigned to the
workers, optimizing coherence of the assignments in space, time and feature
space using an external memory module. No hard decisions are taken, i.e. each
glimpse point is assigned to all existing workers, albeit with different
importance. Our methods outperform state-of-the-art methods on the largest
human activity recognition dataset available to-date; NTU RGB+D Dataset, and on
a smaller human action recognition dataset Northwestern-UCLA Multiview Action
3D Dataset. Our code is publicly available at
https://github.com/fabienbaradel/glimpse_clouds. | ['Christian Wolf', 'Graham W. Taylor', 'Julien Mille', 'Fabien Baradel'] | 2018-02-22 | glimpse-clouds-human-activity-recognition-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Baradel_Glimpse_Clouds_Human_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Baradel_Glimpse_Clouds_Human_CVPR_2018_paper.pdf | cvpr-2018-6 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 1.23342246e-01 -2.82203376e-01 -2.64998555e-01 -6.47392124e-02
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7476ec08-40e8-4882-9cf1-21dfa0822eaf | nodis-neural-ordinary-differential-scene | 2001.04735 | null | https://arxiv.org/abs/2001.04735v3 | https://arxiv.org/pdf/2001.04735v3.pdf | NODIS: Neural Ordinary Differential Scene Understanding | Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as Mixed-Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. It achieves state-of-the-art results on all three benchmark tasks: scene graph generation (SGGen), classification (SGCls) and visual relationship detection (PredCls) on Visual Genome benchmark. | ['Cong Yuren', 'Wentong Liao', 'Michael Ying Yang', 'Hanno Ackermann', 'Bodo Rosenhahn'] | 2020-01-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3577_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650630.pdf | eccv-2020-8 | ['visual-relationship-detection'] | ['computer-vision'] | [ 7.29247212e-01 5.09646475e-01 -6.72166571e-02 -4.91414130e-01
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48a6a6e3-586b-47ef-a295-4c5ab4b124e1 | natural-evolution-strategy-for-mixed-integer | 2304.10724 | null | https://arxiv.org/abs/2304.10724v1 | https://arxiv.org/pdf/2304.10724v1.pdf | Natural Evolution Strategy for Mixed-Integer Black-Box Optimization | This paper proposes a natural evolution strategy (NES) for mixed-integer black-box optimization (MI-BBO) that appears in real-world problems such as hyperparameter optimization of machine learning and materials design. This problem is difficult to optimize because plateaus where the values do not change appear when the integer variables are relaxed to the continuous ones. CMA-ES w. Margin that addresses the plateaus reportedly showed good performance on MI-BBO benchmark problems. However, it has been observed that the search performance of CMA-ES w. Margin deteriorates when continuous variables contribute more to the objective function value than integer ones. In order to address the problem of CMA-ES w. Margin, we propose Distance-weighted eXponential Natural Evolution Strategy taking account of Implicit Constraint and Integer (DX-NES-ICI). We compare the search performance of DX-NES-ICI with that of CMA-ES w. Margin through numerical experiments. As a result, DX-NES-ICI was up to 3.7 times better than CMA-ES w. Margin in terms of a rate of finding the optimal solutions on benchmark problems where continuous variables contribute more to the objective function value than integer ones. DX-NES-ICI also outperformed CMA-ES w. Margin on problems where CMA-ES w. Margin originally showed good performance. | ['Isao Ono', 'Koki Ikeda'] | 2023-04-21 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 5.87147996e-02 1.50066763e-01 -2.05516070e-01 3.11858237e-01
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8ec2937c-a694-4b4a-8124-984acea3bad6 | squib-effects-of-cognitive-effort-on-the | null | null | https://aclanthology.org/J17-2006 | https://aclanthology.org/J17-2006.pdf | Squib: Effects of Cognitive Effort on the Resolution of Overspecified Descriptions | Studies in referring expression generation (REG) have shown different effects of referential overspecification on the resolution of certain descriptions. To further investigate effects of this kind, this article reports two eye-tracking experiments that measure the time required to recognize target objects based on different kinds of information. Results suggest that referential overspecification may be either helpful or detrimental to identification depending on the kind of information that is actually overspecified, an insight that may be useful for the design of more informed hearer-oriented REG algorithms. | ["Fl{\\'a}vio Luiz Coutinho", "Matheus Mendes de Sant{'}Ana", "r{\\'e}", 'Iv Paraboni', 'Alex Gwo Jen Lan'] | 2017-06-01 | null | null | null | cl-2017-6 | ['referring-expression-generation'] | ['computer-vision'] | [ 6.10275716e-02 1.68061957e-01 -3.71437743e-02 -6.72676086e-01
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92409433-5716-46c4-914d-855d75be3363 | latent-alignment-and-variational-attention | 1807.03756 | null | http://arxiv.org/abs/1807.03756v2 | http://arxiv.org/pdf/1807.03756v2.pdf | Latent Alignment and Variational Attention | Neural attention has become central to many state-of-the-art models in
natural language processing and related domains. Attention networks are an
easy-to-train and effective method for softly simulating alignment; however,
the approach does not marginalize over latent alignments in a probabilistic
sense. This property makes it difficult to compare attention to other alignment
approaches, to compose it with probabilistic models, and to perform posterior
inference conditioned on observed data. A related latent approach, hard
attention, fixes these issues, but is generally harder to train and less
accurate. This work considers variational attention networks, alternatives to
soft and hard attention for learning latent variable alignment models, with
tighter approximation bounds based on amortized variational inference. We
further propose methods for reducing the variance of gradients to make these
approaches computationally feasible. Experiments show that for machine
translation and visual question answering, inefficient exact latent variable
models outperform standard neural attention, but these gains go away when using
hard attention based training. On the other hand, variational attention retains
most of the performance gain but with training speed comparable to neural
attention. | ['Yoon Kim', 'Justin Chiu', 'Alexander M. Rush', 'Yuntian Deng', 'Demi Guo'] | 2018-07-10 | latent-alignment-and-variational-attention-1 | http://papers.nips.cc/paper/8179-latent-alignment-and-variational-attention | http://papers.nips.cc/paper/8179-latent-alignment-and-variational-attention.pdf | neurips-2018-12 | ['hard-attention'] | ['methodology'] | [ 1.95771664e-01 5.17492533e-01 -3.54834855e-01 -3.37379336e-01
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eff20b9c-f7cc-422a-b20a-c022a5706cdc | online-cyber-attack-detection-in-smart-grid-a | 1809.05258 | null | http://arxiv.org/abs/1809.05258v1 | http://arxiv.org/pdf/1809.05258v1.pdf | Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach | Early detection of cyber-attacks is crucial for a safe and reliable operation
of the smart grid. In the literature, outlier detection schemes making
sample-by-sample decisions and online detection schemes requiring perfect
attack models have been proposed. In this paper, we formulate the online
attack/anomaly detection problem as a partially observable Markov decision
process (POMDP) problem and propose a universal robust online detection
algorithm using the framework of model-free reinforcement learning (RL) for
POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based
algorithm in timely and accurate detection of cyber-attacks targeting the smart
grid. | ['Xiaodong Wang', 'Oyetunji Ogundijo', 'Mehmet Necip Kurt', 'Chong Li'] | 2018-09-14 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-2.40395755e-01 6.30498379e-02 -1.69752259e-02 -1.97630785e-02
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bd45d911-1c2f-45f7-9a97-5ac477aedb8a | inversemv-composing-piano-scores-with-a | 2112.15320 | null | https://arxiv.org/abs/2112.15320v1 | https://arxiv.org/pdf/2112.15320v1.pdf | InverseMV: Composing Piano Scores with a Convolutional Video-Music Transformer | Many social media users prefer consuming content in the form of videos rather than text. However, in order for content creators to produce videos with a high click-through rate, much editing is needed to match the footage to the music. This posts additional challenges for more amateur video makers. Therefore, we propose a novel attention-based model VMT (Video-Music Transformer) that automatically generates piano scores from video frames. Using music generated from models also prevent potential copyright infringements that often come with using existing music. To the best of our knowledge, there is no work besides the proposed VMT that aims to compose music for video. Additionally, there lacks a dataset with aligned video and symbolic music. We release a new dataset composed of over 7 hours of piano scores with fine alignment between pop music videos and MIDI files. We conduct experiments with human evaluation on VMT, SeqSeq model (our baseline), and the original piano version soundtrack. VMT achieves consistent improvements over the baseline on music smoothness and video relevance. In particular, with the relevance scores and our case study, our model has shown the capability of multimodality on frame-level actors' movement for music generation. Our VMT model, along with the new dataset, presents a promising research direction toward composing the matching soundtrack for videos. We have released our code at https://github.com/linchintung/VMT | ['Mu Yang', 'Chin-Tung Lin'] | 2021-12-31 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.43775839e-01 -2.66617507e-01 -2.08546117e-01 1.12990819e-01
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da7a4f68-2720-449e-87c4-2a40742ea273 | unmasking-deepfakes-masked-autoencoding | 2306.06881 | null | https://arxiv.org/abs/2306.06881v1 | https://arxiv.org/pdf/2306.06881v1.pdf | Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection | We present a novel approach for the detection of deepfake videos using a pair of vision transformers pre-trained by a self-supervised masked autoencoding setup. Our method consists of two distinct components, one of which focuses on learning spatial information from individual RGB frames of the video, while the other learns temporal consistency information from optical flow fields generated from consecutive frames. Unlike most approaches where pre-training is performed on a generic large corpus of images, we show that by pre-training on smaller face-related datasets, namely Celeb-A (for the spatial learning component) and YouTube Faces (for the temporal learning component), strong results can be obtained. We perform various experiments to evaluate the performance of our method on commonly used datasets namely FaceForensics++ (Low Quality and High Quality, along with a new highly compressed version named Very Low Quality) and Celeb-DFv2 datasets. Our experiments show that our method sets a new state-of-the-art on FaceForensics++ (LQ, HQ, and VLQ), and obtains competitive results on Celeb-DFv2. Moreover, our method outperforms other methods in the area in a cross-dataset setup where we fine-tune our model on FaceForensics++ and test on CelebDFv2, pointing to its strong cross-dataset generalization ability. | ['Ali Etemad', 'Will Hickie', 'Levent Özparlak', 'Mojtaba Kolahdouzi', 'Sayantan Das'] | 2023-06-12 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-1.04160786e-01 -3.13765019e-01 -9.69732702e-02 -3.98682892e-01
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e435ca42-af2e-4d5e-86d6-1978c728e8ca | semi-supervised-acoustic-modelling-for-five | 2004.06480 | null | https://arxiv.org/abs/2004.06480v1 | https://arxiv.org/pdf/2004.06480v1.pdf | Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech | This paper considers the impact of automatic segmentation on the fully-automatic, semi-supervised training of automatic speech recognition (ASR) systems for five-lingual code-switched (CS) speech. Four automatic segmentation techniques were evaluated in terms of the recognition performance of an ASR system trained on the resulting segments in a semi-supervised manner. The system's output was compared with the recognition rates achieved by a semi-supervised system trained on manually assigned segments. Three of the automatic techniques use a newly proposed convolutional neural network (CNN) model for framewise classification, and include a novel form of HMM smoothing of the CNN outputs. Automatic segmentation was applied in combination with automatic speaker diarization. The best-performing segmentation technique was also tested without speaker diarization. An evaluation based on 248 unsegmented soap opera episodes indicated that voice activity detection (VAD) based on a CNN followed by Gaussian mixture modelhidden Markov model smoothing (CNN-GMM-HMM) yields the best ASR performance. The semi-supervised system trained with the resulting segments achieved an overall WER improvement of 1.1% absolute over the system trained with manually created segments. Furthermore, we found that system performance improved even further when the automatic segmentation was used in conjunction with speaker diarization. | ['E. van der Westhuizen', 'E. Yılmaz', 'T. R. Niesler', 'A. Biswas', 'F. de Wet', 'N. Wilkinson'] | 2020-04-08 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.86735231e-01 3.30103487e-01 1.22578219e-02 -5.62109947e-01
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-6.40579820e-01 8.77611697e-01 1.09906040e-01 -6.69188559e-01
-1.51227975e+00 2.28596061e-01 5.70969641e-01 3.38913232e-01
-2.21596122e-01 9.10990953e-01 -2.89359570e-01 -4.24215019e-01
7.03971982e-01 -5.47106385e-01 -2.06524983e-01 -2.96352170e-02
4.25411195e-01 4.34802681e-01 5.74303091e-01 -1.20652223e+00
-1.36998296e-01 4.61519770e-02 -1.72421232e-01 -6.88006103e-01
8.76837552e-01 -2.04156250e-01 2.72446901e-01 9.09183621e-01
1.29425442e+00 1.29641583e-02 -9.03483450e-01 -1.34369537e-01
7.49351531e-02 -3.83515470e-02 4.64724869e-01 -7.52699018e-01
-7.85102785e-01 1.12375665e+00 7.59157836e-01 5.19538701e-01
7.44072020e-01 -2.23065853e-01 7.03710318e-01 3.52637172e-01
-2.50855405e-02 -1.47723484e+00 -3.64448756e-01 6.72079742e-01
8.49145412e-01 -1.16266203e+00 -6.93269014e-01 -3.35105240e-01
-7.93552876e-01 1.43697631e+00 4.10144359e-01 1.30568102e-01
8.72420192e-01 2.84690671e-02 4.85743731e-01 1.29653171e-01
-3.38861942e-01 -3.63856614e-01 2.19988346e-01 6.88540936e-01
6.14249587e-01 3.42212766e-01 -1.08808406e-01 2.57108659e-01
-3.78586173e-01 -3.87939602e-01 5.94947994e-01 9.88603771e-01
-7.56544948e-01 -7.21135974e-01 -6.96406543e-01 2.59544849e-01
-4.80359495e-01 -1.27357796e-01 -5.03937125e-01 6.00037336e-01
-4.04518209e-02 1.46646678e+00 3.29103470e-01 -4.09629196e-01
2.90808111e-01 7.40390718e-01 -9.72501040e-02 -9.81950939e-01
-7.65984058e-01 7.91614413e-01 4.32783842e-01 -1.89572290e-01
-4.04029548e-01 -1.03628635e+00 -1.29147243e+00 1.32640913e-01
-6.83311880e-01 3.67650390e-01 1.38530958e+00 1.13402283e+00
-5.64844459e-02 5.17101467e-01 7.39844382e-01 -8.98623466e-01
-3.17521065e-01 -1.42475152e+00 -5.78675032e-01 -1.54395938e-01
5.45004785e-01 -2.05607519e-01 -8.82793128e-01 5.70572555e-01] | [14.478891372680664, 6.5719075202941895] |
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