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67ec7117-efc2-49f2-8d8b-9f82cf22428c | structured-set-matching-networks-for-one-shot | 1712.01867 | null | http://arxiv.org/abs/1712.01867v2 | http://arxiv.org/pdf/1712.01867v2.pdf | Structured Set Matching Networks for One-Shot Part Labeling | Diagrams often depict complex phenomena and serve as a good test bed for
visual and textual reasoning. However, understanding diagrams using natural
image understanding approaches requires large training datasets of diagrams,
which are very hard to obtain. Instead, this can be addressed as a matching
problem either between labeled diagrams, images or both. This problem is very
challenging since the absence of significant color and texture renders local
cues ambiguous and requires global reasoning. We consider the problem of
one-shot part labeling: labeling multiple parts of an object in a target image
given only a single source image of that category. For this set-to-set matching
problem, we introduce the Structured Set Matching Network (SSMN), a structured
prediction model that incorporates convolutional neural networks. The SSMN is
trained using global normalization to maximize local match scores between
corresponding elements and a global consistency score among all matched
elements, while also enforcing a matching constraint between the two sets. The
SSMN significantly outperforms several strong baselines on three label transfer
scenarios: diagram-to-diagram, evaluated on a new diagram dataset of over 200
categories; image-to-image, evaluated on a dataset built on top of the Pascal
Part Dataset; and image-to-diagram, evaluated on transferring labels across
these datasets. | ['Aniruddha Kembhavi', 'Ali Farhadi', 'Jayant Krishnamurthy', 'Jonghyun Choi'] | 2017-12-05 | structured-set-matching-networks-for-one-shot-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Choi_Structured_Set_Matching_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_Structured_Set_Matching_CVPR_2018_paper.pdf | cvpr-2018-6 | ['set-matching'] | ['computer-vision'] | [ 6.68644249e-01 2.37753317e-01 -1.79259181e-01 -7.71082938e-01
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47c63ebf-464e-4ea1-a5c9-d95738d2b9b6 | g2l-a-global-to-local-alignment-method-for | null | null | https://www.sciencedirect.com/science/article/pii/S1877050922012170 | https://www.sciencedirect.com/science/article/pii/S1877050922012170 | G2L: A Global to Local Alignment Method for Unsupervised Domain Adaptive Semantic Segmentation | Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer knowledge from a source dataset with dense pixel-level annotations to an unlabeled target dataset. However, the performance of UDA methods often suffers from the domain shift, which is the discrepancy between the feature distributions of the two domains. There have been several attempts to match these distributions at the image level marginally. However, due to the so-called category-level domain shift, such global alignments do not guarantee a good separability of deep features extracted from different categories in the target domain. As a result, the generated pseudo-labels can be noisy and thus poison the learning process on the target domain. Some recent methods focus on denoising the pseudo-labels online using category-wise information. This paper introduces a novel UDA method called Global-to-Local alignment (G2L) that leverages fine-grained adversarial training and a newly proposed chromatic Fourier transform to address the image-level domain shift from a global perspective. Next, our method deals with the category-level domain shift under a local view. Specifically, we propose a long-tail category rating strategy as well as apply dynamic confidence thresholds and category-wise priority weights when generating and denoising the pseudo-labels to favor rare categories. Finally, self-distillation is used to boost the final segmentation results. Experiments on popular benchmarks GTA5 → Cityscapes and SYNTHIA → Cityscapes show that our method yields superior accuracy performance than other state-of-the-art methods. | ['Thi-Oanh Nguyen', 'Dinh Viet Sang', 'Kieu Dang Nam', 'Nguyen Viet Manh'] | 2022-09-07 | null | null | null | kes-2022-9 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 5.71680605e-01 3.18748131e-02 -2.33127326e-01 -5.43534577e-01
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529bb926-da95-4b9d-98d4-8987023bdbd9 | cross-functional-analysis-of-generalisation | 2305.12951 | null | https://arxiv.org/abs/2305.12951v1 | https://arxiv.org/pdf/2305.12951v1.pdf | Cross-functional Analysis of Generalisation in Behavioural Learning | In behavioural testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs. Optimising performance on the behavioural tests during training (behavioural learning) would improve coverage of phenomena not sufficiently represented in the i.i.d. data and could lead to seemingly more robust models. However, there is the risk that the model narrowly captures spurious correlations from the behavioural test suite, leading to overestimation and misrepresentation of model performance -- one of the original pitfalls of traditional evaluation. In this work, we introduce BeLUGA, an analysis method for evaluating behavioural learning considering generalisation across dimensions of different granularity levels. We optimise behaviour-specific loss functions and evaluate models on several partitions of the behavioural test suite controlled to leave out specific phenomena. An aggregate score measures generalisation to unseen functionalities (or overfitting). We use BeLUGA to examine three representative NLP tasks (sentiment analysis, paraphrase identification and reading comprehension) and compare the impact of a diverse set of regularisation and domain generalisation methods on generalisation performance. | ['Benjamin Roth', 'Pedro Henrique Luz de Araujo'] | 2023-05-22 | null | null | null | null | ['paraphrase-identification', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.81021726e-01 2.48282954e-01 1.06416769e-01 -5.89420438e-01
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62ad8d6f-e40c-4f86-ba85-bbff74bc0d05 | coda-prompt-continual-decomposed-attention | 2211.13218 | null | https://arxiv.org/abs/2211.13218v2 | https://arxiv.org/pdf/2211.13218v2.pdf | CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning | Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this continual learning problem require extensive rehearsal of previously seen data, which increases memory costs and may violate data privacy. Recently, the emergence of large-scale pre-trained vision transformer models has enabled prompting approaches as an alternative to data-rehearsal. These approaches rely on a key-query mechanism to generate prompts and have been found to be highly resistant to catastrophic forgetting in the well-established rehearsal-free continual learning setting. However, the key mechanism of these methods is not trained end-to-end with the task sequence. Our experiments show that this leads to a reduction in their plasticity, hence sacrificing new task accuracy, and inability to benefit from expanded parameter capacity. We instead propose to learn a set of prompt components which are assembled with input-conditioned weights to produce input-conditioned prompts, resulting in a novel attention-based end-to-end key-query scheme. Our experiments show that we outperform the current SOTA method DualPrompt on established benchmarks by as much as 4.5% in average final accuracy. We also outperform the state of art by as much as 4.4% accuracy on a continual learning benchmark which contains both class-incremental and domain-incremental task shifts, corresponding to many practical settings. Our code is available at https://github.com/GT-RIPL/CODA-Prompt | ['Zsolt Kira', 'Rogerio Feris', 'Rameswar Panda', 'Assaf Arbelle', 'Donghyun Kim', 'Paola Cascante-Bonilla', 'Vyshnavi Gutta', 'Leonid Karlinsky', 'James Seale Smith'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['novel-concepts'] | ['reasoning'] | [ 4.70167249e-01 3.61912930e-03 5.17049953e-02 -3.77867669e-01
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65909c96-46b5-4b35-8543-4191ebf7717a | towards-better-characterization-of-1 | null | null | https://aclanthology.org/2022.acl-long.588 | https://aclanthology.org/2022.acl-long.588.pdf | Towards Better Characterization of Paraphrases | To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better understand the commonly-used MRPC dataset and study how it differs from PAWS, another paraphrase identification dataset. We also perform a detailed study on MRPC and propose improvements to the dataset, showing that it improves generalizability of models trained on the dataset. Lastly, we apply our metrics to filter the output of a paraphrase generation model and show how it can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models. | ['De Wen Soh', 'Timothy Liu'] | null | null | null | null | acl-2022-5 | ['paraphrase-generation', 'paraphrase-identification', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 5.21799862e-01 -3.62944640e-02 -2.86716968e-01 -2.30377346e-01
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7d8a6d8e-339d-4711-925a-b80a6ade5a8f | 190501965 | 1905.01965 | null | http://arxiv.org/abs/1905.01965v1 | http://arxiv.org/pdf/1905.01965v1.pdf | Arabic Text Diacritization Using Deep Neural Networks | Diacritization of Arabic text is both an interesting and a challenging
problem at the same time with various applications ranging from speech
synthesis to helping students learning the Arabic language. Like many other
tasks or problems in Arabic language processing, the weak efforts invested into
this problem and the lack of available (open-source) resources hinder the
progress towards solving this problem. This work provides a critical review for
the currently existing systems, measures and resources for Arabic text
diacritization. Moreover, it introduces a much-needed free-for-all cleaned
dataset that can be easily used to benchmark any work on Arabic diacritization.
Extracted from the Tashkeela Corpus, the dataset consists of 55K lines
containing about 2.3M words. After constructing the dataset, existing tools and
systems are tested on it. The results of the experiments show that the neural
Shakkala system significantly outperforms traditional rule-based approaches and
other closed-source tools with a Diacritic Error Rate (DER) of 2.88% compared
with 13.78%, which the best DER for the non-neural approach (obtained by the
Mishkal tool). | ['Mahmoud Al-Ayyoub', "Bara' Al-Jawarneh", 'Ibraheem Tuffaha', 'Ali Fadel'] | 2019-04-25 | null | null | null | null | ['arabic-text-diacritization'] | ['natural-language-processing'] | [-7.05435723e-02 8.25574547e-02 7.11420998e-02 -3.26045454e-01
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85e0656d-bb90-4bff-b1f7-904d2e7d3e38 | deep-hough-transform-line-priors | 2007.09493 | null | https://arxiv.org/abs/2007.09493v1 | https://arxiv.org/pdf/2007.09493v1.pdf | Deep Hough-Transform Line Priors | Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually annotated datasets. Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features. We add line priors through a trainable Hough transform block into a deep network. Hough transform provides the prior knowledge about global line parameterizations, while the convolutional layers can learn the local gradient-like line features. On the Wireframe (ShanghaiTech) and York Urban datasets we show that adding prior knowledge improves data efficiency as line priors no longer need to be learned from data. Keywords: Hough transform; global line prior, line segment detection. | ['Silvia L. Pintea', 'Yancong Lin', 'Jan C. van Gemert'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4061_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670324.pdf | eccv-2020-8 | ['line-segment-detection'] | ['computer-vision'] | [-1.27046540e-01 1.45316407e-01 -2.89950550e-01 -5.94594419e-01
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968c10ad-7445-46d2-9363-56c4eda96f07 | revisiting-im2gps-in-the-deep-learning-era | 1705.04838 | null | http://arxiv.org/abs/1705.04838v1 | http://arxiv.org/pdf/1705.04838v1.pdf | Revisiting IM2GPS in the Deep Learning Era | Image geolocalization, inferring the geographic location of an image, is a
challenging computer vision problem with many potential applications. The
recent state-of-the-art approach to this problem is a deep image classification
approach in which the world is spatially divided into cells and a deep network
is trained to predict the correct cell for a given image. We propose to combine
this approach with the original Im2GPS approach in which a query image is
matched against a database of geotagged images and the location is inferred
from the retrieved set. We estimate the geographic location of a query image by
applying kernel density estimation to the locations of its nearest neighbors in
the reference database. Interestingly, we find that the best features for our
retrieval task are derived from networks trained with classification loss even
though we do not use a classification approach at test time. Training with
classification loss outperforms several deep feature learning methods (e.g.
Siamese networks with contrastive of triplet loss) more typical for retrieval
applications. Our simple approach achieves state-of-the-art geolocalization
accuracy while also requiring significantly less training data. | ['Nam Vo', 'James Hays', 'Nathan Jacobs'] | 2017-05-13 | revisiting-im2gps-in-the-deep-learning-era-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Vo_Revisiting_IM2GPS_in_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Vo_Revisiting_IM2GPS_in_ICCV_2017_paper.pdf | iccv-2017-10 | ['photo-geolocation-estimation'] | ['computer-vision'] | [-3.29509974e-01 -2.18985572e-01 -2.15587497e-01 -3.84185165e-01
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9a478c3b-7d4c-4397-a8b3-2d897680784a | automated-imbalanced-classification-via | 2205.02553 | null | https://arxiv.org/abs/2205.02553v2 | https://arxiv.org/pdf/2205.02553v2.pdf | Automated Imbalanced Classification via Layered Learning | In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instances of interest close to the decision boundary to drive the resampling process. However, under-sampling the majority class may potentially lead to important information loss. Over-sampling also may increase the chance of overfitting by propagating the information contained in instances from the minority class. The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations. Instead, ICLL follows a layered learning paradigm to model the data in two stages. In the first layer, ICLL learns to distinguish cases close to the decision boundary from cases which are clearly from the majority class, where this dichotomy is defined using a hierarchical clustering analysis. In the subsequent layer, we use instances close to the decision boundary and instances from the minority class to solve the original predictive task. A second contribution of our work is the automatic definition of the layers which comprise the layered learning strategy using a hierarchical clustering model. This is a relevant discovery as this process is usually performed manually according to domain knowledge. We carried out extensive experiments using 100 benchmark data sets. The results show that the proposed method leads to a better performance relatively to several state-of-the-art methods for IBC. | ['Paula Branco', 'Colin Bellinger', 'Luis Torgo', 'Vitor Cerqueira'] | 2022-05-05 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.84260088e-01 1.90615907e-01 -4.45552051e-01 -3.42896968e-01
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5232b07b-0f36-40a8-a033-c4046891d295 | on-the-trade-off-between-redundancy-and-local | 2205.10192 | null | https://arxiv.org/abs/2205.10192v1 | https://arxiv.org/pdf/2205.10192v1.pdf | On the Trade-off between Redundancy and Local Coherence in Summarization | Extractive summarization systems are known to produce poorly coherent and, if not accounted for, highly redundant text. In this work, we tackle the problem of summary redundancy in unsupervised extractive summarization of long, highly-redundant documents. For this, we leverage a psycholinguistic theory of human reading comprehension which directly models local coherence and redundancy. Implementing this theory, our system operates at the proposition level and exploits properties of human memory representations to rank similarly content units that are coherent and non-redundant, hence encouraging the extraction of less redundant final summaries. Because of the impact of the summary length on automatic measures, we control for it by formulating content selection as an optimization problem with soft constraints in the budget of information retrieved. Using summarization of scientific articles as a case study, extensive experiments demonstrate that the proposed systems extract consistently less redundant summaries across increasing levels of document redundancy, whilst maintaining comparable performance (in terms of relevancy and local coherence) against strong unsupervised baselines according to automated evaluations. | ['Shay B. Cohen', 'Matthias Galle', 'Ronald Cardenas'] | 2022-05-20 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.16051888e-01 6.27469718e-01 -1.01299666e-01 3.36096175e-02
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45d031d2-41b7-4a9e-b7c2-d3586acd9f9b | rnn-with-particle-flow-for-probabilistic | 2106.06064 | null | https://arxiv.org/abs/2106.06064v1 | https://arxiv.org/pdf/2106.06064v1.pdf | RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting | Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods. | ['Mark Coates', 'Yingxue Zhang', 'Liheng Ma', 'Soumyasundar Pal'] | 2021-06-10 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-3.66519898e-01 -4.15273994e-01 -3.79579425e-01 -4.04359579e-01
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4f7ab4a3-427f-4f03-a216-49d8ee7ac1ee | the-learnable-typewriter-a-generative | 2302.01660 | null | https://arxiv.org/abs/2302.01660v3 | https://arxiv.org/pdf/2302.01660v3.pdf | The Learnable Typewriter: A Generative Approach to Text Analysis | We present a generative document-specific approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods and in particular those that reconstruct images based on a limited amount of visual elements, called sprites. Taking as input a set of text lines with similar font or handwriting, our approach can learn a large number of different characters and leverage line-level annotations when available. Our contribution is twofold. First, we provide the first adaptation and evaluation of a deep unsupervised multi-object segmentation approach for text line analysis. Since these methods have mainly been evaluated on synthetic data in a completely unsupervised setting, demonstrating that they can be adapted and quantitatively evaluated on real images of text and that they can be trained using weak supervision are significant progresses. Second, we show the potential of our method for new applications, more specifically in the field of paleography, which studies the history and variations of handwriting, and for cipher analysis. We demonstrate our approach on three very different datasets: a printed volume of the Google1000 dataset, the Copiale cipher and historical handwritten charters from the 12th and early 13th century. | ['Mathieu Aubry', 'Tom Monnier', 'Julien Gaubil', 'Nicolas Gonthier', 'Ioannis Siglidis'] | 2023-02-03 | null | null | null | null | ['unsupervised-text-recognition'] | ['computer-vision'] | [ 4.74599212e-01 -1.18819259e-01 9.73693952e-02 -9.95548293e-02
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e92ca7d3-a151-4340-8a3a-408ceb57c3e2 | babynet-reconstructing-3d-faces-of-babies | 2203.05908 | null | https://arxiv.org/abs/2203.05908v1 | https://arxiv.org/pdf/2203.05908v1.pdf | BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs | We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies differs substantially from that of adults, baby-specific facial reconstruction systems are needed. BabyNet consists of two stages: 1) a 3D graph convolutional autoencoder learns a latent space of the baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D latent space based on representative features extracted using transfer learning. In this way, using the pre-trained 3D decoder, we can recover a 3D face from 2D images. We evaluate BabyNet and show that 1) methods based on adult datasets cannot model the 3D facial geometry of babies, which proves the need for a baby-specific method, and 2) BabyNet outperforms classical model-fitting methods even when a baby-specific 3D morphable model, such as BabyFM, is used. | ['Federico M. Sukno', 'Gemma Piella', 'Marius George Linguraru', 'Antonio R. Porras', 'Araceli Morales'] | 2022-03-11 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.03052959e-01 7.64080107e-01 2.89019883e-01 -6.77711248e-01
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fa513282-f86b-452c-bf5b-aebc06957571 | commonsense-knowledge-graph-completion-via | 2305.17019 | null | https://arxiv.org/abs/2305.17019v1 | https://arxiv.org/pdf/2305.17019v1.pdf | Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering | The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and node redundancy, which challenges CSKG representation and completion. On the one hand, edge sparsity limits the performance of graph representation learning; On the other hand, node redundancy makes different nodes corresponding to the same concept have inconsistent relations with other nodes. To address the two problems, we propose a new CSKG completion framework based on Contrastive Pretraining and Node Clustering (CPNC). Contrastive Pretraining constructs positive and negative head-tail node pairs on CSKG and utilizes contrastive learning to obtain better semantic node representation. Node Clustering aggregates nodes with the same concept into a latent concept, assisting the task of CSKG completion. We evaluate our CPNC approach on two CSKG completion benchmarks (CN-100K and ATOMIC), where CPNC outperforms the state-of-the-art methods. Extensive experiments demonstrate that both Contrastive Pretraining and Node Clustering can significantly improve the performance of CSKG completion. The source code of CPNC is publicly available on \url{https://github.com/NUSTM/CPNC}. | ['Rui Xia', 'Xiangqing Shen', 'Siwei Wu'] | 2023-05-26 | null | null | null | null | ['knowledge-graph-completion', 'graph-representation-learning'] | ['knowledge-base', 'methodology'] | [ 6.52916655e-02 3.91524822e-01 -4.87571299e-01 -1.93771183e-01
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39439767-f07d-4dc2-ac86-baccca322d1c | comic-an-unsupervised-change-detection-method | 2304.00721 | null | https://arxiv.org/abs/2304.00721v1 | https://arxiv.org/pdf/2304.00721v1.pdf | COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks | In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods. | ['Pramod K. Varshney', 'Xueqian Wang', 'Zhuoyue Wang', 'Gang Li', 'Chengxi Li'] | 2023-04-03 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.19665861e-01 -3.79020721e-01 2.87168473e-01 2.42378954e-02
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f9febb84-434c-4668-8881-960ae701624e | beam-search-for-learning-a-deep-convolutional | 1612.04774 | null | http://arxiv.org/abs/1612.04774v1 | http://arxiv.org/pdf/1612.04774v1.pdf | Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes | This paper addresses 3D shape recognition. Recent work typically represents a
3D shape as a set of binary variables corresponding to 3D voxels of a uniform
3D grid centered on the shape, and resorts to deep convolutional neural
networks(CNNs) for modeling these binary variables. Robust learning of such
CNNs is currently limited by the small datasets of 3D shapes available, an
order of magnitude smaller than other common datasets in computer vision.
Related work typically deals with the small training datasets using a number of
ad hoc, hand-tuning strategies. To address this issue, we formulate CNN
learning as a beam search aimed at identifying an optimal CNN architecture,
namely, the number of layers, nodes, and their connectivity in the network, as
well as estimating parameters of such an optimal CNN. Each state of the beam
search corresponds to a candidate CNN. Two types of actions are defined to add
new convolutional filters or new convolutional layers to a parent CNN, and thus
transition to children states. The utility function of each action is
efficiently computed by transferring parameter values of the parent CNN to its
children, thereby enabling an efficient beam search. Our experimental
evaluation on the 3D ModelNet dataset demonstrates that our model pursuit using
the beam search yields a CNN with superior performance on 3D shape
classification than the state of the art. | ['Xu Xu', 'Sinisa Todorovic'] | 2016-12-14 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-4.45293747e-02 1.12683877e-01 -2.94578671e-01 -2.39162028e-01
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03ac9c5c-9642-49bf-a4a5-81c720c71bb4 | knowledge-based-recurrent-attentive-neural | 1803.05263 | null | http://arxiv.org/abs/1803.05263v4 | http://arxiv.org/pdf/1803.05263v4.pdf | Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network | At present, the performance of deep neural network in general object
detection is comparable to or even surpasses that of human beings. However, due
to the limitations of deep learning itself, the small proportion of feature
pixels, and the occurence of blur and occlusion, the detection of small objects
in complex scenes is still an open question. But we can not deny that real-time
and accurate object detection is fundamental to automatic perception and
subsequent perception-based decision-making and planning tasks of autonomous
driving.
Considering the characteristics of small objects in autonomous driving scene,
we proposed a novel method named KB-RANN, which based on domain knowledge,
intuitive experience and feature attentive selection. It can focus on
particular parts of image features, and then it tries to stress the importance
of these features and strengthenes the learning parameters of them. Our
comparative experiments on KITTI and COCO datasets show that our proposed
method can achieve considerable results both in speed and accuracy, and can
improve the effect of small object detection through self-selection of
important features and continuous enhancement of proposed method, and deployed
it in our self-developed autonomous driving car. | ['Shitao Chen', 'Zhiqiang Jian', 'Nanning Zheng', 'Kai Yi'] | 2018-03-13 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.10757738e-02 -2.64804393e-01 7.01503158e-02 -4.05712336e-01
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c0b9c29f-7e28-4f5a-89c6-6470c9b33767 | st-2-small-data-text-style-transfer-via-multi | 2004.11742 | null | https://arxiv.org/abs/2004.11742v1 | https://arxiv.org/pdf/2004.11742v1.pdf | ST$^2$: Small-data Text Style Transfer via Multi-task Meta-Learning | Text style transfer aims to paraphrase a sentence in one style into another style while preserving content. Due to lack of parallel training data, state-of-art methods are unsupervised and rely on large datasets that share content. Furthermore, existing methods have been applied on very limited categories of styles such as positive/negative and formal/informal. In this work, we develop a meta-learning framework to transfer between any kind of text styles, including personal writing styles that are more fine-grained, share less content and have much smaller training data. While state-of-art models fail in the few-shot style transfer task, our framework effectively utilizes information from other styles to improve both language fluency and style transfer accuracy. | ['Xiwen Chen', 'Kenny Q. Zhu'] | 2020-04-24 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 5.82199812e-01 -1.56434268e-01 -3.78296673e-01 -7.19785094e-01
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f5236f06-f5d9-4b03-8558-3e0263fccc93 | buffer-balancing-accuracy-efficiency-and | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.pdf | BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration | An ideal point cloud registration framework should have superior accuracy, acceptable efficiency, and strong generalizability. However, this is highly challenging since existing registration techniques are either not accurate enough, far from efficient, or generalized poorly. It remains an open question that how to achieve a satisfying balance between this three key elements. In this paper, we propose BUFFER, a point cloud registration method for balancing accuracy, efficiency, and generalizability. The key to our approach is to take advantage of both point-wise and patch-wise techniques, while overcoming the inherent drawbacks simultaneously. Different from a simple combination of existing methods, each component of our network has been carefully crafted to tackle specific issues. Specifically, a Point-wise Learner is first introduced to enhance computational efficiency by predicting keypoints and improving the representation capacity of features by estimating point orientations, a Patch-wise Embedder which leverages a lightweight local feature learner is then deployed to extract efficient and general patch features. Additionally, an Inliers Generator which combines simple neural layers and general features is presented to search inlier correspondences. Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization. In particular, our method not only reaches the highest success rate on unseen domains, but also is almost 30 times faster than the strong baselines specializing in generalization. Code is available at https://github.com/aosheng1996/BUFFER. | ['Yulan Guo', 'Kai Xu', 'Hanyun Wang', 'Qingyong Hu', 'Sheng Ao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-registration', 'open-question'] | ['computer-vision', 'natural-language-processing'] | [-2.33803928e-01 -1.89021096e-01 -1.51294857e-01 -2.58226633e-01
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89ae5744-dbed-43f9-bef7-4ff38b3380b3 | codereviewer-pre-training-for-automating-code | 2203.09095 | null | https://arxiv.org/abs/2203.09095v2 | https://arxiv.org/pdf/2203.09095v2.pdf | Automating Code Review Activities by Large-Scale Pre-training | Code review is an essential part to software development lifecycle since it aims at guaranteeing the quality of codes. Modern code review activities necessitate developers viewing, understanding and even running the programs to assess logic, functionality, latency, style and other factors. It turns out that developers have to spend far too much time reviewing the code of their peers. Accordingly, it is in significant demand to automate the code review process. In this research, we focus on utilizing pre-training techniques for the tasks in the code review scenario. We collect a large-scale dataset of real-world code changes and code reviews from open-source projects in nine of the most popular programming languages. To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review scenario. To evaluate our model, we focus on three key tasks related to code review activities, including code change quality estimation, review comment generation and code refinement. Furthermore, we establish a high-quality benchmark dataset based on our collected data for these three tasks and conduct comprehensive experiments on it. The experimental results demonstrate that our model outperforms the previous state-of-the-art pre-training approaches in all tasks. Further analysis show that our proposed pre-training tasks and the multilingual pre-training dataset benefit the model on the understanding of code changes and reviews. | ['Neel Sundaresan', 'Shengyu Fu', 'Alexey Svyatkovskiy', 'Jared Green', 'Deep Majumder', 'Grant Jenks', 'Shailesh Jannu', 'Nan Duan', 'Daya Guo', 'Shuai Lu', 'Zhiyu Li'] | 2022-03-17 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 1.03735521e-01 -2.61900663e-01 -3.86780471e-01 -5.49138784e-01
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4f5f2946-2af1-49bf-96d0-8bd83547ebff | som-semantic-obviousness-metric-for-image | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_SOM_Semantic_Obviousness_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_SOM_Semantic_Obviousness_2015_CVPR_paper.pdf | SOM: Semantic Obviousness Metric for Image Quality Assessment | Image quality assessment (IQA) tries to estimate human perception based image visual quality in an objective manner. Existing approaches target this problem with or without reference images. For no-reference image quality assessment, there is no given reference image or any knowledge of the distortion type of the image. Previous approaches measure the image quality from signal level rather than semantic analysis. They typically depend on various features to represent local characteristic of an image. In this paper we propose a new no-reference (NR) image quality assessment (IQA) framework based on semantic obviousness. We discover that semantic-level factors affect human perception of image quality. With such observation, we explore semantic obviousness as a metric to perceive objects of an image. We propose to extract two types of features, one to measure the semantic obviousness of the image and the other to discover local characteristic. Then the two kinds of features are combined for image quality estimation. The principles proposed in our approach can also be incorporated with many existing IQA algorithms to boost their performance. We evaluate our approach on the LIVE dataset. Our approach is demonstrated to be superior to the existing NR-IQA algorithms and comparable to the state-of-the-art full-reference IQA (FR-IQA) methods. Cross-dataset experiments show the generalization ability of our approach. | ['Houqiang Li', 'Wengang Zhou', 'Lei Wu', 'Peng Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['image-quality-estimation', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.51178259e-01 -4.52055424e-01 2.32749097e-02 -5.23902416e-01
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55e72a2c-0679-4ef2-9946-98620f77199e | decoding-the-underlying-meaning-of-multimodal | 2305.17678 | null | https://arxiv.org/abs/2305.17678v2 | https://arxiv.org/pdf/2305.17678v2.pdf | Decoding the Underlying Meaning of Multimodal Hateful Memes | Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme dataset annotated with the underlying hateful contextual reasons. We also define a new conditional generation task that aims to automatically generate underlying reasons to explain hateful memes and establish the baseline performance of state-of-the-art pre-trained language models on this task. We further demonstrate the usefulness of HatReD by analyzing the challenges of the new conditional generation task in explaining memes in seen and unseen domains. The dataset and benchmark models are made available here: https://github.com/Social-AI-Studio/HatRed | ['Roy Ka-Wei Lee', 'Wen-Haw Chong', 'Ming Shan Hee'] | 2023-05-28 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [ 6.70780092e-02 5.83179116e-01 -2.17552707e-01 -1.93599403e-01
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46554a10-9aa3-4001-922c-1b0cb78fa948 | adapting-end-to-end-neural-speaker | 1811.03055 | null | http://arxiv.org/abs/1811.03055v1 | http://arxiv.org/pdf/1811.03055v1.pdf | Adapting End-to-End Neural Speaker Verification to New Languages and Recording Conditions with Adversarial Training | In this article we propose a novel approach for adapting speaker embeddings
to new domains based on adversarial training of neural networks. We apply our
embeddings to the task of text-independent speaker verification, a challenging,
real-world problem in biometric security. We further the development of
end-to-end speaker embedding models by combing a novel 1-dimensional,
self-attentive residual network, an angular margin loss function and
adversarial training strategy. Our model is able to learn extremely compact,
64-dimensional speaker embeddings that deliver competitive performance on a
number of popular datasets using simple cosine distance scoring. One the
NIST-SRE 2016 task we are able to beat a strong i-vector baseline, while on the
Speakers in the Wild task our model was able to outperform both i-vector and
x-vector baselines, showing an absolute improvement of 2.19% over the latter.
Additionally, we show that the integration of adversarial training consistently
leads to a significant improvement over an unadapted model. | ['Patrick Kenny', 'Gautam Bhattacharya', 'Jahangir Alam'] | 2018-11-07 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 3.99483442e-01 1.48363158e-01 1.65179372e-01 -6.10366464e-01
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95658bb7-e618-49e1-9d46-31a838334125 | ta-da-topic-aware-domain-adaptation-for | 2301.06902 | null | https://arxiv.org/abs/2301.06902v1 | https://arxiv.org/pdf/2301.06902v1.pdf | TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract) | Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score. | ['Florin Pop', 'Mihai Dascalu', 'Dumitru-Clementin Cercel', 'Andrei-Marius Avram', 'George-Eduard Zaharia', 'Răzvan-Alexandru Smădu'] | 2022-12-30 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 3.20465595e-01 -1.42363757e-01 -4.97302532e-01 2.32120976e-01
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56a44dbf-7a65-49f3-8c74-7ce5ef6f61ca | pansharpening-prisma-data-for-marine-plastic | null | null | https://ieeexplore.ieee.org/abstract/document/9406795 | https://ieeexplore.ieee.org/abstract/document/9406795 | Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes | Hyperspectral PRISMA images are new and have not yet been evaluated for their ability to detect marine plastic litter. The hyperspectral PRISMA images have a fine spectral resolution, however, their spatial resolution is not high enough to enable the discrimination of small plastic objects in the ocean. Pansharpening with the panchromatic data enhances their spatial resolution and makes their detection capabilities a technological challenge. This study exploits, for the first time, the potential of using satellite hyperspectral data in detecting small-sized marine plastic litter. Controlled experiments with plastic targets of various sizes constructed from several materials have been conducted. The required pre-processing steps have been defined and 13 pansharpening methods have been applied and evaluated for their ability to spectrally discriminate plastics from water. Among them, the PCA-based substitution efficiently separates plastic spectra from water without producing duplicate edges, or pixelation. Plastic targets with size equivalent to 8% of the original hyperspectral image pixel coverage are easily detected. The same targets can also be observed in the panchromatic image, however, they cannot be detected solely by the panchromatic information as they are confused with other appearances. Exploiting spectra derived from the pan-sharpened hyperspectral images, an index combining methodology has been developed, which enables the detection of plastic objects. Although spectra of plastic materials present similarities with water spectra, some spectral characteristics can be utilized for producing marine plastic litter indexes. Based on these indexes, the index combining methodology has successfully detected the plastic targets and differentiated them from other materials. | ['Paolo Corradi', 'Enrico Barbone', 'Giulio Ceriola', 'Antonello Aiello', 'Nicolò Taggio', 'Pol Kolokoussis', 'Konstantinos Topouzelis', 'Vassilia Karathanassi', 'Viktoria Kristollari', 'Maria Kremezi'] | 2021-04-19 | null | null | null | ieee-access-2021-4 | ['pansharpening'] | ['computer-vision'] | [ 8.29016447e-01 -3.77953947e-01 3.03484470e-01 3.23788971e-01
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4c70ef07-0b85-414a-ab3b-ad2f364c9f43 | grasping-field-learning-implicit | 2008.04451 | null | https://arxiv.org/abs/2008.04451v3 | https://arxiv.org/pdf/2008.04451v3.pdf | Grasping Field: Learning Implicit Representations for Human Grasps | Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. | ['Korrawe Karunratanakul', 'Michael Black', 'Yan Zhang', 'Jinlong Yang', 'Siyu Tang', 'Krikamol Muandet'] | 2020-08-10 | null | null | null | null | ['3d-object-reconstruction', 'grasp-generation'] | ['computer-vision', 'computer-vision'] | [-5.08795083e-02 2.30714560e-01 -2.31711958e-02 -1.36251062e-01
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cbbfdebf-bf23-4cfa-bb1d-6ca9e64beaa7 | simple-question-answering-by-attentive | 1606.03391 | null | http://arxiv.org/abs/1606.03391v2 | http://arxiv.org/pdf/1606.03391v2.pdf | Simple Question Answering by Attentive Convolutional Neural Network | This work focuses on answering single-relation factoid questions over
Freebase. Each question can acquire the answer from a single fact of form
(subject, predicate, object) in Freebase. This task, simple question answering
(SimpleQA), can be addressed via a two-step pipeline: entity linking and fact
selection. In fact selection, we match the subject entity in a fact candidate
with the entity mention in the question by a character-level convolutional
neural network (char-CNN), and match the predicate in that fact with the
question by a word-level CNN (word-CNN). This work makes two main
contributions. (i) A simple and effective entity linker over Freebase is
proposed. Our entity linker outperforms the state-of-the-art entity linker over
SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so
that the predicate representation can be matched with the predicate-focused
question representation more effectively. Experiments show that our system sets
new state-of-the-art in this task. | ['Bo-Wen Zhou', 'Hinrich Schütze', 'Bing Xiang', 'Mo Yu', 'Wenpeng Yin'] | 2016-06-10 | simple-question-answering-by-attentive-2 | https://aclanthology.org/C16-1164 | https://aclanthology.org/C16-1164.pdf | coling-2016-12 | ['fact-selection'] | ['natural-language-processing'] | [-2.18841836e-01 9.07842100e-01 -4.25342888e-01 -5.26003003e-01
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fdd79637-58cb-4c79-b6e1-082f09f74f15 | when-high-performing-models-behave-poorly-in | null | null | https://openreview.net/forum?id=9kBDWEmA6i | https://openreview.net/pdf?id=9kBDWEmA6i | When high-performing models behave poorly in practice: periodic sampling can help | Training a deep neural network (DNN) for breast cancer detection from medical images suffers from the (hopefully) low prevalence of the pathology.
For a sensible amount of positive cases, images must be collected from numerous places resulting in large heterogeneous datasets with different acquisition devices, populations, cancer incidences.
Without precaution, this heterogeneity may result in a DNN biased by latent variables a priori independent of the pathology.
This may be dramatic if this DNN is used inside a software to help radiologists to detect cancers.
This work mitigates this issue by acting on how mini-batches for Stochastic Gradient Descent (SGD) algorithms are constructed.
The dataset is divided into homogeneous subsets sharing some attributes (\textit{e.g.} acquisition device, source) called Data Segments (DSs).
Batches are built by sampling each DS periodically with a frequency proportional to the rarest label in the DS and by simultaneously preserving an overall balance between positive and negative labels within the batch.
Periodic sampling is compared to balanced sampling (equal amount of labels within a batch, independently of DS) and to balanced sampling within DS (equal amount of labels within a batch and each DS).
We show, on breast cancer prediction from mammography images of various devices and origins, that periodic sampling leads to better generalization than other sampling strategies. | ['Pierre Fillard', 'Paul Wambergue', 'Yaroslav Nikulin', 'Luis Montero', 'Julien GUILLAUMIN', 'Stanislas Chambon'] | 2021-09-29 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.71789366e-01 3.43309164e-01 -5.43909490e-01 -6.40969574e-01
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b7efefad-b88f-4c0b-a75e-c80df65b551e | low-complexity-approximate-convolutional | 2208.00087 | null | https://arxiv.org/abs/2208.00087v1 | https://arxiv.org/pdf/2208.00087v1.pdf | Low-complexity Approximate Convolutional Neural Networks | In this paper, we present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and parameters (pooling and bias coefficients; and activation function) with efficient approximations capable of extreme reductions in computational complexity. Low-complexity convolution filters are obtained through a binary (zero-one) linear programming scheme based on the Frobenius norm over sets of dyadic rationals. The resulting matrices allow for multiplication-free computations requiring only addition and bit-shifting operations. Such low-complexity structures pave the way for low-power, efficient hardware designs. We applied our approach on three use cases of different complexity: (i) a "light" but efficient ConvNet for face detection (with around 1000 parameters); (ii) another one for hand-written digit classification (with more than 180000 parameters); and (iii) a significantly larger ConvNet: AlexNet with $\approx$1.2 million matrices. We evaluated the overall performance on the respective tasks for different levels of approximations. In all considered applications, very low-complexity approximations have been derived maintaining an almost equal classification performance. | ['A. Leite', 'C. Garcia', 'S. Duffner', 'R. J. Cintra'] | 2022-07-29 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 2.01893285e-01 1.91975862e-01 3.46195102e-01 -4.95052546e-01
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265074d6-3754-4d6e-ace3-2ced1fd6cb81 | biomedicalclinical-nlp | null | null | https://aclanthology.org/C14-3001 | https://aclanthology.org/C14-3001.pdf | Biomedical/Clinical NLP | null | ['Meliha Yeti{\\c{s}}gen', 'Ozlem Uzuner', 'Amber Stubbs'] | 2014-08-01 | biomedicalclinical-nlp-1 | https://aclanthology.org/C14-3001 | https://aclanthology.org/C14-3001.pdf | coling-2014-8 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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605212a0-42b7-4ecd-94d3-18e17b056521 | spatial-aware-token-for-weakly-supervised | 2303.10438 | null | https://arxiv.org/abs/2303.10438v1 | https://arxiv.org/pdf/2303.10438v1.pdf | Spatial-Aware Token for Weakly Supervised Object Localization | Weakly supervised object localization (WSOL) is a challenging task aiming to localize objects with only image-level supervision. Recent works apply visual transformer to WSOL and achieve significant success by exploiting the long-range feature dependency in self-attention mechanism. However, existing transformer-based methods synthesize the classification feature maps as the localization map, which leads to optimization conflicts between classification and localization tasks. To address this problem, we propose to learn a task-specific spatial-aware token (SAT) to condition localization in a weakly supervised manner. Specifically, a spatial token is first introduced in the input space to aggregate representations for localization task. Then a spatial aware attention module is constructed, which allows spatial token to generate foreground probabilities of different patches by querying and to extract localization knowledge from the classification task. Besides, for the problem of sparse and unbalanced pixel-level supervision obtained from the image-level label, two spatial constraints, including batch area loss and normalization loss, are designed to compensate and enhance this supervision. Experiments show that the proposed SAT achieves state-of-the-art performance on both CUB-200 and ImageNet, with 98.45% and 73.13% GT-known Loc, respectively. Even under the extreme setting of using only 1 image per class from ImageNet for training, SAT already exceeds the SOTA method by 2.1% GT-known Loc. Code and models are available at https://github.com/wpy1999/SAT. | ['Zheng-Jun Zha', 'Jiebo Luo', 'Yang Cao', 'Wei Zhai', 'Pingyu Wu'] | 2023-03-18 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 2.74610817e-01 3.49818431e-02 -3.63203138e-01 -5.21417856e-01
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0aec0683-8f27-4036-88b3-dbb0268fb88a | dictionary-attacks-on-speaker-verification | 2204.11304 | null | https://arxiv.org/abs/2204.11304v2 | https://arxiv.org/pdf/2204.11304v2.pdf | Dictionary Attacks on Speaker Verification | In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population. The resulting master voice successfully matches a non-trivial fraction of people in an unknown population. Adversarial waveforms obtained with our approach can match on average 69% of females and 38% of males enrolled in the target system at a strict decision threshold calibrated to yield false alarm rate of 1%. By using the attack with a black-box voice cloning system, we obtain master voices that are effective in the most challenging conditions and transferable between speaker encoders. We also show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems. | ['Nasir Memon', 'Anubhav Jain', 'Pawel Korus', 'Mirko Marras'] | 2022-04-24 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 3.80273491e-01 5.88065565e-01 1.86895490e-01 -9.51855481e-02
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30ed7788-7de9-44ef-8e2b-4c3f008622cd | adaptive-cross-batch-normalization-for-metric | 2303.17127 | null | https://arxiv.org/abs/2303.17127v1 | https://arxiv.org/pdf/2303.17127v1.pdf | Adaptive Cross Batch Normalization for Metric Learning | Metric learning is a fundamental problem in computer vision whereby a model is trained to learn a semantically useful embedding space via ranking losses. Traditionally, the effectiveness of a ranking loss depends on the minibatch size, and is, therefore, inherently limited by the memory constraints of the underlying hardware. While simply accumulating the embeddings across minibatches has proved useful (Wang et al. [2020]), we show that it is equally important to ensure that the accumulated embeddings are up to date. In particular, it is necessary to circumvent the representational drift between the accumulated embeddings and the feature embeddings at the current training iteration as the learnable parameters are being updated. In this paper, we model representational drift as distribution misalignment and tackle it using moment matching. The result is a simple method for updating the stored embeddings to match the first and second moments of the current embeddings at each training iteration. Experiments on three popular image retrieval datasets, namely, SOP, In-Shop, and DeepFashion2, demonstrate that our approach significantly improves the performance in all scenarios. | ['Stephen Gould', 'Anton Van Den Hengel', 'Matt Ma', 'Thalaiyasingam Ajanthan'] | 2023-03-30 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-9.95453894e-02 -2.99157590e-01 -1.37355030e-01 -3.69315535e-01
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cde30961-3d39-4067-a67b-108f5d144448 | sitaka-at-semeval-2017-task-4-sentiment | null | null | https://aclanthology.org/S17-2115 | https://aclanthology.org/S17-2115.pdf | SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features | This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets. | ['Mohammed Jabreel', 'Antonio Moreno'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.77152157e-01 8.80104899e-02 -2.09663883e-01 -5.57782590e-01
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137743e4-501d-45a7-ac27-65270bc51748 | inducing-document-plans-for-concept-to-text | null | null | https://aclanthology.org/D13-1157 | https://aclanthology.org/D13-1157.pdf | Inducing Document Plans for Concept-to-Text Generation | null | ['Ioannis Konstas', 'Mirella Lapata'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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2e9a8eb1-ad1f-419f-bcf9-a462912f069c | scalable-graph-convolutional-network-training | 2212.05009 | null | https://arxiv.org/abs/2212.05009v2 | https://arxiv.org/pdf/2212.05009v2.pdf | Scalable Graph Convolutional Network Training on Distributed-Memory Systems | Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs. | ['Hakan Ferhatosmanoglu', 'Aparajita Haldar', 'Gunduz Vehbi Demirci'] | 2022-12-09 | null | null | null | null | ['hypergraph-partitioning', 'graph-partitioning', 'blocking'] | ['graphs', 'graphs', 'natural-language-processing'] | [-2.47870088e-01 9.51712281e-02 -1.62108347e-01 -2.88924873e-01
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3f25d302-0675-4ee4-93cb-fc1895632cb7 | nebula-i-a-general-framework-for | 2205.09470 | null | https://arxiv.org/abs/2205.09470v1 | https://arxiv.org/pdf/2205.09470v1.pdf | Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters | The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote clusters, huge challenges are faced. In this work, we introduce a general framework, Nebula-I, for collaboratively training deep learning models over remote heterogeneous clusters, the connections between which are low-bandwidth wide area networks (WANs). We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning. To balance the accuracy and communication efficiency, in Nebula-I, parameter-efficient training strategies, hybrid parallel computing methods and adaptive communication acceleration techniques are jointly applied. Meanwhile, security strategies are employed to guarantee the safety, reliability and privacy in intra-cluster computation and inter-cluster communication. Nebula-I is implemented with the PaddlePaddle deep learning framework, which can support collaborative training over heterogeneous hardware, e.g. GPU and NPU. Experiments demonstrate that the proposed framework could substantially maximize the training efficiency while preserving satisfactory NLP performance. By using Nebula-I, users can run large-scale training tasks over cloud clusters with minimum developments, and the utility of existed large pre-trained models could be further promoted. We also introduced new state-of-the-art results on cross-lingual natural language inference tasks, which are generated based upon a novel learning framework and Nebula-I. | ['dianhai yu', 'Yanjun Ma', 'Yu Sun', 'Ge Li', 'Yue Yu', 'Yaqian Han', 'Shaohuai Shi', 'Bin Wang', 'Long Li', 'Yongshuai Hou', 'Peng Liu', 'Shuohuan Wang', 'Yuang Liu', 'Xianjie Mo', 'Siyu Ding', 'Weibao Gong', 'Zhihua Wu', 'Yang Xiang'] | 2022-05-19 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-4.17202652e-01 -4.07391459e-01 -2.54341274e-01 -4.68579739e-01
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67a2daa7-e077-4b8f-9c93-79783c150937 | from-shapley-values-to-generalized-additive | 2209.04012 | null | https://arxiv.org/abs/2209.04012v3 | https://arxiv.org/pdf/2209.04012v3.pdf | From Shapley Values to Generalized Additive Models and back | In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconciliation between the two by establishing a correspondence between Shapley Values and Generalized Additive Models (GAMs). We introduce $n$-Shapley Values, a parametric family of local post-hoc explanation algorithms that explain individual predictions with interaction terms up to order $n$. By varying the parameter $n$, we obtain a sequence of explanations that covers the entire range from Shapley Values up to a uniquely determined decomposition of the function we want to explain. The relationship between $n$-Shapley Values and this decomposition offers a functionally-grounded characterization of Shapley Values, which highlights their limitations. We then show that $n$-Shapley Values, as well as the Shapley Taylor- and Faith-Shap interaction indices, recover GAMs with interaction terms up to order $n$. This implies that the original Shapely Values recover GAMs without variable interactions. Taken together, our results provide a precise characterization of Shapley Values as they are being used in explainable machine learning. They also offer a principled interpretation of partial dependence plots of Shapley Values in terms of the underlying functional decomposition. A package for the estimation of different interaction indices is available at \url{https://github.com/tml-tuebingen/nshap}. | ['Ulrike Von Luxburg', 'Sebastian Bordt'] | 2022-09-08 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 6.43174648e-02 8.56830060e-01 -4.57301468e-01 -5.68998158e-01
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0dce2d27-4e36-478c-abdd-4638f4db0d3b | learning-an-unreferenced-metric-for-online | 2005.00583 | null | https://arxiv.org/abs/2005.00583v1 | https://arxiv.org/pdf/2005.00583v1.pdf | Learning an Unreferenced Metric for Online Dialogue Evaluation | Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to develop automatic dialogue evaluation metrics, but most of them do not generalize to unseen datasets and/or need a human-generated reference response during inference, making it infeasible for online evaluation. Here, we propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances, and leverages the temporal transitions that exist between them. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference. | ['William L. Hamilton', 'Ryan Lowe', 'Prasanna Parthasarathi', 'Koustuv Sinha', 'Jasmine Wang', 'Joelle Pineau'] | 2020-05-01 | learning-an-unreferenced-metric-for-online-1 | https://aclanthology.org/2020.acl-main.220 | https://aclanthology.org/2020.acl-main.220.pdf | acl-2020-6 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.39607036e-01 3.87557030e-01 -1.73324049e-02 -7.81782329e-01
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cb4140f2-d560-4a8c-8960-3c59fa3d0db5 | federated-minimax-optimization-improved | 2203.04850 | null | https://arxiv.org/abs/2203.04850v1 | https://arxiv.org/pdf/2203.04850v1.pdf | Federated Minimax Optimization: Improved Convergence Analyses and Algorithms | In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection of training data in these applications calls for communication-efficient distributed optimization algorithms, such as those used in federated learning, to process the data. In this paper, we analyze Local stochastic gradient descent ascent (SGDA), the local-update version of the SGDA algorithm. SGDA is the core algorithm used in minimax optimization, but it is not well-understood in a distributed setting. We prove that Local SGDA has \textit{order-optimal} sample complexity for several classes of nonconvex-concave and nonconvex-nonconcave minimax problems, and also enjoys \textit{linear speedup} with respect to the number of clients. We provide a novel and tighter analysis, which improves the convergence and communication guarantees in the existing literature. For nonconvex-PL and nonconvex-one-point-concave functions, we improve the existing complexity results for centralized minimax problems. Furthermore, we propose a momentum-based local-update algorithm, which has the same convergence guarantees, but outperforms Local SGDA as demonstrated in our experiments. | ['Pramod K. Varshney', 'Gauri Joshi', 'Rohan Panda', 'Pranay Sharma'] | 2022-03-09 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.18149513e-01 -1.54171839e-01 -4.34318244e-01 -3.97263259e-01
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b308acc4-61f5-4b5b-a735-06251a6e0f7f | low-rank-prune-and-factorize-for-language | 2306.14152 | null | https://arxiv.org/abs/2306.14152v1 | https://arxiv.org/pdf/2306.14152v1.pdf | Low-Rank Prune-And-Factorize for Language Model Compression | The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has been utilized to reduce the number of parameters in PLM. However, it fails to retain satisfactory performance under moderate to high compression rate. In this paper, we identify the \textit{full-rankness} of fine-tuned PLM as the fundamental bottleneck for the failure of matrix factorization and explore the use of network pruning to extract low-rank sparsity pattern desirable to matrix factorization. We find such low-rank sparsity pattern exclusively exists in models generated by first-order pruning, which motivates us to unite the two approaches and achieve more effective model compression. We further propose two techniques: sparsity-aware SVD and mixed-rank fine-tuning, which improve the initialization and training of the compression procedure, respectively. Experiments on GLUE and question-answering tasks show that the proposed method has superior compression-performance trade-off compared to existing approaches. | ['Kenny Q. Zhu', 'Siyu Ren'] | 2023-06-25 | null | null | null | null | ['network-pruning', 'model-compression', 'question-answering'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 3.19103956e-01 9.03866068e-02 -4.28081661e-01 -1.11074060e-01
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04e36eb9-7a33-4a57-b5f3-6f47b5d2d7a3 | bridging-the-gap-between-human-action | 2101.08851 | null | https://arxiv.org/abs/2101.08851v1 | https://arxiv.org/pdf/2101.08851v1.pdf | Bridging the gap between Human Action Recognition and Online Action Detection | Action recognition, early prediction, and online action detection are complementary disciplines that are often studied independently. Most online action detection networks use a pre-trained feature extractor, which might not be optimal for its new task. We address the task-specific feature extraction with a teacher-student framework between the aforementioned disciplines, and a novel training strategy. Our network, Online Knowledge Distillation Action Detection network (OKDAD), embeds online early prediction and online temporal segment proposal subnetworks in parallel. Low interclass and high intraclass similarity are encouraged during teacher training. Knowledge distillation to the OKDAD network is ensured via layer reuse and cosine similarity between teacher-student feature vectors. Layer reuse and similarity learning significantly improve our baseline which uses a generic feature extractor. We evaluate our framework on infrared videos from two popular datasets, NTU RGB+D (action recognition, early prediction) and PKU MMD (action detection). Unlike previous attempts on those datasets, our student networks perform without any knowledge of the future. Even with this added difficulty, we achieve state-of-the-art results on both datasets. Moreover, our networks use infrared from RGB-D cameras, which we are the first to use for online action detection, to our knowledge. | ['Rita Noumeir', 'Alban Main de Boissiere'] | 2021-01-21 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.58274633e-01 2.08981223e-02 -7.10728347e-01 -1.13096841e-01
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c352e563-09e2-4d2c-9826-2fa2e817c4a2 | fgahoi-fine-grained-anchors-for-human-object | 2301.04019 | null | https://arxiv.org/abs/2301.04019v1 | https://arxiv.org/pdf/2301.04019v1.pdf | FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection | Human-Object Interaction (HOI), as an important problem in computer vision, requires locating the human-object pair and identifying the interactive relationships between them. The HOI instance has a greater span in spatial, scale, and task than the individual object instance, making its detection more susceptible to noisy backgrounds. To alleviate the disturbance of noisy backgrounds on HOI detection, it is necessary to consider the input image information to generate fine-grained anchors which are then leveraged to guide the detection of HOI instances. However, it is challenging for the following reasons. i) how to extract pivotal features from the images with complex background information is still an open question. ii) how to semantically align the extracted features and query embeddings is also a difficult issue. In this paper, a novel end-to-end transformer-based framework (FGAHOI) is proposed to alleviate the above problems. FGAHOI comprises three dedicated components namely, multi-scale sampling (MSS), hierarchical spatial-aware merging (HSAM) and task-aware merging mechanism (TAM). MSS extracts features of humans, objects and interaction areas from noisy backgrounds for HOI instances of various scales. HSAM and TAM semantically align and merge the extracted features and query embeddings in the hierarchical spatial and task perspectives in turn. In the meanwhile, a novel training strategy Stage-wise Training Strategy is designed to reduce the training pressure caused by overly complex tasks done by FGAHOI. In addition, we propose two ways to measure the difficulty of HOI detection and a novel dataset, i.e., HOI-SDC for the two challenges (Uneven Distributed Area in Human-Object Pairs and Long Distance Visual Modeling of Human-Object Pairs) of HOI instances detection. | ['Ying WEI', 'Shanze Wang', 'Yuefeng Wang', 'Shuailei Ma'] | 2023-01-08 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 1.75577641e-01 -4.16147470e-01 3.96441907e-01 -2.20523685e-01
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eb96c376-3035-4881-97d1-4181ac1b6da9 | asl-video-corpora-sign-bank-resources | 2201.07899 | null | https://arxiv.org/abs/2201.07899v1 | https://arxiv.org/pdf/2201.07899v1.pdf | ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP) | The American Sign Language Linguistic Research Project (ASLLRP) provides Internet access to high-quality ASL video data, generally including front and side views and a close-up of the face. The manual and non-manual components of the signing have been linguistically annotated using SignStream(R). The recently expanded video corpora can be browsed and searched through the Data Access Interface (DAI 2) we have designed; it is possible to carry out complex searches. The data from our corpora can also be downloaded; annotations are available in an XML export format. We have also developed the ASLLRP Sign Bank, which contains almost 6,000 sign entries for lexical signs, with distinct English-based glosses, with a total of 41,830 examples of lexical signs (in addition to about 300 gestures, over 1,000 fingerspelled signs, and 475 classifier examples). The Sign Bank is likewise accessible and searchable on the Internet; it can also be accessed from within SignStream(R) (software to facilitate linguistic annotation and analysis of visual language data) to make annotations more accurate and efficient. Here we describe the available resources. These data have been used for many types of research in linguistics and in computer-based sign language recognition from video; examples of such research are provided in the latter part of this article. | ['Dimitris Metaxas', 'Augustine Opoku', 'Carol Neidle'] | 2022-01-19 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.31718919e-01 -2.60961890e-01 -4.74558890e-01 -4.55941767e-01
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16e69a31-f044-4006-b4c9-fa0790710c8b | quick-starting-dialog-systems-with-paraphrase | 2204.02546 | null | https://arxiv.org/abs/2204.02546v2 | https://arxiv.org/pdf/2204.02546v2.pdf | Quick Starting Dialog Systems with Paraphrase Generation | Acquiring training data to improve the robustness of dialog systems can be a painstakingly long process. In this work, we propose a method to reduce the cost and effort of creating new conversational agents by artificially generating more data from existing examples, using paraphrase generation. Our proposed approach can kick-start a dialog system with little human effort, and brings its performance to a level satisfactory enough for allowing actual interactions with real end-users. We experimented with two neural paraphrasing approaches, namely Neural Machine Translation and a Transformer-based seq2seq model. We present the results obtained with two datasets in English and in French:~a crowd-sourced public intent classification dataset and our own corporate dialog system dataset. We show that our proposed approach increased the generalization capabilities of the intent classification model on both datasets, reducing the effort required to initialize a new dialog system and helping to deploy this technology at scale within an organization. | ['Marie-Jean Meurs', 'Nada Naji', 'Eric Charton', 'Marc Queudot', 'Raouf Belbahar', 'Louis Marceau'] | 2022-04-06 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.66622758e-01 4.54928964e-01 2.07047820e-01 -7.60485172e-01
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48b72a5f-5e0d-434b-9ced-74f3824d2a4e | rethinking-kernel-methods-for-node | 1910.02548 | null | https://arxiv.org/abs/1910.02548v1 | https://arxiv.org/pdf/1910.02548v1.pdf | Rethinking Kernel Methods for Node Representation Learning on Graphs | Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph representation learning, is still ill-posed and the state-of-the-art methods are heavily based on heuristics. Here, we present a novel theoretical kernel-based framework for node classification that can bridge the gap between these two representation learning problems on graphs. Our approach is motivated by graph kernel methodology but extended to learn the node representations capturing the structural information in a graph. We theoretically show that our formulation is as powerful as any positive semidefinite kernels. To efficiently learn the kernel, we propose a novel mechanism for node feature aggregation and a data-driven similarity metric employed during the training phase. More importantly, our framework is flexible and complementary to other graph-based deep learning models, e.g., Graph Convolutional Networks (GCNs). We empirically evaluate our approach on a number of standard node classification benchmarks, and demonstrate that our model sets the new state of the art. | ['Dimitris N. Metaxas', 'Xi Peng', 'Yu Tian', 'Long Zhao'] | 2019-10-06 | rethinking-kernel-methods-for-node-1 | http://papers.nips.cc/paper/9342-rethinking-kernel-methods-for-node-representation-learning-on-graphs | http://papers.nips.cc/paper/9342-rethinking-kernel-methods-for-node-representation-learning-on-graphs.pdf | neurips-2019-12 | ['graph-similarity'] | ['graphs'] | [-6.48336485e-02 2.40761876e-01 -5.03319919e-01 -1.93768293e-01
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99683b38-4244-41c3-9097-d4d1f22a9810 | metric-learning-for-user-defined-keyword | 2211.00439 | null | https://arxiv.org/abs/2211.00439v1 | https://arxiv.org/pdf/2211.00439v1.pdf | Metric Learning for User-defined Keyword Spotting | The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. The ability to define custom keywords has advantages in terms of user experience. In this paper, we propose a metric learning-based training strategy for user-defined keyword spotting. In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics. Our proposed system does not require an incremental training on the user-defined keywords, and outperforms previous works by a significant margin on the Google Speech Commands dataset using the proposed as well as the existing metrics. | ['Joon Son Chung', 'Youngjoon Jang', 'Byeong-Yeol Kim', 'Youshin Lim', 'Jihwan Park', 'Youkyum Kim', 'Jaemin Jung'] | 2022-11-01 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.40104669e-01 -2.28509858e-01 -1.79890066e-01 -5.90893269e-01
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6967b3fe-509a-4c38-8dc0-997ca4e89dea | deep-retrosynthetic-reaction-prediction-using | null | null | https://pubs.acs.org/doi/10.1021/jacsau.1c00246 | https://pubs.acs.org/doi/pdf/10.1021/jacsau.1c00246 | Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention | As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. Here we propose a local retrosynthesis framework called LocalRetro, motivated by the chemical intuition that the molecular changes occur mostly locally during the chemical reactions. This differs from nearly all existing retrosynthesis methods that suggest reactants based on the global structures of the molecules, often containing fine details not directly relevant to the reactions. This local concept yields local reaction templates involving the atom and bond edits. Because the remote functional groups can also affect the overall reaction path as a secondary aspect, the proposed locally encoded retrosynthesis model is then further refined to account for the nonlocal effects of chemical reaction through a global attention mechanism. Our model shows a promising 89.5 and 99.2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions. We further demonstrate the validity of LocalRetro on a large dataset containing 479 035 reactions (UTPTO-MIT) with comparable round-trip top-1 and top-5 accuracy of 87.0 and 97.4%, respectively. The practical application of the model is also demonstrated by correctly predicting the synthesis pathways of five drug candidate molecules from various literature. | ['Yousung Jung', 'Shuan Chen'] | 2021-08-05 | null | null | null | jacs-au-2021-8 | ['retrosynthesis'] | ['medical'] | [ 4.31692302e-01 1.94816515e-01 -5.85350692e-01 -3.80663462e-02
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45c26a93-c686-47da-9495-cc08e2941aef | imaginator-pre-trained-image-text-joint | 2305.10438 | null | https://arxiv.org/abs/2305.10438v1 | https://arxiv.org/pdf/2305.10438v1.pdf | IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images | Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis "You shall know a word by the company it keeps" (Harris, 1954), whereas modern prediction-based neural network embeddings rely on design choices and hyperparameter optimization. Word embeddings like Word2Vec, GloVe etc. well capture the contextuality and real-world analogies but contemporary convolution-based image embeddings such as VGGNet, AlexNet, etc. do not capture contextual knowledge. The popular king-queen analogy does not hold true for most commonly used vision embeddings. In this paper, we introduce a pre-trained joint embedding (JE), named IMAGINATOR, trained on 21K distinct image objects level from 1M image+text pairs. JE is a way to encode multimodal data into a vector space where the text modality serves as the ground-ing key, which the complementary modality (in this case, the image) is anchored with. IMAGINATOR encapsulates three individual representations: (i) object-object co-location, (ii) word-object co-location, and (iii) word-object correlation. These three ways capture complementary aspects of the two modalities which are further combined to obtain the final JEs. Generated JEs are intrinsically evaluated to assess how well they capture the contextuality and real-world analogies. We also evaluate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Image2Tweet, and (iii) text-based image retrieval. IMAGINATOR establishes a new standard on the aforementioned down-stream tasks by outperforming the current SoTA on all the selected tasks. IMAGINATOR will be made publicly available. The codes are available at https://github.com/varunakk/IMAGINATOR | ['Amit Sheth', 'Amitava Das', 'Aman Chadha', 'Megha Chakraborty', 'Parth Patwa', 'Sathyanarayanan Ramamoorthy', 'Shreyash Mishra', 'S Suryavardan', 'Varuna Krishna'] | 2023-05-12 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-8.89731124e-02 -2.74846137e-01 -2.43724301e-01 -3.09743732e-01
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0641b196-c42c-4cdd-862b-6818535b76e7 | classes-matter-a-fine-grained-adversarial | 2007.09222 | null | https://arxiv.org/abs/2007.09222v1 | https://arxiv.org/pdf/2007.09222v1.pdf | Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation | Despite great progress in supervised semantic segmentation,a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignment from a holistic view, ignoring the underlying class-level data structure in the target domain. To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains. We adopt a fine-grained domain discriminator that not only plays as a domain distinguisher, but also differentiates domains at class level. The traditional binary domain labels are also generalized to domain encodings as the supervision signal to guide the fine-grained feature alignment. An analysis with Class Center Distance (CCD) validates that our fine-grained adversarial strategy achieves better class-level alignment compared to other state-of-the-art methods. Our method is easy to implement and its effectiveness is evaluated on three classical domain adaptation tasks, i.e., GTA5 to Cityscapes, SYNTHIA to Cityscapes and Cityscapes to Cross-City. Large performance gains show that our method outperforms other global feature alignment based and class-wise alignment based counterparts. The code is publicly available at https://github.com/JDAI-CV/FADA. | ['Ling-Yu Duan', 'Wei zhang', 'Haoran Wang', 'Tong Shen', 'Tao Mei'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2246_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590630.pdf | eccv-2020-8 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.31930488e-01 -5.22955656e-02 -3.76074702e-01 -5.16336381e-01
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442bb40d-69a4-4d97-bd09-6024589b61f6 | coherence-modeling-improves-implicit | null | null | https://aclanthology.org/W18-5040 | https://aclanthology.org/W18-5040.pdf | Coherence Modeling Improves Implicit Discourse Relation Recognition | The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora. We introduce an unsupervised learning method on text coherence that could produce numerical representations that improve implicit discourse relation recognition in a semi-supervised manner. We also empirically examine two variants of coherence modeling: order-oriented and topic-oriented negative sampling, showing that, of the two, topic-oriented negative sampling tends to be more effective. | ['Noriki Nishida', 'Hideki Nakayama'] | 2018-07-01 | null | null | null | ws-2018-7 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 2.00241223e-01 1.10682988e+00 -1.07302439e+00 -6.24071181e-01
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dba62dbb-9f7f-463f-8747-dbb87282bc37 | two-stream-amtnet-for-action-detection | 2004.01494 | null | https://arxiv.org/abs/2004.01494v1 | https://arxiv.org/pdf/2004.01494v1.pdf | Two-Stream AMTnet for Action Detection | In this paper, we propose Two-Stream AMTnet, which leverages recent advances in video-based action representation[1] and incremental action tube generation[2]. Majority of the present action detectors follow a frame-based representation, a late-fusion followed by an offline action tube building steps. These are sub-optimal as: frame-based features barely encode the temporal relations; late-fusion restricts the network to learn robust spatiotemporal features; and finally, an offline action tube generation is not suitable for many real-world problems such as autonomous driving, human-robot interaction to name a few. The key contributions of this work are: (1) combining AMTnet's 3D proposal architecture with an online action tube generation technique which allows the model to learn stronger temporal features needed for accurate action detection and facilitates running inference online; (2) an efficient fusion technique allowing the deep network to learn strong spatiotemporal action representations. This is achieved by augmenting the previous Action Micro-Tube (AMTnet) action detection framework in three distinct ways: by adding a parallel motion stIn this paper, we propose a new deep neural network architecture for online action detection, termed ream to the original appearance one in AMTnet; (2) in opposition to state-of-the-art action detectors which train appearance and motion streams separately, and use a test time late fusion scheme to fuse RGB and flow cues, by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time; (3) by introducing an online action tube generation algorithm which works at video-level, and in real-time (when exploiting only appearance features). Two-Stream AMTnet exhibits superior action detection performance over state-of-the-art approaches on the standard action detection benchmarks. | ['Fabio Cuzzolin', 'Suman Saha', 'Gurkirt Singh'] | 2020-04-03 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.04684067e-01 -5.54398373e-02 -4.13923353e-01 4.71494459e-02
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1f828277-8a78-47e2-944e-075934163855 | provable-identifiability-of-two-layer-relu | 2305.04267 | null | https://arxiv.org/abs/2305.04267v1 | https://arxiv.org/pdf/2305.04267v1.pdf | Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization | LASSO regularization is a popular regression tool to enhance the prediction accuracy of statistical models by performing variable selection through the $\ell_1$ penalty, initially formulated for the linear model and its variants. In this paper, the territory of LASSO is extended to two-layer ReLU neural networks, a fashionable and powerful nonlinear regression model. Specifically, given a neural network whose output $y$ depends only on a small subset of input $\boldsymbol{x}$, denoted by $\mathcal{S}^{\star}$, we prove that the LASSO estimator can stably reconstruct the neural network and identify $\mathcal{S}^{\star}$ when the number of samples scales logarithmically with the input dimension. This challenging regime has been well understood for linear models while barely studied for neural networks. Our theory lies in an extended Restricted Isometry Property (RIP)-based analysis framework for two-layer ReLU neural networks, which may be of independent interest to other LASSO or neural network settings. Based on the result, we advocate a neural network-based variable selection method. Experiments on simulated and real-world datasets show promising performance of the variable selection approach compared with existing techniques. | ['Jie Ding', 'Ganghua Wang', 'Gen Li'] | 2023-05-07 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 5.55645645e-01 1.20638460e-01 -4.53275532e-01 -4.38156366e-01
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af0e1118-a0fb-490b-b5b8-e749cd5cd0f2 | fastinst-a-simple-query-based-model-for-real | 2303.08594 | null | https://arxiv.org/abs/2303.08594v2 | https://arxiv.org/pdf/2303.08594v2.pdf | FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation | Recent attention in instance segmentation has focused on query-based models. Despite being non-maximum suppression (NMS)-free and end-to-end, the superiority of these models on high-accuracy real-time benchmarks has not been well demonstrated. In this paper, we show the strong potential of query-based models on efficient instance segmentation algorithm designs. We present FastInst, a simple, effective query-based framework for real-time instance segmentation. FastInst can execute at a real-time speed (i.e., 32.5 FPS) while yielding an AP of more than 40 (i.e., 40.5 AP) on COCO test-dev without bells and whistles. Specifically, FastInst follows the meta-architecture of recently introduced Mask2Former. Its key designs include instance activation-guided queries, dual-path update strategy, and ground truth mask-guided learning, which enable us to use lighter pixel decoders, fewer Transformer decoder layers, while achieving better performance. The experiments show that FastInst outperforms most state-of-the-art real-time counterparts, including strong fully convolutional baselines, in both speed and accuracy. Code can be found at https://github.com/junjiehe96/FastInst . | ['Xuansong Xie', 'Yifeng Geng', 'Pengyu Li', 'Junjie He'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_FastInst_A_Simple_Query-Based_Model_for_Real-Time_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 1.03683487e-01 4.68962751e-02 -3.68231326e-01 -3.18279058e-01
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ef42628d-aaab-433f-b8ca-b209d9b7a7b8 | liveness-score-based-regression-neural | 2302.09461 | null | https://arxiv.org/abs/2302.09461v2 | https://arxiv.org/pdf/2302.09461v2.pdf | Liveness score-based regression neural networks for face anti-spoofing | Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generating pseudo maps and the way in which the users define the labels. In this paper, we propose a liveness score-based regression network for overcoming the dependency on third party networks and users. First, we introduce a new labeling technique, called pseudo-discretized label encoding for generating discretized labels indicating the amount of information related to real images. Secondly, we suggest the expected liveness score based on a regression network for training the difference between the proposed supervision and the expected liveness score. Finally, extensive experiments were conducted on four face anti-spoofing benchmarks to verify our proposed method on both intra-and cross-dataset tests. The experimental results show our approach outperforms previous methods. | ['Changick Kim', 'JinHo Shin', 'Hunjae Yoo', 'Minyoung Jung', 'Youngjun Kwak'] | 2023-02-19 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 5.81329584e-01 6.94194809e-02 -4.50507224e-01 -6.54247344e-01
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4f362727-9a26-4fe4-9287-71c5e38d25a6 | community-detection-using-low-dimensional | 2111.05267 | null | https://arxiv.org/abs/2111.05267v1 | https://arxiv.org/pdf/2111.05267v1.pdf | Community detection using low-dimensional network embedding algorithms | With the increasing relevance of large networks in important areas such as the study of contact networks for spread of disease, or social networks for their impact on geopolitics, it has become necessary to study machine learning tools that are scalable to very large networks, often containing millions of nodes. One major class of such scalable algorithms is known as network representation learning or network embedding. These algorithms try to learn representations of network functionals (e.g.~nodes) by first running multiple random walks and then using the number of co-occurrences of each pair of nodes in observed random walk segments to obtain a low-dimensional representation of nodes on some Euclidean space. The aim of this paper is to rigorously understand the performance of two major algorithms, DeepWalk and node2vec, in recovering communities for canonical network models with ground truth communities. Depending on the sparsity of the graph, we find the length of the random walk segments required such that the corresponding observed co-occurrence window is able to perform almost exact recovery of the underlying community assignments. We prove that, given some fixed co-occurrence window, node2vec using random walks with a low non-backtracking probability can succeed for much sparser networks compared to DeepWalk using simple random walks. Moreover, if the sparsity parameter is low, we provide evidence that these algorithms might not succeed in almost exact recovery. The analysis requires developing general tools for path counting on random networks having an underlying low-rank structure, which are of independent interest. | ['Souvik Dhara', 'Shankar Bhamidi', 'Aman Barot'] | 2021-11-04 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 7.71934912e-02 3.25355738e-01 -2.18687341e-01 6.99104667e-02
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a5351f19-eb31-42b4-8b1a-053f3dcfd08a | act3d-infinite-resolution-action-detection | 2306.17817 | null | https://arxiv.org/abs/2306.17817v1 | https://arxiv.org/pdf/2306.17817v1.pdf | Act3D: Infinite Resolution Action Detection Transformer for Robotic Manipulation | 3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, typically demanding high-resolution 3D perceptual grids that are computationally expensive to process. As a result, most manipulation policies operate directly in 2D, foregoing 3D inductive biases. In this paper, we propose Act3D, a manipulation policy Transformer that casts 6-DoF keypose prediction as 3D detection with adaptive spatial computation. It takes as input 3D feature clouds unprojected from one or more camera views, iteratively samples 3D point grids in free space in a coarse-to-fine manner, featurizes them using relative spatial attention to the physical feature cloud, and selects the best feature point for end-effector pose prediction. Act3D sets a new state-of-the-art in RLbench, an established manipulation benchmark. Our model achieves 10% absolute improvement over the previous SOTA 2D multi-view policy on 74 RLbench tasks and 22% absolute improvement with 3x less compute over the previous SOTA 3D policy. In thorough ablations, we show the importance of relative spatial attention, large-scale vision-language pre-trained 2D backbones, and weight tying across coarse-to-fine attentions. Code and videos are available at our project site: https://act3d.github.io/. | ['Katerina Fragkiadaki', 'Nikolaos Gkanatsios', 'Zhou Xian', 'Theophile Gervet'] | 2023-06-30 | null | null | null | null | ['pose-prediction', 'action-detection', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'robots'] | [-3.83417130e-01 -6.69296160e-02 -4.42399770e-01 1.16030037e-01
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e3569946-73a2-4543-bac0-317e46a9a9ca | deceptive-review-spam-detection-via | null | null | https://aclanthology.org/D16-1187 | https://aclanthology.org/D16-1187.pdf | Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data | null | ['Xiao-Li Li', 'Peng Yang', 'Peng Cheng', 'Peilin Zhao', 'Zhen Hai', 'Guangxia Li'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['spam-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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0159be51-65a4-43b5-8ebb-573caa876b64 | turning-the-pipeline-into-a-loop-iterated | null | null | https://aclanthology.org/W12-1913 | https://aclanthology.org/W12-1913.pdf | Turning the pipeline into a loop: Iterated unsupervised dependency parsing and PoS induction | null | ['Christos Christodoulopoulos', 'Sharon Goldwater', 'Mark Steedman'] | 2012-06-01 | null | null | null | ws-2012-6 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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4af3207b-12e3-47c7-b7cb-88deaa5e0372 | parameter-efficient-image-to-video-transfer | 2206.13559 | null | https://arxiv.org/abs/2206.13559v3 | https://arxiv.org/pdf/2206.13559v3.pdf | ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning | Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full fine-tuning based task adaptation strategy becomes prohibitively costly in terms of model training and storage. This has led to a new research direction in parameter-efficient transfer learning. However, existing attempts typically focus on downstream tasks from the same modality (e.g., image understanding) of the pre-trained model. This creates a limit because in some specific modalities, (e.g., video understanding) such a strong pre-trained model with sufficient knowledge is less or not available. In this work, we investigate such a novel cross-modality transfer learning setting, namely parameter-efficient image-to-video transfer learning. To solve this problem, we propose a new Spatio-Temporal Adapter (ST-Adapter) for parameter-efficient fine-tuning per video task. With a built-in spatio-temporal reasoning capability in a compact design, ST-Adapter enables a pre-trained image model without temporal knowledge to reason about dynamic video content at a small (~8%) per-task parameter cost, requiring approximately 20 times fewer updated parameters compared to previous work. Extensive experiments on video action recognition tasks show that our ST-Adapter can match or even outperform the strong full fine-tuning strategy and state-of-the-art video models, whilst enjoying the advantage of parameter efficiency. The code and model are available at https://github.com/linziyi96/st-adapter | ['Hongsheng Li', 'Jing Shao', 'Xiatian Zhu', 'Ziyi Lin', 'Junting Pan'] | 2022-06-27 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 2.75106817e-01 -1.49089009e-01 -4.03671741e-01 -2.42154554e-01
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e7d7f490-43a1-4ec0-a2e0-cc400866a7ce | asynchronous-decentralized-federated-lifelong | 2303.06783 | null | https://arxiv.org/abs/2303.06783v1 | https://arxiv.org/pdf/2303.06783v1.pdf | Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging | Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized global model to consolidate individual models into one, and the devices train synchronously, which both can be potential bottlenecks for using federated learning. In this paper, we propose a novel method of asynchronous decentralized federated lifelong learning (ADFLL) method that inherits the merits of federated learning and can train on multiple tasks simultaneously without the need for a central node or synchronous training. Thus, overcoming the potential drawbacks of conventional federated learning. We demonstrate excellent performance on the brain tumor segmentation (BRATS) dataset for localizing the left ventricle on multiple image sequences and image orientation. Our framework allows agents to achieve the best performance with a mean distance error of 7.81, better than the conventional all-knowing agent's mean distance error of 11.78, and significantly (p=0.01) better than a conventional lifelong learning agent with a distance error of 15.17 after eight rounds of training. In addition, all ADFLL agents have comparable or better performance than a conventional LL agent. In conclusion, we developed an ADFLL framework with excellent performance and speed-up compared to conventional RL agents. | ['Vishwa S. Parekh', 'Vladimir Braverman', 'Michael A. Jacobs', 'Guangyao Zheng'] | 2023-03-12 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-3.88330370e-01 3.03923547e-01 -4.07491863e-01 -3.05233628e-01
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90df8948-5738-4b6a-9da6-10fff2c356b3 | unpaired-motion-style-transfer-from-video-to | 2005.05751 | null | https://arxiv.org/abs/2005.05751v1 | https://arxiv.org/pdf/2005.05751v1.pdf | Unpaired Motion Style Transfer from Video to Animation | Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most existing data-driven approaches are supervised and rely on paired data, where motions with the same content are performed in different styles. In addition, these approaches are limited to transfer of styles that were seen during training. In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training. Furthermore, our framework is able to extract motion styles directly from videos, bypassing 3D reconstruction, and apply them to the 3D input motion. Our style transfer network encodes motions into two latent codes, for content and for style, each of which plays a different role in the decoding (synthesis) process. While the content code is decoded into the output motion by several temporal convolutional layers, the style code modifies deep features via temporally invariant adaptive instance normalization (AdaIN). Moreover, while the content code is encoded from 3D joint rotations, we learn a common embedding for style from either 3D or 2D joint positions, enabling style extraction from videos. Our results are comparable to the state-of-the-art, despite not requiring paired training data, and outperform other methods when transferring previously unseen styles. To our knowledge, we are the first to demonstrate style transfer directly from videos to 3D animations - an ability which enables one to extend the set of style examples far beyond motions captured by MoCap systems. | ['Daniel Cohen-Or', 'Yijia Weng', 'Kfir Aberman', 'Dani Lischinski', 'Baoquan Chen'] | 2020-05-12 | null | null | null | null | ['motion-style-transfer'] | ['computer-code'] | [ 3.87925088e-01 -1.47924289e-01 -1.92072049e-01 -3.07819963e-01
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ad459cba-dc32-496d-a673-86800e882dcc | diffurec-a-diffusion-model-for-sequential | 2304.00686 | null | https://arxiv.org/abs/2304.00686v3 | https://arxiv.org/pdf/2304.00686v3.pdf | DiffuRec: A Diffusion Model for Sequential Recommendation | Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors. These vectors have limited capability in capturing items' latent aspects and users' diverse preferences. As a new generative paradigm, Diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this paper, we make the very first attempt to adapt Diffusion model to SR and propose DiffuRec, for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec, which reflect user's multiple interests and item's various aspects adaptively. In diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterwards, the item representation is fed into an Approximator for target item representation reconstruction. In reversion phase, based on user's historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin. | ['Chenliang Li', 'Aixin Sun', 'Zihao Li'] | 2023-04-03 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-5.95255420e-02 -3.58980864e-01 -5.27127445e-01 -3.43871146e-01
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3384bd1a-2063-4c30-a1c9-974c3f16c5fc | a-survey-on-recent-deep-learning-driven | 2110.02511 | null | https://arxiv.org/abs/2110.02511v1 | https://arxiv.org/pdf/2110.02511v1.pdf | A Survey on Recent Deep Learning-driven Singing Voice Synthesis Systems | Singing voice synthesis (SVS) is a task that aims to generate audio signals according to musical scores and lyrics. With its multifaceted nature concerning music and language, producing singing voices indistinguishable from that of human singers has always remained an unfulfilled pursuit. Nonetheless, the advancements of deep learning techniques have brought about a substantial leap in the quality and naturalness of synthesized singing voice. This paper aims to review some of the state-of-the-art deep learning-driven SVS systems. We intend to summarize their deployed model architectures and identify the strengths and limitations for each of the introduced systems. Thereby, we picture the recent advancement trajectory of this field and conclude the challenges left to be resolved both in commercial applications and academic research. | ['Yi-Wen Liu', 'Xiao-Han Wang', 'Ching-Ting Cheng', 'Yung-Chuan Chang', 'Fu-Rong Yang', 'Yin-Ping Cho'] | 2021-10-06 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 7.42356703e-02 -1.15094427e-02 1.10765107e-01 6.26157373e-02
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ed312bf8-0087-4299-b641-a94ed9bc4d7b | amr-parsing-with-action-pointer-transformer | 2104.14674 | null | https://arxiv.org/abs/2104.14674v3 | https://arxiv.org/pdf/2104.14674v3.pdf | AMR Parsing with Action-Pointer Transformer | Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best Smatch score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding. | ['Radu Florian', 'Ramón Fernandez Astudillo', 'Tahira Naseem', 'Jiawei Zhou'] | 2021-04-29 | null | https://aclanthology.org/2021.naacl-main.443 | https://aclanthology.org/2021.naacl-main.443.pdf | naacl-2021-4 | ['hard-attention'] | ['methodology'] | [ 7.04504192e-01 9.04200435e-01 -2.58505970e-01 -5.35803080e-01
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0066bafe-4205-4bed-a37c-cdad9e968557 | transferability-properties-of-graph-neural | 2112.04629 | null | https://arxiv.org/abs/2112.04629v3 | https://arxiv.org/pdf/2112.04629v3.pdf | Transferability Properties of Graph Neural Networks | Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably successful at learning representations from data supported on moderate-scale graphs. However, they are difficult to learn on large-scale graphs. In this paper, we study the problem of training GNNs on graphs of moderate size and transferring them to large-scale graphs. We use graph limits called graphons to define limit objects for graph filters and GNNs -- graphon filters and graphon neural networks (WNNs) -- which we interpret as generative models for graph filters and GNNs. We then show that graphon filters and WNNs can be approximated by graph filters and GNNs sampled from them on weighted and stochastic graphs. Because the error of these approximations can be upper bounded, by a triangle inequality argument we can further bound the error of transferring a graph filter or a GNN across graphs. Our results show that (i) the transference error decreases with the graph size, and (ii) that graph filters have a transferability-discriminability tradeoff that in GNNs is alleviated by the scattering behavior of the nonlinearity. These findings are demonstrated empirically in a movie recommendation problem and in a decentralized control task. | ['Alejandro Ribeiro', 'Luiz F. O. Chamon', 'Luana Ruiz'] | 2021-12-09 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 9.37045366e-02 6.01802111e-01 3.18622366e-02 -1.53856918e-01
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da88bca8-734c-44ab-9634-da79df268846 | real-time-automatic-fetal-brain-extraction-in | 1710.09338 | null | http://arxiv.org/abs/1710.09338v1 | http://arxiv.org/pdf/1710.09338v1.pdf | Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning | Brain segmentation is a fundamental first step in neuroimage analysis. In the
case of fetal MRI, it is particularly challenging and important due to the
arbitrary orientation of the fetus, organs that surround the fetal head, and
intermittent fetal motion. Several promising methods have been proposed but are
limited in their performance in challenging cases and in real-time
segmentation. We aimed to develop a fully automatic segmentation method that
independently segments sections of the fetal brain in 2D fetal MRI slices in
real-time. To this end, we developed and evaluated a deep fully convolutional
neural network based on 2D U-net and autocontext, and compared it to two
alternative fast methods based on 1) a voxelwise fully convolutional network
and 2) a method based on SIFT features, random forest and conditional random
field. We trained the networks with manual brain masks on 250 stacks of
training images, and tested on 17 stacks of normal fetal brain images as well
as 18 stacks of extremely challenging cases based on extreme motion, noise, and
severely abnormal brain shape. Experimental results show that our U-net
approach outperformed the other methods and achieved average Dice metrics of
96.52% and 78.83% in the normal and challenging test sets, respectively. With
an unprecedented performance and a test run time of about 1 second, our network
can be used to segment the fetal brain in real-time while fetal MRI slices are
being acquired. This can enable real-time motion tracking, motion detection,
and 3D reconstruction of fetal brain MRI. | ['Simon K. Warfield', 'Abdelhakim Ouaalam', 'Clemente Velasco-Annis', 'Seyed Raein Hashemi', 'Deniz Erdogmus', 'Ali Gholipour', 'Seyed Sadegh Mohseni Salehi', 'Judy A. Estroff'] | 2017-10-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.57114738e-01 1.21490695e-01 3.49366337e-01 -5.22125125e-01
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4099e9ab-296b-4bff-afe0-4b0c5d4dc649 | chinese-discourse-segmentation-using | 1809.01497 | null | http://arxiv.org/abs/1809.01497v1 | http://arxiv.org/pdf/1809.01497v1.pdf | Chinese Discourse Segmentation Using Bilingual Discourse Commonality | Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and
is a fundamental task in discourse analysis. For Chinese, previous researches
identify EDUs just through discriminating the functions of punctuations. In
this paper, we argue that Chinese EDUs may not end at the punctuation positions
and should follow the definition of EDU in RST-DT. With this definition, we
conduct Chinese discourse segmentation with the help of English labeled
data.Using discourse commonality between English and Chinese, we design an
adversarial neural network framework to extract common language-independent
features and language-specific features which are useful for discourse
segmentation, when there is no or only a small scale of Chinese labeled data
available. Experiments on discourse segmentation demonstrate that our models
can leverage common features from bilingual data, and learn efficient
Chinese-specific features from a small amount of Chinese labeled data,
outperforming the baseline models. | ['Jingfeng Yang', 'Sujian Li'] | 2018-08-30 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.05086485e-01 1.74012512e-01 -4.53694284e-01 -3.39172602e-01
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e9a54172-5447-43cb-bf03-b2b1b65d1dd0 | sentiment-analysis-using-aligned-word | 2305.15380 | null | https://arxiv.org/abs/2305.15380v1 | https://arxiv.org/pdf/2305.15380v1.pdf | Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages | In this paper, we present an approach for translating word embeddings from a majority language into 4 minority languages: Erzya, Moksha, Udmurt and Komi-Zyrian. Furthermore, we align these word embeddings and present a novel neural network model that is trained on English data to conduct sentiment analysis and then applied on endangered language data through the aligned word embeddings. To test our model, we annotated a small sentiment analysis corpus for the 4 endangered languages and Finnish. Our method reached at least 56\% accuracy for each endangered language. The models and the sentiment corpus will be released together with this paper. Our research shows that state-of-the-art neural models can be used with endangered languages with the only requirement being a dictionary between the endangered language and a majority language. | ['Jack Rueter', 'Mika Hämäläinen', 'Khalid Alnajjar'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.35242963e-01 1.54169098e-01 -4.27632660e-01 -3.68196636e-01
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0c6b3dcd-2115-488b-a9dc-9bac8a5fad18 | logit-clipping-for-robust-learning-against | 2212.04055 | null | https://arxiv.org/abs/2212.04055v3 | https://arxiv.org/pdf/2212.04055v3.pdf | Mitigating Memorization of Noisy Labels by Clipping the Model Prediction | In the presence of noisy labels, designing robust loss functions is critical for securing the generalization performance of deep neural networks. Cross Entropy (CE) loss has been shown to be not robust to noisy labels due to its unboundedness. To alleviate this issue, existing works typically design specialized robust losses with the symmetric condition, which usually lead to the underfitting issue. In this paper, our key idea is to induce a loss bound at the logit level, thus universally enhancing the noise robustness of existing losses. Specifically, we propose logit clipping (LogitClip), which clamps the norm of the logit vector to ensure that it is upper bounded by a constant. In this manner, CE loss equipped with our LogitClip method is effectively bounded, mitigating the overfitting to examples with noisy labels. Moreover, we present theoretical analyses to certify the noise-tolerant ability of LogitClip. Extensive experiments show that LogitClip not only significantly improves the noise robustness of CE loss, but also broadly enhances the generalization performance of popular robust losses. | ['Yixuan Li', 'Bo An', 'Gang Niu', 'Lei Feng', 'Renchunzi Xie', 'Huiping Zhuang', 'Hongxin Wei'] | 2022-12-08 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 6.29324466e-02 -6.97409511e-02 2.00403240e-02 -4.79227036e-01
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6882efa2-2419-40c9-8ba6-08fc2fcb2b42 | directional-graph-networks-1 | 2010.02863 | null | https://arxiv.org/abs/2010.02863v4 | https://arxiv.org/pdf/2010.02863v4.pdf | Directional Graph Networks | The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this limitation, we propose the first globally consistent anisotropic kernels for GNNs, allowing for graph convolutions that are defined according to topologicaly-derived directional flows. First, by defining a vector field in the graph, we develop a method of applying directional derivatives and smoothing by projecting node-specific messages into the field. Then, we propose the use of the Laplacian eigenvectors as such vector field. We show that the method generalizes CNNs on an $n$-dimensional grid and is provably more discriminative than standard GNNs regarding the Weisfeiler-Lehman 1-WL test. We evaluate our method on different standard benchmarks and see a relative error reduction of 8% on the CIFAR10 graph dataset and 11% to 32% on the molecular ZINC dataset, and a relative increase in precision of 1.6% on the MolPCBA dataset. An important outcome of this work is that it enables graph networks to embed directions in an unsupervised way, thus allowing a better representation of the anisotropic features in different physical or biological problems. | ['Pietro Liò', 'Gabriele Corso', 'William L. Hamilton', 'Vincent Létourneau', 'Saro Passaro', 'Dominique Beaini'] | 2020-10-06 | directional-graph-networks | https://openreview.net/forum?id=FUdBF49WRV1 | https://openreview.net/pdf?id=FUdBF49WRV1 | null | ['graph-regression'] | ['graphs'] | [ 5.30352630e-02 2.09671587e-01 9.14144423e-03 -3.96154612e-01
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b7047f7e-a6cb-40a0-9e72-5ea426ebc63b | peach-pre-training-sequence-to-sequence | 2304.01282 | null | https://arxiv.org/abs/2304.01282v2 | https://arxiv.org/pdf/2304.01282v2.pdf | PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation | Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language modeling and text-denoising objectives on monolingual data. Multilingual pre-training on monolingual data ignores the availability of parallel data in many language pairs. Also, some other works integrate the available human-generated parallel translation data in their pre-training. This kind of parallel data is definitely helpful, but it is limited even in high-resource language pairs. This paper introduces a novel semi-supervised method, SPDG, that generates high-quality pseudo-parallel data for multilingual pre-training. First, a denoising model is pre-trained on monolingual data to reorder, add, remove, and substitute words, enhancing the pre-training documents' quality. Then, we generate different pseudo-translations for each pre-training document using dictionaries for word-by-word translation and applying the pre-trained denoising model. The resulting pseudo-parallel data is then used to pre-train our multilingual sequence-to-sequence model, PEACH. Our experiments show that PEACH outperforms existing approaches used in training mT5 and mBART on various translation tasks, including supervised, zero- and few-shot scenarios. Moreover, PEACH's ability to transfer knowledge between similar languages makes it particularly useful for low-resource languages. Our results demonstrate that with high-quality dictionaries for generating accurate pseudo-parallel, PEACH can be valuable for low-resource languages. | ['Azadeh Shakery', 'Yadollah Yaghoobzadeh', 'Sara Tavakoli', 'Amirhossein Abaskohi', 'Alireza Salemi'] | 2023-04-03 | null | null | null | null | ['word-translation', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [ 0.07543976 -0.38729203 -0.3408765 -0.21735054 -1.3659414 -0.74106634
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a24736af-061a-4479-9f13-7889c76fc8da | keeping-the-questions-conversational-using | 2304.07125 | null | https://arxiv.org/abs/2304.07125v1 | https://arxiv.org/pdf/2304.07125v1.pdf | Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering | Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given question. However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis. The task of question rewriting has the potential to address the challenges of resolving dependencies amongst the contextual turns by transforming them into intent-explicit questions. Nonetheless, the solution of rewriting the implicit questions comes with some potential challenges such as resulting in verbose questions and taking conversational aspect out of the scenario by generating self-contained questions. In this paper, we propose a novel framework, CONVSR (CONVQA using Structured Representations) for capturing and generating intermediate representations as conversational cues to enhance the capability of the QA model to better interpret the incomplete questions. We also deliberate how the strengths of this task could be leveraged in a bid to design more engaging and eloquent conversational agents. We test our model on the QuAC and CANARD datasets and illustrate by experimental results that our proposed framework achieves a better F1 score than the standard question rewriting model. | ['Adnan Mahmood', 'Wei Emma Zhang', 'Quan Z. Sheng', 'Munazza Zaib'] | 2023-04-14 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 4.63706702e-01 8.77295792e-01 2.92955369e-01 -6.67904317e-01
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4208656d-99c7-4d7c-be25-835f1e7608b3 | word-and-document-embedding-with-vmf-mixture | null | null | https://aclanthology.org/P19-1321 | https://aclanthology.org/P19-1321.pdf | Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors | Word embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained. However, the words from a given language can be organized in various natural groupings, such as syntactic word classes (e.g. nouns, adjectives, verbs) and semantic themes (e.g. sports, politics, sentiment). Our hypothesis in this paper is that embedding models can be improved by explicitly imposing a cluster structure on the set of context word vectors. To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors. We show that this results in word vectors which are qualitatively different from those obtained with existing word embedding models. We furthermore show that our embedding model can also be used to learn high-quality document representations. | ['Steven Schockaert', 'Shoaib Jameel'] | 2019-07-01 | null | null | null | acl-2019-7 | ['document-embedding'] | ['methodology'] | [-1.63268164e-01 8.23724717e-02 -5.64702988e-01 -4.32518512e-01
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d06ffe52-d01a-40cf-b2c8-6d162d59a3aa | virtual-testbed-for-monocular-visual | 2007.00737 | null | https://arxiv.org/abs/2007.00737v1 | https://arxiv.org/pdf/2007.00737v1.pdf | Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems | Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded or denied. However, testing and comparing visual navigation algorithms remains a challenge since visual data is expensive to gather. Conducting flight tests in a virtual environment is an attractive solution prior to committing to outdoor testing. This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz. This tool was created to ultimately find a visual odometry algorithm appropriate for further GPS-denied navigation research on fixed-wing aircraft, even though all of the algorithms were designed for other modalities. This testbed was used to evaluate three current state-of-the-art, open-source monocular visual odometry algorithms on a fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and ORB-SLAM2 (with loop closures disabled). | ['Scott L. Nykl', 'Robert C. Leishman', 'Kyung Kim'] | 2020-07-01 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.73777753e-01 -3.08527529e-01 1.86085403e-01 -1.39958695e-01
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b643a5d9-ec38-41d1-8565-ab65f195d2e5 | feature-representation-learning-for-click | 2302.02241 | null | https://arxiv.org/abs/2302.02241v1 | https://arxiv.org/pdf/2302.02241v1.pdf | Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives | Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneously with other parameters in the model. With the development of CTR models, feature representation learning has become a trending topic and has been extensively studied by both industrial and academic researchers in recent years. This survey aims at summarizing the feature representation learning in a broader picture and pave the way for future research. To achieve such a goal, we first present a taxonomy of current research methods on feature representation learning following two main issues: (i) which feature to represent and (ii) how to represent these features. Then we give a detailed description of each method regarding these two issues. Finally, the review concludes with a discussion on the future directions of this field. | ['Xue Liu', 'Xiuqiang He', 'Chen Ma', 'Haolun Wu', 'Dugang Liu', 'Xing Tang', 'Fuyuan Lyu'] | 2023-02-04 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 2.01161072e-01 -3.31276387e-01 -7.59021878e-01 -5.20570815e-01
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58cd269e-3bcc-497c-8880-ec0d76b19d74 | a-swarm-variant-for-the-schrodinger-solver | 2104.04795 | null | https://arxiv.org/abs/2104.04795v2 | https://arxiv.org/pdf/2104.04795v2.pdf | A Swarm Variant for the Schrödinger Solver | This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks. It adopts PSO's major advantages such as search space exploration and higher robustness to local minima compared to gradient-descent optimizers such as Adam. Neural network based solvers endowed with gradient optimization are now being used to approximate solutions to Differential Equations. Here, we demonstrate the novelty of EM-PSO in approximating gradients and leveraging the property in solving the Schr\"odinger equation, for the Particle-in-a-Box problem. We also provide the optimal set of hyper-parameters supported by mathematical proofs, suited for our algorithm. | ['Snehanshu Saha', 'Anwesh Bhattacharya', 'Omatharv Bharat Vaidya', 'Urvil Nileshbhai Jivani'] | 2021-04-10 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-2.54934698e-01 1.94106232e-02 -5.16054500e-03 2.06867028e-02
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5e7ad0da-0234-40ab-b45a-6f6de50d0ee7 | best-bert-pre-training-for-sign-language | 2302.05075 | null | https://arxiv.org/abs/2302.05075v3 | https://arxiv.org/pdf/2302.05075v3.pdf | BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization | In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model. Considering the dominance of hand and body in sign language expression, we organize them as pose triplet units and feed them into the Transformer backbone in a frame-wise manner. Pre-training is performed via reconstructing the masked triplet unit from the corrupted input sequence, which learns the hierarchical correlation context cues among internal and external triplet units. Notably, different from the highly semantic word token in BERT, the pose unit is a low-level signal originally located in continuous space, which prevents the direct adoption of the BERT cross-entropy objective. To this end, we bridge this semantic gap via coupling tokenization of the triplet unit. It adaptively extracts the discrete pseudo label from the pose triplet unit, which represents the semantic gesture/body state. After pre-training, we fine-tune the pre-trained encoder on the downstream SLR task, jointly with the newly added task-specific layer. Extensive experiments are conducted to validate the effectiveness of our proposed method, achieving new state-of-the-art performance on all four benchmarks with a notable gain. | ['Houqiang Li', 'Jiaxin Shi', 'Wengang Zhou', 'Hezhen Hu', 'Weichao Zhao'] | 2023-02-10 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.75590944e-01 1.63534015e-01 -2.79239118e-01 -5.67764580e-01
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0fd278fa-5cd5-4df0-9239-85db36ae118a | salient-object-detection-in-video-using-deep | 1810.07097 | null | http://arxiv.org/abs/1810.07097v1 | http://arxiv.org/pdf/1810.07097v1.pdf | Salient Object Detection in Video using Deep Non-Local Neural Networks | Detection of salient objects in image and video is of great importance in
many computer vision applications. In spite of the fact that the state of the
art in saliency detection for still images has been changed substantially over
the last few years, there have been few improvements in video saliency
detection. This paper investigates the use of recently introduced non-local
neural networks in video salient object detection. Non-local neural networks
are applied to capture global dependencies and hence determine the salient
objects. The effect of non-local operations is studied separately on static and
dynamic saliency detection in order to exploit both appearance and motion
features. A novel deep non-local neural network architecture is introduced for
video salient object detection and tested on two well-known datasets DAVIS and
FBMS. The experimental results show that the proposed algorithm outperforms
state-of-the-art video saliency detection methods. | ['Mohammad Shokri', 'Kimya Taba', 'Ahad Harati'] | 2018-10-16 | null | null | null | null | ['video-salient-object-detection', 'video-saliency-detection'] | ['computer-vision', 'computer-vision'] | [ 5.60856104e-01 -2.85449207e-01 -2.30065629e-01 -1.65387839e-01
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4a9c4d54-0780-41c8-a2c4-d941c379478f | hybrid-distillation-connecting-masked | 2306.15876 | null | https://arxiv.org/abs/2306.15876v1 | https://arxiv.org/pdf/2306.15876v1.pdf | Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners | Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their pros and cons in specific scenarios, i.e., CL and supervised pre-training excel at capturing longer-range global patterns and enabling better feature discrimination, while MIM can introduce more local and diverse attention across all transformer layers. In this paper, we explore how to obtain a model that combines their strengths. We start by examining previous feature distillation and mask feature reconstruction methods and identify their limitations. We find that their increasing diversity mainly derives from the asymmetric designs, but these designs may in turn compromise the discrimination ability. In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model. Hybrid Distill imitates the token relations of the MIM teacher to alleviate attention collapse, as well as distills the feature maps of the supervised/CL teacher to enable discrimination. Furthermore, a progressive redundant token masking strategy is also utilized to reduce the distilling costs and avoid falling into local optima. Experiment results prove that Hybrid Distill can achieve superior performance on different benchmarks. | ['Qi Tian', 'Hongkai Xiong', 'Junni Zou', 'Wenrui Dai', 'Jin Li', 'Yaoming Wang', 'Xiaopeng Zhang', 'Bowen Shi'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 1.08548239e-01 -2.22278297e-01 -2.65880585e-01 -3.08312744e-01
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aa39f889-ec54-4d11-bbb6-61951fcf0067 | on-search-strategies-for-document-level | 2306.05116 | null | https://arxiv.org/abs/2306.05116v1 | https://arxiv.org/pdf/2306.05116v1.pdf | On Search Strategies for Document-Level Neural Machine Translation | Compared to sentence-level systems, document-level neural machine translation (NMT) models produce a more consistent output across a document and are able to better resolve ambiguities within the input. There are many works on document-level NMT, mostly focusing on modifying the model architecture or training strategy to better accommodate the additional context-input. On the other hand, in most works, the question on how to perform search with the trained model is scarcely discussed, sometimes not mentioned at all. In this work, we aim to answer the question how to best utilize a context-aware translation model in decoding. We start with the most popular document-level NMT approach and compare different decoding schemes, some from the literature and others proposed by us. In the comparison, we are using both, standard automatic metrics, as well as specific linguistic phenomena on three standard document-level translation benchmarks. We find that most commonly used decoding strategies perform similar to each other and that higher quality context information has the potential to further improve the translation. | ['Hermann Ney', 'Christian Herold'] | 2023-06-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.68191886e-01 -1.22829288e-01 -3.80018026e-01 -2.73985833e-01
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8fc25ac8-07c8-4384-ae01-484115246528 | do-images-really-do-the-talking-analysing-the | 2108.03886 | null | https://arxiv.org/abs/2108.03886v1 | https://arxiv.org/pdf/2108.03886v1.pdf | Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification | A meme is an part of media created to share an opinion or emotion across the internet. Due to its popularity, memes have become the new forms of communication on social media. However, due to its nature, they are being used in harmful ways such as trolling and cyberbullying progressively. Various data modelling methods create different possibilities in feature extraction and turning them into beneficial information. The variety of modalities included in data plays a significant part in predicting the results. We try to explore the significance of visual features of images in classifying memes. Memes are a blend of both image and text, where the text is embedded into the image. We try to incorporate the memes as troll and non-trolling memes based on the images and the text on them. However, the images are to be analysed and combined with the text to increase performance. Our work illustrates different textual analysis methods and contrasting multimodal methods ranging from simple merging to cross attention to utilising both worlds' - best visual and textual features. The fine-tuned cross-lingual language model, XLM, performed the best in textual analysis, and the multimodal transformer performs the best in multimodal analysis. | ['Bharathi Raja Chakravarthi', 'B Bharathi', 'Sathiyaraj Thangasamy', 'Ratnasingam Sakuntharaj', 'Sajeetha Thavareesan', 'Ruba Priyadharshini', 'Adeep Hande', 'Siddhanth U Hegde'] | 2021-08-09 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.34225816e-01 -2.10412011e-01 -3.40150669e-02 -5.87959215e-03
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a3649590-1f6d-406b-807a-787cfbcd62ad | rethinking-bisenet-for-real-time-semantic | 2104.13188 | null | https://arxiv.org/abs/2104.13188v1 | https://arxiv.org/pdf/2104.13188v1.pdf | Rethinking BiSeNet For Real-time Semantic Segmentation | BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However, its principle of adding an extra path to encode spatial information is time-consuming, and the backbones borrowed from pretrained tasks, e.g., image classification, may be inefficient for image segmentation due to the deficiency of task-specific design. To handle these problems, we propose a novel and efficient structure named Short-Term Dense Concatenate network (STDC network) by removing structure redundancy. Specifically, we gradually reduce the dimension of feature maps and use the aggregation of them for image representation, which forms the basic module of STDC network. In the decoder, we propose a Detail Aggregation module by integrating the learning of spatial information into low-level layers in single-stream manner. Finally, the low-level features and deep features are fused to predict the final segmentation results. Extensive experiments on Cityscapes and CamVid dataset demonstrate the effectiveness of our method by achieving promising trade-off between segmentation accuracy and inference speed. On Cityscapes, we achieve 71.9% mIoU on the test set with a speed of 250.4 FPS on NVIDIA GTX 1080Ti, which is 45.2% faster than the latest methods, and achieve 76.8% mIoU with 97.0 FPS while inferring on higher resolution images. | ['Xiaolin Wei', 'Junfeng Luo', 'Zhenhua Chai', 'Xiaoming Wei', 'Junshi Huang', 'Shenqi Lai', 'Mingyuan Fan'] | 2021-04-27 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fan_Rethinking_BiSeNet_for_Real-Time_Semantic_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 7.13090301e-02 -2.37447500e-01 -6.77968487e-02 -4.82484549e-01
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a8774a2c-d05f-4453-8396-938ce6a84572 | vote-n-rank-revision-of-benchmarking-with | 2210.05769 | null | https://arxiv.org/abs/2210.05769v3 | https://arxiv.org/pdf/2210.05769v3.pdf | Vote'n'Rank: Revision of Benchmarking with Social Choice Theory | The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation capabilities from multiple perspectives. Although the paradigm is shifting towards more fine-grained evaluation across diverse tasks, the delicate question of how to aggregate the performances has received particular interest in the community. In general, benchmarks follow the unspoken utilitarian principles, where the systems are ranked based on their mean average score over task-specific metrics. Such aggregation procedure has been viewed as a sub-optimal evaluation protocol, which may have created the illusion of progress. This paper proposes Vote'n'Rank, a framework for ranking systems in multi-task benchmarks under the principles of the social choice theory. We demonstrate that our approach can be efficiently utilised to draw new insights on benchmarking in several ML sub-fields and identify the best-performing systems in research and development case studies. The Vote'n'Rank's procedures are more robust than the mean average while being able to handle missing performance scores and determine conditions under which the system becomes the winner. | ['Ekaterina Artemova', 'Daniel Karabekyan', 'Tatiana Shavrina', 'Elena Tutubalina', 'Andrey Kravchenko', 'Mikhail Florinskiy', 'Vladislav Mikhailov', 'Mark Rofin'] | 2022-10-11 | null | null | null | null | ['skills-evaluation', 'result-aggregation'] | ['computer-vision', 'methodology'] | [ 2.33094946e-01 1.89179797e-02 -3.10433894e-01 -5.52678585e-01
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9f1f647c-cc3f-45d7-bcbd-4796a0cbfc0e | audio-visual-scene-aware-dialog | 1901.09107 | null | https://arxiv.org/abs/1901.09107v2 | https://arxiv.org/pdf/1901.09107v2.pdf | Audio-Visual Scene-Aware Dialog | We introduce the task of scene-aware dialog. Our goal is to generate a complete and natural response to a question about a scene, given video and audio of the scene and the history of previous turns in the dialog. To answer successfully, agents must ground concepts from the question in the video while leveraging contextual cues from the dialog history. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) Dataset. For each of more than 11,000 videos of human actions from the Charades dataset, our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using both qualitative and quantitative metrics. Our results indicate that models must utilize all the available inputs (video, audio, question, and dialog history) to perform best on this dataset. | ['Peter Anderson', 'Chiori Hori', 'Tim K. Marks', 'Vincent Cartillier', 'Stefan Lee', 'Huda Alamri', 'Jue Wang', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Anoop Cherian', 'Abhishek Das'] | 2019-01-25 | null | null | null | null | ['scene-aware-dialogue'] | ['computer-vision'] | [ 1.80849716e-01 1.26441732e-01 1.84210896e-01 -7.51648307e-01
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df2c891d-7390-4ed3-9f51-9503dd620297 | d2gclf-document-to-graph-classifier-for-legal | null | null | https://aclanthology.org/2022.findings-naacl.170 | https://aclanthology.org/2022.findings-naacl.170.pdf | D2GCLF: Document-to-Graph Classifier for Legal Document Classification | Legal document classification is an essential task in law intelligence to automate the labor-intensive law case filing process. Unlike traditional document classification problems, legal documents should be classified by reasons and facts instead of topics. We propose a Document-to-Graph Classifier (D2GCLF), which extracts facts as relations between key participants in the law case and represents a legal document with four relation graphs. Each graph is responsible for capturing different relations between the litigation participants. We further develop a graph attention network on top of the four relation graphs to classify the legal documents. Experiments on a real-world legal document dataset show that D2GCLF outperforms the state-of-the-art methods in terms of accuracy. | ['Ruofan Wang', 'Benjamin Liu', 'Robert Amor', 'Kaiqi Zhao', 'Qiqi Wang'] | null | null | null | null | findings-naacl-2022-7 | ['document-classification'] | ['natural-language-processing'] | [ 1.33315787e-01 4.67842162e-01 -7.97698319e-01 -3.30197573e-01
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668b72e5-13c7-4ee6-9bd9-030642018f21 | sampling-is-matter-point-guided-3d-human-mesh-1 | 2304.09502 | null | https://arxiv.org/abs/2304.09502v1 | https://arxiv.org/pdf/2304.09502v1.pdf | Sampling is Matter: Point-guided 3D Human Mesh Reconstruction | This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex. To resolve this problem, we propose to design a simple feature sampling scheme. The key idea is to sample features in the embedded space by following the guide of points, which are estimated as projection results of 3D mesh vertices (i.e., ground truth). This helps the model to concentrate more on vertex-relevant features in the 2D space, thus leading to the reconstruction of the natural human pose. Furthermore, we apply progressive attention masking to precisely estimate local interactions between vertices even under severe occlusions. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of 3D human mesh reconstruction. The code and model are publicly available at: https://github.com/DCVL-3D/PointHMR_release. | ['Wonjun Kim', 'Gi-Mun Um', 'Hyukmin Kwon', 'Hyunwoo Park', 'Mi-Gyeong Gwon', 'Jeonghwan Kim'] | 2023-04-19 | sampling-is-matter-point-guided-3d-human-mesh | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Sampling_Is_Matter_Point-Guided_3D_Human_Mesh_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.95191836e-02 2.26233438e-01 1.05263423e-02 -1.97337732e-01
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05b0b39c-d651-4ec0-88bd-834fe8506e54 | not-a-cute-stroke-analysis-of-rule-and-neural | null | null | https://aclanthology.org/2020.louhi-1.4 | https://aclanthology.org/2020.louhi-1.4.pdf | Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports | We present an in-depth comparison of three clinical information extraction (IE) systems designed to perform entity recognition and negation detection on brain imaging reports: EdIE-R, a bespoke rule-based system, and two neural network models, EdIE-BiLSTM and EdIE-BERT, both multi-task learning models with a BiLSTM and BERT encoder respectively. We compare our models both on an in-sample and an out-of-sample dataset containing mentions of stroke findings and draw on our error analysis to suggest improvements for effective annotation when building clinical NLP models for a new domain. Our analysis finds that our rule-based system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset. On the other hand, the neural models do not generalise negation to the out-of-sample dataset, despite metrics on the in-sample dataset suggesting otherwise. | ['William Whiteley', 'Richard Tobin', 'Claire Grover', 'Beatrice Alex', 'Andreas Grivas'] | null | null | null | null | emnlp-louhi-2020-11 | ['negation-detection'] | ['natural-language-processing'] | [ 2.37731859e-01 7.59024322e-01 -2.70881027e-01 -6.40331507e-01
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eb1eabd7-77ef-4902-8621-a69f6e14f8d4 | a-quantitative-metric-for-privacy-leakage-in | 2102.13472 | null | https://arxiv.org/abs/2102.13472v1 | https://arxiv.org/pdf/2102.13472v1.pdf | A Quantitative Metric for Privacy Leakage in Federated Learning | In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. However, the gradient itself might carry enough information for precise inference of the original data. By reporting their parameter gradients to the central server, client datasets are exposed to inference attacks from adversaries. In this paper, we propose a quantitative metric based on mutual information for clients to evaluate the potential risk of information leakage in their gradients. Mutual information has received increasing attention in the machine learning and data mining community over the past few years. However, existing mutual information estimation methods cannot handle high-dimensional variables. In this paper, we propose a novel method to approximate the mutual information between the high-dimensional gradients and batched input data. Experimental results show that the proposed metric reliably reflect the extent of information leakage in federated learning. In addition, using the proposed metric, we investigate the influential factors of risk level. It is proven that, the risk of information leakage is related to the status of the task model, as well as the inherent data distribution. | ['Jing Xiao', 'Jianzong Wang', 'Xinghua Zhu', 'Yong liu'] | 2021-02-24 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-3.00640941e-01 -1.18873440e-01 -1.74345151e-01 -4.12362605e-01
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d43aa7e5-2921-4138-bcb9-3df66e8e6249 | safe-model-based-design-of-experiments-using | 2011.10009 | null | https://arxiv.org/abs/2011.10009v2 | https://arxiv.org/pdf/2011.10009v2.pdf | Safe model-based design of experiments using Gaussian processes | Construction of kinetic models has become an indispensable step in the development and scale up of processes in the industry. Model-based design of experiments (MBDoE) has been widely used for the purpose of improving parameter precision in nonlinear dynamic systems. This process needs to account for both parametric and structural uncertainty, as the feasibility constraints imposed on the system may well turn out to be violated leading to unsafe experimental conditions when an optimally designed experiment is performed. In this work, a Gaussian process is utilized in a two-fold manner: 1) to quantify the uncertainty realization of the physical system and calculate the plant-model mismatch, 2) to compute the optimal experimental design while accounting for the parametric uncertainty. This method provides a guarantee for the probabilistic satisfaction of the constraints in the context of model-based design of experiments. The method is assisted with the use of adaptive trust-regions in order to facilitate a satisfactory local approximation. The proposed method is able to allow the design of optimal experiments starting from limited preliminary knowledge of the parameter set, leading to a safe exploration of the parameter space. The performance of this method is demonstrated through illustrative case studies regarding the parameter identification of the kinetic model in flow reactors. | ['Federico Galvanin', 'Panagiotis Petsagkourakis'] | 2020-11-19 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.08320974e-01 2.58132696e-01 3.70445102e-01 2.20362961e-01
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d8ceaae0-450f-4b45-9f8e-cc877fdad235 | deftri-a-few-shot-label-fused-contextual | null | null | https://aclanthology.org/2022.ecnlp-1.1 | https://aclanthology.org/2022.ecnlp-1.1.pdf | DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce | Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for automated defect triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels fused text embeddings to improve contextual representations from human-generated product defects. For our multi-label text classification defect triage task, we also introduce a Walmart proprietary dataset of product defects using weak supervision and adversarial learning, in a few-shot setting. | ['Ipsita Mohanty'] | null | null | null | null | ecnlp-acl-2022-5 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.46264327e-01 2.02444583e-01 1.81957081e-01 -8.93740773e-01
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c68ddb69-4a08-42fb-ba21-4be398cea727 | a-haar-wavelet-based-perceptual-similarity | 1607.06140 | null | http://arxiv.org/abs/1607.06140v4 | http://arxiv.org/pdf/1607.06140v4.pdf | A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment | In most practical situations, the compression or transmission of images and
videos creates distortions that will eventually be perceived by a human
observer. Vice versa, image and video restoration techniques, such as
inpainting or denoising, aim to enhance the quality of experience of human
viewers. Correctly assessing the similarity between an image and an undistorted
reference image as subjectively experienced by a human viewer can thus lead to
significant improvements in any transmission, compression, or restoration
system. This paper introduces the Haar wavelet-based perceptual similarity
index (HaarPSI), a novel and computationally inexpensive similarity measure for
full reference image quality assessment. The HaarPSI utilizes the coefficients
obtained from a Haar wavelet decomposition to assess local similarities between
two images, as well as the relative importance of image areas. The consistency
of the HaarPSI with the human quality of experience was validated on four large
benchmark databases containing thousands of differently distorted images. On
these databases, the HaarPSI achieves higher correlations with human opinion
scores than state-of-the-art full reference similarity measures like the
structural similarity index (SSIM), the feature similarity index (FSIM), and
the visual saliency-based index (VSI). Along with the simple computational
structure and the short execution time, these experimental results suggest a
high applicability of the HaarPSI in real world tasks. | ['Thomas Wiegand', 'Gitta Kutyniok', 'Sebastian Bosse', 'Rafael Reisenhofer'] | 2016-07-20 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 3.96456957e-01 -4.12464410e-01 1.10500410e-01 -5.21161482e-02
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ced03bbe-086a-4b0f-a13d-2ce4b267ef40 | xnli-evaluating-cross-lingual-sentence | 1809.05053 | null | http://arxiv.org/abs/1809.05053v1 | http://arxiv.org/pdf/1809.05053v1.pdf | XNLI: Evaluating Cross-lingual Sentence Representations | State-of-the-art natural language processing systems rely on supervision in
the form of annotated data to learn competent models. These models are
generally trained on data in a single language (usually English), and cannot be
directly used beyond that language. Since collecting data in every language is
not realistic, there has been a growing interest in cross-lingual language
understanding (XLU) and low-resource cross-language transfer. In this work, we
construct an evaluation set for XLU by extending the development and test sets
of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15
languages, including low-resource languages such as Swahili and Urdu. We hope
that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence
understanding by providing an informative standard evaluation task. In
addition, we provide several baselines for multilingual sentence understanding,
including two based on machine translation systems, and two that use parallel
data to train aligned multilingual bag-of-words and LSTM encoders. We find that
XNLI represents a practical and challenging evaluation suite, and that directly
translating the test data yields the best performance among available
baselines. | ['Ruty Rinott', 'Holger Schwenk', 'Adina Williams', 'Veselin Stoyanov', 'Samuel R. Bowman', 'Guillaume Lample', 'Alexis Conneau'] | 2018-09-13 | xnli-evaluating-cross-lingual-sentence-1 | https://aclanthology.org/D18-1269 | https://aclanthology.org/D18-1269.pdf | emnlp-2018-10 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.94987729e-01 4.99707125e-02 -4.08437222e-01 -7.83045113e-01
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f3851e62-da16-42ae-b5c9-3c0c5671881f | hhp-net-a-light-heteroscedastic-neural | 2111.01440 | null | https://arxiv.org/abs/2111.01440v2 | https://arxiv.org/pdf/2111.01440v2.pdf | HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty | In this paper we introduce a novel method to estimate the head pose of people in single images starting from a small set of head keypoints. To this purpose, we propose a regression model that exploits keypoints computed automatically by 2D pose estimation algorithms and outputs the head pose represented by yaw, pitch, and roll. Our model is simple to implement and more efficient with respect to the state of the art -- faster in inference and smaller in terms of memory occupancy -- with comparable accuracy. Our method also provides a measure of the heteroscedastic uncertainties associated with the three angles, through an appropriately designed loss function; we show there is a correlation between error and uncertainty values, thus this extra source of information may be used in subsequent computational steps. As an example application, we address social interaction analysis in images: we propose an algorithm for a quantitative estimation of the level of interaction between people, starting from their head poses and reasoning on their mutual positions. The code is available at https://github.com/cantarinigiorgio/HHP-Net. | ['Francesca Odone', 'Nicoletta Noceti', 'Federico Figari Tomenotti', 'Giorgio Cantarini'] | 2021-11-02 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.38442296e-01 2.62087554e-01 2.28254616e-01 -6.12223566e-01
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285e7dd3-aa99-47f4-be82-fe3a36785be8 | k-salsa-k-anonymous-synthetic-averaging-of | 2303.10824 | null | https://arxiv.org/abs/2303.10824v1 | https://arxiv.org/pdf/2303.10824v1.pdf | k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment | The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training data. However, the personally-identifying nature of retinal images, encompassing the unique vascular structure of each individual, often prevents this data from being shared openly. While prior works have explored image de-identification strategies based on synthetic averaging of images in other domains (e.g. facial images), existing techniques face difficulty in preserving both privacy and clinical utility in retinal images, as we demonstrate in our work. We therefore introduce k-SALSA, a generative adversarial network (GAN)-based framework for synthesizing retinal fundus images that summarize a given private dataset while satisfying the privacy notion of k-anonymity. k-SALSA brings together state-of-the-art techniques for training and inverting GANs to achieve practical performance on retinal images. Furthermore, k-SALSA leverages a new technique, called local style alignment, to generate a synthetic average that maximizes the retention of fine-grain visual patterns in the source images, thus improving the clinical utility of the generated images. On two benchmark datasets of diabetic retinopathy (EyePACS and APTOS), we demonstrate our improvement upon existing methods with respect to image fidelity, classification performance, and mitigation of membership inference attacks. Our work represents a step toward broader sharing of retinal images for scientific collaboration. Code is available at https://github.com/hcholab/k-salsa. | ['Hyunghoon Cho', 'Michael Morley', 'Hyunwoo J. Kim', 'Hyeonjin Park', 'Minkyu Jeon'] | 2023-03-20 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 5.20661354e-01 2.85349935e-01 -1.41338274e-01 -5.11627614e-01
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7ab30a36-39e3-4771-b7e3-27fd70908bf8 | artificial-influence-an-analysis-of-ai-driven | 2303.08721 | null | https://arxiv.org/abs/2303.08721v1 | https://arxiv.org/pdf/2303.08721v1.pdf | Artificial Influence: An Analysis Of AI-Driven Persuasion | Persuasion is a key aspect of what it means to be human, and is central to business, politics, and other endeavors. Advancements in artificial intelligence (AI) have produced AI systems that are capable of persuading humans to buy products, watch videos, click on search results, and more. Even systems that are not explicitly designed to persuade may do so in practice. In the future, increasingly anthropomorphic AI systems may form ongoing relationships with users, increasing their persuasive power. This paper investigates the uncertain future of persuasive AI systems. We examine ways that AI could qualitatively alter our relationship to and views regarding persuasion by shifting the balance of persuasive power, allowing personalized persuasion to be deployed at scale, powering misinformation campaigns, and changing the way humans can shape their own discourse. We consider ways AI-driven persuasion could differ from human-driven persuasion. We warn that ubiquitous highlypersuasive AI systems could alter our information environment so significantly so as to contribute to a loss of human control of our own future. In response, we examine several potential responses to AI-driven persuasion: prohibition, identification of AI agents, truthful AI, and legal remedies. We conclude that none of these solutions will be airtight, and that individuals and governments will need to take active steps to guard against the most pernicious effects of persuasive AI. | ['Thomas Woodside', 'Matthew Burtell'] | 2023-03-15 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 5.72719753e-01 8.76706779e-01 -2.67074406e-01 -4.36470121e-01
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c3d34bc6-5495-4a77-9871-c3c2d99807f6 | adversarial-representation-learning-for-text | 1908.10534 | null | https://arxiv.org/abs/1908.10534v1 | https://arxiv.org/pdf/1908.10534v1.pdf | Adversarial Representation Learning for Text-to-Image Matching | For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem. Its challenges originate from the large word variance in the text domain as well as the difficulty of accurately measuring the distance between the features of the two modalities. Most prior work focuses on the latter challenge, by introducing loss functions that help the network learn better feature representations but fail to account for the complexity of the textual input. With that in mind, we introduce TIMAM: a Text-Image Modality Adversarial Matching approach that learns modality-invariant feature representations using adversarial and cross-modal matching objectives. In addition, we demonstrate that BERT, a publicly-available language model that extracts word embeddings, can successfully be applied in the text-to-image matching domain. The proposed approach achieves state-of-the-art cross-modal matching performance on four widely-used publicly-available datasets resulting in absolute improvements ranging from 2% to 5% in terms of rank-1 accuracy. | ['Ioannis A. Kakadiaris', 'Xiang Xu', 'Nikolaos Sarafianos'] | 2019-08-28 | adversarial-representation-learning-for-text-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Sarafianos_Adversarial_Representation_Learning_for_Text-to-Image_Matching_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Sarafianos_Adversarial_Representation_Learning_for_Text-to-Image_Matching_ICCV_2019_paper.pdf | iccv-2019-10 | ['person-search'] | ['computer-vision'] | [ 5.46094954e-01 -2.86345065e-01 -2.18238816e-01 -3.50835621e-01
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57c629ed-c974-4901-be37-f0ea59a8d664 | going-deeper-with-brain-morphometry-using | 2009.03303 | null | https://arxiv.org/abs/2009.03303v1 | https://arxiv.org/pdf/2009.03303v1.pdf | Going deeper with brain morphometry using neural networks | Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convolutional neural networks can infer morphometric measurements within a few seconds. Nevertheless, the accuracy of the devised model for insightful bio-markers (mean curvature and thickness) remains unsatisfactory. In this paper, we propose a more accurate and efficient neural network model for brain morphometry named HerstonNet. More specifically, we develop a 3D ResNet-based neural network to learn rich features directly from MRI, design a multi-scale regression scheme by predicting morphometric measures at feature maps of different resolutions, and leverage a robust optimization method to avoid poor quality minima and reduce the prediction variance. As a result, HerstonNet improves the existing approach by 24.30% in terms of intraclass correlation coefficient (agreement measure) to FreeSurfer silver-standards while maintaining a competitive run-time. | ['Olivier Salvado', 'Clinton Fookes', 'Vincent Doré', 'Léo Lebrat', 'Jason Dowling', 'Pierrick Bourgeat', 'Rodrigo Santa Cruz', 'Jurgen Fripp'] | 2020-09-07 | null | null | null | null | ['brain-morphometry'] | ['medical'] | [ 2.11887155e-02 1.19919248e-01 1.42678574e-01 -5.23720622e-01
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e2e8f063-42b0-44dc-8e08-00fb1864af67 | dyadformer-a-multi-modal-transformer-for-long | 2109.09487 | null | https://arxiv.org/abs/2109.09487v1 | https://arxiv.org/pdf/2109.09487v1.pdf | Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions | Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset. | ['Cristina Palmero', 'Sergio Escalera', 'Thomas B. Moeslund', 'David Leiva', 'Georgina Guilera', 'David Gallardo-Pujol', 'Julio C. S. Jacques Junior', 'Sorina Smeureanu', 'Javier Selva', 'Albert Clapés', 'David Curto'] | 2021-09-20 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [-2.40646034e-01 2.90294252e-02 4.91533522e-03 -7.48938978e-01
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3.30578834e-01 9.75301147e-01 2.32362285e-01 -3.60140830e-01
4.24716234e-01 3.54360133e-01 8.02297056e-01 -5.14192522e-01
-9.61176336e-01 -7.82129765e-02 3.75409335e-01 -6.61711514e-01
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2.41799027e-01 -2.67959893e-01 4.68197435e-01 9.34168279e-01
5.39554060e-01 7.02208042e-01 4.77590084e-01 -8.97358179e-01
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1.17518805e-01 -1.07687831e+00 -4.83482257e-02 -4.28716987e-02] | [13.322381019592285, 4.966462135314941] |
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