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ae2b5023-e014-4140-9dba-9f9dcb053400 | intent-discovery-with-or-without-labeled-data | null | null | https://openreview.net/forum?id=YZXFpEU8UHi | https://openreview.net/pdf?id=YZXFpEU8UHi | Intent Discovery With Or Without Labeled Data Using Dependency Parser | In dialogue applications, machine learning classification models are often used to classify user utterances into different intents that help to understand the users.
In real world scenarios, however, some utterances may not belong to any of the anticipated intent categories.
Furthermore, supervised classification models are not a viable solution when data of a new domain is introduced without the corresponding labels.
In this work, we present a clustering and evaluation approach that can be used in semi-supervised or unsupervised modes, depending on the (non-)availability of training data for new intent discovery.
This method assigns meaningful intent-labels by determining the optimal number of clusters and evaluating the performance of the clustering results. In addition, it assigns a TF-IDF score to individual samples within a cluster. | ['Anonymous'] | 2020-10-15 | null | null | null | neurips-workshop-hamlets-2020-12 | ['intent-discovery'] | ['natural-language-processing'] | [ 1.07320383e-01 1.65054306e-01 -1.72472924e-01 -8.60876024e-01
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1bee6427-3b75-4db7-8973-3dbcd33cc4f1 | meta-dm-applications-of-diffusion-models-on | 2305.08092 | null | https://arxiv.org/abs/2305.08092v1 | https://arxiv.org/pdf/2305.08092v1.pdf | Meta-DM: Applications of Diffusion Models on Few-Shot Learning | In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a generalized data processing module for FSL problems based on diffusion models. Meta-DM is a simple yet effective module that can be easily integrated with existing FSL methods, leading to significant performance improvements in both supervised and unsupervised settings. We provide a theoretical analysis of Meta-DM and evaluate its performance on several algorithms. Our experiments show that combining Meta-DM with certain methods achieves state-of-the-art results. | ['Hui Tian', 'YuQi Yang', 'Jiarun Liu', 'Xiurong Jiang', 'Wentao Hu'] | 2023-05-14 | null | null | null | null | ['unsupervised-few-shot-image-classification'] | ['computer-vision'] | [ 4.44224328e-02 -8.18921179e-02 -5.09895146e-01 -2.17391357e-01
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f44d9ce2-9d96-4ca0-a2a8-26528bb3f897 | contextual-adversarial-attack-against-aerial | 2302.13487 | null | https://arxiv.org/abs/2302.13487v1 | https://arxiv.org/pdf/2302.13487v1.pdf | Contextual adversarial attack against aerial detection in the physical world | Deep Neural Networks (DNNs) have been extensively utilized in aerial detection. However, DNNs' sensitivity and vulnerability to maliciously elaborated adversarial examples have progressively garnered attention. Recently, physical attacks have gradually become a hot issue due to they are more practical in the real world, which poses great threats to some security-critical applications. In this paper, we take the first attempt to perform physical attacks in contextual form against aerial detection in the physical world. We propose an innovative contextual attack method against aerial detection in real scenarios, which achieves powerful attack performance and transfers well between various aerial object detectors without smearing or blocking the interested objects to hide. Based on the findings that the targets' contextual information plays an important role in aerial detection by observing the detectors' attention maps, we propose to make full use of the contextual area of the interested targets to elaborate contextual perturbations for the uncovered attacks in real scenarios. Extensive proportionally scaled experiments are conducted to evaluate the effectiveness of the proposed contextual attack method, which demonstrates the proposed method's superiority in both attack efficacy and physical practicality. | ['Shaohui Mei', 'Mingyang Ma', 'Yuru Su', 'Xiaofei Wang', 'Jiawei Lian'] | 2023-02-27 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 4.50759113e-01 -1.61228389e-01 2.97224134e-01 2.65314519e-01
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bb8d19f8-80d4-4277-8dc1-ac24193dc1ac | task-oriented-dialog-systems-that-consider | 1911.10484 | null | https://arxiv.org/abs/1911.10484v2 | https://arxiv.org/pdf/1911.10484v2.pdf | Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context | Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches takes this property into account. We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. Specifically, we first use dialog states to summarize the dialog history, and then discover all possible mappings from every dialog state to its different valid system actions. During dialog system training, we enable the current dialog state to map to all valid system actions discovered in the previous process to create additional state-action pairs. By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. Experimental results show that the proposed framework consistently improves dialog policy diversity, and results in improved response diversity and appropriateness. Our model obtains state-of-the-art results on MultiWOZ. | ['Yichi Zhang', 'Zhou Yu', 'Zhijian Ou'] | 2019-11-24 | null | null | null | null | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [ 5.35697676e-02 1.82266235e-01 -4.12933975e-01 -7.62506723e-01
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42707463-ee57-4691-a95a-de511b9093f7 | rank-over-class-the-untapped-potential-of | 2009.05160 | null | https://arxiv.org/abs/2009.05160v4 | https://arxiv.org/pdf/2009.05160v4.pdf | Rank over Class: The Untapped Potential of Ranking in Natural Language Processing | Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection. With such a powerful solution, it is often tempting to use it as the go-to tool for all NLP problems since when you are holding a hammer, everything looks like a nail. However, we argue here that many tasks which are currently addressed using classification are in fact being shoehorned into a classification mould and that if we instead address them as a ranking problem, we not only improve the model, but we achieve better performance. We propose a novel end-to-end ranking approach consisting of a Transformer network responsible for producing representations for a pair of text sequences, which are in turn passed into a context aggregating network outputting ranking scores used to determine an ordering to the sequences based on some notion of relevance. We perform numerous experiments on publicly-available datasets and investigate the applications of ranking in problems often solved using classification. In an experiment on a heavily-skewed sentiment analysis dataset, converting ranking results to classification labels yields an approximately 22% improvement over state-of-the-art text classification, demonstrating the efficacy of text ranking over text classification in certain scenarios. | ['Amir Atapour-Abarghouei', 'Andrew Stephen McGough', 'Stephen Bonner'] | 2020-09-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 6.61395967e-01 -1.13377430e-01 -1.65915310e-01 -7.91191041e-01
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e28bfb2d-d140-4b77-8d46-34a55a2259d8 | representation-learning-by-rotating-your | 1705.11136 | null | http://arxiv.org/abs/1705.11136v2 | http://arxiv.org/pdf/1705.11136v2.pdf | Representation Learning by Rotating Your Faces | The large pose discrepancy between two face images is one of the fundamental
challenges in automatic face recognition. Conventional approaches to
pose-invariant face recognition either perform face frontalization on, or learn
a pose-invariant representation from, a non-frontal face image. We argue that
it is more desirable to perform both tasks jointly to allow them to leverage
each other. To this end, this paper proposes a Disentangled Representation
learning-Generative Adversarial Network (DR-GAN) with three distinct novelties.
First, the encoder-decoder structure of the generator enables DR-GAN to learn a
representation that is both generative and discriminative, which can be used
for face image synthesis and pose-invariant face recognition. Second, this
representation is explicitly disentangled from other face variations such as
pose, through the pose code provided to the decoder and pose estimation in the
discriminator. Third, DR-GAN can take one or multiple images as the input, and
generate one unified identity representation along with an arbitrary number of
synthetic face images. Extensive quantitative and qualitative evaluation on a
number of controlled and in-the-wild databases demonstrate the superiority of
DR-GAN over the state of the art in both learning representations and rotating
large-pose face images. | ['Xiaoming Liu', 'Luan Tran', 'Xi Yin'] | 2017-05-31 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.58527058e-01 2.12342486e-01 -7.47273713e-02 -5.98179758e-01
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41fc62c3-2c69-4f5f-bae2-7bebfc46696f | cross-lingual-contrastive-learning-for-fine | null | null | https://aclanthology.org/2022.acl-long.159 | https://aclanthology.org/2022.acl-long.159.pdf | Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages | Fine-grained entity typing (FGET) aims to classify named entity mentions into fine-grained entity types, which is meaningful for entity-related NLP tasks. For FGET, a key challenge is the low-resource problem — the complex entity type hierarchy makes it difficult to manually label data. Especially for those languages other than English, human-labeled data is extremely scarce. In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. Specifically, we use multi-lingual pre-trained language models (PLMs) as the backbone to transfer the typing knowledge from high-resource languages (such as English) to low-resource languages (such as Chinese). Furthermore, we introduce entity-pair-oriented heuristic rules as well as machine translation to obtain cross-lingual distantly-supervised data, and apply cross-lingual contrastive learning on the distantly-supervised data to enhance the backbone PLMs. Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages, even without any language-specific human-labeled data. Our code is also available at https://github.com/thunlp/CrossET. | ['Suncong Zheng', 'Hao Fei', 'Zhou Botong', 'Maosong Sun', 'Zhiyuan Liu', 'Weize Chen', 'Yuqi Luo', 'Xu Han'] | null | null | null | null | acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [-5.24280667e-01 -4.63799722e-02 -6.32354498e-01 -5.63962519e-01
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50a39763-3dd4-4dbf-aeaf-cda8f6c836bc | effective-cascade-dual-decoder-model-for | 2106.14163 | null | https://arxiv.org/abs/2106.14163v1 | https://arxiv.org/pdf/2106.14163v1.pdf | Effective Cascade Dual-Decoder Model for Joint Entity and Relation Extraction | Extracting relational triples from texts is a fundamental task in knowledge graph construction. The popular way of existing methods is to jointly extract entities and relations using a single model, which often suffers from the overlapping triple problem. That is, there are multiple relational triples that share the same entities within one sentence. In this work, we propose an effective cascade dual-decoder approach to extract overlapping relational triples, which includes a text-specific relation decoder and a relation-corresponded entity decoder. Our approach is straightforward: the text-specific relation decoder detects relations from a sentence according to its text semantics and treats them as extra features to guide the entity extraction; for each extracted relation, which is with trainable embedding, the relation-corresponded entity decoder detects the corresponding head and tail entities using a span-based tagging scheme. In this way, the overlapping triple problem is tackled naturally. Experiments on two public datasets demonstrate that our proposed approach outperforms state-of-the-art methods and achieves better F1 scores under the strict evaluation metric. Our implementation is available at https://github.com/prastunlp/DualDec. | ['Xiliang Zhang', 'Huimin Ren', 'Lianbo Ma'] | 2021-06-27 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-2.19603963e-02 5.25770247e-01 -4.49477822e-01 -2.46056661e-01
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17d473b6-f47f-4805-bec8-3f8f90700a40 | covidexpert-a-triplet-siamese-neural-network | 2302.09004 | null | https://arxiv.org/abs/2302.09004v1 | https://arxiv.org/pdf/2302.09004v1.pdf | CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19 | Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensive hyperparameter adjustment and are unsuitable for small datasets. By leveraging the data in a comparatively small dataset, few-shot learning algorithms aim to reduce the requirement of large datasets. This inspired us to develop a few-shot learning model for early detection of COVID-19 to reduce the post-effect of this dangerous disease. The proposed architecture combines few-shot learning with an ensemble of pre-trained convolutional neural networks to extract feature vectors from CT scan images for similarity learning. The proposed Triplet Siamese Network as the few-shot learning model classified CT scan images into Normal, COVID-19, and Community-Acquired Pneumonia. The suggested model achieved an overall accuracy of 98.719%, a specificity of 99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT scans per category for training data. | ['Enamul Hassan', 'Gourab Roy', 'Tareque Rahman Ornob'] | 2023-02-17 | null | null | null | null | ['few-shot-image-classification', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 2.01848060e-01 -3.49148542e-01 7.16929138e-02 -2.78441459e-01
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5bfef72f-db96-42dc-b21f-38010f63e2b5 | dark-beyond-deep-a-paradigm-shift-to | 2004.09044 | null | https://arxiv.org/abs/2004.09044v1 | https://arxiv.org/pdf/2004.09044v1.pdf | Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense | Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artificial intelligence (AI) system is challenged to develop "common sense", enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of "why" and "how", beyond the dominant "what" and "where" framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the "dark matter" of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks. | ['Song-Chun Zhu', 'Feng Gao', 'Tao Gao', 'Yixin Zhu', 'Ying Nian Wu', 'Lifeng Fan', 'Siyuan Qi', 'Siyuan Huang', 'Hangxin Liu', 'Mark Edmonds', 'Joshua B. Tenenbaum', 'Chi Zhang'] | 2020-04-20 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 2.10674316e-01 2.55508512e-01 9.63623673e-02 -1.43545657e-01
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b20b2692-d8af-42b3-a4ff-3f999704b234 | augdiff-diffusion-based-feature-augmentation | 2303.06371 | null | https://arxiv.org/abs/2303.06371v1 | https://arxiv.org/pdf/2303.06371v1.pdf | AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image | Multiple Instance Learning (MIL), a powerful strategy for weakly supervised learning, is able to perform various prediction tasks on gigapixel Whole Slide Images (WSIs). However, the tens of thousands of patches in WSIs usually incur a vast computational burden for image augmentation, limiting the MIL model's improvement in performance. Currently, the feature augmentation-based MIL framework is a promising solution, while existing methods such as Mixup often produce unrealistic features. To explore a more efficient and practical augmentation method, we introduce the Diffusion Model (DM) into MIL for the first time and propose a feature augmentation framework called AugDiff. Specifically, we employ the generation diversity of DM to improve the quality of feature augmentation and the step-by-step generation property to control the retention of semantic information. We conduct extensive experiments over three distinct cancer datasets, two different feature extractors, and three prevalent MIL algorithms to evaluate the performance of AugDiff. Ablation study and visualization further verify the effectiveness. Moreover, we highlight AugDiff's higher-quality augmented feature over image augmentation and its superiority over self-supervised learning. The generalization over external datasets indicates its broader applications. | ['Yongbing Zhang', 'Haoqian Wang', 'Yifeng Wang', 'Liuxi Dai', 'Zhuchen Shao'] | 2023-03-11 | null | null | null | null | ['whole-slide-images', 'image-augmentation', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.21680772e-01 2.61683874e-02 -5.42052925e-01 -2.43139938e-01
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be5598d3-4b0a-425c-ba3a-1122a66e8085 | manipulated-object-proposal-a-discriminative | 1509.00651 | null | http://arxiv.org/abs/1509.00651v3 | http://arxiv.org/pdf/1509.00651v3.pdf | Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition | Detecting and recognizing objects interacting with humans lie in the center
of first-person (egocentric) daily activity recognition. However, due to noisy
camera motion and frequent changes in viewpoint and scale, most of the previous
egocentric action recognition methods fail to capture and model highly
discriminative object features. In this work, we propose a novel pipeline for
first-person daily activity recognition, aiming at more discriminative object
feature representation and object-motion feature fusion. Our object feature
extraction and representation pipeline is inspired by the recent success of
object hypotheses and deep convolutional neural network based detection
frameworks. Our key contribution is a simple yet effective manipulated object
proposal generation scheme. This scheme leverages motion cues such as motion
boundary and motion magnitude (in contrast, camera motion is usually considered
as "noise" for most previous methods) to generate a more compact and
discriminative set of object proposals, which are more closely related to the
objects which are being manipulated. Then, we learn more discriminative object
detectors from these manipulated object proposals based on region-based
convolutional neural network (R-CNN). Meanwhile, we develop a network based
feature fusion scheme which better combines object and motion features. We show
in experiments that the proposed framework significantly outperforms the
state-of-the-art recognition performance on a challenging first-person daily
activity benchmark. | ['Jian-Feng Wang', 'Bingbing Ni', 'Meng Wang', 'Jun Yuan', 'Changzhi Luo', 'Shuicheng Yan'] | 2015-09-02 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 2.04916552e-01 -5.59505761e-01 -2.95835435e-01 -2.60929167e-01
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f923db3f-4681-4116-82b5-58c53d2e129b | unimib-at-trec-2021-clinical-trials-track | 2207.13514 | null | https://arxiv.org/abs/2207.13514v1 | https://arxiv.org/pdf/2207.13514v1.pdf | UNIMIB at TREC 2021 Clinical Trials Track | This contribution summarizes the participation of the UNIMIB team to the TREC 2021 Clinical Trials Track. We have investigated the effect of different query representations combined with several retrieval models on the retrieval performance. First, we have implemented a neural re-ranking approach to study the effectiveness of dense text representations. Additionally, we have investigated the effectiveness of a novel decision-theoretic model for relevance estimation. Finally, both of the above relevance models have been compared with standard retrieval approaches. In particular, we combined a keyword extraction method with a standard retrieval process based on the BM25 model and a decision-theoretic relevance model that exploits the characteristics of this particular search task. The obtained results show that the proposed keyword extraction method improves 84% of the queries over the TREC's median NDCG@10 measure when combined with either traditional or decision-theoretic relevance models. Moreover, regarding RPEC@10, the employed decision-theoretic model improves 85% of the queries over the reported TREC's median value. | ['Gabriella Pasi', 'Oscar Espitia', 'Georgios Peikos'] | 2022-07-27 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 3.34823072e-01 1.68791324e-01 -2.92219400e-01 1.19381838e-01
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0e4a7195-9e5a-4cd5-9877-6cc87902a7b8 | improving-machine-translation-of-rare-and | null | null | https://aclanthology.org/2021.wmt-1.66 | https://aclanthology.org/2021.wmt-1.66.pdf | Improving Machine Translation of Rare and Unseen Word Senses | The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge. Since word senses are not represented uniformly in the parallel corpora used for training, there is an excessive use of the most frequent sense in MT output. In this work, we propose CmBT (Contextually-mined Back-Translation), an approach for improving multi-sense word translation leveraging pre-trained cross-lingual contextual word representations (CCWRs). Because of their contextual sensitivity and their large pre-training data, CCWRs can easily capture word senses that are missing or very rare in parallel corpora used to train MT. Specifically, CmBT applies bilingual lexicon induction on CCWRs to mine sense-specific target sentences from a monolingual dataset, and then back-translates these sentences to generate a pseudo parallel corpus as additional training data for an MT system. We test the translation quality of ambiguous words on the MuCoW test suite, which was built to test the word sense disambiguation effectiveness of MT systems. We show that our system improves on the translation of difficult unseen and low frequency word senses. | ['Anna Korhonen', 'Alexander Fraser', 'Dario Stojanovski', 'Qianchu Liu', 'Viktor Hangya'] | null | null | null | null | wmt-emnlp-2021-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.94830298e-01 -2.73638725e-01 -2.87036419e-01 -4.09301102e-01
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3cd3dd7e-c183-46d9-9f62-308a010ae2c1 | a-unified-deep-learning-architecture-for | 1802.00385 | null | http://arxiv.org/abs/1802.00385v2 | http://arxiv.org/pdf/1802.00385v2.pdf | A Unified Deep Learning Architecture for Abuse Detection | Hate speech, offensive language, sexism, racism and other types of abusive
behavior have become a common phenomenon in many online social media platforms.
In recent years, such diverse abusive behaviors have been manifesting with
increased frequency and levels of intensity. This is due to the openness and
willingness of popular media platforms, such as Twitter and Facebook, to host
content of sensitive or controversial topics. However, these platforms have not
adequately addressed the problem of online abusive behavior, and their
responsiveness to the effective detection and blocking of such inappropriate
behavior remains limited.
In the present paper, we study this complex problem by following a more
holistic approach, which considers the various aspects of abusive behavior. To
make the approach tangible, we focus on Twitter data and analyze user and
textual properties from different angles of abusive posting behavior. We
propose a deep learning architecture, which utilizes a wide variety of
available metadata, and combines it with automatically-extracted hidden
patterns within the text of the tweets, to detect multiple abusive behavioral
norms which are highly inter-related. We apply this unified architecture in a
seamless, transparent fashion to detect different types of abusive behavior
(hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for
any tuning of the model architecture for each task. We test the proposed
approach with multiple datasets addressing different and multiple abusive
behaviors on Twitter. Our results demonstrate that it largely outperforms the
state-of-art methods (between 21 and 45\% improvement in AUC, depending on the
dataset). | ['Antigoni-Maria Founta', 'Nicolas Kourtellis', 'Despoina Chatzakou', 'Athena Vakali', 'Jeremy Blackburn', 'Ilias Leontiadis'] | 2018-02-01 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-2.52619624e-01 -2.10994527e-01 -2.44909510e-01 -1.80296779e-01
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bff75d7b-5220-4552-8386-3497aed534bc | track-to-detect-and-segment-an-online-multi | 2103.08808 | null | https://arxiv.org/abs/2103.08808v1 | https://arxiv.org/pdf/2103.08808v1.pdf | Track to Detect and Segment: An Online Multi-Object Tracker | Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets, including MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS (instance segmentation tracking). Project page: https://jialianwu.com/projects/TraDeS.html. | ['Junsong Yuan', 'Ming Yang', 'Yu Wang', 'Liangchen Song', 'Jiale Cao', 'Jialian Wu'] | 2021-03-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-instance-segmentation', 'online-multi-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.03505898e-01 -2.96148837e-01 -4.68899995e-01 -1.12850636e-01
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d84d71f7-791d-454e-ae5f-9879518e099f | scaling-up-trustless-dnn-inference-with-zero | 2210.08674 | null | https://arxiv.org/abs/2210.08674v1 | https://arxiv.org/pdf/2210.08674v1.pdf | Scaling up Trustless DNN Inference with Zero-Knowledge Proofs | As ML models have increased in capabilities and accuracy, so has the complexity of their deployments. Increasingly, ML model consumers are turning to service providers to serve the ML models in the ML-as-a-service (MLaaS) paradigm. As MLaaS proliferates, a critical requirement emerges: how can model consumers verify that the correct predictions were served, in the face of malicious, lazy, or buggy service providers? In this work, we present the first practical ImageNet-scale method to verify ML model inference non-interactively, i.e., after the inference has been done. To do so, we leverage recent developments in ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge), a form of zero-knowledge proofs. ZK-SNARKs allows us to verify ML model execution non-interactively and with only standard cryptographic hardness assumptions. In particular, we provide the first ZK-SNARK proof of valid inference for a full resolution ImageNet model, achieving 79\% top-5 accuracy. We further use these ZK-SNARKs to design protocols to verify ML model execution in a variety of scenarios, including for verifying MLaaS predictions, verifying MLaaS model accuracy, and using ML models for trustless retrieval. Together, our results show that ZK-SNARKs have the promise to make verified ML model inference practical. | ['Yi Sun', 'Ion Stoica', 'Tatsunori Hashimoto', 'Daniel Kang'] | 2022-10-17 | null | null | null | null | ['snarks'] | ['natural-language-processing'] | [-2.01642543e-01 2.01792255e-01 -3.27976495e-01 -3.47068638e-01
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434b4a01-3027-490d-bedc-b80802d877bc | medlens-improve-mortality-prediction-via | 2305.11742 | null | https://arxiv.org/abs/2305.11742v1 | https://arxiv.org/pdf/2305.11742v1.pdf | MedLens: Improve mortality prediction via medical signs selecting and regression interpolation | Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR, which exceeds the previous benchmark. | ['Weinan Dai', 'Chengjie Mou', 'Jun Wu', 'Xuesong Ye'] | 2023-05-19 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-1.38377234e-01 -1.66575417e-01 -2.92736411e-01 -5.45293570e-01
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7325b9d0-e66d-417b-a35c-4f008a1334e7 | a-novel-challenge-set-for-hebrew | 2010.02864 | null | https://arxiv.org/abs/2010.02864v1 | https://arxiv.org/pdf/2010.02864v1.pdf | A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration | One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses in order to properly evaluate performance, nor to train effective classifiers. In this paper we address the issue of unbalanced morphological ambiguities in Hebrew. We offer a challenge set for Hebrew homographs -- the first of its kind -- containing substantial attestation of each analysis of 21 Hebrew homographs. We show that the current SOTA of Hebrew disambiguation performs poorly on cases of unbalanced ambiguity. Leveraging our new dataset, we achieve a new state-of-the-art for all 21 words, improving the overall average F1 score from 0.67 to 0.95. Our resulting annotated datasets are made publicly available for further research. | ['Reut Tsarfaty', 'Moshe Koppel', 'Shaltiel Shmidman', 'Joshua Guedalia', 'Avi Shmidman'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.297 | https://aclanthology.org/2020.findings-emnlp.297.pdf | findings-of-the-association-for-computational | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 2.00311884e-01 3.61941099e-01 -9.99554768e-02 -3.26853633e-01
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a6892ac2-2932-4752-837e-ad398decd3ca | prediction-of-repurposed-drugs-for-treating | 2003.14333 | null | https://arxiv.org/abs/2003.14333v2 | https://arxiv.org/pdf/2003.14333v2.pdf | Prediction of repurposed drugs for treating lung injury in COVID-19 | Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, brought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Inhibition of Angiotensin-converting enzyme 2 (ACE2) caused by spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19, based on their abilities to reverse the gene expression patterns in HCC515 cells treated with ACE2 inhibitor and in human COVID-19 patient lung tissues. Further bioinformatics analysis shows that twelve significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues, including signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and Chemokine signaling pathways. All these twelve pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares eleven of twelve pathways with COL-3 with common target genes such as RHOA. It also uniquely targets genes related to lung injury, such as CALR and MMP14. In summary, this study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have the potential as repurposed drugs for its treatment. | ['Lana Garmire', 'Bing He'] | 2020-03-30 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 3.77304181e-02 -8.10939372e-01 -1.80716753e-01 4.13083822e-01
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569c2745-f506-4ed9-8580-0c7cbe10236d | learning-to-guide-multiple-heterogeneous | 2205.05784 | null | https://arxiv.org/abs/2205.05784v1 | https://arxiv.org/pdf/2205.05784v1.pdf | Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II | Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating. However, in real-world scenarios, expert data is limited and it is desired to train an agent that learns a behavior policy general enough to handle situations that were not demonstrated by the human expert. Another alternative is to learn these policies with no supervision via deep reinforcement learning, however, these algorithms require a large amount of computing time to perform well on complex tasks with high-dimensional state and action spaces, such as those found in StarCraft II. Automatic curriculum learning is a recent mechanism comprised of techniques designed to speed up deep reinforcement learning by adjusting the difficulty of the current task to be solved according to the agent's current capabilities. Designing a proper curriculum, however, can be challenging for sufficiently complex tasks, and thus we leverage human demonstrations as a way to guide agent exploration during training. In this work, we aim to train deep reinforcement learning agents that can command multiple heterogeneous actors where starting positions and overall difficulty of the task are controlled by an automatically-generated curriculum from a single human demonstration. Our results show that an agent trained via automated curriculum learning can outperform state-of-the-art deep reinforcement learning baselines and match the performance of the human expert in a simulated command and control task in StarCraft II modeled over a real military scenario. | ['Derrik E. Asher', 'Anjon Basak', 'John Richardson', 'Mark Mittrick', 'Vinicius G. Goecks', 'James Hare', 'Nicholas Waytowich'] | 2022-05-11 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-4.15361635e-02 6.98293000e-02 -4.04352576e-01 -2.19786853e-01
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d2149889-73d6-4737-aa4a-23a3ff17478d | lg4av-combining-language-models-and-graph | 2109.01479 | null | https://arxiv.org/abs/2109.01479v1 | https://arxiv.org/pdf/2109.01479v1.pdf | LG4AV: Combining Language Models and Graph Neural Networks for Author Verification | The automatic verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media platforms. Therefore, it is important that authorship information in frequently used web services and platforms is correct. The question whether a given document is written by a given author is commonly referred to as authorship verification (AV). While AV is a widely investigated problem in general, only few works consider settings where the documents are short and written in a rather uniform style. This makes most approaches unpractical for online databases and knowledge graphs in the scholarly domain. Here, authorships of scientific publications have to be verified, often with just abstracts and titles available. To this point, we present our novel approach LG4AV which combines language models and graph neural networks for authorship verification. By directly feeding the available texts in a pre-trained transformer architecture, our model does not need any hand-crafted stylometric features that are not meaningful in scenarios where the writing style is, at least to some extent, standardized. By the incorporation of a graph neural network structure, our model can benefit from relations between authors that are meaningful with respect to the verification process. For example, scientific authors are more likely to write about topics that are addressed by their co-authors and twitter users tend to post about the same subjects as people they follow. We experimentally evaluate our model and study to which extent the inclusion of co-authorships enhances verification decisions in bibliometric environments. | ['Gerd Stumme', 'Maximilian Stubbemann'] | 2021-09-03 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.01853618e-01 2.28207305e-01 -3.52139622e-01 -5.40138930e-02
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66b3239d-593f-4a10-ab74-9111e410a54c | lasuie-unifying-information-extraction-with | 2304.06248 | null | https://arxiv.org/abs/2304.06248v1 | https://arxiv.org/pdf/2304.06248v1.pdf | LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model | Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature which has been extensively utilized in IE community, should also be beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. A heterogeneous structure inductor is explored to unsupervisedly induce rich heterogeneous structural representations by post-training an existing GLM. In particular, a structural broadcaster is devised to compact various latent trees into explicit high-order forests, helping to guide a better generation during decoding. We finally introduce a task-oriented structure fine-tuning mechanism, further adjusting the learned structures to most coincide with the end-task's need. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system. Further in-depth analyses show that our GLM learns rich task-adaptive structural bias that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying. Source codes are open at https://github.com/ChocoWu/LasUIE. | ['Tat-Seng Chua', 'Min Zhang', 'Meishan Zhang', 'Libo Qin', 'Fei Li', 'Bobo Li', 'Jingye Li', 'Shengqiong Wu', 'Hao Fei'] | 2023-04-13 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 4.10187751e-01 5.46303213e-01 -4.66940403e-01 -6.16754830e-01
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634f8784-dc0b-4e95-9069-6531ef4280f8 | trust-and-reliance-in-consensus-based | 2304.11279 | null | https://arxiv.org/abs/2304.11279v1 | https://arxiv.org/pdf/2304.11279v1.pdf | Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent | The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no "true" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on platforms like Twitter, is a global problem for which there is currently no good solution. As an explainable AI (XAI) system, the agent provides explanations for its decisions on the misinformed nature of social media content. In this late-breaking study, we explored the roles of trust (attitude) and reliance (behaviour) as key elements of XAI user experience (UX) and whether these influenced the illusion of consensus. Findings show no effect of trust, but an effect of reliance on consensus-based explanations. This work may guide the design of anti-misinformation systems that use XAI, especially the user-centred design of explanations. | ['Katie Seaborn', 'Hiroki Oura', 'Yeongdae Kim', 'Takane Ueno'] | 2023-04-22 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-2.66313463e-01 7.25057006e-01 -3.14196080e-01 -3.70252192e-01
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4023a0ac-081e-4579-8972-a7a80ec3a29a | record-deduplication-for-entity-distribution | 2306.06246 | null | https://arxiv.org/abs/2306.06246v1 | https://arxiv.org/pdf/2306.06246v1.pdf | Record Deduplication for Entity Distribution Modeling in ASR Transcripts | Voice digital assistants must keep up with trending search queries. We rely on a speech recognition model using contextual biasing with a rapidly updated set of entities, instead of frequent model retraining, to keep up with trends. There are several challenges with this approach: (1) the entity set must be frequently reconstructed, (2) the entity set is of limited size due to latency and accuracy trade-offs, and (3) finding the true entity distribution for biasing is complicated by ASR misrecognition. We address these challenges and define an entity set by modeling customers true requested entity distribution from ASR output in production using record deduplication, a technique from the field of entity resolution. Record deduplication resolves or deduplicates coreferences, including misrecognitions, of the same latent entity. Our method successfully retrieves 95% of misrecognized entities and when used for contextual biasing shows an estimated 5% relative word error rate reduction. | ['Kanna Shimizu', 'Carl Wivagg', 'Chung Hoon Hong', 'Tianyu Huang'] | 2023-06-09 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [ 9.53238606e-02 1.18490115e-01 -4.31951702e-01 -1.71995804e-01
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9ca37bd1-f928-4f79-b6ed-4270766b3954 | adversarial-seeded-sequence-growing-for | 1908.02422 | null | https://arxiv.org/abs/1908.02422v1 | https://arxiv.org/pdf/1908.02422v1.pdf | Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization | Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up. However, most of existing frameworks rely on Class Activation Sequence (CAS) to localize actions by minimizing the video-level classification loss, which exploits the most discriminative parts of actions but ignores the minor regions. In this paper, we propose a novel weakly-supervised framework by adversarial learning of two modules for eliminating such demerits. Specifically, the first module is designed as a well-designed Seeded Sequence Growing (SSG) Network for progressively extending seed regions (namely the highly reliable regions initialized by a CAS-based framework) to their expected boundaries. The second module is a specific classifier for mining trivial or incomplete action regions, which is trained on the shared features after erasing the seeded regions activated by SSG. In this way, a whole network composed of these two modules can be trained in an adversarial manner. The goal of the adversary is to mine features that are difficult for the action classifier. That is, erasion from SSG will force the classifier to discover minor or even new action regions on the input feature sequence, and the classifier will drive the seeds to grow, alternately. At last, we could obtain the action locations and categories from the well-trained SSG and the classifier. Extensive experiments on two public benchmarks THUMOS'14 and ActivityNet1.3 demonstrate the impressive performance of our proposed method compared with the state-of-the-arts. | ['ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Yi Niu', 'Fei Wu', 'Futai Zou', 'Chengwei Zhang'] | 2019-08-07 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 7.04067171e-01 3.20699215e-01 -4.27881092e-01 -1.91435609e-02
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55069bca-256f-4104-a2fa-ee5f1f59db3e | singing-voice-synthesis-using-deep | 1906.08977 | null | https://arxiv.org/abs/1906.08977v1 | https://arxiv.org/pdf/1906.08977v1.pdf | Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling | This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS). Singing voice differs from speech and it contains more local dynamic movements of acoustic features, e.g., vibratos. Therefore, our method adopts deep autoregressive (DAR) models to predict the F0 and spectral features of singing voice in order to better describe the dependencies among the acoustic features of consecutive frames. For F0 modeling, discretized F0 values are used and the influences of the history length in DAR are analyzed by experiments. An F0 post-processing strategy is also designed to alleviate the inconsistency between the predicted F0 contours and the F0 values determined by music notes. Furthermore, we extend the DAR model to deal with continuous spectral features, and a prenet module with self-attention layers is introduced to process historical frames. Experiments on a Chinese singing voice corpus demonstrate that our method using DARs can produce F0 contours with vibratos effectively, and can achieve better objective and subjective performance than the conventional method using recurrent neural networks (RNNs). | ['Li-Rong Dai', 'Zhen-Hua Ling', 'Yuan-Hao Yi', 'Yang Ai'] | 2019-06-21 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-2.12319493e-01 -4.15627062e-01 1.30434707e-01 3.32123749e-02
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a313acce-647a-4b25-a113-4ac8e0da7547 | power-up-what-can-generative-models-do-for | 2307.02243 | null | https://arxiv.org/abs/2307.02243v1 | https://arxiv.org/pdf/2307.02243v1.pdf | Power-up! What Can Generative Models Do for Human Computation Workflows? | We are amidst an explosion of artificial intelligence research, particularly around large language models (LLMs). These models have a range of applications across domains like medicine, finance, commonsense knowledge graphs, and crowdsourcing. Investigation into LLMs as part of crowdsourcing workflows remains an under-explored space. The crowdsourcing research community has produced a body of work investigating workflows and methods for managing complex tasks using hybrid human-AI methods. Within crowdsourcing, the role of LLMs can be envisioned as akin to a cog in a larger wheel of workflows. From an empirical standpoint, little is currently understood about how LLMs can improve the effectiveness of crowdsourcing workflows and how such workflows can be evaluated. In this work, we present a vision for exploring this gap from the perspectives of various stakeholders involved in the crowdsourcing paradigm -- the task requesters, crowd workers, platforms, and end-users. We identify junctures in typical crowdsourcing workflows at which the introduction of LLMs can play a beneficial role and propose means to augment existing design patterns for crowd work. | ['Ujwal Gadiraju', 'Gaole He', 'Garrett Allen'] | 2023-07-05 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-4.10616472e-02 4.27278847e-01 1.78627521e-01 -1.92999169e-01
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61100f8d-f69d-4388-861e-fc1035aa0366 | sparse-to-dense-motion-transfer-for-face | 2109.00471 | null | https://arxiv.org/abs/2109.00471v2 | https://arxiv.org/pdf/2109.00471v2.pdf | Sparse to Dense Motion Transfer for Face Image Animation | Face image animation from a single image has achieved remarkable progress. However, it remains challenging when only sparse landmarks are available as the driving signal. Given a source face image and a sequence of sparse face landmarks, our goal is to generate a video of the face imitating the motion of landmarks. We develop an efficient and effective method for motion transfer from sparse landmarks to the face image. We then combine global and local motion estimation in a unified model to faithfully transfer the motion. The model can learn to segment the moving foreground from the background and generate not only global motion, such as rotation and translation of the face, but also subtle local motion such as the gaze change. We further improve face landmark detection on videos. With temporally better aligned landmark sequences for training, our method can generate temporally coherent videos with higher visual quality. Experiments suggest we achieve results comparable to the state-of-the-art image driven method on the same identity testing and better results on cross identity testing. | ['Guodong Guo', 'Tianyi Wu', 'Ruiqi Zhao'] | 2021-09-01 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 2.43059963e-01 -2.14216903e-01 -1.24173760e-01 -3.78595769e-01
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66dc9a20-7e24-4ac6-8663-fe40cd139911 | atm-r-an-adaptive-tradeoff-model-with | 2301.03317 | null | https://arxiv.org/abs/2301.03317v1 | https://arxiv.org/pdf/2301.03317v1.pdf | ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization | The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and welldistributed feasible solutions. To complete this goal, there should be a tradeoff among feasibility, diversity, and convergence. However, it is nontrivial to balance these three elements simultaneously by using a single tradeoff model since the importance of each element varies in different evolutionary phases. As an alternative, we adapt different tradeoff models in different phases and propose a novel algorithm called ATM-R. In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from "the tradeoff between feasibility and diversity" to "the tradeoff between diversity and convergence", which can facilitate the discovering of enough feasible regions and speed up the search for the feasible Pareto optima in succession. In the feasible phase, the tradeoff between diversity and convergence is considered to attain a set of well-converged and well-distributed feasible solutions. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a wide range of benchmark test functions demonstrate that ATM-R is effective and competitive, compared against five state-of-the-art constrained multiobjective optimization evolutionary algorithms. | ['Zhi-Zhong Liu', 'Xian-Bing Meng', 'Yunchuan Qin', 'Bing-Chuan Wang'] | 2023-01-09 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-1.36571556e-01 -6.93854451e-01 -2.17544377e-01 -1.97102979e-01
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80278995-d96e-483e-b3ae-114c6c4d9cc2 | the-pcg-aiid-system-for-l3das22-challenge | 2202.10017 | null | https://arxiv.org/abs/2202.10017v1 | https://arxiv.org/pdf/2202.10017v1.pdf | The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition | This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D speech enhancement in office reverberant environment. We proposed a two-stage framework to address multi-channel speech denoising and dereverberation. In the first stage, a multiple input and multiple output (MIMO) network is applied to remove background noise while maintaining the spatial characteristics of multi-channel signals. In the second stage, a multiple input and single output (MISO) network is applied to enhance the speech from desired direction and post-filtering. As a result, our system ranked 3rd place in ICASSP2022 L3DAS22 challenge and significantly outperforms the baseline system, while achieving 3.2% WER and 0.972 STOI on the blind test-set. | ['Zhaoxia Li', 'Guohui Cui', 'Yun Liu', 'Dawei Luo', 'Yuanyuan Zhu', 'Jingdong Li'] | 2022-02-21 | null | null | null | null | ['speech-denoising'] | ['speech'] | [-1.12613097e-01 -1.35450944e-01 6.35507584e-01 -1.46187410e-01
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af8468c9-bbbc-4676-beb8-a984a9fb4f43 | adaptive-feature-processing-for-robust-human | 1901.02858 | null | http://arxiv.org/abs/1901.02858v1 | http://arxiv.org/pdf/1901.02858v1.pdf | Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset | Human Activity Recognition (HAR) is a key building block of many emerging
applications such as intelligent mobility, sports analytics, ambient-assisted
living and human-robot interaction. With robust HAR, systems will become more
human-aware, leading towards much safer and empathetic autonomous systems.
While human pose detection has made significant progress with the dawn of deep
convolutional neural networks (CNNs), the state-of-the-art research has almost
exclusively focused on a single sensing modality, especially video. However, in
safety critical applications it is imperative to utilize multiple sensor
modalities for robust operation. To exploit the benefits of state-of-the-art
machine learning techniques for HAR, it is extremely important to have
multimodal datasets. In this paper, we present a novel, multi-modal sensor
dataset that encompasses nine indoor activities, performed by 16 participants,
and captured by four types of sensors that are commonly used in indoor
applications and autonomous vehicles. This multimodal dataset is the first of
its kind to be made openly available and can be exploited for many applications
that require HAR, including sports analytics, healthcare assistance and indoor
intelligent mobility. We propose a novel data preprocessing algorithm to enable
adaptive feature extraction from the dataset to be utilized by different
machine learning algorithms. Through rigorous experimental evaluations, this
paper reviews the performance of machine learning approaches to posture
recognition, and analyses the robustness of the algorithms. When performing HAR
with the RGB-Depth data from our new dataset, machine learning algorithms such
as a deep neural network reached a mean accuracy of up to 96.8% for
classification across all stationary and dynamic activities | ['Varuna De Silva', 'Ahmet Kondoz', 'Mirco Moencks', 'Jamie Roche'] | 2019-01-09 | null | null | null | null | ['sports-analytics', 'multimodal-activity-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.82789847e-01 1.48194600e-02 -2.50138253e-01 -2.55705714e-01
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15cb399b-0314-4afe-b818-5f1f6ac8f2d2 | rayleigh-gauss-newton-optimization-with | 2106.10558 | null | https://arxiv.org/abs/2106.10558v4 | https://arxiv.org/pdf/2106.10558v4.pdf | Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlo | Variational Monte Carlo (VMC) is an approach for computing ground-state wavefunctions that has recently become more powerful due to the introduction of neural network-based wavefunction parametrizations. However, efficiently training neural wavefunctions to converge to an energy minimum remains a difficult problem. In this work, we analyze optimization and sampling methods used in VMC and introduce alterations to improve their performance. First, based on theoretical convergence analysis in a noiseless setting, we motivate a new optimizer that we call the Rayleigh-Gauss-Newton method, which can improve upon gradient descent and natural gradient descent to achieve superlinear convergence at no more than twice the computational cost. Second, in order to realize this favorable comparison in the presence of stochastic noise, we analyze the effect of sampling error on VMC parameter updates and experimentally demonstrate that it can be reduced by the parallel tempering method. In particular, we demonstrate that RGN can be made robust to energy spikes that occur when the sampler moves between metastable regions of configuration space. Finally, putting theory into practice, we apply our enhanced optimization and sampling methods to the transverse-field Ising and XXZ models on large lattices, yielding ground-state energy estimates with remarkably high accuracy after just 200 parameter updates. | ['Michael Lindsey', 'Robert J. Webber'] | 2021-06-19 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 2.47557670e-01 -2.83092201e-01 1.31835207e-01 -1.61770016e-01
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77f72c1f-a40e-4408-b7b9-bf2d8d6bf8f6 | dial2desc-end-to-end-dialogue-description | 1811.00185 | null | http://arxiv.org/abs/1811.00185v1 | http://arxiv.org/pdf/1811.00185v1.pdf | Dial2Desc: End-to-end Dialogue Description Generation | We first propose a new task named Dialogue Description (Dial2Desc). Unlike
other existing dialogue summarization tasks such as meeting summarization, we
do not maintain the natural flow of a conversation but describe an object or an
action of what people are talking about. The Dial2Desc system takes a dialogue
text as input, then outputs a concise description of the object or the action
involved in this conversation. After reading this short description, one can
quickly extract the main topic of a conversation and build a clear picture in
his mind, without reading or listening to the whole conversation. Based on the
existing dialogue dataset, we build a new dataset, which has more than one
hundred thousand dialogue-description pairs. As a step forward, we demonstrate
that one can get more accurate and descriptive results using a new neural
attentive model that exploits the interaction between utterances from different
speakers, compared with other baselines. | ['Zhou Zhao', 'Junpei Zhou', 'Haojie Pan', 'Min Yang', 'Deng Cai', 'Yan Liu'] | 2018-11-01 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 2.78836936e-01 7.24964797e-01 5.18207774e-02 -6.53286040e-01
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ab57a255-df4e-40c3-a142-a5da8a435e17 | action-conditioned-on-demand-motion | 2207.08164 | null | https://arxiv.org/abs/2207.08164v1 | https://arxiv.org/pdf/2207.08164v1.pdf | Action-conditioned On-demand Motion Generation | We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evaluated on three public datasets (HumanAct12, UESTC, and MoCap). Furthermore, we provide both qualitative evaluations and quantitative metrics demonstrating several first-known customization capabilities afforded by our framework, including mode discovery, interpolation, and trajectory customization. These capabilities significantly widen the spectrum of potential applications of such motion generation models. The novel on-demand generative capabilities are enabled by innovations in both the encoder and decoder architectures: (i) Encoder: Utilizing contrastive learning in low-dimensional latent space to create a hierarchical embedding of motion sequences, where not only the codes of different action types form different groups, but within an action type, codes of similar inherent patterns (motion styles) cluster together, making them readily discoverable; (ii) Decoder: Using a hierarchical decoding strategy where the motion trajectory is reconstructed first and then used to reconstruct the whole motion sequence. Such an architecture enables effective trajectory control. Our code is released on the Github page: https://github.com/roychowdhuryresearch/ODMO | ['Vwani Roychowdhury', 'Mingjian Lu', 'YiPeng Zhang', 'QIUJING LU'] | 2022-07-17 | null | null | null | null | ['human-action-generation'] | ['computer-vision'] | [-1.38876885e-01 -1.54957563e-01 -3.58750403e-01 2.84129716e-02
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af290059-d916-46f8-a53f-a87d92fde8f3 | proposal-distribution-calibration-for-few | 2212.07618 | null | https://arxiv.org/abs/2212.07618v1 | https://arxiv.org/pdf/2212.07618v1.pdf | Proposal Distribution Calibration for Few-Shot Object Detection | Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance, i.e., holistic pre-training on base classes, then partial fine-tuning in a balanced setting with all classes. Since unlabeled instances are suppressed as backgrounds in the base training phase, the learned RPN is prone to produce biased proposals for novel instances, resulting in dramatic performance degradation. Unfortunately, the extreme data scarcity aggravates the proposal distribution bias, hindering the RoI head from evolving toward novel classes. In this paper, we introduce a simple yet effective proposal distribution calibration (PDC) approach to neatly enhance the localization and classification abilities of the RoI head by recycling its localization ability endowed in base training and enriching high-quality positive samples for semantic fine-tuning. Specifically, we sample proposals based on the base proposal statistics to calibrate the distribution bias and impose additional localization and classification losses upon the sampled proposals for fast expanding the base detector to novel classes. Experiments on the commonly used Pascal VOC and MS COCO datasets with explicit state-of-the-art performances justify the efficacy of our PDC for FSOD. Code is available at github.com/Bohao-Lee/PDC. | ['Qixiang Ye', 'Xiangyang Ji', 'Xiaozhong Chen', 'Mengnan Shi', 'Chang Liu', 'Bohao Li'] | 2022-12-15 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 3.56753945e-01 1.08022586e-01 -3.61680299e-01 -3.45473349e-01
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b928f95c-5191-44e8-a9cc-34f2de0a87fc | exploring-transformers-for-on-line | 2307.01663 | null | https://arxiv.org/abs/2307.01663v2 | https://arxiv.org/pdf/2307.01663v2.pdf | Exploring Transformers for On-Line Handwritten Signature Verification | The application of mobile biometrics as a user-friendly authentication method has increased in the last years. Recent studies have proposed novel behavioral biometric recognition systems based on Transformers, which currently outperform the state of the art in several application scenarios. On-line handwritten signature verification aims to verify the identity of subjects, based on their biometric signatures acquired using electronic devices such as tablets or smartphones. This paper investigates the suitability of architectures based on recent Transformers for on-line signature verification. In particular, four different configurations are studied, two of them rely on the Vanilla Transformer encoder, and the two others have been successfully applied to the tasks of gait and activity recognition. We evaluate the four proposed configurations according to the experimental protocol proposed in the SVC-onGoing competition. The results obtained in our experiments are promising, and promote the use of Transformers for on-line signature verification. | ['Javier Ortega-Garcia', 'Julian Fierrez', 'Giuseppe Stragapede', 'Paula Delgado-Santos', 'Ruben Vera-Rodriguez', 'Ruben Tolosana', 'Pietro Melzi'] | 2023-07-04 | null | null | null | null | ['activity-recognition'] | ['computer-vision'] | [ 4.71218020e-01 -3.47454756e-01 -1.92537084e-01 -2.26025090e-01
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9a0ffbf5-e518-4693-8d6e-bc5d3c54ab82 | inducing-distant-supervision-in-suggestion | 1709.07403 | null | http://arxiv.org/abs/1709.07403v2 | http://arxiv.org/pdf/1709.07403v2.pdf | Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings | Mining suggestion expressing sentences from a given text is a less
investigated sentence classification task, and therefore lacks hand labeled
benchmark datasets. In this work, we propose and evaluate two approaches for
distant supervision in suggestion mining. The distant supervision is obtained
through a large silver standard dataset, constructed using the text from
wikiHow and Wikipedia. Both the approaches use a LSTM based neural network
architecture to learn a classification model for suggestion mining, but vary in
their method to use the silver standard dataset. The first approach directly
trains the classifier using this dataset, while the second approach only learns
word embeddings from this dataset. In the second approach, we also learn POS
embeddings, which interestingly gives the best classification accuracy. | ['Sapna Negi', 'Paul Buitelaar'] | 2017-09-21 | null | null | null | null | ['suggestion-mining'] | ['natural-language-processing'] | [ 3.41330767e-01 5.69494784e-01 -4.87840056e-01 -5.94463170e-01
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337289ef-872e-466a-a166-1f0c2fb4bf96 | place-recognition-in-forests-with-urquhart | 2010.03026 | null | https://arxiv.org/abs/2010.03026v2 | https://arxiv.org/pdf/2010.03026v2.pdf | Place Recognition in Forests with Urquhart Tessellations | In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest. We propose a framework that uses these descriptors to detect previously seen observations and landmark correspondences, even with partial overlap and noise. We run loop closure detection experiments in simulation and real-world data map-merging from different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and show that our method outperforms state-of-the-art approaches in accuracy and robustness. | ['Vijay Kumar', 'Roseli A. F. Romero', 'Vaibhav Arcot', 'Xu Liu', 'Steven W. Chen', 'Avraham Cohen', 'Guilherme V. Nardari'] | 2020-09-23 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 3.26806784e-01 -2.98315883e-01 8.49214792e-02 -2.80981392e-01
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9281343a-947c-4873-865f-cd16d99dda50 | a-low-rank-weighted-graph-convolutional | null | null | https://ieeexplore.ieee.org/document/8594887 | https://ieeexplore.ieee.org/document/8594887 | A Low Rank Weighted Graph Convolutional Approach to Weather Prediction | Weather forecasting is an important but challenging problem as one must contend with the inherent non-linearities and spatiotemporal autocorrelation present in the data. This paper presents a novel deep learning approach based on a coupled weighted graph convolutional LSTM (WGC-LSTM) to address these challenges. Specifically, our proposed approach uses an LSTM to capture the inherent temporal autocorrelation of the data and a graph convolution to model its spatial relationships. As the weather condition can be influenced by various spatial factors besides the distance between locations, e.g., topography, prevailing winds and jet streams, imposing a fixed graph structure based on the proximity between locations is insufficient to train a robust deep learning model. Instead, our proposed approach treats the adjacency matrix of the graph as a model parameter that can be learned from the training data. However, this introduces an additional O(|V |2) parameters to be estimated, where V is the number of locations. With large graphs this may also lead to slower performance as well as susceptibility to overfitting. We propose a modified version of our approach that can address this difficulty by assuming that the adjacency matrix is either sparse or low rank. Experimental results using two real-world weather datasets show that WGCLSTM outperforms all other baseline methods for the majority of the evaluated locations. | ['Lifeng Luo', 'Pang-Ning Tan', 'Tyler Wilson'] | 2018-11-17 | null | null | null | ieee-2018-11 | ['weather-forecasting'] | ['miscellaneous'] | [-1.69363007e-01 -2.33773679e-01 2.70871460e-01 -3.28685969e-01
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3ba88b0e-5717-4a27-b570-1e8b2648f246 | weakly-supervised-graph-clustering | null | null | https://openreview.net/forum?id=gaYko_Y2_l | https://openreview.net/pdf?id=gaYko_Y2_l | Weakly Supervised Graph Clustering | Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications. While this task is common, more complex cases arise in practice—can we cluster nodes better with some graph-level side information or in a weakly supervised manner as, for example, identifying potential fraud users in a social network given additional labels of fraud communities. This triggers an interesting problem which we define as Weakly Supervised Graph Clustering (WSGC). In this paper, we firstly discuss the various possible settings of WSGC, formally. Upon such discussion, we investigate a particular task of weakly supervised graph clustering by making use of the graph labels and node features, with the assistance of a hierarchical graph that further characterizes the connections between different graphs. To address this task, we propose Gaussian Mixture Graph Convolutional Network (GMGCN), a simple yet effective framework for learning node representations under the supervision of graph labels guided by a proposed consensus loss and then inferring the category of each node via a Gaussian Mixture Layer (GML). Extensive experiments are conducted to test the rationality of the formulation of weakly supervised graph clustering. The experimental results show that, with the assistance of graph labels, the weakly supervised graph clustering method has a great improvement over the traditional graph clustering method. | ['Hong Cheng', 'Junzhou Huang', 'Peilin Zhao', 'Xi Xiao', 'Wenbing Huang', 'Yu Rong', 'Tingyang Xu', 'Tian Bian'] | 2021-09-29 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.43410371e-04 5.52468598e-01 -2.69176692e-01 -2.40946665e-01
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579afcf6-72f2-4dc5-85e4-a4c353f25dec | cs-af-a-cost-sensitive-multi-classifier | 2004.12064 | null | https://arxiv.org/abs/2004.12064v2 | https://arxiv.org/pdf/2004.12064v2.pdf | CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification | Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple classifiers via fusion approaches shows to be more effective and robust. Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e.g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach. Furthermore, in skin lesion analysis, the data of certain classes (e.g., the benign lesions) is usually abundant making them an over-represented majority, while the data of some other classes (e.g., the cancerous lesions) is deficient, making them an underrepresented minority. It is more crucial to precisely identify the samples from an underrepresented (i.e., in terms of the amount of data) but more important minority class (e.g., certain cancerous lesion). In other words, misclassifying a more severe lesion to a benign or less severe lesion should have relative more cost (e.g., money, time and even lives). To address such challenges, we present CS-AF, a cost-sensitive multi-classifier active fusion framework for skin lesion classification. In the experimental evaluation, we prepared 96 base classifiers (of 12 CNN architectures) on the ISIC research datasets. Our experimental results show that our framework consistently outperforms the static fusion competitors. | ['J. Morris Chang', 'Di Zhuang', 'Keyu Chen'] | 2020-04-25 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.02556300e-01 -1.17587252e-02 -4.27952796e-01 -2.05135211e-01
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78950cc9-78ad-4314-b59d-1d2c1d63480a | dice-loss-for-data-imbalanced-nlp-tasks | 1911.02855 | null | https://arxiv.org/abs/1911.02855v3 | https://arxiv.org/pdf/1911.02855v3.pdf | Dice Loss for Data-imbalanced NLP Tasks | Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training. The most commonly used cross entropy (CE) criteria is actually an accuracy-oriented objective, and thus creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function, while at test time F1 score concerns more about positive examples. In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks. Dice loss is based on the Sorensen-Dice coefficient or Tversky index, which attaches similar importance to false positives and false negatives, and is more immune to the data-imbalance issue. To further alleviate the dominating influence from easy-negative examples in training, we propose to associate training examples with dynamically adjusted weights to deemphasize easy-negative examples.Theoretical analysis shows that this strategy narrows down the gap between the F1 score in evaluation and the dice loss in training. With the proposed training objective, we observe significant performance boost on a wide range of data imbalanced NLP tasks. Notably, we are able to achieve SOTA results on CTB5, CTB6 and UD1.4 for the part of speech tagging task; SOTA results on CoNLL03, OntoNotes5.0, MSRA and OntoNotes4.0 for the named entity recognition task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification. | ['Fei Wu', 'Xiaofei Sun', 'Junjun Liang', 'Yuxian Meng', 'Jiwei Li', 'Xiaoya Li'] | 2019-11-07 | dice-loss-for-data-imbalanced-nlp-tasks-1 | https://aclanthology.org/2020.acl-main.45 | https://aclanthology.org/2020.acl-main.45.pdf | acl-2020-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 1.81984529e-01 3.36781800e-01 -3.61773789e-01 -6.11222148e-01
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dd0d11d6-7054-4753-a161-df80dbfc955d | mitigating-negative-transfer-with-task | 2307.03377 | null | https://arxiv.org/abs/2307.03377v1 | https://arxiv.org/pdf/2307.03377v1.pdf | Mitigating Negative Transfer with Task Awareness for Sexism, Hate Speech, and Toxic Language Detection | This paper proposes a novelty approach to mitigate the negative transfer problem. In the field of machine learning, the common strategy is to apply the Single-Task Learning approach in order to train a supervised model to solve a specific task. Training a robust model requires a lot of data and a significant amount of computational resources, making this solution unfeasible in cases where data are unavailable or expensive to gather. Therefore another solution, based on the sharing of information between tasks, has been developed: Multi-Task Learning (MTL). Despite the recent developments regarding MTL, the problem of negative transfer has still to be solved. Negative transfer is a phenomenon that occurs when noisy information is shared between tasks, resulting in a drop in performance. This paper proposes a new approach to mitigate the negative transfer problem based on the task awareness concept. The proposed approach results in diminishing the negative transfer together with an improvement of performance over classic MTL solution. Moreover, the proposed approach has been implemented in two unified architectures to detect Sexism, Hate Speech, and Toxic Language in text comments. The proposed architectures set a new state-of-the-art both in EXIST-2021 and HatEval-2019 benchmarks. | ['Damiano Spina', 'Paolo Rosso', 'Angel Felipe Magnossão de Paula'] | 2023-07-07 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.46736610e-01 1.25442639e-01 2.25512281e-01 -2.12403312e-01
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eb1ed678-597f-4913-890a-f1754296c99e | impact-of-dataset-on-acoustic-models-for | 2203.13590 | null | https://arxiv.org/abs/2203.13590v1 | https://arxiv.org/pdf/2203.13590v1.pdf | Impact of Dataset on Acoustic Models for Automatic Speech Recognition | In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic modelling. With the current advancement of deep learning, the Gaussian Mixture Model (GMM) from acoustic models has been replaced with Deep Neural Network, namely DNN-HMM Acoustic Models. The GMM models are widely used to create the alignments of the training data for the hybrid deep neural network model, thus making it an important task to create accurate alignments. Many factors such as training dataset size, training data augmentation, model hyperparameters, etc., affect the model learning. Traditionally in machine learning, larger datasets tend to have better performance, while smaller datasets tend to trigger over-fitting. The collection of speech data and their accurate transcriptions is a significant challenge that varies over different languages, and in most cases, it might be limited to big organizations. Moreover, in the case of available large datasets, training a model using such data requires additional time and computing resources, which may not be available. While the data about the accuracy of state-of-the-art ASR models on open-source datasets are published, the study about the impact of the size of a dataset on acoustic models is not readily available. This work aims to investigate the impact of dataset size variations on the performance of various GMM-HMM Acoustic Models and their respective computational costs. | ['Siddhesh Singh'] | 2022-03-25 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [-1.43073753e-01 -1.74231678e-01 1.71745986e-01 -5.21153033e-01
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7af0dea5-5d6a-4586-ad71-13268e90da18 | an-efficient-method-for-face-quality | 2207.09505 | null | https://arxiv.org/abs/2207.09505v1 | https://arxiv.org/pdf/2207.09505v1.pdf | An Efficient Method for Face Quality Assessment on the Edge | Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach's efficiency through comparison with SOTA face quality regression models on different data sets and real-life scenarios. | ['Cevahir Çığla', 'Burak Oğuz Özkalaycı', 'Sefa Burak Okcu'] | 2022-07-19 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.96928722e-01 -1.83063596e-01 2.23739415e-01 -5.79284489e-01
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288e3e44-cffb-4989-b8cb-cb284be0e805 | improving-continuous-time-conflict-based | 2101.09723 | null | https://arxiv.org/abs/2101.09723v2 | https://arxiv.org/pdf/2101.09723v2.pdf | Improving Continuous-time Conflict Based Search | Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and $2^k$-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multiagent path finding in continuous-time domains. | ['Roni Stern', 'Eli Boyarski', 'Konstantin Yakovlev', 'Anton Andreychuk'] | 2021-01-24 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 6.69639036e-02 2.31593698e-01 -2.88184762e-01 1.17475748e-01
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716bcd3a-608b-441d-8b4b-133877714eec | quantum-pufferfish-privacy-a-flexible-privacy | 2306.13054 | null | https://arxiv.org/abs/2306.13054v1 | https://arxiv.org/pdf/2306.13054v1.pdf | Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems | We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and addresses limitations of quantum differential privacy by offering flexibility in specifying private information, feasible measurements, and domain knowledge. We show that QPP can be equivalently formulated in terms of the Datta-Leditzky information spectrum divergence, thus providing the first operational interpretation thereof. We reformulate this divergence as a semi-definite program and derive several properties of it, which are then used to prove convexity, composability, and post-processing of QPP mechanisms. Parameters that guarantee QPP of the depolarization mechanism are also derived. We analyze the privacy-utility tradeoff of general QPP mechanisms and, again, study the depolarization mechanism as an explicit instance. The QPP framework is then applied to privacy auditing for identifying privacy violations via a hypothesis testing pipeline that leverages quantum algorithms. Connections to quantum fairness and other quantum divergences are also explored and several variants of QPP are examined. | ['Mark M. Wilde', 'Ziv Goldfeld', 'Theshani Nuradha'] | 2023-06-22 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 4.78905618e-01 3.29400480e-01 1.50353787e-03 -4.40266281e-01
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5fa89616-9dc2-470d-8bc4-e8a8de7dfd06 | perception-de-la-langue-francaise-parlee | null | null | https://aclanthology.org/F12-1103 | https://aclanthology.org/F12-1103.pdf | Perception de la Langue fran\ccaise Parl\'ee Compl\'et\'ee (LPC) et effet d'expertise chez les normo-entendants (French Cued Speech perception and expertise effect in hearing people) [in French] | null | ["C{\\'e}cile Colin", 'Anne-Sophie Tilmant', "Cl{\\'e}mence Bayard", 'Jacqueline Leybaert'] | 2012-06-01 | perception-de-la-langue-franccaise-parlee | https://aclanthology.org/F12-1103 | https://aclanthology.org/F12-1103.pdf | jeptalnrecital-2012-6 | ['lipreading'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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3b17f0e7-ea74-4b76-a1d2-2f6eec755b91 | wikir-a-python-toolkit-for-building-a-large | 1912.01901 | null | https://arxiv.org/abs/1912.01901v4 | https://arxiv.org/pdf/1912.01901v4.pdf | WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset | Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large amounts of annotated data to be effective. Since most standard ad-hoc information retrieval datasets publicly available for academic research (e.g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets. These models (e.g. DUET, Conv-KNRM) are trained and evaluated on data collected from commercial search engines not publicly available for academic research which is a problem for reproducibility and the advancement of research. In this paper, we propose WIKIR: an open-source toolkit to automatically build large-scale English information retrieval datasets based on Wikipedia. WIKIR is publicly available on GitHub. We also provide wikIR78k and wikIRS78k: two large-scale publicly available datasets that both contain 78,628 queries and 3,060,191 (query, relevant documents) pairs. | ['Jean-Pierre Chevallet', 'Jibril Frej', 'Didier Schwab'] | 2019-12-04 | wikir-a-python-toolkit-for-building-a-large-1 | https://aclanthology.org/2020.lrec-1.237 | https://aclanthology.org/2020.lrec-1.237.pdf | lrec-2020-5 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-6.81521535e-01 -4.40119475e-01 -3.69623363e-01 -1.46903053e-01
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ee0a6df8-609a-426d-b288-0424142588ce | a-new-benchmark-for-evaluation-of-cross | 1912.07200 | null | https://arxiv.org/abs/1912.07200v2 | https://arxiv.org/pdf/1912.07200v2.pdf | A Broader Study of Cross-Domain Few-Shot Learning | Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learning problem, where there is a large shift between base and novel class domains. While investigations of the cross-domain few-shot scenario exist, these works are limited to natural images that still contain a high degree of visual similarity. No work yet exists that examines few-shot learning across different imaging methods seen in real world scenarios, such as aerial and medical imaging. In this paper, we propose the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods. This includes natural images, such as crop disease images, but additionally those that present with an increasing dissimilarity to natural images, such as satellite images, dermatology images, and radiology images. Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning. The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. Performance gains previously observed with methods specialized for cross-domain few-shot learning vanish in this more challenging benchmark. Finally, accuracy of all methods tend to correlate with dataset similarity to natural images, verifying the value of the benchmark to better represent the diversity of data seen in practice and guiding future research. | ['Tajana Rosing', 'Leonid Karlinsky', 'Yunhui Guo', 'Kate Saenko', 'James V. Codella', 'John R. Smith', 'Rogerio Feris', 'Noel C. Codella'] | 2019-12-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5643_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720120.pdf | eccv-2020-8 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 6.03573084e-01 -1.23624772e-01 -2.90900737e-01 -1.55905992e-01
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8a1c638f-8030-461a-996f-1ff35b5f14b7 | empirical-exploration-of-zone-by-zone-energy | 2304.13175 | null | https://arxiv.org/abs/2304.13175v1 | https://arxiv.org/pdf/2304.13175v1.pdf | Empirical Exploration of Zone-by-zone Energy Flexibility: a Non-intrusive Load Disaggregation Approach for Commercial Buildings | Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in commercial buildings. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a virtual zonal power meter). Three university buildings in Northern California were selected to test the proposed load disaggregation method. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. The proposed method enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants. | ['Jacques A. de Chalendar', 'Ram Rajagopal', 'Maomao Hu'] | 2023-04-25 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.16987032e-01 -3.34373564e-02 -1.59793451e-01 2.05318034e-02
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7f1faf35-0a62-40f0-ba0c-5fc2d353974e | data-summarization-at-scale-a-two-stage | 1806.02815 | null | http://arxiv.org/abs/1806.02815v1 | http://arxiv.org/pdf/1806.02815v1.pdf | Data Summarization at Scale: A Two-Stage Submodular Approach | The sheer scale of modern datasets has resulted in a dire need for
summarization techniques that identify representative elements in a dataset.
Fortunately, the vast majority of data summarization tasks satisfy an intuitive
diminishing returns condition known as submodularity, which allows us to find
nearly-optimal solutions in linear time. We focus on a two-stage submodular
framework where the goal is to use some given training functions to reduce the
ground set so that optimizing new functions (drawn from the same distribution)
over the reduced set provides almost as much value as optimizing them over the
entire ground set. In this paper, we develop the first streaming and
distributed solutions to this problem. In addition to providing strong
theoretical guarantees, we demonstrate both the utility and efficiency of our
algorithms on real-world tasks including image summarization and ride-share
optimization. | ['Amin Karbasi', 'Morteza Zadimoghaddam', 'Marko Mitrovic', 'Ehsan Kazemi'] | 2018-06-07 | data-summarization-at-scale-a-two-stage-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2144 | http://proceedings.mlr.press/v80/mitrovic18a/mitrovic18a.pdf | icml-2018-7 | ['data-summarization'] | ['miscellaneous'] | [ 2.40720034e-01 3.19333732e-01 -5.72990119e-01 -2.36960277e-01
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cd32a5de-d2cf-47cd-b187-6afcbf48a976 | parallel-coordinates-for-discovery-of | 2305.18434 | null | https://arxiv.org/abs/2305.18434v1 | https://arxiv.org/pdf/2305.18434v1.pdf | Parallel Coordinates for Discovery of Interpretable Machine Learning Models | This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes and hyperblocks (HBs) simple to understand for end users. It is suggested to use mixed and pure hyperblocks in the proposed data classifier algorithm Hyper. It is shown that Hyper models generalize decision trees. The algorithm is presented in several settings and options to discover interactively or automatically overlapping or non-overlapping hyperblocks. Additionally, the use of hyperblocks in conjunction with language descriptions of visual patterns is demonstrated. The benchmark data from the UCI ML repository were used to evaluate the Hyper algorithm. It enabled the discovery of mixed and pure HBs evaluated using 10-fold cross validation. Connections among hyperblocks, dimension reduction and visualization have been established. The capability of end users to find and observe hyperblocks, as well as the ability of side-by-side visualizations to make patterns evident, are among major advantages ofhyperblock technology and the Hyper algorithm. A new method to visualize incomplete n-D data with missing values is proposed, while the traditional parallel coordinates do not support it. The ability of HBs to better prevent both overgeneralization and overfitting of data over decision trees is demonstrated as another benefit of the hyperblocks. The features of VisCanvas 2.0 software tool that implements Hyper technology are presented. | ['Boris Kovalerchuk', 'Dustin Hayes'] | 2023-05-28 | null | null | null | null | ['dimensionality-reduction', 'interpretable-machine-learning'] | ['methodology', 'methodology'] | [-3.38372886e-01 3.24947029e-01 -1.56496137e-01 -2.91749984e-01
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040b5217-cba9-45fe-a0d6-552f21923467 | common-conversational-community-prototype | 2001.06910 | null | https://arxiv.org/abs/2001.06910v1 | https://arxiv.org/pdf/2001.06910v1.pdf | Common Conversational Community Prototype: Scholarly Conversational Assistant | This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions. Specifically, we propose to develop and operate a prototype conversational search system for scholarly activities. This Scholarly Conversational Assistant would serve as a useful tool, a means to create datasets, and a platform for running evaluation challenges by groups across the community. This article results from discussions of a working group at Dagstuhl Seminar 19461 on Conversational Search. | ['Svitlana Vakulenko', 'Martin Potthast', 'Lucie Flekova', 'Matthias Hagen', 'Hamed Zamani', 'Mark Sanderson', 'Rosie Jones', 'Krisztian Balog', 'Filip Radlinski'] | 2020-01-19 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-5.24794817e-01 1.84660301e-01 -5.20024598e-01 -2.78725713e-01
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8e8dc237-29c3-41d7-b18f-fb01ee261376 | improved-drug-target-interaction-prediction | 2110.07347 | null | https://arxiv.org/abs/2110.07347v2 | https://arxiv.org/pdf/2110.07347v2.pdf | Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer | The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieved better performance than molecular docking, existing models often neglect certain aspects of the intermolecular information, hindering the performance of prediction. We recognize this problem and propose a novel approach named Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively, and shows superior generalization ability to unseen receptor proteins. Furthermore, IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. | ['Tie-Yan Liu', 'Nanning Zheng', 'Jian Yin', 'Bin Shao', 'Liang He', 'Yifan Deng', 'Tong Wang', 'Yusong Wang', 'Siyuan Liu'] | 2021-10-14 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.69280827e-01 -2.21096814e-01 -3.45304281e-01 -1.44330636e-01
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803aed5a-0cde-4f3a-9d2f-2e245b564c51 | fpga-based-acceleration-system-for-visual | 1810.05367 | null | http://arxiv.org/abs/1810.05367v2 | http://arxiv.org/pdf/1810.05367v2.pdf | FPGA-based Acceleration System for Visual Tracking | Visual tracking is one of the most important application areas of computer
vision. At present, most algorithms are mainly implemented on PCs, and it is
difficult to ensure real-time performance when applied in the real scenario. In
order to improve the tracking speed and reduce the overall power consumption of
visual tracking, this paper proposes a real-time visual tracking algorithm
based on DSST(Discriminative Scale Space Tracking) approach. We implement a
hardware system on Xilinx XC7K325T FPGA platform based on our proposed visual
tracking algorithm. Our hardware system can run at more than 153 frames per
second. In order to reduce the resource occupation, our system adopts the batch
processing method in the feature extraction module. In the filter processing
module, the FFT IP core is time-division multiplexed. Therefore, our hardware
system utilizes LUTs and storage blocks of 33% and 40%, respectively. Test
results show that the proposed visual tracking hardware system has excellent
performance. | ['Yunxu Sun', 'Peng Gao', 'Ke Song', 'Chun Yuan'] | 2018-10-12 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [ 8.33218545e-02 -6.83827162e-01 3.98793742e-02 9.12395567e-02
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7ed14f12-20bb-4df7-8d32-1f0fe4eecf3c | transferd2-automated-defect-detection | 2302.13317 | null | https://arxiv.org/abs/2302.13317v1 | https://arxiv.org/pdf/2302.13317v1.pdf | TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques | Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects and extend its application to new unseen target objects. We present a data enhancement technique to generate a large dataset from the small source dataset for building a classifier. We then integrate three different pre-trained models (Xception, ResNet101V2, and InceptionResNetV2) into the classifier network and compare their performance on source and target data. We use the classifier to detect the presence of imperfections on the unseen target data using pseudo-bounding boxes. Our results show that ResNet101V2 performs best on the source data with an accuracy of 95.72%. Xception performs best on the target data with an accuracy of 91.00% and also provides a more accurate prediction of the defects on the target images. Throughout the experiment, the results also indicate that the choice of a pre-trained model is not dependent on the depth of the network. Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data. | ['Rickey Dubay', 'Monica Wachowicz', 'Joshua Pickard', 'Hung Cao', 'Atah Nuh Mih'] | 2023-02-26 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.69661462e-01 1.21451430e-02 2.00695410e-01 -3.78775954e-01
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8119b98f-ac91-49f4-ac28-957c25870ca3 | the-agent-based-modelling-for-human-behaviour | 2302.01789 | null | https://arxiv.org/abs/2302.01789v2 | https://arxiv.org/pdf/2302.01789v2.pdf | The Agent-based Modelling for Human Behaviour Special Issue | If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we do? Starting with the origins of life provides us with fascinating answers to some of these questions. However, some researchers choose to bring their studies closer to the present day. We are after all, human. It has been a few billion years since our ancestors were self-replicating molecules. Thus, more direct studies of ourselves and our human societies can reveal truths that may lead to practical knowledge. The papers in this special issue bring together scientists who choose to perform this kind of research. | ['Peter J. Bentley', 'Soo Ling Lim'] | 2023-02-03 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.27638608e-01 2.38080874e-01 1.25449114e-02 -2.33243704e-02
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359dbade-7521-450a-8e3e-54a4c9f79525 | migs-meta-image-generation-from-scene-graphs | 2110.11918 | null | https://arxiv.org/abs/2110.11918v1 | https://arxiv.org/pdf/2110.11918v1.pdf | MIGS: Meta Image Generation from Scene Graphs | Generation of images from scene graphs is a promising direction towards explicit scene generation and manipulation. However, the images generated from the scene graphs lack quality, which in part comes due to high difficulty and diversity in the data. We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks. By sampling the data in a task-driven fashion, we train the generator using meta-learning on different sets of tasks that are categorized based on the scene attributes. Our results show that using this meta-learning approach for the generation of images from scene graphs achieves state-of-the-art performance in terms of image quality and capturing the semantic relationships in the scene. Project Website: https://migs2021.github.io/ | ['Nassir Navab', 'Helisa Dhamo', 'Sabrina Musatian', 'Azade Farshad'] | 2021-10-22 | null | null | null | null | ['scene-generation', 'image-generation-from-scene-graphs'] | ['computer-vision', 'computer-vision'] | [ 3.69197130e-01 1.30665645e-01 1.09476000e-01 -3.44495893e-01
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bccfd920-de23-474d-8972-8012b69d65ce | une-version-polyatomique-de-l-algorithme | 2204.13557 | null | https://arxiv.org/abs/2204.13557v1 | https://arxiv.org/pdf/2204.13557v1.pdf | Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions | Nous nous int\'eressons \`a la reconstruction parcimonieuse d'images \`a l'aide du probl\`eme d'optimisation r\'egularis\'e LASSO. Dans de nombreuses applications pratiques, les grandes dimensions des objets \`a reconstruire limitent, voire emp\^echent, l'utilisation des m\'ethodes de r\'esolution proximales classiques. C'est le cas par exemple en radioastronomie. Nous d\'etaillons dans cet article le fonctionnement de l'algorithme \textit{Frank-Wolfe Polyatomique}, sp\'ecialement d\'evelopp\'e pour r\'esoudre le probl\`eme LASSO dans ces contextes exigeants. Nous d\'emontrons sa sup\'eriorit\'e par rapport aux m\'ethodes proximales dans des situations en grande dimension avec des mesures de Fourier, lors de la r\'esolution de probl\`emes simul\'es inspir\'es de la radio-interf\'erom\'etrie. -- We consider the problem of recovering sparse images by means of the penalised optimisation problem LASSO. For various practical applications, it is impossible to rely on the proximal solvers commonly used for that purpose due to the size of the objects to recover, as it is the case for radio astronomy. In this article we explain the mechanisms of the \textit{Polyatomic Frank-Wolfe algorithm}, specifically designed to minimise the LASSO problem in such challenging contexts. We demonstrate in simulated problems inspired from radio-interferometry the preeminence of this algorithm over the proximal methods for high dimensional images with Fourier measurements. | ['Julien Fageot', 'Matthieu Simeoni', 'Adrian Jarret'] | 2022-04-28 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 2.54656732e-01 2.54763931e-01 5.44420540e-01 -1.38921849e-02
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9374bb0e-cd2f-467f-8873-0cd2ecd4a478 | revisiting-the-uniform-information-density | 2109.11635 | null | https://arxiv.org/abs/2109.11635v1 | https://arxiv.org/pdf/2109.11635v1.pdf | Revisiting the Uniform Information Density Hypothesis | The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehension and linguistic acceptability as well. Further, it is unclear how uniformity in a linguistic signal -- or lack thereof -- should be measured, and over which linguistic unit, e.g., the sentence or language level, this uniformity should hold. Here we investigate these facets of the UID hypothesis using reading time and acceptability data. While our reading time results are generally consistent with previous work, they are also consistent with a weakly super-linear effect of surprisal, which would be compatible with UID's predictions. For acceptability judgments, we find clearer evidence that non-uniformity in information density is predictive of lower acceptability. We then explore multiple operationalizations of UID, motivated by different interpretations of the original hypothesis, and analyze the scope over which the pressure towards uniformity is exerted. The explanatory power of a subset of the proposed operationalizations suggests that the strongest trend may be a regression towards a mean surprisal across the language, rather than the phrase, sentence, or document -- a finding that supports a typical interpretation of UID, namely that it is the byproduct of language users maximizing the use of a (hypothetical) communication channel. | ['Roger Levy', 'Ryan Cotterell', 'Lena Jäger', 'Patrick Haller', 'Tiago Pimentel', 'Clara Meister'] | 2021-09-23 | null | https://aclanthology.org/2021.emnlp-main.74 | https://aclanthology.org/2021.emnlp-main.74.pdf | emnlp-2021-11 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.29015455e-01 5.25190175e-01 -4.44621295e-01 -6.69793963e-01
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07bef5b5-2fd1-41c3-a1b4-195405c220c2 | isolated-sign-recognition-from-rgb-video | null | null | https://openaccess.thecvf.com/content/CVPR2021W/ChaLearn/html/De_Coster_Isolated_Sign_Recognition_From_RGB_Video_Using_Pose_Flow_and_CVPRW_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021W/ChaLearn/papers/De_Coster_Isolated_Sign_Recognition_From_RGB_Video_Using_Pose_Flow_and_CVPRW_2021_paper.pdf | Isolated Sign Recognition from RGB Video using Pose Flow and Self-Attention | Automatic sign language recognition lies at the intersection of natural language processing (NLP) and computer vision. The highly successful transformer architectures, based on multi-head attention, originate from the field of NLP. The Video Transformer Network (VTN) is an adaptation of this concept for tasks that require video understanding, e.g., action recognition. However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain. In this work, we reduce the impact of this data limitation by automatically pre-extracting useful information from the sign language videos. In our approach, different types of information are offered to a VTN in a multi-modal setup. It includes per-frame human pose keypoints (extracted by OpenPose) to capture the body movement and hand crops to capture the (evolution of) hand shapes. We evaluate our method on the recently released AUTSL dataset for isolated sign recognition and obtain 92.92% accuracy on the test set using only RGB data. For comparison: the VTN architecture without hand crops and pose flow achieved 82% accuracy. A qualitative inspection of our model hints at further potential of multi-modal multi-head attention in a sign language recognition context. | ['Joni Dambre', 'Mieke Van Herreweghe', 'Mathieu De Coster'] | 2021-06-11 | null | null | null | computer-vision-and-pattern-recognition | ['sign-language-recognition'] | ['computer-vision'] | [ 2.04868257e-01 -6.85952976e-02 5.21433763e-02 -2.14126170e-01
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a1cafe2c-8b39-4d61-b17c-99bb5e830db8 | privacy-preserving-visual-feature-descriptors | 2006.06634 | null | https://arxiv.org/abs/2006.06634v3 | https://arxiv.org/pdf/2006.06634v3.pdf | Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings | Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for the applications of visual localization and mapping as well as face authentication. Compared to the original features, our approach makes it significantly more difficult for an adversary to recover private information. | ['Johannes L. Schönberger', 'Mihai Dusmanu', 'Marc Pollefeys', 'Sudipta N. Sinha'] | 2020-06-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.70411366e-01 -1.71869159e-01 5.70285320e-03 -3.62916589e-01
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9098b8b6-08ce-41e2-9b4b-8ee6c5e0c0b2 | cross-class-feature-augmentation-for-class | 2304.01899 | null | https://arxiv.org/abs/2304.01899v1 | https://arxiv.org/pdf/2304.01899v1.pdf | Cross-Class Feature Augmentation for Class Incremental Learning | We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the past to complement training examples rather than simply play a role as a teacher for knowledge distillation towards subsequent models. The proposed approach has a unique perspective to utilize the previous knowledge in class incremental learning since it augments features of arbitrary target classes using examples in other classes via adversarial attacks on a previously learned classifier. By allowing the cross-class feature augmentations, each class in the old tasks conveniently populates samples in the feature space, which alleviates the collapse of the decision boundaries caused by sample deficiency for the previous tasks, especially when the number of stored exemplars is small. This idea can be easily incorporated into existing class incremental learning algorithms without any architecture modification. Extensive experiments on the standard benchmarks show that our method consistently outperforms existing class incremental learning methods by significant margins in various scenarios, especially under an environment with an extremely limited memory budget. | ['Bohyung Han', 'Jaeyoo Park', 'TaeHoon Kim'] | 2023-04-04 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 4.57731396e-01 1.61228091e-01 -4.91999477e-01 -1.75572276e-01
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d9ef1e06-fbca-40eb-a67a-29e7795db565 | ded-diagnostic-evidence-distillation-for-acne | null | null | http://dx.doi.org/10.1016/j.eswa.2023.120312 | http://dx.doi.org/10.1016/j.eswa.2023.120312 | DED: Diagnostic Evidence Distillation for acne severity grading on face images | Acne seriously affects people’s daily life. Acne severity level grading plays a decisive role in the cure. However, the acne criterion is not unified in the medical field. Most of the current studies explore the application of advanced visual models on acne severity grading but lack the adaptation to the characteristics of acne diagnosis. Meanwhile, some studies propose specifically adapted methods for respectively used acne criteria but can not be used on other acne criteria. In this study, we propose an acne diagnosis method, Diagnostic Evidence Distillation (DED), that suitably adapts the characteristics of acne diagnosis and can be applied to diagnose under different acne criteria. Firstly, we fully investigate and analyze the commonality of various acne criteria and condense the face acne diagnosis into an unconventional image classification problem based on the diagnostic evidence of type and number of fine-grained lesions distributed on a whole face. Next, we propose the DED framework to adapt the characteristics of acne diagnosis. This framework uses the teacher-student structure of knowledge distillation methods to bring the diagnostic evidence, which is not available for new patients but only in training data, into the diagnosis model. To break the limitation of different criteria, the framework uses convolutional neural networks (CNNs) as backbones to imitate the global estimation of dermatology. We also propose a subtask joint learning for the teacher network to enhance its guidance to the student. The DED is applied to diagnose acne on two datasets ACNE04 and PLSBRACNE01 based on different mainstream acne criteria. The experimental results demonstrate that on both datasets, the DED effectively improves the diagnosis performance, exceeds the state-of-the-art and reaches the diagnostic level of dermatologists. The precision, sensitivity, specificity, Youden Index and accuracy reach 85.31%, 84.83%, 94.66%, 79.48% and 86.06% on the ACNE04 dataset and 69.16%, 65.62%, 88.93%, 54.54% and 67.56% on the PLSBRACNE01 dataset, respectively. | ['Jing Yang', 'Haiyan You', 'Xiguang Liu', 'Yi Guan', 'Zhaoyang Ma', 'Dongxin Chen', 'Jingchi Jiang', 'Yi Lin'] | 2023-10-05 | null | null | null | expert-systems-with-applications-2023-10 | ['acne-severity-grading', 'specificity'] | ['medical', 'natural-language-processing'] | [-1.68486103e-01 -1.58649713e-01 -2.33773589e-01 -8.79181102e-02
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acc882be-f04c-4226-b5f5-fef176e13164 | accurate-ground-truth-depth-image-generation | 2207.07016 | null | https://arxiv.org/abs/2207.07016v2 | https://arxiv.org/pdf/2207.07016v2.pdf | Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets | Accurate three-dimensional perception is a fundamental task in several computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains insufficient owing to the inherent noise from a single-view environment. Recently, several studies have focused on the single-view depth enhancement of RGB-D cameras. Recent research has proposed deep-learning-based approaches that typically train networks using high-quality supervised depth datasets, which indicates that the quality of the ground-truth (GT) depth dataset is a top-most important factor for accurate system; however, such high-quality GT datasets are difficult to obtain. In this study, we developed a novel method for high-quality GT depth generation based on an RGB-D stream dataset. First, we defined consecutive depth frames in a local spatial region as a local frame set. Then, the depth frames were aligned to a certain frame in the local frame set using an unsupervised point cloud registration scheme. The registration parameters were trained based on an overfit-training scheme, which was primarily used to construct a single GT depth image for each frame set. The final GT depth dataset was constructed using several local frame sets, and each local frame set was trained independently. The primary advantage of this study is that a high-quality GT depth dataset can be constructed under various scanning environments using only the RGB-D stream dataset. Moreover, our proposed method can be used as a new benchmark GT dataset for accurate performance evaluations. We evaluated our GT dataset on previously benchmarked GT depth datasets and demonstrated that our method is superior to state-of-the-art depth enhancement frameworks. | ['Minyoung Chung', 'Yeong-Gil Shin', 'Minchang Kim', 'Jiwan Kim'] | 2022-07-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 5.00784874e-01 -2.80198246e-01 9.12751555e-02 -5.27314425e-01
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19c71260-9d68-4687-94ae-ab7b4b930122 | spatial-and-semantic-consistency | 2109.05686 | null | https://arxiv.org/abs/2109.05686v1 | https://arxiv.org/pdf/2109.05686v1.pdf | Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition | While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial locations of the same attribute should be consistent between different pedestrian images, \eg, the ``hat" attribute and the ``boots" attribute are always located at the top and bottom of the picture respectively. In addition, the inherent semantic feature of the ``hat" attribute should be consistent, whether it is a baseball cap, beret, or helmet. To fully exploit inter-image relations and aggregate human prior in the model learning process, we construct a Spatial and Semantic Consistency (SSC) framework that consists of two complementary regularizations to achieve spatial and semantic consistency for each attribute. Specifically, we first propose a spatial consistency regularization to focus on reliable and stable attribute-related regions. Based on the precise attribute locations, we further propose a semantic consistency regularization to extract intrinsic and discriminative semantic features. We conduct extensive experiments on popular benchmarks including PA100K, RAP, and PETA. Results show that the proposed method performs favorably against state-of-the-art methods without increasing parameters. | ['Kaiqi Huang', 'Xiaotang Chen', 'Jian Jia'] | 2021-09-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jia_Spatial_and_Semantic_Consistency_Regularizations_for_Pedestrian_Attribute_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jia_Spatial_and_Semantic_Consistency_Regularizations_for_Pedestrian_Attribute_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-2.94600129e-01 -3.80042762e-01 -1.48611655e-02 -8.51562262e-01
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5e819187-7583-4cbf-a0cd-9795446b813e | that-is-a-suspicious-reaction-interpreting | null | null | https://openreview.net/forum?id=UrtZRnO6VB | https://openreview.net/pdf?id=UrtZRnO6VB | "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks | Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, the same issue remains less explored in natural language processing. Our work presents a model-agnostic detector of adversarial text examples. The approach identifies patterns in the logits of the target classifier when perturbing the input text. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['adversarial-text'] | ['adversarial'] | [ 4.51701254e-01 -1.45176351e-01 -2.14413881e-01 -1.61840826e-01
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594317e9-e1ad-41d3-bf69-63648cb5f924 | diversity-based-generalization-for-neural | 2002.10937 | null | https://arxiv.org/abs/2002.10937v2 | https://arxiv.org/pdf/2002.10937v2.pdf | Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift | Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data. In this paper, we propose a novel method for domain adaptation of single-task text classification problems based on a simple but effective idea of diversity-based generalization that does not require unlabeled target data but still matches the state-of-the-art in performance. Diversity plays the role of promoting the model to better generalize and be indiscriminate towards domain shift by forcing the model not to rely on same features for prediction. We apply this concept on the most explainable component of neural networks, the attention layer. To generate sufficient diversity, we create a multi-head attention model and infuse a diversity constraint between the attention heads such that each head will learn differently. We further expand upon our model by tri-training and designing a procedure with an additional diversity constraint between the attention heads of the tri-trained classifiers. Extensive evaluation using the standard benchmark dataset of Amazon reviews and a newly constructed dataset of Crisis events shows that our fully unsupervised method matches with the competing baselines that uses unlabeled target data. Our results demonstrate that machine learning architectures that ensure sufficient diversity can generalize better; encouraging future research to design ubiquitously usable learning models without using unlabeled target data. | ['Huzefa Rangwala', 'Jitin Krishnan', 'Hemant Purohit'] | 2020-02-25 | null | null | null | null | ['unsupervised-text-classification'] | ['natural-language-processing'] | [ 2.69634277e-01 3.23725700e-01 -4.42483276e-01 -7.99292386e-01
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447b9fbf-b198-4615-86cf-6be5021f362d | incorporating-l2-phonemes-using-articulatory | 2306.02534 | null | https://arxiv.org/abs/2306.02534v1 | https://arxiv.org/pdf/2306.02534v1.pdf | Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition | The limited availability of non-native speech datasets presents a major challenge in automatic speech recognition (ASR) to narrow the performance gap between native and non-native speakers. To address this, the focus of this study is on the efficient incorporation of the L2 phonemes, which in this work refer to Korean phonemes, through articulatory feature analysis. This not only enables accurate modeling of pronunciation variants but also allows for the utilization of both native Korean and English speech datasets. We employ the lattice-free maximum mutual information (LF-MMI) objective in an end-to-end manner, to train the acoustic model to align and predict one of multiple pronunciation candidates. Experimental results show that the proposed method improves ASR accuracy for Korean L2 speech by training solely on L1 speech data. Furthermore, fine-tuning on L2 speech improves recognition accuracy for both L1 and L2 speech without performance trade-offs. | ['Myungwoo Oh', 'Haram Lee', 'Jisung Wang'] | 2023-06-05 | null | null | null | null | ['robust-speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 2.17684895e-01 -9.28954408e-02 -2.16195226e-01 -3.10645431e-01
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7703f605-5314-491e-a53b-f1359159014c | low-complexity-cnns-for-acoustic-scene-1 | 2208.01555 | null | https://arxiv.org/abs/2208.01555v1 | https://arxiv.org/pdf/2208.01555v1.pdf | Low-complexity CNNs for Acoustic Scene Classification | This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K parameters, and (b) there should be a maximum of 30 millions multiply-accumulate operations (MACs) per inference. In this report, we present low-complexity systems for ASC that follow the rules intended for the task. | ['Mark D. Plumbley', 'Wenwu Wang', 'Xubo Liu', 'James A King', 'Arshdeep Singh'] | 2022-08-02 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 1.73553228e-01 -2.40704283e-01 3.98835927e-01 -7.36970305e-01
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7dd85aca-949d-49c2-901d-cde17838d1d8 | explaining-transition-systems-through-program | 1705.08320 | null | http://arxiv.org/abs/1705.08320v1 | http://arxiv.org/pdf/1705.08320v1.pdf | Explaining Transition Systems through Program Induction | Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which capture the invariant structure in the observed data. We introduce
the $\pi$-machine (program-induction machine) -- an architecture able to induce
interpretable LISP-like programs from observed data traces. We propose an
optimisation procedure for program learning based on backpropagation, gradient
descent and A* search. We apply the proposed method to three problems: system
identification of dynamical systems, explaining the behaviour of a DQN agent
and learning by demonstration in a human-robot interaction scenario. Our
experimental results show that the $\pi$-machine can efficiently induce
interpretable programs from individual data traces. | ['Subramanian Ramamoorthy', 'Svetlin Penkov'] | 2017-05-23 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.36676443e-01 6.00251436e-01 -2.53386289e-01 -5.58077276e-01
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506b09f2-528b-4ef6-9392-d2bb4f43278d | structure-representation-network-and | 2210.03061 | null | https://arxiv.org/abs/2210.03061v1 | https://arxiv.org/pdf/2210.03061v1.pdf | Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal | Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or non-uniform distributions can be intractable, since fog's attenuation and airlight (or veiling effect) significantly weaken the background scene information in the input image. To address this problem, we introduce a structure-representation network with uncertainty feedback learning. Specifically, we extract the feature representations from a pre-trained Vision Transformer (DINO-ViT) module to recover the background information. To guide our network to focus on non-uniform fog areas, and then remove the fog accordingly, we introduce the uncertainty feedback learning, which produces the uncertainty maps, that have higher uncertainty in denser fog regions, and can be regarded as an attention map that represents fog's density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying light colors that are possibly present in the input image. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively compared to the state-of-the-art methods in handling dense and non-uniform fog or smoke. | ['Robby T. Tan', 'Wenhan Yang', 'Wending Yan', 'Yeying Jin'] | 2022-10-06 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.28846258e-01 -2.67801136e-01 5.27168274e-01 -2.58161068e-01
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5d4951e8-2d2c-401d-bf09-8ef14848d13e | an-inductive-bias-for-distances-neural-nets-1 | 2002.05825 | null | https://arxiv.org/abs/2002.05825v3 | https://arxiv.org/pdf/2002.05825v3.pdf | An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality | Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guarantees--and empirically--as an inductive bias. Deep metric learning architectures that respect the triangle inequality rely, almost exclusively, on Euclidean distance in the latent space. Though effective, this fails to model two broad classes of subadditive distances, common in graphs and reinforcement learning: asymmetric metrics, and metrics that cannot be embedded into Euclidean space. To address these problems, we introduce novel architectures that are guaranteed to satisfy the triangle inequality. We prove our architectures universally approximate norm-induced metrics on $\mathbb{R}^n$, and present a similar result for modified Input Convex Neural Networks. We show that our architectures outperform existing metric approaches when modeling graph distances and have a better inductive bias than non-metric approaches when training data is limited in the multi-goal reinforcement learning setting. | ['Silviu Pitis', 'Jimmy Ba', 'Harris Chan', 'Kiarash Jamali'] | 2020-02-14 | null | https://openreview.net/forum?id=HJeiDpVFPr | https://openreview.net/pdf?id=HJeiDpVFPr | iclr-2020-1 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-5.87873533e-02 2.39848599e-01 -1.64885804e-01 -6.56735599e-01
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35276aff-8215-41eb-8957-68160136948c | counterfactual-explanations-for-survival | null | null | https://www.researchgate.net/publication/352203927_Counterfactual_Explanations_for_Survival_Prediction_of_Cardiovascular_ICU_Patients | https://www.researchgate.net/publication/352203927_Counterfactual_Explanations_for_Survival_Prediction_of_Cardiovascular_ICU_Patients | Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients | In recent years, machine learning methods have been rapidly implemented in the medical domain. However, current state-of-the-art methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to develop interpretable machine learning methods. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual explanation solution for predicting the survival of cardiovascular ICU patients, by representing their electronic health record as a sequence of medical events, and generating counterfactuals by adopting and employing a text style-transfer technique. Experimental results on the MIMIC-III dataset strongly suggest that text style-transfer methods can … | ['Panagiotis Papapetrou', 'Isak Samsten', 'Zhendong Wang'] | 2021-06-15 | null | null | null | international-conference-on-artificial-6 | ['style-transfer', 'interpretable-machine-learning', 'counterfactual-explanation', 'text-style-transfoer'] | ['computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [ 6.90648556e-01 1.06835794e+00 -3.00796121e-01 -5.41094005e-01
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13241be9-fcc2-414a-b62c-5b877b8434db | automated-translation-of-rebar-information | 2110.15448 | null | https://arxiv.org/abs/2110.15448v1 | https://arxiv.org/pdf/2110.15448v1.pdf | Automated Translation of Rebar Information from GPR Data into As-Built BIM: A Deep Learning-based Approach | Building Information Modeling (BIM) is increasingly used in the construction industry, but existing studies often ignore embedded rebars. Ground Penetrating Radar (GPR) provides a potential solution to develop as-built BIM with surface elements and rebars. However, automatically translating rebars from GPR into BIM is challenging since GPR cannot provide any information about the scanned element. Thus, we propose an approach to link GPR data and BIM according to Faster R-CNN. A label is attached to each element scanned by GPR for capturing the labeled images, which are used with other images to build a 3D model. Meanwhile, Faster R-CNN is introduced to identify the labels, and the projection relationship between images and the model is used to localize the scanned elements in the 3D model. Two concrete buildings is selected to evaluate the proposed approach, and the results reveal that our method could accurately translate the rebars from GPR data into corresponding elements in BIM with correct distributions. | ['Abbas Rashidi', 'Ge Ou', 'Zhongming Xiang'] | 2021-10-28 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.94613799e-01 -2.96046063e-02 3.70067745e-01 -4.16671902e-01
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05d8fdf7-be72-4dc3-8233-58d73b3a45b2 | learning-to-control-highly-accelerated | 1904.03665 | null | http://arxiv.org/abs/1904.03665v1 | http://arxiv.org/pdf/1904.03665v1.pdf | Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots | High-speed and high-acceleration movements are inherently hard to control.
Applying learning to the control of such motions on anthropomorphic robot arms
can improve the accuracy of the control but might damage the system. The
inherent exploration of learning approaches can lead to instabilities and the
robot reaching joint limits at high speeds. Having hardware that enables safe
exploration of high-speed and high-acceleration movements is therefore
desirable. To address this issue, we propose to use robots actuated by
Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees
of freedom (DoFs) robot arm that reaches high joint angle accelerations of up
to 28000 deg/s^2 while avoiding dangerous joint limits thanks to the
antagonistic actuation and limits on the air pressure ranges. With this robot
arm, we are able to tune control parameters using Bayesian optimization
directly on the hardware without additional safety considerations. The achieved
tracking performance on a fast trajectory exceeds previous results on
comparable PAM-driven robots. We also show that our system can be controlled
well on slow trajectories with PID controllers due to careful construction
considerations such as minimal bending of cables, lightweight kinematics and
minimal contact between PAMs and PAMs with the links. Finally, we propose a
novel technique to control the the co-contraction of antagonistic muscle pairs.
Experimental results illustrate that choosing the optimal co-contraction level
is vital to reach better tracking performance. Through the use of PAM-driven
robots and learning, we do a small step towards the future development of
robots capable of more human-like motions. | ['Dieter Büchler', 'Jan Peters', 'Roberto Calandra'] | 2019-04-07 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.92641303e-01 8.32225859e-01 -3.66499960e-01 3.18421990e-01
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76f6e84c-f8a2-49dd-bcfe-14fb02a977cc | redet-a-rotation-equivariant-detector-for | 2103.07733 | null | https://arxiv.org/abs/2103.07733v1 | https://arxiv.org/pdf/2103.07733v1.pdf | ReDet: A Rotation-equivariant Detector for Aerial Object Detection | Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model the orientation variation, large amounts of rotation augmented data is needed to train an accurate object detector. In this paper, we propose a Rotation-equivariant Detector (ReDet) to address these issues, which explicitly encodes rotation equivariance and rotation invariance. More precisely, we incorporate rotation-equivariant networks into the detector to extract rotation-equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size. Based on the rotation-equivariant features, we also present Rotation-invariant RoI Align (RiRoI Align), which adaptively extracts rotation-invariant features from equivariant features according to the orientation of RoI. Extensive experiments on several challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that our method can achieve state-of-the-art performance on the task of aerial object detection. Compared with previous best results, our ReDet gains 1.2, 3.5 and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing the number of parameters by 60\% (313 Mb vs. 121 Mb). The code is available at: \url{https://github.com/csuhan/ReDet}. | ['Gui-Song Xia', 'Nan Xue', 'Jian Ding', 'Jiaming Han'] | 2021-03-13 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-4.79878820e-02 -5.10742307e-01 1.84415445e-01 -2.78212219e-01
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5913d714-56d8-4815-938a-359fb535935b | visual7w-grounded-question-answering-in | 1511.03416 | null | http://arxiv.org/abs/1511.03416v4 | http://arxiv.org/pdf/1511.03416v4.pdf | Visual7W: Grounded Question Answering in Images | We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's capacity for deep image understanding. Previous works have
established a loose, global association between QA sentences and images.
However, many questions and answers, in practice, relate to local regions in
the images. We establish a semantic link between textual descriptions and image
regions by object-level grounding. It enables a new type of QA with visual
answers, in addition to textual answers used in previous work. We study the
visual QA tasks in a grounded setting with a large collection of 7W
multiple-choice QA pairs. Furthermore, we evaluate human performance and
several baseline models on the QA tasks. Finally, we propose a novel LSTM model
with spatial attention to tackle the 7W QA tasks. | ['Li Fei-Fei', 'Michael Bernstein', 'Yuke Zhu', 'Oliver Groth'] | 2015-11-11 | visual7w-grounded-question-answering-in-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Visual7W_Grounded_Question_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Visual7W_Grounded_Question_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.66144475e-01 1.97594434e-01 2.11056605e-01 -5.23412168e-01
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28d86d1f-07f5-49ac-b8d3-bd017685e160 | scene-restoration-from-scaffold-occlusion | 2305.18810 | null | https://arxiv.org/abs/2305.18810v1 | https://arxiv.org/pdf/2305.18810v1.pdf | Scene restoration from scaffold occlusion using deep learning-based methods | The occlusion issues of computer vision (CV) applications in construction have attracted significant attention, especially those caused by the wide-coverage, crisscrossed, and immovable scaffold. Intuitively, removing the scaffold and restoring the occluded visual information can provide CV agents with clearer site views and thus help them better understand the construction scenes. Therefore, this study proposes a novel two-step method combining pixel-level segmentation and image inpainting for restoring construction scenes from scaffold occlusion. A low-cost data synthesis method based only on unlabeled data is developed to address the shortage dilemma of labeled data. Experiments on the synthesized test data show that the proposed method achieves performances of 92% mean intersection over union (MIoU) for scaffold segmentation and over 82% structural similarity (SSIM) for scene restoration from scaffold occlusion. | ['Xiaowei Luo', 'Muyang Liu', 'Yuexiong Ding'] | 2023-05-30 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.63063383e-01 1.24659866e-01 -3.11031342e-02 4.70839366e-02
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289450a7-0e7d-4b27-988f-acb781faaae2 | molecular-orbital-based-machine-learning-for | 2207.08317 | null | https://arxiv.org/abs/2207.08317v1 | https://arxiv.org/pdf/2207.08317v1.pdf | Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression | We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML (KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, GDB-13-T) and open-shell (QMSpin) molecules. | ['Thomas F. Miller III', 'Vignesh C. Bhethanabotla', 'J. Emiliano Deustua', 'Jiace Sun', 'Lixue Cheng'] | 2022-07-17 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-6.39278516e-02 4.51529771e-03 -2.28929788e-01 1.50866687e-01
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a7797099-1daa-41ca-a7a8-d1e184e507bb | aunet-learning-relations-between-action-units | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.pdf | AUNet: Learning Relations Between Action Units for Face Forgery Detection | Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same domain. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods during training. Observing that face manipulation may alter the relation between different facial action units (AU), we propose the Action Units Relation Learning framework to improve the generality of forgery detection. In specific, it consists of the Action Units Relation Transformer (ART) and the Tampered AU Prediction (TAP). The ART constructs the relation between different AUs with AU-agnostic Branch and AU-specific Branch, which complement each other and work together to exploit forgery clues. In the Tampered AU Prediction, we tamper AU-related regions at the image level and develop challenging pseudo samples at the feature level. The model is then trained to predict the tampered AU regions with the generated location-specific supervision. Experimental results demonstrate that our method can achieve state-of-the-art performance in both the in-dataset and cross-dataset evaluations. | ['Weiming Hu', 'Bing Li', 'Zhipeng Zhang', 'Yufan Liu', 'Weiming Bai'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 4.38785672e-01 1.64558813e-02 1.49774864e-01 -3.19027960e-01
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6aea2f8d-b91d-43fe-afd7-ad71482d6b8d | semantic-term-blurring-and-stochastic | 1811.02456 | null | http://arxiv.org/abs/1811.02456v1 | http://arxiv.org/pdf/1811.02456v1.pdf | Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification | The abundance of text data being produced in the modern age makes it
increasingly important to intuitively group, categorize, or classify text data
by theme for efficient retrieval and search. Yet, the high dimensionality and
imprecision of text data, or more generally language as a whole, prove to be
challenging when attempting to perform unsupervised document clustering. In
this thesis, we present two novel methods for improving unsupervised document
clustering/classification by theme. The first is to improve document
representations. We look to exploit "term neighborhoods" and "blur" semantic
weight across neighboring terms. These neighborhoods are located in the
semantic space afforded by "word embeddings." The second method is for cluster
revision, based on what we deem as "stochastic barcoding", or "S- Barcode"
patterns. Text data is inherently high dimensional, yet clustering typically
takes place in a low dimensional representation space. Our method utilizes
lower dimension clustering results as initial cluster configurations, and
iteratively revises the configuration in the high dimensional space. We show
with experimental results how both of the two methods improve the quality of
document clustering. While this thesis elaborates on the two new conceptual
contributions, a joint thesis by David Yan details the feature transformation
and software architecture we developed for unsupervised document
classification. | ['Robert Frank Martorano III'] | 2018-11-06 | null | null | null | null | ['unsupervised-text-classification'] | ['natural-language-processing'] | [ 6.26963750e-03 -3.06060255e-01 -4.49603908e-02 -4.95031178e-01
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ef194b6c-2467-4ba0-b21a-a651d90152a8 | 2d-motion-detection-using-snns-with-graphene | 2111.15250 | null | https://arxiv.org/abs/2111.15250v1 | https://arxiv.org/pdf/2111.15250v1.pdf | 2D-Motion Detection using SNNs with Graphene-Insulator-Graphene Memristive Synapses | The event-driven nature of spiking neural networks makes them biologically plausible and more energy-efficient than artificial neural networks. In this work, we demonstrate motion detection of an object in a two-dimensional visual field. The network architecture presented here is biologically plausible and uses CMOS analog leaky integrate-and-fire neurons and ultra-low power multi-layer RRAM synapses. Detailed transistorlevel SPICE simulations show that the proposed structure can accurately and reliably detect complex motions of an object in a two-dimensional visual field. | ['Anjan Chakravorty', 'Bhaswar Chakrabarti', 'Suresh Balanethiram', 'Karthi Srinivasan', 'Shubham Pande'] | 2021-11-30 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 5.44728577e-01 -5.26844203e-01 3.85158360e-01 1.42734975e-01
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aebcfb5a-c2ec-4b66-80d3-cba79a4c1fde | learning-6d-pose-estimation-from-synthetic | 2208.14288 | null | https://arxiv.org/abs/2208.14288v2 | https://arxiv.org/pdf/2208.14288v2.pdf | 6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping | 6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However, their generalization capabilities in real-world applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation. 6IMPOSE consists of four modules: First, a data generation pipeline that employs the 3D software suite Blender to create synthetic RGBD image datasets with 6D pose annotations. Second, an annotated RGBD dataset of five household objects generated using the proposed pipeline. Third, a real-time two-stage 6D pose estimation approach that integrates the object detector YOLO-V4 and a streamlined, real-time version of the 6D pose estimation algorithm PVN3D optimized for time-sensitive robotics applications. Fourth, a codebase designed to facilitate the integration of the vision system into a robotic grasping experiment. Our approach demonstrates the efficient generation of large amounts of photo-realistic RGBD images and the successful transfer of the trained inference model to robotic grasping experiments, achieving an overall success rate of 87% in grasping five different household objects from cluttered backgrounds under varying lighting conditions. This is made possible by the fine-tuning of data generation and domain randomization techniques, and the optimization of the inference pipeline, overcoming the generalization and performance shortcomings of the original PVN3D algorithm. Finally, we make the code, synthetic dataset, and all the pretrained models available on Github. | ['Marco Caccamo', 'Cristina Piazza', 'Daniele Bernardini', 'Lukas Dirnberger', 'Hongpeng Cao'] | 2022-08-30 | null | null | null | null | ['6d-pose-estimation-1', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 8.75941068e-02 -1.31741658e-01 3.58591825e-01 -3.93451571e-01
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5f48aae3-745b-45eb-bab1-d8a5bc7238d4 | pedestrain-detection-for-low-light-vision | 2303.12725 | null | https://arxiv.org/abs/2303.12725v1 | https://arxiv.org/pdf/2303.12725v1.pdf | Pedestrain detection for low-light vision proposal | The demand for pedestrian detection has created a challenging problem for various visual tasks such as image fusion. As infrared images can capture thermal radiation information, image fusion between infrared and visible images could significantly improve target detection under environmental limitations. In our project, we would approach by preprocessing our dataset with image fusion technique, then using Vision Transformer model to detect pedestrians from the fused images. During the evaluation procedure, a comparison would be made between YOLOv5 and the revised ViT model performance on our fused images | ['Wenliang Jia', 'Ruiling Ma', 'Zhipeng Chang'] | 2023-03-17 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 3.96299094e-01 -5.38710117e-01 1.89091474e-01 -3.33389282e-01
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fcfeb2a4-5243-46b8-a1a2-c50541085066 | simple-and-effective-multi-paragraph-reading | 1710.10723 | null | http://arxiv.org/abs/1710.10723v2 | http://arxiv.org/pdf/1710.10723v2.pdf | Simple and Effective Multi-Paragraph Reading Comprehension | We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during training, and use a shared-normalization training objective
that encourages the model to produce globally correct output. We combine this
method with a state-of-the-art pipeline for training models on document QA
data. Experiments demonstrate strong performance on several document QA
datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion
of TriviaQA, a large improvement from the 56.7 F1 of the previous best system. | ['Christopher Clark', 'Matt Gardner'] | 2017-10-29 | simple-and-effective-multi-paragraph-reading-1 | https://aclanthology.org/P18-1078 | https://aclanthology.org/P18-1078.pdf | acl-2018-7 | ['triviaqa'] | ['miscellaneous'] | [ 2.43978739e-01 4.32140291e-01 -4.40670699e-02 -7.89994121e-01
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05788dd0-bffc-4c21-85a2-bb06456703b6 | forcing-the-whole-video-as-background-an | 2207.06659 | null | https://arxiv.org/abs/2207.06659v1 | https://arxiv.org/pdf/2207.06659v1.pdf | Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization | With video-level labels, weakly supervised temporal action localization (WTAL) applies a localization-by-classification paradigm to detect and classify the action in untrimmed videos. Due to the characteristic of classification, class-specific background snippets are inevitably mis-activated to improve the discriminability of the classifier in WTAL. To alleviate the disturbance of background, existing methods try to enlarge the discrepancy between action and background through modeling background snippets with pseudo-snippet-level annotations, which largely rely on artificial hypotheticals. Distinct from the previous works, we present an adversarial learning strategy to break the limitation of mining pseudo background snippets. Concretely, the background classification loss forces the whole video to be regarded as the background by a background gradient reinforcement strategy, confusing the recognition model. Reversely, the foreground(action) loss guides the model to focus on action snippets under such conditions. As a result, competition between the two classification losses drives the model to boost its ability for action modeling. Simultaneously, a novel temporal enhancement network is designed to facilitate the model to construct temporal relation of affinity snippets based on the proposed strategy, for further improving the performance of action localization. Finally, extensive experiments conducted on THUMOS14 and ActivityNet1.2 demonstrate the effectiveness of the proposed method. | ['Zhongming Chen', 'Jiaruo Yu', 'Yongxin Ge', 'Ziqiang Li'] | 2022-07-14 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 5.71049690e-01 -1.99223962e-03 -5.45180559e-01 2.85908151e-02
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85399f21-4a47-4f74-ab4a-5386deab3bef | non-local-graph-neural-networks | 2005.14612 | null | https://arxiv.org/abs/2005.14612v2 | https://arxiv.org/pdf/2005.14612v2.pdf | Non-Local Graph Neural Networks | Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortative graphs. In this work, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. Based on it, we develop various non-local GNNs. We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs. Experimental results demonstrate that our non-local GNNs significantly outperform previous state-of-the-art methods on seven benchmark datasets of disassortative graphs, in terms of both model performance and efficiency. | ['Shuiwang Ji', 'Meng Liu', 'Zhengyang Wang'] | 2020-05-29 | null | https://openreview.net/forum?id=heqv8eIweMY | https://openreview.net/pdf?id=heqv8eIweMY | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-2.71147579e-01 -8.78395885e-02 -3.38461310e-01 -3.32288206e-01
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4e7b38d3-8ae6-4761-9ae8-1858e2b0ca35 | filler-word-detection-and-classification-a | 2203.15135 | null | https://arxiv.org/abs/2203.15135v2 | https://arxiv.org/pdf/2203.15135v2.pdf | Filler Word Detection and Classification: A Dataset and Benchmark | Filler words such as `uh' or `um' are sounds or words people use to signal they are pausing to think. Finding and removing filler words from recordings is a common and tedious task in media editing. Automatically detecting and classifying filler words could greatly aid in this task, but few studies have been published on this problem to date. A key reason is the absence of a dataset with annotated filler words for model training and evaluation. In this work, we present a novel speech dataset, PodcastFillers, with 35K annotated filler words and 50K annotations of other sounds that commonly occur in podcasts such as breaths, laughter, and word repetitions. We propose a pipeline that leverages VAD and ASR to detect filler candidates and a classifier to distinguish between filler word types. We evaluate our proposed pipeline on PodcastFillers, compare to several baselines, and present a detailed ablation study. In particular, we evaluate the importance of using ASR and how it compares to a transcription-free approach resembling keyword spotting. We show that our pipeline obtains state-of-the-art results, and that leveraging ASR strongly outperforms a keyword spotting approach. We make PodcastFillers publicly available, in the hope that our work serves as a benchmark for future research. | ['Justin Salamon', 'Juan-Pablo Caceres', 'Ge Zhu'] | 2022-03-28 | null | null | null | null | ['sound-event-localization-and-detection', 'keyword-spotting'] | ['audio', 'speech'] | [ 3.00734192e-01 -6.14731014e-02 6.16477728e-02 -2.04084948e-01
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0ae5738b-0cfa-4b1a-b8c5-1bb5cd055764 | comparing-span-extraction-methods-for | null | null | https://aclanthology.org/2021.spnlp-1.8 | https://aclanthology.org/2021.spnlp-1.8.pdf | Comparing Span Extraction Methods for Semantic Role Labeling | In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL). While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on popular benchmarks, the potential costs and benefits of structured decoding in these models have become less clear. With extensive experiments on PropBank SRL datasets, we find that more structured decoding methods outperform BIO-tagging when using static (word type) embeddings across all experimental settings. However, when used in conjunction with pre-trained contextualized word representations, the benefits are diminished. We also experiment in cross-genre and cross-lingual settings and find similar trends. We further perform speed comparisons and provide analysis on the accuracy-efficiency trade-offs among different decoding methods. | ['Eduard Hovy', 'Emma Strubell', 'Zhisong Zhang'] | null | null | null | null | acl-spnlp-2021-8 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 6.49373412e-01 3.06894004e-01 -7.07817197e-01 -5.99284947e-01
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3e5b2625-19ba-401a-9bd2-8bfa02ed4c5b | divide-and-conquer-from-complexity-to | null | null | https://aclanthology.org/2020.sdp-1.40 | https://aclanthology.org/2020.sdp-1.40.pdf | Divide and Conquer: From Complexity to Simplicity for Lay Summarization | We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide-and-conquer approach. First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method. Next, we perform abstractive summarization on these extractions and systematically merge the abstractions. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task. | ['Vasudha Bhatnagar', 'Alka Khurana', 'Swagata Duari', 'Neha Tomar', 'Ankush Khanna', 'Anurag Joshi', 'Jaspreet Singh Dhani', 'Saachi .', 'Rochana Chaturvedi'] | null | null | null | null | emnlp-sdp-2020-11 | ['lay-summarization'] | ['natural-language-processing'] | [ 3.41763347e-01 6.36783242e-01 -4.04745311e-01 -1.64410979e-01
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6be1524b-dd48-43d1-b74f-9ffb5792ab73 | weakly-supervised-learning-significantly | 2211.15924 | null | https://arxiv.org/abs/2211.15924v1 | https://arxiv.org/pdf/2211.15924v1.pdf | Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT | Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing strong supervision on what constitutes positive and negative samples. This constitutes a major obstacle for the development of DL models in radiology--in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)--where labels must come from manual annotations by expert radiologists at the image or slice-level. These differ from examination-level annotations, which are coarser but cheaper, and could be extracted from radiology reports using natural language processing techniques. This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a multiple instance learning problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve detection performance. We find that strong supervision (i.e., learning with local image-level annotations) and weak supervision (i.e., learning with only global examination-level labels) achieve comparable performance in examination-level hemorrhage detection (the task of selecting the images in an examination that show signs of hemorrhage) as well as in image-level hemorrhage detection (highlighting those signs within the selected images). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all for these tasks, drastically reducing the time and cost involved in collecting and curating datasets. | ['Jeremias Sulam', 'Paul H. Yi', 'Jacopo Teneggi'] | 2022-11-29 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.45276198e-01 4.01261717e-01 -1.36911109e-01 -5.54481208e-01
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4d15d3f2-1c0f-427f-a1b9-2aeeec3e175f | first-order-transition-in-trigonal-structure | 2012.01863 | null | https://arxiv.org/abs/2012.01863v1 | https://arxiv.org/pdf/2012.01863v1.pdf | First order transition in trigonal structure ${\textbf{Ca}}{\textbf{Mn}}_{2}{\textbf{P}}_{2}$ | We report structural and physical properties of the single crystalline ${\mathrm{Ca}}{\mathrm{Mn}}_{2}{\mathrm{P}}_{2}$. The X-ray diffraction(XRD) results show that ${\mathrm{Ca}}{\mathrm{Mn}}_{2}{\mathrm{P}}_{2}$ adopts the trigonal ${\mathrm{Ca}}{\mathrm{Al}}_{2}{\mathrm{Si}}_{2}$-type structure. Temperature dependent electrical resistivity $\rho(T)$ measurements indicate an insulating ground state for ${\mathrm{Ca}}{\mathrm{Mn}}_{2}{\mathrm{P}}_{2}$ with activation energies of 40 meV and 0.64 meV for two distinct regions, respectively. Magnetization measurements show no apparent magnetic phase transition under 400 K. Different from other ${\mathrm{A}}{\mathrm{Mn}}_{2}{\mathrm{Pn}}_{2}$ (A = Ca, Sr, and Ba, and Pn = P, As, and Sb) compounds with the same structure, heat capacity $C_{\mathrm{p}}(T)$ and $\rho(T)$ reveal that ${\mathrm{Ca}}{\mathrm{Mn}}_{2}{\mathrm{P}}_{2}$ has a first-order transition at $T$ = 69.5 K and the transition temperature shifts to high temperature upon increasing pressure. The emergence of plenty of new Raman modes below the transition, clearly suggests a change in symmetry accompanying the transition. The combination of the structural, transport, thermal and magnetic measurements, points to an unusual origin of the transition. | ['J. L. Luo', 'G. Li', 'T. Xiang', 'Q. M. Zhang', 'L. L. Sun', 'K. Liu', 'Z. Li', 'X. B. Zhou', 'C. Mu', 'S. H. Na', 'D. S. Wu', 'W. Wu', 'J. Guo', 'Z. Y. Mi', 'F. Jin', 'Y. J. Li'] | 2020-12-03 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 4.40951854e-01 3.49625707e-01 5.49579673e-02 3.34088430e-02
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44d69134-c13b-471a-87f2-04558418781d | multi-dimensional-refinement-graph | 2306.15321 | null | https://arxiv.org/abs/2306.15321v1 | https://arxiv.org/pdf/2306.15321v1.pdf | Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition | Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of inter-class data. Moreover, the noisy data from pose extraction increases the challenge of fine-grained recognition. In this work, we propose a flexible attention block called Channel-Variable Spatial-Temporal Attention (CVSTA) to enhance the discriminative power of spatial-temporal joints and obtain a more compact intra-class feature distribution. Based on CVSTA, we construct a Multi-Dimensional Refinement Graph Convolutional Network (MDR-GCN), which can improve the discrimination among channel-, joint- and frame-level features for fine-grained actions. Furthermore, we propose a Robust Decouple Loss (RDL), which significantly boosts the effect of the CVSTA and reduces the impact of noise. The proposed method combining MDR-GCN with RDL outperforms the known state-of-the-art skeleton-based approaches on fine-grained datasets, FineGym99 and FSD-10, and also on the coarse dataset NTU-RGB+D X-view version. | ['Gao Huang', 'Fei-Long Wang', 'Si-Fan Zhang', 'Kai-Yuan Liu', 'Jin-Rong Zhang', 'Yu-Ning Ding', 'Sheng-Lan Liu'] | 2023-06-27 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.73969084e-01 -3.33203375e-01 -2.75527716e-01 -3.02430779e-01
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94cbbd99-73ad-427c-9b7a-d16313a72940 | contextual-information-and-commonsense-based | 2207.13254 | null | https://arxiv.org/abs/2207.13254v1 | https://arxiv.org/pdf/2207.13254v1.pdf | Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs' advantages thoroughly, and perform poorly for the conversations lacking explicit emotional expressions. In order to fully leverage the latent knowledge related to the emotional expressions in utterances, we propose a novel ERC model CISPER with the new paradigm of prompt and language model (LM) tuning. Specifically, CISPER is equipped with the prompt blending the contextual information and commonsense related to the interlocutor's utterances, to achieve ERC more effectively. Our extensive experiments demonstrate CISPER's superior performance over the state-of-the-art ERC models, and the effectiveness of leveraging these two kinds of significant prompt information for performance gains. To reproduce our experimental results conveniently, CISPER's sourcecode and the datasets have been shared at https://github.com/DeqingYang/CISPER. | ['Yanghua Xiao', 'Zhiyao Zhang', 'Caiyan Cao', 'Siyu Yuan', 'Deqing Yang', 'Jingjie Yi'] | 2022-07-27 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.99992269e-01 -1.31462160e-02 -9.83696058e-02 -6.61754310e-01
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57886316-24f1-4e91-a600-5fa170206780 | ukp-square-an-interactive-tool-for-teaching | 2305.19748 | null | https://arxiv.org/abs/2305.19748v2 | https://arxiv.org/pdf/2305.19748v2.pdf | UKP-SQuARE: An Interactive Tool for Teaching Question Answering | The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. Additionally, the breadth of QA derived from this exponential growth makes it an ideal scenario for teaching related NLP topics such as information retrieval, explainability, and adversarial attacks among others. In this paper, we introduce UKP-SQuARE as a platform for QA education. This platform provides an interactive environment where students can run, compare, and analyze various QA models from different perspectives, such as general behavior, explainability, and robustness. Therefore, students can get a first-hand experience in different QA techniques during the class. Thanks to this, we propose a learner-centered approach for QA education in which students proactively learn theoretical concepts and acquire problem-solving skills through interactive exploration, experimentation, and practical assignments, rather than solely relying on traditional lectures. To evaluate the effectiveness of UKP-SQuARE in teaching scenarios, we adopted it in a postgraduate NLP course and surveyed the students after the course. Their positive feedback shows the platform's effectiveness in their course and invites a wider adoption. | ['Iryna Gurevych', 'Haritz Puerto', 'Haishuo Fang'] | 2023-05-31 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-2.38131523e-01 2.15987965e-01 2.20657662e-01 -2.70980746e-01
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3f96ce72-a196-4ab3-a43c-e062fd272e12 | hyperparameter-optimization-quantum-assisted | 2303.15053 | null | https://arxiv.org/abs/2303.15053v1 | https://arxiv.org/pdf/2303.15053v1.pdf | Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC | Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient hyperparameter search algorithms. This work studies the potential of using model performance prediction to aid the HPO process carried out on High Performance Computing systems. In addition, a quantum annealer is used to train the performance predictor and a method is proposed to overcome some of the problems derived from the current limitations in quantum systems as well as to increase the stability of solutions. This allows for achieving results on a quantum machine comparable to those obtained on a classical machine, showing how quantum computers could be integrated within classical machine learning tuning pipelines. Furthermore, results are presented from the development of a containerized benchmark based on an AI-model for collision event reconstruction that allows us to compare and assess the suitability of different hardware accelerators for training deep neural networks. | ['Eduard Cuba', 'Juan Pablo García Amboage', 'David Southwick', 'Maria Girone', 'Eric Wulff'] | 2023-03-27 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-8.84764194e-02 -1.66768894e-01 1.66897088e-01 -3.02590489e-01
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1fbec278-cb39-497d-a46f-072bcfc39454 | mdetr-modulated-detection-for-end-to-end | 2104.12763 | null | https://arxiv.org/abs/2104.12763v2 | https://arxiv.org/pdf/2104.12763v2.pdf | MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding | Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. We use a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. We pre-train the network on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. We then fine-tune on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation, achieving state-of-the-art results on popular benchmarks. We also investigate the utility of our model as an object detector on a given label set when fine-tuned in a few-shot setting. We show that our pre-training approach provides a way to handle the long tail of object categories which have very few labelled instances. Our approach can be easily extended for visual question answering, achieving competitive performance on GQA and CLEVR. The code and models are available at https://github.com/ashkamath/mdetr. | ['Gabriel Synnaeve', 'Nicolas Carion', 'Ishan Misra', 'Yann Lecun', 'Mannat Singh', 'Aishwarya Kamath'] | 2021-04-26 | null | null | null | null | ['referring-image-matting-refmatte-rw100', 'referring-image-matting-keyword-based', 'referring-expression-segmentation', 'referring-image-matting-expression-based', 'phrase-grounding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.25477332e-01 2.99417466e-01 -3.34030725e-02 -6.14177525e-01
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5.00562668e-01 -5.78240991e-01 -1.90780073e-01 4.00585264e-01] | [10.47502613067627, 1.4396226406097412] |
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