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6579fc6c-2f80-4b12-9368-22725200f725 | rethinking-gnn-based-entity-alignment-on | 2304.03468 | null | https://arxiv.org/abs/2304.03468v2 | https://arxiv.org/pdf/2304.03468v2.pdf | Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method | The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However, we have observed that the oversimplified settings of the existing common EA datasets are distant from real-world scenarios, which obstructs a full understanding of the advancements achieved by recent methods. This phenomenon makes us ponder: Do existing GNN-based EA methods really make great progress? In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e.g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities. First, we sweep the unreasonable settings, and propose two new HHKG datasets that closely mimic real-world EA scenarios. Then, based on the proposed datasets, we conduct extensive experiments to evaluate previous representative EA methods, and reveal interesting findings about the progress of GNN-based EA methods. We find that the structural information becomes difficult to exploit but still valuable in aligning HHKGs. This phenomenon leads to inferior performance of existing EA methods, especially GNN-based methods. Our findings shed light on the potential problems resulting from an impulsive application of GNN-based methods as a panacea for all EA datasets. Finally, we introduce a simple but effective method: Simple-HHEA, which comprehensively utilizes entity name, structure, and temporal information. Experiment results show Simple-HHEA outperforms previous models on HHKG datasets. | ['HuaWei Shen', 'Zixuan Li', 'Fei Sun', 'Yuanzhuo Wang', 'Fenglong Su', 'Yinghan Shen', 'Chengjin Xu', 'Xuhui Jiang'] | 2023-04-07 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-1.22409895e-01 1.95557088e-01 -3.77926588e-01 3.72925284e-03
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86e9e96e-9026-46da-b1e9-162b85889536 | learning-axioms-to-compute-verifiable | null | null | https://openreview.net/forum?id=PkqwRo2wjuW | https://openreview.net/pdf?id=PkqwRo2wjuW | Learning Axioms to Compute Verifiable Symbolic Expression Equivalence Proofs Using Graph-to-Sequence Networks | We target the problem of proving the semantic equivalence between two complex expressions represented as typed trees, and demonstrate our system on expressions from a rich multi-type symbolic language for linear algebra. We propose the first graph-to-sequence deep learning system to generate axiomatic proofs of equivalence between program pairs. We generate expressions which include scalars, vectors and matrices and 16 distinct operators combining them, with 147 distinct axioms of equivalence. We study the robustness of the system to generate proofs of increasing length, demonstrating how incremental graph-to-sequence networks can learn to represent complex and verifiable symbolic reasoning. It achieves 93% average true positive coverage on 10,000 test cases while ensuring zero false positives by design. | ['Theo Barolett', 'Louis-Noel Pouchet', 'Steven James Kommrusch'] | 2021-01-01 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 2.91346222e-01 4.38654333e-01 -3.66184711e-01 -2.54138023e-01
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d63df5b2-efc8-4ee6-bf2b-064343e94701 | difficulty-aware-simulator-for-open-set | 2207.10024 | null | https://arxiv.org/abs/2207.10024v1 | https://arxiv.org/pdf/2207.10024v1.pdf | Difficulty-Aware Simulator for Open Set Recognition | Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we present a novel framework, DIfficulty-Aware Simulator (DIAS), that generates fakes with diverse difficulty levels to simulate the real world. We first investigate fakes from generative adversarial network (GAN) in the classifier's viewpoint and observe that these are not severely challenging. This leads us to define the criteria for difficulty by regarding samples generated with GANs having moderate-difficulty. To produce hard-difficulty examples, we introduce Copycat, imitating the behavior of the classifier. Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively. As a result, DIAS outperforms state-of-the-art methods with both metrics of AUROC and F-score. Our code is available at https://github.com/wjun0830/Difficulty-Aware-Simulator. | ['Jae-Pil Heo', 'Cheol-Ho Cho', 'Hyun Seok Seong', 'Junho Park', 'WonJun Moon'] | 2022-07-20 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-1.22759305e-01 2.54816413e-01 4.03510220e-02 -2.28261024e-01
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56b74c59-fd70-45d1-8d16-a145fa1583e9 | neural-message-passing-with-edge-updates-for | 1806.03146 | null | http://arxiv.org/abs/1806.03146v1 | http://arxiv.org/pdf/1806.03146v1.pdf | Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials | Neural message passing on molecular graphs is one of the most promising
methods for predicting formation energy and other properties of molecules and
materials. In this work we extend the neural message passing model with an edge
update network which allows the information exchanged between atoms to depend
on the hidden state of the receiving atom. We benchmark the proposed model on
three publicly available datasets (QM9, The Materials Project and OQMD) and
show that the proposed model yields superior prediction of formation energies
and other properties on all three datasets in comparison with the best
published results. Furthermore we investigate different methods for
constructing the graph used to represent crystalline structures and we find
that using a graph based on K-nearest neighbors achieves better prediction
accuracy than using maximum distance cutoff or the Voronoi tessellation graph. | ['Mikkel N. Schmidt', 'Peter Bjørn Jørgensen', 'Karsten Wedel Jacobsen'] | 2018-06-08 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 1.35239527e-01 1.14055410e-01 -3.44791323e-01 -3.14886481e-01
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33dd9dd8-d454-42bf-87a8-5ccf2c0953d6 | fm-vit-flexible-modal-vision-transformers-for | 2305.03277 | null | https://arxiv.org/abs/2305.03277v1 | https://arxiv.org/pdf/2305.03277v1.pdf | FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing | The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack detection (PAD) has two defects: (1) The framework based on multi-modal fusion requires providing modalities consistent with the training input, which seriously limits the deployment scenario. (2) The performance of ConvNet-based model on high fidelity datasets is increasingly limited. In this work, we present a pure transformer-based framework, dubbed the Flexible Modal Vision Transformer (FM-ViT), for face anti-spoofing to flexibly target any single-modal (i.e., RGB) attack scenarios with the help of available multi-modal data. Specifically, FM-ViT retains a specific branch for each modality to capture different modal information and introduces the Cross-Modal Transformer Block (CMTB), which consists of two cascaded attentions named Multi-headed Mutual-Attention (MMA) and Fusion-Attention (MFA) to guide each modal branch to mine potential features from informative patch tokens, and to learn modality-agnostic liveness features by enriching the modal information of own CLS token, respectively. Experiments demonstrate that the single model trained based on FM-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin, and approaches the multi-modal frameworks introduced with smaller FLOPs and model parameters. | ['Guodong Guo', 'Stan Z. Li', 'Du Zhang', 'Zhen Lei', 'Yanyan Liang', 'Jun Wan', 'Chenxu Zhao', 'Zitong Yu', 'Zichang Tan', 'Ajian Liu'] | 2023-05-05 | null | null | null | null | ['face-presentation-attack-detection', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 3.14361930e-01 -3.03295672e-01 -1.61388442e-01 -6.10749051e-02
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e59d199a-31a5-4fae-9298-bdd3f1cd2c39 | xihe-a-3d-vision-based-lighting-estimation | 2106.15280 | null | https://arxiv.org/abs/2106.15280v1 | https://arxiv.org/pdf/2106.15280v1.pdf | Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality | Omnidirectional lighting provides the foundation for achieving spatially-variant photorealistic 3D rendering, a desirable property for mobile augmented reality applications. However, in practice, estimating omnidirectional lighting can be challenging due to limitations such as partial panoramas of the rendering positions, and the inherent environment lighting and mobile user dynamics. A new opportunity arises recently with the advancements in mobile 3D vision, including built-in high-accuracy depth sensors and deep learning-powered algorithms, which provide the means to better sense and understand the physical surroundings. Centering the key idea of 3D vision, in this work, we design an edge-assisted framework called Xihe to provide mobile AR applications the ability to obtain accurate omnidirectional lighting estimation in real time. Specifically, we develop a novel sampling technique that efficiently compresses the raw point cloud input generated at the mobile device. This technique is derived based on our empirical analysis of a recent 3D indoor dataset and plays a key role in our 3D vision-based lighting estimator pipeline design. To achieve the real-time goal, we develop a tailored GPU pipeline for on-device point cloud processing and use an encoding technique that reduces network transmitted bytes. Finally, we present an adaptive triggering strategy that allows Xihe to skip unnecessary lighting estimations and a practical way to provide temporal coherent rendering integration with the mobile AR ecosystem. We evaluate both the lighting estimation accuracy and time of Xihe using a reference mobile application developed with Xihe's APIs. Our results show that Xihe takes as fast as 20.67ms per lighting estimation and achieves 9.4% better estimation accuracy than a state-of-the-art neural network. | ['Tian Guo', 'Yiqin Zhao'] | 2021-05-30 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.87299466e-02 -6.26272202e-01 2.26388022e-01 -2.16700807e-01
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630973bf-6164-45a2-9d30-cb378747fab7 | infrared-and-visible-image-fusion-via-dual | 2210.11018 | null | https://arxiv.org/abs/2210.11018v2 | https://arxiv.org/pdf/2210.11018v2.pdf | An Attention-Guided and Wavelet-Constrained Generative Adversarial Network for Infrared and Visible Image Fusion | The GAN-based infrared and visible image fusion methods have gained ever-increasing attention due to its effectiveness and superiority. However, the existing methods adopt the global pixel distribution of source images as the basis for discrimination, which fails to focus on the key modality information. Moreover, the dual-discriminator based methods suffer from the confrontation between the discriminators. To this end, we propose an attention-guided and wavelet-constrained GAN for infrared and visible image fusion (AWFGAN). In this method, two unique discrimination strategies are designed to improve the fusion performance. Specifically, we introduce the spatial attention modules (SAM) into the generator to obtain the spatial attention maps, and then the attention maps are utilized to force the discrimination of infrared images to focus on the target regions. In addition, we extend the discrimination range of visible information to the wavelet subspace, which can force the generator to restore the high-frequency details of visible images. Ablation experiments demonstrate the effectiveness of our method in eliminating the confrontation between discriminators. And the comparison experiments on public datasets demonstrate the effectiveness and superiority of the proposed method. | ['Xin Yang', 'Jing Li', 'Hongtao Huo', 'Renhua Wang', 'Xiaowen Liu'] | 2022-10-20 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 3.97536099e-01 -5.67919195e-01 1.94911882e-01 -9.10013542e-02
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9704b123-f2cd-456d-bbba-893cf98f0a15 | schemaless-queries-over-document-tables-with | 1911.09356 | null | https://arxiv.org/abs/1911.09356v1 | https://arxiv.org/pdf/1911.09356v1.pdf | Schemaless Queries over Document Tables with Dependencies | Unstructured enterprise data such as reports, manuals and guidelines often contain tables. The traditional way of integrating data from these tables is through a two-step process of table detection/extraction and mapping the table layouts to an appropriate schema. This can be an expensive process. In this paper we show that by using semantic technologies (RDF/SPARQL and database dependencies) paired with a simple but powerful way to transform tables with non-relational layouts, it is possible to offer query answering services over these tables with minimal manual work or domain-specific mappings. Our method enables users to exploit data in tables embedded in documents with little effort, not only for simple retrieval queries, but also for structured queries that require joining multiple interrelated tables. | ['Cristina Cornelio', 'Mustafa Canim', 'Mariano Rodrigez Muro', 'Arun Iyengar', 'Ryan Musa'] | 2019-11-21 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-1.07642680e-01 4.77402240e-01 -7.32942224e-02 -4.73754734e-01
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82419feb-3511-4b9a-ab30-b8f7d105c255 | on-the-use-of-the-gram-matrix-for | 2306.12949 | null | https://arxiv.org/abs/2306.12949v1 | https://arxiv.org/pdf/2306.12949v1.pdf | On the use of the Gram matrix for multivariate functional principal components analysis | Dimension reduction is crucial in functional data analysis (FDA). The key tool to reduce the dimension of the data is functional principal component analysis. Existing approaches for functional principal component analysis usually involve the diagonalization of the covariance operator. With the increasing size and complexity of functional datasets, estimating the covariance operator has become more challenging. Therefore, there is a growing need for efficient methodologies to estimate the eigencomponents. Using the duality of the space of observations and the space of functional features, we propose to use the inner-product between the curves to estimate the eigenelements of multivariate and multidimensional functional datasets. The relationship between the eigenelements of the covariance operator and those of the inner-product matrix is established. We explore the application of these methodologies in several FDA settings and provide general guidance on their usability. | ['Norma Bargary', 'Andrew J. Simpkin', 'Edward Gunning', 'Steven Golovkine'] | 2023-06-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 8.63320157e-02 -4.12684143e-01 7.92750418e-02 -6.77120164e-02
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77de3c6f-16e0-44cd-a3e3-118740719f2b | reconstruction-regularized-deep-metric | 2007.13547 | null | https://arxiv.org/abs/2007.13547v1 | https://arxiv.org/pdf/2007.13547v1.pdf | Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification | In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images features, as well as labels, we attempt to explore a latent space, where images and labels are embedded via two unique deep neural networks, respectively. To capture the relationships between image features and labels, we aim to learn a \emph{two-way} deep distance metric over the embedding space from two different views, i.e., the distance between one image and its labels is not only smaller than those distances between the image and its labels' nearest neighbors, but also smaller than the distances between the labels and other images corresponding to the labels' nearest neighbors. Moreover, a reconstruction module for recovering correct labels is incorporated into the whole framework as a regularization term, such that the label embedding space is more representative. Our model can be trained in an end-to-end manner. Experimental results on publicly available image datasets corroborate the efficacy of our method compared with the state-of-the-arts. | ['Peng Gao', 'Changsheng Li', 'Kai Zheng', 'Chong Liu', 'Lixin Duan'] | 2020-07-27 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.06716821e-01 -1.34358436e-01 -8.23681280e-02 -7.86299109e-01
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8a47fe4b-752a-43af-a172-744f300d7128 | moprd-a-multidisciplinary-open-peer-review | 2212.04972 | null | https://arxiv.org/abs/2212.04972v1 | https://arxiv.org/pdf/2212.04972v1.pdf | MOPRD: A multidisciplinary open peer review dataset | Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as they are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response to this problem, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we design a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications. | ['Xiaodong Shi', 'Yidong Chen', 'Zhangping Zhou', 'Jiaxin Song', 'Jialiang Lin'] | 2022-12-09 | null | null | null | null | ['review-generation', 'comment-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-2.55973071e-01 1.66685089e-01 -8.78229380e-01 2.34721135e-02
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3b980a21-1061-4331-b21b-447937ef719e | improving-low-resource-named-entity | null | null | https://aclanthology.org/2020.acl-main.523 | https://aclanthology.org/2020.acl-main.523.pdf | Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling | Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate. Current models for jointly learning sentence and token labeling are limited to binary classification. We present a joint model that supports multi-class classification and introduce a simple variant of self-attention that allows the model to learn scaling factors. Our model produces 3.78{\%}, 4.20{\%}, 2.08{\%} improvements in F1 over the BiLSTM-CRF baseline on e-commerce product titles in three different low-resource languages: Vietnamese, Thai, and Indonesian, respectively. | ['Sharifah Mahani Aljunied', 'Canasai Kruengkrai', 'Thien Hai Nguyen', 'Lidong Bing'] | 2020-07-01 | null | null | null | acl-2020-6 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-4.56061512e-02 1.76681146e-01 -5.74050546e-01 -8.18207681e-01
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67a44b4d-6045-431b-b45b-da46ad596d6b | deepsportlab-a-unified-framework-for-ball | 2112.00627 | null | https://arxiv.org/abs/2112.00627v1 | https://arxiv.org/pdf/2112.00627v1.pdf | DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes | This paper presents a unified framework to (i) locate the ball, (ii) predict the pose, and (iii) segment the instance mask of players in team sports scenes. Those problems are of high interest in automated sports analytics, production, and broadcast. A common practice is to individually solve each problem by exploiting universal state-of-the-art models, \eg, Panoptic-DeepLab for player segmentation. In addition to the increased complexity resulting from the multiplication of single-task models, the use of the off-the-shelf models also impedes the performance due to the complexity and specificity of the team sports scenes, such as strong occlusion and motion blur. To circumvent those limitations, our paper proposes to train a single model that simultaneously predicts the ball and the player mask and pose by combining the part intensity fields and the spatial embeddings principles. Part intensity fields provide the ball and player location, as well as player joints location. Spatial embeddings are then exploited to associate player instance pixels to their respective player center, but also to group player joints into skeletons. We demonstrate the effectiveness of the proposed model on the DeepSport basketball dataset, achieving comparable performance to the SoA models addressing each individual task separately. | ['Christophe De Vleeschouwer', 'Amirafshar Moshtaghpour', 'Niels Sayez', 'Maxime Istasse', 'Gabriel Van Zandycke', 'Seyed Abolfazl Ghasemzadeh'] | 2021-12-01 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-1.86722845e-01 -1.09341756e-01 -1.86129317e-01 -7.96934590e-02
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02344553-5ce2-4222-b770-3f2975ef570f | sentence-level-discourse-parsing-as-text-to | null | null | https://openreview.net/forum?id=MzT29q_1ez8 | https://openreview.net/pdf?id=MzT29q_1ez8 | Sentence-Level Discourse Parsing as Text-to-Text Generation | Previous studies have made great advances in RST discourse parsing through neural frameworks or efficient features, but they split the parsing process into two subtasks and heavily depended on gold segmentation. In this paper, we introduce an end-to-end method for sentence-level RST discourse parsing via transforming it into a text-to-text generation task. Our method unifies the traditional two-stage parsing and generates the parsing tree directly from the input text without requiring a complicated model. Moreover, the EDU segmentation can be simultaneously generated and extracted from the parsing tree. Experimental results on the RST Discourse Treebank demonstrate that our proposed method outperforms existing methods in both tasks of sentence-level RST parsing and discourse segmentation. Considering the lack of annotated data in RST parsing, we also create high-quality augmented data based on several filtering strategies, which further improves the performance. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['discourse-segmentation', 'discourse-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.54846168e-01 9.28908885e-01 -1.86659634e-01 -4.46554452e-01
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e3e508f8-4fad-4de2-8800-507fa3490a89 | bayling-bridging-cross-lingual-alignment-and | 2306.10968 | null | https://arxiv.org/abs/2306.10968v2 | https://arxiv.org/pdf/2306.10968v2.pdf | BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models | Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation. Advancing from foundation LLMs to instructionfollowing LLMs, instruction tuning plays a vital role in aligning LLMs to human preferences. However, the existing LLMs are usually focused on English, leading to inferior performance in non-English languages. In order to improve the performance for non-English languages, it is necessary to collect language-specific training data for foundation LLMs and construct language-specific instructions for instruction tuning, both of which are heavy loads. To minimize human workload, we propose to transfer the capabilities of language generation and instruction following from English to other languages through an interactive translation task. We have developed BayLing, an instruction-following LLM by utilizing LLaMA as the foundation LLM and automatically constructing interactive translation instructions for instructing tuning. Extensive assessments demonstrate that BayLing achieves comparable performance to GPT-3.5-turbo, despite utilizing a considerably smaller parameter size of only 13 billion. Experimental results on translation tasks show that BayLing achieves 95% of single-turn translation capability compared to GPT-4 with automatic evaluation and 96% of interactive translation capability compared to GPT-3.5-turbo with human evaluation. To estimate the performance on general tasks, we created a multi-turn instruction test set called BayLing-80. The experimental results on BayLing-80 indicate that BayLing achieves 89% of performance compared to GPT-3.5-turbo. BayLing also demonstrates outstanding performance on knowledge assessment of Chinese GaoKao and English SAT, second only to GPT-3.5-turbo among a multitude of instruction-following LLMs. Demo, homepage, code and models of BayLing are available. | ['Yang Feng', 'Xilin Chen', 'Yunji Chen', 'Shangtong Gui', 'Mengyu Bu', 'Langlin Huang', 'Yan Zhou', 'Zhengrui Ma', 'Zhuocheng Zhang', 'Qingkai Fang', 'Shaolei Zhang'] | 2023-06-19 | null | null | null | null | ['text-generation', 'instruction-following'] | ['natural-language-processing', 'natural-language-processing'] | [-1.94591895e-01 -1.62161112e-01 -4.38619524e-01 -3.76559585e-01
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609c7cb0-f45d-463c-9216-58de13301c40 | feature-space-selection-and-combination-for | null | null | https://aclanthology.org/W13-1712 | https://aclanthology.org/W13-1712.pdf | Feature Space Selection and Combination for Native Language Identification | null | ["Serge L{\\'e}ger", 'Marine Carpuat', 'Cyril Goutte'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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88c99951-1bf6-4a35-8abb-fc68cc168d08 | flexible-option-learning-1 | 2112.03097 | null | https://arxiv.org/abs/2112.03097v1 | https://arxiv.org/pdf/2112.03097v1.pdf | Flexible Option Learning | Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more efficiently over time. Although option learning was initially formulated in a way that allows updating many options simultaneously, using off-policy, intra-option learning (Sutton, Precup & Singh, 1999), many of the recent hierarchical reinforcement learning approaches only update a single option at a time: the option currently executing. We revisit and extend intra-option learning in the context of deep reinforcement learning, in order to enable updating all options consistent with current primitive action choices, without introducing any additional estimates. Our method can therefore be naturally adopted in most hierarchical RL frameworks. When we combine our approach with the option-critic algorithm for option discovery, we obtain significant improvements in performance and data-efficiency across a wide variety of domains. | ['Doina Precup', 'Martin Klissarov'] | 2021-12-06 | flexible-option-learning | http://proceedings.neurips.cc/paper/2021/hash/24cceab7ffc1118f5daaace13c670885-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/24cceab7ffc1118f5daaace13c670885-Paper.pdf | neurips-2021-12 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.19770274e-01 2.32216433e-01 -5.57458818e-01 -3.68669555e-02
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13138563-e0ca-465e-85df-1a69578c4bb3 | boss-bottom-up-cross-modal-semantic | 2207.04211 | null | https://arxiv.org/abs/2207.04211v1 | https://arxiv.org/pdf/2207.04211v1.pdf | BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval | Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially impacts a wide variety of real-world applications, such as internet search and fashion retrieval. In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image. This task is challenging since it necessitates learning and understanding the composite image-text representation by incorporating cross-granular semantic updates. In this paper, we tackle this task by a novel \underline{\textbf{B}}ottom-up cr\underline{\textbf{O}}ss-modal \underline{\textbf{S}}emantic compo\underline{\textbf{S}}ition (\textbf{BOSS}) with Hybrid Counterfactual Training framework, which sheds new light on the CIR task by studying it from two previously overlooked perspectives: \emph{implicitly bottom-up composition of visiolinguistic representation} and \emph{explicitly fine-grained correspondence of query-target construction}. On the one hand, we leverage the implicit interaction and composition of cross-modal embeddings from the bottom local characteristics to the top global semantics, preserving and transforming the visual representation conditioned on language semantics in several continuous steps for effective target image search. On the other hand, we devise a hybrid counterfactual training strategy that can reduce the model's ambiguity for similar queries. | ['Yueting Zhuang', 'Siliang Tang', 'Juncheng Li', 'Shengyu Zhang', 'Haochen Shi', 'Mengze Li', 'Jiannan Guo', 'Wenqiao Zhang'] | 2022-07-09 | null | null | null | null | ['content-based-image-retrieval', 'semantic-composition'] | ['computer-vision', 'natural-language-processing'] | [ 5.89568377e-01 6.26179129e-02 -2.68563420e-01 -2.28216320e-01
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05c1be73-4e75-48e4-b709-7a55df28db9a | a-contrast-adaptive-method-for-simultaneous | 2005.05135 | null | https://arxiv.org/abs/2005.05135v2 | https://arxiv.org/pdf/2005.05135v2.pdf | A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis | Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer. | ['Koen van Leemput', 'Mark Mühlau', 'Dominik S. Meier', 'Oula Puonti', 'Jens Wuerfel', 'Hartwig R. Siebner', 'Stefano Cerri'] | 2020-05-11 | null | null | null | null | ['3d-medical-imaging-segmentation', 'brain-image-segmentation', 'brain-lesion-segmentation-from-mri'] | ['medical', 'medical', 'medical'] | [ 3.92476827e-01 -9.36658755e-02 3.04440349e-01 -5.88874221e-01
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8324bf74-8911-4311-8b2c-d6d1602700cf | pushing-the-limits-of-simple-pipelines-for | 2204.07305 | null | https://arxiv.org/abs/2204.07305v1 | https://arxiv.org/pdf/2204.07305v1.pdf | Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference | Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for more realistic and practical settings of few-shot image classification. To this end, we explore few-shot learning from the perspective of neural network architecture, as well as a three stage pipeline of network updates under different data supplies, where unsupervised external data is considered for pre-training, base categories are used to simulate few-shot tasks for meta-training, and the scarcely labelled data of an novel task is taken for fine-tuning. We investigate questions such as: (1) How pre-training on external data benefits FSL? (2) How state-of-the-art transformer architectures can be exploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we show that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. Our code and demo are available at https://hushell.github.io/pmf. | ['Timothy M. Hospedales', 'Minyoung Kim', 'Jan Stühmer', 'Da Li', 'Shell Xu Hu'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.12728882e-01 3.01186163e-02 -2.33121365e-01 -4.40129310e-01
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58a52f1a-96c2-46e8-b5b5-606f7c509e7c | model-based-image-signal-processors-via | 2201.03210 | null | https://arxiv.org/abs/2201.03210v1 | https://arxiv.org/pdf/2201.03210v1.pdf | Model-Based Image Signal Processors via Learnable Dictionaries | Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the direct sensor readings. Despite this, the availability of RAW images is limited in comparison with the abundance and diversity of available RGB data. Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. Our proposed invertible model, capable of bidirectional mapping between RAW and RGB domains, employs end-to-end learning of rich parameter representations, i.e. dictionaries, that are free from direct parametric supervision and additionally enable simple and plausible data augmentation. We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. Additionally, we show that our ISP can learn meaningful mappings from few data samples, and that denoising models trained with our dictionary-based data augmentation are competitive despite having only few or zero ground-truth labels. | ['Eduardo Pérez-Pellitero', 'Aleš Leonardis', 'Matteo Maggioni', 'Steven McDonagh', 'Marcos V. Conde'] | 2022-01-10 | null | null | null | null | ['color-constancy', 'raw-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 7.49707401e-01 3.43707949e-01 -4.38175462e-02 -4.93220627e-01
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62f15c65-4433-47a1-81ea-e0be53db93b4 | aca-net-towards-lightweight-speaker | 2305.12121 | null | https://arxiv.org/abs/2305.12121v1 | https://arxiv.org/pdf/2305.12121v1.pdf | ACA-Net: Towards Lightweight Speaker Verification using Asymmetric Cross Attention | In this paper, we propose ACA-Net, a lightweight, global context-aware speaker embedding extractor for Speaker Verification (SV) that improves upon existing work by using Asymmetric Cross Attention (ACA) to replace temporal pooling. ACA is able to distill large, variable-length sequences into small, fixed-sized latents by attending a small query to large key and value matrices. In ACA-Net, we build a Multi-Layer Aggregation (MLA) block using ACA to generate fixed-sized identity vectors from variable-length inputs. Through global attention, ACA-Net acts as an efficient global feature extractor that adapts to temporal variability unlike existing SV models that apply a fixed function for pooling over the temporal dimension which may obscure information about the signal's non-stationary temporal variability. Our experiments on the WSJ0-1talker show ACA-Net outperforms a strong baseline by 5\% relative improvement in EER using only 1/5 of the parameters. | ['Bin Ma', 'Eng Siong Chng', 'Shengkui Zhao', 'Chongjia Ni', 'Trung Hieu Nguyen', 'Yukun Ma', 'Chong Zhang', 'Dianwen Ng', 'Tuan Truong', 'Jia Qi Yip'] | 2023-05-20 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 8.18847492e-02 -1.14009358e-01 -9.86815691e-02 -6.69683397e-01
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0590627b-257d-41b0-9393-e0a4a0bb99c0 | the-memad-submission-to-the-wmt18-multimodal | 1808.10802 | null | http://arxiv.org/abs/1808.10802v2 | http://arxiv.org/pdf/1808.10802v2.pdf | The MeMAD Submission to the WMT18 Multimodal Translation Task | This paper describes the MeMAD project entry to the WMT Multimodal Machine
Translation Shared Task.
We propose adapting the Transformer neural machine translation (NMT)
architecture to a multi-modal setting. In this paper, we also describe the
preliminary experiments with text-only translation systems leading us up to
this choice.
We have the top scoring system for both English-to-German and
English-to-French, according to the automatic metrics for flickr18.
Our experiments show that the effect of the visual features in our system is
small. Our largest gains come from the quality of the underlying text-only NMT
system. We find that appropriate use of additional data is effective. | ['Raúl Vázquez', 'Jörg Tiedemann', 'Mats Sjöberg', 'Phu Pham', 'Stig-Arne Grönroos', 'Raphael Troncy', 'Mikko Kurimo', 'Jorma Laaksonen', 'Benoit Huet', 'Umut Sulubacak', 'Bernard Merialdo'] | 2018-08-31 | the-memad-submission-to-the-wmt18-multimodal-1 | https://aclanthology.org/W18-6439 | https://aclanthology.org/W18-6439.pdf | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.94699451e-01 -1.55383721e-01 -1.89842284e-01 -2.54333377e-01
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4425c818-1319-48e1-9b6a-33c6fbc63bc7 | a-60-ghz-radar-sensor-for-micron-scale-motion | 2107.10993 | null | https://arxiv.org/abs/2107.10993v1 | https://arxiv.org/pdf/2107.10993v1.pdf | A 60-GHz Radar Sensor for Micron-Scale Motion Detection | A compact, continuous-wave, mmWave radar sensor is developed for non-contact detection of micron-scale motions. This board-integrated radar system consists of a pair of mmWave transmitter and receiver, two series-fed microstrip patch arrays, an IF subsystem, and a microcontroller. Working at 60-GHz frequency, this super-heterodyne, digital-IF radar sensor exhibits an agile sensitivity and a robust anti-noise performance. Assisted by a gradient-descent DC offset estimation and a phase demodulation algorithm based on extended differential and cross-multiply, a 45-{\mu}m pendulum swing can be experimentally detected with a 20.6-dB SNR, verifying its strong ability in Doppler micro-motion detections. | ['Lixin Ran', 'Jie Wang', 'Bin Zhang', 'Chengkai Zhu', 'Marcel Balle'] | 2021-07-23 | null | null | null | null | ['motion-detection', 'contact-detection'] | ['computer-vision', 'robots'] | [ 2.60011673e-01 -7.31152445e-02 2.47861147e-01 -1.91317230e-01
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521ab34e-b3d0-4d8d-aed1-258a13c05055 | vectorfusion-text-to-svg-by-abstracting-pixel | 2211.11319 | null | https://arxiv.org/abs/2211.11319v1 | https://arxiv.org/pdf/2211.11319v1.pdf | VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models | Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be scaled to any size, and are compact. We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. By optimizing a differentiable vector graphics rasterizer, our method, VectorFusion, distills abstract semantic knowledge out of a pretrained diffusion model. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. To accelerate generation and improve fidelity, VectorFusion also initializes from an image sample. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches. See our project webpage at https://ajayj.com/vectorfusion . | ['Pieter Abbeel', 'Amber Xie', 'Ajay Jain'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-3d'] | ['computer-vision'] | [ 4.61222261e-01 2.05934942e-01 -1.36241123e-01 -2.63668895e-01
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a45df01d-0f23-4679-a2a4-129b9a5f9da4 | multivent-multilingual-videos-of-events-with | 2307.03153 | null | https://arxiv.org/abs/2307.03153v1 | https://arxiv.org/pdf/2307.03153v1.pdf | MultiVENT: Multilingual Videos of Events with Aligned Natural Text | Everyday news coverage has shifted from traditional broadcasts towards a wide range of presentation formats such as first-hand, unedited video footage. Datasets that reflect the diverse array of multimodal, multilingual news sources available online could be used to teach models to benefit from this shift, but existing news video datasets focus on traditional news broadcasts produced for English-speaking audiences. We address this limitation by constructing MultiVENT, a dataset of multilingual, event-centric videos grounded in text documents across five target languages. MultiVENT includes both news broadcast videos and non-professional event footage, which we use to analyze the state of online news videos and how they can be leveraged to build robust, factually accurate models. Finally, we provide a model for complex, multilingual video retrieval to serve as a baseline for information retrieval using MultiVENT. | ['Benjamin Van Durme', 'Reno Kriz', 'David Etter', 'Kate Sanders'] | 2023-07-06 | null | null | null | null | ['video-retrieval', 'retrieval', 'information-retrieval'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [-3.31787080e-01 -2.50125736e-01 -7.47146428e-01 -2.90136933e-01
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13b2c341-ed11-41d7-b269-dcadf780dd04 | boundary-content-graph-neural-network-for | 2008.01432 | null | https://arxiv.org/abs/2008.01432v1 | https://arxiv.org/pdf/2008.01432v1.pdf | Boundary Content Graph Neural Network for Temporal Action Proposal Generation | Temporal action proposal generation plays an important role in video action understanding, which requires localizing high-quality action content precisely. However, generating temporal proposals with both precise boundaries and high-quality action content is extremely challenging. To address this issue, we propose a novel Boundary Content Graph Neural Network (BC-GNN) to model the insightful relations between the boundary and action content of temporal proposals by the graph neural networks. In BC-GNN, the boundaries and content of temporal proposals are taken as the nodes and edges of the graph neural network, respectively, where they are spontaneously linked. Then a novel graph computation operation is proposed to update features of edges and nodes. After that, one updated edge and two nodes it connects are used to predict boundary probabilities and content confidence score, which will be combined to generate a final high-quality proposal. Experiments are conducted on two mainstream datasets: ActivityNet-1.3 and THUMOS14. Without the bells and whistles, BC-GNN outperforms previous state-of-the-art methods in both temporal action proposal and temporal action detection tasks. | ['Yunhai Tong', 'Junhui Liu', 'Yingying Wang', 'Yueran Bai', 'Yang Yang', 'Qiyue Liu'] | 2020-08-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6021_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730120.pdf | eccv-2020-8 | ['action-understanding', 'temporal-action-proposal-generation'] | ['computer-vision', 'computer-vision'] | [ 3.18359435e-01 5.10571264e-02 -4.31189716e-01 -2.05928177e-01
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dda4f50a-aa3f-4148-b091-56ef3e1dbbc7 | relative-acoustic-features-for-distance | 2212.01306 | null | https://arxiv.org/abs/2212.01306v1 | https://arxiv.org/pdf/2212.01306v1.pdf | Relative Acoustic Features for Distance Estimation in Smart-Homes | Any audio recording encapsulates the unique fingerprint of the associated acoustic environment, namely the background noise and reverberation. Considering the scenario of a room equipped with a fixed smart speaker device with one or more microphones and a wearable smart device (watch, glasses or smartphone), we employed the improved proportionate normalized least mean square adaptive filter to estimate the relative room impulse response mapping the audio recordings of the two devices. We performed inter-device distance estimation by exploiting a new set of features obtained extending the definition of some acoustic attributes of the room impulse response to its relative version. In combination with the sparseness measure of the estimated relative room impulse response, the relative features allow precise inter-device distance estimation which can be exploited for tasks such as best microphone selection or acoustic scene analysis. Experimental results from simulated rooms of different dimensions and reverberation times demonstrate the effectiveness of this computationally lightweight approach for smart home acoustic ranging applications | ['Patrick A. Naylor', 'Daniel Barreda', 'Francesco Nespoli'] | 2022-12-02 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 5.01875699e-01 -1.98764443e-01 8.79918933e-01 -2.06805319e-01
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1804d334-6505-4cc2-b49a-7f01839286b0 | tcfimt-temporal-counterfactual-forecasting | 2212.08890 | null | https://arxiv.org/abs/2212.08890v1 | https://arxiv.org/pdf/2212.08890v1.pdf | TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective | Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods. | ['Xiangnan Feng', 'Changjie Fan', 'Tangjie Lv', 'Yu Ding', 'Runze Wu', 'Wei Huang', 'Mingming Gong', 'Yu Xiong', 'Zhipeng Hu', 'Guifeng Wang', 'Pengfei Xi'] | 2022-12-17 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 5.96477270e-01 -1.75737321e-01 -1.12895274e+00 -3.81847590e-01
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fae57213-f030-4d59-90aa-032680c1950b | fully-convolutional-multi-scale-residual | 1801.05173 | null | http://arxiv.org/abs/1801.05173v1 | http://arxiv.org/pdf/1801.05173v1.pdf | Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers | Deep fully convolutional neural network (FCN) based architectures have shown
great potential in medical image segmentation. However, such architectures
usually have millions of parameters and inadequate number of training samples
leading to over-fitting and poor generalization. In this paper, we present a
novel highly parameter and memory efficient FCN based architecture for medical
image analysis. We propose a novel up-sampling path which incorporates long
skip and short-cut connections to overcome the feature map explosion in FCN
like architectures. In order to processes the input images at multiple scales
and view points simultaneously, we propose to incorporate Inception module's
parallel structures. We also propose a novel dual loss function whose weighting
scheme allows to combine advantages of cross-entropy and dice loss. We have
validated our proposed network architecture on two publicly available datasets,
namely: (i) Automated Cardiac Disease Diagnosis Challenge (ACDC-2017), (ii)
Left Ventricular Segmentation Challenge (LV-2011). Our approach in ACDC-2017
challenge stands second place for segmentation and first place in automated
cardiac disease diagnosis tasks with an accuracy of 100%. In the LV-2011
challenge our approach attained 0.74 Jaccard index, which is so far the highest
published result in fully automated algorithms. From the segmentation we
extracted clinically relevant cardiac parameters and hand-crafted features
which reflected the clinical diagnostic analysis to train an ensemble system
for cardiac disease classification. Our approach combined both cardiac
segmentation and disease diagnosis into a fully automated framework which is
computational efficient and hence has the potential to be incorporated in
computer-aided diagnosis (CAD) tools for clinical application. | ['Ganapathy Krishnamurthi', 'Mahendra Khened', 'Varghese Alex Kollerathu'] | 2018-01-16 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.06652492e-01 1.19366311e-01 2.45935246e-01 -4.63500082e-01
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9c523629-4cbd-4daf-ae25-814d3a3b6a8f | beyond-static-features-for-temporally | 2011.08627 | null | https://arxiv.org/abs/2011.08627v4 | https://arxiv.org/pdf/2011.08627v4.pdf | Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video | Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency issue due to a strong dependency on a static feature of the current frame. In this regard, we present a temporally consistent mesh recovery system (TCMR). It effectively focuses on the past and future frames' temporal information without being dominated by the current static feature. Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy. We also release the codes. For the demo video, see https://youtu.be/WB3nTnSQDII. For the codes, see https://github.com/hongsukchoi/TCMR_RELEASE. | ['Ju Yong Chang', 'Kyoung Mu Lee', 'Gyeongsik Moon', 'Hongsuk Choi'] | 2020-11-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-2.65009642e-01 -5.50311446e-01 -1.16555020e-01 -1.38575554e-01
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4aded5bf-5f44-4569-bff7-dc34f7ee22ff | efficient-visual-fault-detection-for-freight | 2307.00701 | null | https://arxiv.org/abs/2307.00701v1 | https://arxiv.org/pdf/2307.00701v1.pdf | Efficient Visual Fault Detection for Freight Train Braking System via Heterogeneous Self Distillation in the Wild | Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment. Although deep learning-based approaches have excelled in object detection, the efficiency of freight train fault detection is still insufficient to apply in real-world engineering. This paper proposes a heterogeneous self-distillation framework to ensure detection accuracy and speed while satisfying low resource requirements. The privileged information in the output feature knowledge can be transferred from the teacher to the student model through distillation to boost performance. We first adopt a lightweight backbone to extract features and generate a new heterogeneous knowledge neck. Such neck models positional information and long-range dependencies among channels through parallel encoding to optimize feature extraction capabilities. Then, we utilize the general distribution to obtain more credible and accurate bounding box estimates. Finally, we employ a novel loss function that makes the network easily concentrate on values near the label to improve learning efficiency. Experiments on four fault datasets reveal that our framework can achieve over 37 frames per second and maintain the highest accuracy in comparison with traditional distillation approaches. Moreover, compared to state-of-the-art methods, our framework demonstrates more competitive performance with lower memory usage and the smallest model size. | ['Guodong Sun', 'Mingying Li', 'Yang Zhou', 'Huilin Pan', 'Yang Zhang'] | 2023-07-03 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.44756019e-01 -1.72483310e-01 -1.11474514e-01 -3.01666170e-01
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662d6863-c1bd-413f-93cd-cf22dae102dd | an-encoder-decoder-based-framework-for-hindi | null | null | https://link.springer.com/article/10.1007/s11042-021-11106-5 | https://link.springer.com/article/10.1007/s11042-021-11106-5 | An encoder-decoder based framework for hindi image caption generation | In recent times, research activity on image caption generation has attracted several researchers. The present work attempt to address the problem of Hindi image caption generation using Hindi Visual genome dataset. Hindi is the official and most spoken language in India. In a linguistically diverse country like India, it is essential to provide a means that can help the people to understand the visual entities in their native languages. In this paper, an encoder-decoder based architecture is proposed where Convolutional Neural Network (CNN) is employed for encoding visual features of an image and stacked Long Short-Term Memory (sLSTM) in combination with both uni-directional LSTM and bi-directional LSTM for generating the captions in Hindi. For encoding the visual feature representation of an image, V GG19 based pre-trained model is used and sLSTM architecture is employed for caption generation at the decoder side. The model is tested over Hindi visual genome dataset to validate the proposed approach’s performance and cross-verification is carried out for English captions with Flickr dataset. The experimental results of the proposed approach manifest that the model is qualitatively and quantitatively better than state-of-the-art approaches for Hindi caption generation. | ['Sivaji Bandyopadhyay', 'Thoudam Doren Singh', 'Alok Singh'] | 2021-07-09 | null | null | null | multimedia-tools-and-applications-2021-7 | ['hindi-image-captioning'] | ['computer-vision'] | [ 5.87484956e-01 5.78004122e-02 2.70534605e-01 -4.04597670e-01
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c00c991c-ccb2-4ef7-b079-458bda8f3bb5 | personalized-review-generation-by-expanding | null | null | https://aclanthology.org/P18-2112 | https://aclanthology.org/P18-2112.pdf | Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations | In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect-level information via an aspect encoder that learns aspect-aware user and item representations. An attention fusion layer is applied to control generation by attending on the outputs of multiple encoders. Experimental results show that our model successfully learns representations capable of generating coherent and diverse reviews. In addition, the learned aspect-aware representations discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences. | ['Jianmo Ni', 'Julian McAuley'] | 2018-07-01 | null | null | null | acl-2018-7 | ['review-generation'] | ['natural-language-processing'] | [ 3.80041271e-01 9.44072962e-01 -4.86360520e-01 -6.73708975e-01
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7c2b3224-4c76-41fc-b34c-4a41146e445c | dtg-ssod-dense-teacher-guidance-for-semi | 2207.05536 | null | https://arxiv.org/abs/2207.05536v1 | https://arxiv.org/pdf/2207.05536v1.pdf | DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection | The Mean-Teacher (MT) scheme is widely adopted in semi-supervised object detection (SSOD). In MT, the sparse pseudo labels, offered by the final predictions of the teacher (e.g., after Non Maximum Suppression (NMS) post-processing), are adopted for the dense supervision for the student via hand-crafted label assignment. However, the sparse-to-dense paradigm complicates the pipeline of SSOD, and simultaneously neglects the powerful direct, dense teacher supervision. In this paper, we attempt to directly leverage the dense guidance of teacher to supervise student training, i.e., the dense-to-dense paradigm. Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels. INC leads the student to group candidate boxes into clusters in NMS as the teacher does, which is implemented by learning grouping information revealed in NMS procedure of the teacher. After obtaining the same grouping scheme as the teacher via INC, the student further imitates the rank distribution of the teacher over clustered candidates through Rank Matching. With the proposed INC and RM, we integrate Dense Teacher Guidance into Semi-Supervised Object Detection (termed DTG-SSOD), successfully abandoning sparse pseudo labels and enabling more informative learning on unlabeled data. On COCO benchmark, our DTG-SSOD achieves state-of-the-art performance under various labelling ratios. For example, under 10% labelling ratio, DTG-SSOD improves the supervised baseline from 26.9 to 35.9 mAP, outperforming the previous best method Soft Teacher by 1.9 points. | ['Shanshan Zhang', 'Ding Liang', 'Yichao Wu', 'Yujie Wang', 'Xiang Li', 'Gang Li'] | 2022-07-12 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.44025069e-01 5.26273727e-01 -1.76588133e-01 -4.38945204e-01
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2341c774-03c4-4c0b-bc0a-ef4c69b62578 | lifting-interpretability-performance-trade | 2002.04267 | null | https://arxiv.org/abs/2002.04267v1 | https://arxiv.org/pdf/2002.04267v1.pdf | Lifting Interpretability-Performance Trade-off via Automated Feature Engineering | Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, achieving satisfactory accuracy of interpretable models require more time-consuming work related to feature engineering. Can we train interpretable and accurate models, without timeless feature engineering? We propose a method that uses elastic black-boxes as surrogate models to create a simpler, less opaque, yet still accurate and interpretable glass-box models. New models are created on newly engineered features extracted with the help of a surrogate model. We supply the analysis by a large-scale benchmark on several tabular data sets from the OpenML database. There are two results 1) extracting information from complex models may improve the performance of linear models, 2) questioning a common myth that complex machine learning models outperform linear models. | ['Alicja Gosiewska', 'Przemyslaw Biecek'] | 2020-02-11 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.07630618e-01 6.96894884e-01 -1.30482718e-01 -9.12667751e-01
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6fdbf615-1212-44b3-8272-4aac09e5f825 | instant-volumetric-head-avatars | 2211.12499 | null | https://arxiv.org/abs/2211.12499v2 | https://arxiv.org/pdf/2211.12499v2.pdf | Instant Volumetric Head Avatars | We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance field based on neural graphics primitives embedded around a parametric face model. Our pipeline is trained on a single monocular RGB portrait video that observes the subject under different expressions and views. While state-of-the-art methods take up to several days to train an avatar, our method can reconstruct a digital avatar in less than 10 minutes on modern GPU hardware, which is orders of magnitude faster than previous solutions. In addition, it allows for the interactive rendering of novel poses and expressions. By leveraging the geometry prior of the underlying parametric face model, we demonstrate that INSTA extrapolates to unseen poses. In quantitative and qualitative studies on various subjects, INSTA outperforms state-of-the-art methods regarding rendering quality and training time. | ['Justus Thies', 'Timo Bolkart', 'Wojciech Zielonka'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model'] | ['computer-vision'] | [ 1.97969787e-02 3.61667812e-01 6.25945389e-01 -3.07276875e-01
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3f0e97a7-e366-4ba5-8036-374497190cd8 | machine-learning-based-missing-values | 2306.06338 | null | https://arxiv.org/abs/2306.06338v1 | https://arxiv.org/pdf/2306.06338v1.pdf | Machine Learning Based Missing Values Imputation in Categorical Datasets | This study explored the use of machine learning algorithms for predicting and imputing missing values in categorical datasets. We focused on ensemble models that use the error correction output codes (ECOC) framework, including SVM-based and KNN-based ensemble models, as well as an ensemble classifier that combines SVM, KNN, and MLP models. We applied these algorithms to three datasets: the CPU dataset, the hypothyroid dataset, and the Breast Cancer dataset. Our experiments showed that the machine learning algorithms were able to achieve good performance in predicting and imputing the missing values, with some variations depending on the specific dataset and missing value pattern. The ensemble models using the error correction output codes (ECOC) framework were particularly effective in improving the accuracy and robustness of the predictions, compared to individual models. However, there are also challenges and limitations to using deep learning for missing value imputation, including the need for large amounts of labeled data and the potential for overfitting. Further research is needed to evaluate the effectiveness and efficiency of deep learning algorithms for missing value imputation and to develop strategies for addressing the challenges and limitations that may arise. | ['Arshad Khan', 'Asfandyar Khan', 'Majid Khan', 'Laila iftikhar', 'Muhammad Ishaq'] | 2023-06-10 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 2.32630953e-01 -1.14189267e-01 -3.04560006e-01 -8.54213893e-01
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c50f5729-5cef-48df-a48e-745850117325 | ir-lpr-large-scale-of-iranian-license-plate | 2209.04680 | null | https://arxiv.org/abs/2209.04680v1 | https://arxiv.org/pdf/2209.04680v1.pdf | IR-LPR: Large Scale of Iranian License Plate Recognition Dataset | Object detection has always been practical. There are so many things in our world that recognizing them can not only increase our automatic knowledge of the surroundings, but can also be lucrative for those interested in starting a new business. One of these attractive objects is the license plate (LP). In addition to the security uses that license plate detection can have, it can also be used to create creative businesses. With the development of object detection methods based on deep learning models, an appropriate and comprehensive dataset becomes doubly important. But due to the frequent commercial use of license plate datasets, there are limited datasets not only in Iran but also in the world. The largest Iranian dataset for detection license plates has 1,466 images. Also, the largest Iranian dataset for recognizing the characters of a license plate has 5,000 images. We have prepared a complete dataset including 20,967 car images along with all the detection annotation of the whole license plate and its characters, which can be useful for various purposes. Also, the total number of license plate images for character recognition application is 27,745 images. | ['Mohammad Ali Keyvanrad', 'Mahdi Naghibi', 'Mohammad Mohsen Talaie', 'Seyyede Mahila Moghadami', 'Melika Sabaghian', 'Mahdi Rahmani'] | 2022-09-10 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-1.72164515e-01 -7.65124142e-01 8.25318173e-02 -1.10876866e-01
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ef6f4483-9b51-4442-acd1-e461f15bf6ff | multilevel-regression-with-poststratification | 2009.06615 | null | https://arxiv.org/abs/2009.06615v1 | https://arxiv.org/pdf/2009.06615v1.pdf | Multilevel regression with poststratification for the national level Viber/Street poll on the 2020 presidential election in Belarus | Independent sociological polls are forbidden in Belarus. Online polls performed without sound scientific rigour do not yield representative results. Yet, both inside and outside Belarus it is of great importance to obtain precise estimates of the ratings of all candidates. These ratings could function as reliable proxies for the election's outcomes. We conduct an independent poll based on the combination of the data collected via Viber and on the streets of Belarus. The Viber and the street data samples consist of almost 45000 and 1150 unique observations respectively. Bayesian regressions with poststratification were build to estimate ratings of the candidates and rates of early voting turnout for the population as a whole and within various focus subgroups. We show that both the officially announced results of the election and early voting rates are highly improbable. With a probability of at least 95%, Sviatlana Tikhanouskaya's rating lies between 75% and 80%, whereas Aliaksandr Lukashenka's rating lies between 13% and 18% and early voting rate predicted by the method ranges from 9% to 13% of those who took part in the election. These results contradict the officially announced outcomes, which are 10.12%, 80.11%, and 49.54% respectively and lie far outside even the 99.9% credible intervals predicted by our model. The only marginal groups of people where the upper bounds of the 99.9% credible intervals of the rating of Lukashenka are above 50% are people older than 60 and uneducated people. For all other marginal subgroups, including rural residents, even the upper bounds of 99.9% credible intervals for Lukashenka are far below 50%. The same is true for the population as a whole. Thus, with a probability of at least 99.9% Lukashenka could not have had enough electoral support to win the 2020 presidential election in Belarus. | ['Ales Zahorski'] | 2020-09-14 | null | null | null | null | ['pre-election-ratings-estimation'] | ['reasoning'] | [-5.85638762e-01 6.54252052e-01 -4.06499207e-01 -3.60258609e-01
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e02a2908-5ed4-48d2-b09b-88f8c40bb2fb | funny3-at-semeval-2020-task-7-humor-detection | null | null | https://aclanthology.org/2020.semeval-1.132 | https://aclanthology.org/2020.semeval-1.132.pdf | Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System | This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 {``}Assessing the Funniness of Edited News Headlines{''}. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline. | ['Kuan Tang', 'Xuefeng Luo'] | 2020-12-01 | null | null | null | semeval-2020 | ['humor-detection'] | ['natural-language-processing'] | [-4.03511226e-02 6.63272798e-01 7.12445304e-02 -7.26804554e-01
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38839726-2928-4706-ad63-39a2f3c28dec | learning-crosslingual-word-embeddings-without | 1606.09403 | null | http://arxiv.org/abs/1606.09403v1 | http://arxiv.org/pdf/1606.09403v1.pdf | Learning Crosslingual Word Embeddings without Bilingual Corpora | Crosslingual word embeddings represent lexical items from different languages
in the same vector space, enabling transfer of NLP tools. However, previous
attempts had expensive resource requirements, difficulty incorporating
monolingual data or were unable to handle polysemy. We address these drawbacks
in our method which takes advantage of a high coverage dictionary in an EM
style training algorithm over monolingual corpora in two languages. Our model
achieves state-of-the-art performance on bilingual lexicon induction task
exceeding models using large bilingual corpora, and competitive results on the
monolingual word similarity and cross-lingual document classification task. | ['Long Duong', 'Steven Bird', 'Trevor Cohn', 'Tengfei Ma', 'Hiroshi Kanayama'] | 2016-06-30 | learning-crosslingual-word-embeddings-without-1 | https://aclanthology.org/D16-1136 | https://aclanthology.org/D16-1136.pdf | emnlp-2016-11 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-5.16981304e-01 -5.74529469e-01 -8.32676589e-01 -1.90790460e-01
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74fa342d-0d6a-456c-bee9-34f3ae829475 | a-local-machine-learning-approach-for | 2302.10810 | null | https://arxiv.org/abs/2302.10810v1 | https://arxiv.org/pdf/2302.10810v1.pdf | A Local Machine Learning Approach for Fingerprint-based Indoor Localization | Machine learning (ML) solutions to indoor localization problems have become popular in recent years due to high positioning accuracy and low cost of implementation. This paper proposes a novel local nonparametric approach for solving localizations from high-dimensional Received Signal Strength Indicator (RSSI) values. Our approach consists of a sequence of classification algorithms that sequentially narrows down the possible space for location solutions into smaller neighborhoods. The idea of this sequential classification method is similar to the decision tree algorithm, but a key difference is our splitting of the dataset at each node is not based on features of input (i.e. RSSI values), but some discrete-valued variables generated from the output variable (i.e. the 3D real-world coordinates). The strength of our localization solution can be tuned to problem specifics by the appropriate choice of how to sequentially partition the the space of location into smaller neighborhoods. Using the publicly available indoor localization dataset UJIIndoorLoc, we evaluate our proposed method vs. the global ML algorithms for the dataset. The primary contribution of this paper is to introduce a novel local ML solution for indoor localization problems. | ['Haiqing Xu', 'Xiao Meng', 'Brian Evans', 'Nora Agah'] | 2023-02-21 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-4.09480184e-02 -2.87145972e-01 -2.00403363e-01 -6.57119930e-01
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0b57cffb-11d9-4567-9718-5247196b3f34 | cycle-self-training-for-semi-supervised | 2207.05334 | null | https://arxiv.org/abs/2207.05334v1 | https://arxiv.org/pdf/2207.05334v1.pdf | Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting | Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student framework and have achieved state-of-the-art results. However, the teacher network is tightly coupled with the student network since the teacher is an exponential moving average (EMA) of the student, which causes a performance bottleneck. To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2. Based on these networks, a cycle self-training mechanism is built, i.e., S1${\rightarrow}$T1${\rightarrow}$S2${\rightarrow}$T2${\rightarrow}$S1. For S${\rightarrow}$T, we also utilize the EMA weights of the students to update the teachers. For T${\rightarrow}$S, instead of providing supervision for its own student S1(S2) directly, the teacher T1(T2) generates pseudo-labels for the student S2(S1), which looses the coupling effect. Moreover, owing to the property of EMA, the teacher is most likely to accumulate the biases from the student and make the mistakes irreversible. To mitigate the problem, we also propose a distribution consistency reweighting strategy, where pseudo-labels are reweighted based on distribution consistency across the teachers T1 and T2. With the strategy, the two students S2 and S1 can be trained robustly with noisy pseudo labels to avoid confirmation biases. Extensive experiments prove the superiority of CST by consistently improving the AP over the baseline and outperforming state-of-the-art methods by 2.1% absolute AP improvements with scarce labeled data. | ['Peng Wu', 'Feng Dai', 'Chunpeng Wu', 'Bo wang', 'Bin Chen', 'Hao liu'] | 2022-07-12 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 6.24059588e-02 4.07139540e-01 -3.06948692e-01 -4.68652576e-01
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f998e9fc-20a6-48e2-ab10-c14139336bab | mobilestereonet-towards-lightweight-deep | 2108.09770 | null | https://arxiv.org/abs/2108.09770v1 | https://arxiv.org/pdf/2108.09770v1.pdf | MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching | Recent methods in stereo matching have continuously improved the accuracy using deep models. This gain, however, is attained with a high increase in computation cost, such that the network may not fit even on a moderate GPU. This issue raises problems when the model needs to be deployed on resource-limited devices. For this, we propose two light models for stereo vision with reduced complexity and without sacrificing accuracy. Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively. To this end, we leverage 2D MobileNet blocks and extend them to 3D for stereo vision application. Besides, a new cost volume is proposed to boost the accuracy of the 2D model, making it performing close to 3D networks. Experiments show that the proposed 2D/3D networks effectively reduce the computational expense (27%/95% and 72%/38% fewer parameters/operations in 2D and 3D models, respectively) while upholding the accuracy. Our code is available at https://github.com/cogsys-tuebingen/mobilestereonet. | ['Andreas Zell', 'Rafia Rahim', 'Samuel Woerz', 'Faranak Shamsafar'] | 2021-08-22 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-4.30309735e-02 -1.95081290e-02 -6.96757296e-03 -3.24843973e-01
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bc1dbf87-55d4-465d-abaf-e248c9382585 | learning-monocular-depth-in-dynamic-scenes | 2102.02629 | null | https://arxiv.org/abs/2102.02629v1 | https://arxiv.org/pdf/2102.02629v1.pdf | Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency | We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we highlight the fundamental difference between inverse and forward projection while modeling the individual motion of each rigid object, and propose a geometrically correct projection pipeline using a neural forward projection module. Second, we design a unified instance-aware photometric and geometric consistency loss that holistically imposes self-supervisory signals for every background and object region. Lastly, we introduce a general-purpose auto-annotation scheme using any off-the-shelf instance segmentation and optical flow models to produce video instance segmentation maps that will be utilized as input to our training pipeline. These proposed elements are validated in a detailed ablation study. Through extensive experiments conducted on the KITTI and Cityscapes dataset, our framework is shown to outperform the state-of-the-art depth and motion estimation methods. Our code, dataset, and models are available at https://github.com/SeokjuLee/Insta-DM . | ['In So Kweon', 'Stephen Lin', 'Sunghoon Im', 'Seokju Lee'] | 2021-02-04 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.74062678e-02 -5.88689670e-02 -1.48355439e-01 -4.96055484e-01
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3b5d57fa-7f01-450c-b87b-44de578c6f26 | solving-math-word-problems-with-process-and | 2211.14275 | null | https://arxiv.org/abs/2211.14275v1 | https://arxiv.org/pdf/2211.14275v1.pdf | Solving math word problems with process- and outcome-based feedback | Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervise the reasoning process itself? Differences between these approaches might naturally be expected not just in final-answer errors but also in reasoning errors, which can be difficult to detect and are problematic in many real-world domains such as education. We run the first comprehensive comparison between process- and outcome-based approaches trained on a natural language task, GSM8K. We find that pure outcome-based supervision produces similar final-answer error rates with less label supervision. However, for correct reasoning steps we find it necessary to use process-based supervision or supervision from learned reward models that emulate process-based feedback. In total, we improve the previous best results from 16.8% $\to$ 12.7% final-answer error and 14.0% $\to$ 3.4% reasoning error among final-answer-correct solutions. | ['Irina Higgins', 'Geoffrey Irving', 'Antonia Creswell', 'Lisa Wang', 'Noah Siegel', 'Francis Song', 'Ramana Kumar', 'Nate Kushman', 'Jonathan Uesato'] | 2022-11-25 | null | null | null | null | ['gsm8k', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 2.82353342e-01 7.90469110e-01 1.31348133e-01 -7.87377954e-01
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136d480f-72ea-4987-aded-176ad01f80d2 | motion-corrected-multishot-mri-reconstruction | 1902.07430 | null | https://arxiv.org/abs/1902.07430v6 | https://arxiv.org/pdf/1902.07430v6.pdf | Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding | Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times. | ['Siddique Latif', 'Muhammad Usman', 'Muhammad Umar Farooq', 'Junaid Qadir', 'Muhammad Asim'] | 2019-02-20 | null | null | null | null | ['motion-correction-in-multishot-mri'] | ['medical'] | [ 4.86233562e-01 -3.05596918e-01 2.91897565e-01 -2.27473408e-01
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58cf9032-2e26-494f-83d8-baf4a27ce3b2 | fast-interpretable-greedy-tree-sums-figs | 2201.11931 | null | https://arxiv.org/abs/2201.11931v3 | https://arxiv.org/pdf/2201.11931v3.pdf | Fast Interpretable Greedy-Tree Sums | Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the CART algorithm to simultaneously grow a flexible number of trees in summation. By combining logical rules with addition, FIGS is able to adapt to additive structure while remaining highly interpretable. Extensive experiments on real-world datasets show that FIGS achieves state-of-the-art prediction performance. To demonstrate the usefulness of FIGS in high-stakes domains, we adapt FIGS to learn clinical decision instruments (CDIs), which are tools for guiding clinical decision-making. Specifically, we introduce a variant of FIGS known as G-FIGS that accounts for the heterogeneity in medical data. G-FIGS derives CDIs that reflect domain knowledge and enjoy improved specificity (by up to 20% over CART) without sacrificing sensitivity or interpretability. To provide further insight into FIGS, we prove that FIGS learns components of additive models, a property we refer to as disentanglement. Further, we show (under oracle conditions) that unconstrained tree-sum models leverage disentanglement to generalize more efficiently than single decision tree models when fitted to additive regression functions. Finally, to avoid overfitting with an unconstrained number of splits, we develop Bagging-FIGS, an ensemble version of FIGS that borrows the variance reduction techniques of random forests. Bagging-FIGS enjoys competitive performance with random forests and XGBoost on real-world datasets. | ['Bin Yu', 'Aaron Kornblith', 'Matthew Epland', 'Omer Ronen', 'James Duncan', 'Abhineet Agarwal', 'Keyan Nasseri', 'Chandan Singh', 'Yan Shuo Tan'] | 2022-01-28 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 4.75649208e-01 7.70311058e-01 -6.87221169e-01 -6.34852767e-01
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e35a5860-8baf-491e-8774-04625ea5f977 | learning-to-synthesize-volumetric-meshes-from | 2203.15155 | null | https://arxiv.org/abs/2203.15155v1 | https://arxiv.org/pdf/2203.15155v1.pdf | Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints | Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts. Obtaining accurate volumetric meshes for the deformed elastomer can provide direct contact information and benefit robotic grasping and manipulation. This paper focuses on learning to synthesize the volumetric mesh of the elastomer based on the image imprints acquired from vision-based tactile sensors. Synthetic image-mesh pairs and real-world images are gathered from 3D finite element methods (FEM) and physical sensors, respectively. A graph neural network (GNN) is introduced to learn the image-to-mesh mappings with supervised learning. A self-supervised adaptation method and image augmentation techniques are proposed to transfer networks from simulation to reality, from primitive contacts to unseen contacts, and from one sensor to another. Using these learned and adapted networks, our proposed method can accurately reconstruct the deformation of the real-world tactile sensor elastomer in various domains, as indicated by the quantitative and qualitative results. | ['Jeroen van Baar', 'Masayoshi Tomizuka', 'Siddarth Jain', 'Xinghao Zhu'] | 2022-03-29 | null | null | null | null | ['image-augmentation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 8.83402884e-01 1.82646796e-01 3.10902387e-01 -3.86034608e-01
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a17aba57-6034-4017-abd8-5d877bd6b6db | combinatorial-neural-bandits | 2306.00242 | null | https://arxiv.org/abs/2306.00242v1 | https://arxiv.org/pdf/2306.00242v1.pdf | Combinatorial Neural Bandits | We consider a contextual combinatorial bandit problem where in each round a learning agent selects a subset of arms and receives feedback on the selected arms according to their scores. The score of an arm is an unknown function of the arm's feature. Approximating this unknown score function with deep neural networks, we propose algorithms: Combinatorial Neural UCB ($\texttt{CN-UCB}$) and Combinatorial Neural Thompson Sampling ($\texttt{CN-TS}$). We prove that $\texttt{CN-UCB}$ achieves $\tilde{\mathcal{O}}(\tilde{d} \sqrt{T})$ or $\tilde{\mathcal{O}}(\sqrt{\tilde{d} T K})$ regret, where $\tilde{d}$ is the effective dimension of a neural tangent kernel matrix, $K$ is the size of a subset of arms, and $T$ is the time horizon. For $\texttt{CN-TS}$, we adapt an optimistic sampling technique to ensure the optimism of the sampled combinatorial action, achieving a worst-case (frequentist) regret of $\tilde{\mathcal{O}}(\tilde{d} \sqrt{TK})$. To the best of our knowledge, these are the first combinatorial neural bandit algorithms with regret performance guarantees. In particular, $\texttt{CN-TS}$ is the first Thompson sampling algorithm with the worst-case regret guarantees for the general contextual combinatorial bandit problem. The numerical experiments demonstrate the superior performances of our proposed algorithms. | ['Min-hwan Oh', 'Kyuwook Chai', 'TaeHyun Hwang'] | 2023-05-31 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.77830160e-01 3.78827304e-01 -6.56558752e-01 -2.82770902e-01
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411817b4-c996-4baf-a7ef-6959f0ac6087 | randomized-low-rank-dynamic-mode | 1512.03526 | null | http://arxiv.org/abs/1512.03526v1 | http://arxiv.org/pdf/1512.03526v1.pdf | Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection | This paper introduces a fast algorithm for randomized computation of a
low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this
matrix to represent the development of a spatial grid through time e.g. data
from a static video source. DMD was originally introduced in the fluid
mechanics community, but is also suitable for motion detection in video streams
and its use for background subtraction has received little previous
investigation. In this study we present a comprehensive evaluation of
background subtraction, using the randomized DMD and compare the results with
leading robust principal component analysis algorithms. The results are
convincing and show the random DMD is an efficient and powerful approach for
background modeling, allowing processing of high resolution videos in
real-time. Supplementary materials include implementations of the algorithms in
Python. | ['N. Benjamin Erichson', 'Carl Donovan'] | 2015-12-11 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.53587538e-01 -7.74396777e-01 6.51101589e-01 1.64262667e-01
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61418cfd-8054-4fb3-a712-20b5a10b90f6 | cost-aware-learning-of-relevant-contextual | 2305.14120 | null | https://arxiv.org/abs/2305.14120v2 | https://arxiv.org/pdf/2305.14120v2.pdf | Cost-aware learning of relevant contextual variables within Bayesian optimization | Contextual Bayesian Optimization (CBO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions with respect to design variables, while simultaneously efficiently integrating relevant contextual information regarding the environment, such as experimental conditions. However, in many practical scenarios, the relevance of contextual variables is not necessarily known beforehand. Moreover, the contextual variables can sometimes be optimized themselves, a setting that current CBO algorithms do not take into account. Optimizing contextual variables may be costly, which raises the question of determining a minimal relevant subset. In this paper, we frame this problem as a cost-aware model selection BO task and address it using a novel method, Sensitivity-Analysis-Driven Contextual BO (SADCBO). We learn the relevance of context variables by sensitivity analysis of the posterior surrogate model at specific input points, whilst minimizing the cost of optimization by leveraging recent developments on early stopping for BO. We empirically evaluate our proposed SADCBO against alternatives on synthetic experiments together with extensive ablation studies, and demonstrate a consistent improvement across examples. | ['Samuel Kaski', 'Louis Filstroff', 'Sabina Sloman', 'Armi Tiihonen', 'S. T. John', 'Ayush Bharti', 'Julien Martinelli'] | 2023-05-23 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 6.55790687e-01 -3.83352369e-01 -1.88844368e-01 -3.26208860e-01
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c2f1b1c8-2850-4c34-a623-6333a8b433a7 | swingnn-rethinking-permutation-invariance-in | 2307.01646 | null | https://arxiv.org/abs/2307.01646v1 | https://arxiv.org/pdf/2307.01646v1.pdf | SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation | Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more modes; 2) their optimal one-step denoising scores are the score functions of Gaussian mixtures with more components. Motivated by this analysis, we propose a non-invariant diffusion model, called $\textit{SwinGNN}$, which employs an efficient edge-to-edge 2-WL message passing network and utilizes shifted window based self-attention inspired by SwinTransformers. Further, through systematic ablations, we identify several critical training and sampling techniques that significantly improve the sample quality of graph generation. At last, we introduce a simple post-processing trick, $\textit{i.e.}$, randomly permuting the generated graphs, which provably converts any graph generative model to a permutation-invariant one. Extensive experiments on synthetic and real-world protein and molecule datasets show that our SwinGNN achieves state-of-the-art performances. Our code is released at https://github.com/qiyan98/SwinGNN . | ['Lele Wang', 'Renjie Liao', 'Yang song', 'Zhengyang Liang', 'Qi Yan'] | 2023-07-04 | null | null | null | null | ['graph-generation'] | ['graphs'] | [ 5.28954089e-01 1.28065899e-01 -6.62879348e-02 -2.40983829e-01
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5020dfd6-3f72-49a6-a30c-abcbef816fe4 | knowledge-graph-question-answering-using | 2103.06752 | null | https://arxiv.org/abs/2103.06752v2 | https://arxiv.org/pdf/2103.06752v2.pdf | Knowledge Graph Question Answering using Graph-Pattern Isomorphism | Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges. | ['Ricardo Usbeck', 'Axel-Cyrille Ngonga Ngomo', 'Hardik Topiwala', 'Diego Moussallem', 'Rricha Jalota', 'Daniel Vollmers'] | 2021-03-11 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-2.92839974e-01 3.49506706e-01 2.43712589e-01 -5.00467837e-01
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38ac1a01-71b5-4eee-9cf4-f9e8b1e852da | pygod-a-python-library-for-graph-outlier | 2204.12095 | null | https://arxiv.org/abs/2204.12095v1 | https://arxiv.org/pdf/2204.12095v1.pdf | PyGOD: A Python Library for Graph Outlier Detection | PyGOD is an open-source Python library for detecting outliers on graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for node-, edge-, subgraph-, and graph-level outlier detection, under a unified, well-documented API designed for use by both researchers and practitioners. To overcome the scalability issue in large graphs, we provide advanced functionalities for selected models, including mini-batch and sampling. PyGOD is equipped with best practices to foster code reliability and maintainability, including unit testing, continuous integration, and code coverage. To foster accessibility, PyGOD is released under a permissive BSD-license at https://github.com/pygod-team/pygod/ and the Python Package Index (PyPI). | ['Philip S. Yu', 'Zhihao Jia', 'George H. Chen', 'Kai Shu', 'Hao Peng', 'Canyu Chen', 'Kaize Ding', 'Ruitong Zhang', 'Xiyang Hu', 'Xueying Ding', 'Yue Zhao', 'Yingtong Dou', 'Kay Liu'] | 2022-04-26 | null | null | null | null | ['graph-outlier-detection'] | ['graphs'] | [-6.97232366e-01 -6.35913387e-02 -1.92724213e-01 -3.47805172e-02
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03186375-c15e-4f6d-9cec-4b14532cdf75 | how-to-train-your-dragan-a-task-oriented | 2211.10065 | null | https://arxiv.org/abs/2211.10065v1 | https://arxiv.org/pdf/2211.10065v1.pdf | How to train your draGAN: A task oriented solution to imbalanced classification | The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago. Though GAN based models seem promising, there has been a lack of purpose built architectures for solving the aforementioned problem, as most previous studies focus on applying already existing models. This paper proposes a unique, performance-oriented, data-generating strategy that utilizes a new architecture, coined draGAN, to generate both minority and majority samples. The samples are generated with the objective of optimizing the classification model's performance, rather than similarity to the real data. We benchmark our approach against state-of-the-art methods from the SMOTE family and competitive GAN based approaches on 94 tabular datasets with varying degrees of imbalance and linearity. Empirically we show the superiority of draGAN, but also highlight some of its shortcomings. All code is available on: https://github.com/LeonGuertler/draGAN. | ['Anh Tuan Luu', 'Andri Ashfahani', 'Leon O. Guertler'] | 2022-11-18 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.08674772e-01 2.70079553e-01 -4.39303517e-01 -5.10640442e-01
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42c9bf6e-7abd-45a4-b546-6a89a8520afc | simulation-algorithms-for-markovian-and-non | 2302.02812 | null | https://arxiv.org/abs/2302.02812v1 | https://arxiv.org/pdf/2302.02812v1.pdf | Simulation algorithms for Markovian and non-Markovian epidemics | Researchers have employed stochastic simulations to determine the validity of their theoretical findings and to study analytically intractable spreading dynamics. In both cases, the correctness and efficiency of the simulation algorithm are of paramount importance. We prove in this article that the Next Reaction Method and the non-Markovian Gillespie algorithm, two algorithms for simulating non-Markovian epidemics, are statistically equivalent. We also study the performance and applicability under various circumstances through complexity analyses and numerical experiments. In our numerical simulations, we apply the Next Reaction Method and the Gillespie algorithm to epidemic simulations on time-varying networks and epidemic simulations with cooperative infections. Both tasks have only been done using the Gillespie algorithm, while we show that the Next Reaction Method is a good alternative. We believe this article may also serve as a guide for choosing simulation algorithms that are both correct and efficient for researchers from epidemiology and beyond. | ['Guohao Dou'] | 2023-02-06 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 7.52186254e-02 -1.96468174e-01 1.77280039e-01 3.29059601e-01
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037871a7-47b5-4284-8bb2-f109293436d1 | liar-liar-pants-on-fire-a-new-benchmark-1 | null | null | https://aclanthology.org/P17-2067 | https://aclanthology.org/P17-2067.pdf | ``Liar, Liar Pants on Fire'': A New Benchmark Dataset for Fake News Detection | Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model. | ['William Yang Wang'] | 2017-07-01 | null | null | null | acl-2017-7 | ['deception-detection'] | ['miscellaneous'] | [-1.56271011e-02 -9.55954101e-03 -7.92847276e-01 -3.11325908e-01
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370bc393-2853-4a96-845f-1c8cc2fe7c37 | a-cosine-similarity-based-method-for-out-of | 2306.14920 | null | https://arxiv.org/abs/2306.14920v1 | https://arxiv.org/pdf/2306.14920v1.pdf | A Cosine Similarity-based Method for Out-of-Distribution Detection | The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods. | ['Hoang Thanh-Tung', 'Khoa D Doan', 'Thanh Nguyen-Tang', 'The-Anh Ta', 'Nguyen Hung-Quang', 'Nguyen Ngoc-Hieu'] | 2023-06-23 | null | null | null | null | ['out-of-distribution-detection'] | ['computer-vision'] | [-9.19692442e-02 -7.55462945e-02 -5.09382427e-01 -5.36446095e-01
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87c8a7fa-3585-4d78-921b-879b1224a23d | deep-learning-for-smile-recognition | 1602.00172 | null | http://arxiv.org/abs/1602.00172v2 | http://arxiv.org/pdf/1602.00172v2.pdf | Deep Learning For Smile Recognition | Inspired by recent successes of deep learning in computer vision, we propose
a novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) database, significantly outperforming existing approaches based on
hand-crafted features with accuracies ranging from 65.55% to 79.67%. The
novelty of this approach includes a comprehensive model selection of the
architecture parameters, allowing to find an appropriate architecture for each
expression such as smile. This is feasible because all experiments were run on
a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations
on a CPU. | ['Patrick O. Glauner'] | 2016-01-30 | null | null | null | null | ['smile-recognition'] | ['computer-vision'] | [ 7.93147609e-02 -1.32871538e-01 -1.02576390e-01 -6.09126568e-01
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8ee4bb24-d047-4193-a476-0f494c6c27e2 | a-comparative-study-of-pre-trained-speech-and | 2304.11472 | null | https://arxiv.org/abs/2304.11472v1 | https://arxiv.org/pdf/2304.11472v1.pdf | A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition | Pre-trained models (PTMs) have shown great promise in the speech and audio domain. Embeddings leveraged from these models serve as inputs for learning algorithms with applications in various downstream tasks. One such crucial task is Speech Emotion Recognition (SER) which has a wide range of applications, including dynamic analysis of customer calls, mental health assessment, and personalized language learning. PTM embeddings have helped advance SER, however, a comprehensive comparison of these PTM embeddings that consider multiple facets such as embedding model architecture, data used for pre-training, and the pre-training procedure being followed is missing. A thorough comparison of PTM embeddings will aid in the faster and more efficient development of models and enable their deployment in real-world scenarios. In this work, we exploit this research gap and perform a comparative analysis of embeddings extracted from eight speech and audio PTMs (wav2vec 2.0, data2vec, wavLM, UniSpeech-SAT, wav2clip, YAMNet, x-vector, ECAPA). We perform an extensive empirical analysis with four speech emotion datasets (CREMA-D, TESS, SAVEE, Emo-DB) by training three algorithms (XGBoost, Random Forest, FCN) on the derived embeddings. The results of our study indicate that the best performance is achieved by algorithms trained on embeddings derived from PTMs trained for speaker recognition followed by wav2clip and UniSpeech-SAT. This can relay that the top performance by embeddings from speaker recognition PTMs is most likely due to the model taking up information about numerous speech features such as tone, accent, pitch, and so on during its speaker recognition training. Insights from this work will assist future studies in their selection of embeddings for applications related to SER. | ['Rajesh Sharma', 'Arun Balaji Buduru', 'Orchid Chetia Phukan'] | 2023-04-22 | null | null | null | null | ['speaker-recognition', 'speech-emotion-recognition'] | ['speech', 'speech'] | [-1.51922181e-01 -4.77306321e-02 -5.33865299e-03 -4.51880842e-01
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e23d3a33-4418-480c-a822-ba2d39e7c5e8 | how-multi-is-multi-document-summarization | 2210.12688 | null | https://arxiv.org/abs/2210.12688v1 | https://arxiv.org/pdf/2210.12688v1.pdf | How "Multi" is Multi-Document Summarization? | The task of multi-document summarization (MDS) aims at models that, given multiple documents as input, are able to generate a summary that combines disperse information, originally spread across these documents. Accordingly, it is expected that both reference summaries in MDS datasets, as well as system summaries, would indeed be based on such dispersed information. In this paper, we argue for quantifying and assessing this expectation. To that end, we propose an automated measure for evaluating the degree to which a summary is ``disperse'', in the sense of the number of source documents needed to cover its content. We apply our measure to empirically analyze several popular MDS datasets, with respect to their reference summaries, as well as the output of state-of-the-art systems. Our results show that certain MDS datasets barely require combining information from multiple documents, where a single document often covers the full summary content. Overall, we advocate using our metric for assessing and improving the degree to which summarization datasets require combining multi-document information, and similarly how summarization models actually meet this challenge. Our code is available in https://github.com/ariecattan/multi_mds. | ['Ido Dagan', 'Ori Ernst', 'Arie Cattan', 'Ruben Wolhandler'] | 2022-10-23 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 1.55588686e-01 3.54960591e-01 -1.60177499e-01 -1.25372425e-01
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bdaad125-0364-4eca-9a04-716649e051ab | writer-aware-cnn-for-parsimonious-hmm-based | 1812.09809 | null | https://arxiv.org/abs/1812.09809v2 | https://arxiv.org/pdf/1812.09809v2.pdf | Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text Recognition | Recently, the hybrid convolutional neural network hidden Markov model (CNN-HMM) has been introduced for offline handwritten Chinese text recognition (HCTR) and has achieved state-of-the-art performance. However, modeling each of the large vocabulary of Chinese characters with a uniform and fixed number of hidden states requires high memory and computational costs and makes the tens of thousands of HMM state classes confusing. Another key issue of CNN-HMM for HCTR is the diversified writing style, which leads to model strain and a significant performance decline for specific writers. To address these issues, we propose a writer-aware CNN based on parsimonious HMM (WCNN-PHMM). First, PHMM is designed using a data-driven state-tying algorithm to greatly reduce the total number of HMM states, which not only yields a compact CNN by state sharing of the same or similar radicals among different Chinese characters but also improves the recognition accuracy due to the more accurate modeling of tied states and the lower confusion among them. Second, WCNN integrates each convolutional layer with one adaptive layer fed by a writer-dependent vector, namely, the writer code, to extract the irrelevant variability in writer information to improve recognition performance. The parameters of writer-adaptive layers are jointly optimized with other network parameters in the training stage, while a multiple-pass decoding strategy is adopted to learn the writer code and generate recognition results. Validated on the ICDAR 2013 competition of CASIA-HWDB database, the more compact WCNN-PHMM of a 7360-class vocabulary can achieve a relative character error rate (CER) reduction of 16.6% over the conventional CNN-HMM without considering language modeling. By adopting a powerful hybrid language model (N-gram language model and recurrent neural network language model), the CER of WCNN-PHMM is reduced to 3.17%. | ['Jia-Ming Wang', 'Zi-Rui Wang', 'Jun Du'] | 2018-12-24 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 1.00669883e-01 -4.63623136e-01 -3.84845704e-01 -3.65897447e-01
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232e44ff-4f4e-4359-9176-fd9985583e76 | deam-dialogue-coherence-evaluation-using-amr | 2203.09711 | null | https://arxiv.org/abs/2203.09711v1 | https://arxiv.org/pdf/2203.09711v1.pdf | DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations | Automatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyper-parameter tuning and comparison between models. Although recently proposed trainable conversation-level metrics have shown encouraging results, the quality of the metrics is strongly dependent on the quality of training data. Prior works mainly resort to heuristic text-level manipulations (e.g. utterances shuffling) to bootstrap incoherent conversations (negative examples) from coherent dialogues (positive examples). Such approaches are insufficient to appropriately reflect the incoherence that occurs in interactions between advanced dialogue models and humans. To tackle this problem, we propose DEAM, a Dialogue coherence Evaluation metric that relies on Abstract Meaning Representation (AMR) to apply semantic-level Manipulations for incoherent (negative) data generation. AMRs naturally facilitate the injection of various types of incoherence sources, such as coreference inconsistency, irrelevancy, contradictions, and decrease engagement, at the semantic level, thus resulting in more natural incoherent samples. Our experiments show that DEAM achieves higher correlations with human judgments compared to baseline methods on several dialog datasets by significant margins. We also show that DEAM can distinguish between coherent and incoherent dialogues generated by baseline manipulations, whereas those baseline models cannot detect incoherent examples generated by DEAM. Our results demonstrate the potential of AMR-based semantic manipulations for natural negative example generation. | ['Nanyun Peng', 'Aram Galstyan', 'Nuan Wen', 'Sarik Ghazarian'] | 2022-03-18 | null | https://aclanthology.org/2022.acl-long.57 | https://aclanthology.org/2022.acl-long.57.pdf | acl-2022-5 | ['dialogue-evaluation', 'coherence-evaluation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.04077676e-01 6.12670958e-01 4.30145860e-02 -5.08960545e-01
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9a2024b1-e828-432e-92ce-594c65eb9cef | estimates-for-the-branching-factors-of-atari | 2107.02385 | null | https://arxiv.org/abs/2107.02385v2 | https://arxiv.org/pdf/2107.02385v2.pdf | Estimates for the Branching Factors of Atari Games | The branching factor of a game is the average number of new states reachable from a given state. It is a widely used metric in AI research on board games, but less often computed or discussed for videogames. This paper provides estimates for the branching factors of 103 Atari 2600 games, as implemented in the Arcade Learning Environment (ALE). Depending on the game, ALE exposes between 3 and 18 available actions per frame of gameplay, which is an upper bound on branching factor. This paper shows, based on an enumeration of the first 1 million distinct states reachable in each game, that the average branching factor is usually much lower, in many games barely above 1. In addition to reporting the branching factors, this paper aims to clarify what constitutes a distinct state in ALE. | ['Mark J. Nelson'] | 2021-07-06 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.33835727e-01 2.43251994e-01 1.08004939e-02 1.85755998e-01
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507960a9-aa19-4891-aa49-da5c37c3ee39 | an-effective-optimization-method-for-neural | null | null | https://aclanthology.org/2020.wat-1.2 | https://aclanthology.org/2020.wat-1.2.pdf | An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario | In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms. Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality compared to the English-Persian Statistical Machine Translation (SMT) paradigm. | ['Raul Aranovich', 'Benyamin Ahmadnia'] | null | null | null | null | aacl-wat-2020-12 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 5.00294685e-01 1.24954499e-01 -6.45874441e-01 -1.88517779e-01
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a203344a-4e97-49ba-ba65-a6b2fdede169 | instance-incremental-scene-graph-generation | 2302.10425 | null | https://arxiv.org/abs/2302.10425v1 | https://arxiv.org/pdf/2302.10425v1.pdf | Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows | This work introduces a new task of instance-incremental scene graph generation: Given an empty room of the point cloud, representing it as a graph and automatically increasing novel instances. A graph denoting the object layout of the scene is finally generated. It is an important task since it helps to guide the insertion of novel 3D objects into a real-world scene in vision-based applications like augmented reality. It is also challenging because the complexity of the real-world point cloud brings difficulties in learning object layout experiences from the observation data (non-empty rooms with labeled semantics). We model this task as a conditional generation problem and propose a 3D autoregressive framework based on normalizing flows (3D-ANF) to address it. We first represent the point cloud as a graph by extracting the containing label semantics and contextual relationships. Next, a model based on normalizing flows is introduced to map the conditional generation of graphic elements into the Gaussian process. The mapping is invertible. Thus, the real-world experiences represented in the observation data can be modeled in the training phase, and novel instances can be sequentially generated based on the Gaussian process in the testing phase. We implement this new task on the dataset of 3D point-based scenes (3DSSG and 3RScan) and evaluate the performance of our method. Experiments show that our method generates reliable novel graphs from the real-world point cloud and achieves state-of-the-art performance on the benchmark dataset. | ['Pengxiang Ding', 'Jinghang Xu', 'Jianqin Yin', 'Chao Qi'] | 2023-02-21 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.10317001e-01 2.90883332e-01 5.65518618e-01 -2.72415757e-01
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98bbc33c-2060-4c8b-a22b-67b7f89f5cda | prediction-calibration-for-generalized-few | 2210.08290 | null | https://arxiv.org/abs/2210.08290v1 | https://arxiv.org/pdf/2210.08290v1.pdf | Prediction Calibration for Generalized Few-shot Semantic Segmentation | Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation FSS, which is limited to segmenting novel classes only, GFSS is much under-studied despite being more practical. Existing approach to GFSS is based on classifier parameter fusion whereby a newly trained novel class classifier and a pre-trained base class classifier are combined to form a new classifier. As the training data is dominated by base classes, this approach is inevitably biased towards the base classes. In this work, we propose a novel Prediction Calibration Network PCN to address this problem. Instead of fusing the classifier parameters, we fuse the scores produced separately by the base and novel classifiers. To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced. It is known that the lower-level features are useful of detecting edge information in an input image than higher-level features. Thus, we build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. However, transformers are computationally demanding. Crucially, to make the proposed cross-attention module training tractable at the pixel level, this module is designed based on feature-score cross-covariance and episodically trained to be generalizable at inference time. Extensive experiments on PASCAL-$5^{i}$ and COCO-$20^{i}$ show that our PCN outperforms the state-the-the-art alternatives by large margins. | ['Tao Xiang', 'Yi-Zhe Song', 'Da Li', 'Sen He', 'Zhihe Lu'] | 2022-10-15 | null | null | null | null | ['generalized-few-shot-semantic-segmentation'] | ['computer-vision'] | [ 6.88735783e-01 1.48943901e-01 -2.25539893e-01 -4.99291927e-01
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86808383-9461-40b2-8304-0d421a930778 | on-the-fly-strategy-adaptation-for-ad-hoc | 2203.08015 | null | https://arxiv.org/abs/2203.08015v1 | https://arxiv.org/pdf/2203.08015v1.pdf | On-the-fly Strategy Adaptation for ad-hoc Agent Coordination | Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advances are significant, the vast majority of focus has been on the self-play paradigm. This often results in a coordination problem, caused by agents learning to make use of arbitrary conventions when playing with themselves. This means that even the strongest self-play agents may have very low cross-play with other agents, including other initializations of the same algorithm. In this paper we propose to solve this problem by adapting agent strategies on the fly, using a posterior belief over the other agents' strategy. Concretely, we consider the problem of selecting a strategy from a finite set of previously trained agents, to play with an unknown partner. We propose an extension of the classic statistical technique, Gibbs sampling, to update beliefs about other agents and obtain close to optimal ad-hoc performance. Despite its simplicity, our method is able to achieve strong cross-play with unseen partners in the challenging card game of Hanabi, achieving successful ad-hoc coordination without knowledge of the partner's strategy a priori. | ['Stephen J. Roberts', 'Jack Parker-Holder', 'Jaleh Zand'] | 2022-03-08 | null | null | null | null | ['game-of-hanabi'] | ['playing-games'] | [ 8.13781843e-02 1.56231463e-01 3.96190017e-01 3.95250954e-02
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9cfc68dd-508e-4675-a790-cbccf721d966 | development-and-evaluation-of-an-online-home | 2304.11770 | null | https://arxiv.org/abs/2304.11770v1 | https://arxiv.org/pdf/2304.11770v1.pdf | Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources | In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50\% of the flexible loads, which is possible due to the use of the stationary energy storage. | ['Stephanie Stockar', 'Rachel Blaser', 'Prasad Dev Hanumalagutti', 'Mithun Goutham', 'Cory Miller', 'Xiaoling Chen'] | 2023-04-23 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.60721824e-01 3.33372325e-01 7.01827630e-02 1.25350744e-01
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73368113-947f-4369-9d03-420c624cd915 | predicting-affinity-ties-in-a-surname-network | 2306.01218 | null | https://arxiv.org/abs/2306.01218v1 | https://arxiv.org/pdf/2306.01218v1.pdf | Predicting affinity ties in a surname network | From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago. | ['Naim Bro', 'Marcelo Mendoza'] | 2023-06-02 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-4.20849323e-01 7.07360029e-01 -6.87832952e-01 -2.53547907e-01
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73a74ff2-972e-406a-af07-28047888a8fc | interpretable-bangla-sarcasm-detection-using | 2303.12772 | null | https://arxiv.org/abs/2303.12772v1 | https://arxiv.org/pdf/2303.12772v1.pdf | Interpretable Bangla Sarcasm Detection using BERT and Explainable AI | A positive phrase or a sentence with an underlying negative motive is usually defined as sarcasm that is widely used in today's social media platforms such as Facebook, Twitter, Reddit, etc. In recent times active users in social media platforms are increasing dramatically which raises the need for an automated NLP-based system that can be utilized in various tasks such as determining market demand, sentiment analysis, threat detection, etc. However, since sarcasm usually implies the opposite meaning and its detection is frequently a challenging issue, data meaning extraction through an NLP-based model becomes more complicated. As a result, there has been a lot of study on sarcasm detection in English over the past several years, and there's been a noticeable improvement and yet sarcasm detection in the Bangla language's state remains the same. In this article, we present a BERT-based system that can achieve 99.60\% while the utilized traditional machine learning algorithms are only capable of achieving 89.93\%. Additionally, we have employed Local Interpretable Model-Agnostic Explanations that introduce explainability to our system. Moreover, we have utilized a newly collected bangla sarcasm dataset, BanglaSarc that was constructed specifically for the evaluation of this study. This dataset consists of fresh records of sarcastic and non-sarcastic comments, the majority of which are acquired from Facebook and YouTube comment sections. | ['Md. Golam Rabiul Alam', 'Sudipta Mondal', 'Elizabeth Antora Modhu', 'Zeba Tahsin Hossain', 'Tasnim Sakib Apon', 'Ramisa Anan'] | 2023-03-22 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-1.07060038e-01 3.23506713e-01 -3.88847381e-01 -3.89547676e-01
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ce454b4a-58de-4ab8-b7c2-ca7321bae8c5 | reconstruction-and-analysis-of-negatively | 2211.05489 | null | https://arxiv.org/abs/2211.05489v1 | https://arxiv.org/pdf/2211.05489v1.pdf | Reconstruction and analysis of negatively buoyant jets with interpretable machine learning | In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. To minimize harmful effects and assess environmental impact, a detailed numerical investigation is necessary. The selection of appropriate geometry and working conditions for minimizing such effects often requires numerous experiments and numerical simulations. For this reason, the application of machine learning models is proposed. Several models including Support Vector Regression, Artificial Neural Networks, Random Forests, XGBoost, CatBoost and LightGBM were trained. The dataset was built with numerous OpenFOAM simulations, which were validated by experimental data from previous research. The best prediction was obtained by Artificial Neural Network with an average of R2 0.98 and RMSE 0.28. In order to understand the working of the machine learning model and the influence of all parameters on the geometrical characteristics of inclined buoyant jets, the SHAP feature interpretation method was used. | ['Lado Kranjčević', 'Ante Sikirica', 'Luka Grbčić', 'Marta Alvir'] | 2022-11-10 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-2.26714373e-01 -3.43868077e-01 1.82446197e-01 -1.30910128e-01
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24ea09ad-55eb-42da-bfd0-e8a612be5083 | class-specific-anchoring-proposal-for-3d | 1907.09081 | null | https://arxiv.org/abs/1907.09081v1 | https://arxiv.org/pdf/1907.09081v1.pdf | Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images | Detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in three-dimension (3D), such as in the case of autonomous driving vehicles. Therefore, accurately and efficiently detecting objects in the three-dimensional setting is becoming increasingly relevant to a wide range of industrial applications, and thus is progressively attracting the attention of researchers. Building systems to detect objects in 3D is a challenging task though, because it relies on the multi-modal fusion of data derived from different sources. In this paper, we study the effects of anchoring using the current state-of-the-art 3D object detector and propose Class-specific Anchoring Proposal (CAP) strategy based on object sizes and aspect ratios based clustering of anchors. The proposed anchoring strategy significantly increased detection accuracy's by 7.19%, 8.13% and 8.8% on Easy, Moderate and Hard setting of the pedestrian class, 2.19%, 2.17% and 1.27% on Easy, Moderate and Hard setting of the car class and 12.1% on Easy setting of cyclist class. We also show that the clustering in anchoring process also enhances the performance of the regional proposal network in proposing regions of interests significantly. Finally, we propose the best cluster numbers for each class of objects in KITTI dataset that improves the performance of detection model significantly. | ['Humayun Irshad', 'Amir Hossein Raffiee'] | 2019-07-22 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.32208064e-01 7.44378865e-02 6.97189569e-02 -9.26142633e-02
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f92a7b64-b496-49b4-9817-d162d16cadf6 | no-more-reviewer-2-subverting-automatic-paper | 2303.14443 | null | https://arxiv.org/abs/2303.14443v1 | https://arxiv.org/pdf/2303.14443v1.pdf | No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning | The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems use statistical topic models to characterize the content of submissions and automate the assignment to reviewers. In this paper, we show that this automation can be manipulated using adversarial learning. We propose an attack that adapts a given paper so that it misleads the assignment and selects its own reviewers. Our attack is based on a novel optimization strategy that alternates between the feature space and problem space to realize unobtrusive changes to the paper. To evaluate the feasibility of our attack, we simulate the paper-reviewer assignment of an actual security conference (IEEE S&P) with 165 reviewers on the program committee. Our results show that we can successfully select and remove reviewers without access to the assignment system. Moreover, we demonstrate that the manipulated papers remain plausible and are often indistinguishable from benign submissions. | ['Konrad Rieck', 'Thorsten Holz', 'Doreen Riepel', 'Jonas Möller', 'Erwin Quiring', 'Thorsten Eisenhofer'] | 2023-03-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 1.48613602e-01 1.54304847e-01 3.93241458e-02 -5.50003611e-02
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8dcfcd43-3cfd-4c4b-931f-e0308d90dc4a | learning-to-automatically-solve-algebra-word | null | null | https://aclanthology.org/P14-1026 | https://aclanthology.org/P14-1026.pdf | Learning to Automatically Solve Algebra Word Problems | null | ['Nate Kushman', 'Luke Zettlemoyer', 'Regina Barzilay', 'Yoav Artzi'] | 2014-06-01 | null | null | null | acl-2014-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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480b4d94-f840-4026-ab92-c0e184616772 | mutual-information-estimation-as-a-difference | null | null | https://openreview.net/forum?id=J7FaSJw-xCM | https://openreview.net/pdf?id=J7FaSJw-xCM | Mutual Information Estimation as a Difference of Entropies for Unsupervised Representation Learning | Contrastive loss has been successfully exploited in the latest visual unsupervised representation learning methods. Contrastive loss is based on a lower-bound estimation of mutual information where its known limitations include batch size dependency expressed as $O(log (n))$. It is also commonly known as negative sampling size problem. To cope with the limitation, non-contrastive methods have been proposed and they have been shown to achieve outstanding performance. The non-contrastive methods, however, are limited in that they are not based on principled designs and their learning dynamics can be unstable. In this work, we derive a principled non-contrastive method where mutual information is estimated as a difference of entropies and thus no need for negative sampling. With our best knowledge, this is the first successful implementation of difference of entropies for visual unsupervised representation learning. Our method performs on par with or better than the state-of-the-art contrastive and non-contrastive methods. The main idea of our approach is to extend Shannon entropy $H(\rmZ)$ to von Neumann entropy $S(\rmZ)$. The von Neumann entropy can be shown to be a lower bound of Shannon entropy and it can be stably estimated with a small sample size. Additionally, we prove that the conditional entropy term $H(\rmZ_1|\rmZ_2)$ is upper bounded by the negative cosine similarity for the case of weak Gaussian noise augmentation. Even though the derivation is limited to a special case of augmentation, it provides a justification of cosine similarity as the measure between positive samples. | ['Wonjong Rhee', 'Jaeill Kim'] | 2021-09-29 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.41732353e-01 2.58328170e-01 2.93958616e-02 -6.87205791e-02
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70b96eda-f02a-401d-946d-858d5dd791c7 | towards-robust-ranker-for-text-retrieval | 2206.08063 | null | https://arxiv.org/abs/2206.08063v1 | https://arxiv.org/pdf/2206.08063v1.pdf | Towards Robust Ranker for Text Retrieval | A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever. In this work, we first identify two major barriers to a robust ranker, i.e., inherent label noises caused by a well-trained retriever and non-ideal negatives sampled for a high-capable ranker. Thereby, we propose multiple retrievers as negative generators improve the ranker's robustness, where i) involving extensive out-of-distribution label noises renders the ranker against each noise distribution, and ii) diverse hard negatives from a joint distribution are relatively close to the ranker's negative distribution, leading to more challenging thus effective training. To evaluate our robust ranker (dubbed R$^2$anker), we conduct experiments in various settings on the popular passage retrieval benchmark, including BM25-reranking, full-ranking, retriever distillation, etc. The empirical results verify the new state-of-the-art effectiveness of our model. | ['Daxin Jiang', 'Binxing Jiao', 'Guodong Long', 'Can Xu', 'Chongyang Tao', 'Xiubo Geng', 'Tao Shen', 'Yucheng Zhou'] | 2022-06-16 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 6.22096211e-02 -3.43913287e-01 -1.46285862e-01 -1.44982159e-01
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b2e87300-7204-48b4-bdc5-312b92018413 | what-would-elsa-do-freezing-layers-during | 1911.03090 | null | https://arxiv.org/abs/1911.03090v1 | https://arxiv.org/pdf/1911.03090v1.pdf | What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning | Pretrained transformer-based language models have achieved state of the art across countless tasks in natural language processing. These models are highly expressive, comprising at least a hundred million parameters and a dozen layers. Recent evidence suggests that only a few of the final layers need to be fine-tuned for high quality on downstream tasks. Naturally, a subsequent research question is, "how many of the last layers do we need to fine-tune?" In this paper, we precisely answer this question. We examine two recent pretrained language models, BERT and RoBERTa, across standard tasks in textual entailment, semantic similarity, sentiment analysis, and linguistic acceptability. We vary the number of final layers that are fine-tuned, then study the resulting change in task-specific effectiveness. We show that only a fourth of the final layers need to be fine-tuned to achieve 90% of the original quality. Surprisingly, we also find that fine-tuning all layers does not always help. | ['Raphael Tang', 'Jaejun Lee', 'Jimmy Lin'] | 2019-11-08 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.17030501e-01 6.14320338e-02 7.37421215e-02 -8.03706467e-01
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5c94bca3-1dbf-4d08-8316-6b0c34a3aeab | cns-net-conservative-novelty-synthesizing | 2305.01236 | null | https://arxiv.org/abs/2305.01236v1 | https://arxiv.org/pdf/2305.01236v1.pdf | CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario | We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. In this paper, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. Moreover, we also build a new large-scale malware dataset, named MAL-100, to fill the gap of lacking large open-set malware benchmark dataset. Experimental results on two widely used malware datasets and our MAL-100 demonstrate the effectiveness of our model compared with other representative methods. | ['Yuanyuan Xu', 'Yuxia Sun', 'Shiheng Ma', 'Song Guo', 'Jingcai Guo'] | 2023-05-02 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.28867525e-01 -4.83717799e-01 -1.65286541e-01 -2.06791729e-01
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bc2e28a2-c0bc-4d90-b605-a2de8b463a16 | a-multi-modal-method-for-satire-detection | 2010.06671 | null | https://arxiv.org/abs/2010.06671v1 | https://arxiv.org/pdf/2010.06671v1.pdf | A Multi-Modal Method for Satire Detection using Textual and Visual Cues | Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines. | ['David A. Broniatowski', 'Pedram Hosseini', 'Or Levi', 'Lily Li'] | 2020-10-13 | null | https://aclanthology.org/2020.nlp4if-1.4 | https://aclanthology.org/2020.nlp4if-1.4.pdf | nlp4if-coling-2020-12 | ['image-forensics', 'satire-detection'] | ['computer-vision', 'natural-language-processing'] | [ 3.33711296e-01 -7.63280615e-02 1.16074823e-01 -8.55281055e-02
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34c2b0e8-fbdc-4ee5-963e-9c3c413ed7a1 | adbert-an-effective-few-shot-learning | null | null | https://aclanthology.org/2022.wnut-1.19 | https://aclanthology.org/2022.wnut-1.19.pdf | AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements | The tremendous increase in social media usage for sharing Television (TV) experiences has provided a unique opportunity in the Public Health and Marketing sectors to understand viewer engagement and attitudes through viewer-generated content on social media. However, this opportunity also comes with associated technical challenges. Specifically, given a televised event and related tweets about this event, we need methods to effectively align these tweets and the corresponding event. In this paper, we consider the specific ecosystem of the Superbowl 2020 and map viewer tweets to advertisements they are referring to. Our proposed model, AdBERT, is an effective few-shot learning framework that is able to handle the technical challenges of establishing ad-relatedness, class imbalance as well as the scarcity of labeled data. As part of this study, we have curated and developed two datasets that can prove to be useful for Social TV research: 1) dataset of ad-related tweets and 2) dataset of ad descriptions of Superbowl advertisements. Explaining connections to SentenceBERT, we describe the advantages of AdBERT that allow us to make the most out of a challenging and interesting dataset which we will open-source along with the models developed in this paper. | ['Jaideep Srivastava', 'Jisu Huh', 'Maral Abdollahi', 'Roopana Chenchu', 'Debarati Das'] | null | null | null | null | coling-wnut-2022-10 | ['marketing'] | ['miscellaneous'] | [ 1.13806836e-01 2.94961095e-01 -3.28534037e-01 -6.78206027e-01
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ebee31a2-462d-4bfc-8d46-e91831f6be1c | factual-and-informative-review-generation-for | 2209.12613 | null | https://arxiv.org/abs/2209.12613v2 | https://arxiv.org/pdf/2209.12613v2.pdf | Factual and Informative Review Generation for Explainable Recommendation | Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language 'rationales' for the jointly predicted rating. However, previous studies found that existing models often generate repetitive, universally applicable, and generic explanations, resulting in uninformative rationales. Further, our analysis shows that previous models' generated content often contain factual hallucinations. These issues call for novel solutions that could generate both informative and factually grounded explanations. Inspired by recent success in using retrieved content in addition to parametric knowledge for generation, we propose to augment the generator with a personalized retriever, where the retriever's output serves as external knowledge for enhancing the generator. Experiments on Yelp, TripAdvisor, and Amazon Movie Reviews dataset show our model could generate explanations that more reliably entail existing reviews, are more diverse, and are rated more informative by human evaluators. | ['Bodhisattwa Prasad Majumder', 'Julian McAuley', 'Sameer Singh', 'Zhouhang Xie'] | 2022-09-12 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 1.01593271e-01 1.04619622e+00 -2.14366361e-01 -7.59719372e-01
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a06faee4-a3d1-42d7-bc86-6ead19e8166e | optimization-based-eye-tracking-using | 2303.04997 | null | https://arxiv.org/abs/2303.04997v1 | https://arxiv.org/pdf/2303.04997v1.pdf | Optimization-Based Eye Tracking using Deflectometric Information | Eye tracking is an important tool with a wide range of applications in Virtual, Augmented, and Mixed Reality (VR/AR/MR) technologies. State-of-the-art eye tracking methods are either reflection-based and track reflections of sparse point light sources, or image-based and exploit 2D features of the acquired eye image. In this work, we attempt to significantly improve reflection-based methods by utilizing pixel-dense deflectometric surface measurements in combination with optimization-based inverse rendering algorithms. Utilizing the known geometry of our deflectometric setup, we develop a differentiable rendering pipeline based on PyTorch3D that simulates a virtual eye under screen illumination. Eventually, we exploit the image-screen-correspondence information from the captured measurements to find the eye's rotation, translation, and shape parameters with our renderer via gradient descent. In general, our method does not require a specific pattern and can work with ordinary video frames of the main VR/AR/MR screen itself. We demonstrate real-world experiments with evaluated mean relative gaze errors below 0.45 degrees at a precision better than 0.11 degrees. Moreover, we show an improvement of 6X over a representative reflection-based state-of-the-art method in simulation. | ['Florian Willomitzer', 'Oliver Cossairt', 'Jiazhang Wang', 'Tianfu Wang'] | 2023-03-09 | null | null | null | null | ['mixed-reality', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 2.40934432e-01 -1.53547257e-01 5.17811120e-01 -1.06909297e-01
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f902f257-f34a-4352-85c0-f231bf625250 | interpretable-battery-cycle-life-range | 2204.12420 | null | https://arxiv.org/abs/2204.12420v2 | https://arxiv.org/pdf/2204.12420v2.pdf | Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level | Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot. | ['Sebastien Gros', 'Torsten Wik', 'Faisal Altaf', 'Yang Su', 'Huang Zhang'] | 2022-04-26 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.26517528e-01 -1.55335531e-01 -4.17090565e-01 -5.52078545e-01
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71f6869e-7643-40c2-a933-3b9ec6cbc9ec | an-empirical-study-of-pre-trained-language | 2303.10368 | null | https://arxiv.org/abs/2303.10368v1 | https://arxiv.org/pdf/2303.10368v1.pdf | An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering | Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT, which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA. | ['Zafar Ali', 'Jeff Z. Pan', 'Jiaoyan Chen', 'Dehai Min', 'Guilin Qi', 'Yike Wu', 'Nan Hu'] | 2023-03-18 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-3.71524692e-01 3.96802366e-01 -1.76409721e-01 -3.35702807e-01
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2c73c34d-077f-4baa-85a7-66eadad25c34 | learning-from-video-and-text-via-large-scale | 1707.09074 | null | http://arxiv.org/abs/1707.09074v1 | http://arxiv.org/pdf/1707.09074v1.pdf | Learning from Video and Text via Large-Scale Discriminative Clustering | Discriminative clustering has been successfully applied to a number of
weakly-supervised learning tasks. Such applications include person and action
recognition, text-to-video alignment, object co-segmentation and colocalization
in videos and images. One drawback of discriminative clustering, however, is
its limited scalability. We address this issue and propose an online
optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We
apply the proposed method to the problem of weakly supervised learning of
actions and actors from movies together with corresponding movie scripts. The
scaling up of the learning problem to 66 feature length movies enables us to
significantly improve weakly supervised action recognition. | ['Piotr Bojanowski', 'Jean-Baptiste Alayrac', 'Antoine Miech', 'Ivan Laptev', 'Josef Sivic'] | 2017-07-27 | learning-from-video-and-text-via-large-scale-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Miech_Learning_From_Video_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Miech_Learning_From_Video_ICCV_2017_paper.pdf | iccv-2017-10 | ['weakly-supervised-action-recognition', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 4.00610596e-01 -4.16856170e-01 -3.60288948e-01 -5.70020378e-01
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11871f1f-f56f-4f37-9973-d3abb5b7f5ce | bl-research-at-semeval-2022-task-8-using | null | null | https://aclanthology.org/2022.semeval-1.173 | https://aclanthology.org/2022.semeval-1.173.pdf | BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity | This paper presents our system for document-level semantic textual similarity (STS) evaluation at SemEval-2022 Task 8: “Multilingual News Article Similarity”. The semantic information used is obtained by using different semantic models ranging from the extraction of key terms and named entities to the document classification and obtaining similarity from automatic summarization of documents. All these semantic information’s are then used as features to feed a supervised system in order to evaluate the degree of similarity of a pair of documents. We obtained a Pearson correlation score of 0.706 compared to the best score of 0.818 from teams that participated in this task. | ['Youssef Miloudi', 'Christophe Bortolaso', 'Mokhtar Boumedyen Billami', 'Camille Gosse', 'Karim Boutamine', 'Mohamed Mehdi Kandi', 'Sebastien Dufour'] | null | null | null | null | semeval-naacl-2022-7 | ['document-classification'] | ['natural-language-processing'] | [ 2.53721252e-02 2.08943635e-01 -8.72176364e-02 -5.00546396e-01
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fa7e47ee-b4d5-442f-8e3a-c8d3743a9aca | octree-guided-cnn-with-spherical-kernels-for | 1903.00343 | null | http://arxiv.org/abs/1903.00343v1 | http://arxiv.org/pdf/1903.00343v1.pdf | Octree guided CNN with Spherical Kernels for 3D Point Clouds | We propose an octree guided neural network architecture and spherical
convolutional kernel for machine learning from arbitrary 3D point clouds. The
network architecture capitalizes on the sparse nature of irregular point
clouds, and hierarchically coarsens the data representation with space
partitioning. At the same time, the proposed spherical kernels systematically
quantize point neighborhoods to identify local geometric structures in the
data, while maintaining the properties of translation-invariance and asymmetry.
We specify spherical kernels with the help of network neurons that in turn are
associated with spatial locations. We exploit this association to avert dynamic
kernel generation during network training that enables efficient learning with
high resolution point clouds. The effectiveness of the proposed technique is
established on the benchmark tasks of 3D object classification and
segmentation, achieving new state-of-the-art on ShapeNet and RueMonge2014
datasets. | ['Huan Lei', 'Ajmal Mian', 'Naveed Akhtar'] | 2019-02-28 | octree-guided-cnn-with-spherical-kernels-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lei_Octree_Guided_CNN_With_Spherical_Kernels_for_3D_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lei_Octree_Guided_CNN_With_Spherical_Kernels_for_3D_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-2.79965073e-01 -3.43340635e-02 -1.73127893e-02 -3.21581990e-01
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3cb17e4e-af01-434d-a9e6-8a372b915908 | ditto-in-the-house-building-articulation | 2302.01295 | null | https://arxiv.org/abs/2302.01295v1 | https://arxiv.org/pdf/2302.01295v1.pdf | Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception | Virtualizing the physical world into virtual models has been a critical technique for robot navigation and planning in the real world. To foster manipulation with articulated objects in everyday life, this work explores building articulation models of indoor scenes through a robot's purposeful interactions in these scenes. Prior work on articulation reasoning primarily focuses on siloed objects of limited categories. To extend to room-scale environments, the robot has to efficiently and effectively explore a large-scale 3D space, locate articulated objects, and infer their articulations. We introduce an interactive perception approach to this task. Our approach, named Ditto in the House, discovers possible articulated objects through affordance prediction, interacts with these objects to produce articulated motions, and infers the articulation properties from the visual observations before and after each interaction. It tightly couples affordance prediction and articulation inference to improve both tasks. We demonstrate the effectiveness of our approach in both simulation and real-world scenes. Code and additional results are available at https://ut-austin-rpl.github.io/HouseDitto/ | ['Yuke Zhu', 'Zhenyu Jiang', 'Cheng-Chun Hsu'] | 2023-02-02 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-5.24124615e-02 4.22251999e-01 -3.19163352e-02 -2.13591173e-01
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963bb0b5-8824-47bf-8875-50a692f78aec | cycnn-a-rotation-invariant-cnn-using-polar | 2007.10588 | null | https://arxiv.org/abs/2007.10588v1 | https://arxiv.org/pdf/2007.10588v1.pdf | CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers | Deep Convolutional Neural Networks (CNNs) are empirically known to be invariant to moderate translation but not to rotation in image classification. This paper proposes a deep CNN model, called CyCNN, which exploits polar mapping of input images to convert rotation to translation. To deal with the cylindrical property of the polar coordinates, we replace convolution layers in conventional CNNs to cylindrical convolutional (CyConv) layers. A CyConv layer exploits the cylindrically sliding windows (CSW) mechanism that vertically extends the input-image receptive fields of boundary units in a convolutional layer. We evaluate CyCNN and conventional CNN models for classification tasks on rotated MNIST, CIFAR-10, and SVHN datasets. We show that if there is no data augmentation during training, CyCNN significantly improves classification accuracies when compared to conventional CNN models. Our implementation of CyCNN is publicly available on https://github.com/mcrl/CyCNN. | ['Jaejin Lee', 'Hyungmo Kim', 'Wooekun Jung', 'Jinpyo Kim'] | 2020-07-21 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-1.24213777e-01 1.20706499e-01 -3.01904052e-01 -3.15544903e-01
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4f7a100f-d576-4ca3-a63c-6e9dc2104bda | any-speaker-adaptive-text-to-speech-synthesis | 2211.09383 | null | https://arxiv.org/abs/2211.09383v2 | https://arxiv.org/pdf/2211.09383v2.pdf | Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models | There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling. However, existing methods on any-speaker adaptive TTS have achieved unsatisfactory performance, due to their suboptimal accuracy in mimicking the target speakers' styles. In this work, we present Grad-StyleSpeech, which is an any-speaker adaptive TTS framework that is based on a diffusion model that can generate highly natural speech with extremely high similarity to target speakers' voice, given a few seconds of reference speech. Grad-StyleSpeech significantly outperforms recent speaker-adaptive TTS baselines on English benchmarks. Audio samples are available at https://nardien.github.io/grad-stylespeech-demo. | ['Sung Ju Hwang', 'Dongchan Min', 'Minki Kang'] | 2022-11-17 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 1.18108466e-03 3.56090404e-02 1.63335335e-02 -5.44189990e-01
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a1592792-1364-42b9-8087-0d5f4ef5be41 | genas-neural-architecture-search-with-better | 2305.08611 | null | https://arxiv.org/abs/2305.08611v2 | https://arxiv.org/pdf/2305.08611v2.pdf | GeNAS: Neural Architecture Search with Better Generalization | Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization. We demonstrate that the flatness of the loss surface can be a promising proxy for predicting the generalization capability of neural network architectures. We evaluate our proposed method on various search spaces, showing similar or even better performance compared to the state-of-the-art NAS methods. Notably, the resultant architecture found by flatness measure generalizes robustly to various shifts in data distribution (e.g. ImageNet-V2,-A,-O), as well as various tasks such as object detection and semantic segmentation. Code is available at https://github.com/clovaai/GeNAS. | ['Youngjoon Yoo', 'Dongyoon Han', 'Geondo Park', 'Joonsang Yu', 'JoonHyun Jeong'] | 2023-05-15 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.35029852e-03 9.72453132e-02 -9.77520347e-02 -4.05674934e-01
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7897cffc-c1ce-4736-b74d-3829d72e5834 | theoretical-analysis-on-the-efficiency-of | 2306.10023 | null | https://arxiv.org/abs/2306.10023v1 | https://arxiv.org/pdf/2306.10023v1.pdf | Theoretical Analysis on the Efficiency of Interleaved Comparisons | This study presents a theoretical analysis on the efficiency of interleaving, an efficient online evaluation method for rankings. Although interleaving has already been applied to production systems, the source of its high efficiency has not been clarified in the literature. Therefore, this study presents a theoretical analysis on the efficiency of interleaving methods. We begin by designing a simple interleaving method similar to ordinary interleaving methods. Then, we explore a condition under which the interleaving method is more efficient than A/B testing and find that this is the case when users leave the ranking depending on the item's relevance, a typical assumption made in click models. Finally, we perform experiments based on numerical analysis and user simulation, demonstrating that the theoretical results are consistent with the empirical results. | ['Makoto P. Kato', 'Hajime Morita', 'Kojiro Iizuka'] | 2023-05-31 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-2.22902030e-01 -3.63527894e-01 -3.36830825e-01 -8.12217668e-02
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a5274c86-992a-499f-a149-2cd4e7e097b0 | a-full-image-full-resolution-end-to-end | 1909.06751 | null | https://arxiv.org/abs/1909.06751v1 | https://arxiv.org/pdf/1909.06751v1.pdf | A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection | Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods. | ['Luisa Verdoliva', 'Diego Gragnaniello', 'Giovanni Poggi', 'Francesco Marra'] | 2019-09-15 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.36969137e-01 3.61329205e-02 1.68429524e-01 -1.56753749e-01
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316dd4bf-c5b5-4c34-960b-00d70bc09600 | the-change-that-matters-in-discourse-parsing-1 | 2203.11317 | null | https://arxiv.org/abs/2203.11317v1 | https://arxiv.org/pdf/2203.11317v1.pdf | The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error | Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside of the training distribution's coverage, diminishing the practical utility of existing models. There is need for a measure that can inform us to what extent our model generalizes from the training to the test sample when these samples may be drawn from distinct distributions. While this can be estimated via distribution shift, we argue that this does not directly correlate with change in the observed error of a classifier (i.e. error-gap). Thus, we propose to use a statistic from the theoretical domain adaptation literature which can be directly tied to error-gap. We study the bias of this statistic as an estimator of error-gap both theoretically and through a large-scale empirical study of over 2400 experiments on 6 discourse datasets from domains including, but not limited to: news, biomedical texts, TED talks, Reddit posts, and fiction. Our results not only motivate our proposal and help us to understand its limitations, but also provide insight on the properties of discourse models and datasets which improve performance in domain adaptation. For instance, we find that non-news datasets are slightly easier to transfer to than news datasets when the training and test sets are very different. Our code and an associated Python package are available to allow practitioners to make more informed model and dataset choices. | ['Malihe Alikhani', 'Seong Jae Hwang', 'Anthony Sicilia', 'Katherine Atwell'] | 2022-03-21 | null | https://aclanthology.org/2022.findings-acl.68 | https://aclanthology.org/2022.findings-acl.68.pdf | findings-acl-2022-5 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.28056514e-01 4.57049012e-01 -5.91503263e-01 -5.67179799e-01
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ea27eaaa-e393-491c-ad79-2622bb8dad9c | one-shot-object-localization-in-medical | 2012.07043 | null | https://arxiv.org/abs/2012.07043v2 | https://arxiv.org/pdf/2012.07043v2.pdf | Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images | Deep learning networks have shown promising performance for accurate object localization in medial images, but require large amount of annotated data for supervised training, which is expensive and expertise burdensome. To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage. Our main idea comes from that tissues and organs from different human bodies have a similar relative position and context. Therefore, we could predict the relative positions of their non-local patches, thus locate the target organ. Our framework is composed of three parts: (1) A projection network trained to predict the 3D offset between any two patches from the same volume, where human annotations are not required. In the inference stage, it takes one given landmark in a reference image as a support patch and predicts the offset from a random patch to the corresponding landmark in the test (query) volume. (2) A coarse-to-fine framework contains two projection networks, providing more accurate localization of the target. (3) Based on the coarse-to-fine model, we transfer the organ boundingbox (B-box) detection to locating six extreme points along x, y and z directions in the query volume. Experiments on multi-organ localization from head-and-neck (HaN) CT volumes showed that our method acquired competitive performance in real time, which is more accurate and 10^5 times faster than template matching methods with the same setting. Code is available: https://github.com/LWHYC/RPR-Loc. | ['Guotai Wang', 'Shaoting Zhang', 'Hao Fu', 'Ran Gu', 'Wei Xu', 'Wenhui Lei'] | 2020-12-13 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-8.21518451e-02 2.49295443e-01 -2.09795877e-01 -4.11581904e-01
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81f19c1b-8bab-4e08-93ff-4b343536a5a3 | what-is-wrong-with-you-leveraging-user | 2203.13927 | null | https://arxiv.org/abs/2203.13927v1 | https://arxiv.org/pdf/2203.13927v1.pdf | What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation | Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect. In this work, we propose to use information that can be automatically extracted from the next user utterance, such as its sentiment or whether the user explicitly ends the conversation, as a proxy to measure the quality of the previous system response. This allows us to train on a massive set of dialogs with weak supervision, without requiring manual system turn quality annotations. Experiments show that our model is comparable to models trained on human annotated data. Furthermore, our model generalizes across both spoken and written open-domain dialog corpora collected from real and paid users. | ['Dilek Hakkani-Tur', 'Yang Liu', 'Alexandros Papangelis', 'Behnam Hedayatnia', 'Sarik Ghazarian'] | 2022-03-25 | null | https://aclanthology.org/2022.findings-acl.331 | https://aclanthology.org/2022.findings-acl.331.pdf | findings-acl-2022-5 | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-1.44309610e-01 3.90452981e-01 -1.36530250e-01 -9.02862132e-01
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f2ebd3ee-9a71-4711-9d51-0c993081e21b | towards-end-to-end-handwritten-document | 2209.15362 | null | https://arxiv.org/abs/2209.15362v2 | https://arxiv.org/pdf/2209.15362v2.pdf | Towards End-to-end Handwritten Document Recognition | Handwritten text recognition has been widely studied in the last decades for its numerous applications. Nowadays, the state-of-the-art approach consists in a three-step process. The document is segmented into text lines, which are then ordered and recognized. However, this three-step approach has many drawbacks. The three steps are treated independently whereas they are closely related. Errors accumulate from one step to the other. The ordering step is based on heuristic rules which prevent its use for documents with a complex layouts or for heterogeneous documents. The need for additional physical segmentation annotations for training the segmentation stage is inherent to this approach. In this thesis, we propose to tackle these issues by performing the handwritten text recognition of whole document in an end-to-end way. To this aim, we gradually increase the difficulty of the recognition task, moving from isolated lines to paragraphs, and then to whole documents. We proposed an approach at the line level, based on a fully convolutional network, in order to design a first generic feature extraction step for the handwriting recognition task. Based on this preliminary work, we studied two different approaches to recognize handwritten paragraphs. We reached state-of-the-art results at paragraph level on the RIMES 2011, IAM and READ 2016 datasets and outperformed the line-level state of the art on these datasets. We finally proposed the first end-to-end approach dedicated to the recognition of both text and layout, at document level. Characters and layout tokens are sequentially predicted following a learned reading order. We proposed two new metrics we used to evaluate this task on the RIMES 2009 and READ 2016 dataset, at page level and double-page level. | ['Denis Coquenet'] | 2022-09-30 | null | null | null | null | ['handwriting-recognition', 'handwritten-document-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.24108928e-01 -2.32534096e-01 1.79089844e-01 -4.87992108e-01
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1.62980244e-01 7.83334970e-01 -2.82805022e-02 -2.17726156e-01
6.44087195e-01 2.97474772e-01 6.65274382e-01 -4.17566478e-01
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2.54180849e-01 6.43078029e-01 6.60421729e-01 -9.89118457e-01
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1.09583519e-01 -1.75924212e-01 -7.42993429e-02 2.01411501e-01] | [11.711353302001953, 2.5666182041168213] |
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