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4af09dc2-089d-4fe8-8711-42c52639837c | deepe-a-deep-neural-network-for-knowledge | 2211.0462 | null | https://arxiv.org/abs/2211.04620v1 | https://arxiv.org/pdf/2211.04620v1.pdf | DeepE: a deep neural network for knowledge graph embedding | Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow models might suffice for accurately representing the structure of the simple KGs. In this paper, we propose a neural network based model, named DeepE, to address the problem, which stacks multiple building blocks to predict the tail entity based on the head entity and the relation. Each building block is an addition of a linear and a non-linear function. The stacked building blocks are equivalent to a group of learning functions with different non-linear depth. Hence, DeepE allows deep functions to learn deep features, and shallow functions to learn shallow features. Through extensive experiments, we find DeepE outperforms other state-of-the-art baseline methods. A major advantage of DeepE is the robustness. DeepE achieves a Mean Rank (MR) score that is 6%, 30%, 65% lower than the best baseline methods on FB15k-237, WN18RR and YAGO3-10. Our design makes it possible to train much deeper networks on KGE, e.g. 40 layers on FB15k-237, and without scarifying precision on simple relations. | ['Ding Ziqi', 'Yin Chang', 'Huang Shujian', 'Shen Si', 'Zhu Danhao'] | 2022-11-09 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-5.01565754e-01 4.59791929e-01 -4.71084535e-01 -2.59728104e-01
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5aedaa28-9819-492e-b78e-3a42e478f468 | similarity-preserving-adversarial-graph | 2306.13854 | null | https://arxiv.org/abs/2306.13854v1 | https://arxiv.org/pdf/2306.13854v1.pdf | Similarity Preserving Adversarial Graph Contrastive Learning | Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features. Among various GNN models, graph contrastive learning (GCL) based methods specifically suffer from adversarial attacks due to their inherent design that highly depends on the self-supervision signals derived from the original graph, which however already contains noise when the graph is attacked. To achieve adversarial robustness against such attacks, existing methods adopt adversarial training (AT) to the GCL framework, which considers the attacked graph as an augmentation under the GCL framework. However, we find that existing adversarially trained GCL methods achieve robustness at the expense of not being able to preserve the node feature similarity. In this paper, we propose a similarity-preserving adversarial graph contrastive learning (SP-AGCL) framework that contrasts the clean graph with two auxiliary views of different properties (i.e., the node similarity-preserving view and the adversarial view). Extensive experiments demonstrate that SP-AGCL achieves a competitive performance on several downstream tasks, and shows its effectiveness in various scenarios, e.g., a network with adversarial attacks, noisy labels, and heterophilous neighbors. Our code is available at https://github.com/yeonjun-in/torch-SP-AGCL. | ['Chanyoung Park', 'Kanghoon Yoon', 'Yeonjun In'] | 2023-06-24 | null | null | null | null | ['adversarial-robustness', 'contrastive-learning', 'contrastive-learning'] | ['adversarial', 'computer-vision', 'methodology'] | [ 1.27234414e-01 8.70701745e-02 -2.89384872e-02 1.41200155e-01
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bb5104d1-6ad1-4e18-85e9-12cb882f715b | on-deep-speaker-embeddings-for-text | 1804.1008 | null | http://arxiv.org/abs/1804.10080v1 | http://arxiv.org/pdf/1804.10080v1.pdf | On deep speaker embeddings for text-independent speaker recognition | We investigate deep neural network performance in the textindependent speaker
recognition task. We demonstrate that using angular softmax activation at the
last classification layer of a classification neural network instead of a
simple softmax activation allows to train a more generalized discriminative
speaker embedding extractor. Cosine similarity is an effective metric for
speaker verification in this embedding space. We also address the problem of
choosing an architecture for the extractor. We found that deep networks with
residual frame level connections outperform wide but relatively shallow
architectures. This paper also proposes several improvements for previous
DNN-based extractor systems to increase the speaker recognition accuracy. We
show that the discriminatively trained similarity metric learning approach
outperforms the standard LDA-PLDA method as an embedding backend. The results
obtained on Speakers in the Wild and NIST SRE 2016 evaluation sets demonstrate
robustness of the proposed systems when dealing with close to real-life
conditions. | ['Vadim Shchemelinin', 'Sergey Novoselov', 'Andrey Shulipa', 'Alexandr Kozlov', 'Ivan Kremnev'] | 2018-04-26 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 1.01998240e-01 -1.72598228e-01 1.63182020e-01 -9.57312346e-01
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8d461abd-ff95-4c13-9cdf-e2a4fa5cc967 | destseg-segmentation-guided-denoising-student | 2211.11317 | null | https://arxiv.org/abs/2211.11317v2 | https://arxiv.org/pdf/2211.11317v2.pdf | DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection | Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous data, we introduce a denoising procedure that allows the student network to learn more robust representations. From synthetically corrupted normal images, we train the student network to match the teacher network feature of the same images without corruption. Second, to fuse the multi-level S-T features adaptively, we train a segmentation network with rich supervision from synthetic anomaly masks, achieving a substantial performance improvement. Experiments on the industrial inspection benchmark dataset demonstrate that our method achieves state-of-the-art performance, 98.6% on image-level AUC, 75.8% on pixel-level average precision, and 76.4% on instance-level average precision. | ['Ting Chen', 'Jiulong Shan', 'Ping Huang', 'Xi Li', 'Shiyu Li', 'Xuan Zhang'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['one-class-classification'] | ['miscellaneous'] | [ 5.81898689e-01 2.18337670e-01 1.17472634e-01 -5.33429742e-01
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5c7c087c-0ab7-49be-a00b-b7194cf0ca59 | a-probabilistic-logic-based-commonsense | 2211.16822 | null | https://arxiv.org/abs/2211.16822v2 | https://arxiv.org/pdf/2211.16822v2.pdf | A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods | Commonsense knowledge-graphs (CKGs) are important resources towards building machines that can 'reason' on text or environmental inputs and make inferences beyond perception. While current CKGs encode world knowledge for a large number of concepts and have been effectively utilized for incorporating commonsense in neural models, they primarily encode declarative or single-condition inferential knowledge and assume all conceptual beliefs to have the same likelihood. Further, these CKGs utilize a limited set of relations shared across concepts and lack a coherent knowledge organization structure resulting in redundancies as well as sparsity across the larger knowledge graph. Consequently, today's CKGs, while useful for a first level of reasoning, do not adequately capture deeper human-level commonsense inferences which can be more nuanced and influenced by multiple contextual or situational factors. Accordingly, in this work, we study how commonsense knowledge can be better represented by -- (i) utilizing a probabilistic logic representation scheme to model composite inferential knowledge and represent conceptual beliefs with varying likelihoods and (ii) incorporating a hierarchical conceptual ontology to identify salient concept-relevant relations and organize beliefs at different conceptual levels. Our resulting knowledge representation framework can encode a wider variety of world knowledge and represent beliefs flexibly using grounded concepts as well as free-text phrases. As a result, the framework can be utilized as both a traditional free-text knowledge graph and a grounded logic-based inference system more suitable for neuro-symbolic applications. We describe how we extend the PrimeNet knowledge base with our framework through crowd-sourcing and expert-annotation, and demonstrate its application for more interpretable passage-based semantic parsing and question answering. | ['Kenneth Kwok', 'Dongkyu Choi', 'Liu Yan', 'Shantanu Jaiswal'] | 2022-11-30 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 2.25803137e-01 7.04890966e-01 -3.16004723e-01 -4.43001151e-01
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5f2b16d9-f212-4326-8df9-9c9bf3d9486c | black-box-coreset-variational-inference | 2211.02377 | null | https://arxiv.org/abs/2211.02377v2 | https://arxiv.org/pdf/2211.02377v2.pdf | Black-box Coreset Variational Inference | Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either selecting subsets of the observed datapoints, or jointly performing approximate inference and optimizing pseudodata in the observed space akin to inducing points methods in Gaussian Processes. So far, both approaches are limited by complexities in evaluating their objectives for general purpose models, and require generating samples from a typically intractable posterior over the coreset throughout inference and testing. In this work, we present a black-box variational inference framework for coresets that overcomes these constraints and enables principled application of variational coresets to intractable models, such as Bayesian neural networks. We apply our techniques to supervised learning problems, and compare them with existing approaches in the literature for data summarization and inference. | ['Theofanis Karaletsos', 'Hippolyt Ritter', 'Dionysis Manousakas'] | 2022-11-04 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.14186209e-01 3.41932565e-01 -4.42902029e-01 -3.74053955e-01
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b6166c78-1582-4787-88c4-28357caa2d83 | pricure-privacy-preserving-collaborative | 2102.09751 | null | https://arxiv.org/abs/2102.09751v1 | https://arxiv.org/pdf/2102.09751v1.pdf | PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting | When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regulations and lack of trust among collaborating parties. If done in a privacy-preserving manner, predictive analytics can benefit from the collective prediction capability of multiple parties holding complementary datasets on the same machine learning task. This paper presents PRICURE, a system that combines complementary strengths of secure multi-party computation (SMPC) and differential privacy (DP) to enable privacy-preserving collaborative prediction among multiple model owners. SMPC enables secret-sharing of private models and client inputs with non-colluding secure servers to compute predictions without leaking model parameters and inputs. DP masks true prediction results via noisy aggregation so as to deter a semi-honest client who may mount membership inference attacks. We evaluate PRICURE on neural networks across four datasets including benchmark medical image classification datasets. Our results suggest PRICURE guarantees privacy for tens of model owners and clients with acceptable accuracy loss. We also show that DP reduces membership inference attack exposure without hurting accuracy. | ['Birhanu Eshete', 'Ismat Jarin'] | 2021-02-19 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.09374303e-01 4.39138979e-01 6.59865141e-02 -8.09557736e-01
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c2848340-de93-4559-98f8-9183c4cbabd0 | does-local-pruning-offer-task-specific-models | null | null | https://aclanthology.org/2021.ranlp-srw.17 | https://aclanthology.org/2021.ranlp-srw.17.pdf | Does local pruning offer task-specific models to learn effectively ? | The need to deploy large-scale pre-trained models on edge devices under limited computational resources has led to substantial research to compress these large models. However, less attention has been given to compress the task-specific models. In this work, we investigate the different methods of unstructured pruning on task-specific models for Aspect-based Sentiment Analysis (ABSA) tasks. Specifically, we analyze differences in the learning dynamics of pruned models by using the standard pruning techniques to achieve high-performing sparse networks. We develop a hypothesis to demonstrate the effectiveness of local pruning over global pruning considering a simple CNN model. Later, we utilize the hypothesis to demonstrate the efficacy of the pruned state-of-the-art model compared to the over-parameterized state-of-the-art model under two settings, the first considering the baselines for the same task used for generating the hypothesis, i.e., aspect extraction and the second considering a different task, i.e., sentiment analysis. We also provide discussion related to the generalization of the pruning hypothesis. | ['Mohna Chakraborty', 'Abhishek Kumar Mishra'] | null | null | null | null | ranlp-2021-9 | ['aspect-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.22357142e-01 3.98440838e-01 -1.32374376e-01 -3.87050509e-01
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f8e4a838-dfb7-4409-bebf-f07301327f6b | multi-view-vision-prompt-fusion-network-can | 2304.10224 | null | https://arxiv.org/abs/2304.10224v1 | https://arxiv.org/pdf/2304.10224v1.pdf | Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning? | Point cloud based 3D deep model has wide applications in many applications such as autonomous driving, house robot, and so on. Inspired by the recent prompt learning in natural language processing, this work proposes a novel Multi-view Vision-Prompt Fusion Network (MvNet) for few-shot 3D point cloud classification. MvNet investigates the possibility of leveraging the off-the-shelf 2D pre-trained models to achieve the few-shot classification, which can alleviate the over-dependence issue of the existing baseline models towards the large-scale annotated 3D point cloud data. Specifically, MvNet first encodes a 3D point cloud into multi-view image features for a number of different views. Then, a novel multi-view prompt fusion module is developed to effectively fuse information from different views to bridge the gap between 3D point cloud data and 2D pre-trained models. A set of 2D image prompts can then be derived to better describe the suitable prior knowledge for a large-scale pre-trained image model for few-shot 3D point cloud classification. Extensive experiments on ModelNet, ScanObjectNN, and ShapeNet datasets demonstrate that MvNet achieves new state-of-the-art performance for 3D few-shot point cloud image classification. The source code of this work will be available soon. | ['Hongyuan Zhu', 'Tao Chen', 'Xin Chen', 'Bo Zhang', 'Baopu Li', 'Haoyang Peng'] | 2023-04-20 | null | null | null | null | ['3d-point-cloud-classification', 'few-shot-3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.92093188e-01 -2.88271099e-01 -1.50102243e-01 -5.75263917e-01
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89598b92-cce0-4bf5-8b7b-95b28b088cdc | symmetry-as-a-representation-of-intuitive | 2206.02019 | null | https://arxiv.org/abs/2206.02019v1 | https://arxiv.org/pdf/2206.02019v1.pdf | Symmetry as a Representation of Intuitive Geometry? | Recognition of geometrical patterns seems to be an important aspect of human intelligence. Geometric pattern recognition is used in many intelligence tests, including Dehaene's odd-one-out test of Core Geometry (CG)) based on intuitive geometrical concepts (Dehaene et al., 2006). Earlier work has developed a symmetry-based cognitive model of Dehaene's test and demonstrated performance comparable to that of humans. In this work, we further investigate the role of symmetry in geometrical intuition and build a cognitive model for the 2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma 2021). In contrast to Dehaene's test, 2-AFC leaves almost no space for cognitive models based on generalization over multiple examples. Our symmetry-based model achieves an accuracy comparable to the human average on the 2-AFC test and appears to capture an essential part of intuitive geometry. | ['Ashok Goel', 'Snejana Shegheva', 'Wangcheng Xu'] | 2022-06-04 | null | null | null | null | ['odd-one-out'] | ['reasoning'] | [-3.34071010e-01 4.01577264e-01 4.65542346e-01 -4.26840305e-01
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b0db9676-5177-4386-ba6b-05e886519b32 | explaining-deep-neural-networks-for-point | 2207.12984 | null | https://arxiv.org/abs/2207.12984v1 | https://arxiv.org/pdf/2207.12984v1.pdf | Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations | Explaining decisions made by deep neural networks is a rapidly advancing research topic. In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data. In this paper, we propose a novel approach to generate coarse visual explanations of networks designed to classify unstructured 3D data, namely point clouds. Our method uses gradients flowing back to the final feature map layers and maps these values as contributions of the corresponding points in the input point cloud. Due to dimensionality disagreement and lack of spatial consistency between input points and final feature maps, our approach combines gradients with points dropping to compute explanations of different parts of the point cloud iteratively. The generality of our approach is tested on various point cloud classification networks, including 'single object' networks PointNet, PointNet++, DGCNN, and a 'scene' network VoteNet. Our method generates symmetric explanation maps that highlight important regions and provide insight into the decision-making process of network architectures. We perform an exhaustive evaluation of trust and interpretability of our explanation method against comparative approaches using quantitative, quantitative and human studies. All our code is implemented in PyTorch and will be made publicly available. | ['Nicolas Schönborn', 'Muhammad Sarmad', 'Jawad Tayyub'] | 2022-07-26 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.27464935e-01 7.61492431e-01 3.13235447e-02 -7.64875591e-01
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c5a7a8cb-b5ef-4917-82f5-96c4700b884f | mockingbird-defending-against-deep-learning | 1902.06626 | null | https://arxiv.org/abs/1902.06626v3 | https://arxiv.org/pdf/1902.06626v3.pdf | Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces | Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity even when the traffic is protected by encryption, a VPN, or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. Existing WF defenses are either very expensive in terms of bandwidth and latency overheads (e.g. two-to-three times as large or slow) or ineffective against the latest attacks. In this paper, we explore a novel defense, Mockingbird, based on the idea of adversarial examples that have been shown to undermine machine-learning classifiers in other domains. Since the attacker gets to design his classifier based on the defense design, we first demonstrate that at least one technique for generating adversarial-example based traces fails to protect against an attacker using adversarial training for robust classification. We then propose Mockingbird, a technique for generating traces that resists adversarial training by moving randomly in the space of viable traces and not following more predictable gradients. The technique drops the accuracy of the state-of-the-art attack hardened with adversarial training from 98% to as low as 29% while incurring only 56% bandwidth overhead. The attack accuracy is generally lower than state-of-the-art defenses, and much lower when considering Top-2 accuracy, while incurring lower overheads in most settings. | ['Matthew Wright', 'Nate Mathews', 'Mohammad Saidur Rahman', 'Mohsen Imani'] | 2019-02-18 | null | null | null | null | ['website-fingerprinting-defense'] | ['adversarial'] | [ 1.30657151e-01 -4.54617739e-02 -3.01766038e-01 -1.39268180e-02
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c9abe1b0-5f09-409e-bc60-048536ad63a1 | reflection-removal-using-ghosting-cues | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Shih_Reflection_Removal_Using_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shih_Reflection_Removal_Using_2015_CVPR_paper.pdf | Reflection Removal Using Ghosting Cues | Photographs taken through glass windows often contain both the desired scene and undesired reflections. Separating the reflection and transmission layers is an important but ill-posed problem that has both aesthetic and practical applications. In this work, we introduce the use of ghosting cues that introduce asymmetry between the layers, thereby helping to significantly reduce the ill-posedness of the problem. Such cues arise from shifted double reflections of the reflected scene off the glass surface. In double-pane windows, each pane reflects shifted and attenuated versions of objects on the same side of the glass as the camera. For single-pane windows, ghosting cues arise from shifted reflections on the two surfaces of the glass pane. Even though the ghosting is sometimes barely perceptible by humans, we can still exploit the cue for layer separation. In this work, we model the ghosted reflection using a double-impulse convolution kernel, and automatically estimate the spatial separation and relative attenuation of the ghosted reflection components. To separate the layers, we propose an algorithm that uses a Gaussian Mixture Model for regularization. Our method is automatic and requires only a single input image. We demonstrate that our approach removes a large fraction of reflections on both synthetic and real-world inputs. | ['William T. Freeman', 'Fredo Durand', 'YiChang Shih', 'Dilip Krishnan'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['reflection-removal'] | ['computer-vision'] | [ 8.63912046e-01 1.28670752e-01 7.04143524e-01 -1.00837238e-02
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2004b293-5cea-46cd-8a48-f06fcfc77a94 | l2-regularization-versus-batch-and-weight | 1706.0535 | null | http://arxiv.org/abs/1706.05350v1 | http://arxiv.org/pdf/1706.05350v1.pdf | L2 Regularization versus Batch and Weight Normalization | Batch Normalization is a commonly used trick to improve the training of deep
neural networks. These neural networks use L2 regularization, also called
weight decay, ostensibly to prevent overfitting. However, we show that L2
regularization has no regularizing effect when combined with normalization.
Instead, regularization has an influence on the scale of weights, and thereby
on the effective learning rate. We investigate this dependence, both in theory,
and experimentally. We show that popular optimization methods such as ADAM only
partially eliminate the influence of normalization on the learning rate. This
leads to a discussion on other ways to mitigate this issue. | ['Twan van Laarhoven'] | 2017-06-16 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.49074584e-01 1.99745357e-01 -2.64696509e-01 -4.00775671e-01
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1f51a567-771b-4b62-9871-164b3abd1728 | uima-ruta-workbench-rule-based-text | null | null | https://aclanthology.org/C14-2007 | https://aclanthology.org/C14-2007.pdf | UIMA Ruta Workbench: Rule-based Text Annotation | null | ['Frank Puppe', 'Philip-Daniel Beck', 'Martin Toepfer', 'Georg Fette', 'Peter Kluegl'] | 2014-08-01 | uima-ruta-workbench-rule-based-text-1 | https://aclanthology.org/C14-2007 | https://aclanthology.org/C14-2007.pdf | coling-2014-8 | ['text-annotation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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3f763683-5272-4f5c-9e4b-c4c34da99286 | tutorial-on-agent-based-models-in-netlogo | 1808.09499 | null | http://arxiv.org/abs/1808.09499v1 | http://arxiv.org/pdf/1808.09499v1.pdf | Tutorial on agent-based models in NetLogo applied to immunology and virology | This tutorial introduces participants to the design and implementation of an
agent-based model using NetLogo through one of two different projects:
modelling T cell movement within a lymph node or modelling the progress of a
viral infection in an in vitro cell culture monolayer. Each project is broken
into a series of incremental steps of increasing complexity. Each step is
described in detail and the code to type in is initially provided. However,
each project has room to grow in complexity and biological realism so
participants are encouraged to expand their project beyond the scope of the
tutorial or to develop a project of their own. | [] | 2018-08-28 | null | null | null | null | ['virology'] | ['miscellaneous'] | [-2.41577595e-01 3.00499558e-01 -1.42237991e-01 -2.77743824e-02
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b3fe13c5-9ef6-4cfe-a635-fb6fa74da38a | end-to-end-text-dependent-speaker | 1509.08062 | null | http://arxiv.org/abs/1509.08062v1 | http://arxiv.org/pdf/1509.08062v1.pdf | End-to-End Text-Dependent Speaker Verification | In this paper we present a data-driven, integrated approach to speaker
verification, which maps a test utterance and a few reference utterances
directly to a single score for verification and jointly optimizes the system's
components using the same evaluation protocol and metric as at test time. Such
an approach will result in simple and efficient systems, requiring little
domain-specific knowledge and making few model assumptions. We implement the
idea by formulating the problem as a single neural network architecture,
including the estimation of a speaker model on only a few utterances, and
evaluate it on our internal "Ok Google" benchmark for text-dependent speaker
verification. The proposed approach appears to be very effective for big data
applications like ours that require highly accurate, easy-to-maintain systems
with a small footprint. | ['Samy Bengio', 'Noam Shazeer', 'Ignacio Moreno', 'Georg Heigold'] | 2015-09-27 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [-1.94006283e-02 1.20869465e-01 8.34672749e-02 -1.05245638e+00
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43042317-f3f3-46d8-9674-7657b99f1a09 | finalmlp-an-enhanced-two-stream-mlp-model-for-1 | 2304.00902 | null | https://arxiv.org/abs/2304.00902v3 | https://arxiv.org/pdf/2304.00902v3.pdf | FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction | Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature gating and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models. | ['Zhenhua Dong', 'Yuru Li', 'Guohao Cai', 'Liangcai Su', 'Jieming Zhu', 'Kelong Mao'] | 2023-04-03 | finalmlp-an-enhanced-two-stream-mlp-model-for | https://arxiv.org/abs/2304.00902 | https://arxiv.org/pdf/2304.00902 | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 1.04166307e-01 -2.31318176e-01 -6.40039980e-01 -6.07118428e-01
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4fc18b03-a61d-44b0-a63d-56c39fec8cc6 | latent-aspect-detection-from-online | 2204.06964 | null | https://arxiv.org/abs/2204.06964v1 | https://arxiv.org/pdf/2204.06964v1.pdf | Latent Aspect Detection from Online Unsolicited Customer Reviews | Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs, and hence, maintain revenues and mitigate customer churn. Existing methods focus on detecting the surface form of an aspect by training supervised learning methods that fall short when aspects are latent in reviews. In this paper, we propose an unsupervised method to extract latent occurrences of aspects. Specifically, we assume that a customer undergoes a two-stage hypothetical generative process when writing a review: (1) deciding on an aspect amongst the set of aspects available for the product or service, and (2) writing the opinion words that are more interrelated to the chosen aspect from the set of all words available in a language. We employ latent Dirichlet allocation to learn the latent aspects distributions for generating the reviews. Experimental results on benchmark datasets show that our proposed method is able to improve the state of the art when the aspects are latent with no surface form in reviews. | ['Hossein Fani', 'Arash Mansouri', 'Mohammad Forouhesh'] | 2022-04-14 | null | null | null | null | ['latent-aspect-detection', 'hidden-aspect-detection', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.84834117e-01 2.31935784e-01 -8.16412032e-01 -6.27689600e-01
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dc567886-53d4-4306-82fb-e18bdd995c10 | adapt-vision-language-navigation-with | 2205.15509 | null | https://arxiv.org/abs/2205.15509v1 | https://arxiv.org/pdf/2205.15509v1.pdf | ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts | Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi-modal inputs. In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT), which provides the VLN agent with action prompts to enable the explicit learning of action-level modality alignment to pursue successful navigation. Specifically, an action prompt is defined as a modality-aligned pair of an image sub-prompt and a text sub-prompt, where the former is a single-view observation and the latter is a phrase like ''walk past the chair''. When starting navigation, the instruction-related action prompt set is retrieved from a pre-built action prompt base and passed through a prompt encoder to obtain the prompt feature. Then the prompt feature is concatenated with the original instruction feature and fed to a multi-layer transformer for action prediction. To collect high-quality action prompts into the prompt base, we use the Contrastive Language-Image Pretraining (CLIP) model which has powerful cross-modality alignment ability. A modality alignment loss and a sequential consistency loss are further introduced to enhance the alignment of the action prompt and enforce the agent to focus on the related prompt sequentially. Experimental results on both R2R and RxR show the superiority of ADAPT over state-of-the-art methods. | ['Xiaodan Liang', 'Jianzhuang Liu', 'Xiwen Liang', 'Zicong Chen', 'Yi Zhu', 'Bingqian Lin'] | 2022-05-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.pdf | cvpr-2022-1 | ['vision-language-navigation'] | ['computer-vision'] | [ 4.70301718e-01 -1.21179529e-01 -4.61841971e-01 -5.02229929e-01
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2b4f897d-ad0b-4d72-8f3f-1ce1a292681c | subdivision-based-mesh-convolution-networks | 2106.02285 | null | https://arxiv.org/abs/2106.02285v2 | https://arxiv.org/pdf/2106.02285v2.pdf | Subdivision-Based Mesh Convolution Networks | Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure, in which each face in a closed 2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these two observations, this paper presents SubdivNet, an innovative and versatile CNN framework for 3D triangle meshes with Loop subdivision sequence connectivity. Making an analogy between mesh faces and pixels in a 2D image allows us to present a mesh convolution operator to aggregate local features from nearby faces. By exploiting face neighborhoods, this convolution can support standard 2D convolutional network concepts, e.g. variable kernel size, stride, and dilation. Based on the multi-resolution hierarchy, we make use of pooling layers which uniformly merge four faces into one and an upsampling method which splits one face into four. Thereby, many popular 2D CNN architectures can be easily adapted to process 3D meshes. Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach. Extensive evaluation and various applications demonstrate SubdivNet's effectiveness and efficiency. | ['Ralph R. Martin', 'Tai-Jiang Mu', 'Jiahui Huang', 'Jun-Xiong Cai', 'Meng-Hao Guo', 'Zheng-Ning Liu', 'Shi-Min Hu'] | 2021-06-04 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-4.37008850e-02 1.97601527e-01 -4.99956161e-02 -4.80150729e-02
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11ad861c-a4cb-4605-aab1-ab1273000c9b | let-s-do-a-thought-experiment-using | 2306.14308 | null | https://arxiv.org/abs/2306.14308v1 | https://arxiv.org/pdf/2306.14308v1.pdf | Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning | Language models still struggle on moral reasoning, despite their impressive performance in many other tasks. In particular, the Moral Scenarios task in MMLU (Multi-task Language Understanding) is among the worst performing tasks for many language models, including GPT-3. In this work, we propose a new prompting framework, Thought Experiments, to teach language models to do better moral reasoning using counterfactuals. Experiment results show that our framework elicits counterfactual questions and answers from the model, which in turn helps improve the accuracy on Moral Scenarios task by 9-16% compared to other zero-shot baselines. Interestingly, unlike math reasoning tasks, zero-shot Chain-of-Thought (CoT) reasoning doesn't work out of the box, and even reduces accuracy by around 4% compared to direct zero-shot. We further observed that with minimal human supervision in the form of 5 few-shot examples, the accuracy of the task can be improved to as much as 80%. | ['Jilin Chen', 'Alex Beutel', 'Ahmad Beirami', 'Swaroop Mishra', 'Xiao Ma'] | 2023-06-25 | null | null | null | null | ['multi-task-language-understanding', 'moral-scenarios'] | ['methodology', 'miscellaneous'] | [-1.16228787e-02 1.02701521e+00 -1.63436517e-01 -3.38398188e-01
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3e225fb4-67f6-4af7-9788-0cc1dbf533f2 | novel-fuzzy-approach-to-antimicrobial-peptide | null | null | https://openreview.net/forum?id=x0tzOYvapDl | https://openreview.net/pdf?id=x0tzOYvapDl | Novel fuzzy approach to Antimicrobial Peptide Activity Prediction: A tale of limited and imbalanced data that models won’t hear | Antimicrobial peptides have gained immense attention in recent years due to their potential for developing novel antibacterial medicines, next-generation anti-cancer treatment regimes, etc. Owing to the significant cost and time required for wet lab-based AMP screening, researchers have framed the task as an ML problem. However, traditional models rely on the unrealistic premise of large medical data availability to achieve significant performance levels; otherwise, they overfit, decreasing model precision. The collection of such labeled medical data is a challenging and expensive task in itself. The current study is the first to examine models in a real-world setting, training them on restricted and highly imbalanced data. A Fuzzy Intelligence based model is proposed for short (<30 aa) AMP activity prediction, and its ability to learn on limited and severely skewed high-dimensional space mapping is demonstrated over a set of experiments. The proposed model significantly outperforms state-of-the-art ML models trained on the same data. The findings demonstrate the model's efficacy as a potential method for in silico AMP activity prediction. | ['Vinay Kumar', 'Rahul Upadhyay', 'Aviral Chharia'] | 2021-09-24 | null | null | null | neurips-workshop-ai4scien-2021-12 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 7.60009885e-01 -3.31113994e-01 -3.59585971e-01 -2.60367453e-01
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217ae64c-47a1-47ac-9a27-2fd58c4cdb38 | offline-pre-trained-multi-agent-decision-1 | 2112.02845 | null | https://arxiv.org/abs/2112.02845v3 | https://arxiv.org/pdf/2112.02845v3.pdf | Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks | Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the increased interactions among agents and with the enviroment. Yet, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL. We investigate the generalisation of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to the online fine-tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages transformer's modelling ability of sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A crucial benefit of MADT is that it learns generalisable policies that can transfer between different types of agents under different task scenarios. On StarCraft II offline dataset, MADT outperforms the state-of-the-art offline RL baselines. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency, and enjoys strong performance both few-short and zero-shot cases. To our best knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalisability enhancements in MARL. | ['Bo Xu', 'Jun Wang', 'Haifeng Zhang', 'Ying Wen', 'Weinan Zhang', 'Xiyun Li', 'Chenyang Le', 'Yaodong Yang', 'Muning Wen', 'Linghui Meng'] | 2021-12-06 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.07521096e-01 -2.16576532e-01 -2.90923983e-01 1.85956299e-01
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b91d2af0-acbf-4d36-8387-0a8f6a213786 | pose-robust-face-recognition-via-deep | 1803.00839 | null | http://arxiv.org/abs/1803.00839v1 | http://arxiv.org/pdf/1803.00839v1.pdf | Pose-Robust Face Recognition via Deep Residual Equivariant Mapping | Face recognition achieves exceptional success thanks to the emergence of deep
learning. However, many contemporary face recognition models still perform
relatively poor in processing profile faces compared to frontal faces. A key
reason is that the number of frontal and profile training faces are highly
imbalanced - there are extensively more frontal training samples compared to
profile ones. In addition, it is intrinsically hard to learn a deep
representation that is geometrically invariant to large pose variations. In
this study, we hypothesize that there is an inherent mapping between frontal
and profile faces, and consequently, their discrepancy in the deep
representation space can be bridged by an equivariant mapping. To exploit this
mapping, we formulate a novel Deep Residual EquivAriant Mapping (DREAM) block,
which is capable of adaptively adding residuals to the input deep
representation to transform a profile face representation to a canonical pose
that simplifies recognition. The DREAM block consistently enhances the
performance of profile face recognition for many strong deep networks,
including ResNet models, without deliberately augmenting training data of
profile faces. The block is easy to use, light-weight, and can be implemented
with a negligible computational overhead. | ['Kaidi Cao', 'Yu Rong', 'Chen Change Loy', 'Xiaoou Tang', 'Cheng Li'] | 2018-03-02 | pose-robust-face-recognition-via-deep-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Cao_Pose-Robust_Face_Recognition_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Pose-Robust_Face_Recognition_CVPR_2018_paper.pdf | cvpr-2018-6 | ['robust-face-recognition'] | ['computer-vision'] | [ 2.75663853e-01 8.76833051e-02 -1.20825227e-02 -6.98722422e-01
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717b81fc-c3c3-491c-b5c2-7eaa3819849e | venn-diagram-multi-label-class-interpretation | 2305.01044 | null | https://arxiv.org/abs/2305.01044v2 | https://arxiv.org/pdf/2305.01044v2.pdf | Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer with Color and Sharpness Enhancement | DFU is a severe complication of diabetes that can lead to amputation of the lower limb if not treated properly. Inspired by the 2021 Diabetic Foot Ulcer Grand Challenge, researchers designed automated multi-class classification of DFU, including infection, ischaemia, both of these conditions, and none of these conditions. However, it remains a challenge as classification accuracy is still not satisfactory. This paper proposes a Venn Diagram interpretation of multi-label CNN-based method, utilizing different image enhancement strategies, to improve the multi-class DFU classification. We propose to reduce the four classes into two since both class wounds can be interpreted as the simultaneous occurrence of infection and ischaemia and none class wounds as the absence of infection and ischaemia. We introduce a novel Venn Diagram representation block in the classifier to interpret all four classes from these two classes. To make our model more resilient, we propose enhancing the perceptual quality of DFU images, particularly blurry or inconsistently lit DFU images, by performing color and sharpness enhancements on them. We also employ a fine-tuned optimization technique, adaptive sharpness aware minimization, to improve the CNN model generalization performance. The proposed method is evaluated on the test dataset of DFUC2021, containing 5,734 images and the results are compared with the top-3 winning entries of DFUC2021. Our proposed approach outperforms these existing approaches and achieves Macro-Average F1, Recall and Precision scores of 0.6592, 0.6593, and 0.6652, respectively.Additionally, We perform ablation studies and image quality measurements to further interpret our proposed method. This proposed method will benefit patients with DFUs since it tackles the inconsistencies in captured images and can be employed for a more robust remote DFU wound classification. | ['Md Kamrul Hasan', 'Moi Hoon Yap', 'Md Mahamudul Hasan'] | 2023-05-01 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 5.11137486e-01 -3.73039931e-01 -2.16784239e-01 -7.21534342e-02
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19a2473d-7114-4a46-93f7-cef1deab0972 | dadin-domain-adversarial-deep-interest | 2305.12058 | null | https://arxiv.org/abs/2305.12058v1 | https://arxiv.org/pdf/2305.12058v1.pdf | DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems | Click-Through Rate (CTR) prediction is one of the main tasks of the recommendation system, which is conducted by a user for different items to give the recommendation results. Cross-domain CTR prediction models have been proposed to overcome problems of data sparsity, long tail distribution of user-item interactions, and cold start of items or users. In order to make knowledge transfer from source domain to target domain more smoothly, an innovative deep learning cross-domain CTR prediction model, Domain Adversarial Deep Interest Network (DADIN) is proposed to convert the cross-domain recommendation task into a domain adaptation problem. The joint distribution alignment of two domains is innovatively realized by introducing domain agnostic layers and specially designed loss, and optimized together with CTR prediction loss in a way of adversarial training. It is found that the Area Under Curve (AUC) of DADIN is 0.08% higher than the most competitive baseline on Huawei dataset and is 0.71% higher than its competitors on Amazon dataset, achieving the state-of-the-art results on the basis of the evaluation of this model performance on two real datasets. The ablation study shows that by introducing adversarial method, this model has respectively led to the AUC improvements of 2.34% on Huawei dataset and 16.67% on Amazon dataset. | ['Yinghao Chen', 'Ri Su', 'Feng Liu', 'Shaojie Zhao', 'Muzhou Hou', 'Menglin Kong'] | 2023-05-20 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-2.24549398e-01 -4.58363295e-01 -3.82092804e-01 -3.62589329e-01
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ce187a49-685a-4148-8a30-9830716dcc24 | towards-robust-neural-retrieval-with-source | null | null | https://aclanthology.org/2022.coling-1.89 | https://aclanthology.org/2022.coling-1.89.pdf | Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning | Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation. | ['Avirup Sil', 'Heng Ji', 'Vittorio Castelli', 'Martin Franz', 'Md Arafat Sultan', 'Vikas Yadav', 'Revanth Gangi Reddy'] | null | null | null | null | coling-2022-10 | ['passage-retrieval'] | ['natural-language-processing'] | [ 4.30209666e-01 -1.71562448e-01 -2.93806404e-01 -2.54320651e-01
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a28a0f20-8aa4-472c-bc4e-8e13f88d198a | ferv39k-a-large-scale-multi-scene-dataset-for | 2203.09463 | null | https://arxiv.org/abs/2203.09463v2 | https://arxiv.org/pdf/2203.09463v2.pdf | FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos | Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the "Happy" expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event. To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the well-designed workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frameworks were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project will be available. | ['Wenqiang Zhang', 'Weifeng Ge', 'Wei zhang', 'Shuyong Gao', 'Zhongying Liu', 'Yiwen Huang', 'Yixuan Sun', 'Yan Wang'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.pdf | cvpr-2022-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.75771528e-01 -4.24786210e-01 -6.06887303e-02 -8.76365125e-01
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5a160376-0746-4874-9b6a-47fc23f92b4a | ukp-square-v2-explainability-and-adversarial | 2208.09316 | null | https://arxiv.org/abs/2208.09316v3 | https://arxiv.org/pdf/2208.09316v3.pdf | UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA | Question Answering (QA) systems are increasingly deployed in applications where they support real-world decisions. However, state-of-the-art models rely on deep neural networks, which are difficult to interpret by humans. Inherently interpretable models or post hoc explainability methods can help users to comprehend how a model arrives at its prediction and, if successful, increase their trust in the system. Furthermore, researchers can leverage these insights to develop new methods that are more accurate and less biased. In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations. While saliency maps are useful to inspect the importance of each input token for the model's prediction, graph-based explanations from external Knowledge Graphs enable the users to verify the reasoning behind the model prediction. In addition, we provide multiple adversarial attacks to compare the robustness of QA models. With these explainability methods and adversarial attacks, we aim to ease the research on trustworthy QA models. SQuARE is available on https://square.ukp-lab.de. | ['Leonardo F. R. Ribeiro', 'Sewin Tariverdian', 'Tim Baumgärtner', 'Haritz Puerto', 'Iryna Gurevych', 'Hossain Shaikh Saadi', 'Kexin Wang', 'Hao Zhang', 'Rachneet Sachdeva'] | 2022-08-19 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 9.41063836e-02 8.47310185e-01 -8.28407332e-02 -6.25648975e-01
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1250641d-2a9e-487d-be47-c5cd74ddb9d6 | decour-a-corpus-of-deceptive-statements-in | null | null | https://aclanthology.org/L12-1188 | https://aclanthology.org/L12-1188.pdf | DeCour: a corpus of DEceptive statements in Italian COURts | In criminal proceedings, sometimes it is not easy to evaluate the sincerity of oral testimonies. DECOUR - DEception in COURt corpus - has been built with the aim of training models suitable to discriminate, from a stylometric point of view, between sincere and deceptive statements. DECOUR is a collection of hearings held in four Italian Courts, in which the speakers lie in front of the judge. These hearings become the object of a specific criminal proceeding for calumny or false testimony, in which the deceptiveness of the statements of the defendant is ascertained. Thanks to the final Court judgment, that points out which lies are told, each utterance of the corpus has been annotated as true, uncertain or false, according to its degree of truthfulness. Since the judgment of deceptiveness follows a judicial inquiry, the annotation has been realized with a greater degree of confidence than ever before. Moreover, in Italy this is the first corpus of deceptive texts not relying on ‘mock' lies created in laboratory conditions, but which has been collected in a natural environment. | ['Massimo Poesio', 'Tommaso Fornaciari'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['deception-detection'] | ['miscellaneous'] | [ 1.75273582e-01 3.57479483e-01 1.61175981e-01 -4.12626475e-01
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a574e221-6417-4059-bea9-6cf39c54ffb9 | keycld-learning-constrained-lagrangian | 2206.1103 | null | https://arxiv.org/abs/2206.11030v1 | https://arxiv.org/pdf/2206.11030v1.pdf | KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images | We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. Interpreting this state as Cartesian coordinates coupled with explicit holonomic constraints, allows expressing the dynamics with a constrained Lagrangian. Our method explicitly models kinetic and potential energy, thus allowing energy based control. We are the first to demonstrate learning of Lagrangian dynamics from images on the dm_control pendulum, cartpole and acrobot environments. This is a step forward towards learning Lagrangian dynamics from real-world images, since previous work in literature was only applied to minimalistic images with monochromatic shapes on empty backgrounds. Please refer to our project page for code and additional results: https://rdaems.github.io/keycld/ | ['Guillaume Crevecoeur', 'Francis wyffels', 'Jeroen Taets', 'Rembert Daems'] | 2022-06-22 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-5.23639321e-01 -5.52244000e-02 -2.61592060e-01 9.45282802e-02
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923516d0-d176-4548-84a0-2c2297c6fb3a | banglahatebert-bert-for-abusive-language | null | null | https://aclanthology.org/2022.restup-1.2 | https://aclanthology.org/2022.restup-1.2.pdf | BanglaHateBERT: BERT for Abusive Language Detection in Bengali | This paper introduces BanglaHateBERT, a retrained BERT model for abusive language detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive, and hateful corpus that we have collected from different sources and made available to the public. Furthermore, we have collected and manually annotated 15K Bengali hate speech balanced dataset and made it publicly available for the research community. We used existing pre-trained BanglaBERT model and retrained it with 1.5 million offensive posts. We presented the results of a detailed comparison between generic pre-trained language model and retrained with the abuse-inclined version. In all datasets, BanglaHateBERT outperformed the corresponding available BERT model. | ['Mourad Oussalah', 'Nabil Arhab', 'Mainul Haque', 'Md Saroar Jahan'] | null | null | null | null | restup-lrec-2022-6 | ['abusive-language'] | ['natural-language-processing'] | [-3.50084484e-01 -7.93855563e-02 5.02137095e-02 -2.58175969e-01
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bf941f6e-9dfa-4334-a277-113d739e3e77 | paracolorizer-realistic-image-colorization | 2208.08295 | null | https://arxiv.org/abs/2208.08295v1 | https://arxiv.org/pdf/2208.08295v1.pdf | ParaColorizer: Realistic Image Colorization using Parallel Generative Networks | Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learning-based methods currently in use produce acceptable results for straightforward cases but usually fail to restore the contextual information in the absence of clear figure-ground separation. Also, the images suffer from color bleeding and desaturated backgrounds since a single model trained on full image features is insufficient for learning the diverse data modes. To address these issues, we present a parallel GAN-based colorization framework. In our approach, each separately tailored GAN pipeline colorizes the foreground (using object-level features) or the background (using full-image features). The foreground pipeline employs a Residual-UNet with self-attention as its generator trained using the full-image features and the corresponding object-level features from the COCO dataset. The background pipeline relies on full-image features and additional training examples from the Places dataset. We design a DenseFuse-based fusion network to obtain the final colorized image by feature-based fusion of the parallelly generated outputs. We show the shortcomings of the non-perceptual evaluation metrics commonly used to assess multi-modal problems like image colorization and perform extensive performance evaluation of our framework using multiple perceptual metrics. Our approach outperforms most of the existing learning-based methods and produces results comparable to the state-of-the-art. Further, we performed a runtime analysis and obtained an average inference time of 24ms per image. | ['Sanjay Singh', 'Sumeet Saurav', 'Abeer Banerjee', 'Himanshu Kumar'] | 2022-08-17 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 6.70238435e-01 -1.90481409e-01 2.59943247e-01 -1.96186870e-01
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300a2bb1-d905-4f47-8739-95b6701e81d4 | qmix-monotonic-value-function-factorisation | 1803.11485 | null | http://arxiv.org/abs/1803.11485v2 | http://arxiv.org/pdf/1803.11485v2.pdf | QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning | In many real-world settings, a team of agents must coordinate their behaviour
while acting in a decentralised way. At the same time, it is often possible to
train the agents in a centralised fashion in a simulated or laboratory setting,
where global state information is available and communication constraints are
lifted. Learning joint action-values conditioned on extra state information is
an attractive way to exploit centralised learning, but the best strategy for
then extracting decentralised policies is unclear. Our solution is QMIX, a
novel value-based method that can train decentralised policies in a centralised
end-to-end fashion. QMIX employs a network that estimates joint action-values
as a complex non-linear combination of per-agent values that condition only on
local observations. We structurally enforce that the joint-action value is
monotonic in the per-agent values, which allows tractable maximisation of the
joint action-value in off-policy learning, and guarantees consistency between
the centralised and decentralised policies. We evaluate QMIX on a challenging
set of StarCraft II micromanagement tasks, and show that QMIX significantly
outperforms existing value-based multi-agent reinforcement learning methods. | ['Mikayel Samvelyan', 'Shimon Whiteson', 'Jakob Foerster', 'Tabish Rashid', 'Gregory Farquhar', 'Christian Schroeder de Witt'] | 2018-03-30 | qmix-monotonic-value-function-factorisation-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2389 | http://proceedings.mlr.press/v80/rashid18a/rashid18a.pdf | icml-2018-7 | ['smac-1'] | ['playing-games'] | [-3.35715741e-01 2.62894064e-01 -4.59349573e-01 -1.89715419e-02
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fb33eb13-b7f3-468a-ab0d-9bd33dab86f9 | daccord-un-jeu-de-donnees-pour-la-detection | null | null | http://talnarchives.atala.org/TALN/TALN-2023/459882.html | http://talnarchives.atala.org/TALN/TALN-2023/459882.pdf | DACCORD : un jeu de données pour la Détection Automatique d'énonCés COntRaDictoires en français | La tâche de détection automatique de contradictions logiques entre énoncés en TALN est une tâche de classification binaire, où chaque paire de phrases reçoit une étiquette selon que les deux phrases se contredisent ou non. Elle peut être utilisée afin de lutter contre la désinformation. Dans cet article, nous présentons DACCORD, un jeu de données dédié à la tâche de détection automatique de contradictions entre phrases en français. Le jeu de données élaboré est actuellement composé de 1034 paires de phrases. Il couvre les thématiques de l'invasion de la Russie en Ukraine en 2022, de la pandémie de Covid-19 et de la crise climatique. Pour mettre en avant les possibilités de notre jeu de données, nous évaluons les performances de certains modèles de transformeurs sur lui. Nous constatons qu'il constitue pour eux un défi plus élevé que les jeux de données existants pour le français, qui sont déjà peu nombreux.
In NLP, the automatic detection of logical contradictions between statements is a binary classification task, in which a pair of sentences receives a label according to whether or not the two sentences contradict each other. This task has many potential applications, including combating disinformation. In this article, we present DACCORD, a new dataset dedicated to the task of automatically detecting contradictions between sentences in French. The dataset is currently composed of 1034 sentence pairs. It covers the themes of Russia's invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. To highlight the possibilities of our dataset, we evaluate the performance of some recent Transformer models on it. We conclude that our dataset is considerably more challenging than the few existing datasets for French. | ['Simon Robillard', 'Richard Moot', 'Maximos Skandalis'] | 2023-06-08 | null | null | null | actes-de-18e-conference-en-recherche-d | ['sentence-pair-classification'] | ['natural-language-processing'] | [-1.40026540e-01 -2.09278494e-01 1.44577399e-01 -3.89322817e-01
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f3f06fff-bcc4-4333-bb94-3dc70baf2239 | backdoor-attacks-against-incremental-learners | 2305.18384 | null | https://arxiv.org/abs/2305.18384v1 | https://arxiv.org/pdf/2305.18384v1.pdf | Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study | Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. However, the adversarial robustness of incremental learners has not been widely verified, leaving potential security risks. Specifically, for poisoning-based backdoor attacks, we argue that the nature of streaming data in IL provides great convenience to the adversary by creating the possibility of distributed and cross-task attacks -- an adversary can affect \textbf{any unknown} previous or subsequent task by data poisoning \textbf{at any time or time series} with extremely small amount of backdoor samples injected (e.g., $0.1\%$ based on our observations). To attract the attention of the research community, in this paper, we empirically reveal the high vulnerability of 11 typical incremental learners against poisoning-based backdoor attack on 3 learning scenarios, especially the cross-task generalization effect of backdoor knowledge, while the poison ratios range from $5\%$ to as low as $0.1\%$. Finally, the defense mechanism based on activation clustering is found to be effective in detecting our trigger pattern to mitigate potential security risks. | ['Xiangyang Ji', 'Junjun Jiang', 'Deming Zhai', 'Xianming Liu', 'Yiqi Zhong'] | 2023-05-28 | null | null | null | null | ['data-poisoning', 'backdoor-attack', 'adversarial-robustness', 'incremental-learning'] | ['adversarial', 'adversarial', 'adversarial', 'methodology'] | [-3.28271389e-02 -1.14932559e-01 -8.98169577e-02 3.06345582e-01
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457b89d5-a2a5-47aa-a762-2378f20e9ee6 | customized-attention-mechanism-for-relation | null | null | https://aclanthology.org/Y18-1067 | https://aclanthology.org/Y18-1067.pdf | Customized Attention Mechanism for Relation Classification | null | ['Hongyin Zan', 'Lijuan Zhou', 'Yang Wen', 'Shirong Shen'] | null | null | null | null | paclic-2018-12 | ['relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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b8ada2a3-f6fe-48aa-9bc7-ac945f64ae56 | learning-logic-rules-for-document-level-1 | 2111.05407 | null | https://arxiv.org/abs/2111.05407v1 | https://arxiv.org/pdf/2111.05407v1.pdf | Learning Logic Rules for Document-level Relation Extraction | Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor. The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules. Those two modules can be efficiently optimized with the expectation-maximization (EM) algorithm. By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation. Empirical results show that LogiRE significantly outperforms several strong baselines in terms of relation performance (1.8 F1 score) and logical consistency (over 3.3 logic score). Our code is available at https://github.com/rudongyu/LogiRE. | ['Lei LI', 'Yong Yu', 'Weinan Zhang', 'Hao Zhou', 'Lin Qiu', 'Jiangtao Feng', 'Changzhi Sun', 'Dongyu Ru'] | 2021-11-09 | learning-logic-rules-for-document-level | https://aclanthology.org/2021.emnlp-main.95 | https://aclanthology.org/2021.emnlp-main.95.pdf | emnlp-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.36287460e-01 8.73527169e-01 -6.49095416e-01 -5.32820046e-01
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33418b9e-fa7b-41f5-b23e-b8bdab91a4f6 | person-re-identification-via-recurrent | 1701.06351 | null | http://arxiv.org/abs/1701.06351v1 | http://arxiv.org/pdf/1701.06351v1.pdf | Person Re-Identification via Recurrent Feature Aggregation | We address the person re-identification problem by effectively exploiting a
globally discriminative feature representation from a sequence of tracked human
regions/patches. This is in contrast to previous person re-id works, which rely
on either single frame based person to person patch matching, or graph based
sequence to sequence matching. We show that a progressive/sequential fusion
framework based on long short term memory (LSTM) network aggregates the
frame-wise human region representation at each time stamp and yields a sequence
level human feature representation. Since LSTM nodes can remember and propagate
previously accumulated good features and forget newly input inferior ones, even
with simple hand-crafted features, the proposed recurrent feature aggregation
network (RFA-Net) is effective in generating highly discriminative sequence
level human representations. Extensive experimental results on two person
re-identification benchmarks demonstrate that the proposed method performs
favorably against state-of-the-art person re-identification methods. | ['Bingbing Ni', 'Zhichao Song', 'Yichao Yan', 'Chao Ma', 'Yan Yan', 'Xiaokang Yang'] | 2017-01-23 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 4.46887523e-01 -4.23830420e-01 8.23950544e-02 -2.62960225e-01
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563df64a-ffe4-4eae-a8c5-41d9927dff10 | open-world-weakly-supervised-object | 2304.08271 | null | https://arxiv.org/abs/2304.08271v2 | https://arxiv.org/pdf/2304.08271v2.pdf | Open-World Weakly-Supervised Object Localization | While remarkable success has been achieved in weakly-supervised object localization (WSOL), current frameworks are not capable of locating objects of novel categories in open-world settings. To address this issue, we are the first to introduce a new weakly-supervised object localization task called OWSOL (Open-World Weakly-Supervised Object Localization). During training, all labeled data comes from known categories and, both known and novel categories exist in the unlabeled data. To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to generate a complete G-CAM (Generalized Class Activation Map) for object localization, without the requirement of bounding box annotation. As no class label is available for the unlabelled data, we conduct clustering over the full training set and design a novel multiple semantic centroids-driven contrastive loss for representation learning. We re-organize two widely used datasets, i.e., ImageNet-1K and iNatLoc500, and propose OpenImages150 to serve as evaluation benchmarks for OWSOL. Extensive experiments demonstrate that the proposed method can surpass all baselines by a large margin. We believe that this work can shift the close-set localization towards the open-world setting and serve as a foundation for subsequent works. Code will be released at https://github.com/ryylcc/OWSOL. | ['Mike Zheng Shou', 'Linlin Shen', 'Haozhe Liu', 'Yuexiang Li', 'Zhaochuan Luo', 'Jinheng Xie'] | 2023-04-17 | null | null | null | null | ['object-localization', 'weakly-supervised-object-localization'] | ['computer-vision', 'computer-vision'] | [-6.71586022e-02 -5.46582900e-02 -5.48532844e-01 -4.82334852e-01
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810ea8f0-f9d7-4b19-85d9-21a397a9178e | a-comprehensive-survey-of-image-augmentation | 2205.01491 | null | https://arxiv.org/abs/2205.01491v2 | https://arxiv.org/pdf/2205.01491v2.pdf | A Comprehensive Survey of Image Augmentation Techniques for Deep Learning | Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution. Then, the algorithms are split into three categories; model-free, model-based, and optimizing policy-based. The model-free category employs image processing methods while the model-based method leverages trainable image generation models. In contrast, the optimizing policy-based approach aims to find the optimal operations or their combinations. Furthermore, we discuss the current trend of common applications with two more active topics, leveraging different ways to understand image augmentation, such as group and kernel theory, and deploying image augmentation for unsupervised learning. Based on the analysis, we believe that our survey gives a better understanding helpful to choose suitable methods or design novel algorithms for practical applications. | ['Dong Sun Park', 'Alvaro Fuentes', 'Sook Yoon', 'Mingle Xu'] | 2022-05-03 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 0.6221214 0.22223897 -0.53224826 -0.28402048 -0.2129071 -0.22528484
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03f3b9df-128b-4001-99db-4c8c37591972 | mediapipe-and-cnns-for-real-time-asl-gesture | 2305.05296 | null | https://arxiv.org/abs/2305.05296v3 | https://arxiv.org/pdf/2305.05296v3.pdf | Mediapipe and CNNs for Real-Time ASL Gesture Recognition | This research paper describes a realtime system for identifying American Sign Language (ASL) movements that employs modern computer vision and machine learning approaches. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classification. The testing results show that the suggested system can detect all ASL alphabets with an accuracy of 99.95%, indicating its potential for use in communication devices for people with hearing impairments. The proposed approach can also be applied to additional sign languages with similar hand motions, potentially increasing the quality of life for people with hearing loss. Overall, the study demonstrates the effectiveness of using Mediapipe and CNN for real-time sign language recognition, making a significant contribution to the field of computer vision and machine learning. | ['Ayush Sinha', 'Ashutosh Bajpai', 'Rupesh Kumar'] | 2023-05-09 | null | null | null | null | ['sign-language-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [-8.60301331e-02 -4.10790980e-01 -4.38915998e-01 2.02984530e-02
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99052d22-009d-4252-b543-203f5ec868be | deep-learning-based-image-retrieval-in-the | 2107.03648 | null | https://arxiv.org/abs/2107.03648v1 | https://arxiv.org/pdf/2107.03648v1.pdf | Deep Learning Based Image Retrieval in the JPEG Compressed Domain | Content-based image retrieval (CBIR) systems on pixel domain use low-level features, such as colour, texture and shape, to retrieve images. In this context, two types of image representations i.e. local and global image features have been studied in the literature. Extracting these features from pixel images and comparing them with images from the database is very time-consuming. Therefore, in recent years, there has been some effort to accomplish image analysis directly in the compressed domain with lesser computations. Furthermore, most of the images in our daily transactions are stored in the JPEG compressed format. Therefore, it would be ideal if we could retrieve features directly from the partially decoded or compressed data and use them for retrieval. Here, we propose a unified model for image retrieval which takes DCT coefficients as input and efficiently extracts global and local features directly in the JPEG compressed domain for accurate image retrieval. The experimental findings indicate that our proposed model performed similarly to the current DELG model which takes RGB features as an input with reference to mean average precision while having a faster training and retrieval speed. | ['Mohammed Javed', 'Bulla Rajesh', 'Shrikant Temburwar'] | 2021-07-08 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 4.57269371e-01 -7.58449018e-01 -1.77526593e-01 -5.45918107e-01
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869a5ee7-cfe9-4104-ac5a-83038206e0d8 | variables-effecting-photomosaic | 1611.03318 | null | http://arxiv.org/abs/1611.03318v1 | http://arxiv.org/pdf/1611.03318v1.pdf | Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets | Unmanned aerial vehicles now make it possible to obtain high quality aerial
imagery at a low cost, but processing those images into a single, useful entity
is neither simple nor seamless. Specifically, there are factors that must be
addressed when merging multiple images into a single coherent one. While
ortho-rectification can be done, it tends to be expensive and time consuming.
Image stitching offers a more economical, low-tech approach. However direct
application tends to fail for low-elevation imagery due to one or more factors
including insufficient keypoints, parallax issues, and homogeneity of the
surveyed area. This paper discusses these problems and possible solutions when
using techniques such as image stitching and structure from motion for
generating ortho-rectified imagery. These are presented in terms of actual
Irish projects including the Boland's Mills building in Dublin's city centre,
the Kilmoon Cross Farm, and the Richview buildings on the University College
Dublin campus. Implications for various Irish industries are explained in terms
of both urban and rural projects. | ['Jonathan Byrne', 'Debra Laefer'] | 2016-11-10 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 6.07603133e-01 -3.55401844e-01 1.38920397e-01 -3.63124877e-01
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2195fddc-f6f3-4617-9903-6b76168af9b7 | learning-realistic-patterns-from-unrealistic | 2009.10007 | null | https://arxiv.org/abs/2009.10007v2 | https://arxiv.org/pdf/2009.10007v2.pdf | Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization | Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such private data. We explore the feasibility of learning implicitly from unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network (DNN). As such, neuronal excitation serves as a pseudo-generative model. The stimuli data is used to train new classification models. Furthermore, we extend this framework to inhibit representations that are associated with specific individuals. We use sleep monitoring data from both an open and a large closed clinical study and evaluate whether (1) end-users can create and successfully use customized classification models for sleep apnea detection, and (2) the identity of participants in the study is protected. Extensive comparative empirical investigation shows that different algorithms trained on the stimuli are able generalize successfully on the same task as the original model. However, architectural and algorithmic similarity between new and original models play an important role in performance. For similar architectures, the performance is close to that of using the true data (e.g., Accuracy difference of 0.56\%, Kappa coefficient difference of 0.03-0.04). Further experiments show that the stimuli can to a large extent successfully anonymize participants of the clinical studies. | ['Lars Aakerøy', 'Britt Øverland', 'Knut Liestøl', 'Mohan Kankanhalli', 'Stein Kristiansen', 'Sigurd Steinshamn', 'Konstantinos Nikolaidis', 'Vera Goebel', 'Thomas Plagemann', 'Harriet Akre', 'Gunn Marit Traaen'] | 2020-09-21 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 4.06278610e-01 5.32416880e-01 -7.01086894e-02 -6.56004667e-01
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0d8612b0-972c-4a24-a8c6-4bf0f9c428e7 | a-bag-of-visual-words-model-for-medical-image | 2007.09464 | null | https://arxiv.org/abs/2007.09464v1 | https://arxiv.org/pdf/2007.09464v1.pdf | A Bag of Visual Words Model for Medical Image Retrieval | Medical Image Retrieval is a challenging field in Visual information retrieval, due to the multi-dimensional and multi-modal context of the underlying content. Traditional models often fail to take the intrinsic characteristics of data into consideration, and have thus achieved limited accuracy when applied to medical images. The Bag of Visual Words (BoVW) is a technique that can be used to effectively represent intrinsic image features in vector space, so that applications like image classification and similar-image search can be optimized. In this paper, we present a MedIR approach based on the BoVW model for content-based medical image retrieval. As medical images as multi-dimensional, they exhibit underlying cluster and manifold information which enhances semantic relevance and allows for label uniformity. Hence, the BoVW features extracted for each image are used to train a supervised machine learning classifier based on positive and negative training images, for extending content based image retrieval. During experimental validation, the proposed model performed very well, achieving a Mean Average Precision of 88.89% during top-3 image retrieval experiments. | ['Sowmya Kamath S', 'Karthik K'] | 2020-07-18 | null | null | null | null | ['medical-image-retrieval', 'content-based-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [ 3.99085730e-01 -4.29753631e-01 -6.90985441e-01 -2.45578527e-01
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95c66a95-fc7f-4d4c-9eff-33cdee269f5a | a-multimodal-method-based-on-cross-attention | 2305.14142 | null | https://arxiv.org/abs/2305.14142v1 | https://arxiv.org/pdf/2305.14142v1.pdf | A multimodal method based on cross-attention and convolution for postoperative infection diagnosis | Postoperative infection diagnosis is a common and serious complication that generally poses a high diagnostic challenge. This study focuses on PJI, a type of postoperative infection. X-ray examination is an imaging examination for suspected PJI patients that can evaluate joint prostheses and adjacent tissues, and detect the cause of pain. Laboratory examination data has high sensitivity and specificity and has significant potential in PJI diagnosis. In this study, we proposed a self-supervised masked autoencoder pre-training strategy and a multimodal fusion diagnostic network MED-NVC, which effectively implements the interaction between two modal features through the feature fusion network of CrossAttention. We tested our proposed method on our collected PJI dataset and evaluated its performance and feasibility through comparison and ablation experiments. The results showed that our method achieved an ACC of 94.71% and an AUC of 98.22%, which is better than the latest method and also reduces the number of parameters. Our proposed method has the potential to provide clinicians with a powerful tool for enhancing accuracy and efficiency. | ['Hongwei Shi', 'Xianjie Liu'] | 2023-05-23 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-1.04999980e-02 -2.84418106e-01 -2.34719396e-01 2.24799767e-01
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70af6ae7-86c4-4b3d-8712-62344751de6f | single-image-deraining-using-scale-aware | 1712.0683 | null | http://arxiv.org/abs/1712.06830v1 | http://arxiv.org/pdf/1712.06830v1.pdf | Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network | Given a single input rainy image, our goal is to visually remove rain streaks
and the veiling effect caused by scattering and transmission of rain streaks
and rain droplets. We are particularly concerned with heavy rain, where rain
streaks of various sizes and directions can overlap each other and the veiling
effect reduces contrast severely. To achieve our goal, we introduce a
scale-aware multi-stage convolutional neural network. Our main idea here is
that different sizes of rain-streaks visually degrade the scene in different
ways. Large nearby streaks obstruct larger regions and are likely to reflect
specular highlights more prominently than smaller distant streaks. These
different effects of different streaks have their own characteristics in their
image features, and thus need to be treated differently. To realize this, we
create parallel sub-networks that are trained and made aware of these different
scales of rain streaks. To our knowledge, this idea of parallel sub-networks
that treats the same class of objects according to their unique sub-classes is
novel, particularly in the context of rain removal. To verify our idea, we
conducted experiments on both synthetic and real images, and found that our
method is effective and outperforms the state-of-the-art methods. | ['Loong-Fah Cheong', 'Robby T. Tan', 'Ruoteng Li'] | 2017-12-19 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 7.11018369e-02 -4.74182874e-01 7.18927383e-01 -3.88806373e-01
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47e58bc5-85c2-40e6-8add-213e2bc88e17 | a-dual-branch-self-supervised-representation | 2303.11019 | null | https://arxiv.org/abs/2303.11019v1 | https://arxiv.org/pdf/2303.11019v1.pdf | A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images | Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creating such datasets for whole slide images (WSIs) in histopathology is a challenging task due to their gigapixel size. In recent years, self-supervised learning (SSL) has emerged as an alternative solution to reduce the annotation overheads in WSIs, as it does not require labels for training. These SSL approaches, however, are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features. In this paper, we propose a Dual-branch SSL Framework for WSI tumour segmentation (DSF-WSI) that can effectively learn image features from multi-resolution WSIs. Our DSF-WSI connected two branches and jointly learnt low and high resolution WSIs in a self-supervised manner. Moreover, we introduced a novel Context-Target Fusion Module (CTFM) and a masked jigsaw pretext task to align the learnt multi-resolution features. Furthermore, we designed a Dense SimSiam Learning (DSL) strategy to maximise the similarity of different views of WSIs, enabling the learnt representations to be more efficient and discriminative. We evaluated our method using two public datasets on breast and liver cancer segmentation tasks. The experiment results demonstrated that our DSF-WSI can effectively extract robust and efficient representations, which we validated through subsequent fine-tuning and semi-supervised settings. Our proposed method achieved better accuracy than other state-of-the-art approaches. Code is available at https://github.com/Dylan-H-Wang/dsf-wsi. | ['Jinman Kim', 'Euijoon Ahn', 'Hao Wang'] | 2023-03-20 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.18931133e-01 6.68466790e-04 -3.33077490e-01 -6.00076795e-01
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a732c99c-ab14-4951-a8ae-cd547b673525 | imbalanced-classification-in-medical-imaging | 2210.12234 | null | https://arxiv.org/abs/2210.12234v2 | https://arxiv.org/pdf/2210.12234v2.pdf | Imbalanced Classification in Medical Imaging via Regrouping | We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image classification shows that this natural idea can substantially boost the classification performance as measured by average precision (approximately area-under-the-precision-recall-curve, or AUPRC), which is more appropriate for evaluating imbalanced classification than other metrics such as balanced accuracy. | ['Ju Sun', 'Ying Cui', 'Rui Zhang', 'Yash Travadi', 'Le Peng'] | 2022-10-21 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.21374238e-01 3.96165371e-01 -8.46271217e-01 -7.42938697e-01
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c979c9da-6aa3-49b0-b65c-0b1df39ec2ba | bev-locator-an-end-to-end-visual-semantic | 2211.14927 | null | https://arxiv.org/abs/2211.14927v1 | https://arxiv.org/pdf/2211.14927v1.pdf | BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images | Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV (Birds-Eye-View) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map queries sequence. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-locator is capable to estimate the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052m, 0.135m and 0.251$^\circ$ in lateral, longitudinal translation and heading angle degree. | ['Stefan Poslad', 'Liang Li', 'Tao Peng', 'Wenqiang Zhou', 'Meng Xu', 'Zhihuang Zhang'] | 2022-11-27 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-4.99634564e-01 -2.48638373e-02 -2.19679505e-01 -8.09447050e-01
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0497a41b-ddea-43e5-b442-ff55eb1425a0 | drugst-one-a-plug-and-play-solution-for | 2305.15453 | null | https://arxiv.org/abs/2305.15453v2 | https://arxiv.org/pdf/2305.15453v2.pdf | Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing | In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. | ['Suryadipto Sarkar', 'Julio Saez-Rodriguez', 'Sepideh Sadegh', 'Miles Mee', 'Noel Malod-Dognin', 'Mohamed Helmy', 'Jan Baumbach', 'Olga Zolotareva', 'Ruisheng Wang', 'Ugur Turhan', 'Nico Trummer', 'Ron Shamir', 'Gideon Shaked', 'Nataša Pržulj', 'Dexter Pratt', 'Julian M. Poschenrieder', 'Rudolf T. Pillich', 'Alexander R. Pico', 'Mhaned Oubounyt', 'Julian Matschinske', 'Quirin Manz', 'Joseph Loscalzo', 'Sebastian Lobentanzer', 'Markus List', 'Hagai Levi', 'Olga Lazareva', 'Max Kotlyar', 'Igor Jurisica', 'Markus Hoffmann', 'Carlos Garcia-Hernandez', 'Maria L. Elkjaer', 'Jing Chen', 'David B. Blumenthal', 'Sylvie Baier', 'Gary D. Bader', 'Klaudia Adamowicz', 'Mark Abovsky', 'Michael Hartung', 'Andreas Maier'] | 2023-05-24 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 6.40228838e-02 -2.49145746e-01 -5.66343427e-01 1.03690803e-01
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b6b431db-61c5-41cb-8f6c-f476fe65749c | improving-named-entity-recognition-by-jointly | 1807.06683 | null | http://arxiv.org/abs/1807.06683v1 | http://arxiv.org/pdf/1807.06683v1.pdf | Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags | Previous studies have shown that linguistic features of a word such as
possession, genitive or other grammatical cases can be employed in word
representations of a named entity recognition (NER) tagger to improve the
performance for morphologically rich languages. However, these taggers require
external morphological disambiguation (MD) tools to function which are hard to
obtain or non-existent for many languages. In this work, we propose a model
which alleviates the need for such disambiguators by jointly learning NER and
MD taggers in languages for which one can provide a list of candidate
morphological analyses. We show that this can be done independent of the
morphological annotation schemes, which differ among languages. Our experiments
employing three different model architectures that join these two tasks show
that joint learning improves NER performance. Furthermore, the morphological
disambiguator's performance is shown to be competitive. | ['Tunga Güngör', 'Suzan Üsküdarlı', 'Onur Güngör'] | 2018-07-17 | null | null | null | null | ['morphological-disambiguation'] | ['natural-language-processing'] | [-2.72945493e-01 -7.03823715e-02 -1.94935516e-01 -3.93523455e-01
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6b60f966-d1b6-4fb4-9699-2a843d47eb67 | selfact-personalized-activity-recognition | 2304.0953 | null | https://arxiv.org/abs/2304.09530v1 | https://arxiv.org/pdf/2304.09530v1.pdf | SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning | Supervised Deep Learning (DL) models are currently the leading approach for sensor-based Human Activity Recognition (HAR) on wearable and mobile devices. However, training them requires large amounts of labeled data whose collection is often time-consuming, expensive, and error-prone. At the same time, due to the intra- and inter-variability of activity execution, activity models should be personalized for each user. In this work, we propose SelfAct: a novel framework for HAR combining self-supervised and active learning to mitigate these problems. SelfAct leverages a large pool of unlabeled data collected from many users to pre-train through self-supervision a DL model, with the goal of learning a meaningful and efficient latent representation of sensor data. The resulting pre-trained model can be locally used by new users, which will fine-tune it thanks to a novel unsupervised active learning strategy. Our experiments on two publicly available HAR datasets demonstrate that SelfAct achieves results that are close to or even better than the ones of fully supervised approaches with a small number of active learning queries. | ['Claudio Bettini', 'Samuele Valente', 'Gabriele Civitarese', 'Luca Arrotta'] | 2023-04-19 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.89514428e-01 7.81234652e-02 -7.50093520e-01 -6.24821603e-01
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c22e3e67-17f1-4811-b43f-75a96ab72ecf | knowledge-refined-denoising-network-for | 2304.14987 | null | https://arxiv.org/abs/2304.14987v1 | https://arxiv.org/pdf/2304.14987v1.pdf | Knowledge-refined Denoising Network for Robust Recommendation | Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \textit{task-irrelevant knowledge propagation} and \textit{vulnerability to interaction noise}, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL. | ['Yunjun Gao', 'Yujia Hu', 'Lu Chen', 'YUREN MAO', 'Yuntao Du', 'Xinjun Zhu'] | 2023-04-28 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-2.76578903e-01 -9.27661285e-02 -3.61911118e-01 -1.61865383e-01
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18b4ef7e-46c4-4d5e-92b3-b0f11afed8bd | combining-rules-and-embeddings-via-neuro | 2109.09566 | null | https://arxiv.org/abs/2109.09566v1 | https://arxiv.org/pdf/2109.09566v1.pdf | Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion | Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings. In particular, rule-based KBC has led to interpretable rules while being comparable in performance with graph embeddings. Even within rule-based KBC, there exist different approaches that lead to rules of varying quality and previous work has not always been precise in highlighting these differences. Another issue that plagues most rule-based KBC is the non-uniformity of relation paths: some relation sequences occur in very few paths while others appear very frequently. In this paper, we show that not all rule-based KBC models are the same and propose two distinct approaches that learn in one case: 1) a mixture of relations and the other 2) a mixture of paths. When implemented on top of neuro-symbolic AI, which learns rules by extending Boolean logic to real-valued logic, the latter model leads to superior KBC accuracy outperforming state-of-the-art rule-based KBC by 2-10% in terms of mean reciprocal rank. Furthermore, to address the non-uniformity of relation paths, we combine rule-based KBC with graph embeddings thus improving our results even further and achieving the best of both worlds. | ['Alexander Gray', 'Salim Roukos', 'Francois Luus', 'Pavan Kapanipathi', 'Ibrahim Abdelaziz', 'Breno W. S. R. Carvalho', 'Prithviraj Sen'] | 2021-09-16 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 4.70179208e-02 5.39760292e-01 -3.74523014e-01 -1.25495046e-01
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b084ee29-cc8d-45df-81b6-4b2023b126ba | exploiting-global-and-local-attentions-for | 2104.08126 | null | https://arxiv.org/abs/2104.08126v1 | https://arxiv.org/pdf/2104.08126v1.pdf | Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images | Heavy rain removal from a single image is the task of simultaneously eliminating rain streaks and fog, which can dramatically degrade the quality of captured images. Most existing rain removal methods do not generalize well for the heavy rain case. In this work, we propose a novel network architecture consisting of three sub-networks to remove heavy rain from a single image without estimating rain streaks and fog separately. The first sub-net, a U-net-based architecture that incorporates our Spatial Channel Attention (SCA) blocks, extracts global features that provide sufficient contextual information needed to remove atmospheric distortions caused by rain and fog. The second sub-net learns the additive residues information, which is useful in removing rain streak artifacts via our proposed Residual Inception Modules (RIM). The third sub-net, the multiplicative sub-net, adopts our Channel-attentive Inception Modules (CIM) and learns the essential brighter local features which are not effectively extracted in the SCA and additive sub-nets by modulating the local pixel intensities in the derained images. Our three clean image results are then combined via an attentive blending block to generate the final clean image. Our method with SCA, RIM, and CIM significantly outperforms the previous state-of-the-art single-image deraining methods on the synthetic datasets, shows considerably cleaner and sharper derained estimates on the real image datasets. We present extensive experiments and ablation studies supporting each of our method's contributions on both synthetic and real image datasets. | ['Munchurl Kim', 'Juan Luis Gonzalez', 'Dac Tung Vu'] | 2021-04-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.30557883e-01 -2.88046598e-01 6.57985151e-01 -3.37588459e-01
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c2a59c61-6fb3-4ba6-9a58-358922d1568f | action-parsing-using-context-features | 2205.10008 | null | https://arxiv.org/abs/2205.10008v1 | https://arxiv.org/pdf/2205.10008v1.pdf | Action parsing using context features | We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm temporally segments the video sequence into action segments. The optimal temporal segmentation is found using a dynamic programming search algorithm that optimizes the overall classification confidence score. The classification score of each segment is determined using local features calculated from that segment as well as context features calculated from other candidate action segments of the sequence. Experimental results on the Breakfast activity data-set showed improved segmentation accuracy compared to existing state-of-the-art parsing techniques. | ['Nagita Mehrseresht'] | 2022-05-20 | null | null | null | null | ['action-segmentation', 'action-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 7.50804007e-01 -2.14138087e-02 -7.83240318e-01 -4.78530884e-01
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ed631da5-f2eb-4f93-905d-57a16d4a155d | a-lightweight-deep-learning-based-cloud | 2105.00967 | null | https://arxiv.org/abs/2105.00967v1 | https://arxiv.org/pdf/2105.00967v1.pdf | A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features | Clouds are a very important factor in the availability of optical remote sensing images. Recently, deep learning-based cloud detection methods have surpassed classical methods based on rules and physical models of clouds. However, most of these deep models are very large which limits their applicability and explainability, while other models do not make use of the full spectral information in multi-spectral images such as Sentinel-2. In this paper, we propose a lightweight network for cloud detection, fusing multi-scale spectral and spatial features (CDFM3SF) and tailored for processing all spectral bands in Sentinel- 2A images. The proposed method consists of an encoder and a decoder. In the encoder, three input branches are designed to handle spectral bands at their native resolution and extract multiscale spectral features. Three novel components are designed: a mixed depth-wise separable convolution (MDSC) and a shared and dilated residual block (SDRB) to extract multi-scale spatial features, and a concatenation and sum (CS) operation to fuse multi-scale spectral and spatial features with little calculation and no additional parameters. The decoder of CD-FM3SF outputs three cloud masks at the same resolution as input bands to enhance the supervision information of small, middle and large clouds. To validate the performance of the proposed method, we manually labeled 36 Sentinel-2A scenes evenly distributed over mainland China. The experiment results demonstrate that CD-FM3SF outperforms traditional cloud detection methods and state-of-theart deep learning-based methods in both accuracy and speed. | ['Matthieu Molinier', 'Xiao Xiang Zhu', 'Lichao Mou', 'Shaojie Luo', 'Canliang Jian', 'Zhongwen Hu', 'Zhaocong Wu', 'Jun Li'] | 2021-04-29 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 2.34911814e-01 -8.01365435e-01 2.75864869e-01 -3.77317905e-01
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70c29f84-affa-4a96-bb5d-124526a97cb3 | incorporating-textual-evidence-in-visual | 1911.09334 | null | https://arxiv.org/abs/1911.09334v1 | https://arxiv.org/pdf/1911.09334v1.pdf | Incorporating Textual Evidence in Visual Storytelling | Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering. | ['Tianyi Li', 'Sujian Li'] | 2019-11-21 | incorporating-textual-evidence-in-visual-1 | https://aclanthology.org/W19-8102 | https://aclanthology.org/W19-8102.pdf | ws-2019-11 | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.86889464e-01 -1.28020823e-01 -2.80473918e-01 -3.25858027e-01
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7c9badd7-6a40-4298-8b05-b9840b118f7f | humble-teacher-and-eager-student-dual-network | 2011.12498 | null | https://arxiv.org/abs/2011.12498v4 | https://arxiv.org/pdf/2011.12498v4.pdf | An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation | Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, the methods degenerate and predict every pixel in unlabeled images as background. This is because contradictory predictions are gradually pushed to the background class due to highly imbalanced class distribution. But this is not an issue in supervised learning because it has accurate labels. This inspires us to stabilize the training by obtaining reliable pseudo labels. Specifically, we learn two networks to mutually teach each other. In particular, for each image, we compose an easy-hard pair by applying different augmentations and feed them to both networks. The more reliable predictions on easy images in each network are used to teach the other network to learn about the corresponding hard images. The approach successfully avoids degeneration and achieves promising results on public datasets. The source code and pretrained models have been released at https://github.com/xierc/Semi_Human_Pose. | ['Yizhou Wang', 'Wenjun Zeng', 'Chunyu Wang', 'Rongchang Xie'] | 2020-11-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xie_An_Empirical_Study_of_the_Collapsing_Problem_in_Semi-Supervised_2D_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xie_An_Empirical_Study_of_the_Collapsing_Problem_in_Semi-Supervised_2D_ICCV_2021_paper.pdf | iccv-2021-1 | ['semi-supervised-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.83792317e-01 5.95304668e-01 -4.55120564e-01 -7.10265100e-01
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a4b097b1-2b72-4a89-9541-2b3ecd97956a | a-deep-transfer-learning-method-for-cross | null | null | https://aclanthology.org/2022.lrec-1.330 | https://aclanthology.org/2022.lrec-1.330.pdf | A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference | Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), has been one of the central tasks in Artificial Intelligence (AI) and Natural Language Processing (NLP). RTE between the two pieces of texts is a crucial problem, and it adds further challenges when involving two different languages, i.e., in the cross-lingual scenario. This paper proposes an effective transfer learning approach for cross-lingual NLI. We perform experiments on English-Hindi language pairs in the cross-lingual setting to find out that our novel loss formulation could enhance the performance of the baseline model by up to 2%. To assess the effectiveness of our method further, we perform additional experiments on every possible language pair using four European languages, namely French, German, Bulgarian, and Turkish, on top of XNLI dataset. Evaluation results yield up to 10% performance improvement over the respective baseline models, in some cases surpassing the state-of-the-art (SOTA). It is also to be noted that our proposed model has 110M parameters which is much lesser than the SOTA model having 220M parameters. Finally, we argue that our transfer learning-based loss objective is model agnostic and thus can be used with other deep learning-based architectures for cross-lingual NLI. | ['Asif Ekbal', 'Tanik Saikh', 'Baban Gain', 'Arkadipta De', 'Dibyanayan Bandyopadhyay'] | null | null | null | null | lrec-2022-6 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-2.04106439e-02 -4.11929097e-03 -2.97352895e-02 -4.89193022e-01
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e11b0897-2238-48b3-96d3-e40f6df82207 | pix2vox-context-aware-3d-reconstruction-from | 1901.11153 | null | https://arxiv.org/abs/1901.11153v2 | https://arxiv.org/pdf/1901.11153v2.pdf | Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images | Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method. | ['Hongxun Yao', 'Shangchen Zhou', 'Xiaoshuai Sun', 'Haozhe Xie', 'Shengping Zhang'] | 2019-01-31 | pix2vox-context-aware-3d-reconstruction-from-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Xie_Pix2Vox_Context-Aware_3D_Reconstruction_From_Single_and_Multi-View_Images_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xie_Pix2Vox_Context-Aware_3D_Reconstruction_From_Single_and_Multi-View_Images_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.71545539e-02 -1.84455022e-01 3.95364724e-02 -4.24600303e-01
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1a352704-cbde-4ddb-b2eb-d7c749c7d1e5 | coding-kendalls-shape-trajectories-for-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Tanfous_Coding_Kendalls_Shape_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Tanfous_Coding_Kendalls_Shape_CVPR_2018_paper.pdf | Coding Kendall's Shape Trajectories for 3D Action Recognition | Suitable shape representations as well as their temporal evolution, termed trajectories, often lie to non-linear manifolds. This puts an additional constraint (i.e., non-linearity) in using conventional machine learning techniques for the purpose of classification, event detection, prediction, etc. This paper accommodates the well-known Sparse Coding and Dictionary Learning to the Kendall's shape space and illustrates effective coding of 3D skeletal sequences for action recognition. Grounding on the Riemannian geometry of the shape space, an intrinsic sparse coding and dictionary learning formulation is proposed for static skeletal shapes to overcome the inherent non-linearity of the manifold. As a main result, initial trajectories give rise to sparse code functions with suitable computational properties, including sparsity and vector space representation. To achieve action recognition, two different classification schemes were adopted. A bi-directional LSTM is directly performed on sparse code functions, while a linear SVM is applied after representing sparse code functions using Fourier temporal pyramid. Experiments conducted on three publicly available datasets show the superiority of the proposed approach compared to existing Riemannian representations and its competitiveness with respect to other recently-proposed approaches. When the benefits of invariance are maintained from the Kendall's shape representation, our approach not only overcomes the problem of non-linearity but also yields to discriminative sparse code functions. | ['Hassen Drira', 'Boulbaba Ben Amor', 'Amor Ben Tanfous'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.90697417e-01 -2.61739135e-01 -2.51626045e-01 -1.29137695e-01
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8237992a-d30e-4c53-91a0-ee2fc4ea93a0 | indic-punct-an-automatic-punctuation | 2203.16825 | null | https://arxiv.org/abs/2203.16825v1 | https://arxiv.org/pdf/2203.16825v1.pdf | indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages | Automatic Speech Recognition (ASR) generates text which is most of the times devoid of any punctuation. Absence of punctuation is text can affect readability. Also, down stream NLP tasks such as sentiment analysis, machine translation, greatly benefit by having punctuation and sentence boundary information. We present an approach for automatic punctuation of text using a pretrained IndicBERT model. Inverse text normalization is done by hand writing weighted finite state transducer (WFST) grammars. We have developed this tool for 11 Indic languages namely Hindi, Tamil, Telugu, Kannada, Gujarati, Marathi, Odia, Bengali, Assamese, Malayalam and Punjabi. All code and data is publicly. available | ['Vivek Raghavan', 'Harveen Singh Chadha', 'Priyanshi Shah', 'Rishabh Gaur', 'Ankur Dhuriya', 'Neeraj Chhimwal', 'Anirudh Gupta'] | 2022-03-31 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 5.08396745e-01 2.65319765e-01 2.65354365e-01 -3.70102197e-01
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b3fe1935-d7e1-4c11-85a8-9b03bf459ce6 | robust-preference-learning-for-storytelling | 2210.07792 | null | https://arxiv.org/abs/2210.07792v2 | https://arxiv.org/pdf/2210.07792v2.pdf | Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning | Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control for story preference utilize prompt engineering which is labor intensive and often inconsistent. They may also use logit-manipulation methods which require annotated datasets to exist for the desired attributes. To address these issues, we first train a contrastive bi-encoder model to align stories with corresponding human critiques, named CARP, building a general purpose preference model. This is subsequently used as a reward function to fine-tune a generative language model via reinforcement learning. However, simply fine-tuning a generative language model with a contrastive reward model does not always reliably result in a story generation system capable of generating stories that meet user preferences. To increase story generation robustness we further fine-tune the contrastive reward model using a prompt-learning technique. A human participant study is then conducted comparing generations from our full system, ablations, and two baselines. We show that the full fine-tuning pipeline results in a story generator preferred over a LLM 20x as large as well as logit-based methods. This motivates the use of contrastive learning for general purpose human preference modeling. | ['Mark Riedl', 'Spencer Frazier', 'Ian Yang', 'Anbang Ye', 'Michael Pieler', 'Shahbuland Matiana', 'Alexander Havrilla', 'Louis Castricato'] | 2022-10-14 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 5.31837285e-01 5.15783310e-01 -1.90504029e-01 -5.32405972e-01
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ecbe894d-1915-404f-a6c2-0ed2ce0b433c | nerfool-uncovering-the-vulnerability-of | 2306.06359 | null | https://arxiv.org/abs/2306.06359v1 | https://arxiv.org/pdf/2306.06359v1.pdf | NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations | Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes. While adversarial robustness is essential for real-world applications, little study has been devoted to understanding its implication on GNeRF. We hypothesize that because GNeRF is implemented by conditioning on the source views from new scenes, which are often acquired from the Internet or third-party providers, there are potential new security concerns regarding its real-world applications. Meanwhile, existing understanding and solutions for neural networks' adversarial robustness may not be applicable to GNeRF, due to its 3D nature and uniquely diverse operations. To this end, we present NeRFool, which to the best of our knowledge is the first work that sets out to understand the adversarial robustness of GNeRF. Specifically, NeRFool unveils the vulnerability patterns and important insights regarding GNeRF's adversarial robustness. Built upon the above insights gained from NeRFool, we further develop NeRFool+, which integrates two techniques capable of effectively attacking GNeRF across a wide range of target views, and provide guidelines for defending against our proposed attacks. We believe that our NeRFool/NeRFool+ lays the initial foundation for future innovations in developing robust real-world GNeRF solutions. Our codes are available at: https://github.com/GATECH-EIC/NeRFool. | ['Yingyan Lin', 'Shunyao Zhang', 'Shang Wu', 'Souvik Kundu', 'Ye Yuan', 'Yonggan Fu'] | 2023-06-10 | null | null | null | null | ['adversarial-robustness', 'novel-view-synthesis'] | ['adversarial', 'computer-vision'] | [ 1.81860402e-01 -3.04153282e-02 2.05820948e-01 -2.57919699e-01
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84e79e1f-5c97-407c-8c23-f19589c97788 | identifying-the-key-components-in-resnet-50 | 2110.1416 | null | https://arxiv.org/abs/2110.14160v2 | https://arxiv.org/pdf/2110.14160v2.pdf | Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation | Although deep learning based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on the prediction performance. The training setting includes various interdependent components, such as objective function, data sampling strategy and data augmentation approach. To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components. Extensive experiments are conducted on a publicly-available dataset EyePACS. We demonstrate that (1) the DR grading framework is sensitive to input resolution, objective function, and composition of data augmentation, (2) using mean square error as the loss function can effectively improve the performance with respect to a task-specific evaluation metric, namely the quadratically-weighted Kappa, (3) utilizing eye pairs boosts the performance of DR grading and (4) using data resampling to address the problem of imbalanced data distribution in EyePACS hurts the performance. Based on these observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves the state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42670 fundus images) with only image-level labels. We also examine the proposed training practices on other fundus datasets and other network architectures to evaluate their generalizability. Our codes and pre-trained model are available at https://github.com/YijinHuang/pytorch-classification. | ['Xiaoying Tang', 'Roger Tam', 'Junyan Lyu', 'Pujin Cheng', 'Li Lin', 'Yijin Huang'] | 2021-10-27 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-7.26499110e-02 7.94796459e-03 -3.04100096e-01 -6.33217931e-01
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75d20efb-5568-4968-97ec-84b839a93e53 | dense-pixel-to-pixel-harmonization-via | 2303.01681 | null | https://arxiv.org/abs/2303.01681v1 | https://arxiv.org/pdf/2303.01681v1.pdf | Dense Pixel-to-Pixel Harmonization via Continuous Image Representation | High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural Networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any hand-crafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution Image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code will be available at https://github.com/WindVChen/INR-Harmonization. | ['Zhenwei Shi', 'Keyan Chen', 'Zhengxia Zou', 'Yilan Zhang', 'Jianqi Chen'] | 2023-03-03 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 5.14877379e-01 -2.13145241e-01 5.05544767e-02 -1.43144295e-01
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a57eef98-3dac-451e-b515-5bad5f47c890 | a-framework-for-building-closed-domain-chat | 1910.13826 | null | https://arxiv.org/abs/1910.13826v4 | https://arxiv.org/pdf/1910.13826v4.pdf | A Framework for Building Closed-Domain Chat Dialogue Systems | This paper presents HRIChat, a framework for developing closed-domain chat dialogue systems. Being able to engage in chat dialogues has been found effective for improving communication between humans and dialogue systems. This paper focuses on closed-domain systems because they would be useful when combined with task-oriented dialogue systems in the same domain. HRIChat enables domain-dependent language understanding so that it can deal well with domain-specific utterances. In addition, HRIChat makes it possible to integrate state transition network-based dialogue management and reaction-based dialogue management. FoodChatbot, which is an application in the food and restaurant domain, has been developed and evaluated through a user study. Its results suggest that reasonably good systems can be developed with HRIChat. This paper also reports lessons learned from the development and evaluation of FoodChatbot. | ['Mikio Nakano', 'Kazunori Komatani'] | 2019-10-30 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.43226761e-01 8.07292581e-01 1.14408962e-01 -6.22571290e-01
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ee9d4716-1283-41d4-bfb6-ad679d96f507 | eval-explainable-video-anomaly-localization | 2212.079 | null | https://arxiv.org/abs/2212.07900v1 | https://arxiv.org/pdf/2212.07900v1.pdf | EVAL: Explainable Video Anomaly Localization | We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art. | ['Erik Learned-Miller', 'Michael J. Jones', 'Ashish Singh'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Singh_EVAL_Explainable_Video_Anomaly_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [-1.07805550e-01 -3.00977647e-01 3.84400506e-03 -3.98611754e-01
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d31d2465-3a71-4498-bde2-03e1263669f0 | teamtat-a-collaborative-text-annotation-tool | 2004.11894 | null | https://arxiv.org/abs/2004.11894v1 | https://arxiv.org/pdf/2004.11894v1.pdf | TeamTat: a collaborative text annotation tool | Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for image display, project management, and multi-user team annotation. In response, we developed TeamTat (teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC, (uploaded locally or automatically retrieved from PubMed or PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotators convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus-quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality. | ['Dongseop Kwon', 'Sun Kim', 'Rezarta Islamaj', 'Zhiyong Lu'] | 2020-04-24 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 3.27216804e-01 3.82041521e-02 -3.85110945e-01 -4.50592905e-01
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49877157-72c8-46b4-aa4a-f041ecc6b72e | cross-domain-labeled-lda-for-cross-domain | 1809.0582 | null | http://arxiv.org/abs/1809.05820v1 | http://arxiv.org/pdf/1809.05820v1.pdf | Cross-Domain Labeled LDA for Cross-Domain Text Classification | Cross-domain text classification aims at building a classifier for a target
domain which leverages data from both source and target domain. One promising
idea is to minimize the feature distribution differences of the two domains.
Most existing studies explicitly minimize such differences by an exact
alignment mechanism (aligning features by one-to-one feature alignment,
projection matrix etc.). Such exact alignment, however, will restrict models'
learning ability and will further impair models' performance on classification
tasks when the semantic distributions of different domains are very different.
To address this problem, we propose a novel group alignment which aligns the
semantics at group level. In addition, to help the model learn better semantic
groups and semantics within these groups, we also propose a partial supervision
for model's learning in source domain. To this end, we embed the group
alignment and a partial supervision into a cross-domain topic model, and
propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and
Reuters dataset, extensive quantitative (classification, perplexity etc.) and
qualitative (topic detection) experiments are conducted to show the
effectiveness of the proposed group alignment and partial supervision. | ['Baoyu Jing', 'Deqing Wang', 'Chenwei Lu', 'Fuzhen Zhuang', 'Cheng Niu'] | 2018-09-16 | null | null | null | null | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-6.47412986e-02 -4.08665789e-03 -4.88191456e-01 -8.54474604e-01
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aa87b3cb-9701-4cfa-ba41-6cc319b7b667 | event-temporal-relation-extraction-with | 2302.04985 | null | https://arxiv.org/abs/2302.04985v1 | https://arxiv.org/pdf/2302.04985v1.pdf | Event Temporal Relation Extraction with Bayesian Translational Model | Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge. In this study, we introduce Bayesian-Trans, a Bayesian learning-based method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions. Compared to conventional neural approaches, instead of performing point estimation to find the best set parameters, the proposed model infers the parameters' posterior distribution directly, enhancing the model's capability to encode and express uncertainty about the predictions. Experimental results on the three widely used datasets show that Bayesian-Trans outperforms existing approaches for event temporal relation extraction. We additionally present detailed analyses on uncertainty quantification, comparison of priors, and ablation studies, illustrating the benefits of the proposed approach. | ['Yulan He', 'Gabriele Pergola', 'Xingwei Tan'] | 2023-02-10 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.70547634e-01 2.88242579e-01 -4.54777867e-01 -5.34157276e-01
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46b93c35-c2bf-4f52-b0af-c62ca4b465f9 | predicting-lexical-complexity-in-english | 2102.08773 | null | https://arxiv.org/abs/2102.08773v2 | https://arxiv.org/pdf/2102.08773v2.pdf | Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset | Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as Complex Word Identification (CWI) and is often modelled as a supervised classification problem. For training such systems, annotated datasets in which words and sometimes multi-word expressions are labelled regarding complexity are required. In this paper we analyze previous work carried out in this task and investigate the properties of CWI datasets for English. We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2.0. We present experiments using both new and old datasets to investigate the nature of lexical complexity. We found that a Likert-scale annotation protocol provides an objective setting that is superior for identifying the complexity of words compared to a binary annotation protocol. We release a new dataset using our new protocol to promote the task of Lexical Complexity Prediction. | ['Marcos Zampieri', 'Richard Evans', 'Matthew Shardlow'] | 2021-02-17 | null | null | null | null | ['lexical-complexity-prediction', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.44856650e-01 2.07880080e-01 -2.09797516e-01 -4.17557597e-01
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581799cd-7ee4-4f6e-975c-e6391383b8d5 | self-supervised-point-cloud-completion-via | 2111.10701 | null | https://arxiv.org/abs/2111.10701v1 | https://arxiv.org/pdf/2111.10701v1.pdf | Self-Supervised Point Cloud Completion via Inpainting | When navigating in urban environments, many of the objects that need to be tracked and avoided are heavily occluded. Planning and tracking using these partial scans can be challenging. The aim of this work is to learn to complete these partial point clouds, giving us a full understanding of the object's geometry using only partial observations. Previous methods achieve this with the help of complete, ground-truth annotations of the target objects, which are available only for simulated datasets. However, such ground truth is unavailable for real-world LiDAR data. In this work, we present a self-supervised point cloud completion algorithm, PointPnCNet, which is trained only on partial scans without assuming access to complete, ground-truth annotations. Our method achieves this via inpainting. We remove a portion of the input data and train the network to complete the missing region. As it is difficult to determine which regions were occluded in the initial cloud and which were synthetically removed, our network learns to complete the full cloud, including the missing regions in the initial partial cloud. We show that our method outperforms previous unsupervised and weakly-supervised methods on both the synthetic dataset, ShapeNet, and real-world LiDAR dataset, Semantic KITTI. | ['David Held', 'Arpit Jangid', 'Brian Okorn', 'Himangi Mittal'] | 2021-11-21 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 1.00650124e-01 2.37063408e-01 -3.07553951e-02 -5.45849860e-01
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62c8711c-b95e-4b24-84db-cd6e399e181d | sparse-neural-additive-model-interpretable | 2202.12482 | null | https://arxiv.org/abs/2202.12482v1 | https://arxiv.org/pdf/2202.12482v1.pdf | Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity | Interpretable machine learning has demonstrated impressive performance while preserving explainability. In particular, neural additive models (NAM) offer the interpretability to the black-box deep learning and achieve state-of-the-art accuracy among the large family of generalized additive models. In order to empower NAM with feature selection and improve the generalization, we propose the sparse neural additive models (SNAM) that employ the group sparsity regularization (e.g. Group LASSO), where each feature is learned by a sub-network whose trainable parameters are clustered as a group. We study the theoretical properties for SNAM with novel techniques to tackle the non-parametric truth, thus extending from classical sparse linear models such as the LASSO, which only works on the parametric truth. Specifically, we show that SNAM with subgradient and proximal gradient descents provably converges to zero training loss as $t\to\infty$, and that the estimation error of SNAM vanishes asymptotically as $n\to\infty$. We also prove that SNAM, similar to LASSO, can have exact support recovery, i.e. perfect feature selection, with appropriate regularization. Moreover, we show that the SNAM can generalize well and preserve the `identifiability', recovering each feature's effect. We validate our theories via extensive experiments and further testify to the good accuracy and efficiency of SNAM. | ['Ian J. Barnett', 'Pratik Chaudhari', 'Zhiqi Bu', 'Shiyun Xu'] | 2022-02-25 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.67499804e-01 5.01600027e-01 -5.05959868e-01 -4.07512307e-01
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3a1b9ea9-3993-4009-a8be-8e835fec720b | character-centric-storytelling | 1909.07863 | null | https://arxiv.org/abs/1909.07863v3 | https://arxiv.org/pdf/1909.07863v3.pdf | Character-Centric Storytelling | Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains. Though existing models generate narratives that read subjectively well, there could be cases when these models miss out on generating stories that account and address all prospective human and animal characters in the image sequences. Considering this scenario, we propose a model that implicitly learns relationships between provided characters and thereby generates stories with respective characters in scope. We use the VIST dataset for this purpose and report numerous statistics on the dataset. Eventually, we describe the model, explain the experiment and discuss our current status and future work. | ['Jorma Laaksonen', 'Aditya Surikuchi'] | 2019-09-17 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.98472553e-01 3.90093476e-01 -1.72113910e-01 -3.51374418e-01
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17cb48f0-d3ed-41ff-88b6-eaed8d2fed9e | multiple-document-datasets-pre-training | 2012.14163 | null | https://arxiv.org/abs/2012.14163v2 | https://arxiv.org/pdf/2012.14163v2.pdf | Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks | In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line segmentation task and more generally the layout analysis problem as a pixel-wise classification task then our model outputs a pixel-labeling of the input images. We show that Doc-UFCN outperforms state-of-the-art methods on various datasets and also demonstrate that the pre-trained parts on natural scene images are not required to reach good results. In addition, we show that pre-training on multiple document datasets can improve the performances. We evaluate the models using various metrics to have a fair and complete comparison between the methods. | ['Thierry Paquet', 'Christopher Kermorvant', 'Mélodie Boillet'] | 2020-12-28 | null | null | null | null | ['document-layout-analysis', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 2.06176743e-01 -1.03946254e-01 7.58345127e-02 -3.97601426e-01
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d0fd6fc0-0bba-494a-8e7e-03340d99337b | facial-emotion-recognition-with-noisy-multi | 2010.09849 | null | https://arxiv.org/abs/2010.09849v2 | https://arxiv.org/pdf/2010.09849v2.pdf | Facial Emotion Recognition with Noisy Multi-task Annotations | Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with noisy multi-task annotations. For this new problem, we suggest a formulation from the point of joint distribution match view, which aims at learning more reliable correlations among raw facial images and multi-task labels, resulting in the reduction of noise influence. In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game. Evaluation throughout extensive experiments studies the real setups of the suggested new problem, as well as the clear superiority of the proposed method over the state-of-the-art competing methods on either the synthetic noisy labeled CIFAR-10 or practical noisy multi-task labeled RAF and AffectNet. The code is available at https://github.com/sanweiliti/noisyFER. | ['Luc van Gool', 'Danda Pani Paudel', 'Zhiwu Huang', 'Siwei Zhang'] | 2020-10-19 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 3.36182155e-02 -3.75485495e-02 3.50895137e-01 -7.65154302e-01
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a4140cbf-2603-422e-9dfc-80841557c368 | explicit-interaction-network-for-aspect | 2106.11148 | null | https://arxiv.org/abs/2106.11148v2 | https://arxiv.org/pdf/2106.11148v2.pdf | Explicit Interaction Network for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divided into 3 atom subtasks, namely target detection, opinion detection and sentiment classification. We argue that the proper subtask combination, compositional feature extraction for target-opinion pairs, and interaction between subtasks would be the key to success. Prior work, however, may fail on `one-to-many' or `many-to-one' situations or derive non-existent sentiment triplets due to defective subtask formulation, sub-optimal feature representation or the lack of subtask interaction. In this paper, we divide ASTE into target-opinion joint detection and sentiment classification subtasks, which is in line with human cognition, and correspondingly utilize sequence encoder and table encoder to handle them. Table encoder extracts sentiment at token-pair level, so that the compositional feature between targets and opinions can be easily captured. To establish explicit interaction between subtasks, we utilize the table representation to guide the sequence encoding, and inject the sequence features back into the table encoder. Experiments show that our model outperforms state-of-the-art methods on six popular ASTE datasets. | ['Zhifang Sui', 'Baobao Chang', 'Runxin Xu', 'Damai Dai', 'Tianyu Liu', 'Peiyi Wang'] | 2021-06-21 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 4.05056447e-01 -1.79362416e-01 -3.33601743e-01 -7.12449193e-01
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65f0f444-335a-4441-8f31-3ecf937dc569 | unsupervised-opinion-summarisation-in-the | 2211.14923 | null | https://arxiv.org/abs/2211.14923v1 | https://arxiv.org/pdf/2211.14923v1.pdf | Unsupervised Opinion Summarisation in the Wasserstein Space | Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus of opinion summarisation. To address these challenges we present \textit{WassOS}, an unsupervised abstractive summarization model which makes use of the Wasserstein distance. A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces. The summary distribution is obtained using the Wasserstein barycenter of the semantic and syntactic distributions. A latent variable sampled from the summary distribution is fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments on multiple datasets including Twitter clusters, Reddit threads, and reviews show that WassOS almost always outperforms the state-of-the-art on ROUGE metrics and consistently produces the best summaries with respect to meaning preservation according to human evaluations. | ['Maria Liakata', 'Rob Procter', 'Adam Tsakalidis', 'Iman Munire Bilal', 'Jiayu Song'] | 2022-11-27 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.06004232e-01 4.19780105e-01 -5.15023433e-02 -4.19307202e-01
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c975c367-1620-468d-bc17-84733b07efdb | dc-former-diverse-and-compact-transformer-for | 2302.14335 | null | https://arxiv.org/abs/2302.14335v1 | https://arxiv.org/pdf/2302.14335v1.pdf | DC-Former: Diverse and Compact Transformer for Person Re-Identification | In person re-identification (re-ID) task, it is still challenging to learn discriminative representation by deep learning, due to limited data. Generally speaking, the model will get better performance when increasing the amount of data. The addition of similar classes strengthens the ability of the classifier to identify similar identities, thereby improving the discrimination of representation. In this paper, we propose a Diverse and Compact Transformer (DC-Former) that can achieve a similar effect by splitting embedding space into multiple diverse and compact subspaces. Compact embedding subspace helps model learn more robust and discriminative embedding to identify similar classes. And the fusion of these diverse embeddings containing more fine-grained information can further improve the effect of re-ID. Specifically, multiple class tokens are used in vision transformer to represent multiple embedding spaces. Then, a self-diverse constraint (SDC) is applied to these spaces to push them away from each other, which makes each embedding space diverse and compact. Further, a dynamic weight controller(DWC) is further designed for balancing the relative importance among them during training. The experimental results of our method are promising, which surpass previous state-of-the-art methods on several commonly used person re-ID benchmarks. | ['Wei Chu', 'Yuan Cheng', 'Ruobing Zheng', 'Jianan Zhao', 'Furong Xu', 'Meng Wang', 'Cheng Zou', 'Wen Li'] | 2023-02-28 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-3.28314215e-01 -4.55395728e-01 -2.39601597e-01 -4.02641892e-01
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9e6fffbb-0331-40ce-9257-f3b82dbd3ab5 | insmos-instance-aware-moving-object | 2303.03909 | null | https://arxiv.org/abs/2303.03909v1 | https://arxiv.org/pdf/2303.03909v1.pdf | InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data | Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our approach not only predicts point-wise moving labels but also detects instance information of main traffic participants. Such a design helps determine which instances are actually moving and which ones are temporarily static in the current scene. Our method exploits a sequence of point clouds as input and quantifies them into 4D voxels. We use 4D sparse convolutions to extract motion features from the 4D voxels and inject them into the current scan. Then, we extract spatio-temporal features from the current scan for instance detection and feature fusion. Finally, we design an upsample fusion module to output point-wise labels by fusing the spatio-temporal features and predicted instance information. We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation. Furthermore, our method shows superior performance on the Apollo dataset with a pre-trained model on SemanticKITTI, indicating that our method generalizes well in different scenes.The code and pre-trained models of our method will be released at https://github.com/nubot-nudt/InsMOS. | ['Xieyuanli Chen', 'Zhiqiang Zheng', 'Huimin Lu', 'Ruibin Guo', 'Chenghao Shi', 'Neng Wang'] | 2023-03-07 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 9.73354280e-02 -5.30859888e-01 -2.43216932e-01 -5.52177310e-01
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3f999c0f-b744-4cde-ad75-b2ceae5f8fc8 | optimizing-prediction-of-mgmt-promoter | 2201.04416 | null | https://arxiv.org/abs/2201.04416v2 | https://arxiv.org/pdf/2201.04416v2.pdf | Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning | Glioblastoma Multiforme (GBM) is a malignant brain cancer forming around 48% of al brain and Central Nervous System (CNS) cancers. It is estimated that annually over 13,000 deaths occur in the US due to GBM, making it crucial to have early diagnosis systems that can lead to predictable and effective treatment. The most common treatment after GBM diagnosis is chemotherapy, which works by sending rapidly dividing cells to apoptosis. However, this form of treatment is not effective when the MGMT promoter sequence is methylated, and instead leads to severe side effects decreasing patient survivability. Therefore, it is important to be able to identify the MGMT promoter methylation status through non-invasive magnetic resonance imaging (MRI) based machine learning (ML) models. This is accomplished using the Brain Tumor Segmentation (BraTS) 2021 dataset, which was recently used for an international Kaggle competition. We developed four primary models - two radiomic models and two CNN models - each solving the binary classification task with progressive improvements. We built a novel ML model termed as the Intermediate State Generator which was used to normalize the slice thicknesses of all MRI scans. With further improvements, our best model was able to achieve performance significantly ($p < 0.05$) better than the best performing Kaggle model with a 6% increase in average cross-validation accuracy. This improvement could potentially lead to a more informed choice of chemotherapy as a treatment option, prolonging lives of thousands of patients with GBM each year. | ['Sauman Das'] | 2022-01-12 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 5.05813181e-01 2.34133661e-01 -1.35657743e-01 -2.31869012e-01
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ec08210c-3738-48a6-b7eb-8a7307bcbb5f | disentangling-task-relations-for-few-shot | 2211.08588 | null | https://arxiv.org/abs/2211.08588v1 | https://arxiv.org/pdf/2211.08588v1.pdf | Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering | Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently with only few examples, by leveraging prior knowledge from historical tasks. However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scenarios where tasks are heterogeneous and lie in different distributions. As such, existing methods may suffer from their globally knowledge-shared mechanisms to handle the task heterogeneity. On the other hand, inherent task relation are not explicitly captured, making task knowledge unorganized and hard to transfer to new tasks. Thus, we explore a new FSTC setting where tasks can come from a diverse range of data sources. To address the task heterogeneity, we propose a self-supervised hierarchical task clustering (SS-HTC) method. SS-HTC not only customizes cluster-specific knowledge by dynamically organizing heterogeneous tasks into different clusters in hierarchical levels but also disentangles underlying relations between tasks to improve the interpretability. Extensive experiments on five public FSTC benchmark datasets demonstrate the effectiveness of SS-HTC. | ['Yu Zhang', 'Ying WEI', 'Zheng Li', 'Juan Zha'] | 2022-11-16 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.34564239e-01 -2.66107053e-01 -1.20911062e-01 -5.42326033e-01
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31461026-ca0e-435b-bacd-d310fef6b4db | improving-code-summarization-with-block-wise | 2103.07845 | null | https://arxiv.org/abs/2103.07845v2 | https://arxiv.org/pdf/2103.07845v2.pdf | Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting | Automatic code summarization frees software developers from the heavy burden of manual commenting and benefits software development and maintenance. Abstract Syntax Tree (AST), which depicts the source code's syntactic structure, has been incorporated to guide the generation of code summaries. However, existing AST based methods suffer from the difficulty of training and generate inadequate code summaries. In this paper, we present the Block-wise Abstract Syntax Tree Splitting method (BASTS for short), which fully utilizes the rich tree-form syntax structure in ASTs, for improving code summarization. BASTS splits the code of a method based on the blocks in the dominator tree of the Control Flow Graph, and generates a split AST for each code split. Each split AST is then modeled by a Tree-LSTM using a pre-training strategy to capture local non-linear syntax encoding. The learned syntax encoding is combined with code encoding, and fed into Transformer to generate high-quality code summaries. Comprehensive experiments on benchmarks have demonstrated that BASTS significantly outperforms state-of-the-art approaches in terms of various evaluation metrics. To facilitate reproducibility, our implementation is available at https://github.com/XMUDM/BASTS. | ['Rongxin Wu', 'Hui Li', 'Jianqiang Chen', 'Junqing Zhuang', 'Zhichao Ouyang', 'Chen Lin'] | 2021-03-14 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.17798078e-01 9.72737372e-02 -3.71455193e-01 -3.49443436e-01
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709a226e-fb3b-4de6-8900-885fe72bedf3 | lift-learn-physics-informed-machine-learning | 1912.08177 | null | https://arxiv.org/abs/1912.08177v5 | https://arxiv.org/pdf/1912.08177v5.pdf | Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems | We present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system's governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh-Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model. | ['Boris Kramer', 'Karen Willcox', 'Elizabeth Qian', 'Benjamin Peherstorfer'] | 2019-12-17 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.06332742e-01 8.58618170e-02 1.07642800e-01 1.33205235e-01
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bfce42f5-5568-4ceb-9e57-975d880b352e | multi-singer-fast-multi-singer-singing-voice-1 | 2112.10358 | null | https://arxiv.org/abs/2112.10358v1 | https://arxiv.org/pdf/2112.10358v1.pdf | Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus | High-fidelity multi-singer singing voice synthesis is challenging for neural vocoder due to the singing voice data shortage, limited singer generalization, and large computational cost. Existing open corpora could not meet requirements for high-fidelity singing voice synthesis because of the scale and quality weaknesses. Previous vocoders have difficulty in multi-singer modeling, and a distinct degradation emerges when conducting unseen singer singing voice generation. To accelerate singing voice researches in the community, we release a large-scale, multi-singer Chinese singing voice dataset OpenSinger. To tackle the difficulty in unseen singer modeling, we propose Multi-Singer, a fast multi-singer vocoder with generative adversarial networks. Specifically, 1) Multi-Singer uses a multi-band generator to speed up both training and inference procedure. 2) to capture and rebuild singer identity from the acoustic feature (i.e., mel-spectrogram), Multi-Singer adopts a singer conditional discriminator and conditional adversarial training objective. 3) to supervise the reconstruction of singer identity in the spectrum envelopes in frequency domain, we propose an auxiliary singer perceptual loss. The joint training approach effectively works in GANs for multi-singer voices modeling. Experimental results verify the effectiveness of OpenSinger and show that Multi-Singer improves unseen singer singing voices modeling in both speed and quality over previous methods. The further experiment proves that combined with FastSpeech 2 as the acoustic model, Multi-Singer achieves strong robustness in the multi-singer singing voice synthesis pipeline. Samples are available at https://Multi-Singer.github.io/ | ['Zhou Zhao', 'Chenye Cui', 'Jinglin Liu', 'Yi Ren', 'Feiyang Chen', 'Rongjie Huang'] | 2021-12-20 | multi-singer-fast-multi-singer-singing-voice | https://dl.acm.org/doi/pdf/10.1145/3474085.3475437 | https://dl.acm.org/doi/pdf/10.1145/3474085.3475437 | mm-21-proceedings-of-the-29th-acm | ['audio-generation', 'singing-voice-synthesis'] | ['audio', 'speech'] | [-8.98959674e-03 -2.66447544e-01 1.10117845e-01 2.12547123e-01
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c7d8b413-1cf1-4d61-b439-9c6f0a0ddcdd | exploring-sequence-to-sequence-transformer | 2211.06478 | null | https://arxiv.org/abs/2211.06478v1 | https://arxiv.org/pdf/2211.06478v1.pdf | Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting | In this paper, we present a novel approach to adapt a sequence-to-sequence Transformer-Transducer ASR system to the keyword spotting (KWS) task. We achieve this by replacing the keyword in the text transcription with a special token <kw> and training the system to detect the <kw> token in an audio stream. At inference time, we create a decision function inspired by conventional KWS approaches, to make our approach more suitable for the KWS task. Furthermore, we introduce a specific keyword spotting loss by adapting the sequence-discriminative Minimum Bayes-Risk training technique. We find that our approach significantly outperforms ASR based KWS systems. When compared with a conventional keyword spotting system, our proposal has similar performance while bringing the advantages and flexibility of sequence-to-sequence training. Additionally, when combined with the conventional KWS system, our approach can improve the performance at any operation point. | ['Quan Wang', 'Liam Fowl', 'Angelo Scorza Scarpati', 'Ignacio López Moreno', 'Guanlong Zhao', 'Beltrán Labrador'] | 2022-11-11 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 5.69744706e-01 -3.80739160e-02 -1.87923864e-01 -2.42907807e-01
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ad2601e9-3e08-4641-afe1-89ef7397b685 | classification-and-reconstruction-of-optical | 2012.02185 | null | https://arxiv.org/abs/2012.02185v1 | https://arxiv.org/pdf/2012.02185v1.pdf | Classification and reconstruction of optical quantum states with deep neural networks | We apply deep-neural-network-based techniques to quantum state classification and reconstruction. We demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural networks (CNNs) can successfully classify several types of states distorted by, e.g., additive Gaussian noise or photon loss. We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection. Secondly, we demonstrate reconstruction of quantum-state density matrices using neural networks that incorporate quantum-physics knowledge. The knowledge is implemented as custom neural-network layers that convert outputs from standard feedforward neural networks to valid descriptions of quantum states. Any standard feed-forward neural-network architecture can be adapted for quantum state tomography (QST) with our method. We present further demonstrations of our proposed [arXiv:2008.03240] QST technique with conditional generative adversarial networks (QST-CGAN). We motivate our choice of a learnable loss function within an adversarial framework by demonstrating that the QST-CGAN outperforms, across a range of scenarios, generative networks trained with standard loss functions. For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state. The QST-CGAN reconstructs states using up to two orders of magnitude fewer iterative steps than a standard iterative maximum likelihood (iMLE) method. Further, the QST-CGAN can reconstruct both pure and mixed states from two orders of magnitude fewer randomly chosen data points than iMLE. | ['Anton Frisk Kockum', 'Franco Nori', 'Carlos Sánchez Muñoz', 'Shahnawaz Ahmed'] | 2020-12-03 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.15439188e-01 2.34346688e-01 3.74481887e-01 -2.96540499e-01
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57b45776-fcd0-44f0-8b7d-79ec24a5b5df | 3d-meta-segmentation-neural-network | 2110.04297 | null | https://arxiv.org/abs/2110.04297v1 | https://arxiv.org/pdf/2110.04297v1.pdf | 3D Meta-Segmentation Neural Network | Though deep learning methods have shown great success in 3D point cloud part segmentation, they generally rely on a large volume of labeled training data, which makes the model suffer from unsatisfied generalization abilities to unseen classes with limited data. To address this problem, we present a novel meta-learning strategy that regards the 3D shape segmentation function as a task. By training over a number of 3D part segmentation tasks, our method is capable to learn the prior over the respective 3D segmentation function space which leads to an optimal model that is rapidly adapting to new part segmentation tasks. To implement our meta-learning strategy, we propose two novel modules: meta part segmentation learner and part segmentation learner. During the training process, the part segmentation learner is trained to complete a specific part segmentation task in the few-shot scenario. In the meantime, the meta part segmentation learner is trained to capture the prior from multiple similar part segmentation tasks. Based on the learned information of task distribution, our meta part segmentation learner is able to dynamically update the part segmentation learner with optimal parameters which enable our part segmentation learner to rapidly adapt and have great generalization ability on new part segmentation tasks. We demonstrate that our model achieves superior part segmentation performance with the few-shot setting on the widely used dataset: ShapeNet. | ['Yi Fang', 'Yu Hao'] | 2021-10-08 | null | null | null | null | ['3d-part-segmentation', '3d-point-cloud-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.62817910e-01 1.34623080e-01 -2.28805915e-01 -3.76861244e-01
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6890955d-11ea-433b-9a6e-7e0b0940c847 | 190910158 | 1909.10158 | null | https://arxiv.org/abs/1909.10158v2 | https://arxiv.org/pdf/1909.10158v2.pdf | Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data | A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text generation from structured and unstructured data. We consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively. Table-to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models. Experimental results demonstrate that a basic attention-based seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks. Code is available at https://github.com/h-shahidi/2birds-gen. | ['Hamidreza Shahidi', 'Ming Li', 'Jimmy Lin'] | 2019-09-23 | two-birds-one-stone-a-simple-unified-model | https://aclanthology.org/2020.acl-main.355 | https://aclanthology.org/2020.acl-main.355.pdf | acl-2020-6 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.50658065e-01 4.94823545e-01 2.26663604e-01 -3.03065866e-01
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07b986f5-894f-4993-ba72-07ddfdda8a2c | bridging-the-gap-between-reality-and-ideality | 2205.05889 | null | https://arxiv.org/abs/2205.05889v1 | https://arxiv.org/pdf/2205.05889v1.pdf | Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction | Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems from the unreasonable benchmark construction process, which is inconsistent with the nature of entity matching and therefore leads to biased evaluations of current EM approaches. To this end, we build a new EM corpus and re-construct EM benchmarks to challenge critical assumptions implicit in the previous benchmark construction process by step-wisely changing the restricted entities, balanced labels, and single-modal records in previous benchmarks into open entities, imbalanced labels, and multimodal records in an open environment. Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching. The constructed benchmarks and code are publicly released | ['Xiuwen Zhu', 'Minlong Lu', 'Hui Chen', 'Feiyu Xiong', 'Le Sun', 'Xianpei Han', 'Cheng Fu', 'Hongyu Lin', 'Tianshu Wang'] | 2022-05-12 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.14196703e-01 4.37248260e-01 -2.02482209e-01 -3.82480145e-01
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f13b0dea-44af-4d1e-905c-2d25b3afea01 | improving-performance-of-automatic-keyword | 2211.05031 | null | https://arxiv.org/abs/2211.05031v1 | https://arxiv.org/pdf/2211.05031v1.pdf | Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness | Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. This paper proposes a simple but effective post-processing-based universal approach to improve the performance of any AKE methods, via an enhanced level of semantic-awareness supported by PoS-tagging. To demonstrate the performance of the proposed approach, we considered word types retrieved from a PoS-tagging step and two representative sources of semantic information -- specialised terms defined in one or more context-dependent thesauri, and named entities in Wikipedia. The above three steps can be simply added to the end of any AKE methods as part of a post-processor, which simply re-evaluate all candidate keywords following some context-specific and semantic-aware criteria. For five state-of-the-art (SOTA) AKE methods, our experimental results with 17 selected datasets showed that the proposed approach improved their performances both consistently (up to 100\% in terms of improved cases) and significantly (between 10.2\% and 53.8\%, with an average of 25.8\%, in terms of F1-score and across all five methods), especially when all the three enhancement steps are used. Our results have profound implications considering the ease to apply our proposed approach to any AKE methods and to further extend it. | ['Shujun Li', 'Jie Guo', 'Yang Xu', 'Jason R. C. Nurse', 'Enes Altuncu'] | 2022-11-09 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 5.69081664e-01 1.72055051e-01 -3.37725207e-02 8.67248103e-02
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94d520f1-5761-4071-83c7-4861b10db14c | dr-2track-towards-real-time-visual-tracking | 2008.03912 | null | https://arxiv.org/abs/2008.03912v1 | https://arxiv.org/pdf/2008.03912v1.pdf | DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression | Visual tracking has yielded promising applications with unmanned aerial vehicle (UAV). In literature, the advanced discriminative correlation filter (DCF) type trackers generally distinguish the foreground from the background with a learned regressor which regresses the implicit circulated samples into a fixed target label. However, the predefined and unchanged regression target results in low robustness and adaptivity to uncertain aerial tracking scenarios. In this work, we exploit the local maximum points of the response map generated in the detection phase to automatically locate current distractors. By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity. Substantial experiments conducted on three challenging UAV benchmarks demonstrate both excellent performance and extraordinary speed (~50fps on a cheap CPU) of our tracker. | ['Yiming Li', 'Chen Feng', 'Fangqiang Ding', 'Changhong Fu', 'Jin Jin'] | 2020-08-10 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-8.99125859e-02 -4.08500761e-01 -1.34320766e-01 1.08819678e-01
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15e08f9d-47f6-4245-bc7f-7fe0ba8b26d1 | scene-as-occupancy | 2306.02851 | null | https://arxiv.org/abs/2306.02851v3 | https://arxiv.org/pdf/2306.02851v3.pdf | Scene as Occupancy | Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-aware representation that quantizes the physical 3D scene into structured grid map with semantic labels per cell, termed as 3D Occupancy, would be desirable. Compared to the form of bounding box, a key insight behind occupancy is that it could capture the fine-grained details of critical obstacles in the scene, and thereby facilitate subsequent tasks. Prior or concurrent literature mainly concentrate on a single scene completion task, where we might argue that the potential of this occupancy representation might obsess broader impact. In this paper, we propose OccNet, a multi-view vision-centric pipeline with a cascade and temporal voxel decoder to reconstruct 3D occupancy. At the core of OccNet is a general occupancy embedding to represent 3D physical world. Such a descriptor could be applied towards a wide span of driving tasks, including detection, segmentation and planning. To validate the effectiveness of this new representation and our proposed algorithm, we propose OpenOcc, the first dense high-quality 3D occupancy benchmark built on top of nuScenes. Empirical experiments show that there are evident performance gain across multiple tasks, e.g., motion planning could witness a collision rate reduction by 15%-58%, demonstrating the superiority of our method. | ['Hongyang Li', 'Dahua Lin', 'Ping Luo', 'Lewei Lu', 'Yi Gu', 'Li Chen', 'Hanming Deng', 'Silei Wu', 'Tai Wang', 'Chonghao Sima', 'Wenwen Tong'] | 2023-06-05 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.67077526e-01 8.70845765e-02 -2.68408079e-02 -2.17183962e-01
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64b1d415-26e1-4e10-939e-cc54f138f0e2 | 3d-instances-as-1d-kernels | 2207.07372 | null | https://arxiv.org/abs/2207.07372v2 | https://arxiv.org/pdf/2207.07372v2.pdf | 3D Instances as 1D Kernels | We introduce a 3D instance representation, termed instance kernels, where instances are represented by one-dimensional vectors that encode the semantic, positional, and shape information of 3D instances. We show that instance kernels enable easy mask inference by simply scanning kernels over the entire scenes, avoiding the heavy reliance on proposals or heuristic clustering algorithms in standard 3D instance segmentation pipelines. The idea of instance kernel is inspired by recent success of dynamic convolutions in 2D/3D instance segmentation. However, we find it non-trivial to represent 3D instances due to the disordered and unstructured nature of point cloud data, e.g., poor instance localization can significantly degrade instance representation. To remedy this, we construct a novel 3D instance encoding paradigm. First, potential instance centroids are localized as candidates. Then, a candidate merging scheme is devised to simultaneously aggregate duplicated candidates and collect context around the merged centroids to form the instance kernels. Once instance kernels are available, instance masks can be reconstructed via dynamic convolutions whose weights are conditioned on instance kernels. The whole pipeline is instantiated with a dynamic kernel network (DKNet). Results show that DKNet outperforms the state of the arts on both ScanNetV2 and S3DIS datasets with better instance localization. Code is available: https://github.com/W1zheng/DKNet. | ['Weicai Zhong', 'Zhiguo Cao', 'Hao Lu', 'Shuaiyuan Du', 'Min Shi', 'Yizheng Wu'] | 2022-07-15 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 6.81834370e-02 2.34310940e-01 -9.32788402e-02 -4.25666392e-01
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0b007f64-3dfe-4f0c-a0eb-090723ce90e8 | an-approach-to-human-iris-recognition-using | 2009.0588 | null | https://arxiv.org/abs/2009.05880v1 | https://arxiv.org/pdf/2009.05880v1.pdf | An approach to human iris recognition using quantitative analysis of image features and machine learning | The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris recognition is proposed in four steps. (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM, and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4) classification (applying multi-layer neural network) to classify 2000 iris images of CASIA-Iris-Interval dataset obtained from 200 volunteers. The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%. | ['Donya Khaledyan', 'Abolfazl Zargari Khuzani', 'Najmeh Mashhadi', 'Morteza Heidari'] | 2020-09-12 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 3.07131648e-01 -4.28613871e-01 -2.27433994e-01 -9.73521397e-02
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5.78310490e-01 -4.35751349e-01 -7.06687331e-01 1.27901092e-01] | [3.7488532066345215, -3.6278018951416016] |
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