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65d4d63b-8276-4339-9214-5be0ea9b1b32 | one-2-3-45-any-single-image-to-3d-mesh-in-45 | 2306.16928 | null | https://arxiv.org/abs/2306.16928v1 | https://arxiv.org/pdf/2306.16928v1.pdf | One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization | Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models. | ['Hao Su', 'Zexiang Xu', 'Mukund Varma T', 'Linghao Chen', 'Haian Jin', 'Chao Xu', 'Minghua Liu'] | 2023-06-29 | null | null | null | null | ['3d-reconstruction', 'image-to-3d', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.21338347e-01 5.96313141e-02 2.04362229e-01 -2.42065743e-01
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c6b52c4e-6058-46bc-b476-9b92b6aa7e86 | tgrnet-a-table-graph-reconstruction-network | 2106.10598 | null | https://arxiv.org/abs/2106.10598v3 | https://arxiv.org/pdf/2106.10598v3.pdf | TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition | A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research. Considering large-scale tabular data in online and offline documents, automatic table recognition has attracted increasing attention from the document analysis community. Though human can easily understand the structure of tables, it remains a challenge for machines to understand that, especially due to a variety of different table layouts and styles. Existing methods usually model a table as either the markup sequence or the adjacency matrix between different table cells, failing to address the importance of the logical location of table cells, e.g., a cell is located in the first row and the second column of the table. In this paper, we reformulate the problem of table structure recognition as the table graph reconstruction, and propose an end-to-end trainable table graph reconstruction network (TGRNet) for table structure recognition. Specifically, the proposed method has two main branches, a cell detection branch and a cell logical location branch, to jointly predict the spatial location and the logical location of different cells. Experimental results on three popular table recognition datasets and a new dataset with table graph annotations (TableGraph-350K) demonstrate the effectiveness of the proposed TGRNet for table structure recognition. Code and annotations will be made publicly available. | ['Qingyong Li', 'DaCheng Tao', 'Wen Wang', 'Baosheng Yu', 'Wenyuan Xue'] | 2021-06-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xue_TGRNet_A_Table_Graph_Reconstruction_Network_for_Table_Structure_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xue_TGRNet_A_Table_Graph_Reconstruction_Network_for_Table_Structure_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['table-recognition', 'cell-detection', 'graph-reconstruction'] | ['computer-vision', 'computer-vision', 'graphs'] | [ 9.47787240e-02 -8.19656551e-02 -4.76309717e-01 -3.11311692e-01
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2a4b9e90-28f3-4284-969a-17ef1c997ed9 | 3d-human-pose-estimation-for-free-form | 2110.08314 | null | https://arxiv.org/abs/2110.08314v1 | https://arxiv.org/pdf/2110.08314v1.pdf | 3D Human Pose Estimation for Free-form Activity Using WiFi Signals | WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, we combine signal separation and joint movement modeling to achieve free-form activity tracking. Our system first identifies the moving limbs by leveraging the two-dimensional angle of arrival of the signals reflected off the human body and separates the entangled signals for each limb. Then, it tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect is environment-independent and achieves centimeter-level accuracy for free-form activity tracking under various challenging environments including the none-line-of-sight (NLoS) scenarios. | ['Jie Yang', 'Yili Ren'] | 2021-10-15 | null | null | null | null | ['3d-human-pose-tracking'] | ['computer-vision'] | [ 1.42914951e-01 -2.07946748e-01 -1.35335207e-01 9.87147987e-02
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d9f433f5-d932-4af6-bd8a-06a18722c15c | robust-design-of-deep-neural-networks-against | 1911.04636 | null | https://arxiv.org/abs/1911.04636v1 | https://arxiv.org/pdf/1911.04636v1.pdf | Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory | Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the DNN. In this work, we take a control theoretic approach to the problem of robustness in DNNs. We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally. We then proceed to prove stability and robustness globally for the entire DNN. We develop empirically tight bounds on the response of the output layer, or any hidden layer, to adversarial perturbations added to the input, or the input of hidden layers. Recent works have proposed spectral norm regularization as a solution for improving robustness against l2 adversarial attacks. Our results give new insights into how spectral norm regularization can mitigate the adversarial effects. Finally, we evaluate the power of our approach on a variety of data sets and network architectures and against some of the well-known adversarial attacks. | ['Arash Rahnama', 'Andre T. Nguyen', 'Edward Raff'] | 2019-11-12 | robust-design-of-deep-neural-networks-against-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Rahnama_Robust_Design_of_Deep_Neural_Networks_Against_Adversarial_Attacks_Based_CVPR_2020_paper.pdf | cvpr-2020-6 | ['robust-design'] | ['miscellaneous'] | [ 1.91881180e-01 3.84757429e-01 3.59346330e-01 6.40499145e-02
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f43b5ee1-a07e-4655-92ab-a8dac2cff93a | autonomous-driving-in-reality-with | 1801.05299 | null | http://arxiv.org/abs/1801.05299v2 | http://arxiv.org/pdf/1801.05299v2.pdf | Autonomous Driving in Reality with Reinforcement Learning and Image Translation | Supervised learning is widely used in training autonomous driving vehicle.
However, it is trained with large amount of supervised labeled data.
Reinforcement learning can be trained without abundant labeled data, but we
cannot train it in reality because it would involve many unpredictable
accidents. Nevertheless, training an agent with good performance in virtual
environment is relatively much easier. Because of the huge difference between
virtual and real, how to fill the gap between virtual and real is challenging.
In this paper, we proposed a novel framework of reinforcement learning with
image semantic segmentation network to make the whole model adaptable to
reality. The agent is trained in TORCS, a car racing simulator. | ['Bingyu Kong', 'Bowen Tan', 'Nayun Xu'] | 2018-01-13 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-3.85526925e-01 1.39354095e-01 -1.96780130e-01 -6.02240264e-01
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fba08ba2-dde8-47b3-b97f-65f48eef31c4 | difficulty-aware-meta-learning-for-rare | 1907.00354 | null | https://arxiv.org/abs/1907.00354v2 | https://arxiv.org/pdf/1907.00354v2.pdf | Difficulty-aware Meta-learning for Rare Disease Diagnosis | Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images. Our key approach is to first train and construct a meta-learning model from data of common diseases, then adapt the model to perform rare disease classification.To achieve this, we develop the difficulty-aware meta-learning method that dynamically monitors the importance of learning tasks during the meta-optimization stage. To evaluate our method, we use the recent ISIC 2018 skin lesion classification dataset, and show that with only five samples per class, our model can quickly adapt to classify unseen classes by a high AUC of 83.3%. Also, we evaluated several rare disease classification results in the public Dermofit Image Library to demonstrate the potential of our method for real clinical practice. | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaomeng Li', 'Lequan Yu', 'Lei Xing', 'Yueming Jin'] | 2019-06-30 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.90636945e-01 1.87145844e-01 -5.03033638e-01 -3.05249274e-01
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79a0b260-6aa7-4694-9313-e2a4a2cb12b3 | xmem-long-term-video-object-segmentation-with | 2207.07115 | null | https://arxiv.org/abs/2207.07115v2 | https://arxiv.org/pdf/2207.07115v2.pdf | XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model | We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature memory model tightly links memory consumption and accuracy. In contrast, following the Atkinson-Shiffrin model, we develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores: a rapidly updated sensory memory, a high-resolution working memory, and a compact thus sustained long-term memory. Crucially, we develop a memory potentiation algorithm that routinely consolidates actively used working memory elements into the long-term memory, which avoids memory explosion and minimizes performance decay for long-term prediction. Combined with a new memory reading mechanism, XMem greatly exceeds state-of-the-art performance on long-video datasets while being on par with state-of-the-art methods (that do not work on long videos) on short-video datasets. Code is available at https://hkchengrex.github.io/XMem | ['Alexander G. Schwing', 'Ho Kei Cheng'] | 2022-07-14 | null | null | null | null | ['semi-supervised-video-object-segmentation', '2d-human-pose-estimation', '3d-absolute-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.63447359e-02 -2.32936159e-01 -4.40419316e-01 -1.97971240e-01
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245604a9-b398-4823-8c58-71f5fe589868 | drafting-in-collectible-card-games-via | null | null | https://ieeexplore.ieee.org/document/9291616 | https://www.sbgames.org/proceedings2020/ComputacaoFull/209690.pdf | Drafting in Collectible Card Games via Reinforcement Learning | Collectible card games are played by tens of millions of players worldwide. Their intricate rules and diverse cards make them much harder than traditional card games. To win, players must be proficient in two interdependent tasks: deck building and battling. In this paper, we present a deep reinforcement learning approach for deck building in arena mode - an understudied game mode present in many collectible card games. In arena, the players build decks immediately before battling by drafting one card at a time from randomly presented candidates. We investigate three variants of the approach and perform experiments on Legends of Code and Magic, a collectible card game designed for AI research. Results show that our learned draft strategies outperform those of the best agents of the game. Moreover, a participant of the Strategy Card Game AI competition improves from tenth to fourth place when coupled with our best draft agent. | ['Luiz Chaimowicz', 'Anderson Rocha Tavares', 'Ronaldo Vieira'] | 2020-11-07 | null | null | null | null | ['card-games'] | ['playing-games'] | [-3.45820069e-01 -1.35066926e-01 1.23591714e-01 2.54106969e-01
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ae6ad664-3a9d-4425-a1e5-2b1ff786ef17 | scelmo-source-code-embeddings-from-language-1 | 2004.13214 | null | https://arxiv.org/abs/2004.13214v1 | https://arxiv.org/pdf/2004.13214v1.pdf | SCELMo: Source Code Embeddings from Language Models | Continuous embeddings of tokens in computer programs have been used to support a variety of software development tools, including readability, code search, and program repair. Contextual embeddings are common in natural language processing but have not been previously applied in software engineering. We introduce a new set of deep contextualized word representations for computer programs based on language models. We train a set of embeddings using the ELMo (embeddings from language models) framework of Peters et al (2018). We investigate whether these embeddings are effective when fine-tuned for the downstream task of bug detection. We show that even a low-dimensional embedding trained on a relatively small corpus of programs can improve a state-of-the-art machine learning system for bug detection. | ['Rafael - Michael Karampatsis', 'Charles Sutton'] | 2020-04-28 | null | https://openreview.net/forum?id=ryxnJlSKvr | https://openreview.net/pdf?id=ryxnJlSKvr | null | ['program-repair', 'code-search', 'code-search', 'program-repair'] | ['computer-code', 'computer-code', 'computer-vision', 'reasoning'] | [-1.49023458e-01 7.60878175e-02 -3.66755992e-01 -1.53853595e-01
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eba6ab80-46cc-43d9-870f-a1c924564303 | abstractive-text-image-summarization-using | null | null | https://aclanthology.org/D18-1438 | https://aclanthology.org/D18-1438.pdf | Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN | Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary. Most previous research summarizes texts or images separately. Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization. This paper proposes an abstractive text-image summarization model using the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously, and then to align the sentences and images in summaries. A multi-modal attentional mechanism is proposed to attend original sentences, images, and captions when decoding. The DailyMail dataset is extended by collecting images and captions from the Web. Experiments show our model outperforms the neural abstractive and extractive text summarization methods that do not consider images. In addition, our model can generate informative summaries of images. | ['Hai Zhuge', 'Jingqiang Chen'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.87422097e-01 3.43064904e-01 -2.71210760e-01 -3.67079616e-01
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16b3f846-56e2-4601-83d4-a16a97eeb6f1 | concentration-of-polynomial-random-matrices | 2209.02655 | null | https://arxiv.org/abs/2209.02655v2 | https://arxiv.org/pdf/2209.02655v2.pdf | Concentration of polynomial random matrices via Efron-Stein inequalities | Analyzing concentration of large random matrices is a common task in a wide variety of fields. Given independent random variables, many tools are available to analyze random matrices whose entries are linear in the variables, e.g. the matrix-Bernstein inequality. However, in many applications, we need to analyze random matrices whose entries are polynomials in the variables. These arise naturally in the analysis of spectral algorithms, e.g., Hopkins et al. [STOC 2016], Moitra-Wein [STOC 2019]; and in lower bounds for semidefinite programs based on the Sum of Squares hierarchy, e.g. Barak et al. [FOCS 2016], Jones et al. [FOCS 2021]. In this work, we present a general framework to obtain such bounds, based on the matrix Efron-Stein inequalities developed by Paulin-Mackey-Tropp [Annals of Probability 2016]. The Efron-Stein inequality bounds the norm of a random matrix by the norm of another simpler (but still random) matrix, which we view as arising by "differentiating" the starting matrix. By recursively differentiating, our framework reduces the main task to analyzing far simpler matrices. For Rademacher variables, these simpler matrices are in fact deterministic and hence, analyzing them is far easier. For general non-Rademacher variables, the task reduces to scalar concentration, which is much easier. Moreover, in the setting of polynomial matrices, our results generalize the work of Paulin-Mackey-Tropp. Using our basic framework, we recover known bounds in the literature for simple "tensor networks" and "dense graph matrices". Using our general framework, we derive bounds for "sparse graph matrices", which were obtained only recently by Jones et al. [FOCS 2021] using a nontrivial application of the trace power method, and was a core component in their work. We expect our framework to be helpful for other applications involving concentration phenomena for nonlinear random matrices. | ['Madhur Tulsiani', 'Goutham Rajendran'] | 2022-09-06 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 8.49786922e-02 1.46017641e-01 -5.69600519e-03 4.89956051e-01
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1acee791-f999-4e0f-8397-620d44a2c725 | learning-theorem-proving-components | 2107.10034 | null | https://arxiv.org/abs/2107.10034v1 | https://arxiv.org/pdf/2107.10034v1.pdf | Learning Theorem Proving Components | Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic. The clause selection heuristics in such systems are, however, often evaluating clauses in isolation, ignoring other clauses. This has changed recently by equipping the E/ENIGMA system with a graph neural network (GNN) that chooses the next given clause based on its evaluation in the context of previously selected clauses. In this work, we describe several algorithms and experiments with ENIGMA, advancing the idea of contextual evaluation based on learning important components of the graph of clauses. | ['Josef Urban', 'Miroslav Olšák', 'Jan Jakubův', 'Karel Chvalovský'] | 2021-07-21 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.74244088e-01 9.35137868e-01 -5.60825884e-01 -2.77929276e-01
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b0403ed9-1c6e-4f69-be51-190b5a14189d | squeeze-excitation-embedded-attention-unet | 2305.07850 | null | https://arxiv.org/abs/2305.07850v1 | https://arxiv.org/pdf/2305.07850v1.pdf | Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation | Deep Learning based techniques have gained significance over the past few years in the field of medicine. They are used in various applications such as classifying medical images, segmentation and identification. The existing architectures such as UNet, Attention UNet and Attention Residual UNet are already currently existing methods for the same application of brain tumor segmentation, but none of them address the issue of how to extract the features in channel level. In this paper, we propose a new architecture called Squeeze Excitation Embedded Attention UNet (SEEA-UNet), this architecture has both Attention UNet and Squeeze Excitation Network for better results and predictions, this is used mainly because to get information at both Spatial and channel levels. The proposed model was compared with the existing architectures based on the comparison it was found out that for lesser number of epochs trained, the proposed model performed better. Binary focal loss and Jaccard Coefficient were used to monitor the model's performance. | ['Sathiya Narayanan', 'Lalitha G', 'John Rohit Ernest', 'Gaurav Prasanna'] | 2023-05-13 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 2.97740698e-01 2.77610630e-01 9.33648199e-02 -2.31561691e-01
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65871c02-ea86-4683-a077-9556e2ddcd47 | sharing-cultural-heritage-the-clavius-on-the | null | null | https://aclanthology.org/L14-1317 | https://aclanthology.org/L14-1317.pdf | Sharing Cultural Heritage: the Clavius on the Web Project | In the last few years the amount of manuscripts digitized and made available on the Web has been constantly increasing. However, there is still a considarable lack of results concerning both the explicitation of their content and the tools developed to make it available. The objective of the Clavius on the Web project is to develop a Web platform exposing a selection of Christophorus Clavius letters along with three different levels of analysis: linguistic, lexical and semantic. The multilayered annotation of the corpus involves a XML-TEI encoding followed by a tokenization step where each token is univocally identified through a CTS urn notation and then associated to a part-of-speech and a lemma. The text is lexically and semantically annotated on the basis of a lexicon and a domain ontology, the former structuring the most relevant terms occurring in the text and the latter representing the domain entities of interest (e.g. people, places, etc.). Moreover, each entity is connected to linked and non linked resources, including DBpedia and VIAF. Finally, the results of the three layers of analysis are gathered and shown through interactive visualization and storytelling techniques. A demo version of the integrated architecture was developed. | ['Matteo Abrate', 'Lorenzo Mancini', 'Andrea Marchetti', 'Irene Pedretti', 'Damiana Luzzi', 'Silvia Piccini', 'Emiliano Giovannetti', 'Angelo Mario Del Grosso', 'Angelica Lo Duca'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.73977956e-01 3.36982459e-01 2.11406848e-03 -5.41644134e-02
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8f8ba9f0-a790-46f4-99d2-220f50474da2 | learning-with-fantasy-semantic-aware-virtual | 2304.00426 | null | https://arxiv.org/abs/2304.00426v1 | https://arxiv.org/pdf/2304.00426v1.pdf | Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes. One tempting method to mitigate this problem is to apply an additional naive supervised contrastive learning (SCL) in the base session. Unfortunately, we find that although SCL can create a slightly better representation separation among different base classes, it still struggles to separate base classes and new classes. Inspired by the observations made, we propose Semantic-Aware Virtual Contrastive model (SAVC), a novel method that facilitates separation between new classes and base classes by introducing virtual classes to SCL. These virtual classes, which are generated via pre-defined transformations, not only act as placeholders for unseen classes in the representation space, but also provide diverse semantic information. By learning to recognize and contrast in the fantasy space fostered by virtual classes, our SAVC significantly boosts base class separation and novel class generalization, achieving new state-of-the-art performance on the three widely-used FSCIL benchmark datasets. Code is available at: https://github.com/zysong0113/SAVC. | ['Yonghong Tian', 'Li Yuan', 'Peixi Peng', 'Yujun Shi', 'Yifan Zhao', 'Zeyin Song'] | 2023-04-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Song_Learning_With_Fantasy_Semantic-Aware_Virtual_Contrastive_Constraint_for_Few-Shot_Class-Incremental_CVPR_2023_paper.pdf | cvpr-2023-1 | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.39537942e-01 -2.65409816e-02 -2.56109118e-01 -3.43444765e-01
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df590546-b5fe-4dc3-b926-851556e69202 | prospect-expanded-conditioning-for-the | 2305.16225 | null | https://arxiv.org/abs/2305.16225v2 | https://arxiv.org/pdf/2305.16225v2.pdf | ProSpect: Expanded Conditioning for the Personalization of Attribute-aware Image Generation | Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes like material, style, layout, etc. remains a challenge, leading to a lack of disentanglement and editability. To address this, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low- to high-frequency information, providing a new perspective on representing, generating, and editing images. We develop Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called ProSpect. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer stronger disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image/text-guided material/style/layout transfer/editing, achieving previously unattainable results with a single image input without fine-tuning the diffusion models. | ['Changsheng Xu', 'Oliver Deussen', 'Tong-Yee Lee', 'Chongyang Ma', 'Haibin Huang', 'Nisha Huang', 'Fan Tang', 'WeiMing Dong', 'Yuxin Zhang'] | 2023-05-25 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 7.87798047e-01 -1.09163038e-02 -6.86786184e-03 -3.16731513e-01
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52e51671-82f1-4852-8ddd-81a2a73ce71b | building-spatio-temporal-transformers-for | 2206.04785 | null | https://arxiv.org/abs/2206.04785v1 | https://arxiv.org/pdf/2206.04785v1.pdf | Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation | Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera. Although existing works use intermediate heatmap-based representations to counter distortion with some success, addressing self-occlusion remains an open problem. In this work, we leverage information from past frames to guide our self-attention-based 3D HPE estimation procedure -- Ego-STAN. Specifically, we build a spatio-temporal Transformer model that attends to semantically rich convolutional neural network-based feature maps. We also propose feature map tokens: a new set of learnable parameters to attend to these feature maps. Finally, we demonstrate Ego-STAN's superior performance on the xR-EgoPose dataset where it achieves a 30.6% improvement on the overall mean per-joint position error, while leading to a 22% drop in parameters compared to the state-of-the-art. | ['Paul Fieguth', 'Sirisha Rambhatla', 'Norikatsu Sumi', 'Saad Hossain', 'Kimathi Kaai', 'JinMan Park'] | 2022-06-09 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.05561383e-01 3.02738816e-01 1.47488505e-01 -5.79912961e-01
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87c7f5d2-b6f1-4fb7-abd1-6a6db777c072 | magsac-marginalizing-sample-consensus | 1803.07469 | null | https://arxiv.org/abs/1803.07469v2 | https://arxiv.org/pdf/1803.07469v2.pdf | MAGSAC: marginalizing sample consensus | A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating the noise sigma, it is marginalized over a range of noise scales. The optimized model is obtained by weighted least-squares fitting where the weights come from the marginalization over sigma of the point likelihoods of being inliers. A new quality function is proposed not requiring sigma and, thus, a set of inliers to determine the model quality. Also, a new termination criterion for RANSAC is built on the proposed marginalization approach. Applying sigma-consensus, MAGSAC is proposed with no need for a user-defined sigma and improving the accuracy of robust estimation significantly. It is superior to the state-of-the-art in terms of geometric accuracy on publicly available real-world datasets for epipolar geometry (F and E) and homography estimation. In addition, applying sigma-consensus only once as a post-processing step to the RANSAC output always improved the model quality on a wide range of vision problems without noticeable deterioration in processing time, adding a few milliseconds. The source code is at https://github.com/danini/magsac. | ['Jiri Matas', 'Daniel Barath', 'Jana Noskova'] | 2018-03-20 | magsac-marginalizing-sample-consensus-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Barath_MAGSAC_Marginalizing_Sample_Consensus_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Barath_MAGSAC_Marginalizing_Sample_Consensus_CVPR_2019_paper.pdf | cvpr-2019-6 | ['homography-estimation'] | ['computer-vision'] | [-1.00266024e-01 -3.76817495e-01 3.81157190e-01 -3.29755038e-01
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e6b33e2d-88af-44bf-9ce3-5d56342152c9 | ced-color-event-camera-dataset | 1904.10772 | null | http://arxiv.org/abs/1904.10772v1 | http://arxiv.org/pdf/1904.10772v1.pdf | CED: Color Event Camera Dataset | Event cameras are novel, bio-inspired visual sensors, whose pixels output
asynchronous and independent timestamped spikes at local intensity changes,
called 'events'. Event cameras offer advantages over conventional frame-based
cameras in terms of latency, high dynamic range (HDR) and temporal resolution.
Until recently, event cameras have been limited to outputting events in the
intensity channel, however, recent advances have resulted in the development of
color event cameras, such as the Color-DAVIS346. In this work, we present and
release the first Color Event Camera Dataset (CED), containing 50 minutes of
footage with both color frames and events. CED features a wide variety of
indoor and outdoor scenes, which we hope will help drive forward event-based
vision research. We also present an extension of the event camera simulator
ESIM that enables simulation of color events. Finally, we present an evaluation
of three state-of-the-art image reconstruction methods that can be used to
convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to
visualise the event stream, and for use in downstream vision applications. | ['Cedric Scheerlinck', 'Nick Barnes', 'Timo Stoffregen', 'Henri Rebecq', 'Davide Scaramuzza', 'Robert Mahony'] | 2019-04-24 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 4.85359997e-01 -9.10475135e-01 6.23567164e-01 -3.22351366e-01
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389b955d-6dc0-48f2-858a-643f988b07d8 | a-novel-bistatic-joint-radar-communication | 2006.16591 | null | https://arxiv.org/abs/2006.16591v1 | https://arxiv.org/pdf/2006.16591v1.pdf | A Novel Bistatic Joint Radar-Communication System in Multi-path Environments | Radar detection and communication can be operated simultaneously in joint radar-communication (JRC) system. In this paper, we propose a bistatic JRC system which is applicable in multi-path environments. Basing on a novel joint waveform, a joint detection process is designed for both target detection and channel estimation. Meanwhile, a low-cost channel equalization method that utilizes the channel state information acquired from the detection process is proposed. The numerical results show that the symbol error rate (SER) of the proposed system is similar to that of the binary frequency shift keying system, and the signal to noise ratio requirement in multi-path environments is less than 2 dB higher compared with that in single-path environment to reach a SER of 10-5. Besides, the knowledge of the embedded information is not required for the joint detection process and the detection performance is robust to unknown information. | ['Jiazhi Ma', 'Jialei Liu', 'Longfei Shi', 'Yuan Quan'] | 2020-06-30 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 5.61837494e-01 -3.72276992e-01 3.01599264e-01 7.32233524e-02
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0a7baa2b-295a-4442-807d-36bd53ba45b6 | marginalized-latent-semantic-encoder-for-zero | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Ding_Marginalized_Latent_Semantic_Encoder_for_Zero-Shot_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Marginalized_Latent_Semantic_Encoder_for_Zero-Shot_Learning_CVPR_2019_paper.pdf | Marginalized Latent Semantic Encoder for Zero-Shot Learning | Zero-shot learning has been well explored to precisely identify new unobserved classes through a visual-semantic function obtained from the existing objects. However, there exist two challenging obstacles: one is that the human-annotated semantics are insufficient to fully describe the visual samples; the other is the domain shift across existing and new classes. In this paper, we attempt to exploit the intrinsic relationship in the semantic manifold when given semantics are not enough to describe the visual objects, and enhance the generalization ability of the visual-semantic function with marginalized strategy. Specifically, we design a Marginalized Latent Semantic Encoder (MLSE), which is learned on the augmented seen visual features and the latent semantic representation. Meanwhile, latent semantics are discovered under an adaptive graph reconstruction scheme based on the provided semantics. Consequently, our proposed algorithm could enrich visual characteristics from seen classes, and well generalize to unobserved classes. Experimental results on zero-shot benchmarks demonstrate that the proposed model delivers superior performance over the state-of-the-art zero-shot learning approaches.
| [' Hongfu Liu', 'Zhengming Ding'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['graph-reconstruction'] | ['graphs'] | [ 2.98988968e-01 2.32365415e-01 -5.19448221e-01 -4.53659117e-01
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d5ea921f-b772-4c22-aeab-1aa7a5540cb0 | sparse-text-generation | 2004.02644 | null | https://arxiv.org/abs/2004.02644v3 | https://arxiv.org/pdf/2004.02644v3.pdf | Sparse Text Generation | Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncation techniques, as in top-$k$ or nucleus sampling. This creates a mismatch between training and testing conditions. In this paper, we use the recently introduced entmax transformation to train and sample from a natively sparse language model, avoiding this mismatch. The result is a text generator with favorable performance in terms of fluency and consistency, fewer repetitions, and n-gram diversity closer to human text. In order to evaluate our model, we propose three new metrics for comparing sparse or truncated distributions: $\epsilon$-perplexity, sparsemax score, and Jensen-Shannon divergence. Human-evaluated experiments in story completion and dialogue generation show that entmax sampling leads to more engaging and coherent stories and conversations. | ['André F. T. Martins', 'Zita Marinho', 'Pedro Henrique Martins'] | 2020-04-06 | null | https://aclanthology.org/2020.emnlp-main.348 | https://aclanthology.org/2020.emnlp-main.348.pdf | emnlp-2020-11 | ['story-completion'] | ['natural-language-processing'] | [ 2.11761743e-01 4.15787488e-01 -1.77808911e-01 -3.95722508e-01
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5c08feff-dc9c-4b69-995c-172036cd2254 | matching-web-tables-to-dbpedia-a-feature | null | null | https://www.semanticscholar.org/paper/Matching-Web-Tables-To-DBpedia-A-Feature-Utility-Ritze-Bizer/74c2c4dc375515a2dc6e3d73993c3ad2d77b0757 | https://openproceedings.org/2017/conf/edbt/paper-148.pdf | Matching web tables to DBpedia-A feature utility study | Relational HTML tables on the Web contain data describing a multitude of entities and covering a wide range of topics. Thus, web tables are very useful for filling missing values in cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. Before web table data can be used to fill missing values, the tables need to be matched to the knowledge base in question. This involves three matching tasks: table-to-class matching, rowto-instance matching, and attribute-to-property matching. Various matching approaches have been proposed for each of these tasks. Unfortunately, the existing approaches are evaluated using different web table corpora. Each individual approach also only exploits a subset of the web table and knowledge base features that are potentially helpful for the matching tasks. These two shortcomings make it difficult to compare the different matching approaches and to judge the impact of each feature on the overall matching results. This paper contributes to improve the understanding of the utility of different features for web table to knowledge base matching by reimplementing different matching techniques as well as similarity score aggregation methods from literature within a single matching framework and evaluating different combinations of these techniques against a single gold standard. The gold standard consists of class-, instance-, and property correspondences between the DBpedia knowledge base and web tables from the Web Data Commons web table corpus. | ['Christian Bizer', 'Dominique Ritze'] | 2017-03-01 | null | null | null | edbt-2017-3 | ['table-annotation', 'row-annotation', 'table-annotation', 'table-type-detection', 'columns-property-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.65961945e-01 2.87913978e-01 -4.87457573e-01 -2.31867850e-01
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6bef4209-a407-4554-96e5-b90886855c3f | sentiment-uncertainty-and-spam-in-twitter | 1509.07612 | null | http://arxiv.org/abs/1509.07612v1 | http://arxiv.org/pdf/1509.07612v1.pdf | Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis | State of the art benchmarks for Twitter Sentiment Analysis do not consider
the fact that for more than half of the tweets from the public stream a
distinct sentiment cannot be chosen. This paper provides a new perspective on
Twitter Sentiment Analysis by highlighting the necessity of explicitly
incorporating uncertainty. Moreover, a dataset of high quality to evaluate
solutions for this new problem is introduced and made publicly available. | ['Nils Haldenwang', 'Oliver Vornberger'] | 2015-09-25 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.87254861e-01 1.32083178e-01 -5.98883927e-01 -7.75792241e-01
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abbf03b7-cb88-476e-9b41-aec9e574e9e2 | integrative-imaging-informatics-for-cancer | 2210.03151 | null | https://arxiv.org/abs/2210.03151v1 | https://arxiv.org/pdf/2210.03151v1.pdf | Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO) | Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using convolutional neural networks, and iv) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists. Following the implementation of the framework in Docker containers, it was applied to two retrospective glioma datasets collected from the Washington University School of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30) comprising preoperative MRI scans from patients with pathologically confirmed gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA datasets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor segmentation for WUSM and MDA, respectively. This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology datasets and demonstrating a high potential for integration as an assistive tool in clinical practice. | ['Daniel S. Marcus', 'Aristeidis Sotiras', 'Caroline Chung', 'Sherry Thorpe', 'Yuzhuo Su', 'Michael Adams', 'John Wood', 'Pamela Lamontagne', 'Matthew Kelsey', 'Divya Yadav', 'Isabelle Hren', 'Mahati Mokkarala', 'Mina Mousa', 'Syed Amaan Abidi', 'Satrajit Chakrabarty'] | 2022-10-06 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.75263369e-01 1.95496500e-01 4.30874005e-02 -3.98465723e-01
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e3fffbcd-7822-42d0-9ab8-cef54ac0ba52 | balanced-coarsening-for-multilevel-hypergraph | 2106.07501 | null | https://arxiv.org/abs/2106.07501v1 | https://arxiv.org/pdf/2106.07501v1.pdf | Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy | We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT algorithm, we generate a prior hypergraph under a relaxed balance constraint. With the prior hypergraph, we have defined the Wasserstein discrepancy to coordinate the optimal transport of coarsening process. And the optimal transport matrix is solved by Sinkhorn algorithm. Our coarsening scheme fully takes into account the minimization of connectivity metric (objective function). For the initial partitioning stage, we define a normalized cut function induced by Fiedler vector, which is theoretically proved to be a concave function. Thereby, a three-point algorithm is designed to find the best cut under the balance constraint. | ['Xu Liu', 'Licheng Jiao', 'Jiaxuan Zhao', 'Zhicheng Guo'] | 2021-06-14 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 4.18211371e-02 3.18600833e-01 -3.04825723e-01 -6.29133284e-02
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f85d64ac-a969-41f5-bcce-84f8348aaef3 | exploration-by-self-supervised-exploitation | 2302.11563 | null | https://arxiv.org/abs/2302.11563v2 | https://arxiv.org/pdf/2302.11563v2.pdf | Exploration by self-supervised exploitation | Reinforcement learning can solve decision-making problems and train an agent to behave in an environment according to a predesigned reward function. However, such an approach becomes very problematic if the reward is too sparse and the agent does not come across the reward during the environmental exploration. The solution to such a problem may be in equipping the agent with an intrinsic motivation, which will provide informed exploration, during which the agent is likely to also encounter external reward. Novelty detection is one of the promising branches of intrinsic motivation research. We present Self-supervised Network Distillation (SND), a class of internal motivation algorithms based on the distillation error as a novelty indicator, where the target model is trained using self-supervised learning. We adapted three existing self-supervised methods for this purpose and experimentally tested them on a set of ten environments that are considered difficult to explore. The results show that our approach achieves faster growth and higher external reward for the same training time compared to the baseline models, which implies improved exploration in a very sparse reward environment. | ['Igor Farkaš', 'Michal Chovanec', 'Matej Pecháč'] | 2023-02-22 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 2.10527763e-01 4.15176690e-01 -3.44679177e-01 -2.51777202e-01
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f8083e68-2f56-49db-84be-62e5193b84b2 | multi-frame-super-resolution-from-noisy-data | 2103.13778 | null | https://arxiv.org/abs/2103.13778v1 | https://arxiv.org/pdf/2103.13778v1.pdf | Multi-frame Super-resolution from Noisy Data | Obtaining high resolution images from low resolution data with clipped noise is algorithmically challenging due to the ill-posed nature of the problem. So far such problems have hardly been tackled, and the few existing approaches use simplistic regularisers. We show the usefulness of two adaptive regularisers based on anisotropic diffusion ideas: Apart from evaluating the classical edge-enhancing anisotropic diffusion regulariser, we introduce a novel non-local one with one-sided differences and superior performance. It is termed sector diffusion. We combine it with all six variants of the classical super-resolution observational model that arise from permutations of its three operators for warping, blurring, and downsampling. Surprisingly, the evaluation in a practically relevant noisy scenario produces a different ranking than the one in the noise-free setting in our previous work (SSVM 2017). | ['Joachim Weickert', 'Kireeti Bodduna'] | 2021-03-25 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 6.68360829e-01 -3.40151303e-02 6.54825568e-01 1.41847774e-01
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65e36d76-5e6a-49a9-ba13-86c58c3d75da | training-augmentation-with-adversarial | 1806.02782 | null | http://arxiv.org/abs/1806.02782v2 | http://arxiv.org/pdf/1806.02782v2.pdf | Training Augmentation with Adversarial Examples for Robust Speech Recognition | This paper explores the use of adversarial examples in training speech
recognition systems to increase robustness of deep neural network acoustic
models. During training, the fast gradient sign method is used to generate
adversarial examples augmenting the original training data. Different from
conventional data augmentation based on data transformations, the examples are
dynamically generated based on current acoustic model parameters. We assess the
impact of adversarial data augmentation in experiments on the Aurora-4 and
CHiME-4 single-channel tasks, showing improved robustness against noise and
channel variation. Further improvement is obtained when combining adversarial
examples with teacher/student training, leading to a 23% relative word error
rate reduction on Aurora-4. | ['Mei-Yuh Hwang', 'Mari Ostendorf', 'Ching-Feng Yeh', 'Sining Sun', 'Lei Xie'] | 2018-06-07 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 5.08589387e-01 4.45296168e-01 3.27781349e-01 -3.79070073e-01
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e39ff1ce-39fe-40f6-a242-e48a3e4fdd94 | investigating-non-local-features-for-neural-1 | null | null | https://openreview.net/forum?id=n-pZduaeJvB | https://openreview.net/pdf?id=n-pZduaeJvB | Investigating Non-local Features for Neural Constituency Parsing | Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent -gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1). Our parser also outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['constituency-parsing'] | ['natural-language-processing'] | [-1.63937047e-01 3.55398268e-01 -3.13681394e-01 -8.73985410e-01
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5ddf8185-5b9e-4da8-9d34-21393bf40843 | sister-help-data-augmentation-for-frame | 2109.07725 | null | https://arxiv.org/abs/2109.07725v1 | https://arxiv.org/pdf/2109.07725v1.pdf | Sister Help: Data Augmentation for Frame-Semantic Role Labeling | While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address these gaps. We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. Our rule-based approach defines the notion of a sister lexical unit and generates frame-specific augmented data for training. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation: we obtain a large improvement to prior results on frame identification and argument identification for FrameNet, utilizing both full-text and lexicographic annotations under FrameNet. Our findings on data augmentation highlight the value of automatic resource creation for improved models in frame-semantic parsing. | ['Swabha Swayamdipta', 'Miriam R. L. Petruck', 'Ayush Pancholy'] | 2021-09-16 | null | https://aclanthology.org/2021.law-1.8 | https://aclanthology.org/2021.law-1.8.pdf | emnlp-law-dmr-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.80411613e-01 8.60525906e-01 -6.57805800e-01 -6.87205315e-01
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3bc98bf5-6e7a-4786-93be-70d0d8fe2508 | improving-multilingual-neural-machine-1 | null | null | https://aclanthology.org/2020.loresmt-1.8 | https://aclanthology.org/2020.loresmt-1.8.pdf | Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese | Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs’ joint training. This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. The first strategy is about dynamical learning word similarity of tokens in the shared space among source languages while another one attempts to augment the translation ability of rare words through updating their embeddings during the training. Besides, we leverage monolingual data for multilingual MT systems to increase the amount of synthetic parallel corpora while dealing with the data sparsity problem. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs and released our datasets for the research community. | ['Le-Minh Nguyen', 'Khac-Quy Dinh', 'Thanh-Le Ha', 'Phuong-Thai Nguyen', 'Thi-Vinh Ngo'] | null | null | null | null | loresmt-aacl-2020-12 | ['word-similarity'] | ['natural-language-processing'] | [-2.89359957e-01 -2.68765062e-01 -5.13957739e-01 -2.61783779e-01
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249f47e1-39b9-41a3-b85c-1fa22b1cd7fd | a-high-frequency-focused-network-for | 2303.11701 | null | https://arxiv.org/abs/2303.11701v1 | https://arxiv.org/pdf/2303.11701v1.pdf | A High-Frequency Focused Network for Lightweight Single Image Super-Resolution | Lightweight neural networks for single-image super-resolution (SISR) tasks have made substantial breakthroughs in recent years. Compared to low-frequency information, high-frequency detail is much more difficult to reconstruct. Most SISR models allocate equal computational resources for low-frequency and high-frequency information, which leads to redundant processing of simple low-frequency information and inadequate recovery of more challenging high-frequency information. We propose a novel High-Frequency Focused Network (HFFN) through High-Frequency Focused Blocks (HFFBs) that selectively enhance high-frequency information while minimizing redundant feature computation of low-frequency information. The HFFB effectively allocates more computational resources to the more challenging reconstruction of high-frequency information. Moreover, we propose a Local Feature Fusion Block (LFFB) effectively fuses features from multiple HFFBs in a local region, utilizing complementary information across layers to enhance feature representativeness and reduce artifacts in reconstructed images. We assess the efficacy of our proposed HFFN on five benchmark datasets and show that it significantly enhances the super-resolution performance of the network. Our experimental results demonstrate state-of-the-art performance in reconstructing high-frequency information while using a low number of parameters. | ['Yudong Zhang', 'Junsheng Zhou', 'Yanhui Gu', 'Zhichao Zheng', 'Yi Chen', 'Xiaotian Weng'] | 2023-03-21 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 4.10507947e-01 -3.57966006e-01 -9.96416509e-02 -2.87596405e-01
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95c7d4f5-a7a1-4aaf-9e60-23e84cd37581 | multi-dialectal-representation-learning-of | 2307.01209 | null | https://arxiv.org/abs/2307.01209v1 | https://arxiv.org/pdf/2307.01209v1.pdf | Multi-Dialectal Representation Learning of Sinitic Phonology | Machine learning techniques have shown their competence for representing and reasoning in symbolic systems such as language and phonology. In Sinitic Historical Phonology, notable tasks that could benefit from machine learning include the comparison of dialects and reconstruction of proto-languages systems. Motivated by this, this paper provides an approach for obtaining multi-dialectal representations of Sinitic syllables, by constructing a knowledge graph from structured phonological data, then applying the BoxE technique from knowledge base learning. We applied unsupervised clustering techniques to the obtained representations to observe that the representations capture phonemic contrast from the input dialects. Furthermore, we trained classifiers to perform inference of unobserved Middle Chinese labels, showing the representations' potential for indicating archaic, proto-language features. The representations can be used for performing completion of fragmented Sinitic phonological knowledge bases, estimating divergences between different characters, or aiding the exploration and reconstruction of archaic features. | ['Zhibai Jia'] | 2023-06-30 | null | null | null | null | ['representation-learning'] | ['methodology'] | [ 1.92617290e-02 2.82520831e-01 -2.69869089e-01 -3.27983439e-01
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5b054dda-ac11-4bc7-90f4-cddcb17398cb | a-survey-on-applications-of-artificial | 2007.02202 | null | https://arxiv.org/abs/2007.02202v2 | https://arxiv.org/pdf/2007.02202v2.pdf | A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 | The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19. | ['Keqin Li', 'Zhaolei Zhang', 'Philip S. Yu', 'Kenli Li', 'Jianguo Chen'] | 2020-07-04 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 1.99282423e-01 -5.87141216e-01 1.35580167e-01 2.02564508e-01
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acdec856-e3fe-4bf2-8358-e87aa9f301e9 | approximated-prompt-tuning-for-vision | 2306.15706 | null | https://arxiv.org/abs/2306.15706v1 | https://arxiv.org/pdf/2306.15706v1.pdf | Approximated Prompt Tuning for Vision-Language Pre-trained Models | Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +6.6% accuracy and -64.62% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches. | ['Rongrong Ji', 'Guannan Jiang', 'Annan Shu', 'Pingyang Dai', 'Yiyi Zhou', 'Shubin Huang', 'Qiong Wu'] | 2023-06-27 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [-9.37697757e-03 -1.78771362e-01 -2.58493960e-01 -1.49107084e-01
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4a73442d-84bb-4edc-a456-8c5857152fcd | gcdh-lt-edi-eacl2021-xlm-roberta-for-hope | null | null | https://aclanthology.org/2021.ltedi-1.19 | https://aclanthology.org/2021.ltedi-1.19.pdf | GCDH@LT-EDI-EACL2021: XLM-RoBERTa for Hope Speech Detection in English, Malayalam, and Tamil | This paper describes approaches to identify Hope Speech in short, informal texts in English, Malayalam and Tamil using different machine learning techniques. We demonstrate that even very simple baseline algorithms perform reasonably well on this task if provided with enough training data. However, our best performing algorithm is a cross-lingual transfer learning approach in which we fine-tune XLM-RoBERTa. | ['Aravind Krishnan', 'Franziska Pannach', 'Stefan Ziehe'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [ 8.04418400e-02 1.58821680e-02 -4.21884298e-01 -5.02954900e-01
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0ac53112-e098-4a60-9de0-eb4e50eee927 | mixedteacher-knowledge-distillation-for-fast | 2306.09859 | null | https://arxiv.org/abs/2306.09859v1 | https://arxiv.org/pdf/2306.09859v1.pdf | MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection | For a very long time, unsupervised learning for anomaly detection has been at the heart of image processing research and a stepping stone for high performance industrial automation process. With the emergence of CNN, several methods have been proposed such as Autoencoders, GAN, deep feature extraction, etc. In this paper, we propose a new method based on the promising concept of knowledge distillation which consists of training a network (the student) on normal samples while considering the output of a larger pretrained network (the teacher). The main contributions of this paper are twofold: First, a reduced student architecture with optimal layer selection is proposed, then a new Student-Teacher architecture with network bias reduction combining two teachers is proposed in order to jointly enhance the performance of anomaly detection and its localization accuracy. The proposed texture anomaly detector has an outstanding capability to detect defects in any texture and a fast inference time compared to the SOTA methods. | ['Mahmoud Soua', 'Hichem Snoussi', 'Simon Thomine'] | 2023-06-16 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 2.80880511e-01 1.92592904e-01 2.88241953e-01 -2.56696075e-01
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65bdba68-bc54-40f5-957a-ad244e5564d8 | consistency-regularization-for-domain | 2208.11084 | null | https://arxiv.org/abs/2208.11084v1 | https://arxiv.org/pdf/2208.11084v1.pdf | Consistency Regularization for Domain Adaptation | Collection of real world annotations for training semantic segmentation models is an expensive process. Unsupervised domain adaptation (UDA) tries to solve this problem by studying how more accessible data such as synthetic data can be used to train and adapt models to real world images without requiring their annotations. Recent UDA methods applies self-learning by training on pixel-wise classification loss using a student and teacher network. In this paper, we propose the addition of a consistency regularization term to semi-supervised UDA by modelling the inter-pixel relationship between elements in networks' output. We demonstrate the effectiveness of the proposed consistency regularization term by applying it to the state-of-the-art DAFormer framework and improving mIoU19 performance on the GTA5 to Cityscapes benchmark by 0.8 and mIou16 performance on the SYNTHIA to Cityscapes benchmark by 1.2. | ['Basura Fernando', 'Kian Boon Koh'] | 2022-08-23 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 4.32942420e-01 5.97272515e-01 -9.49949846e-02 -6.53881669e-01
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401d1b45-2cd1-4a79-9997-9bf4549f1c22 | graformer-graph-convolution-transformer-for | 2109.08364 | null | https://arxiv.org/abs/2109.08364v1 | https://arxiv.org/pdf/2109.08364v1.pdf | GraFormer: Graph Convolution Transformer for 3D Pose Estimation | Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation. To alleviate this issue, we propose GraFormer, a novel transformer architecture combined with graph convolution for 3D pose estimation. The proposed GraFormer comprises two repeatedly stacked core modules, GraAttention and ChebGConv block. GraAttention enables all 2D joints to interact in global receptive field without weakening the graph structure information of joints, which introduces vital features for later modules. Unlike vanilla graph convolutions that only model the apparent relationship of joints, ChebGConv block enables 2D joints to interact in the high-order sphere, which formulates their hidden implicit relations. We empirically show the superiority of GraFormer through conducting extensive experiments across popular benchmarks. Specifically, GraFormer outperforms state of the art on Human3.6M dataset while using 18$\%$ parameters. The code is available at https://github.com/Graformer/GraFormer . | ['Weiqiang Wang', 'Jianbin Jiao', 'Qixiang Ye', 'Yunjie Tian', 'Weixi Zhao'] | 2021-09-17 | null | null | null | null | ['3d-pose-estimation', 'implicit-relations'] | ['computer-vision', 'natural-language-processing'] | [-5.61059415e-01 5.04318953e-01 8.90137702e-02 -2.21375078e-01
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19b23bc7-3285-4f3e-9fd8-48d427ef1853 | automated-static-camera-calibration-with | 2304.10814 | null | https://arxiv.org/abs/2304.10814v1 | https://arxiv.org/pdf/2304.10814v1.pdf | Automated Static Camera Calibration with Intelligent Vehicles | Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated geo-referenced camera calibration. Our method requires a calibration vehicle equipped with a combined GNSS/RTK receiver and an inertial measurement unit (IMU) for self-localization. In order to remove any requirements for the target's appearance and the local traffic conditions, we propose a novel approach using hypothesis filtering. Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle. Furthermore, we do not limit road access for other road users during calibration. We demonstrate the feasibility and accuracy of our approach by evaluating our approach on synthetic datasets as well as a real-world connected intersection, and deploying the calibration on real infrastructure. Our source code is publicly available. | ['Vasileios Belagiannis', 'Michael Buchholz', 'Jan Strohbeck', 'Adrian Holzbock', 'Alexander Tsaregorodtsev'] | 2023-04-21 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 5.58315739e-02 1.11371271e-01 3.26393917e-02 -4.20416921e-01
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5d4141fe-552f-4b6c-aae3-46aa7a5e26ba | notes-on-using-determinantal-point-processes | 1410.6975 | null | http://arxiv.org/abs/1410.6975v1 | http://arxiv.org/pdf/1410.6975v1.pdf | Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering | In this paper, we compare three initialization schemes for the KMEANS
clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and
3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k
needs to be set by the user of the algorithms. (Kang 2013) recently proposed a
novel use of determinantal point processes for sampling the initial centroids
for the KMEANS algorithm (we call it KMEANSD++). They, however, do not provide
any evaluation establishing that KMEANSD++ is better than other algorithms. In
this paper, we show that the performance of KMEANSD++ is comparable to KMEANS++
(both of which are better than KMEANSRAND) with KMEANSD++ having an additional
that it can automatically approximate the value of k. | ['Krzysztof Choromanski', 'Apoorv Agarwal', 'Anna Choromanska'] | 2014-10-26 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-3.81410271e-01 -1.21825196e-01 -2.39861701e-02 9.94424298e-02
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20ef2c32-a426-4112-8e18-671a1b42caed | reservoir-computing-via-quantum-recurrent | 2211.02612 | null | https://arxiv.org/abs/2211.02612v1 | https://arxiv.org/pdf/2211.02612v1.pdf | Reservoir Computing via Quantum Recurrent Neural Networks | Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based methods require significant computational resources to perform the gradient-based optimization of a larger number of quantum circuit parameters. The major drawback is that such quantum gradient calculation requires a large amount of circuit evaluation, posing challenges in current near-term quantum hardware and simulation software. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU. The main idea to this RC approach is that the QRNN with randomly initialized weights is treated as a dynamical system and only the final classical linear layer is trained. Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series prediction tasks. Since the QRNN training complexity is significantly reduced, the proposed model trains notably faster. In this work we also compare to corresponding classical RNN-based RC implementations and show that the quantum version learns faster by requiring fewer training epochs in most cases. Our results demonstrate a new possibility to utilize quantum neural network for sequential modeling with greater quantum hardware efficiency, an important design consideration for noisy intermediate-scale quantum (NISQ) computers. | ['Charlee Stefanski', 'Vladimir Rastunkov', 'Amol Deshmukh', 'Daniel Fry', 'Samuel Yen-Chi Chen'] | 2022-11-04 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.61640966e-01 -1.73290864e-01 1.54605195e-01 -2.68786307e-02
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f2b28f89-3b2e-4093-b8a7-c1b5f1e36951 | cxr-net-an-encoder-decoder-encoder-multitask | 2110.10813 | null | https://arxiv.org/abs/2110.10813v1 | https://arxiv.org/pdf/2110.10813v1.pdf | CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images | Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed to detect COVID-19 pneumonia using CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing commonly used visual explanation methods are either too noisy or imprecise, with low resolution, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable deep learning framework (CXRNet) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation from CXR images. The proposed framework is based on a new Encoder-Decoder-Encoder multitask architecture, allowing for both disease classification and visual explanation. The method has been evaluated on real world CXR datasets from both public and private data sources, including: healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases The experimental results demonstrate that the proposed method can achieve a satisfactory level of accuracy and provide fine-resolution classification activation maps for visual explanation in lung disease detection. The Average Accuracy, the Precision, Recall and F1-score of COVID-19 pneumonia reached 0.879, 0.985, 0.992 and 0.989, respectively. We have also found that using lung segmented (CXR) images can help improve the performance of the model. The proposed method can provide more detailed high resolution visual explanation for the classification decision, compared to current state-of-the-art visual explanation methods and has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis. | ['Stephen White', 'Haoming Chen', 'Ascanio Tridente', 'Symeon Lechareas', 'Nina Dempsey', 'Lianghao Han', 'Tam Sobeih', 'Liangxiu Han', 'Xin Zhang'] | 2021-10-20 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 9.65529084e-02 -2.38176703e-01 -1.75164431e-01 1.97115578e-02
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d98dcfa4-8a00-45bb-a8de-c51d29c88411 | accurate-molecular-orbital-based-machine | 2204.09831 | null | https://arxiv.org/abs/2204.09831v1 | https://arxiv.org/pdf/2204.09831v1.pdf | Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space | We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [J. Chem. Theory Comput., 15, 6668 (2019)] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantage of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and having improved performance with an increasing number of training examples. The resulting clusters from supervised or unsupervised clustering is further combined with scalable Gaussian process regression (GPR) or linear regression (LR) to learn molecular energies accurately by generating a local regression model in each cluster. Among all four combinations of regressors and clustering methods, GMM combined with scalable exact Gaussian process regression (GMM/GPR) is the most efficient training protocol for MOB-ML. The numerical tests of molecular energy learning on thermalized datasets of drug-like molecules demonstrate the improved accuracy, transferability, and learning efficiency of GMM/GPR over not only other training protocols for MOB-ML, i.e., supervised regression-clustering combined with GPR(RC/GPR) and GPR without clustering. GMM/GPR also provide the best molecular energy predictions compared with the ones from literature on the same benchmark datasets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in wall-clock training time compared with scalable exact GPR with a training size of 6500 QM7b-T molecules. | ['Thomas F. Miller III', 'Jiace Sun', 'Lixue Cheng'] | 2022-04-21 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.38997154e-02 -2.05514506e-01 -1.44032389e-01 -1.69007853e-01
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918931a2-ca26-4895-b8a1-2cbf8042e9f8 | increasing-robustness-for-cross-domain | null | null | https://aclanthology.org/2022.wnut-1.20 | https://aclanthology.org/2022.wnut-1.20.pdf | Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data | Automatically detecting the intent of an utterance is important for various downstream natural language processing tasks. This task is also called Dialogue Act Classification (DAC) and was primarily researched on spoken one-to-one conversations. The rise of social media has made this an interesting data source to explore within DAC, although it comes with some difficulties: non-standard form, variety of language types (across and within platforms), and quickly evolving norms. We therefore investigate the robustness of DAC on social media data in this paper. More concretely, we provide a benchmark that includes cross-domain data splits, as well as a variety of improvements on our transformer-based baseline. Our experiments show that lexical normalization is not beneficial in this setup, balancing the labels through resampling is beneficial in some cases, and incorporating context is crucial for this task and leads to the highest performance improvements 7 F1 percentage points in-domain and 20 cross-domain). | ['Rob van der Goot', 'Nikolaj Wallenius', 'Marcus Vielsted'] | null | null | null | null | coling-wnut-2022-10 | ['lexical-normalization', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.79797158e-01 2.35826537e-01 -1.87346339e-01 -5.82482934e-01
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5e4bf20c-e6b2-4842-bc7a-39ddcede32c1 | an-unsupervised-learning-model-for-deformable | 1802.02604 | null | http://arxiv.org/abs/1802.02604v3 | http://arxiv.org/pdf/1802.02604v3.pdf | An Unsupervised Learning Model for Deformable Medical Image Registration | We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph . | ['John Guttag', 'Mert R. Sabuncu', 'Guha Balakrishnan', 'Adrian V. Dalca', 'Amy Zhao'] | 2018-02-07 | an-unsupervised-learning-model-for-deformable-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Balakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Balakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.pdf | cvpr-2018-6 | ['deformable-medical-image-registration'] | ['medical'] | [ 2.11862803e-01 1.38701051e-01 -2.12309748e-01 -7.45149136e-01
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1bb3a292-c76b-4887-bda3-f388460058a0 | interpreting-forecasted-vital-signs-using-n | 2306.14016 | null | https://arxiv.org/abs/2306.14016v1 | https://arxiv.org/pdf/2306.14016v1.pdf | Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients | Detecting and predicting septic shock early is crucial for the best possible outcome for patients. Accurately forecasting the vital signs of patients with sepsis provides valuable insights to clinicians for timely interventions, such as administering stabilizing drugs or optimizing infusion strategies. Our research examines N-BEATS, an interpretable deep-learning forecasting model that can forecast 3 hours of vital signs for sepsis patients in intensive care units (ICUs). In this work, we use the N-BEATS interpretable configuration to forecast the vital sign trends and compare them with the actual trend to understand better the patient's changing condition and the effects of infused drugs on their vital signs. We evaluate our approach using the publicly available eICU Collaborative Research Database dataset and rigorously evaluate the vital sign forecasts using out-of-sample evaluation criteria. We present the performance of our model using error metrics, including mean squared error (MSE), mean average percentage error (MAPE), and dynamic time warping (DTW), where the best scores achieved are 18.52e-4, 7.60, and 17.63e-3, respectively. We analyze the samples where the forecasted trend does not match the actual trend and study the impact of infused drugs on changing the actual vital signs compared to the forecasted trend. Additionally, we examined the mortality rates of patients where the actual trend and the forecasted trend did not match. We observed that the mortality rate was higher (92%) when the actual and forecasted trends closely matched, compared to when they were not similar (84%). | ['Jang Yong Kim', 'Yonghwan Kim', 'San Lee', 'Choongmin Kim', 'Marium Hassan', 'Naveen Thangavelu', 'Anubhav Bhatti'] | 2023-06-24 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [-8.99564996e-02 -4.10841197e-01 3.22254866e-01 -2.23454520e-01
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51db38be-a270-4439-9373-d53119ed134b | collaborative-recognition-of-feasible-region | 2103.00947 | null | https://arxiv.org/abs/2103.00947v2 | https://arxiv.org/pdf/2103.00947v2.pdf | Collaborative Recognition of Feasible Region with Aerial and Ground Robots through DPCN | Ground robots always get collision in that only if they get close to the obstacles, can they sense the danger and take actions, which is usually too late to avoid the crash, causing severe damage to the robots. To address this issue, we present collaboration of aerial and ground robots in recognition of feasible region. Taking the aerial robots' advantages of having large scale variance of view points of the same route which the ground robots is on, the collaboration work provides global information of road segmentation for the ground robot, thus enabling it to obtain feasible region and adjust its pose ahead of time. Under normal circumstance, the transformation between these two devices can be obtained by GPS yet with much error, directly causing inferior influence on recognition of feasible region. Thereby, we utilize the state-of-the-art research achievements in matching heterogeneous sensor measurements called deep phase correlation network(DPCN), which has excellent performance on heterogeneous mapping, to refine the transformation. The network is light-weighted and promising for better generalization. We use Aero-Ground dataset which consists of heterogeneous sensor images and aerial road segmentation images. The results show that our collaborative system has great accuracy, speed and stability. | ['Rong Xiong', 'Yue Wang', 'Zexi Chen', 'Zheyuan Huang', 'Yunshuang Li'] | 2021-03-01 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-2.16942281e-02 -1.91859342e-02 1.97355561e-02 -2.15848088e-01
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df302808-2dcc-41dd-92fe-5b7c58fef2a4 | xraysyn-realistic-view-synthesis-from-a | 2012.02407 | null | https://arxiv.org/abs/2012.02407v2 | https://arxiv.org/pdf/2012.02407v2.pdf | XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors | A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. Hence, radiograph analysis naturally requires physicians to relate the prior about 3D human anatomy to 2D radiographs. Synthesizing novel radiographic views in a small range can assist physicians in interpreting anatomy more reliably; however, radiograph view synthesis is heavily ill-posed, lacking in paired data, and lacking in differentiable operations to leverage learning-based approaches. To address these problems, we use Computed Tomography (CT) for radiograph simulation and design a differentiable projection algorithm, which enables us to achieve geometrically consistent transformations between the radiography and CT domains. Our method, XraySyn, can synthesize novel views on real radiographs through a combination of realistic simulation and finetuning on real radiographs. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels. | ['Rama Chellappa', 'Shaohua Kevin Zhou', 'Jiebo Luo', 'Gina Wong', 'Haofu Liao', 'Cheng Peng'] | 2020-12-04 | null | null | null | null | ['3d-aware-image-synthesis', 'bone-suppression-from-dual-energy-chest-x'] | ['computer-vision', 'medical'] | [ 5.12824237e-01 6.75221741e-01 -2.52393782e-02 -3.46769392e-01
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3e8947e4-81af-4ec5-be06-4b600c92203a | classbases-at-case-2022-multilingual-protest | 2301.06617 | null | https://arxiv.org/abs/2301.06617v1 | https://arxiv.org/pdf/2301.06617v1.pdf | ClassBases at CASE-2022 Multilingual Protest Event Detection Tasks: Multilingual Protest News Detection and Automatically Replicating Manually Created Event Datasets | In this report, we describe our ClassBases submissions to a shared task on multilingual protest event detection. For the multilingual protest news detection, we participated in subtask-1, subtask-2, and subtask-4, which are document classification, sentence classification, and token classification. In subtask-1, we compare XLM-RoBERTa-base, mLUKE-base, and XLM-RoBERTa-large on finetuning in a sequential classification setting. We always use a combination of the training data from every language provided to train our multilingual models. We found that larger models seem to work better and entity knowledge helps but at a non-negligible cost. For subtask-2, we only submitted an mLUKE-base system for sentence classification. For subtask-4, we only submitted an XLM-RoBERTa-base for token classification system for sequence labeling. For automatically replicating manually created event datasets, we participated in COVID-related protest events from the New York Times news corpus. We created a system to process the crawled data into a dataset of protest events. | ['Peratham Wiriyathammabhum'] | 2023-01-16 | null | null | null | null | ['document-classification', 'sentence-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-2.19370008e-01 7.79372454e-02 -1.68433979e-01 -3.66951972e-01
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a9f8e153-3731-4388-8c9b-bcd1f75a305f | discourse-aware-neural-rewards-for-coherent | 1805.03766 | null | http://arxiv.org/abs/1805.03766v1 | http://arxiv.org/pdf/1805.03766v1.pdf | Discourse-Aware Neural Rewards for Coherent Text Generation | In this paper, we investigate the use of discourse-aware rewards with
reinforcement learning to guide a model to generate long, coherent text. In
particular, we propose to learn neural rewards to model cross-sentence ordering
as a means to approximate desired discourse structure. Empirical results
demonstrate that a generator trained with the learned reward produces more
coherent and less repetitive text than models trained with cross-entropy or
with reinforcement learning with commonly used scores as rewards. | ['Po-Sen Huang', 'Xiaodong He', 'Yejin Choi', 'Jianfeng Gao', 'Asli Celikyilmaz', 'Antoine Bosselut'] | 2018-05-10 | discourse-aware-neural-rewards-for-coherent-1 | https://aclanthology.org/N18-1016 | https://aclanthology.org/N18-1016.pdf | naacl-2018-6 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.56074548e-01 9.72067356e-01 -3.41673255e-01 -5.29465556e-01
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49b3fe0a-6f81-42ca-b384-657ce66e9e5f | privacy-preserving-chaotic-extreme-learning | 2208.02587 | null | https://arxiv.org/abs/2208.02587v1 | https://arxiv.org/pdf/2208.02587v1.pdf | Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption | The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted form using Fully Homomorphic Encryption where the weights and biases are generated using a logistic map instead of uniform distribution. Our proposed method has performed either better or similar to the Traditional Extreme Learning Machine on most of the datasets. | ['Vadlamani Ravi', 'Syed Imtiaz Ahamed'] | 2022-08-04 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-3.78635526e-01 -1.08029768e-01 1.99662879e-01 -7.39496052e-01
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362a2212-ad00-4051-a9a7-86f774c3f1b0 | leveraging-algorithmic-fairness-to-mitigate | 2211.10209 | null | https://arxiv.org/abs/2211.10209v1 | https://arxiv.org/pdf/2211.10209v1.pdf | Leveraging Algorithmic Fairness to Mitigate Blackbox Attribute Inference Attacks | Machine learning (ML) models have been deployed for high-stakes applications, e.g., healthcare and criminal justice. Prior work has shown that ML models are vulnerable to attribute inference attacks where an adversary, with some background knowledge, trains an ML attack model to infer sensitive attributes by exploiting distinguishable model predictions. However, some prior attribute inference attacks have strong assumptions about adversary's background knowledge (e.g., marginal distribution of sensitive attribute) and pose no more privacy risk than statistical inference. Moreover, none of the prior attacks account for class imbalance of sensitive attribute in datasets coming from real-world applications (e.g., Race and Sex). In this paper, we propose an practical and effective attribute inference attack that accounts for this imbalance using an adaptive threshold over the attack model's predictions. We exhaustively evaluate our proposed attack on multiple datasets and show that the adaptive threshold over the model's predictions drastically improves the attack accuracy over prior work. Finally, current literature lacks an effective defence against attribute inference attacks. We investigate the impact of fairness constraints (i.e., designed to mitigate unfairness in model predictions) during model training on our attribute inference attack. We show that constraint based fairness algorithms which enforces equalized odds acts as an effective defense against attribute inference attacks without impacting the model utility. Hence, the objective of algorithmic fairness and sensitive attribute privacy are aligned. | ['Antoine Boutet', 'Vasisht Duddu', 'Jan Aalmoes'] | 2022-11-18 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 7.34087288e-01 5.04885375e-01 -5.37158012e-01 -8.72617960e-01
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33491721-5d3d-4b36-b83d-8f66db2e6dd4 | semi-supervised-seizure-prediction-with | 1806.08235 | null | http://arxiv.org/abs/1806.08235v1 | http://arxiv.org/pdf/1806.08235v1.pdf | Semi-supervised Seizure Prediction with Generative Adversarial Networks | In this article, we propose an approach that can make use of not only labeled
EEG signals but also the unlabeled ones which is more accessible. We also
suggest the use of data fusion to further improve the seizure prediction
accuracy. Data fusion in our vision includes EEG signals, cardiogram signals,
body temperature and time. We use the short-time Fourier transform on 28-s EEG
windows as a pre-processing step. A generative adversarial network (GAN) is
trained in an unsupervised manner where information of seizure onset is
disregarded. The trained Discriminator of the GAN is then used as feature
extractor. Features generated by the feature extractor are classified by two
fully-connected layers (can be replaced by any classifier) for the labeled EEG
signals. This semi-supervised seizure prediction method achieves area under the
operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp
EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively.
Unsupervised training without the need of labeling is important because not
only it can be performed in real-time during EEG signal recording, but also it
does not require feature engineering effort for each patient. | ['Mohammad Reza Bonyadi', 'Nhan Duy Truong', 'Levin Kuhlmann', 'Omid Kavehei'] | 2018-06-20 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 4.96504962e-01 1.92339301e-01 3.83630455e-01 -5.40680587e-01
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9327237a-1ac5-40a9-b264-0ce36d14fcee | lisac-fsdm-usmba-at-semeval-2021-task-5 | null | null | https://aclanthology.org/2021.semeval-1.116 | https://aclanthology.org/2021.semeval-1.116.pdf | LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model | Toxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text. In this paper, we describe our solutions to tackle toxic spans detection. The first solution, which follows a supervised approach, is based on SpanBERT model. This latter is intended to better embed and predict spans of text. The second solution, which adopts an unsupervised approach, combines linear support vector machine with the Local Interpretable Model-Agnostic Explanations (LIME). This last is used to interpret predictions of learning-based models. Our supervised model outperformed the unsupervised model and achieved the f-score of 67,84{\%} (ranked 22/85) in Task 5 at SemEval-2021: Toxic Spans Detection. | ['Hamza Alami', 'Ahmed Alami', 'Abdessamad Benlahbib'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 3.07882488e-01 2.99020201e-01 -1.98490039e-01 1.54507011e-01
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b6aae83b-5c14-4061-b44e-c9edf716b861 | bico-net-regress-globally-match-locally-for | 2205.03536 | null | https://arxiv.org/abs/2205.03536v1 | https://arxiv.org/pdf/2205.03536v1.pdf | BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation | The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2) systematic noises in depth images. Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented from RGB-D images by locally matching pairs of oriented points between the model and camera space. To this end, we propose a novel Bi-directional Correspondence Mapping Network (BiCo-Net) to first generate point clouds guided by a typical pose regression, which can thus incorporate pose-sensitive information to optimize generation of local coordinates and their normal vectors. As pose predictions via geometric computation only rely on one single pair of local oriented points, our BiCo-Net can achieve robustness against sparse and occluded point clouds. An ensemble of redundant pose predictions from locally matching and direct pose regression further refines final pose output against noisy observations. Experimental results on three popularly benchmarking datasets can verify that our method can achieve state-of-the-art performance, especially for the more challenging severe occluded scenes. Source codes are available at https://github.com/Gorilla-Lab-SCUT/BiCo-Net. | ['Kui Jia', 'Ke Chen', 'Yichen Zhang', 'Zelin Xu'] | 2022-05-07 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 0.15228197 0.16492835 -0.04087397 -0.40674803 -1.128731 -0.5151114
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02fc5b0d-9725-4657-997c-6bcab9ebc360 | gmsf-global-matching-scene-flow | 2305.17432 | null | https://arxiv.org/abs/2305.17432v1 | https://arxiv.org/pdf/2305.17432v1.pdf | GMSF: Global Matching Scene Flow | We tackle the task of scene flow estimation from point clouds. Given a source and a target point cloud, the objective is to estimate a translation from each point in the source point cloud to the target, resulting in a 3D motion vector field. Previous dominant scene flow estimation methods require complicated coarse-to-fine or recurrent architectures as a multi-stage refinement. In contrast, we propose a significantly simpler single-scale one-shot global matching to address the problem. Our key finding is that reliable feature similarity between point pairs is essential and sufficient to estimate accurate scene flow. To this end, we propose to decompose the feature extraction step via a hybrid local-global-cross transformer architecture which is crucial to accurate and robust feature representations. Extensive experiments show that GMSF sets a new state-of-the-art on multiple scene flow estimation benchmarks. On FlyingThings3D, with the presence of occlusion points, GMSF reduces the outlier percentage from the previous best performance of 27.4% to 11.7%. On KITTI Scene Flow, without any fine-tuning, our proposed method shows state-of-the-art performance. | ['Michael Felsberg', 'Maria Magnusson', 'Per-Erik Forssén', 'Bastian Wandt', 'Johan Edstedt', 'Yushan Zhang'] | 2023-05-27 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-1.48080643e-02 -7.97455609e-01 -4.27023694e-02 -2.62332976e-01
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885aa2a1-59a3-427b-9cad-886ffa1fe3db | speech-drives-templates-co-speech-gesture | 2108.08020 | null | https://arxiv.org/abs/2108.08020v2 | https://arxiv.org/pdf/2108.08020v2.pdf | Speech Drives Templates: Co-Speech Gesture Synthesis with Learned Templates | Co-speech gesture generation is to synthesize a gesture sequence that not only looks real but also matches with the input speech audio. Our method generates the movements of a complete upper body, including arms, hands, and the head. Although recent data-driven methods achieve great success, challenges still exist, such as limited variety, poor fidelity, and lack of objective metrics. Motivated by the fact that the speech cannot fully determine the gesture, we design a method that learns a set of gesture template vectors to model the latent conditions, which relieve the ambiguity. For our method, the template vector determines the general appearance of a generated gesture sequence, while the speech audio drives subtle movements of the body, both indispensable for synthesizing a realistic gesture sequence. Due to the intractability of an objective metric for gesture-speech synchronization, we adopt the lip-sync error as a proxy metric to tune and evaluate the synchronization ability of our model. Extensive experiments show the superiority of our method in both objective and subjective evaluations on fidelity and synchronization. | ['YiHao Zhi', 'Shenghua Gao', 'Wen Liu', 'Zhi Tu', 'Shenhan Qian'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Qian_Speech_Drives_Templates_Co-Speech_Gesture_Synthesis_With_Learned_Templates_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Qian_Speech_Drives_Templates_Co-Speech_Gesture_Synthesis_With_Learned_Templates_ICCV_2021_paper.pdf | iccv-2021-1 | ['gesture-generation'] | ['robots'] | [ 1.45732954e-01 -1.24835178e-01 -1.11358993e-01 -2.83202171e-01
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2a26c658-7d02-4900-8562-37e7d8a5adca | deep-demosaicing-for-polarimetric-filter | 2211.13732 | null | https://arxiv.org/abs/2211.13732v1 | https://arxiv.org/pdf/2211.13732v1.pdf | Deep Demosaicing for Polarimetric Filter Array Cameras | Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner. Such filter arrays work as the Bayer pattern for colour cameras, sharing similar advantages and drawbacks. Among the others, the raw image must be demosaiced considering the local variations of the PFA and the characteristics of the imaged scene. Non-linear effects, like the cross-talk among neighbouring pixels, are difficult to explicitly model and suggest the potential advantage of a data-driven learning approach. However, the PFA cannot be removed from the sensor, making it difficult to acquire the ground-truth polarization state for training. In this work we propose a novel CNN-based model which directly demosaics the raw camera image to a per-pixel Stokes vector. Our contribution is twofold. First, we propose a network architecture composed by a sequence of Mosaiced Convolutions operating coherently with the local arrangement of the different filters. Second, we introduce a new method, employing a consumer LCD screen, to effectively acquire real-world data for training. The process is designed to be invariant by monitor gamma and external lighting conditions. We extensively compared our method against algorithmic and learning-based demosaicing techniques, obtaining a consistently lower error especially in terms of polarisation angle. | ['Andrea Torsello', 'Tehreem Fatima', 'Filippo Bergamasco', 'Mara Pistellato'] | 2022-11-24 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 5.69543779e-01 -2.48745471e-01 5.71737647e-01 -3.49204242e-01
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8f5fe3e5-036c-4960-9e66-540720586223 | gibert-enhancing-bert-with-linguistic | null | null | https://aclanthology.org/2021.findings-emnlp.200 | https://aclanthology.org/2021.findings-emnlp.200.pdf | GiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method | Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words – either through masking or next sentence prediction – and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through unsupervised pre-training. We propose a novel method to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT. When injecting counter-fitted and dependency-based embeddings, the performance improvements on multiple semantic similarity datasets indicate that such information is beneficial and currently missing from the original model. Our qualitative analysis shows that counter-fitted embedding injection is particularly beneficial, with notable improvements on examples that require synonym resolution. | ['Maria Liakata', 'Marek Rei', 'Nicole Peinelt'] | null | null | null | null | findings-emnlp-2021-11 | ['unsupervised-pre-training'] | ['methodology'] | [ 1.08298056e-01 3.64646912e-01 -6.02489054e-01 -4.08037156e-01
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ce400647-6cf3-4be2-a3e7-f62072e1ae3c | idol-indicator-oriented-logic-pre-training | 2306.15273 | null | https://arxiv.org/abs/2306.15273v1 | https://arxiv.org/pdf/2306.15273v1.pdf | IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning | In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a long way to go when it comes to logical reasoning. Although some methods for it have been put forward, they either are designed in a quite complicated way or rely too much on external structures. In this paper, we proposed IDOL (InDicator-Oriented Logic Pre-training), an easy-to-understand but highly effective further pre-training task which logically strengthens the pre-trained models with the help of 6 types of logical indicators and a logically rich dataset LGP (LoGic Pre-training). IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2.0 while keeping competitive general language understanding ability through testing on tasks in GLUE. Besides, at the beginning of the era of large language models, we take several of them like ChatGPT into comparison and find that IDOL still shows its advantage. | ['Shijin Wang', 'Yiming Cui', 'Ziqing Yang', 'Zihang Xu'] | 2023-06-27 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension', 'logical-reasoning'] | ['natural-language-processing', 'natural-language-processing', 'reasoning'] | [-1.37632862e-01 5.85755527e-01 5.57600521e-03 -5.67345321e-01
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96a5fb0a-7b93-418d-bb53-509ef17f3417 | no-reference-image-quality-assessment-metric | 1901.05811 | null | https://arxiv.org/abs/1901.05811v2 | https://arxiv.org/pdf/1901.05811v2.pdf | No reference image quality assessment metric based on regional mutual information among images | With the inclusion of camera in daily life, an automatic no reference image quality evaluation index is required for automatic classification of images. The present manuscripts proposes a new No Reference Regional Mutual Information based technique for evaluating the quality of an image. We use regional mutual information on subsets of the complete image. Proposed technique is tested on four benchmark natural image databases, and one benchmark synthetic database. A comparative analysis with classical and state-of-art methods indicate superiority of the present technique for high quality images and comparable for other images of the respective databases. | ['Rahul Upadhyay', 'Vinay Kumar', 'Vivek Singh Bawa'] | 2019-01-17 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.54147729e-01 -3.25307399e-01 -7.68915266e-02 -4.45580989e-01
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e6807ede-fe6c-4457-bfb4-2622a84838de | i-know-what-you-do-not-know-knowledge-graph | 2208.09828 | null | https://arxiv.org/abs/2208.09828v3 | https://arxiv.org/pdf/2208.09828v3.pdf | I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning | Knowledge graph (KG) embedding seeks to learn vector representations for entities and relations. Conventional models reason over graph structures, but they suffer from the issues of graph incompleteness and long-tail entities. Recent studies have used pre-trained language models to learn embeddings based on the textual information of entities and relations, but they cannot take advantage of graph structures. In the paper, we show empirically that these two kinds of features are complementary for KG embedding. To this end, we propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complementarity of graph structures and text information. Its graph embedding model employs Transformer to reconstruct the representation of an entity from its neighborhood subgraph. Its text embedding model uses a pre-trained language model to generate entity representations from the soft prompts of their names, descriptions, and relational neighbors. To let the two model promote each other, we propose co-distillation learning that allows them to distill selective knowledge from each other's prediction logits. In our co-distillation learning, each model serves as both a teacher and a student. Experiments on benchmark datasets demonstrate that the two models outperform their related baselines, and the ensemble method CoLE with co-distillation learning advances the state-of-the-art of KG embedding. | ['Guangyao Li', 'Zequn Sun', 'Wei Hu', 'Yang Liu'] | 2022-08-21 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.74302483e-01 8.17340374e-01 -6.34006560e-01 -2.16957882e-01
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b741a17d-4b24-46bd-9f45-2772c8951a0c | end-to-end-multi-person-pose-estimation-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.pdf | End-to-End Multi-Person Pose Estimation With Transformers | Current methods of multi-person pose estimation typically treat the localization and association of body joints separately. In this paper, we propose the first fully end-to-end multi-person Pose Estimation framework with TRansformers, termed PETR. Our method views pose estimation as a hierarchical set prediction problem and effectively removes the need for many hand-crafted modules like RoI cropping, NMS and grouping post-processing. In PETR, multiple pose queries are learned to directly reason a set of full-body poses. Then a joint decoder is utilized to further refine the poses by exploring the kinematic relations between body joints. With the attention mechanism, the proposed method is able to adaptively attend to the features most relevant to target keypoints, which largely overcomes the feature misalignment difficulty in pose estimation and improves the performance considerably. Extensive experiments on the MS COCO and CrowdPose benchmarks show that PETR plays favorably against state-of-the-art approaches in terms of both accuracy and efficiency. The code and models are available at https://github.com/hikvision-research/opera. | ['Wenming Tan', 'Ye Ren', 'Liangqi Li', 'Xing Wei', 'Dahu Shi'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['multi-person-pose-estimation'] | ['computer-vision'] | [-2.48320460e-01 -2.31092097e-03 -1.50714386e-02 -3.32573473e-01
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5f812ce6-fbe4-4ac4-b735-a962b054f956 | evaluating-open-domain-dialogues-in-latent | 2305.16967 | null | https://arxiv.org/abs/2305.16967v3 | https://arxiv.org/pdf/2305.16967v3.pdf | Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information | The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics. | ['Xiaohui Cui', 'Aline Villavicencio', 'Wenge Rong', 'Chenghua Lin', 'Bohao Yang', 'Kun Zhao'] | 2023-05-26 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.24787778e-01 1.23106919e-01 2.75306314e-01 -7.37133563e-01
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3877de66-1ee0-4e8d-9dbf-86e28eb94f16 | global-aggregation-then-local-distribution | 2107.13154 | null | https://arxiv.org/abs/2107.13154v1 | https://arxiv.org/pdf/2107.13154v1.pdf | Global Aggregation then Local Distribution for Scene Parsing | Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherently limited to model such dependencies due to the naive structure in its building modules (\eg, local convolution kernel). While recent global aggregation methods are beneficial for long-range structure information modelling, they would oversmooth and bring noise to the regions containing fine details (\eg,~boundaries and small objects), which are very much cared for the semantic segmentation task. To alleviate this problem, we propose to explore the local context for making the aggregated long-range relationship being distributed more accurately in local regions. In particular, we design a novel local distribution module which models the affinity map between global and local relationship for each pixel adaptively. Integrating existing global aggregation modules, we show that our approach can be modularized as an end-to-end trainable block and easily plugged into existing semantic segmentation networks, giving rise to the \emph{GALD} networks. Despite its simplicity and versatility, our approach allows us to build new state of the art on major semantic segmentation benchmarks including Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff. Code and trained models are released at \url{https://github.com/lxtGH/GALD-DGCNet} to foster further research. | ['Tao Xiang', 'Xiatian Zhu', 'Yunhai Tong', 'Kuiyuan Yang', 'Guangliang Cheng', 'Li Zhang', 'Xiangtai Li'] | 2021-07-28 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 7.15174302e-02 2.90725768e-01 -1.80181593e-01 -7.37947762e-01
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0c41aba4-658a-491d-bcb2-cb040e7c17aa | pose2seg-detection-free-human-instance | 1803.10683 | null | http://arxiv.org/abs/1803.10683v3 | http://arxiv.org/pdf/1803.10683v3.pdf | Pose2Seg: Detection Free Human Instance Segmentation | The standard approach to image instance segmentation is to perform the object
detection first, and then segment the object from the detection bounding-box.
More recently, deep learning methods like Mask R-CNN perform them jointly.
However, little research takes into account the uniqueness of the "human"
category, which can be well defined by the pose skeleton. Moreover, the human
pose skeleton can be used to better distinguish instances with heavy occlusion
than using bounding-boxes. In this paper, we present a brand new pose-based
instance segmentation framework for humans which separates instances based on
human pose, rather than proposal region detection. We demonstrate that our
pose-based framework can achieve better accuracy than the state-of-art
detection-based approach on the human instance segmentation problem, and can
moreover better handle occlusion. Furthermore, there are few public datasets
containing many heavily occluded humans along with comprehensive annotations,
which makes this a challenging problem seldom noticed by researchers.
Therefore, in this paper we introduce a new benchmark "Occluded Human
(OCHuman)", which focuses on occluded humans with comprehensive annotations
including bounding-box, human pose and instance masks. This dataset contains
8110 detailed annotated human instances within 4731 images. With an average
0.67 MaxIoU for each person, OCHuman is the most complex and challenging
dataset related to human instance segmentation. Through this dataset, we want
to emphasize occlusion as a challenging problem for researchers to study. | ['Rui-Long Li', 'Shi-Min Hu', 'Hao-Zhi Huang', 'Song-Hai Zhang', 'Zixi Cai', 'Han Xi', 'Xin Dong', 'Paul L. Rosin', 'Dingcheng Yang'] | 2018-03-28 | pose2seg-detection-free-human-instance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Pose2Seg_Detection_Free_Human_Instance_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Pose2Seg_Detection_Free_Human_Instance_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['human-instance-segmentation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.20675245e-02 2.58387625e-01 -2.76258111e-01 -2.52207071e-01
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094ad3c8-edfa-4525-a78c-3beb505cab19 | hypershot-few-shot-learning-by-kernel | 2203.11378 | null | https://arxiv.org/abs/2203.11378v1 | https://arxiv.org/pdf/2203.11378v1.pdf | HyperShot: Few-Shot Learning by Kernel HyperNetworks | Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one element represents each class. We propose HyperShot - the fusion of kernels and hypernetwork paradigm. Compared to reference approaches that apply a gradient-based adjustment of the parameters, our model aims to switch the classification module parameters depending on the task's embedding. In practice, we utilize a hypernetwork, which takes the aggregated information from support data and returns the classifier's parameters handcrafted for the considered problem. Moreover, we introduce the kernel-based representation of the support examples delivered to hypernetwork to create the parameters of the classification module. Consequently, we rely on relations between embeddings of the support examples instead of direct feature values provided by the backbone models. Thanks to this approach, our model can adapt to highly different tasks. | ['Przemysław Spurek', 'Jacek Tabor', 'Maciej Zięba', 'Konrad Karanowski', 'Marcin Przewięźlikowski', 'Marcin Sendera'] | 2022-03-21 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.25642025e-01 4.43456769e-01 -2.44460702e-01 -4.27416354e-01
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3de9e9f6-5155-44c9-b99e-755116305bc3 | topological-parallax-a-geometric | 2306.11835 | null | https://arxiv.org/abs/2306.11835v1 | https://arxiv.org/pdf/2306.11835v1.pdf | Topological Parallax: A Geometric Specification for Deep Perception Models | For safety and robustness of AI systems, we introduce topological parallax as a theoretical and computational tool that compares a trained model to a reference dataset to determine whether they have similar multiscale geometric structure. Our proofs and examples show that this geometric similarity between dataset and model is essential to trustworthy interpolation and perturbation, and we conjecture that this new concept will add value to the current debate regarding the unclear relationship between overfitting and generalization in applications of deep-learning. In typical DNN applications, an explicit geometric description of the model is impossible, but parallax can estimate topological features (components, cycles, voids, etc.) in the model by examining the effect on the Rips complex of geodesic distortions using the reference dataset. Thus, parallax indicates whether the model shares similar multiscale geometric features with the dataset. Parallax presents theoretically via topological data analysis [TDA] as a bi-filtered persistence module, and the key properties of this module are stable under perturbation of the reference dataset. | ['Paul Bendich', 'Nirav Patel', 'Gabrielle Angeloro', 'Michael J. Catanzaro', 'Abraham D. Smith'] | 2023-06-20 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-6.19088784e-02 -3.50176692e-02 6.56331033e-02 2.37952154e-02
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627acde1-1edb-4792-ba11-a97cc2e588ce | towards-robust-multivariate-time-series | 2207.09572 | null | https://arxiv.org/abs/2207.09572v3 | https://arxiv.org/pdf/2207.09572v3.pdf | Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms | This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that negatively impact the forecasting of a target time series via making strategic, sparse (imperceptible) modifications to the past observations of a small number of other time series. To mitigate the impact of such attack, we have developed two defense strategies. First, we extend a previously developed randomized smoothing technique in classification to multivariate forecasting scenarios. Second, we develop an adversarial training algorithm that learns to create adversarial examples and at the same time optimizes the forecasting model to improve its robustness against such adversarial simulation. Extensive experiments on real-world datasets confirm that our attack schemes are powerful and our defense algorithms are more effective compared with baseline defense mechanisms. | ['Jun Huan', 'Hilaf Hasson', 'Trong Nghia Hoang', 'Youngsuk Park', 'Linbo Liu'] | 2022-07-19 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 3.60968053e-01 5.97173311e-02 1.61283642e-01 -2.45558426e-01
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7f1e5bc8-6c3b-4f49-aca1-9adfb69b73e7 | fast-inference-in-capsule-networks-using | 1904.07304 | null | http://arxiv.org/abs/1904.07304v1 | http://arxiv.org/pdf/1904.07304v1.pdf | Fast Inference in Capsule Networks Using Accumulated Routing Coefficients | We present a method for fast inference in Capsule Networks (CapsNets) by
taking advantage of a key insight regarding the routing coefficients that link
capsules between adjacent network layers. Since the routing coefficients are
responsible for assigning object parts to wholes, and an object whole generally
contains similar intra-class and dissimilar inter-class parts, the routing
coefficients tend to form a unique signature for each object class. For fast
inference, a network is first trained in the usual manner using examples from
the training dataset. Afterward, the routing coefficients associated with the
training examples are accumulated offline and used to create a set of "master"
routing coefficients. During inference, these master routing coefficients are
used in place of the dynamically calculated routing coefficients. Our method
effectively replaces the for-loop iterations in the dynamic routing procedure
with a single matrix multiply operation, providing a significant boost in
inference speed. Compared with the dynamic routing procedure, fast inference
decreases the test accuracy for the MNIST, Background MNIST, Fashion MNIST, and
Rotated MNIST datasets by less than 0.5% and by approximately 5% for CIFAR10. | ['Ishan Patel', 'K. P. Unnikrishnan', 'Zhen Zhao', 'Gursharan Sandhu', 'Ashley Kleinhans'] | 2019-04-15 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 7.46301860e-02 7.81018212e-02 -1.69369519e-01 -6.40467286e-01
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f18049c8-e7cb-471e-91d7-3a0953b5c024 | stock-prediction-a-method-based-on-extraction | 1707.07585 | null | http://arxiv.org/abs/1707.07585v1 | http://arxiv.org/pdf/1707.07585v1.pdf | Stock Prediction: a method based on extraction of news features and recurrent neural networks | This paper proposed a method for stock prediction. In terms of feature
extraction, we extract the features of stock-related news besides stock prices.
We first select some seed words based on experience which are the symbols of
good news and bad news. Then we propose an optimization method and calculate
the positive polar of all words. After that, we construct the features of news
based on the positive polar of their words. In consideration of sequential
stock prices and continuous news effects, we propose a recurrent neural network
model to help predict stock prices. Compared to SVM classifier with price
features, we find our proposed method has an over 5% improvement on stock
prediction accuracy in experiments. | ['Hongfei Yan', 'Weizheng Chen', 'Zeya Zhang'] | 2017-07-19 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.00064135e-01 -4.99565691e-01 -7.13138878e-01 -1.91326827e-01
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9cc502f1-b344-4b74-9a18-57da5dcc0011 | distribution-network-fault-prediction | 2306.12724 | null | https://arxiv.org/abs/2306.12724v1 | https://arxiv.org/pdf/2306.12724v1.pdf | Distribution Network Fault Prediction Utilising Protection Relay Disturbance Recordings And Machine Learning | As society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization, isolation, and supply restoration processes to minimize fault duration. This paper proposes a machine learning based fault prediction method that aims to predict incipient faults, allowing T/DSOs to take action before the fault occurs and prevent customer outages. | ['Ari Salo', 'Anna Kulmala', 'Henry Niveri', 'Petri Hovila', 'Viktor Olsson', 'Karl Bäckström', 'Ebrahim Balouji'] | 2023-06-22 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-5.68640567e-02 -1.06587879e-01 -1.95131600e-01 -2.91394651e-01
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d96d7025-8bc2-44e9-8dd5-9238229ef208 | context-dependent-embedding-utterance | 2304.08216 | null | https://arxiv.org/abs/2304.08216v2 | https://arxiv.org/pdf/2304.08216v2.pdf | Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) has been gaining increasing importance as conversational agents become more and more common. Recognizing emotions is key for effective communication, being a crucial component in the development of effective and empathetic conversational agents. Knowledge and understanding of the conversational context are extremely valuable for identifying the emotions of the interlocutor. We thus approach Emotion Recognition in Conversations leveraging the conversational context, i.e., taking into attention previous conversational turns. The usual approach to model the conversational context has been to produce context-independent representations of each utterance and subsequently perform contextual modeling of these. Here we propose context-dependent embedding representations of each utterance by leveraging the contextual representational power of pre-trained transformer language models. In our approach, we feed the conversational context appended to the utterance to be classified as input to the RoBERTa encoder, to which we append a simple classification module, thus discarding the need to deal with context after obtaining the embeddings since these constitute already an efficient representation of such context. We also investigate how the number of introduced conversational turns influences our model performance. The effectiveness of our approach is validated on the open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset. | ['Joao Paulo Carvalho', 'Isabel Dias', 'Helena Moniz', 'Patrícia Pereira'] | 2023-04-17 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.03514320e-02 2.78134406e-01 2.60074914e-01 -6.83883786e-01
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99af6a4b-9cc0-4ec4-94dd-13293013b3ef | on-breast-cancer-detection-an-application-of | 1711.07831 | null | http://arxiv.org/abs/1711.07831v4 | http://arxiv.org/pdf/1711.07831v4.pdf | On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset | This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1992) by measuring their classification test accuracy and their
sensitivity and specificity values. The said dataset consists of features which
were computed from digitized images of FNA tests on a breast mass (Wolberg,
Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the
dataset was partitioned in the following fashion: 70% for training phase, and
30% for the testing phase. The hyper-parameters used for all the classifiers
were manually assigned. Results show that all the presented ML algorithms
performed well (all exceeded 90% test accuracy) on the classification task. The
MLP algorithm stands out among the implemented algorithms with a test accuracy
of ~99.04%. | ['Abien Fred Agarap'] | 2017-11-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.79276371e-01 1.62023768e-01 -5.73784828e-01 -5.25931954e-01
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1e093b2d-39e8-4e9c-b735-39f57a7759c3 | minimize-exposure-bias-of-seq2seq-models-in | 2009.07503 | null | https://arxiv.org/abs/2009.07503v2 | https://arxiv.org/pdf/2009.07503v2.pdf | Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction | Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order on the unordered triplets and involves a large decoding length associated with error accumulation. These introduce exposure bias, which may cause the models overfit to the frequent label combination, thus deteriorating the generalization. We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets. We evaluate our model on two datasets, DuIE and NYT, and systematically study how exposure bias alters the performance of Seq2Seq models. Experiments show that the state-of-the-art Seq2Seq model overfits to both datasets while Seq2UMTree shows significantly better generalization. Our code is available at https://github.com/WindChimeRan/OpenJERE . | ['Ranran Haoran Zhang', 'Daisuke Kawahara', 'Sadao Kurohashi', 'Heng Ji', 'Daojian Zeng', 'Qianying Liu', 'Fei Cheng', 'Aysa Xuemo Fan'] | 2020-09-16 | null | https://aclanthology.org/2020.findings-emnlp.23 | https://aclanthology.org/2020.findings-emnlp.23.pdf | findings-of-the-association-for-computational | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 5.94119489e-01 1.35006636e-01 -2.92877525e-01 -5.28636277e-01
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f455cc67-e299-4885-8ccb-5de35310682b | distribution-based-emotion-recognition-in | 2211.04834 | null | https://arxiv.org/abs/2211.04834v1 | https://arxiv.org/pdf/2211.04834v1.pdf | Distribution-based Emotion Recognition in Conversation | Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence. This paper proposes a distribution-based framework that formulates ERC as a sequence-to-sequence problem for emotion distribution estimation. The inherent ambiguity of emotions and the subjectivity of human perception lead to disagreements in emotion labels, which is handled naturally in our framework from the perspective of uncertainty estimation in emotion distributions. A Bayesian training loss is introduced to improve the uncertainty estimation by conditioning each emotional state on an utterance-specific Dirichlet prior distribution. Experimental results on the IEMOCAP dataset show that ERC outperformed the single-utterance-based system, and the proposed distribution-based ERC methods have not only better classification accuracy, but also show improved uncertainty estimation. | ['Philip C. Woodland', 'Chao Zhang', 'Wen Wu'] | 2022-11-09 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.43030817e-02 2.39889234e-01 2.00658366e-01 -1.07073665e+00
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c2e8973d-ceda-4b85-84f5-95e1c7864dea | mappsent-a-textual-mapping-approach-for | null | null | https://aclanthology.org/R17-1040 | https://aclanthology.org/R17-1040.pdf | MappSent: a Textual Mapping Approach for Question-to-Question Similarity | Since the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. Mikolov et al. (2013) have demonstrated that words and phrases exhibit linear structures that allow to meaningfully combine words by an element-wise addition of their vector representations. Recently, Arora et al. (2017) have shown that removing the projections of the weighted average sum of word embedding vectors on their first principal components, outperforms sophisticated supervised methods including RNN{'}s and LSTM{'}s. Inspired by Mikolov et al. (2013) and Arora et al. (2017) findings and by a bilingual word mapping technique presented in Artetxe et al. (2016), we introduce MappSent, a novel approach for textual similarity. Based on a linear sentence embedding representation, its principle is to build a matrix that maps sentences in a joint-subspace where similar sets of sentences are pushed closer. We evaluate our approach on the SemEval 2016/2017 question-to-question similarity task and show that overall MappSent achieves competitive results and outperforms in most cases state-of-art methods. | ['Hern', 'Amir Hazem', 'Nicolas ez', 'Basma El Amel Boussaha'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['question-similarity'] | ['natural-language-processing'] | [ 1.83199927e-01 1.04610890e-01 -1.99381202e-01 -2.49499083e-01
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1d749fd1-28a8-4c5e-9cb5-66ee8ded04d7 | addressing-class-imbalance-in-grammatical | null | null | https://aclanthology.org/W15-5902 | https://aclanthology.org/W15-5902.pdf | Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization | null | ['Anoop Kunchukuttan', 'Pushpak Bhattacharyya'] | 2015-12-01 | null | null | null | ws-2015-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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67e3e0fa-0d1c-409d-89bf-f348384b938c | learning-to-learn-unlearned-feature-for-brain | 2305.08878 | null | https://arxiv.org/abs/2305.08878v1 | https://arxiv.org/pdf/2305.08878v1.pdf | Learning to Learn Unlearned Feature for Brain Tumor Segmentation | We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks. Our approach is based on active learning and meta-learning. One of the difficulties in medical image segmentation is the lack of datasets with proper annotations, because it requires doctors to tag reliable annotation and there are many variants of a disease, such as glioma and brain metastasis, which are the different types of brain tumor and have different structural features in MR images. Therefore, it is impossible to produce the large-scale medical image datasets for all types of diseases. In this paper, we show a transfer learning method from high grade glioma to brain metastasis, and demonstrate that the proposed algorithm achieves balanced parameters for both glioma and brain metastasis domains within a few steps. | ['Seunghong Choi', 'Jungwoo Lee', 'Seokhyeon Ha', 'Yeongmo Kim', 'Seungyub Han'] | 2023-05-13 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 3.27943474e-01 5.32518983e-01 -5.48635602e-01 -4.44578290e-01
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b9620532-6c05-476b-852f-3c22771bb23c | federated-survival-forests | 2302.02807 | null | https://arxiv.org/abs/2302.02807v1 | https://arxiv.org/pdf/2302.02807v1.pdf | Federated Survival Forests | Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. Despite the widespread development of federated learning in recent AI research, only a few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data. | ['Matteo Matteucci', 'Alberto Archetti'] | 2023-02-06 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.62328959e-01 -1.51453659e-01 -3.98276359e-01 -5.77803791e-01
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75e7942a-060a-4c7f-ac41-edb9f59fbf8c | 3d-fully-convolutional-networks-for | 1612.03925 | null | http://arxiv.org/abs/1612.03925v2 | http://arxiv.org/pdf/1612.03925v2.pdf | 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study | This study investigates a 3D and fully convolutional neural network (CNN) for
subcortical brain structure segmentation in MRI. 3D CNN architectures have been
generally avoided due to their computational and memory requirements during
inference. We address the problem via small kernels, allowing deeper
architectures. We further model both local and global context by embedding
intermediate-layer outputs in the final prediction, which encourages
consistency between features extracted at different scales and embeds
fine-grained information directly in the segmentation process. Our model is
efficiently trained end-to-end on a graphics processing unit (GPU), in a single
stage, exploiting the dense inference capabilities of fully CNNs.
We performed comprehensive experiments over two publicly available datasets.
First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then,
we report a {\em large-scale} multi-site evaluation over 1112 unregistered
subject datasets acquired from 17 different sites (ABIDE dataset), with ages
ranging from 7 to 64 years, showing that our method is robust to various
acquisition protocols, demographics and clinical factors. Our method yielded
segmentations that are highly consistent with a standard atlas-based approach,
while running in a fraction of the time needed by atlas-based methods and
avoiding registration/normalization steps. This makes it convenient for massive
multi-site neuroanatomical imaging studies. To the best of our knowledge, our
work is the first to study subcortical structure segmentation on such
large-scale and heterogeneous data. | ['J. Dolz', 'I. Ben Ayed', 'C. Desrosiers'] | 2016-12-12 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [-3.89946327e-02 2.49853581e-01 -2.03438923e-02 -7.06202030e-01
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0892c8d3-37d6-41f0-a442-4d041ae6919c | on-the-distribution-of-penultimate | 2107.01900 | null | https://arxiv.org/abs/2107.01900v2 | https://arxiv.org/pdf/2107.01900v2.pdf | On The Distribution of Penultimate Activations of Classification Networks | This paper studies probability distributions of penultimate activations of classification networks. We show that, when a classification network is trained with the cross-entropy loss, its final classification layer forms a Generative-Discriminative pair with a generative classifier based on a specific distribution of penultimate activations. More importantly, the distribution is parameterized by the weights of the final fully-connected layer, and can be considered as a generative model that synthesizes the penultimate activations without feeding input data. We empirically demonstrate that this generative model enables stable knowledge distillation in the presence of domain shift, and can transfer knowledge from a classifier to variational autoencoders and generative adversarial networks for class-conditional image generation. | ['Suha Kwak', 'Yoonho Lee', 'Minkyo Seo'] | 2021-07-05 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.73929137e-01 7.69121826e-01 -9.56593528e-02 -2.10997209e-01
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673ffe0c-a890-42e1-8bb8-072039bf2ea5 | using-cameras-for-precise-measurement-of-two | 1904.13187 | null | https://arxiv.org/abs/1904.13187v2 | https://arxiv.org/pdf/1904.13187v2.pdf | Using cameras for precise measurement of two-dimensional plant features: CASS | Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing. We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotyping will allow data capture from different cameras and environments, with comparison on the same physical scale. We abbreviate this method as CASS, for CAmera aS Scanner. Code and data is available at http://doi.org/10.5281/zenodo.3677473. | ['Germán A Holguín', 'Amy Tabb', 'Rachel Naegele'] | 2019-04-30 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 5.72951436e-01 -4.76809591e-01 4.42830324e-02 -1.30123839e-01
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87fc594b-30e9-4211-b65e-8b07eb53e182 | enhancing-adversarial-robustness-via-score | 2307.04333 | null | https://arxiv.org/abs/2307.04333v1 | https://arxiv.org/pdf/2307.04333v1.pdf | Enhancing Adversarial Robustness via Score-Based Optimization | Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations. Developing algorithms that can mitigate the effects of these attacks is crucial for ensuring the safe use of artificial intelligence. Recent studies have suggested that score-based diffusion models are effective in adversarial defenses. However, existing diffusion-based defenses rely on the sequential simulation of the reversed stochastic differential equations of diffusion models, which are computationally inefficient and yield suboptimal results. In this paper, we introduce a novel adversarial defense scheme named ScoreOpt, which optimizes adversarial samples at test-time, towards original clean data in the direction guided by score-based priors. We conduct comprehensive experiments on multiple datasets, including CIFAR10, CIFAR100 and ImageNet. Our experimental results demonstrate that our approach outperforms existing adversarial defenses in terms of both robustness performance and inference speed. | ['Zhihua Zhang', 'Weijian Luo', 'Boya Zhang'] | 2023-07-10 | null | null | null | null | ['adversarial-defense', 'adversarial-robustness'] | ['adversarial', 'adversarial'] | [ 5.45172542e-02 -1.58317685e-01 2.56226033e-01 -1.70418307e-01
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f87594bd-ee5f-4120-9e75-b73135cb16f2 | face-hallucination-with-finishing-touches | 2002.03308 | null | https://arxiv.org/abs/2002.03308v2 | https://arxiv.org/pdf/2002.03308v2.pdf | Face Hallucination with Finishing Touches | Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods. | ['Yang Zhang', 'Xin Yu', 'Xiaobo Lu', 'Jun Li', 'Ping Liu', 'Ivor W. Tsang'] | 2020-02-09 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.22230746e-01 4.56511438e-01 1.81480706e-01 -4.50906634e-01
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ef573a5a-dc0c-42ef-82c4-292296b27c4a | fantasia3d-disentangling-geometry-and | 2303.13873 | null | https://arxiv.org/abs/2303.13873v2 | https://arxiv.org/pdf/2303.13873v2.pdf | Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation | Automatic 3D content creation has achieved rapid progress recently due to the availability of pre-trained, large language models and image diffusion models, forming the emerging topic of text-to-3D content creation. Existing text-to-3D methods commonly use implicit scene representations, which couple the geometry and appearance via volume rendering and are suboptimal in terms of recovering finer geometries and achieving photorealistic rendering; consequently, they are less effective for generating high-quality 3D assets. In this work, we propose a new method of Fantasia3D for high-quality text-to-3D content creation. Key to Fantasia3D is the disentangled modeling and learning of geometry and appearance. For geometry learning, we rely on a hybrid scene representation, and propose to encode surface normal extracted from the representation as the input of the image diffusion model. For appearance modeling, we introduce the spatially varying bidirectional reflectance distribution function (BRDF) into the text-to-3D task, and learn the surface material for photorealistic rendering of the generated surface. Our disentangled framework is more compatible with popular graphics engines, supporting relighting, editing, and physical simulation of the generated 3D assets. We conduct thorough experiments that show the advantages of our method over existing ones under different text-to-3D task settings. Project page and source codes: https://fantasia3d.github.io/. | ['Kui Jia', 'Ningxin Jiao', 'Yongwei Chen', 'Rui Chen'] | 2023-03-24 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.88463539e-01 -1.12290859e-01 3.47277313e-01 1.53455082e-02
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f211ee68-c671-4b3c-bb8e-d0e40567faab | curriculum-deepsdf | 2003.08593 | null | https://arxiv.org/abs/2003.08593v3 | https://arxiv.org/pdf/2003.08593v3.pdf | Curriculum DeepSDF | When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this paper, we design a "shape curriculum" for learning continuous Signed Distance Function (SDF) on shapes, namely Curriculum DeepSDF. Inspired by how humans learn, Curriculum DeepSDF organizes the learning task in ascending order of difficulty according to the following two criteria: surface accuracy and sample difficulty. The former considers stringency in supervising with ground truth, while the latter regards the weights of hard training samples near complex geometry and fine structure. More specifically, Curriculum DeepSDF learns to reconstruct coarse shapes at first, and then gradually increases the accuracy and focuses more on complex local details. Experimental results show that a carefully-designed curriculum leads to significantly better shape reconstructions with the same training data, training epochs and network architecture as DeepSDF. We believe that the application of shape curricula can benefit the training process of a wide variety of 3D shape representation learning methods. | ['Haidong Zhu', 'Yueqi Duan', 'Li Yi', 'Leonidas J. Guibas', 'Ram Nevatia', 'He Wang'] | 2020-03-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/441_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530052.pdf | eccv-2020-8 | ['3d-shape-representation'] | ['computer-vision'] | [-6.01759441e-02 1.76595494e-01 1.16049752e-01 -4.96969610e-01
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555578ab-94f6-482c-9d1e-9916c8854dda | fusing-visual-appearance-and-geometry-for | 2302.11458 | null | https://arxiv.org/abs/2302.11458v1 | https://arxiv.org/pdf/2302.11458v1.pdf | Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking | In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, which uses geometry, to additionally consider surface appearance. In general, object surfaces contain local characteristics from text, graphics, and patterns, as well as global differences from distinct materials and colors. To incorporate this visual information, two modalities are developed. For local characteristics, keypoint features are used to minimize distances between points from keyframes and the current image. For global differences, a novel region approach is developed that considers multiple regions on the object surface. In addition, it allows the modeling of external geometries. Experiments on the YCB-Video and OPT datasets demonstrate that our approach ICG+ performs best on both datasets, outperforming both conventional and deep learning-based methods. At the same time, the algorithm is highly efficient and runs at more than 300 Hz. The source code of our tracker is publicly available. | ['Rudolph Triebel', 'Dongheui Lee', 'Florian Steidle', 'Anne E. Reichert', 'Mariam Elsayed', 'Manuel Stoiber'] | 2023-02-22 | null | null | null | null | ['6d-pose-estimation-using-rgbd', '6d-pose-estimation-1', '3d-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.83672830e-01 -4.13687497e-01 -1.13886446e-01 1.71010643e-02
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4ca6e930-114b-49d2-ba03-24393132d301 | investigating-poor-performance-regions-of | 2306.12507 | null | https://arxiv.org/abs/2306.12507v1 | https://arxiv.org/pdf/2306.12507v1.pdf | Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection | Interpreting machine learning models remains a challenge, hindering their adoption in clinical settings. This paper proposes leveraging Local Interpretable Model-Agnostic Explanations (LIME) to provide interpretable descriptions of black box classification models in high-stakes sepsis detection. By analyzing misclassified instances, significant features contributing to suboptimal performance are identified. The analysis reveals regions where the classifier performs poorly, allowing the calculation of error rates within these regions. This knowledge is crucial for cautious decision-making in sepsis detection and other critical applications. The proposed approach is demonstrated using the eICU dataset, effectively identifying and visualizing regions where the classifier underperforms. By enhancing interpretability, our method promotes the adoption of machine learning models in clinical practice, empowering informed decision-making and mitigating risks in critical scenarios. | ['Jang Yong Kim', 'Yonghwan Kim', 'Choongmin Kim', 'San Lee', 'Surajsinh Parmar', 'Mozhgan Salimiparsa'] | 2023-06-21 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 6.75852656e-01 5.04632711e-01 -4.24129009e-01 -3.84436607e-01
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6480672c-6ad3-4862-93e8-43b76a78ede4 | attention-based-convolutional-recurrent | 1907.02230 | null | https://arxiv.org/abs/1907.02230v1 | https://arxiv.org/pdf/1907.02230v1.pdf | Attention based Convolutional Recurrent Neural Network for Environmental Sound Classification | Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose an convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and achieved the state-of-the-art performance in terms of classification accuracy. | ['Shugong Xu', 'Shunqing Zhang', 'Tianhao Qiao', 'Zhichao Zhang', 'Shan Cao'] | 2019-07-04 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.97929561e-01 -8.38716626e-01 4.51542288e-01 -2.42205858e-01
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f69f11b2-4a7b-4455-a7a6-6485472effc0 | pmc-vqa-visual-instruction-tuning-for-medical | 2305.10415 | null | https://arxiv.org/abs/2305.10415v5 | https://arxiv.org/pdf/2305.10415v5.pdf | PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering | In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. Firstly, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction, we propose a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model. Secondly, we establish a scalable pipeline to construct a large-scale medical visual question-answering dataset, named PMC-VQA, which contains 227k VQA pairs of 149k images that cover various modalities or diseases. Thirdly, we pre-train our proposed model on PMC-VQA and then fine-tune it on multiple public benchmarks, e.g., VQA-RAD and SLAKE, outperforming existing work by a large margin. Additionally, we propose a test set that has undergone manual verification, which is significantly more challenging, even the best models struggle to solve. | ['Weidi Xie', 'Yanfeng Wang', 'Ya zhang', 'Weixiong Lin', 'Ziheng Zhao', 'Chaoyi Wu', 'Xiaoman Zhang'] | 2023-05-17 | null | null | null | null | ['generative-visual-question-answering'] | ['computer-vision'] | [ 3.99808586e-01 5.57560563e-01 1.70667857e-01 -3.23536605e-01
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a39ad28f-c45c-4560-a4c0-d02bb5c8f6f4 | low-rank-approximation-for-general-tensor | 2207.07417 | null | https://arxiv.org/abs/2207.07417v2 | https://arxiv.org/pdf/2207.07417v2.pdf | Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions | We study low rank approximation of tensors, focusing on the tensor train and Tucker decompositions, as well as approximations with tree tensor networks and more general tensor networks. For tensor train decomposition, we give a bicriteria $(1 + \eps)$-approximation algorithm with a small bicriteria rank and $O(q \cdot \nnz(A))$ running time, up to lower order terms, which improves over the additive error algorithm of \cite{huber2017randomized}. We also show how to convert the algorithm of \cite{huber2017randomized} into a relative error algorithm, but their algorithm necessarily has a running time of $O(qr^2 \cdot \nnz(A)) + n \cdot \poly(qk/\eps)$ when converted to a $(1 + \eps)$-approximation algorithm with bicriteria rank $r$. To the best of our knowledge, our work is the first to achieve polynomial time relative error approximation for tensor train decomposition. Our key technique is a method for obtaining subspace embeddings with a number of rows polynomial in $q$ for a matrix which is the flattening of a tensor train of $q$ tensors. We extend our algorithm to tree tensor networks. In addition, we extend our algorithm to tensor networks with arbitrary graphs (which we refer to as general tensor networks), by using a result of \cite{ms08_simulating_quantum_tensor_contraction} and showing that a general tensor network of rank $k$ can be contracted to a binary tree network of rank $k^{O(\deg(G)\tw(G))}$, allowing us to reduce to the case of tree tensor networks. Finally, we give new fixed-parameter tractable algorithms for the tensor train, Tucker, and CP decompositions, which are simpler than those of \cite{swz19_tensor_low_rank} since they do not make use of polynomial system solvers. Our technique of Gaussian subspace embeddings with exactly $k$ rows (and thus exponentially small success probability) may be of independent interest. | ['Ziyu Zhang', 'David P. Woodruff', 'Arvind V. Mahankali'] | 2022-07-15 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.03622341e-01 2.01268703e-01 1.50721282e-01 1.67320624e-01
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d722a54d-a8bf-4924-a088-cbb41128cd1a | nonlinear-acoustic-echo-cancellation-with | 2106.13754 | null | https://arxiv.org/abs/2106.13754v1 | https://arxiv.org/pdf/2106.13754v1.pdf | Nonlinear Acoustic Echo Cancellation with Deep Learning | We propose a nonlinear acoustic echo cancellation system, which aims to model the echo path from the far-end signal to the near-end microphone in two parts. Inspired by the physical behavior of modern hands-free devices, we first introduce a novel neural network architecture that is specifically designed to model the nonlinear distortions these devices induce between receiving and playing the far-end signal. To account for variations between devices, we construct this network with trainable memory length and nonlinear activation functions that are not parameterized in advance, but are rather optimized during the training stage using the training data. Second, the network is succeeded by a standard adaptive linear filter that constantly tracks the echo path between the loudspeaker output and the microphone. During training, the network and filter are jointly optimized to learn the network parameters. This system requires 17 thousand parameters that consume 500 Million floating-point operations per second and 40 Kilo-bytes of memory. It also satisfies hands-free communication timing requirements on a standard neural processor, which renders it adequate for embedding on hands-free communication devices. Using 280 hours of real and synthetic data, experiments show advantageous performance compared to competing methods. | ['Baruch Berdugo', 'Israel Cohen', 'Amir Ivry'] | 2021-06-25 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.42495260e-01 -7.77800232e-02 4.88089383e-01 -2.94963956e-01
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34017115-81c9-4b39-a5aa-969c2e7626e3 | krf-keypoint-refinement-with-fusion-network | 2210.03437 | null | https://arxiv.org/abs/2210.03437v1 | https://arxiv.org/pdf/2210.03437v1.pdf | KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation | Existing refinement methods gradually lose their ability to further improve pose estimation methods' accuracy. In this paper, we propose a new refinement pipeline, Keypoint Refinement with Fusion Network (KRF), for 6D pose estimation, especially for objects with serious occlusion. The pipeline consists of two steps. It first completes the input point clouds via a novel point completion network. The network uses both local and global features, considering the pose information during point completion. Then, it registers the completed object point cloud with corresponding target point cloud by Color supported Iterative KeyPoint (CIKP). The CIKP method introduces color information into registration and registers point cloud around each keypoint to increase stability. The KRF pipeline can be integrated with existing popular 6D pose estimation methods, e.g. the full flow bidirectional fusion network, to further improved their pose estimation accuracy. Experiments show that our method outperforms the state-of-the-art method from 93.9\% to 94.4\% on YCB-Video dataset and from 64.4\% to 66.8\% on Occlusion LineMOD dataset. Our source code is available at https://github.com/zhanhz/KRF. | ['Yong-Jin Liu', 'Long Zeng', 'Yu-Ping Wang', 'Yiheng Han', 'Irvin Haozhe Zhan'] | 2022-10-07 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [-2.10301787e-01 -3.23056340e-01 -1.73722953e-01 -7.23035261e-02
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5c324067-332a-4a6d-85b4-61e1d65ad3a8 | a-retrieve-and-rewrite-initialization-method | null | null | https://aclanthology.org/2020.acl-main.320 | https://aclanthology.org/2020.acl-main.320.pdf | A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation | The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance. The initialization stage is very important since bad initialization may wrongly squeeze the search space, and too much noise introduced in this stage may hurt the final performance. In this paper, we propose a novel retrieval and rewriting based method to better initialize unsupervised translation models. We first retrieve semantically comparable sentences from monolingual corpora of two languages and then rewrite the target side to minimize the semantic gap between the source and retrieved targets with a designed rewriting model. The rewritten sentence pairs are used to initialize SMT models which are used to generate pseudo data for two NMT models, followed by the iterative back-translation. Experiments show that our method can build better initial unsupervised translation models and improve the final translation performance by over 4 BLEU scores. Our code is released at https://github.com/Imagist-Shuo/RRforUNMT.git. | ['Shuai Ma', 'Yu Wu', 'Ming Zhou', 'Shuo Ren', 'Shujie Liu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.64333159e-01 -1.42312706e-01 -3.70607316e-01 -4.70072806e-01
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2f1dbe3d-87be-4c0d-9245-6972ad1e995c | sequence-to-set-semantic-tagging-for-complex | null | null | https://aclanthology.org/2020.bionlp-1.2 | https://aclanthology.org/2020.bionlp-1.2.pdf | Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention | Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature. These may not explicitly refer to entities or canonical concept forms occurring in a fact-based knowledge source, e.g. the UMLS ontology. Moreover, hidden associations between related concepts meaningful in the current context, may not exist within a single document, but across documents in the collection. Predicting semantic concept tags of documents can therefore serve to associate documents related in unseen contexts, or categorize them, in information filtering or retrieval scenarios. Thus, inspired by the success of sequence-to-sequence neural models, we develop a novel sequence-to-set framework with attention, for learning document representations in a unique unsupervised setting, using no human-annotated document labels or external knowledge resources and only corpus-derived term statistics to drive the training, that can effect term transfer within a corpus for semantically tagging a large collection of documents. Our sequence-to-set modeling approach to predict semantic tags, gives to the best of our knowledge, the state-of-the-art for both, an unsupervised query expansion (QE) task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25{--}based document retrieval system; and also over the MLTM system baseline baseline (Soleimani and Miller, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del.icio.us and Ohsumed datasets. We make our code and data publicly available. | ['Eric Fosler-Lussier', 'Juanxi Li', 'Simon Lin', 'Manirupa Das', 'Yungui Huang', 'Steve Rust', 'Rajiv Ramnath'] | 2020-07-01 | null | null | null | ws-2020-7 | ['text-categorization'] | ['natural-language-processing'] | [ 7.58252621e-01 1.37132689e-01 -3.81013006e-01 -1.86357036e-01
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1bc3695b-c376-4c24-aace-4f564baea06d | global-constraints-with-prompting-for-zero | 2302.04459 | null | https://arxiv.org/abs/2302.04459v1 | https://arxiv.org/pdf/2302.04459v1.pdf | Global Constraints with Prompting for Zero-Shot Event Argument Classification | Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candidate role with the target argument span. Then, a pre-trained language model scores the new passages, making the initial prediction. Our novel prompt templates can easily adapt to all events and argument types without manual effort. Next, the model regularizes the prediction by global constraints exploiting cross-task, cross-argument, and cross-event relations. Extensive experiments demonstrate our model's effectiveness: it outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, respectively, without given argument spans. We have made our code publicly available. | ['Yangqiu Song', 'Hongming Zhang', 'Zizheng Lin'] | 2023-02-09 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.89885193e-01 2.24830538e-01 -6.42715514e-01 -4.42141771e-01
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9d3f20b8-541a-46fe-96d2-ce294a2c761f | toward-forgetting-sensitive-referring | 2007.08672 | null | https://arxiv.org/abs/2007.08672v1 | https://arxiv.org/pdf/2007.08672v1.pdf | Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures | To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as "Referring Expression Generation." As speakers repeatedly refer to similar objects, they tend to re-use properties from previous descriptions, in part to help the listener, and in part due to cognitive availability of those properties in working memory (WM). Because different theories of working memory "forgetting" necessarily lead to differences in cognitive availability, we hypothesize that they will similarly result in generation of different referring expressions. To design effective intelligent agents, it is thus necessary to determine how different models of forgetting may be differentially effective at producing natural human-like referring expressions. In this work, we computationalize two candidate models of working memory forgetting within a robot cognitive architecture, and demonstrate how they lead to cognitive availability-based differences in generated referring expressions. | ['Kellyn Larson', 'Will Culpepper', 'Torin Johnson', 'Tom Williams'] | 2020-07-16 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 9.78473667e-03 4.99977142e-01 2.73731709e-01 -3.39369625e-01
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-6.99028745e-02 -1.13949287e+00 -2.37553746e-01 1.79531146e-02] | [4.383352756500244, 1.070912480354309] |
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