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b500474a-e5d4-4b24-923b-dfad1455538e | ravitt-random-vision-transformer-tokens | 2306.10959 | null | https://arxiv.org/abs/2306.10959v1 | https://arxiv.org/pdf/2306.10959v1.pdf | RaViTT: Random Vision Transformer Tokens | Vision Transformers (ViTs) have successfully been applied to image classification problems where large annotated datasets are available. On the other hand, when fewer annotations are available, such as in biomedical applications, image augmentation techniques like introducing image variations or combinations have been proposed. However, regarding ViT patch sampling, less has been explored outside grid-based strategies. In this work, we propose Random Vision Transformer Tokens (RaViTT), a random patch sampling strategy that can be incorporated into existing ViTs. We experimentally evaluated RaViTT for image classification, comparing it with a baseline ViT and state-of-the-art (SOTA) augmentation techniques in 4 datasets, including ImageNet-1k and CIFAR-100. Results show that RaViTT increases the accuracy of the baseline in all datasets and outperforms the SOTA augmentation techniques in 3 out of 4 datasets by a significant margin +1.23% to +4.32%. Interestingly, RaViTT accuracy improvements can be achieved even with fewer tokens, thus reducing the computational load of any ViT model for a given accuracy value. | ['Mauricio Cerda', 'Cristóbal A. Navarro', 'Violeta Chang', 'Jorge Jara-Wilde', 'Manuel Zamorano', 'Cristian Muñoz', 'Carlos F. Navarro', 'Felipe A. Quezada'] | 2023-06-19 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.47002411e-01 3.30760151e-01 -1.57518283e-01 -6.48869872e-02
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27f05b2f-068a-436c-a559-319089b18d76 | from-learning-to-match-to-learning-to | null | null | https://aclanthology.org/2021.ccl-1.90 | https://aclanthology.org/2021.ccl-1.90.pdf | From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification | “Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype based oninner-class support sets. However the learning-to-match paradigm focuses on capturing the sim-ilarity knowledge between query and class prototype while fails to consider discriminative infor-mation between different candidate classes. Such information is critical especially when targetclasses are highly confusing and domain shifting exists between training and testing phases. Inthis paper we propose the Global Transformed Prototypical Networks(GTPN) which learns tobuild a few-shot model to directly discriminate between the query and all target classes with bothinner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between allcandidate classes and therefore leads to better classification performance. We conducted exper-iments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance onthe official leaderboard of FewRel 2.0 1.” | ['Sun Le', 'Wu Hua', 'Dai Dai', 'Han Xianpei', 'Lin Hongyu', 'Yan Lingyong', 'Xiao Xinyan', 'Liu Fangchao'] | null | null | null | null | ccl-2021-8 | ['few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.83944863e-02 -3.22630033e-02 -9.56485808e-01 -5.48207581e-01
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d6636eeb-cb9f-4376-8a90-b865b2dbbe51 | knowledge-graph-augmented-language-models-for | 2305.18846 | null | https://arxiv.org/abs/2305.18846v1 | https://arxiv.org/pdf/2305.18846v1.pdf | Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation | Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-grounded dialogue generation methods tackle this problem by leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee that the model utilizes a relevant piece of knowledge from the KG. To overcome this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a framework for generating context-relevant and knowledge-grounded dialogues with the KG. Specifically, our SURGE framework first retrieves the relevant subgraph from the KG, and then enforces consistency across facts by perturbing their word embeddings conditioned by the retrieved subgraph. Then, we utilize contrastive learning to ensure that the generated texts have high similarity to the retrieved subgraphs. We validate our SURGE framework on OpendialKG and KOMODIS datasets, showing that it generates high-quality dialogues that faithfully reflect the knowledge from KG. | ['Sung Ju Hwang', 'Jinheon Baek', 'Jin Myung Kwak', 'Minki Kang'] | 2023-05-30 | null | null | null | null | ['knowledge-graphs', 'word-embeddings', 'dialogue-generation', 'dialogue-generation'] | ['knowledge-base', 'methodology', 'natural-language-processing', 'speech'] | [ 2.86281910e-02 8.55345428e-01 -1.77828774e-01 -1.36054888e-01
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867f88a7-70e6-456c-91ea-66d922c94a52 | krnet-image-denoising-with-kernel-regulation | 1910.08867 | null | https://arxiv.org/abs/1910.08867v1 | https://arxiv.org/pdf/1910.08867v1.pdf | KRNET: Image Denoising with Kernel Regulation Network | One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional neural networks (CNN) have shown the powerful capability to learn image prior information through a stack of layers defined by a combination of kernels (filters) on the input. However, existing CNN-based methods mainly focus on synthetic gray-scale images. These methods still exhibit low performance when tackling multi-channel color image denoising. In this paper, we optimize CNN regularization capability by developing a kernel regulation module. In particular, we propose a kernel regulation network-block, referred to as KR-block, by integrating the merits of both large and small kernels, that can effectively estimate features in solving image denoising. We build a deep CNN-based denoiser, referred to as KRNET, via concatenating multiple KR-blocks. We evaluate KRNET on additive white Gaussian noise (AWGN), multi-channel (MC) noise, and realistic noise, where KRNET obtains significant performance gains over state-of-the-art methods across a wide spectrum of noise levels. | ['Ruogu Fang', 'Xiaoxiao Zhou', 'Peng Liu', 'Junyiyang Li', 'El Basha Mohammad D'] | 2019-10-20 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 1.97297260e-01 -4.66533989e-01 3.41256380e-01 -4.12878126e-01
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dafa5977-77d9-4611-8c05-0dbb698c34ab | optimal-multi-view-correction-of-local-affine | 1905.00519 | null | http://arxiv.org/abs/1905.00519v1 | http://arxiv.org/pdf/1905.00519v1.pdf | Optimal Multi-view Correction of Local Affine Frames | The technique requires the epipolar geometry to be pre-estimated between each
image pair. It exploits the constraints which the camera movement implies, in
order to apply a closed-form correction to the parameters of the input
affinities. Also, it is shown that the rotations and scales obtained by
partially affine-covariant detectors, e.g., AKAZE or SIFT, can be completed to
be full affine frames by the proposed algorithm. It is validated both in
synthetic experiments and on publicly available real-world datasets that the
method always improves the output of the evaluated affine-covariant feature
detectors. As a by-product, these detectors are compared and the ones obtaining
the most accurate affine frames are reported. For demonstrating the
applicability, we show that the proposed technique as a pre-processing step
improves the accuracy of pose estimation for a camera rig, surface normal and
homography estimation. | ['Ivan Eichhardt', 'Daniel Barath'] | 2019-05-01 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 8.43911767e-02 1.70409345e-04 9.03965756e-02 -1.81513175e-01
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e11194ac-0704-48a0-a52e-528e48963b80 | calibrating-cross-modal-feature-for-text | 2304.02278 | null | https://arxiv.org/abs/2304.02278v2 | https://arxiv.org/pdf/2304.02278v2.pdf | Calibrating Cross-modal Features for Text-Based Person Searching | Text-Based Person Searching (TBPS) aims to identify the images of pedestrian targets from a large-scale gallery with given textual caption. For cross-modal TBPS task, it is critical to obtain well-distributed representation in the common embedding space to reduce the inter-modal gap. Furthermore, it is also essential to learn detailed image-text correspondence efficiently to discriminate similar targets and enable fine-grained target search. To address these challenges, we present a simple yet effective method that calibrates cross-modal features from these two perspectives. Our method consists of two novel losses to provide fine-grained cross-modal features. The Sew calibration loss takes the quality of textual captions as guidance and aligns features between image and text modalities. On the other hand, the Masking Caption Modeling (MCM) loss leverages a masked captions prediction task to establish detailed and generic relationships between textual and visual parts. The proposed method is cost-effective and can easily retrieve specific persons with textual captions. The architecture has only a dual-encoder without multi-level branches or extra interaction modules, making a high-speed inference. Our method achieves top results on three popular benchmarks with 73.81%, 74.25% and 57.35% Rank1 accuracy on the CUHK-PEDES, ICFG-PEDES, and RSTPReID, respectively. We hope our scalable method will serve as a solid baseline and help ease future research in TBPS. The code will be publicly available. | ['Jing Liu', 'Yang Liu', 'Tong Yang', 'Sipeng Zhang', 'Donglai Wei'] | 2023-04-05 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 4.97500449e-02 -3.16788673e-01 -2.97041804e-01 -5.02020478e-01
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f705f90a-6b48-4776-ab6a-ab2adbdf2323 | towards-generalized-and-explainable-long | 2210.06282 | null | https://arxiv.org/abs/2210.06282v2 | https://arxiv.org/pdf/2210.06282v2.pdf | Towards Generalized and Explainable Long-Range Context Representation for Dialogue Systems | Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last-$k$ previous utterances. However, this method may not be ideal for conversations containing long-range dependencies. In this work, we propose DialoGX, a novel encoder-decoder based framework for conversational response generation with a generalized and explainable context representation that can look beyond the last-$k$ utterances. Hence the method is adaptive to conversations with long-range dependencies. The main idea of our approach is to identify and utilize the most relevant historical utterances instead of the last-$k$ utterances in chronological order. We study the effectiveness of our proposed method on both dialogue generation (open-domain) and understanding (DST) tasks. DialoGX achieves comparable performance with the state-of-the-art models on DailyDialog dataset. We also observe performance gain in existing DST models with our proposed context representation strategy on MultiWOZ dataset. We justify our context representation through the lens of psycholinguistics and show that the relevance score of previous utterances agrees well with human cognition which makes DialoGX explainable as well. | ['P. K. Srijith', 'Maunendra Sankar Desarkar', 'Suvodip Dey'] | 2022-10-12 | null | null | null | null | ['dialogue-understanding', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.20299301e-02 6.54994607e-01 9.48575735e-02 -8.01617503e-01
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eb881a06-76ff-4b9d-b7fa-a214dc6c3ee9 | dynamic-deep-multi-task-learning-for | 1911.03341 | null | https://arxiv.org/abs/1911.03341v1 | https://arxiv.org/pdf/1911.03341v1.pdf | Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition | Rather than the visual images, the face recognition of the caricatures is far from the performance of the visual images. The challenge is the extreme non-rigid distortions of the caricatures introduced by exaggerating the facial features to strengthen the characters. In this paper, we propose dynamic multi-task learning based on deep CNNs for cross-modal caricature-visual face recognition. Instead of the conventional multi-task learning with fixed weights of the tasks, the proposed dynamic multi-task learning dynamically updates the weights of tasks according to the importance of the tasks, which enables the training of the networks focus on the hard task instead of being stuck in the overtraining of the easy task. The experimental results demonstrate the effectiveness of the dynamic multi-task learning for caricature-visual face recognition. The performance evaluated on the datasets CaVI and WebCaricature show the superiority over the state-of-art methods. The implementation code is available here. | ['Jean-Christophe Burie', 'Zuheng Ming', 'Muhammad Muzzamil Luqman'] | 2019-11-08 | null | null | null | null | ['caricature'] | ['computer-vision'] | [-9.06342417e-02 -2.02703789e-01 2.13620260e-01 -3.78596246e-01
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736b602e-9e38-4e42-8ea1-10d72114258a | forest-an-interactive-multi-tree-synthesizer | 2012.14235 | null | https://arxiv.org/abs/2012.14235v1 | https://arxiv.org/pdf/2012.14235v1.pdf | FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions | Form validators based on regular expressions are often used on digital forms to prevent users from inserting data in the wrong format. However, writing these validators can pose a challenge to some users. We present FOREST, a regular expression synthesizer for digital form validations. FOREST produces a regular expression that matches the desired pattern for the input values and a set of conditions over capturing groups that ensure the validity of integer values in the input. Our synthesis procedure is based on enumerative search and uses a Satisfiability Modulo Theories (SMT) solver to explore and prune the search space. We propose a novel representation for regular expressions synthesis, multi-tree, which induces patterns in the examples and uses them to split the problem through a divide-and-conquer approach. We also present a new SMT encoding to synthesize capture conditions for a given regular expression. To increase confidence in the synthesized regular expression, we implement user interaction based on distinguishing inputs. We evaluated FOREST on real-world form-validation instances using regular expressions. Experimental results show that FOREST successfully returns the desired regular expression in 72% of the instances and outperforms REGEL, a state-of-the-art regular expression synthesizer. | ['Ruben Martins', 'Inês Lynce', 'Miguel Ventura', 'Miguel Terra-Neves', 'Margarida Ferreira'] | 2020-12-28 | null | null | null | null | ['enumerative-search'] | ['computer-code'] | [ 7.53493607e-01 2.75215089e-01 -4.86533731e-01 -4.60821301e-01
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672205a4-4483-4828-a377-8edd09104605 | uszeged-correction-type-sensitive | null | null | https://aclanthology.org/W15-4318 | https://aclanthology.org/W15-4318.pdf | USZEGED: Correction Type-sensitive Normalization of English Tweets Using Efficiently Indexed n-gram Statistics | null | ["Ervin Tasn{\\'a}di", "G{\\'a}bor Berend"] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8c92d80d-c55a-4c5c-a3d0-0cb6e33faecb | reading-between-the-lanes-text-videoqa-on-the | 2307.03948 | null | https://arxiv.org/abs/2307.03948v1 | https://arxiv.org/pdf/2307.03948v1.pdf | Reading Between the Lanes: Text VideoQA on the Road | Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of $3,222$ driving videos collected from multiple countries, annotated with $10,500$ questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering. The dataset is available at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtextvqa | ['C. V. Jawahar', 'Dimosthenis Karatzas', 'Sergi Garcia', 'Minesh Mathew', 'George Tom'] | 2023-07-08 | null | null | null | null | ['scene-text-recognition', 'video-question-answering', 'question-answering'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 9.15177763e-02 -2.09857374e-01 -4.76324052e-01 -7.20090628e-01
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52386d72-359f-4149-b0ef-188bdc059181 | evars-gpr-event-triggered-augmented-refitting | 2107.02463 | null | https://arxiv.org/abs/2107.02463v1 | https://arxiv.org/pdf/2107.02463v1.pdf | EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data | Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned fore-casting model might not be useful anymore. In this paper, we present EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts in the target variable scale of seasonal data. For this purpose, EVARS-GPR com-bines online change point detection with a refitting of the prediction model using data augmentation for samples prior to a change point. Our experiments on sim-ulated data show that EVARS-GPR is applicable for a wide range of output scale changes. EVARS-GPR has on average a 20.8 % lower RMSE on different real-world datasets compared to methods with a similar computational resource con-sumption. Furthermore, we show that our algorithm leads to a six-fold reduction of the averaged runtime in relation to all comparison partners with a periodical refitting strategy. In summary, we present a computationally efficient online fore-casting algorithm for seasonal time series with changes of the target variable scale and demonstrate its functionality on simulated as well as real-world data. All code is publicly available on GitHub: https://github.com/grimmlab/evars-gpr. | ['Dominik G. Grimm', 'Florian Haselbeck'] | 2021-07-06 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 9.54546481e-02 -5.35504341e-01 3.93030763e-01 -2.14799434e-01
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3a94f1f7-885d-42ea-ac1b-50624c24e34f | task-driven-dynamic-fusion-reducing-ambiguity | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Task-Driven_Dynamic_Fusion_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Task-Driven_Dynamic_Fusion_CVPR_2017_paper.pdf | Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description | Integrating complementary features from multiple channels is expected to solve the description ambiguity problem in video captioning, whereas inappropriate fusion strategies often harm rather than help the performance. Existing static fusion methods in video captioning such as concatenation and summation cannot attend to appropriate feature channels, thus fail to adaptively support the recognition of various kinds of visual entities such as actions and objects. This paper contributes to: 1)The first in-depth study of the weakness inherent in data-driven static fusion methods for video captioning. 2) The establishment of a task-driven dynamic fusion (TDDF) method. It can adaptively choose different fusion patterns according to model status. 3) The improvement of video captioning. Extensive experiments conducted on two well-known benchmarks demonstrate that our dynamic fusion method outperforms the state-of-the-art results on MSVD with METEOR scores 0.333, and achieves superior METEOR scores 0.278 on MSR-VTT-10K. Compared to single features, the relative improvement derived from our fusion method are 10.0% and 5.7% respectively on two datasets.
| ['Xishan Zhang', 'Qi Tian', 'Yongdong Zhang', 'Ke Gao', 'Dongming Zhang', 'Jintao Li'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['video-description'] | ['computer-vision'] | [ 4.17998165e-01 -4.18454379e-01 -2.11998463e-01 -2.96239048e-01
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0110b1cd-02cb-4fb8-9698-36c4abb32da6 | physics-informed-transfer-learning-strategy | 2206.06817 | null | https://arxiv.org/abs/2206.06817v1 | https://arxiv.org/pdf/2206.06817v1.pdf | Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations | Since the derivation of the Navier Stokes equations, it has become possible to numerically solve real world viscous flow problems (computational fluid dynamics (CFD)). However, despite the rapid advancements in the performance of central processing units (CPUs), the computational cost of simulating transient flows with extremely small time/grid scale physics is still unrealistic. In recent years, machine learning (ML) technology has received significant attention across industries, and this big wave has propagated various interests in the fluid dynamics community. Recent ML CFD studies have revealed that completely suppressing the increase in error with the increase in interval between the training and prediction times in data driven methods is unrealistic. The development of a practical CFD acceleration methodology that applies ML is a remaining issue. Therefore, the objectives of this study were developing a realistic ML strategy based on a physics-informed transfer learning and validating the accuracy and acceleration performance of this strategy using an unsteady CFD dataset. This strategy can determine the timing of transfer learning while monitoring the residuals of the governing equations in a cross coupling computation framework. Consequently, our hypothesis that continuous fluid flow time series prediction is feasible was validated, as the intermediate CFD simulations periodically not only reduce the increased residuals but also update the network parameters. Notably, the cross coupling strategy with a grid based network model does not compromise the simulation accuracy for computational acceleration. The simulation was accelerated by 1.8 times in the laminar counterflow CFD dataset condition including the parameter updating time. Open source CFD software OpenFOAM and open-source ML software TensorFlow were used in this feasibility study. | ['Sung Joong Kim', 'Ricardo Vinuesa', 'Hamidreza Eivazi', 'Juhyeong Lee', 'Joongoo Jeon'] | 2022-06-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-3.10780317e-01 -5.87536097e-01 4.51257050e-01 9.24132988e-02
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62fcc439-1ae8-4235-aea7-107be68b152d | crowdsourcing-the-perception-of-machine | 2002.01618 | null | https://arxiv.org/abs/2002.01618v1 | https://arxiv.org/pdf/2002.01618v1.pdf | Crowdsourcing the Perception of Machine Teaching | Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt. | ['Jonggi Hong', 'Hernisa Kacorri', 'Kyungjun Lee', 'June Xu'] | 2020-02-05 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.99816585e-01 5.28535657e-02 -9.72026363e-02 -6.80459678e-01
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f065162f-10ef-4d9d-b328-ec29524586b6 | polyretro-few-shot-polymer-retrosynthesis-via | null | null | https://openreview.net/forum?id=JHx9ZDCQEA | https://openreview.net/pdf?id=JHx9ZDCQEA | PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation | Polymers appear everywhere in our daily lives -- fabrics, plastics, rubbers, etc. -- and we could hardly live without them. To make polymers, chemists develop processes that combine smaller building blocks~(monomers) to form long chains or complex networks~(polymers). These processes are called polymerizations and will usually take lots of human efforts to develop. Although machine learning models for small molecules have generated lots of promising results, the prediction problem for polymerization is new and suffers from the scarcity of polymerization datasets available in the field. Furthermore, the problem is made even more challenging by the large size of the polymers and the additional recursive constraints, which are not present in the small molecule problem. In this paper, we make an initial step towards this challenge and propose a learning-based search framework that can automatically identify a sequence of reactions that lead to the polymerization of a target polymer with minimal polymerization data involved. Our method transfers models trained on small molecule datasets for retrosynthesis to check the validity of polymerization reaction. Furthermore, our method also incorporates a template prior learned on a limited amount of polymer data into the framework to adapt the model from small molecule to the polymer domain. We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world. | ['Le Song', 'Rampi Ramprasad', 'Hanjun Dai', 'Chengtao Li', 'Binghong Chen'] | 2021-01-01 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.91556156e-01 3.06066424e-01 -4.35972065e-01 -3.21303532e-02
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da6210df-4c78-4df5-8d3f-45194db421c6 | hierarchical-decision-transformer | 2209.10447 | null | https://arxiv.org/abs/2209.10447v1 | https://arxiv.org/pdf/2209.10447v1.pdf | Hierarchical Decision Transformer | Sequence models in reinforcement learning require task knowledge to estimate the task policy. This paper presents a hierarchical algorithm for learning a sequence model from demonstrations. The high-level mechanism guides the low-level controller through the task by selecting sub-goals for the latter to reach. This sequence replaces the returns-to-go of previous methods, improving its performance overall, especially in tasks with longer episodes and scarcer rewards. We validate our method in multiple tasks of OpenAIGym, D4RL and RoboMimic benchmarks. Our method outperforms the baselines in eight out of ten tasks of varied horizons and reward frequencies without prior task knowledge, showing the advantages of the hierarchical model approach for learning from demonstrations using a sequence model. | ['Luís A. Alexandre', 'André Correia'] | 2022-09-21 | null | null | null | null | ['d4rl'] | ['robots'] | [ 1.03300894e-02 3.42186540e-02 -4.58232582e-01 -1.32826477e-01
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cbf6d306-1443-4c5e-8a67-c002548b8929 | anyonenet-synchronized-speech-and-talking | 2108.04325 | null | https://arxiv.org/abs/2108.04325v2 | https://arxiv.org/pdf/2108.04325v2.pdf | AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Person | Automatically generating videos in which synthesized speech is synchronized with lip movements in a talking head has great potential in many human-computer interaction scenarios. In this paper, we present an automatic method to generate synchronized speech and talking-head videos on the basis of text and a single face image of an arbitrary person as input. In contrast to previous text-driven talking head generation methods, which can only synthesize the voice of a specific person, the proposed method is capable of synthesizing speech for any person that is inaccessible in the training stage. Specifically, the proposed method decomposes the generation of synchronized speech and talking head videos into two stages, i.e., a text-to-speech (TTS) stage and a speech-driven talking head generation stage. The proposed TTS module is a face-conditioned multi-speaker TTS model that gets the speaker identity information from face images instead of speech, which allows us to synthesize a personalized voice on the basis of the input face image. To generate the talking head videos from the face images, a facial landmark-based method that can predict both lip movements and head rotations is proposed. Extensive experiments demonstrate that the proposed method is able to generate synchronized speech and talking head videos for arbitrary persons and non-persons. Synthesized speech shows consistency with the given face regarding to the synthesized voice's timbre and one's appearance in the image, and the proposed landmark-based talking head method outperforms the state-of-the-art landmark-based method on generating natural talking head videos. | ['Scharenborg', 'Lei Xie', 'Jihua Zhu', 'Qicong Xie', 'Xinsheng Wang'] | 2021-08-09 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 2.17494339e-01 3.50753307e-01 4.10070382e-02 -4.45941150e-01
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ba3ae472-d523-43c6-a5ba-08125179fa22 | horizon-free-reinforcement-learning-in-1 | 2305.08359 | null | https://arxiv.org/abs/2305.08359v1 | https://arxiv.org/pdf/2305.08359v1.pdf | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs | Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by $1$, and proved regret bounds that have a polylogarithmic dependence on the planning horizon $H$. However, it remains an open question that if such results can be carried over to adversarial RL, where the reward is adversarially chosen at each episode. In this paper, we answer this question affirmatively by proposing the first horizon-free policy search algorithm. To tackle the challenges caused by exploration and adversarially chosen reward, our algorithm employs (1) a variance-uncertainty-aware weighted least square estimator for the transition kernel; and (2) an occupancy measure-based technique for the online search of a \emph{stochastic} policy. We show that our algorithm achieves an $\tilde{O}\big((d+\log (|\mathcal{S}|^2 |\mathcal{A}|))\sqrt{K}\big)$ regret with full-information feedback, where $d$ is the dimension of a known feature mapping linearly parametrizing the unknown transition kernel of the MDP, $K$ is the number of episodes, $|\mathcal{S}|$ and $|\mathcal{A}|$ are the cardinalities of the state and action spaces. We also provide hardness results and regret lower bounds to justify the near optimality of our algorithm and the unavoidability of $\log|\mathcal{S}|$ and $\log|\mathcal{A}|$ in the regret bound. | ['Quanquan Gu', 'Weitong Zhang', 'Jiafan He', 'Qingyue Zhao', 'Kaixuan Ji'] | 2023-05-15 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.02935903e-01 6.08355284e-01 -1.43979445e-01 4.95599844e-02
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629d983d-6db2-4cfd-ae80-434f3e10c301 | carb-a-crowdsourced-benchmark-for-open-ie | null | null | https://aclanthology.org/D19-1651 | https://aclanthology.org/D19-1651.pdf | CaRB: A Crowdsourced Benchmark for Open IE | Open Information Extraction (Open IE) systems have been traditionally evaluated via manual annotation. Recently, an automated evaluator with a benchmark dataset (OIE2016) was released {--} it scores Open IE systems automatically by matching system predictions with predictions in the benchmark dataset. Unfortunately, our analysis reveals that its data is rather noisy, and the tuple matching in the evaluator has issues, making the results of automated comparisons less trustworthy. We contribute CaRB, an improved dataset and framework for testing Open IE systems. To the best of our knowledge, CaRB is the first crowdsourced Open IE dataset and it also makes substantive changes in the matching code and metrics. NLP experts annotate CaRB{'}s dataset to be more accurate than OIE2016. Moreover, we find that on one pair of Open IE systems, CaRB framework provides contradictory results to OIE2016. Human assessment verifies that CaRB{'}s ranking of the two systems is the accurate ranking. We release the CaRB framework along with its crowdsourced dataset. | ['Sangnie Bhardwaj', 'Samarth Aggarwal', 'Mausam Mausam'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.80394620e-01 6.01905167e-01 -1.54118776e-01 -2.48468980e-01
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633a8166-c82c-4057-b4cc-3a055e44bacd | fsa-net-learning-fine-grained-structure | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_FSA-Net_Learning_Fine-Grained_Structure_Aggregation_for_Head_Pose_Estimation_From_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_FSA-Net_Learning_Fine-Grained_Structure_Aggregation_for_Head_Pose_Estimation_From_CVPR_2019_paper.pdf | FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image | This paper proposes a method for head pose estimation from a single image. Previous methods often predict head poses through landmark or depth estimation and would require more computation than necessary. Our method is based on regression and feature aggregation. For having a compact model, we employ the soft stagewise regression scheme. Existing feature aggregation methods treat inputs as a bag of features and thus ignore their spatial relationship in a feature map. We propose to learn a fine-grained structure mapping for spatially grouping features before aggregation. The fine-grained structure provides part-based information and pooled values. By utilizing learnable and non-learnable importance over the spatial location, different model variants can be generated and form a complementary ensemble. Experiments show that our method outperforms the state-of-the-art methods including both the landmark-free ones and the ones based on landmark or depth estimation. With only a single RGB frame as input, our method even outperforms methods utilizing multi-modality information (RGB-D, RGB-Time) on estimating the yaw angle. Furthermore, the memory overhead of our model is 100 times smaller than those of previous methods.
| [' Yung-Yu Chuang', ' Yen-Yu Lin', ' Yi-Ting Chen', 'Tsun-Yi Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['head-pose-estimation'] | ['computer-vision'] | [-1.52233839e-01 1.29409507e-01 -1.88415408e-01 -8.53111207e-01
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a63e6520-b2e4-4b91-8aec-6068982d7169 | weakly-supervised-discourse-segmentation-for | null | null | https://aclanthology.org/2021.emnlp-main.104 | https://aclanthology.org/2021.emnlp-main.104.pdf | Weakly supervised discourse segmentation for multiparty oral conversations | Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks. While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features foreign to written text. Segmentation is less studied for this type of language, where annotated data is scarce, and existing corpora more heterogeneous. We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts. Supervision is given by a latent model bootstrapped by manually defined heuristic rules that use linguistic and acoustic information. The resulting model improves the original segmenter, especially in contexts where information on speaker turns is lacking or noisy, gaining up to 13% in F-score. Evaluation is performed on data like those used to define our heuristic rules, but also on transcripts from two other corpora. | ['Isabelle Ferrané', 'Thomas Pellegrini', 'Philippe Muller', 'Julie Hunter', 'Lila Gravellier'] | null | null | null | null | emnlp-2021-11 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 5.70036113e-01 8.09293687e-01 -3.57389987e-01 -4.04032052e-01
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fec121ed-3af1-4053-9afe-4925dcc78709 | machine-learning-based-early-detection-of-iot | 2010.11453 | null | https://arxiv.org/abs/2010.11453v1 | https://arxiv.org/pdf/2010.11453v1.pdf | Machine Learning-Based Early Detection of IoT Botnets Using Network-Edge Traffic | In this work, we present a lightweight IoT botnet detection solution, EDIMA, which is designed to be deployed at the edge gateway installed in home networks and targets early detection of botnets prior to the launch of an attack. EDIMA includes a novel two-stage Machine Learning (ML)-based detector developed specifically for IoT bot detection at the edge gateway. The ML-based bot detector first employs ML algorithms for aggregate traffic classification and subsequently Autocorrelation Function (ACF)-based tests to detect individual bots. The EDIMA architecture also comprises a malware traffic database, a policy engine, a feature extractor and a traffic parser. Performance evaluation results show that EDIMA achieves high bot scanning and bot-CnC traffic detection accuracies with very low false positive rates. The detection performance is also shown to be robust to an increase in the number of IoT devices connected to the edge gateway where EDIMA is deployed. Further, the runtime performance analysis of a Python implementation of EDIMA deployed on a Raspberry Pi reveals low bot detection delays and low RAM consumption. EDIMA is also shown to outperform existing detection techniques for bot scanning traffic and bot-CnC server communication. | ['Teng Joon Lim', 'Sahithya Swaminathan', 'Mrinalini Shridhar', 'Ayush Kumar'] | 2020-10-22 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.26715982e-01 -3.89506459e-01 -1.87533364e-01 3.68692607e-01
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4887e875-d1f9-427e-919b-3ab63d47a2bf | improving-perceptual-quality-intelligibility | 2303.09048 | null | https://arxiv.org/abs/2303.09048v1 | https://arxiv.org/pdf/2303.09048v1.pdf | Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms | In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications. Our approach involves adapting the DNS 2020 models to the specific acoustic characteristics of VoIP communications, which includes distortion and artifacts caused by compression, transmission, and platform-specific processing. To this end, we propose a multi-task learning framework for VoIP-DNS that jointly optimizes noise suppression and VoIP-specific acoustics for speech enhancement. We evaluate our approach on a diverse VoIP scenarios and show that it outperforms both industry performance and state-of-the-art methods for speech enhancement on VoIP applications. Our results demonstrate the potential of models trained on DNS-2020 to be improved and tailored to different VoIP platforms using VoIP-DNS, whose findings have important applications in areas such as speech recognition, voice assistants, and telecommunication. | ['Bhiksha Raj', 'Minjeong Kim', 'Kawon Lee', 'Minseon Gwak', 'Hamza Khalid', 'Xuankai Chang', 'Haohui Liu', 'Amanda Shu', 'Yunyang Zeng', 'Shuo Han', 'Ankit Shah', 'Hojeong Lee', 'Shikhar Agnihotri', 'Ojas Bhargave', 'Joseph Konan'] | 2023-03-16 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-6.04244508e-02 -4.91493016e-01 -9.74428356e-02 -1.42290041e-01
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f16dcd2f-041f-4ca6-a32c-fd61725bab1e | single-image-deraining-network-with-rain | 2111.03615 | null | https://arxiv.org/abs/2111.03615v1 | https://arxiv.org/pdf/2111.03615v1.pdf | Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM | Single image deraining is typically addressed as residual learning to predict the rain layer from an input rainy image. For this purpose, an encoder-decoder network draws wide attention, where the encoder is required to encode a high-quality rain embedding which determines the performance of the subsequent decoding stage to reconstruct the rain layer. However, most of existing studies ignore the significance of rain embedding quality, thus leading to limited performance with over/under-deraining. In this paper, with our observation of the high rain layer reconstruction performance by an rain-to-rain autoencoder, we introduce the idea of "Rain Embedding Consistency" by regarding the encoded embedding by the autoencoder as an ideal rain embedding and aim at enhancing the deraining performance by improving the consistency between the ideal rain embedding and the rain embedding derived by the encoder of the deraining network. To achieve this, a Rain Embedding Loss is applied to directly supervise the encoding process, with a Rectified Local Contrast Normalization (RLCN) as the guide that effectively extracts the candidate rain pixels. We also propose Layered LSTM for recurrent deraining and fine-grained encoder feature refinement considering different scales. Qualitative and quantitative experiments demonstrate that our proposed method outperforms previous state-of-the-art methods particularly on a real-world dataset. Our source code is available at http://www.ok.sc.e.titech.ac.jp/res/SIR/. | ['Masatoshi Okutomi', 'Yusuke Monno', 'Yizhou Li'] | 2021-11-05 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.08900875e-01 -7.97686353e-02 1.55296966e-01 -5.30448616e-01
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8ecf12ac-c6f4-4aaf-b99a-45a68e370588 | transfer-learning-for-atomistic-simulations | 2306.01589 | null | https://arxiv.org/abs/2306.01589v3 | https://arxiv.org/pdf/2306.01589v3.pdf | Transfer learning for atomistic simulations using GNNs and kernel mean embeddings | Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, deep learning pipelines are notoriously data-hungry, while generating reference calculations is computationally demanding. To overcome this difficulty, we propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) in describing chemical environments, together with kernel mean embeddings. We extract a feature map from GNNs pre-trained on the OC20 dataset and use it to learn the potential energy surface from system-specific datasets of catalytic processes. Our method is further enhanced by a flexible kernel function that incorporates chemical species information, resulting in improved performance and interpretability. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance, and improving on methods that rely on GNNs or ridge regression alone, as well as similar fine-tuning approaches. We make the code available to the community at https://github.com/IsakFalk/atomistic_transfer_mekrr. | ['Michele Parrinello', 'Massimiliano Pontil', 'Pietro Novelli', 'Luigi Bonati', 'John Falk'] | 2023-06-02 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.16894463e-01 -4.51525599e-02 -1.92998594e-03 -3.07687938e-01
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91ecaadc-3965-471a-857a-d8ce4365a158 | modelling-commonsense-properties-using-pre | 2210.02771 | null | https://arxiv.org/abs/2210.02771v1 | https://arxiv.org/pdf/2210.02771v1.pdf | Modelling Commonsense Properties using Pre-Trained Bi-Encoders | Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding. While contextualised language models are reportedly capable of predicting such commonsense properties with human-level accuracy, we argue that such results have been inflated because of the high similarity between training and test concepts. This means that models which capture concept similarity can perform well, even if they do not capture any knowledge of the commonsense properties themselves. In settings where there is no overlap between the properties that are considered during training and testing, we find that the empirical performance of standard language models drops dramatically. To address this, we study the possibility of fine-tuning language models to explicitly model concepts and their properties. In particular, we train separate concept and property encoders on two types of readily available data: extracted hyponym-hypernym pairs and generic sentences. Our experimental results show that the resulting encoders allow us to predict commonsense properties with much higher accuracy than is possible by directly fine-tuning language models. We also present experimental results for the related task of unsupervised hypernym discovery. | ['Steven Schockaert', 'Luis Espinosa-Anke', 'Amit Gajbhiye'] | 2022-10-06 | null | https://aclanthology.org/2022.coling-1.349 | https://aclanthology.org/2022.coling-1.349.pdf | coling-2022-10 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 5.00889301e-01 3.08495104e-01 -1.18641503e-01 -5.87888956e-01
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c9e30a80-6743-42b5-bdf5-a5837ea8293a | humangan-a-generative-model-of-humans-images | 2103.06902 | null | https://arxiv.org/abs/2103.06902v1 | https://arxiv.org/pdf/2103.06902v1.pdf | HumanGAN: A Generative Model of Humans Images | Generative adversarial networks achieve great performance in photorealistic image synthesis in various domains, including human images. However, they usually employ latent vectors that encode the sampled outputs globally. This does not allow convenient control of semantically-relevant individual parts of the image, and is not able to draw samples that only differ in partial aspects, such as clothing style. We address these limitations and present a generative model for images of dressed humans offering control over pose, local body part appearance and garment style. This is the first method to solve various aspects of human image generation such as global appearance sampling, pose transfer, parts and garment transfer, and parts sampling jointly in a unified framework. As our model encodes part-based latent appearance vectors in a normalized pose-independent space and warps them to different poses, it preserves body and clothing appearance under varying posture. Experiments show that our flexible and general generative method outperforms task-specific baselines for pose-conditioned image generation, pose transfer and part sampling in terms of realism and output resolution. | ['Christian Theobalt', 'Vladislav Golyanik', 'Lingjie Liu', 'Kripasindhu Sarkar'] | 2021-03-11 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 5.00003219e-01 8.43892992e-02 2.46014595e-02 -3.70487630e-01
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2dc4187c-7ed4-4537-bbe4-6413311f2e9f | conservative-optimistic-policy-optimization | 2103.03307 | null | https://arxiv.org/abs/2103.03307v1 | https://arxiv.org/pdf/2103.03307v1.pdf | Conservative Optimistic Policy Optimization via Multiple Importance Sampling | Reinforcement Learning (RL) has been able to solve hard problems such as playing Atari games or solving the game of Go, with a unified approach. Yet modern deep RL approaches are still not widely used in real-world applications. One reason could be the lack of guarantees on the performance of the intermediate executed policies, compared to an existing (already working) baseline policy. In this paper, we propose an online model-free algorithm that solves conservative exploration in the policy optimization problem. We show that the regret of the proposed approach is bounded by $\tilde{\mathcal{O}}(\sqrt{T})$ for both discrete and continuous parameter spaces. | ['Othman Gaizi', 'Achraf Azize'] | 2021-03-04 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.49199083e-01 3.39631349e-01 -2.02443987e-01 5.30612767e-02
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0fa42451-8020-4958-8faa-0d1fd00944cf | on-wind-farm-wake-mixing-strategies-using | 2003.11319 | null | http://arxiv.org/abs/2003.11319v1 | http://arxiv.org/pdf/2003.11319v1.pdf | On wind farm wake mixing strategies using dynamic individual pitch control | Dynamic wind farm control is a new strategy that aims to apply time-varying,
often periodic, control signals on upstream wind turbines to increase the wake
mixing behind the turbine. As a result, wake recovery is accelerated, leading
to a higher power production of downstream turbines. As the amount of interest
in dynamic control strategies for wind turbines in a wind farm is increasing,
different approaches are being proposed. One such novel approach is called
Dynamic Individual Pitch Control (DIPC). In DIPC, each blade pitch angle of a
turbine is controlled independently to dynamically manipulate the direction of
the thrust force vector exerted on the wind. Hence, the direction of the wake
is varied, inducing wake mixing without significant thrust force magnitude
variations on the rotor. In this paper, the effectiveness of different
variations in the thrust direction are evaluated and compared using Large Eddy
Simulation (LES) experiments. | [] | 2020-03-25 | null | null | null | null | ['pitch-control'] | ['audio'] | [-3.28901231e-01 -3.07419747e-01 9.88110155e-02 5.76200962e-01
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e3b556c0-00fc-4d19-9b31-eab6f7d0c409 | imperceptible-adversarial-examples-for-fake | 2106.01615 | null | https://arxiv.org/abs/2106.01615v1 | https://arxiv.org/pdf/2106.01615v1.pdf | Imperceptible Adversarial Examples for Fake Image Detection | Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society. Many methods have been proposed to detect fake images, but they are vulnerable to adversarial perturbations -- intentionally designed noises that can lead to the wrong prediction. Existing methods of attacking fake image detectors usually generate adversarial perturbations to perturb almost the entire image. This is redundant and increases the perceptibility of perturbations. In this paper, we propose a novel method to disrupt the fake image detection by determining key pixels to a fake image detector and attacking only the key pixels, which results in the $L_0$ and the $L_2$ norms of adversarial perturbations much less than those of existing works. Experiments on two public datasets with three fake image detectors indicate that our proposed method achieves state-of-the-art performance in both white-box and black-box attacks. | ['Xi Wu', 'Qi Song', 'Youbing Yin', 'Siwei Lyu', 'Bin Zhu', 'Bin Kong', 'Xin Wang', 'Yuezun Li', 'Quanyu Liao'] | 2021-06-03 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 2.85418391e-01 1.93871185e-01 2.85238743e-01 -1.80196688e-01
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8e9764b9-72ee-416e-bd38-de111ca264d7 | unpwc-svdlo-multi-svd-on-pointpwc-for | 2205.08150 | null | https://arxiv.org/abs/2205.08150v1 | https://arxiv.org/pdf/2205.08150v1.pdf | UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry | High-precision lidar odomety is an essential part of autonomous driving. In recent years, deep learning methods have been widely used in lidar odomety tasks, but most of the current methods only extract the global features of the point clouds. It is impossible to obtain more detailed point-level features in this way. In addition, only the fully connected layer is used to estimate the pose. The fully connected layer has achieved obvious results in the classification task, but the changes in pose are a continuous rather than discrete process, high-precision pose estimation can not be obtained only by using the fully connected layer. Our method avoids problems mentioned above. We use PointPWC as our backbone network. PointPWC is originally used for scene flow estimation. The scene flow estimation task has a strong correlation with lidar odomety. Traget point clouds can be obtained by adding the scene flow and source point clouds. We can achieve the pose directly through ICP algorithm solved by SVD, and the fully connected layer is no longer used. PointPWC extracts point-level features from point clouds with different sampling levels, which solves the problem of too rough feature extraction. We conduct experiments on KITTI, Ford Campus Vision and Lidar DataSe and Apollo-SouthBay Dataset. Our result is comparable with the state-of-the-art unsupervised deep learing method SelfVoxeLO. | ['Yiming Tu'] | 2022-05-17 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-5.11681497e-01 -3.77692133e-01 -1.77146420e-01 -4.84696954e-01
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abef6cdf-79d1-405c-b8d6-4f065e206031 | detecting-cell-and-protein-concentrations-by | 2102.08335 | null | https://arxiv.org/abs/2102.08335v1 | https://arxiv.org/pdf/2102.08335v1.pdf | Detecting cell and protein concentrations by the use of a thermal based sensor | Biosensors are frequently used nowadays for the sake of their attractive capabilities. Because of their high accuracy and precision, they are more and more used in the medical sector. Natural receptors are mostly used, but their use have some specific drawbacks. Therefore, new read-out methods are being developed where there is no need for these receptors. Via a Transient Plane Source (TPS) sensor, the thermal properties of a fluid can be determined. These sensors can detect the capability of a fluid to absorb heat i.e. the thermal effusivity. By the way of monitoring this property, many potential bioprocesses can be monitored. The use of this promising technique was further developed in this research for later use of detecting cell growth and protein concentrations. Firstly, the thermal properties of growth medium and yeast cells were determined. Here, it became clear that the thermal properties change in different concentrations. Also, measurements were performed on protein concentration. No unambiguously results were obtained from these tests. But, the overall results from the use of this sensor are very promising, especially in the cell detection compartment. However, further research will tell about the applicability and sensitivity of this type of sensor. | ['Ronald Thoelen', 'Thijs Vandenryt', 'Seppe Bormans', 'Gilles Oudebrouckx', 'Juul Goossens'] | 2021-02-16 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.97167146e-01 -2.42410794e-01 -3.18970531e-02 9.91972238e-02
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e1849204-162b-414e-b451-8bf5ba592988 | double-a3c-deep-reinforcement-learning-on | 2303.02271 | null | https://arxiv.org/abs/2303.02271v1 | https://arxiv.org/pdf/2303.02271v1.pdf | Double A3C: Deep Reinforcement Learning on OpenAI Gym Games | Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards. Unlike classical Markov Decision Process (MDP) in which agent has full knowledge of its state, rewards, and transitional probability, reinforcement learning utilizes exploration and exploitation for the model uncertainty. Under the condition that the model usually has a large state space, a neural network (NN) can be used to correlate its input state to its output actions to maximize the agent's rewards. However, building and training an efficient neural network is challenging. Inspired by Double Q-learning and Asynchronous Advantage Actor-Critic (A3C) algorithm, we will propose and implement an improved version of Double A3C algorithm which utilizing the strength of both algorithms to play OpenAI Gym Atari 2600 games to beat its benchmarks for our project. | ['Lingjie Kong', 'Jiajie He', 'Yangxin Zhong'] | 2023-03-04 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 9.87103302e-03 4.40984875e-01 -4.57460791e-01 -4.33119666e-03
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fa48a5f8-b8c8-43b6-9d14-7dbc9762bff9 | line-segment-detection-using-transformers | 2101.01909 | null | https://arxiv.org/abs/2101.01909v2 | https://arxiv.org/pdf/2101.01909v2.pdf | Line Segment Detection Using Transformers without Edges | In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a self-attention mechanism, and an encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks. | ['Zhuowen Tu', 'David Cheung', 'Weijian Xu', 'Yifan Xu'] | 2021-01-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper.pdf | cvpr-2021-1 | ['line-segment-detection'] | ['computer-vision'] | [ 1.02912158e-01 9.11056027e-02 -3.60814601e-01 -2.91055769e-01
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adf79f8d-6de4-458c-a244-cc28c1bb1a85 | recurrent-coupled-topic-modeling-over | 2106.13732 | null | https://arxiv.org/abs/2106.13732v1 | https://arxiv.org/pdf/2106.13732v1.pdf | Recurrent Coupled Topic Modeling over Sequential Documents | The abundant sequential documents such as online archival, social media and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the multi-topic-thread evolution. Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward-forward filter algorithm efficiently learns latent timeevolving parameters in a closed-form. In addition, the latent Indian Buffet Process (IBP) compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction. | ['Zhiguo Gong', 'Longbing Cao', 'Jinjin Guo'] | 2021-06-23 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-4.36104946e-02 -1.44445851e-01 -3.45900923e-01 -3.54174405e-01
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1d9354a5-77d3-47c2-a96b-256208fdbe8f | joint-event-and-temporal-relation-extraction | 1909.05360 | null | https://arxiv.org/abs/1909.05360v2 | https://arxiv.org/pdf/1909.05360v2.pdf | Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction | We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F1 improved by 10% and 6.8% on two benchmark datasets respectively. | ['Nanyun Peng', 'Qiang Ning', 'Rujun Han'] | 2019-09-02 | joint-event-and-temporal-relation-extraction-1 | https://aclanthology.org/D19-1041 | https://aclanthology.org/D19-1041.pdf | ijcnlp-2019-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.84051204e-02 3.34150136e-01 -4.95545268e-01 -5.55966973e-01
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26b402ee-ce20-4d83-a93a-3e28de4588ad | visual-writing-prompts-character-grounded | 2301.08571 | null | https://arxiv.org/abs/2301.08571v1 | https://arxiv.org/pdf/2301.08571v1.pdf | Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences | Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent and have more narrativity compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and have more narrativity than stories generated with the current state-of-the-art model. | ['Bernt Schiele', 'Vera Demberg', 'Khushboo Mehra', 'Asad Sayeed', 'Xudong Hong'] | 2023-01-20 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.75577533e-01 3.33879828e-01 -3.53734903e-02 -1.98243439e-01
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af6e292b-388c-4b02-a955-2d87c915ac57 | cae-lo-lidar-odometry-leveraging-fully | 2001.01354 | null | https://arxiv.org/abs/2001.01354v3 | https://arxiv.org/pdf/2001.01354v3.pdf | CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description | As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task. Appropriate data structure and unsupervised deep learning are the keys to achieve an easy adjusted LiDAR odometry solution with high performance. Utilizing compact 2D structured spherical ring projection model and voxel model which preserves the original shape of input data, we propose a fully unsupervised Convolutional Auto-Encoder based LiDAR Odometry (CAE-LO) that detects interest points from spherical ring data using 2D CAE and extracts features from multi-resolution voxel model using 3D CAE. We make several key contributions: 1) experiments based on KITTI dataset show that our interest points can capture more local details to improve the matching success rate on unstructured scenarios and our features outperform state-of-the-art by more than 50% in matching inlier ratio; 2) besides, we also propose a keyframe selection method based on matching pairs transferring, an odometry refinement method for keyframes based on extended interest points from spherical rings, and a backward pose update method. The odometry refinement experiments verify the proposed ideas' feasibility and effectiveness. | ['Juha Hyyppä', 'Jyri Maanpää', 'Yunsheng Wang', 'Qian zhang', 'Deyu Yin', 'Xinlian Liang', 'Hao Ma', 'Ruizhi Chen', 'Jingbin Liu'] | 2020-01-06 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-3.79388213e-01 -3.99531722e-02 -2.13764980e-01 -5.92875063e-01
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d92d7b3e-47d5-4c74-8f20-64f5872c7032 | geometric-based-pruning-rules-for-change | 2306.09555 | null | https://arxiv.org/abs/2306.09555v1 | https://arxiv.org/pdf/2306.09555v1.pdf | Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series | We consider the problem of detecting multiple changes in multiple independent time series. The search for the best segmentation can be expressed as a minimization problem over a given cost function. We focus on dynamic programming algorithms that solve this problem exactly. When the number of changes is proportional to data length, an inequality-based pruning rule encoded in the PELT algorithm leads to a linear time complexity. Another type of pruning, called functional pruning, gives a close-to-linear time complexity whatever the number of changes, but only for the analysis of univariate time series. We propose a few extensions of functional pruning for multiple independent time series based on the use of simple geometric shapes (balls and hyperrectangles). We focus on the Gaussian case, but some of our rules can be easily extended to the exponential family. In a simulation study we compare the computational efficiency of different geometric-based pruning rules. We show that for small dimensions (2, 3, 4) some of them ran significantly faster than inequality-based approaches in particular when the underlying number of changes is small compared to the data length. | ['Vincent Runge', 'Guillem Rigaill', 'Liudmila Pishchagina'] | 2023-06-15 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [ 1.44442663e-01 -2.95683444e-01 4.68755923e-02 -9.66709182e-02
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8abd8802-68a3-4e0e-9007-f5c58af7dfd3 | comma-modeling-relationship-among-motivations | 2209.06470 | null | https://arxiv.org/abs/2209.06470v1 | https://arxiv.org/pdf/2209.06470v1.pdf | COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities | Motivations, emotions, and actions are inter-related essential factors in human activities. While motivations and emotions have long been considered at the core of exploring how people take actions in human activities, there has been relatively little research supporting analyzing the relationship between human mental states and actions. We present the first study that investigates the viability of modeling motivations, emotions, and actions in language-based human activities, named COMMA (Cognitive Framework of Human Activities). Guided by COMMA, we define three natural language processing tasks (emotion understanding, motivation understanding and conditioned action generation), and build a challenging dataset Hail through automatically extracting samples from Story Commonsense. Experimental results on NLP applications prove the effectiveness of modeling the relationship. Furthermore, our models inspired by COMMA can better reveal the essential relationship among motivations, emotions and actions than existing methods. | ['Luxi Xing', 'Guanqun Bi', 'Wei Peng', 'Yue Hu', 'Yuqiang Xie'] | 2022-09-14 | null | https://aclanthology.org/2022.coling-1.15 | https://aclanthology.org/2022.coling-1.15.pdf | coling-2022-10 | ['action-generation'] | ['computer-vision'] | [ 4.04590309e-01 3.33487779e-01 -4.67298061e-01 -3.44075561e-01
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686b627a-1317-449d-9841-02ef3aa15387 | complex-word-identification-challenges-in | 1710.04989 | null | http://arxiv.org/abs/1710.04989v1 | http://arxiv.org/pdf/1710.04989v1.pdf | Complex Word Identification: Challenges in Data Annotation and System Performance | This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes lexical complexity challenging. Our findings show that
most systems performed poorly on the SemEval CWI dataset, and one of the
reasons for that is the way in which human annotation was performed. | ['Lucia Specia', 'Marcos Zampieri', 'Gustavo Paetzold', 'Shervin Malmasi'] | 2017-10-13 | complex-word-identification-challenges-in-1 | https://aclanthology.org/W17-5910 | https://aclanthology.org/W17-5910.pdf | ws-2017-12 | ['complex-word-identification'] | ['natural-language-processing'] | [ 2.61839509e-01 -9.67785716e-02 -1.06389761e-01 -2.74864286e-01
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9527e157-bca4-450b-af37-6ebad56a41b9 | dynamics-regulated-kinematic-policy-for | 2106.05969 | null | https://arxiv.org/abs/2106.05969v3 | https://arxiv.org/pdf/2106.05969v3.pdf | Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation | We propose a method for object-aware 3D egocentric pose estimation that tightly integrates kinematics modeling, dynamics modeling, and scene object information. Unlike prior kinematics or dynamics-based approaches where the two components are used disjointly, we synergize the two approaches via dynamics-regulated training. At each timestep, a kinematic model is used to provide a target pose using video evidence and simulation state. Then, a prelearned dynamics model attempts to mimic the kinematic pose in a physics simulator. By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model. By factoring in the 6DoF pose of objects (e.g., chairs, boxes) in the scene, we demonstrate for the first time, the ability to estimate physically-plausible 3D human-object interactions using a single wearable camera. We evaluate our egocentric pose estimation method in both controlled laboratory settings and real-world scenarios. | ['Kris Kitani', 'Ye Yuan', 'Ryo Hachiuma', 'Zhengyi Luo'] | 2021-06-10 | null | http://proceedings.neurips.cc/paper/2021/hash/d1fe173d08e959397adf34b1d77e88d7-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d1fe173d08e959397adf34b1d77e88d7-Paper.pdf | neurips-2021-12 | ['egocentric-pose-estimation'] | ['computer-vision'] | [-1.58096761e-01 -7.28847086e-02 1.32650509e-01 -1.50063038e-01
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97f641fc-9f7d-4528-ade2-1c11fd8706ff | multitask-learning-for-mental-health | null | null | https://aclanthology.org/e17-1015 | https://aclanthology.org/e17-1015.pdf | Multitask Learning for Mental Health Conditions with Limited Social Media Data | null | ['Dirk Hovy', 'Margaret Mitchell', 'Adrian Benton'] | 2017-04-01 | multitask-learning-for-mental-health-1 | https://aclanthology.org/E17-1015 | https://aclanthology.org/E17-1015.pdf | eacl-2017-4 | ['gender-prediction'] | ['computer-vision'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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fefec4aa-03cc-4bab-ad7d-3fa55f7caf4f | disentangled-image-colorization-via-global | null | null | https://dl.acm.org/doi/abs/10.1145/3550454.3555432 | https://menghanxia.github.io/projects/disco/disco_main.pdf | Disentangled Image Colorization via Global Anchors | Colorization is multimodal by nature and challenges existing frameworks to achieve colorful and structurally consistent results. Even the sophisticated autoregressive model struggles to maintain long-distance color consistency due to the fragility of sequential dependence. To overcome this challenge, we propose a novel colorization framework that disentangles color multimodality and structure consistency through global color anchors, so that both aspects could be learned effectively. Our key insight is that several carefully located anchors could approximately represent the color distribution of an image, and conditioned on the anchor colors, we can predict the image color in a deterministic manner by utilizing internal correlation. To this end, we construct a colorization model with dual branches, where the color modeler predicts the color distribution for anchor color representation, and the color generator predicts the pixel colors by referring the sampled anchor colors. Importantly, the anchors are located under two principles: color independence and global coverage, which is realized with clustering analysis on the deep color features. To simplify the computation, we creatively adopt soft superpixel segmentation to reduce the image primitives, which still nicely reserves the reversibility to pixel-wise representation. Extensive experiments show that our method achieves notable superiority over various mainstream frameworks in perceptual quality. Thanks to anchor-based color representation, our model has the flexibility to support diverse and controllable colorization as well. | ['Jue Wang', 'Tien-Tsin Wong', 'WenBo Hu', 'Menghan Xia'] | 2022-11-30 | null | null | null | siggraph-2022-11 | ['colorization'] | ['computer-vision'] | [-1.43935993e-01 -4.33752060e-01 -1.56258449e-01 -1.87370524e-01
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fead4844-ff5a-4258-9f5f-6c4ea1ad4309 | patmat-person-aware-tuning-of-mask-aware | 2304.06107 | null | https://arxiv.org/abs/2304.06107v1 | https://arxiv.org/pdf/2304.06107v1.pdf | PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting | Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to preserve fine facial details and the identity of the person, despite creating aesthetically convincing image structures and textures. In this work, we propose Person Aware Tuning (PAT) of Mask-Aware Transformer (MAT) for face inpainting, which addresses this issue. Our proposed method, PATMAT, effectively preserves identity by incorporating reference images of a subject and fine-tuning a MAT architecture trained on faces. By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity. Moreover, PATMAT's use of multiple images per anchor during training allows the model to use fewer reference images than competing methods. We demonstrate that PATMAT outperforms state-of-the-art models in terms of image quality, the preservation of person-specific details, and the identity of the subject. Our results suggest that PATMAT can be a promising approach for improving the quality of personalized face inpainting. | ['Fernando de la Torre', 'Chen Henry Wu', 'Jianjin Xu', 'Saman Motamed'] | 2023-04-12 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.01244140e-01 2.87241936e-01 1.55200690e-01 -5.57100892e-01
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67246893-cc2f-4c8e-a565-965332267c6c | majorcom-a-dual-function-radar-communication | 1909.04223 | null | http://arxiv.org/abs/1909.04223v1 | http://arxiv.org/pdf/1909.04223v1.pdf | MAJoRCom: A Dual-Function Radar Communication System Using Index Modulation | Dual-function radar communication (DFRC) systems implement both sensing and
communication using the same hardware. Such schemes are often more efficient in
terms of size, power, and cost, over using distinct radar and communication
systems. Since these functionalities share resources such as spectrum, power,
and antennas, DFRC methods typically entail some degradation in both radar and
communication performance. In this work we propose a DFRC scheme based on the
carrier agile phased array radar (CAESAR), which combines frequency and spatial
agility. The proposed DFRC system, referred to as multi-carrier agile joint
radar communication (MAJoRCom), exploits the inherent spatial and spectral
randomness of CAESAR to convey digital messages in the form of index
modulation. The resulting communication scheme naturally coexists with the
radar functionality, and thus does not come at the cost of reduced radar
performance. We analyze the performance of MAJoRCom, quantifying its achievable
bit rate. In addition, we develop a low complexity decoder and a codebook
design approach, which simplify the recovery of the communicated bits. Our
numerical results demonstrate that MAJoRCom is capable of achieving a bit rate
which is comparable to utilizing independent communication modules without
affecting the radar performance, and that our proposed low-complexity decoder
allows the receiver to reliably recover the transmitted symbols with an
affordable computational burden. | [] | 2019-09-10 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 7.19398975e-01 5.70879467e-02 2.86380202e-01 3.05900164e-03
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543a8c57-e0cf-4d74-860b-f8dc0f4b0ed5 | visual-question-rewriting-for-increasing | 2106.02257 | null | https://arxiv.org/abs/2106.02257v1 | https://arxiv.org/pdf/2106.02257v1.pdf | Visual Question Rewriting for Increasing Response Rate | When a human asks questions online, or when a conversational virtual agent asks human questions, questions triggering emotions or with details might more likely to get responses or answers. we explore how to automatically rewrite natural language questions to improve the response rate from people. In particular, a new task of Visual Question Rewriting(VQR) task is introduced to explore how visual information can be used to improve the new questions. A data set containing around 4K bland questions, attractive questions and images triples is collected. We developed some baseline sequence to sequence models and more advanced transformer based models, which take a bland question and a related image as input and output a rewritten question that is expected to be more attractive. Offline experiments and mechanical Turk based evaluations show that it is possible to rewrite bland questions in a more detailed and attractive way to increase the response rate, and images can be helpful. | ['Xin Wang', 'Yi Zhang', 'Xilian Li', 'Jiayi Wei'] | 2021-06-04 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 2.39362225e-01 5.48831582e-01 4.41210896e-01 -6.59209192e-01
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51740480-9145-42a5-9923-a9d6577abbc8 | skillnet-x-a-multilingual-multitask-model | 2306.16176 | null | https://arxiv.org/abs/2306.16176v1 | https://arxiv.org/pdf/2306.16176v1.pdf | SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills | Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper proposes a general multilingual multitask model, named SkillNet-X, which enables a single model to tackle many different tasks from different languages. To this end, we define several language-specific skills and task-specific skills, each of which corresponds to a skill module. SkillNet-X sparsely activates parts of the skill modules which are relevant either to the target task or the target language. Acting as knowledge transit hubs, skill modules are capable of absorbing task-related knowledge and language-related knowledge consecutively. Based on Transformer, we modify the multi-head attention layer and the feed forward network layer to accommodate skill modules. We evaluate SkillNet-X on eleven natural language understanding datasets in four languages. Results show that SkillNet-X performs better than task-specific baselines and two multitask learning baselines (i.e., dense joint model and Mixture-of-Experts model). Furthermore, skill pre-training further improves the performance of SkillNet-X on almost all datasets. To investigate the generalization of our model, we conduct experiments on two new tasks and find that SkillNet-X significantly outperforms baselines. | ['Shuming Shi', 'Yunbo Cao', 'Bing Qin', 'Shuangzhi Wu', 'Xiaocheng Feng', 'Duyu Tang', 'Fan Zhang', 'Yong Dai', 'Zhangyin Feng'] | 2023-06-28 | null | null | null | null | ['natural-language-understanding'] | ['natural-language-processing'] | [ 2.43885219e-02 -1.36218131e-01 -1.85093299e-01 -3.55199039e-01
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fd38ec16-2bb6-409b-be4c-53774fc7f16e | generating-cyber-threat-intelligence-to | 2108.06862 | null | https://arxiv.org/abs/2108.06862v3 | https://arxiv.org/pdf/2108.06862v3.pdf | Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling | Due to the variety of cyber-attacks or threats, the cybersecurity community enhances the traditional security control mechanisms to an advanced level so that automated tools can encounter potential security threats. Very recently, Cyber Threat Intelligence (CTI) has been presented as one of the proactive and robust mechanisms because of its automated cybersecurity threat prediction. Generally, CTI collects and analyses data from various sources e.g., online security forums, social media where cyber enthusiasts, analysts, even cybercriminals discuss cyber or computer security-related topics and discovers potential threats based on the analysis. As the manual analysis of every such discussion (posts on online platforms) is time-consuming, inefficient, and susceptible to errors, CTI as an automated tool can perform uniquely to detect cyber threats. In this paper, we identify and explore relevant CTI from hacker forums utilizing different supervised (classification) and unsupervised learning (topic modeling) techniques. To this end, we collect data from a real hacker forum and constructed two datasets: a binary dataset and a multi-class dataset. We then apply several classifiers along with deep neural network-based classifiers and use them on the datasets to compare their performances. We also employ the classifiers on a labeled leaked dataset as our ground truth. We further explore the datasets using unsupervised techniques. For this purpose, we leverage two topic modeling algorithms namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). | ['Hei', 'Xiali', 'Mohammad Masudur Rahman', 'Eshtiak Ahmed', 'Farzana Anowar', 'Ashraful Islam', 'Md Imran Hossen'] | 2021-08-16 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.77002768e-02 -1.99605107e-01 -2.60364920e-01 7.42918327e-02
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5e5c6cc1-c852-4b39-bf27-b9b0c759fb98 | unsupervised-semantic-segmentation-by | 2102.06191 | null | https://arxiv.org/abs/2102.06191v3 | https://arxiv.org/pdf/2102.06191v3.pdf | Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals | Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual domain. In this paper, we make a first attempt to tackle the problem on datasets that have been traditionally utilized for the supervised case. To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings. This marks a large deviation from existing works that relied on proxy tasks or end-to-end clustering. Additionally, we argue about the importance of having a prior that contains information about objects, or their parts, and discuss several possibilities to obtain such a prior in an unsupervised manner. Experimental evaluation shows that our method comes with key advantages over existing works. First, the learned pixel embeddings can be directly clustered in semantic groups using K-Means on PASCAL. Under the fully unsupervised setting, there is no precedent in solving the semantic segmentation task on such a challenging benchmark. Second, our representations can improve over strong baselines when transferred to new datasets, e.g. COCO and DAVIS. The code is available. | ['Luc van Gool', 'Stamatios Georgoulis', 'Simon Vandenhende', 'Wouter Van Gansbeke'] | 2021-02-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Van_Gansbeke_Unsupervised_Semantic_Segmentation_by_Contrasting_Object_Mask_Proposals_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Van_Gansbeke_Unsupervised_Semantic_Segmentation_by_Contrasting_Object_Mask_Proposals_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-semantic-segmentation', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 4.96594310e-01 4.00343895e-01 -1.17481604e-01 -5.82237780e-01
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66a5e310-371a-4936-9c28-b018be470d4e | hopc-histogram-of-oriented-principal | 1408.3809 | null | http://arxiv.org/abs/1408.3809v4 | http://arxiv.org/pdf/1408.3809v4.pdf | HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition | Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which change
significantly with viewpoint. In contrast, we directly process the pointclouds
and propose a new technique for action recognition which is more robust to
noise, action speed and viewpoint variations. Our technique consists of a novel
descriptor and keypoint detection algorithm. The proposed descriptor is
extracted at a point by encoding the Histogram of Oriented Principal Components
(HOPC) within an adaptive spatio-temporal support volume around that point.
Based on this descriptor, we present a novel method to detect Spatio-Temporal
Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that
the proposed descriptor and STKP detector outperform state-of-the-art
algorithms on three benchmark human activity datasets. We also introduce a new
multiview public dataset and show the robustness of our proposed method to
viewpoint variations. | ['Du. Q. Huynh', 'Ajmal Mian', 'Arif Mahmood', 'Hossein Rahmani'] | 2014-08-17 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.06601584e-01 -7.39357948e-01 -2.11690113e-01 4.25675288e-02
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3efdf31d-2bb4-4cbd-b33f-a1faa7fff273 | langevin-monte-carlo-for-contextual-bandits | 2206.11254 | null | https://arxiv.org/abs/2206.11254v1 | https://arxiv.org/pdf/2206.11254v1.pdf | Langevin Monte Carlo for Contextual Bandits | We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covariance matrices. Moreover, the Gaussian approximation may not be a good surrogate for the posterior distribution for general reward generating functions. We propose an efficient posterior sampling algorithm, viz., Langevin Monte Carlo Thompson Sampling (LMC-TS), that uses Markov Chain Monte Carlo (MCMC) methods to directly sample from the posterior distribution in contextual bandits. Our method is computationally efficient since it only needs to perform noisy gradient descent updates without constructing the Laplace approximation of the posterior distribution. We prove that the proposed algorithm achieves the same sublinear regret bound as the best Thompson sampling algorithms for a special case of contextual bandits, viz., linear contextual bandits. We conduct experiments on both synthetic data and real-world datasets on different contextual bandit models, which demonstrates that directly sampling from the posterior is both computationally efficient and competitive in performance. | ['Anima Anandkumar', 'Kamyar Azizzadenesheli', 'Eric Mazumdar', 'Hongkai Zheng', 'Pan Xu'] | 2022-06-22 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 5.59098199e-02 -1.59761578e-01 -7.50186324e-01 -1.98222041e-01
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b2eafbdf-40ba-43e8-b28d-967b3fb93856 | linknet-exploiting-encoder-representations | 1707.03718 | null | http://arxiv.org/abs/1707.03718v1 | http://arxiv.org/pdf/1707.03718v1.pdf | LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation | Pixel-wise semantic segmentation for visual scene understanding not only
needs to be accurate, but also efficient in order to find any use in real-time
application. Existing algorithms even though are accurate but they do not focus
on utilizing the parameters of neural network efficiently. As a result they are
huge in terms of parameters and number of operations; hence slow too. In this
paper, we propose a novel deep neural network architecture which allows it to
learn without any significant increase in number of parameters. Our network
uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of
resolution 3x640x360. It gives state-of-the-art performance on CamVid and
comparable results on Cityscapes dataset. We also compare our networks
processing time on NVIDIA GPU and embedded system device with existing
state-of-the-art architectures for different image resolutions. | ['Eugenio Culurciello', 'Abhishek Chaurasia'] | 2017-06-14 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.51649353e-03 -4.21613336e-01 1.60425454e-01 -4.37430352e-01
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4c9bedce-f900-4555-932c-ecc71dd0e846 | investigating-data-memorization-in-3d-latent | 2307.01148 | null | https://arxiv.org/abs/2307.01148v2 | https://arxiv.org/pdf/2307.01148v2.pdf | Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis | Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored. Here, we assess the memorization capacity of 3D latent diffusion models on photon-counting coronary computed tomography angiography and knee magnetic resonance imaging datasets. To detect potential memorization of training samples, we utilize self-supervised models based on contrastive learning. Our results suggest that such latent diffusion models indeed memorize training data, and there is a dire need for devising strategies to mitigate memorization. | ['Theano Papavassiliu', 'Sandy Engelhardt', 'Stefan O. Schoenberg', 'Isabelle Ayx', 'Jannik Kahmann', 'Arman Ghanaat', 'Salman Ul Hassan Dar'] | 2023-07-03 | null | null | null | null | ['contrastive-learning', 'image-generation', 'contrastive-learning', 'memorization'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing'] | [ 5.80208421e-01 8.34078908e-01 -1.27607405e-01 -4.75823611e-01
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a756ecea-01ab-4487-b686-a12bcd3478ea | fully-dense-neural-network-for-the-automatic | 1912.03449 | null | https://arxiv.org/abs/1912.03449v1 | https://arxiv.org/pdf/1912.03449v1.pdf | Fully Dense Neural Network for the Automatic Modulation Recognition | Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR. Based on expert experience and spectrograms, they not only increase the difficulty of preprocessing, but also consume a lot of memory. In order to directly use in-phase and quadrature (IQ) data obtained by the receiver and enhance the efficiency of network extraction features to improve the recognition rate of modulation mode, this paper proposes a new network structure called Fully Dense Neural Network (FDNN). This network uses residual blocks to extract features, dense connect to reduce model size, and adds attentions mechanism to recalibrate. Experiments on RML2016.10a show that this network has a higher recognition rate and lower model complexity. And it shows that the FDNN model with dense connections can not only extract features effectively but also greatly reduce model parameters, which also provides a significant contribution for the application of deep learning to the intelligent radio system. | ['Xiao-Feng Gong', 'Chen Wang', 'Miao Du', 'Ruisen Luo', 'Qin Yu', 'Shaomin Fei'] | 2019-12-07 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [-4.79043722e-02 -4.57465708e-01 4.42832075e-02 -2.17913672e-01
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ad89eecb-a789-4dbb-83c3-09bb70e9acbb | physics-informed-machine-learning-models-for | 1908.10929 | null | https://arxiv.org/abs/1908.10929v1 | https://arxiv.org/pdf/1908.10929v1.pdf | Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing | This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-diffusion equations using a non-negative finite element formulation for different input parameters. Non-negative finite element formulation ensures that the species concentration is non-negative (which is needed for computing QoIs) on coarse computational grids even under high anisotropy. The reactive-mixing model input parameters are a time-scale associated with flipping of velocity, a spatial-scale controlling small/large vortex structures of velocity, a perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. Then, SVR-ROMs are used to predict scaling of QoIs. Qualitatively, SVR-ROMs are able to describe the trends observed in the scaling law associated with QoIs. Fourth, the scaling law's exponent dependence on model inputs/features are evaluated using $k$-means clustering. Finally, in terms of the computational cost, the proposed SVM-ROMs and SVR-ROMs are $\mathcal{O}(10^7)$ times faster than running a high-fidelity numerical simulation for evaluating QoIs. | ['M. K. Mudunuru', 'S. Karra'] | 2019-08-28 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 9.03031379e-02 -6.61309004e-01 -2.72464514e-01 2.54554778e-01
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2c11dd07-e6ad-4003-8706-4f5a69813112 | deep-supervised-learning-for-hyperspectral | null | null | https://doi.org/10.1109/IGARSS.2015.7326945 | https://doi.org/10.1109/IGARSS.2015.7326945 | Deep supervised learning for hyperspectral data classification through convolutional neural networks | Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification. | ['Nikolaos Doulamis', 'Anastasios Doulamis', 'Konstantinos Karantzalos', 'Konstantinos Makantasis'] | 2015-07-26 | null | null | null | 2015-ieee-international-geoscience-and-remote | ['object-recognition', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 8.54733944e-01 -5.27049005e-01 -8.34992528e-02 -4.06070203e-01
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3d90ced9-88ae-4a3d-a634-9fcaa8abef64 | mri-recovery-with-self-calibrated-denoisers | 2304.12890 | null | https://arxiv.org/abs/2304.12890v1 | https://arxiv.org/pdf/2304.12890v1.pdf | MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data | PURPOSE: To present and validate a self-supervised MRI reconstruction method that does not require fully sampled k-space data. METHODS: ReSiDe is inspired by plug-and-play (PnP) methods and employs a denoiser as a regularizer. In contrast to traditional PnP approaches that utilize generic denoisers or train deep learning-based denoisers using high-quality images or image patches, ReSiDe directly trains the denoiser on the image or images being reconstructed from the undersampled data. We introduce two variations of our method, ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements. More importantly, the trained denoisers in ReSiDe-M are stored for PnP recovery without further training. To improve robustness, the denoising strength in ReSiDe-S and ReSiDe- M is auto-tuned using the discrepancy principle. RESULTS: Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. ReSiDe-S and ReSiDe-M outperform other methods in terms of reconstruction signal-to-noise ratio and structural similarity index measure for Studies I and II and in terms of expert scoring for Study III. CONCLUSION: A self-supervised image reconstruction method is presented and validated in both static and dynamic MRI applications. These developments can benefit MRI applications where availability of fully sampled training data is limited. | ['Rizwan Ahmad', 'Philip Schniter', 'Sizhuo Liu'] | 2023-04-25 | null | null | null | null | ['image-reconstruction', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.59559947e-01 -7.60474801e-03 -6.77737966e-02 -4.15280879e-01
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b035b15b-4707-4ae8-8de4-1cba66352677 | cyclic-learning-bridging-image-level-labels | 2306.02691 | null | https://arxiv.org/abs/2306.02691v1 | https://arxiv.org/pdf/2306.02691v1.pdf | Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation | Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation burden, we seek to solve the problem through image-level weakly supervised learning, which is underexplored for nuclei instance segmentation. Compared with most existing methods using other weak annotations (scribble, point, etc.) for nuclei instance segmentation, our method is more labor-saving. The obstacle to using image-level annotations in nuclei instance segmentation is the lack of adequate location information, leading to severe nuclei omission or overlaps. In this paper, we propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem. Cyclic learning comprises a front-end classification task and a back-end semi-supervised instance segmentation task to benefit from multi-task learning (MTL). We utilize a deep learning classifier with interpretability as the front-end to convert image-level labels to sets of high-confidence pseudo masks and establish a semi-supervised architecture as the back-end to conduct nuclei instance segmentation under the supervision of these pseudo masks. Most importantly, cyclic learning is designed to circularly share knowledge between the front-end classifier and the back-end semi-supervised part, which allows the whole system to fully extract the underlying information from image-level labels and converge to a better optimum. Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods. | ['Yan Xu', 'Yubo Fan', 'Jianzhong Shou', 'Maode Lai', 'Bingzheng Wei', 'Zihua Wang', 'Yongjian Wu', 'Yang Zhou'] | 2023-06-05 | null | null | null | null | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 5.44149280e-01 4.49849755e-01 -4.70032007e-01 -3.56544226e-01
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bae79f92-ff2f-4e23-91e8-9576f1b06559 | improved-her2-tumor-segmentation-with-subtype | 2211.06150 | null | https://arxiv.org/abs/2211.06150v1 | https://arxiv.org/pdf/2211.06150v1.pdf | Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks | Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to overfitting. We propose to create synthetic images with semantically-conditioned deep generative networks and to combine subtype-balanced synthetic images with the original dataset to achieve better segmentation performance. We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology. Additionally, we show the capability of diffusion models to conditionally inpaint HER2 tumor areas with modified subtypes. Combining the original dataset with the same amount of diffusion-generated images increased the tumor Dice score from 0.833 to 0.854 and almost halved the variance between the HER2 subtype recalls. These results create the basis for more reliable automatic HER2 analysis with lower performance variance between individual HER2 subtypes. | ['Katharina Breininger', 'Ramona Erber', 'Andreas Maier', 'Peter A. Fasching', 'Matthias W. Beckmann', 'Arndt Hartmann', 'Frauke Wilm', 'Jingna Qiu', 'Carol I. Geppert', 'Matthias Rübner', 'Jana Mönius', 'Mathias Öttl'] | 2022-11-11 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.06175202e-01 6.26605451e-01 6.85837865e-02 -4.69125271e-01
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f905e32d-ba40-4088-ad58-a51f42efad4d | seizure-detection-and-prediction-by-parallel | 2206.09951 | null | https://arxiv.org/abs/2206.09951v1 | https://arxiv.org/pdf/2206.09951v1.pdf | Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks | During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to SOTA CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. To the best of our knowledge, we are the first to parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS DL accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low ADC/DAC resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm$^2$ in a 22nm FDSOI CMOS process. | ['Roman Genov', 'Mostafa Rahimi Azghadi', 'Amirali Amirsoleimani', 'Xuening Dong', 'Corey Lammie', 'Chenqi Li'] | 2022-06-20 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.17074409e-01 -3.71439695e-01 1.11501031e-01 -5.92535175e-02
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f765c014-c91a-4a45-8573-d1e2cd6338fd | learning-graph-embeddings-for-compositional | 2102.01987 | null | https://arxiv.org/abs/2102.01987v3 | https://arxiv.org/pdf/2102.01987v3.pdf | Learning Graph Embeddings for Compositional Zero-shot Learning | In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently from a car. As a solution, we propose a novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers, and latent representations of visual primitives in an end-to-end manner. The key to our approach is exploiting the dependency between states, objects, and their compositions within a graph structure to enforce the relevant knowledge transfer from seen to unseen compositions. By learning a joint compatibility that encodes semantics between concepts, our model allows for generalization to unseen compositions without relying on an external knowledge base like WordNet. We show that in the challenging generalized compositional zero-shot setting our CGE significantly outperforms the state of the art on MIT-States and UT-Zappos. We also propose a new benchmark for this task based on the recent GQA dataset. Code is available at: https://github.com/ExplainableML/czsl | ['Zeynep Akata', 'Federico Tombari', 'Yongqin Xian', 'Muhammad Ferjad Naeem'] | 2021-02-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.90887505e-01 3.11037242e-01 -1.15572125e-01 -2.12583527e-01
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320506ed-3dd4-4604-b907-c69e43bdb58b | progressive-learning-of-3d-reconstruction | 2305.11102 | null | https://arxiv.org/abs/2305.11102v1 | https://arxiv.org/pdf/2305.11102v1.pdf | Progressive Learning of 3D Reconstruction Network from 2D GAN Data | This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent and sometimes GANs output distorted images. This results in degraded reconstruction qualities. In this work, to overcome these limitations of generated datasets, we have two main contributions which lead us to achieve state-of-the-art results on challenging objects: 1) A robust multi-stage learning scheme that gradually relies more on the models own predictions when calculating losses, 2) A novel adversarial learning pipeline with online pseudo-ground truth generations to achieve fine details. Our work provides a bridge from 2D supervisions of GAN models to 3D reconstruction models and removes the expensive annotation efforts. We show significant improvements over previous methods whether they were trained on GAN generated multi-view images or on real images with expensive annotations. Please visit our web-page for 3D visuals: https://research.nvidia.com/labs/adlr/progressive-3d-learning | ['Bryan Catanzaro', 'Andrew Tao', 'Jun Gao', 'Aysegul Dundar'] | 2023-05-18 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 2.29997367e-01 1.56843454e-01 7.52421841e-02 -3.07116538e-01
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095d1e54-216f-435d-90a4-a8e1b4bea7c6 | fedaux-leveraging-unlabeled-auxiliary-data-in | 2102.02514 | null | https://arxiv.org/abs/2102.02514v1 | https://arxiv.org/pdf/2102.02514v1.pdf | FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning | Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled auxiliary set of data into a student model. In this work we propose FedAUX, an extension to FD, which, under the same set of assumptions, drastically improves performance by deriving maximum utility from the unlabeled auxiliary data. FedAUX modifies the FD training procedure in two ways: First, unsupervised pre-training on the auxiliary data is performed to find a model initialization for the distributed training. Second, $(\varepsilon, \delta)$-differentially private certainty scoring is used to weight the ensemble predictions on the auxiliary data according to the certainty of each client model. Experiments on large-scale convolutional neural networks and transformer models demonstrate, that the training performance of FedAUX exceeds SOTA FL baseline methods by a substantial margin in both the iid and non-iid regime, further closing the gap to centralized training performance. Code is available at github.com/fedl-repo/fedaux. | ['Wojciech Samek', 'Roman Rischke', 'Tim Korjakow', 'Felix Sattler'] | 2021-02-04 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-4.77117598e-02 4.70236629e-01 -3.95852596e-01 -6.49802625e-01
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6d88090d-dd65-4247-92aa-e12db76067c7 | exploring-the-impact-of-tunable-agents-in | 2101.11967 | null | https://arxiv.org/abs/2101.11967v1 | https://arxiv.org/pdf/2101.11967v1.pdf | Exploring the Impact of Tunable Agents in Sequential Social Dilemmas | When developing reinforcement learning agents, the standard approach is to train an agent to converge to a fixed policy that is as close to optimal as possible for a single fixed reward function. If different agent behaviour is required in the future, an agent trained in this way must normally be either fully or partially retrained, wasting valuable time and resources. In this study, we leverage multi-objective reinforcement learning to create tunable agents, i.e. agents that can adopt a range of different behaviours according to the designer's preferences, without the need for retraining. We apply this technique to sequential social dilemmas, settings where there is inherent tension between individual and collective rationality. Learning a single fixed policy in such settings leaves one at a significant disadvantage if the opponents' strategies change after learning is complete. In our work, we demonstrate empirically that the tunable agents framework allows easy adaption between cooperative and competitive behaviours in sequential social dilemmas without the need for retraining, allowing a single trained agent model to be adjusted to cater for a wide range of behaviours and opponent strategies. | ['Patrick Mannion', "David O'Callaghan"] | 2021-01-28 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 8.56343582e-02 2.80103981e-01 -8.53122100e-02 -1.26553223e-01
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ad8f289a-b433-46b2-9794-ecd0e7ddc90c | efficient-single-image-depth-estimation-on | 2211.04470 | null | https://arxiv.org/abs/2211.04470v1 | https://arxiv.org/pdf/2211.04470v1.pdf | Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report | Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper. | ['Se Young Chun', 'Seunggyu Lee', 'Joonhee Lee', 'Seongmin Hong', 'Dongwon Park', 'Byeong Hyun Lee', 'Denis Sapozhnikov', 'Marcos V. Conde', 'Zhiguo Cao', 'Zihao Huang', 'Yiran Wang', 'Jiaqi Li', 'Bin Fu', 'Gang Yu', 'Guozhong Luo', 'Zilong Huang', 'Yicheng Wang', 'Ziyu Zhang', 'Lei Lei', 'Xiaotao Wang', 'Yanan Li', 'Difan Xu', 'XueChao Shi', 'Junjun Jiang', 'Xianming Liu', 'Jialei Xu', 'Zehui Chen', 'Zhenyu Li', 'Yujie Ma', 'Maciej Smyl', 'Rafal Rudnicki', 'Maciej Pioro', 'Michal Lopuszynski', 'Piotr Ksiazek', 'Xin Chang', 'Lukasz Treszczotko', 'Radu Timofte', 'Grigory Malivenko', 'Andrey Ignatov'] | 2022-11-07 | null | null | null | null | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 1.44737959e-01 -1.06373370e-01 3.93986970e-01 -2.31138900e-01
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f90ec7e2-56a0-4fce-92e4-3887102d5ff3 | bladder-segmentation-based-on-deep-learning | 2101.06498 | null | https://arxiv.org/abs/2101.06498v1 | https://arxiv.org/pdf/2101.06498v1.pdf | Bladder segmentation based on deep learning approaches: current limitations and lessons | Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumour. Hence, multi region segmentation on patients presenting with symptoms of bladder tumours using deep learning heralds a new level of staging accuracy and prediction of the biologic behaviour of the tumour. Nevertheless, despite the success of these models in other medical problems, progress in multi region bladder segmentation is still at a nascent stage, with just a handful of works tackling a multi region scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted. | ['Jose Dolz', 'K. C. Balaji', 'Chandana Lall', 'Dheeraj R Gopireddy', 'Mark G. Bandyk'] | 2021-01-16 | null | null | null | null | ['bladder-segmentation'] | ['medical'] | [ 5.37262380e-01 3.67793053e-01 -8.98113132e-01 3.17244492e-02
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0e1eef72-62f3-46a8-9cec-2899dbd6565d | a-generative-appearance-model-for-end-to-end | 1811.11611 | null | http://arxiv.org/abs/1811.11611v2 | http://arxiv.org/pdf/1811.11611v2.pdf | A Generative Appearance Model for End-to-end Video Object Segmentation | One of the fundamental challenges in video object segmentation is to find an
effective representation of the target and background appearance. The best
performing approaches resort to extensive fine-tuning of a convolutional neural
network for this purpose. Besides being prohibitively expensive, this strategy
cannot be truly trained end-to-end since the online fine-tuning procedure is
not integrated into the offline training of the network.
To address these issues, we propose a network architecture that learns a
powerful representation of the target and background appearance in a single
forward pass. The introduced appearance module learns a probabilistic
generative model of target and background feature distributions. Given a new
image, it predicts the posterior class probabilities, providing a highly
discriminative cue, which is processed in later network modules. Both the
learning and prediction stages of our appearance module are fully
differentiable, enabling true end-to-end training of the entire segmentation
pipeline. Comprehensive experiments demonstrate the effectiveness of the
proposed approach on three video object segmentation benchmarks. We close the
gap to approaches based on online fine-tuning on DAVIS17, while operating at 15
FPS on a single GPU. Furthermore, our method outperforms all published
approaches on the large-scale YouTube-VOS dataset. | ['Emil Brissman', 'Fahad Shahbaz Khan', 'Martin Danelljan', 'Michael Felsberg', 'Joakim Johnander'] | 2018-11-28 | a-generative-appearance-model-for-end-to-end-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Johnander_A_Generative_Appearance_Model_for_End-To-End_Video_Object_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Johnander_A_Generative_Appearance_Model_for_End-To-End_Video_Object_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.26183385e-01 -1.58151388e-01 -1.99147597e-01 -5.11120498e-01
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2852115a-d3b5-4b90-ace5-fe592914724e | large-scale-historical-watermark-recognition | 1908.10254 | null | https://arxiv.org/abs/1908.10254v1 | https://arxiv.org/pdf/1908.10254v1.pdf | Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach | Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for pattern recognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides important performance boost compared to strong baselines. Our model achieves 55% top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval. | ['Oumayma Bounou', 'Marc Smith', 'Ilaria Pastrolin', 'Mathieu Aubry', 'Xi Shen', 'Spyros Gidaris', 'Olivier Poncet'] | 2019-08-27 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.44417951e-01 -5.68156123e-01 -5.52506030e-01 -3.30774002e-02
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1034b924-347d-45e0-8544-9e8f5fc61dc9 | using-descriptive-video-services-to-create-a | 1503.01070 | null | http://arxiv.org/abs/1503.01070v1 | http://arxiv.org/pdf/1503.01070v1.pdf | Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research | In this work, we introduce a dataset of video annotated with high quality
natural language phrases describing the visual content in a given segment of
time. Our dataset is based on the Descriptive Video Service (DVS) that is now
encoded on many digital media products such as DVDs. DVS is an audio narration
describing the visual elements and actions in a movie for the visually
impaired. It is temporally aligned with the movie and mixed with the original
movie soundtrack. We describe an automatic DVS segmentation and alignment
method for movies, that enables us to scale up the collection of a DVS-derived
dataset with minimal human intervention. Using this method, we have collected
the largest DVS-derived dataset for video description of which we are aware.
Our dataset currently includes over 84.6 hours of paired video/sentences from
92 DVDs and is growing. | ['Aaron Courville', 'Hugo Larochelle', 'Christopher Pal', 'Atousa Torabi'] | 2015-03-03 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.00423610e-01 -2.78666198e-01 -2.45653167e-01 -4.47221726e-01
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20aac081-baa7-4c5d-89f6-301aa54d837a | understanding-contrastive-learning-through | 2306.11526 | null | https://arxiv.org/abs/2306.11526v1 | https://arxiv.org/pdf/2306.11526v1.pdf | Understanding Contrastive Learning Through the Lens of Margins | Self-supervised learning, or SSL, holds the key to expanding the usage of machine learning in real-world tasks by alleviating heavy human supervision. Contrastive learning and its varieties have been SSL strategies in various fields. We use margins as a stepping stone for understanding how contrastive learning works at a deeper level and providing potential directions to improve representation learning. Through gradient analysis, we found that margins scale gradients in three different ways: emphasizing positive samples, de-emphasizing positive samples when angles of positive samples are wide, and attenuating the diminishing gradients as the estimated probability approaches the target probability. We separately analyze each and provide possible directions for improving SSL frameworks. Our experimental results demonstrate that these properties can contribute to acquiring better representations, which can enhance performance in both seen and unseen datasets. | ['JaeHan Park', 'JaeHyun Park', 'Sooill Park', 'Taesoo Kim', 'Daniel Rho'] | 2023-06-20 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.07079464e-01 5.48137836e-02 -9.41896975e-01 -5.33773065e-01
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dd7965e9-4965-4026-bdb0-0121638dc0ca | deep-learning-method-for-object-tracking | 2306.06126 | null | https://arxiv.org/abs/2306.06126v2 | https://arxiv.org/pdf/2306.06126v2.pdf | Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time | Current Deep Learning methods for environment segmentation and velocity estimation rely on Convolutional Recurrent Neural Networks to exploit spatio-temporal relationships within obtained sensor data. These approaches derive scene dynamics implicitly by correlating novel input and memorized data utilizing ConvNets. We show how ConvNets suffer from architectural restrictions for this task. Based on these findings, we then provide solutions to various issues on exploiting spatio-temporal correlations in a sequence of sensor recordings by presenting a novel Recurrent Neural Network unit utilizing Transformer mechanisms. Within this unit, object encodings are tracked across consecutive frames by correlating key-query pairs derived from sensor inputs and memory states, respectively. We then use resulting tracking patterns to obtain scene dynamics and regress velocities. In a last step, the memory state of the Recurrent Neural Network is projected based on extracted velocity estimates to resolve aforementioned spatio-temporal misalignment. | ['Anton Kummert', 'Kevin Kollek', 'Dominic Spata', 'Mirko Meuter', 'Moritz Luszek', 'Marco Braun'] | 2023-06-08 | null | null | null | null | ['object-tracking'] | ['computer-vision'] | [ 4.71858323e-01 -5.29455483e-01 -6.65724352e-02 -1.49571314e-01
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30d539f5-9e21-4278-8e85-b0b4130fe7ff | deep-reinforcement-learning-for-complex-1 | 2001.03877 | null | https://arxiv.org/abs/2001.03877v1 | https://arxiv.org/pdf/2001.03877v1.pdf | Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback | Learning optimal policies from sparse feedback is a known challenge in reinforcement learning. Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm that comes to solve such tasks. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode and then generalizes from that virtual goal to real goals. HER has known flaws and is limited to relatively simple tasks. In this thesis, we present three algorithms based on the existing HER algorithm that improves its performances. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the \textit{instructiveness} of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we designed a filtering process that detects and removes misleading samples that may induce bias throughout the learning process. Lastly, we enable the learning of complex, sequential, tasks using a form of curriculum learning combined with HER. We call this algorithm \textit{Curriculum HER}. To test our algorithms, we built three challenging manipulation environments with sparse reward functions. Each environment has three levels of complexity. Our empirical results show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm. | ['Binyamin Manela'] | 2020-01-12 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 5.22798672e-02 1.57277167e-01 -6.94290847e-02 -1.08025402e-01
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0a5195a3-9212-4629-b433-e147bb5e316a | softtreemax-policy-gradient-with-tree-search | 2209.13966 | null | https://arxiv.org/abs/2209.13966v1 | https://arxiv.org/pdf/2209.13966v1.pdf | SoftTreeMax: Policy Gradient with Tree Search | Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfortunately, they exhibit large variance and subsequently suffer from high-sample complexity since they aggregate gradients over entire trajectories. At the other extreme, planning methods, like tree search, optimize the policy using single-step transitions that consider future lookahead. These approaches have been mainly considered for value-based algorithms. Planning-based algorithms require a forward model and are computationally intensive at each step, but are more sample efficient. In this work, we introduce SoftTreeMax, the first approach that integrates tree-search into policy gradient. Traditionally, gradients are computed for single state-action pairs. Instead, our tree-based policy structure leverages all gradients at the tree leaves in each environment step. This allows us to reduce the variance of gradients by three orders of magnitude and to benefit from better sample complexity compared with standard policy gradient. On Atari, SoftTreeMax demonstrates up to 5x better performance in faster run-time compared with distributed PPO. | ['Gal Chechik', 'Shie Mannor', 'Assaf Hallak', 'Gal Dalal'] | 2022-09-28 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-5.99703416e-02 -6.23451620e-02 -1.02162850e+00 -7.52153844e-02
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eb8f1479-532b-4838-b429-d11406b14f6c | encoding-clinical-priori-in-3d-convolutional | 2011.00263 | null | https://arxiv.org/abs/2011.00263v4 | https://arxiv.org/pdf/2011.00263v4.pdf | Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI | We hypothesize that anatomical priors can be viable mediums to infuse domain-specific clinical knowledge into state-of-the-art convolutional neural networks (CNN) based on the U-Net architecture. We introduce a probabilistic population prior which captures the spatial prevalence and zonal distinction of clinically significant prostate cancer (csPCa), in order to improve its computer-aided detection (CAD) in bi-parametric MR imaging (bpMRI). To evaluate performance, we train 3D adaptations of the U-Net, U-SEResNet, UNet++ and Attention U-Net using 800 institutional training-validation scans, paired with radiologically-estimated annotations and our computed prior. For 200 independent testing bpMRI scans with histologically-confirmed delineations of csPCa, our proposed method of encoding clinical priori demonstrates a strong ability to improve patient-based diagnosis (upto 8.70% increase in AUROC) and lesion-level detection (average increase of 1.08 pAUC between 0.1-10 false positives per patient) across all four architectures. | ['Henkjan Huisman', 'Matin Hosseinzadeh', 'Anindo Saha'] | 2020-10-31 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 5.65652013e-01 7.45995939e-01 -3.68059307e-01 -4.60912585e-01
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84c2e6c7-ee6a-43c3-8f97-b878dcfeb697 | low-resource-neural-machine-translation-with | 2210.06716 | null | https://arxiv.org/abs/2210.06716v1 | https://arxiv.org/pdf/2210.06716v1.pdf | Low-resource Neural Machine Translation with Cross-modal Alignment | How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpora, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal contrastive learning method to learn a shared space for all languages, where both a coarse-grained sentence-level objective and a fine-grained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the cross-modal and cross-lingual alignment with a small amount of image-text pairs and achieves significant improvements over the text-only baseline under both zero-shot and few-shot scenarios. | ['Yang Feng', 'Qingkai Fang', 'Zhe Yang'] | 2022-10-13 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 6.35945052e-02 -6.79823756e-01 -4.21261787e-01 -3.20154071e-01
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e75e2c15-eafe-4dcb-b5eb-ee684574cc7e | self-supervised-assisted-active-learning-for | 2205.07021 | null | https://arxiv.org/abs/2205.07021v1 | https://arxiv.org/pdf/2205.07021v1.pdf | Self-supervised Assisted Active Learning for Skin Lesion Segmentation | Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements. Recently, active learning (AL) strategies strive to reduce annotation costs by querying a small portion of data for annotation, receiving much traction in the field of medical imaging. However, most of the existing AL methods have to initialize models with some randomly selected samples followed by active selection based on various criteria, such as uncertainty and diversity. Such random-start initialization methods inevitably introduce under-value redundant samples and unnecessary annotation costs. For the purpose of addressing the issue, we propose a novel self-supervised assisted active learning framework in the cold-start setting, in which the segmentation model is first warmed up with self-supervised learning (SSL), and then SSL features are used for sample selection via latent feature clustering without accessing labels. We assess our proposed methodology on skin lesions segmentation task. Extensive experiments demonstrate that our approach is capable of achieving promising performance with substantial improvements over existing baselines. | ['Cuntai Guan', 'Bharadwaj Veeravalli', 'Kaixin Xu', 'Zeng Zeng', 'Wenjing Lu', 'Ziyuan Zhao'] | 2022-05-14 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.46992648e-01 4.50082868e-01 -7.64476776e-01 -6.80274308e-01
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4411ab94-41cb-4e40-8583-f673dd44078f | the-performance-impact-of-combining-agent | null | null | https://dl.acm.org/doi/abs/10.1145/3549737.3549773 | https://dl.acm.org/doi/abs/10.1145/3549737.3549773 | The Performance Impact of Combining Agent Factorization with Different Learning Algorithms for Multiagent Coordination | Factorizing a multiagent system refers to partitioning the state-
action space to individual agents and defining the interactions be-
tween those agents. This so-called agent factorization is of much im-
portance in real-world industrial settings, and is a process that can
have significant performance implications. In this work, we explore
if the performance impact of agent factorization is different when
using different learning algorithms in multiagent coordination set-
tings. We evaluated six different agent factorization instances—or
agent definitions—in the warehouse traffic management domain,
comparing the performance of (mainly) two learning algorithms
suitable for learning coordinated multiagent policies: the Evolu-
tionary Strategies (ES), and a genetic algorithm (CCEA) previously
used in this setting. Our results demonstrate that different learning
algorithms are affected in different ways by alternative agent defi-
nitions. Given this, we can deduce that many important multiagent
coordination problems can potentially be solved by an appropriate
agent factorization in conjunction with an appropriate choice of
a learning algorithm. Moreover, our work shows that ES is an ef-
fective learning algorithm for the warehouse traffic management
domain; while, interestingly, celebrated policy gradient methods
do not fare well in this complex real-world problem setting. | ['Georgios Chalkiadakis', 'Stavros Orfanoudakis', 'Andreas Kallinteris'] | 2022-09-09 | null | null | null | setn-2022-9 | ['policy-gradient-methods'] | ['methodology'] | [-8.38891789e-02 -1.74694974e-02 -8.96968618e-02 3.74687314e-01
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098d9e8f-b4e8-4f2f-9092-0c68d0852479 | moms-with-events-multi-object-motion | 2006.06158 | null | https://arxiv.org/abs/2006.06158v2 | https://arxiv.org/pdf/2006.06158v2.pdf | 0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera | Segmentation of moving objects in dynamic scenes is a key process in scene understanding for navigation tasks. Classical cameras suffer from motion blur in such scenarios rendering them effete. On the contrary, event cameras, because of their high temporal resolution and lack of motion blur, are tailor-made for this problem. We present an approach for monocular multi-motion segmentation, which combines bottom-up feature tracking and top-down motion compensation into a unified pipeline, which is the first of its kind to our knowledge. Using the events within a time-interval, our method segments the scene into multiple motions by splitting and merging. We further speed up our method by using the concept of motion propagation and cluster keyslices. The approach was successfully evaluated on both challenging real-world and synthetic scenarios from the EV-IMO, EED, and MOD datasets and outperformed the state-of-the-art detection rate by 12\%, achieving a new state-of-the-art average detection rate of 81.06%, 94.2% and 82.35% on the aforementioned datasets. To enable further research and systematic evaluation of multi-motion segmentation, we present and open-source a new dataset/benchmark called MOD++, which includes challenging sequences and extensive data stratification in-terms of camera and object motion, velocity magnitudes, direction, and rotational speeds. | ['Cornelia Fermüller', 'Chahat Deep Singh', 'Nitin J. Sanket', 'Yiannis Aloimonos', 'Chethan M. Parameshwara'] | 2020-06-11 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.13954358e-01 -7.22224474e-01 1.57171801e-01 -1.11955270e-01
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a9cb774c-143c-4ac7-9e5c-b5dfbd76ab9b | complex-and-precise-movie-and-book | null | null | https://aclanthology.org/L18-1419 | https://aclanthology.org/L18-1419.pdf | Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis | null | ['Stefania Pecore', 'Jeanne Villaneau'] | 2018-05-01 | complex-and-precise-movie-and-book-1 | https://aclanthology.org/L18-1419 | https://aclanthology.org/L18-1419.pdf | lrec-2018-5 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7f964f07-30d8-446d-971a-25a1315b8975 | flow-adapter-architecture-for-unsupervised | 2204.12225 | null | https://arxiv.org/abs/2204.12225v1 | https://arxiv.org/pdf/2204.12225v1.pdf | Flow-Adapter Architecture for Unsupervised Machine Translation | In this work, we propose a flow-adapter architecture for unsupervised NMT. It leverages normalizing flows to explicitly model the distributions of sentence-level latent representations, which are subsequently used in conjunction with the attention mechanism for the translation task. The primary novelties of our model are: (a) capturing language-specific sentence representations separately for each language using normalizing flows and (b) using a simple transformation of these latent representations for translating from one language to another. This architecture allows for unsupervised training of each language independently. While there is prior work on latent variables for supervised MT, to the best of our knowledge, this is the first work that uses latent variables and normalizing flows for unsupervised MT. We obtain competitive results on several unsupervised MT benchmarks. | ['Hinrich Schütze', 'Haris Jabbar', 'Yihong Liu'] | 2022-04-26 | null | https://aclanthology.org/2022.acl-long.89 | https://aclanthology.org/2022.acl-long.89.pdf | acl-2022-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.36572045e-01 2.57838696e-01 -7.19220042e-01 -4.94105458e-01
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eb5c9dd7-10d0-4305-8f82-804463259524 | acceleration-of-subspace-learning-machine-via | 2208.07023 | null | https://arxiv.org/abs/2208.07023v1 | https://arxiv.org/pdf/2208.07023v1.pdf | Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing | Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we investigate two ways to accelerate SLM. First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions. The search of optimal weights in the linear combination is computationally heavy. It is accomplished by probabilistic search in original SLM. The acceleration of SLM by PSO requires 10-20 times fewer iterations. Second, we leverage parallel processing in the SLM implementation. Experimental results show that the accelerated SLM method achieves a speed up factor of 577 in training time while maintaining comparable classification/regression performance of original SLM. | ['C. -C. Jay Kuo', 'Vinod K. Mishra', 'Ethan Harrison', 'Joseph Lin', 'Yuhuai Liu', 'Yijing Yang', 'Hongyu Fu'] | 2022-08-15 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.49375007e-01 -4.63707268e-01 -3.39057952e-01 -8.34206939e-02
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6c51b266-5271-497c-b911-05894aa18c93 | causality-analysis-of-twitter-sentiments-and | null | null | https://aclanthology.org/W18-3102 | https://aclanthology.org/W18-3102.pdf | Causality Analysis of Twitter Sentiments and Stock Market Returns | Sentiment analysis is the process of identifying the opinion expressed in text. Recently, it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mechanical Turk and then we propose a baseline classification model. Then, by using Granger causality of both sentiment datasets with the different stocks, we shows that there is causality between social media and stock market returns (in both directions) for many stocks. Finally, We evaluate this causality analysis by showing that in the event of a specific news on certain dates, there are evidences of trending the same news on Twitter for that stock. | ['Wlodek Zadrozny', 'Bhanu Praneeth', 'Narges Tabari', 'Mirsad Hadzikadic', 'Armin Seyeditabari', 'Piyusha Biswas'] | 2018-07-01 | null | null | null | ws-2018-7 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-7.45056272e-01 -3.01383197e-01 -6.64996088e-01 -3.82317662e-01
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0689ce85-6691-4120-91d4-27887539b1fb | two-for-one-diffusion-models-and-force-fields | 2302.00600 | null | https://arxiv.org/abs/2302.00600v1 | https://arxiv.org/pdf/2302.00600v1.pdf | Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics | Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accurately learning a CG force field remains a challenge. In this work, we leverage connections between score-based generative models, force fields and molecular dynamics to learn a CG force field without requiring any force inputs during training. Specifically, we train a diffusion generative model on protein structures from molecular dynamics simulations, and we show that its score function approximates a force field that can directly be used to simulate CG molecular dynamics. While having a vastly simplified training setup compared to previous work, we demonstrate that our approach leads to improved performance across several small- to medium-sized protein simulations, reproducing the CG equilibrium distribution, and preserving dynamics of all-atom simulations such as protein folding events. | ['Rianne van den Berg', 'Robert Pinsler', 'Frank Noé', 'Cecilia Clementi', 'Marco Federici', 'Daniel Zuegner', 'Chin-wei Huang', 'Victor Garcia Satorras', 'Marloes Arts'] | 2023-02-01 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 1.81798056e-01 -2.01696083e-01 1.37133688e-01 -2.16278628e-01
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b2bec621-09b0-42a9-85b6-ec29084cc376 | adversarial-attacks-on-graph-classification | 2111.02842 | null | https://arxiv.org/abs/2111.02842v1 | https://arxiv.org/pdf/2111.02842v1.pdf | Adversarial Attacks on Graph Classification via Bayesian Optimisation | Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis. The few existing methods often require unrealistic setups, such as access to internal information of the victim models, or an impractically-large number of queries. We present a novel Bayesian optimisation-based attack method for graph classification models. Our method is black-box, query-efficient and parsimonious with respect to the perturbation applied. We empirically validate the effectiveness and flexibility of the proposed method on a wide range of graph classification tasks involving varying graph properties, constraints and modes of attack. Finally, we analyse common interpretable patterns behind the adversarial samples produced, which may shed further light on the adversarial robustness of graph classification models. | ['Xiaowen Dong', 'Michael A. Osborne', 'Arno Blaas', 'Binxin Ru', 'Henry Kenlay', 'Xingchen Wan'] | 2021-11-04 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 6.66250050e-01 1.80422977e-01 -3.87980863e-02 1.15763426e-01
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cf16cc47-e66d-487d-a5a9-0c385f1078bd | deep-learning-and-its-applications-to-wifi | 2207.07859 | null | https://arxiv.org/abs/2207.07859v3 | https://arxiv.org/pdf/2207.07859v3.pdf | SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing | WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we review the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark. | ['Lihua Xie', 'Sumei Sun', 'Chris Xiaoxuan Lu', 'Han Zou', 'Dazhuo Wang', 'Xinyan Chen', 'Jianfei Yang'] | 2022-07-16 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.26351094e-01 -3.90610188e-01 -3.89727950e-01 -4.08331543e-01
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cd0e9cd5-fb12-4a1e-99af-8bc0f3431623 | robust-template-matching-via-hierarchical | 2007.15817 | null | https://arxiv.org/abs/2007.15817v3 | https://arxiv.org/pdf/2007.15817v3.pdf | Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN | Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate if enhancing the CNN's encoding of shape information can produce more distinguishable features that improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results on a standard benchmark. To confirm these results we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM. | ['M. W. Spratling', 'Bo Gao'] | 2020-07-31 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.57490212e-01 -3.45737189e-01 -2.25504357e-02 -4.47329491e-01
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32876027-4057-40a7-b5ac-43b387cc859b | trainable-referring-expression-generation | 1704.03693 | null | http://arxiv.org/abs/1704.03693v1 | http://arxiv.org/pdf/1704.03693v1.pdf | Trainable Referring Expression Generation using Overspecification Preferences | Referring expression generation (REG) models that use speaker-dependent
information require a considerable amount of training data produced by every
individual speaker, or may otherwise perform poorly. In this work we present a
simple REG experiment that allows the use of larger training data sets by
grouping speakers according to their overspecification preferences. Intrinsic
evaluation shows that this method generally outperforms the personalised method
found in previous work. | ['Thiago castro Ferreira', 'Ivandre Paraboni'] | 2017-04-12 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 2.08584871e-02 3.84240538e-01 1.25211719e-02 -8.68413568e-01
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0e1ef304-e8a7-4dfe-a535-eb826d1c39c7 | decentralised-sparse-multi-task-regression | 1912.01417 | null | https://arxiv.org/abs/1912.01417v2 | https://arxiv.org/pdf/1912.01417v2.pdf | Distributed Machine Learning with Sparse Heterogeneous Data | Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneous data sets whose similarity structure is encoded by a graph topology. Precisely, we analyse the case where each node is associated with fitting a sparse linear model, and edges join two nodes if the difference of their solutions is also sparse. We propose a method based on Basis Pursuit Denoising with a total variation penalty, and provide finite sample guarantees for sub-Gaussian design matrices. Taking the root of the tree as a reference node, we show that if the sparsity of the differences across nodes is smaller than the sparsity at the root, then recovery is successful with fewer samples than by solving the problems independently, or by using methods that rely on a large overlap in the signal supports, such as the group Lasso. We consider both the noiseless and noisy setting, and numerically investigate the performance of distributed methods based on Distributed Alternating Direction Methods of Multipliers (ADMM) and hyperspectral unmixing. | ['Patrick Rebeschini', 'Dominic Richards', 'Sahand N. Negahban'] | 2019-12-03 | distributed-machine-learning-with-sparse | http://proceedings.neurips.cc/paper/2021/hash/959776b99b006e5785c3a3364949ce47-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/959776b99b006e5785c3a3364949ce47-Paper.pdf | neurips-2021-12 | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 2.17127785e-01 2.90674657e-01 1.03908859e-03 -2.83686426e-02
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d5840607-7c7c-4497-844b-8c9a17727cdb | incorporating-ultrasound-tongue-images-for | 2305.14933 | null | https://arxiv.org/abs/2305.14933v1 | https://arxiv.org/pdf/2305.14933v1.pdf | Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation | Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes further incorporating ultrasound tongue images to improve lip-based AV-SE systems' performance. Knowledge distillation is employed at the training stage to address the challenge of acquiring ultrasound tongue images during inference, enabling an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model. Experimental results demonstrate significant improvements in the quality and intelligibility of the speech enhanced by the proposed method compared to the traditional audio-lip speech enhancement baselines. Further analysis using phone error rates (PER) of automatic speech recognition (ASR) shows that palatal and velar consonants benefit most from the introduction of ultrasound tongue images. | ['Zhen-Hua Ling', 'Yang Ai', 'Rui-Chen Zheng'] | 2023-05-24 | null | null | null | null | ['automatic-speech-recognition', 'speech-enhancement'] | ['speech', 'speech'] | [ 3.57275933e-01 4.15288538e-01 -3.02751243e-01 -1.47142097e-01
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efdbc9aa-7d45-4d9a-ba9f-68681f95c7bb | dominant-set-clustering-and-pooling-for-multi | 1906.01592 | null | https://arxiv.org/abs/1906.01592v1 | https://arxiv.org/pdf/1906.01592v1.pdf | Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition | View based strategies for 3D object recognition have proven to be very successful. The state-of-the-art methods now achieve over 90% correct category level recognition performance on appearance images. We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets. The key idea is to pool information from views which are similar and thus belong to the same cluster. The pooled feature vectors are then fed as inputs to the same layer, in a recurrent fashion. This recurrent clustering and pooling module, when inserted in an off-the-shelf pretrained CNN, boosts performance for multi-view 3D object recognition, achieving a new state of the art test set recognition accuracy of 93.8% on the ModelNet 40 database. We also explore a fast approximate learning strategy for our cluster-pooling CNN, which, while sacrificing end-to-end learning, greatly improves its training efficiency with only a slight reduction of recognition accuracy to 93.3%. Our implementation is available at https://github.com/fate3439/dscnn. | ['Kaleem Siddiqi', 'Chu Wang', 'Marcello Pelillo'] | 2019-06-04 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.15799680e-01 -2.69366026e-01 -1.06407270e-01 -5.70456147e-01
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832b457f-d8f9-48df-98fd-c1ab7f5c3add | continual-pre-training-mitigates-forgetting | 2205.09357 | null | https://arxiv.org/abs/2205.09357v1 | https://arxiv.org/pdf/2205.09357v1.pdf | Continual Pre-Training Mitigates Forgetting in Language and Vision | Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the stream of non-stationary data. However, pre-training is rarely applied during continual learning. We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks. We show that continually pre-trained models are robust against catastrophic forgetting and we provide strong empirical evidence supporting the fact that self-supervised pre-training is more effective in retaining previous knowledge than supervised protocols. Code is provided at https://github.com/AndreaCossu/continual-pretraining-nlp-vision . | ['Davide Bacciu', 'Vincenzo Lomonaco', 'Lucia Passaro', 'Antonio Carta', 'Tinne Tuytelaars', 'Andrea Cossu'] | 2022-05-19 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 3.71311307e-01 -1.36965767e-01 -7.90951326e-02 -3.45506638e-01
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952929f4-337d-4c21-adbc-924029fed4c0 | disentangling-and-unifying-graph-convolutions | 2003.14111 | null | https://arxiv.org/abs/2003.14111v2 | https://arxiv.org/pdf/2003.14111v2.pdf | Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition | Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extractor. However, existing methods have limitations in achieving (1) unbiased long-range joint relationship modeling under multi-scale operators and (2) unobstructed cross-spacetime information flow for capturing complex spatial-temporal dependencies. In this work, we present (1) a simple method to disentangle multi-scale graph convolutions and (2) a unified spatial-temporal graph convolutional operator named G3D. The proposed multi-scale aggregation scheme disentangles the importance of nodes in different neighborhoods for effective long-range modeling. The proposed G3D module leverages dense cross-spacetime edges as skip connections for direct information propagation across the spatial-temporal graph. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400. | ['Zhiyong Wang', 'Zhenghao Chen', 'Wanli Ouyang', 'Ziyu Liu', 'Hongwen Zhang'] | 2020-03-31 | disentangling-and-unifying-graph-convolutions-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-action-recognition', 'long-range-modeling'] | ['computer-vision', 'natural-language-processing'] | [ 1.30170330e-01 -3.31260383e-01 -4.68215942e-01 4.97910790e-02
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fe936e20-88d4-4cb3-a3ba-08f4a38d2a0c | partial-discharge-direction-of-arrival | 2010.08309 | null | https://arxiv.org/abs/2010.08309v1 | https://arxiv.org/pdf/2010.08309v1.pdf | Partial Discharge Direction of Arrival Estimation in Air-insulated Substation by UHF Wireless Array and RSSI Maximum Likelihood Estimator | The quick detection and localization of partial discharge (PD) in an air-insulated substation (AIS) based on ultrahigh-frequency (UHF) sensor arrays are efficient for power equipment monitoring. The adopted UHF PD time difference of arrival (TDOA) methods mainly use the time difference of electromagnetic wave signals. Thus, the system requires both a high sampling rate and time synchronization accuracy, leading to a high cost and large size. In this study, the feasibility and accuracy of PD DOA in an AIS were investigated using a UHF wireless sensor array and the received signal strength indicator. First, the power pattern of the designed UHF wireless sensor array was obtained via an offline experiment. Then, a statistical approach to the PD DOA method based on the maximum likelihood estimator was employed to obtain the preliminary DOA result. Finally, interpolation and clustering algorithms were used to improve the accuracy of the DOA. A laboratory test was conducted. The average error of the PD DOA was less than 6{\deg}, and the cost-effectiveness and portability were clearly improved. | ['Xiuchen Jiang', 'Gehao Sheng', 'Lingen Luo', 'Bei Han'] | 2020-10-16 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-3.48646075e-01 -4.80205476e-01 4.28901076e-01 -1.33947447e-01
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54498f01-029e-4e17-a5ae-87128671de6a | omninerf-hybriding-omnidirectional-distance | 2209.13433 | null | https://arxiv.org/abs/2209.13433v1 | https://arxiv.org/pdf/2209.13433v1.pdf | OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction | 3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high. Ground-breaking research in the neural radiance field (NeRF) by utilizing Multi-Layer Perceptions has dramatically improved the representation quality of 3D objects. Some later studies improved NeRF by building truncated signed distance fields (TSDFs) but still suffer from the problem of blurred surfaces in 3D reconstruction. In this work, this surface ambiguity is addressed by proposing a novel way of 3D shape representation, OmniNeRF. It is based on training a hybrid implicit field of Omni-directional Distance Field (ODF) and neural radiance field, replacing the apparent density in NeRF with omnidirectional information. Moreover, we introduce additional supervision on the depth map to further improve reconstruction quality. The proposed method has been proven to effectively deal with NeRF defects at the edges of the surface reconstruction, providing higher quality 3D scene reconstruction results. | ['Yi Xu', 'Zirui Wu', 'Bolin Song', 'Jiaming Shen'] | 2022-09-27 | null | null | null | null | ['3d-scene-reconstruction', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 3.21819305e-01 5.93792945e-02 2.31090993e-01 -5.04941761e-01
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98ee04b3-5f5d-4a4d-8620-f0779d919311 | fast-accuracy-estimation-of-deep-learning | 2010.09453 | null | https://arxiv.org/abs/2010.09453v3 | https://arxiv.org/pdf/2010.09453v3.pdf | Fast accuracy estimation of deep learning based multi-class musical source separation | Music source separation represents the task of extracting all the instruments from a given song. Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB, only limited to four instrument classes. Larger datasets and more instruments are costly and time-consuming in collecting data and training deep neural networks (DNNs). In this work, we propose a fast method to evaluate the separability of instruments in any dataset without training and tuning a DNN. This separability measure helps to select appropriate samples for the efficient training of neural networks. Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches such as TasNet or Open-Unmix. Our results contribute to revealing two essential points for audio source separation: 1) the ideal ratio mask, although light and straightforward, provides an accurate measure of the audio separability performance of recent neural nets, and 2) new end-to-end learning methods such as Tasnet, that operate directly on waveforms, are, in fact, internally building a Time-Frequency (TF) representation, so that they encounter the same limitations as the TF based-methods when separating audio pattern overlapping in the TF plane. | ['Milos Cernak', 'Benjamin Ricaud', 'Alexandru Mocanu'] | 2020-10-19 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 1.73235252e-01 -4.47700411e-01 -1.23296522e-01 9.83439386e-02
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bd18fc52-7a13-4068-b931-c94e256f7bfa | action-knowledge-for-video-captioning-with | null | null | https://www.sciencedirect.com/science/article/pii/S1319157823000666 | https://www.sciencedirect.com/science/article/pii/S1319157823000666/pdf | Action knowledge for video captioning with graph neural networks | Many existing video captioning methods capture action information in the video by exploiting features extracted from an action recognition model. However, directly using the action features without object-specific representation may not well capture the object interactions. Consequently, the generated captions may not be accurate enough in describing the action and the object in the scenes. To address this issue, we propose to incorporate the action features as the edge features in a graph neural network where the nodes represent objects, thereby capturing a finer visual representation of object-action-object relationships. Previous graph-based video captioning methods commonly relied on a pretrained object detection model to create the node representations. The object detection model, however, may miss detecting some important objects. To alleviate this problem, we further introduce a grid-based node representation where the nodes are represented by the features extracted from grids of video frames. Using this representation, the important objects in the scenes are captured more thoroughly. To avoid adding any complexity during inference, the knowledge of the proposed graph is transferred to another neural network via knowledge distillation. Our proposed method achieved state-of-the-art results on two popular video captioning datasets, i.e., MSVD and MSR-VTT, on all metrics. The code of our proposed method is available at https://github.com/Sejong-VLI/V2T-Action-Graph-JKSUCIS-2023. | ['Cheol Jeong', 'Fikriansyah Adzaka', 'Bahy Helmi Hartoyo Putra', 'Vania Velda', 'Willy Fitra Hendria'] | 2023-03-16 | null | null | null | journal-of-king-saud-university-computer-and-3 | ['video-captioning', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 2.58499950e-01 1.36012822e-01 -3.76801580e-01 -2.30742842e-01
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d3247487-d041-4762-aa76-02d3b645db14 | pnp-adanet-plug-and-play-adversarial-domain | 1812.07907 | null | http://arxiv.org/abs/1812.07907v1 | http://arxiv.org/pdf/1812.07907v1.pdf | PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation | Deep convolutional networks have demonstrated the state-of-the-art
performance on various medical image computing tasks. Leveraging images from
different modalities for the same analysis task holds clinical benefits.
However, the generalization capability of deep models on test data with
different distributions remain as a major challenge. In this paper, we propose
the PnPAdaNet (plug-and-play adversarial domain adaptation network) for
adapting segmentation networks between different modalities of medical images,
e.g., MRI and CT. We propose to tackle the significant domain shift by aligning
the feature spaces of source and target domains in an unsupervised manner.
Specifically, a domain adaptation module flexibly replaces the early encoder
layers of the source network, and the higher layers are shared between domains.
With adversarial learning, we build two discriminators whose inputs are
respectively multi-level features and predicted segmentation masks. We have
validated our domain adaptation method on cardiac structure segmentation in
unpaired MRI and CT. The experimental results with comprehensive ablation
studies demonstrate the excellent efficacy of our proposed PnP-AdaNet.
Moreover, we introduce a novel benchmark on the cardiac dataset for the task of
unsupervised cross-modality domain adaptation. We will make our code and
database publicly available, aiming to promote future studies on this
challenging yet important research topic in medical imaging. | ['Pheng-Ann Heng', 'Cheng Ouyang', 'Xiahai Zhuang', 'Qi Dou', 'Hao Chen', 'Cheng Chen', 'Ben Glocker'] | 2018-12-19 | null | null | null | null | ['cardiac-segmentation', 'medical-image-generation'] | ['medical', 'medical'] | [ 5.72433531e-01 5.92105277e-02 -3.72489989e-02 -5.52473843e-01
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c7b948ac-2aaa-4bef-97d8-5b019e664c20 | practical-and-scalable-simulations-of-non | 2212.05059 | null | https://arxiv.org/abs/2212.05059v3 | https://arxiv.org/pdf/2212.05059v3.pdf | Practical and scalable simulations of non-Markovian stochastic processes | Discrete stochastic processes are widespread in natural systems with many applications across physics, biochemistry, epidemiology, sociology, and finance. While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying the random phenomena. However, most simulation algorithms assume the system dynamics are memoryless (Markovian assumption), under which assumption, future occurrences only depend on the present state of the system. Mathematically, the Markovian assumption models inter-event times as exponentially distributed variables, which enables the exact simulation of stochastic trajectories using the seminal Gillespie algorithm. Unfortunately, the majority of stochastic systems exhibit properties of memory, an inherently non-Markovian attribute. Non-Markovian systems are notoriously difficult to investigate analytically, and existing numerical methods are computationally costly or only applicable under strong simplifying assumptions, often not compatible with empirical observations. To address these challenges, we have developed the Rejection-based Gillespie algorithm for non-Markovian Reactions (REGIR), a general and scalable framework to simulate non-Markovian stochastic systems with arbitrary inter-event time distributions. REGIR can achieve arbitrary user-defined accuracy while maintaining the same asymptotic computational complexity as the Gillespie algorithm. We illustrate REGIR's modeling capabilities in three important biochemical systems, namely microbial growth dynamics, stem cell differentiation, and RNA transcription. In all three cases, REGIR efficiently models the underlying stochastic processes and demonstrates its utility to accurately investigate complex non-Markovian systems. The algorithm is implemented as a python library REGIR. | ['Maria Rodriguez Martinez', 'Niko Beerenwinkel', 'Miroslav Phan', 'Aurelien Pelissier'] | 2022-12-09 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.85799852e-01 -4.93806332e-01 1.44407079e-01 4.97956127e-01
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3.62856060e-01 -8.27485263e-01 -2.16169685e-01 -5.92779875e-01
1.39582485e-01 -4.74433213e-01 1.08364618e+00 8.59975815e-01
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6.93995655e-02 6.52107060e-01 -1.55968976e+00 7.00104386e-02
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7.08591580e-01 -7.47386754e-01 -5.20775020e-01 7.97896504e-01
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2.13135064e-01 3.56251150e-01 1.10785985e+00 1.09822774e+00
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5.04631341e-01 4.24706012e-01 4.68470573e-01 -6.75476313e-01
-6.32770956e-01 -4.87431824e-01 -8.85830164e-01 1.40523136e-01
1.52252018e-01 -6.69531584e-01 -6.32998765e-01 8.81474093e-02] | [6.106252670288086, 4.218613147735596] |
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