Paper Group NAWR 20
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data. Propagation kernels: efficient graph kernels from propagated information. Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction. Aggregating Deep Pyramidal Representations for Person Re-Idenfitication. VisNet: Deep Convolutional Neural Networks …
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data
| Title | Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data |
| Authors | Caleb Robinson, Le Hou, Kolya Malkin, Rachel Soobitsky, Jacob Czawlytko, Bistra Dilkina, Nebojsa Jojic |
| Abstract | In this paper we propose multi-resolution data fusion methods for deep learning-based high-resolution land cover mapping from aerial imagery. The land cover mapping problem, at country-level scales, is challenging for common deep learning methods due to the scarcity of high-resolution labels, as well as variation in geography and quality of input images. On the other hand, multiple satellite imagery and low-resolution ground truth label sources are widely available, and can be used to improve model training efforts. Our methods include: introducing low-resolution satellite data to smooth quality differences in high-resolution input, exploiting low-resolution labels with a dual loss function, and pairing scarce high-resolution labels with inputs from several points in time. We train models that are able to generalize from a portion of the Northeast United States, where we have high-resolution land cover labels, to the rest of the US. With these models, we produce the first high-resolution (1-meter) land cover map of the contiguous US, consisting of over 8 trillion pixels. We demonstrate the robustness and potential applications of this data in a case study with domain experts and develop a web application to share our results. This work is practically useful, and can be applied to other locations over the earth as high-resolution imagery becomes more widely available even as high-resolution labeled land cover data remains sparse. |
| Tasks | |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Robinson_Large_Scale_High-Resolution_Land_Cover_Mapping_With_Multi-Resolution_Data_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Robinson_Large_Scale_High-Resolution_Land_Cover_Mapping_With_Multi-Resolution_Data_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/large-scale-high-resolution-land-cover |
| Repo | https://github.com/calebrob6/land-cover |
| Framework | tf |
Propagation kernels: efficient graph kernels from propagated information
| Title | Propagation kernels: efficient graph kernels from propagated information |
| Authors | Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting |
| Abstract | We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains. |
| Tasks | Graph Classification |
| Published | 2019-02-01 |
| URL | https://doi.org/10.1007/s10994-015-5517-9 |
| https://link.springer.com/content/pdf/10.1007%2Fs10994-015-5517-9.pdf | |
| PWC | https://paperswithcode.com/paper/propagation-kernels-efficient-graph-kernels |
| Repo | https://github.com/marionmari/propagation_kernels |
| Framework | none |
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
| Title | Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction |
| Authors | Hao Zheng, Faming Fang, Guixu Zhang |
| Abstract | Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging. Inspired by recent deep learning methods, we propose a Cascaded Dilated Dense Network (CDDN) for MRI reconstruction. Dense blocks with residual connection are used to restore clear images step by step and dilated convolution is introduced for expanding receptive field without taking more network parameters. After each sub-network, we use a novel two-step Data Consistency (DC) operation in k-space. We convert the complex result from first DC operation to real-valued images and applied another sampled \emph{k}-space data replacement. Extensive experiments demonstrate that the proposed CDDN with two-step DC achieves state-of-art result. |
| Tasks | |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8451-cascaded-dilated-dense-network-with-two-step-data-consistency-for-mri-reconstruction |
| http://papers.nips.cc/paper/8451-cascaded-dilated-dense-network-with-two-step-data-consistency-for-mri-reconstruction.pdf | |
| PWC | https://paperswithcode.com/paper/cascaded-dilated-dense-network-with-two-step |
| Repo | https://github.com/tinyRattar/CSMRI_0325 |
| Framework | pytorch |
Aggregating Deep Pyramidal Representations for Person Re-Idenfitication
| Title | Aggregating Deep Pyramidal Representations for Person Re-Idenfitication |
| Authors | Niki Martinel, Gian Luca Foresti, Christian Micheloni |
| Abstract | Learning discriminative, view-invariant and multi-scale representations of person appearance with different se- mantic levels is of paramount importance for person Re- Identification (Re-ID). A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation. To improve the achieved results, additional visual attributes and body part-driven models have been considered. How- ever, these require extensive human annotation labor or de- mand additional computational efforts. We argue that a pyramid-inspired method capturing multi-scale information may overcome such requirements. Precisely, multi-scale stripes that represent visual information of a person can be used by a novel architecture factorizing them into latent discriminative factors at multiple semantic levels. A multi- task loss is combined with a curriculum learning strategy to learn a discriminative and invariant person representation which is exploited for triplet-similarity learning. Results on three benchmark Re-ID datasets demonstrate that better performance than existing methods are achieved (e.g., more than 90% accuracy on the Duke-MTMC dataset). |
| Tasks | Person Re-Identification |
| Published | 2019-06-20 |
| URL | http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Martinel_Aggregating_Deep_Pyramidal_Representations_for_Person_Re-Identification_CVPRW_2019_paper.pdf |
| http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Martinel_Aggregating_Deep_Pyramidal_Representations_for_Person_Re-Identification_CVPRW_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/aggregating-deep-pyramidal-representations |
| Repo | https://github.com/iN1k1/deep-pyramidal-representations-person-re-identification |
| Framework | pytorch |
VisNet: Deep Convolutional Neural Networks for Forecasting Atmospheric Visibility
| Title | VisNet: Deep Convolutional Neural Networks for Forecasting Atmospheric Visibility |
| Authors | by Akmaljon PalvanovOrcID and Young Im Cho |
| Abstract | Visibility is a complex phenomenon inspired by emissions and air pollutants or by factors, including sunlight, humidity, temperature, and time, which decrease the clarity of what is visible through the atmosphere. This paper provides a detailed overview of the state-of-the-art contributions in relation to visibility estimation under various foggy weather conditions. We propose VisNet, which is a new approach based on deep integrated convolutional neural networks for the estimation of visibility distances from camera imagery. The implemented network uses three streams of deep integrated convolutional neural networks, which are connected in parallel. In addition, we have collected the largest dataset with three million outdoor images and exact visibility values for this study. To evaluate the model’s performance fairly and objectively, the model is trained on three image datasets with different visibility ranges, each with a different number of classes. Moreover, our proposed model, VisNet, evaluated under dissimilar fog density scenarios, uses a diverse set of images. Prior to feeding the network, each input image is filtered in the frequency domain to remove low-level features, and a spectral filter is applied to each input for the extraction of low-contrast regions. Compared to the previous methods, our approach achieves the highest performance in terms of classification based on three different datasets. Furthermore, our VisNet considerably outperforms not only the classical methods, but also state-of-the-art models of visibility estimation. |
| Tasks | |
| Published | 2019-06-06 |
| URL | https://www.mdpi.com/1424-8220/19/6/1343 |
| https://www.mdpi.com/1424-8220/19/6/1343 | |
| PWC | https://paperswithcode.com/paper/visnet-deep-convolutional-neural-networks-for |
| Repo | https://github.com/JaniceLC/PyTorch-VisNet |
| Framework | pytorch |
RNNs implicitly implement tensor-product representations
| Title | RNNs implicitly implement tensor-product representations |
| Authors | R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky |
| Abstract | Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations. By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated byTPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations |
| Tasks | Representation Learning |
| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=BJx0sjC5FX |
| https://openreview.net/pdf?id=BJx0sjC5FX | |
| PWC | https://paperswithcode.com/paper/rnns-implicitly-implement-tensor-product |
| Repo | https://github.com/tommccoy1/tpdn |
| Framework | pytorch |
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
| Title | Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior |
| Authors | Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang |
| Abstract | This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset. |
| Tasks | Instance Segmentation, Multiple Instance Learning, Semantic Segmentation, Weakly-supervised instance segmentation |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8885-weakly-supervised-instance-segmentation-using-the-bounding-box-tightness-prior |
| http://papers.nips.cc/paper/8885-weakly-supervised-instance-segmentation-using-the-bounding-box-tightness-prior.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-instance-segmentation-using-3 |
| Repo | https://github.com/chengchunhsu/WSIS_BBTP |
| Framework | pytorch |
Curvilinear Distance Metric Learning
| Title | Curvilinear Distance Metric Learning |
| Authors | Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang |
| Abstract | Distance Metric Learning aims to learn an appropriate metric that faithfully measures the distance between two data points. Traditional metric learning methods usually calculate the pairwise distance with fixed distance functions (\emph{e.g.,}\ Euclidean distance) in the projected feature spaces. However, they fail to learn the underlying geometries of the sample space, and thus cannot exactly predict the intrinsic distances between data points. To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair’s nearest points on the learned straight measurer lines. After that, by extending such straight lines to general curved forms, we propose a Curvilinear Distance Metric Learning (CDML) method, which adaptively learns the nonlinear geometries of the training data. By virtue of Weierstrass theorem, the proposed CDML is equivalently parameterized with a 3-order tensor, and the optimization algorithm is designed to learn the tensor parameter. Theoretical analysis is derived to guarantee the effectiveness and soundness of CDML. Extensive experiments on the synthetic and real-world datasets validate the superiority of our method over the state-of-the-art metric learning models. |
| Tasks | Metric Learning |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8675-curvilinear-distance-metric-learning |
| http://papers.nips.cc/paper/8675-curvilinear-distance-metric-learning.pdf | |
| PWC | https://paperswithcode.com/paper/curvilinear-distance-metric-learning |
| Repo | https://github.com/functioncs/CDML |
| Framework | none |
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum
| Title | Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum |
| Authors | Shreyas Saxena, Oncel Tuzel, Dennis Decoste |
| Abstract | Recent works have shown that learning from easier instances first can help deep neural networks (DNNs) generalize better. However, knowing which data to present during different stages of training is a challenging problem. In this work, we address this problem by introducing data parameters. More specifically, we equip each sample and class in a dataset with a learnable parameter (data parameters), which governs their importance in the learning process. During training, at each iteration, as we update the model parameters, we also update the data parameters. These updates are done by gradient descent and do not require hand-crafted rules or design. When applied to image classification task on CIFAR10, CIFAR100,WebVision and ImageNet datasets, and object detection task on KITTI dataset, learning a dynamic curriculum via data parameters leads to consistent gains, without any increase in model complexity or training time. When applied to a noisy dataset, the proposed method learns to learn from clean images and improves over the state-of-the-art methods by 14%. To the best of our knowledge, our work is the first curriculum learning method to show gains on large scale image classification and detection tasks. |
| Tasks | Image Classification, Object Detection |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9289-data-parameters-a-new-family-of-parameters-for-learning-a-differentiable-curriculum |
| http://papers.nips.cc/paper/9289-data-parameters-a-new-family-of-parameters-for-learning-a-differentiable-curriculum.pdf | |
| PWC | https://paperswithcode.com/paper/data-parameters-a-new-family-of-parameters |
| Repo | https://github.com/apple/ml-data-parameters |
| Framework | pytorch |
The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy
| Title | The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy |
| Authors | Qinlan Shen, Carolyn Rose |
| Abstract | Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users{'} ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated. |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3507/ |
| https://www.aclweb.org/anthology/W19-3507 | |
| PWC | https://paperswithcode.com/paper/the-discourse-of-online-content-moderation |
| Repo | https://github.com/qinlans/alw3_data |
| Framework | none |
Annotating and analyzing the interactions between meaning relations
| Title | Annotating and analyzing the interactions between meaning relations |
| Authors | Darina Gold, Venelin Kovatchev, Torsten Zesch |
| Abstract | Pairs of sentences, phrases, or other text pieces can hold semantic relations such as paraphrasing, textual entailment, contradiction, specificity, and semantic similarity. These relations are usually studied in isolation and no dataset exists where they can be compared empirically. Here we present a corpus annotated with these relations and the analysis of these results. The corpus contains 520 sentence pairs, annotated with these relations. We measure the annotation reliability of each individual relation and we examine their interactions and correlations. Among the unexpected results revealed by our analysis is that the traditionally considered direct relationship between paraphrasing and bi-directional entailment does not hold in our data. |
| Tasks | Natural Language Inference, Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4004/ |
| https://www.aclweb.org/anthology/W19-4004 | |
| PWC | https://paperswithcode.com/paper/annotating-and-analyzing-the-interactions |
| Repo | https://github.com/MeDarina/meaning_relations_interaction |
| Framework | none |
A Neural Graph-based Approach to Verbal MWE Identification
| Title | A Neural Graph-based Approach to Verbal MWE Identification |
| Authors | Jakub Waszczuk, Rafael Ehren, Regina Stodden, Laura Kallmeyer |
| Abstract | We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018). |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-5113/ |
| https://www.aclweb.org/anthology/W19-5113 | |
| PWC | https://paperswithcode.com/paper/a-neural-graph-based-approach-to-verbal-mwe |
| Repo | https://github.com/kawu/vine |
| Framework | none |
Enabling Real-time Neural IME with Incremental Vocabulary Selection
| Title | Enabling Real-time Neural IME with Incremental Vocabulary Selection |
| Authors | Jiali Yao, Raphael Shu, Xinjian Li, Katsutoshi Ohtsuki, Hideki Nakayama |
| Abstract | Input method editor (IME) converts sequential alphabet key inputs to words in a target language. It is an indispensable service for billions of Asian users. Although the neural-based language model is extensively studied and shows promising results in sequence-to-sequence tasks, applying a neural-based language model to IME was not considered feasible due to high latency when converting words on user devices. In this work, we articulate the bottleneck of neural IME decoding to be the heavy softmax computation over a large vocabulary. We propose an approach that incrementally builds a subset vocabulary from the word lattice. Our approach always computes the probability with a selected subset vocabulary. When the selected vocabulary is updated, the stale probabilities in previous steps are fixed by recomputing the missing logits. The experiments on Japanese IME benchmark shows an over 50x speedup for the softmax computations comparing to the baseline, reaching real-time speed even on commodity CPU without losing conversion accuracy. The approach is potentially applicable to other incremental sequence-to-sequence decoding tasks such as real-time continuous speech recognition. |
| Tasks | Language Modelling, Speech Recognition |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/N19-2001/ |
| https://www.aclweb.org/anthology/N19-2001 | |
| PWC | https://paperswithcode.com/paper/enabling-real-time-neural-ime-with |
| Repo | https://github.com/jiali-ms/JLM |
| Framework | tf |
GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction
| Title | GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction |
| Authors | Shen Fang 1, 2, Qi Zhang 1, 2, Gaofeng Meng 1, 2, Shiming Xiang 1, 2 and Chunhong Pan 1 |
| Abstract | Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including short-term neighboring and long-term periodic dependencies. What’s more, the spatial correlations over different nodes are both local and non-local. Tocapturetheglobaldynamicspatial-temporalcorrelations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains a multi-resolution temporal module and a global correlated spatial module in sequence, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed method on both the public transportation network and the road network. |
| Tasks | |
| Published | 2019-01-20 |
| URL | https://www.ijcai.org/proceedings/2019/0317.pdf |
| https://www.ijcai.org/proceedings/2019/0317.pdf | |
| PWC | https://paperswithcode.com/paper/gstnet-global-spatial-temporal-network-for |
| Repo | https://github.com/liulingbo918/CSTN |
| Framework | tf |
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
| Title | RangeNet++: Fast and Accurate LiDAR Semantic Segmentation |
| Authors | Andres Milioto, Ignacio Vizzo, Jens Behley, Cyrill Stachniss |
| Abstract | Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-dominantly solved using high-resolution cameras. As a result of that, other sensor modalities potentially useful for this task are often ignored. In this paper, we push the state of the art in LiDAR-only semantic segmentation forward in order to provide another independent source of semantic information to the vehicle. Our approach can accurately perform full semantic segmentation of LiDAR point clouds at sensor frame rate. We exploit range images as an intermediate representation in combination with a Convolutional Neural Network (CNN) exploiting the rotating LiDAR sensor model. To obtain accurate results, we propose a novel post-processing algorithm that deals with problems arising from this intermediate representation such as discretization errors and blurry CNN outputs. We implemented and thoroughly evaluated our approach including several comparisons to the state of the art. Our experiments show that our approach outperforms state-of-the-art approaches, while still running online on a single embedded GPU. The code can be accessed at https://github.com/PRBonn/lidar-bonnetal |
| Tasks | 3D Semantic Segmentation, Autonomous Vehicles, Semantic Segmentation |
| Published | 2019-11-04 |
| URL | http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf |
| http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf | |
| PWC | https://paperswithcode.com/paper/rangenet-fast-and-accurate-lidar-semantic |
| Repo | https://github.com/PRBonn/lidar-bonnetal |
| Framework | pytorch |
