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dc5efa7f-c842-429c-91af-4c47fc1c5e15 | learning-collaborative-generation-correction | 1807.11706 | null | http://arxiv.org/abs/1807.11706v1 | http://arxiv.org/pdf/1807.11706v1.pdf | Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond | Blind image deblurring plays a very important role in many vision and
multimedia applications. Most existing works tend to introduce complex priors
to estimate the sharp image structures for blur kernel estimation. However, it
has been verified that directly optimizing these models is challenging and easy
to fall into degenerate solutions. Although several experience-based heuristic
inference strategies, including trained networks and designed iterations, have
been developed, it is still hard to obtain theoretically guaranteed accurate
solutions. In this work, a collaborative learning framework is established to
address the above issues. Specifically, we first design two modules, named
Generator and Corrector, to extract the intrinsic image structures from the
data-driven and knowledge-based perspectives, respectively. By introducing a
collaborative methodology to cascade these modules, we can strictly prove the
convergence of our image propagations to a deblurring-related optimal solution.
As a nontrivial byproduct, we also apply the proposed method to address other
related tasks, such as image interpolation and edge-preserved smoothing. Plenty
of experiments demonstrate that our method can outperform the state-of-the-art
approaches on both synthetic and real datasets. | ['Zhongxuan Luo', 'Yi He', 'Xin Fan', 'Shichao Cheng', 'Risheng Liu'] | 2018-07-31 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [-1.44433267e-02 -4.26227748e-01 7.22647309e-02 -2.64693618e-01
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9996075f-c522-4a52-94cc-b689eded11db | feature-engineering-in-the-nli-shared-task | null | null | https://aclanthology.info/papers/W13-1730/w13-1730 | https://www.aclweb.org/anthology/W13-1730v2 | Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report | null | ['Barbora Hladka', 'Martin Holub', 'Vincent Kriz'] | 2013-06-01 | feature-engineering-in-the-nli-shared-task-1 | https://aclanthology.org/W13-1730 | https://aclanthology.org/W13-1730.pdf | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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c2c80e11-330e-4719-ad44-fa63691a829b | where-we-are-and-what-we-re-looking-at-query | 2303.04249 | null | https://arxiv.org/abs/2303.04249v1 | https://arxiv.org/pdf/2303.04249v1.pdf | Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes | Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn a single representation of query images, which are then classified at different levels of geographic granularity. These approaches fail to exploit the different visual cues that give context to different hierarchies, such as the country, state, and city level. To this end, we introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels (which we refer to as hierarchies) and the corresponding visual scene information in an image through hierarchical cross-attention. We achieve this by learning a query for each geographic hierarchy and scene type. Furthermore, we learn a separate representation for different environmental scenes, as different scenes in the same location are often defined by completely different visual features. We achieve state of the art street level accuracy on 4 standard geo-localization datasets : Im2GPS, Im2GPS3k, YFCC4k, and YFCC26k, as well as qualitatively demonstrate how our method learns different representations for different visual hierarchies and scenes, which has not been demonstrated in the previous methods. These previous testing datasets mostly consist of iconic landmarks or images taken from social media, which makes them either a memorization task, or biased towards certain places. To address this issue we introduce a much harder testing dataset, Google-World-Streets-15k, comprised of images taken from Google Streetview covering the whole planet and present state of the art results. Our code will be made available in the camera-ready version. | ['Mubarak Shah', 'Vicente Vivanco Cepeda', 'Parth Parag Kulkarni', 'Alec Kerrigan', 'Brandon Clark'] | 2023-03-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Clark_Where_We_Are_and_What_Were_Looking_At_Query_Based_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-based-localization', 'memorization'] | ['computer-vision', 'natural-language-processing'] | [-2.15176135e-01 -2.81641066e-01 -1.43985480e-01 -5.25768816e-01
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4323d593-330f-4c89-8469-61d51160aa42 | efe-end-to-end-frame-to-gaze-estimation | 2305.05526 | null | https://arxiv.org/abs/2305.05526v1 | https://arxiv.org/pdf/2305.05526v1.pdf | EFE: End-to-end Frame-to-Gaze Estimation | Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes. | ['Otmar Hilliges', 'Xucong Zhang', 'Xi Wang', 'Seonwook Park', 'Haldun Balim'] | 2023-05-09 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.83379963e-01 -5.39643597e-03 2.96088755e-02 -7.60029316e-01
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688a8b4d-188d-40fe-bd0e-9d4064368f85 | computing-star-discrepancies-with-numerical | 2306.16998 | null | https://arxiv.org/abs/2306.16998v1 | https://arxiv.org/pdf/2306.16998v1.pdf | Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms | The $L_{\infty}$ star discrepancy is a measure for the regularity of a finite set of points taken from $[0,1)^d$. Low discrepancy point sets are highly relevant for Quasi-Monte Carlo methods in numerical integration and several other applications. Unfortunately, computing the $L_{\infty}$ star discrepancy of a given point set is known to be a hard problem, with the best exact algorithms falling short for even moderate dimensions around 8. However, despite the difficulty of finding the global maximum that defines the $L_{\infty}$ star discrepancy of the set, local evaluations at selected points are inexpensive. This makes the problem tractable by black-box optimization approaches. In this work we compare 8 popular numerical black-box optimization algorithms on the $L_{\infty}$ star discrepancy computation problem, using a wide set of instances in dimensions 2 to 15. We show that all used optimizers perform very badly on a large majority of the instances and that in many cases random search outperforms even the more sophisticated solvers. We suspect that state-of-the-art numerical black-box optimization techniques fail to capture the global structure of the problem, an important shortcoming that may guide their future development. We also provide a parallel implementation of the best-known algorithm to compute the discrepancy. | ['Carola Doerr', 'Luís Paquete', 'Alexandre D. Jesus', 'Jacob de Nobel', 'Diederick Vermetten', 'François Clément'] | 2023-06-29 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-4.81457412e-01 -1.22304037e-02 -7.26312175e-02 -1.95433432e-03
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e4cca3b8-5442-4ddb-8362-818177ffe229 | a-network-resource-allocation-recommendation | 2307.03399 | null | https://arxiv.org/abs/2307.03399v1 | https://arxiv.org/pdf/2307.03399v1.pdf | A Network Resource Allocation Recommendation Method with An Improved Similarity Measure | Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias. | ['Junhua Hu', 'Pei Liang', 'Huiyu Li'] | 2023-07-07 | null | null | null | null | ['recommendation-systems'] | ['miscellaneous'] | [-2.85994828e-01 -5.50258420e-02 -5.42228341e-01 -1.40683487e-01
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b3c726a7-70f9-4072-a991-4629e114e617 | uiai-system-for-short-duration-speaker | 2007.13118 | null | https://arxiv.org/abs/2007.13118v1 | https://arxiv.org/pdf/2007.13118v1.pdf | UIAI System for Short-Duration Speaker Verification Challenge 2020 | In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. Our focus is on Task 1 dedicated to text-dependent speaker verification. We investigate different feature extraction and modeling approaches for automatic speaker verification (ASV) and utterance verification (UV). We have also studied different fusion strategies for combining UV and ASV modules. Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0.072 and an equal error rate (EER) of 2.14% on the evaluation set. The single system consisting of a pass-phrase identification based model with phone-discriminative bottleneck features gives a normalized minDCF of 0.118 and achieves 19% relative improvement over the state-of-the-art challenge baseline. | ['Zheng-Hua Tan', 'Xuechen Liu', 'Tomi Kinnunen', 'Achintya Kumar Sarkar', 'Emmanuel Vincent', 'Ville Vestman', 'Romain Serizel', 'Md Sahidullah'] | 2020-07-26 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 1.39107138e-01 3.70356701e-02 1.87445015e-01 -8.35446119e-01
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98484172-6fc0-4b33-8f5c-9f7506f33260 | probabilistic-conformal-prediction-using | 2206.06584 | null | https://arxiv.org/abs/2206.06584v2 | https://arxiv.org/pdf/2206.06584v2.pdf | Probabilistic Conformal Prediction Using Conditional Random Samples | This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples from an estimated generative model. It is efficient and compatible with either explicit or implicit conditional generative models. Theoretically, we show that PCP guarantees correct marginal coverage with finite samples. Empirically, we study PCP on a variety of simulated and real datasets. Compared to existing methods for conformal inference, PCP provides sharper predictive sets. | ['David M. Blei', 'Mingyuan Zhou', 'Mingzhang Yin', 'Ruijiang Gao', 'Zhendong Wang'] | 2022-06-14 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 4.34588850e-01 5.30331910e-01 -6.88507617e-01 -5.78596890e-01
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6bbcfaed-abb2-41f9-8489-c43aa52ee5fe | neural-network-quantum-state-with-proximal | 2210.16493 | null | https://arxiv.org/abs/2210.16493v1 | https://arxiv.org/pdf/2210.16493v1.pdf | Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlo | Neural network quantum states (NQS), incorporating with variational Monte Carlo (VMC) method, are shown to be a promising way to investigate quantum many-body physics. Whereas vanilla VMC methods perform one gradient update per sample, we introduce a novel objective function with proximal optimization (PO) that enables multiple updates via reusing the mismatched samples. Our VMC-PO method keeps the advantage of the previous importance sampling gradient optimization algorithm [L. Yang, {\it et al}, Phys. Rev. Research {\bf 2}, 012039(R)(2020)] that efficiently uses sampled states. PO mitigates the numerical instabilities during network updates, which is similar to stochastic reconfiguration (SR) methods, but achieves an alternative and simpler implement with lower computational complexity. We investigate the performance of our VMC-PO algorithm for ground-state searching with a 1-dimensional transverse-field Ising model and 2-dimensional Heisenberg antiferromagnet on a square lattice, and demonstrate that the reached ground-state energies are comparable to state-of-the-art results. | ['Ming Xue', 'Feng Chen'] | 2022-10-29 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 3.62863123e-01 -2.36417219e-01 3.00458120e-03 -1.41528070e-01
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2d65d842-b853-4e7c-b992-bb4e3c80f3ff | sampling-equivariant-self-attention-networks | 2111.03420 | null | https://arxiv.org/abs/2111.03420v1 | https://arxiv.org/pdf/2111.03420v1.pdf | Sampling Equivariant Self-attention Networks for Object Detection in Aerial Images | Objects in aerial images have greater variations in scale and orientation than in typical images, so detection is more difficult. Convolutional neural networks use a variety of frequency- and orientation-specific kernels to identify objects subject to different transformations; these require many parameters. Sampling equivariant networks can adjust sampling from input feature maps according to the transformation of the object, allowing a kernel to extract features of an object under different transformations. Doing so requires fewer parameters, and makes the network more suitable for representing deformable objects, like those in aerial images. However, methods like deformable convolutional networks can only provide sampling equivariance under certain circumstances, because of the locations used for sampling. We propose sampling equivariant self-attention networks which consider self-attention restricted to a local image patch as convolution sampling with masks instead of locations, and design a transformation embedding module to further improve the equivariant sampling ability. We also use a novel randomized normalization module to tackle overfitting due to limited aerial image data. We show that our model (i) provides significantly better sampling equivariance than existing methods, without additional supervision, (ii) provides improved classification on ImageNet, and (iii) achieves state-of-the-art results on the DOTA dataset, without increased computation. | ['Shi-Min Hu', 'Ralph R. Martin', 'Xiang-Li Li', 'Guo-Ye Yang'] | 2021-11-05 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.21615577e-01 -2.81887978e-01 -1.17402792e-01 -4.01432276e-01
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6f5178e2-051e-4b47-bc59-a5644a0e1af3 | tgcf-texture-guided-color-fusion-for | 2207.12585 | null | https://arxiv.org/abs/2207.12585v2 | https://arxiv.org/pdf/2207.12585v2.pdf | PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering | As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses the computer algorithms to render a photo into an artistic painting. Recent work has shown that the extraction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed, which fully considers the tonal characteristics and the representative color of the original oil painting, in order to fit the tone of the original oil painting image into the stylized image and make it close to the artist's creative effect. The experiments have validated the efficacy of the proposed model. This method would be more suitable for the works of pointillism painters with a relatively uniform sense of direction, especially for natural scenes. When the original painting brush strokes have a clearer sense of direction, using this method to simulate brushwork texture features can be less satisfactory. | ['Xiaoquan Li', "Li'e Ma", 'Yijun Yan', 'Jing Geng'] | 2022-07-26 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 5.80177546e-01 -1.87303603e-01 6.87745363e-02 1.07945241e-02
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a50baa1f-713f-43f7-b69d-05e9bde953d9 | tsdpmm-incorporating-prior-topic-knowledge | null | null | https://aclanthology.org/D15-1091 | https://aclanthology.org/D15-1091.pdf | TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text Clustering | null | ['Xiao-Li Li', 'Chao Shao', 'Xuzhong Wang', 'Linmei Hu', 'Juanzi Li'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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095f4e95-756c-49ed-b5d4-acf1105d90dc | hypersf-spectral-hypergraph-coarsening-via | 2108.07901 | null | https://arxiv.org/abs/2108.07901v1 | https://arxiv.org/pdf/2108.07901v1.pdf | HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering | Hypergraphs allow modeling problems with multi-way high-order relationships. However, the computational cost of most existing hypergraph-based algorithms can be heavily dependent upon the input hypergraph sizes. To address the ever-increasing computational challenges, graph coarsening can be potentially applied for preprocessing a given hypergraph by aggressively aggregating its vertices (nodes). However, state-of-the-art hypergraph partitioning (clustering) methods that incorporate heuristic graph coarsening techniques are not optimized for preserving the structural (global) properties of hypergraphs. In this work, we propose an efficient spectral hypergraph coarsening scheme (HyperSF) for well preserving the original spectral (structural) properties of hypergraphs. Our approach leverages a recent strongly-local max-flow-based clustering algorithm for detecting the sets of hypergraph vertices that minimize ratio cut. To further improve the algorithm efficiency, we propose a divide-and-conquer scheme by leveraging spectral clustering of the bipartite graphs corresponding to the original hypergraphs. Our experimental results for a variety of hypergraphs extracted from real-world VLSI design benchmarks show that the proposed hypergraph coarsening algorithm can significantly improve the multi-way conductance of hypergraph clustering as well as runtime efficiency when compared with existing state-of-the-art algorithms. | ['Zhuo Feng', 'Zhiqiang Zhao', 'Ali Aghdaei'] | 2021-08-17 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 3.03295970e-01 2.65488505e-01 -4.97735381e-01 3.37235965e-02
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2db15f1f-c652-43ad-b0f1-6c29cd807310 | deep-learning-inversion-of-seismic-data | 1901.07733 | null | https://arxiv.org/abs/1901.07733v2 | https://arxiv.org/pdf/1901.07733v2.pdf | Deep-Learning Inversion of Seismic Data | We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong nonuniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model, as well as the time-varying property of seismic data. To tackle these challenges, we propose end-to-end seismic inversion networks (SeisInvNets) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup, and the global context of its corresponding seismic profile. From the enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct a velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our synthesized and proposed SeisInv data set according to various evaluation metrics. The inversion results are more consistent with the target from the aspects of velocity values, subsurface structures, and geological interfaces. Moreover, the mechanism and the generalization of the proposed method are discussed and verified. Nevertheless, the generalization of deep-learning-based inversion methods on real data is still challenging and considering physics may be one potential solution. | ['Yunhai Wang', 'Yuxiao Ren', 'Bin Liu', 'Yangkang Chen', 'Peng Jiang', 'Shucai Li', 'Senlin Yang'] | 2019-01-23 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 1.18795864e-01 -1.10617109e-01 2.87250787e-01 -1.67293891e-01
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699497af-e769-4860-8681-834b6c299130 | input-sensitive-dense-sparse-primitive | 2306.15155 | null | https://arxiv.org/abs/2306.15155v1 | https://arxiv.org/pdf/2306.15155v1.pdf | Input-sensitive dense-sparse primitive compositions for GNN acceleration | Graph neural networks (GNN) have become an important class of neural network models that have gained popularity in domains such as social and financial network analysis. Different phases of GNN computations can be modeled using both dense and sparse matrix operations. There have been many frameworks and optimization techniques proposed in the literature to accelerate GNNs. However, getting consistently high performance across many input graphs with different sparsity patterns and GNN embedding sizes has remained difficult. In this paper, we propose different algebraic reassociations of GNN computations that lead to novel dense and sparse matrix primitive selections and compositions. We show that the profitability of these compositions depends on the input graph, embedding size, and the target hardware. We developed SENSEi, a system that uses a data-driven adaptive strategy to select the best composition given the input graph and GNN embedding sizes. Our evaluations on a wide range of graphs and embedding sizes show that SENSEi achieves geomean speedups of $1.105\times$ (up to $2.959\times$) and $1.187\times$ (up to $1.99\times$) on graph convolutional networks and geomean speedups of $2.307\times$ (up to $35.866\times$) and $1.44\times$ (up to $5.69\times$) on graph attention networks on CPUs and GPUs respectively over the widely used Deep Graph Library. Further, we show that the compositions yield notable synergistic performance benefits on top of other established sparse optimizations such as sparse matrix tiling by evaluating against a well-tuned baseline. | ['Charith Mendis', 'Josep Torrellas', 'Serif Yesil', 'Gerasimos Gerogiannis', 'Vimarsh Sathia', 'Damitha Lenadora'] | 2023-06-27 | null | null | null | null | ['graph-embedding', 'graph-attention'] | ['graphs', 'graphs'] | [-1.03005268e-01 3.01651154e-02 2.71222722e-02 -2.56835043e-01
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14fc7068-17c8-4afa-92e1-a3dd41faa24d | one-shot-face-reenactment | 1908.03251 | null | https://arxiv.org/abs/1908.03251v1 | https://arxiv.org/pdf/1908.03251v1.pdf | One-shot Face Reenactment | To enable realistic shape (e.g. pose and expression) transfer, existing face reenactment methods rely on a set of target faces for learning subject-specific traits. However, in real-world scenario end-users often only have one target face at hand, rendering existing methods inapplicable. In this work, we bridge this gap by proposing a novel one-shot face reenactment learning framework. Our key insight is that the one-shot learner should be able to disentangle and compose appearance and shape information for effective modeling. Specifically, the target face appearance and the source face shape are first projected into latent spaces with their corresponding encoders. Then these two latent spaces are associated by learning a shared decoder that aggregates multi-level features to produce the final reenactment results. To further improve the synthesizing quality on mustache and hair regions, we additionally propose FusionNet which combines the strengths of our learned decoder and the traditional warping method. Extensive experiments show that our one-shot face reenactment system achieves superior transfer fidelity as well as identity preserving capability than alternatives. More remarkably, our approach trained with only one target image per subject achieves competitive results to those using a set of target images, demonstrating the practical merit of this work. Code, models and an additional set of reenacted faces have been publicly released at the project page. | ['Yunxuan Zhang', 'Siwei Zhang', 'Yue He', 'Chen Change Loy', 'Ziwei Liu', 'Cheng Li'] | 2019-08-05 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 3.40227783e-01 1.61783859e-01 -1.56108648e-01 -6.29697561e-01
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ac32f8d9-8426-4ba7-9e9e-71d1a4edc94c | traffic-net-3d-traffic-monitoring-using-a | 2109.09165 | null | https://arxiv.org/abs/2109.09165v2 | https://arxiv.org/pdf/2109.09165v2.pdf | Traffic-Net: 3D Traffic Monitoring Using a Single Camera | Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data stream to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions. | ['Farzam Mohammad Pour Mir', 'Mohsen Azarmi', 'Mahdi Rezaei'] | 2021-09-19 | null | null | null | null | ['camera-auto-calibration'] | ['computer-vision'] | [-2.48913765e-01 -6.01299226e-01 7.05770552e-02 -4.15445834e-01
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6399f53c-18d7-4fe9-8295-2ff186acaa75 | on-the-effectiveness-of-out-of-distribution | 2306.04934 | null | https://arxiv.org/abs/2306.04934v1 | https://arxiv.org/pdf/2306.04934v1.pdf | On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning | Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the majority classes dominating the feature space. Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect. In this paper, we propose an alternative but easy-to-use and effective solution, Contrastive with Out-of-distribution (OOD) data for Long-Tail learning (COLT), which can effectively exploit OOD data to dynamically re-balance the feature space. We empirically identify the counter-intuitive usefulness of OOD samples in SSL long-tailed learning and principally design a novel SSL method. Concretely, we first localize the `head' and `tail' samples by assigning a tailness score to each OOD sample based on its neighborhoods in the feature space. Then, we propose an online OOD sampling strategy to dynamically re-balance the feature space. Finally, we enforce the model to be capable of distinguishing ID and OOD samples by a distribution-level supervised contrastive loss. Extensive experiments are conducted on various datasets and several state-of-the-art SSL frameworks to verify the effectiveness of the proposed method. The results show that our method significantly improves the performance of SSL on long-tailed datasets by a large margin, and even outperforms previous work which uses external ID data. Our code is available at https://github.com/JianhongBai/COLT. | ['Haoji Hu', 'Huanpeng Chu', 'Yang Feng', 'Jin Hao', 'Hualiang Wang', 'Zuozhu Liu', 'Jianhong Bai'] | 2023-06-08 | null | null | null | null | ['long-tail-learning'] | ['methodology'] | [-1.86046720e-01 -1.90873250e-01 -8.04670095e-01 -5.54203153e-01
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f539d30c-35fa-463d-8050-fb636d2fde47 | a-transfer-learning-and-optimized-cnn-based | 2201.11812 | null | https://arxiv.org/abs/2201.11812v1 | https://arxiv.org/pdf/2201.11812v1.pdf | A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles | Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks. | ['Abdallah Shami', 'Li Yang'] | 2022-01-27 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-4.85836715e-01 -1.26640365e-01 -2.53996342e-01 -4.59400788e-02
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73d03af1-74da-40c5-8e63-48ac48c05a43 | effective-occlusion-handling-for-fast | 1807.04880 | null | https://arxiv.org/abs/1807.04880v3 | https://arxiv.org/pdf/1807.04880v3.pdf | Effective Occlusion Handling for Fast Correlation Filter-based Trackers | Correlation filter-based trackers heavily suffer from the problem of multiple peaks in their response maps incurred by occlusions. Moreover, the whole tracking pipeline may break down due to the uncertainties brought by shifting among peaks, which will further lead to the degraded correlation filter model. To alleviate the drift problem caused by occlusions, we propose a novel scheme to choose the specific filter model according to different scenarios. Specifically, an effective measurement function is designed to evaluate the quality of filter response. A sophisticated strategy is employed to judge whether occlusions occur, and then decide how to update the filter models. In addition, we take advantage of both log-polar method and pyramid-like approach to estimate the best scale of the target. We evaluate our proposed approach on VOT2018 challenge and OTB100 dataset, whose experimental result shows that the proposed tracker achieves the promising performance compared against the state-of-the-art trackers. | ['T. T. Wong', 'Zheng Zhang'] | 2018-07-13 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-2.90759206e-01 -5.91054201e-01 1.57648936e-01 -1.96160510e-01
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62b53329-8661-4d15-b5a9-e97a0fc45774 | conssed-at-semeval-2019-task-3-configurable | null | null | https://aclanthology.org/S19-2027 | https://aclanthology.org/S19-2027.pdf | ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector | This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of context, identify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Others. Our system: ConSSED is a configurable combination of semantic and sentiment neural models. The official task submission achieved a micro-average F1 score of 75.31 which placed us 16th out of 165 participating systems. | ["Rafa{\\l} Po{\\'s}wiata"] | 2019-06-01 | null | null | null | semeval-2019-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 5.16618378e-02 2.72854179e-01 2.92350292e-01 -9.12389219e-01
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917273d3-5f91-41d1-bb87-1bee1696be62 | panoptic-narrative-grounding | 2109.04988 | null | https://arxiv.org/abs/2109.04988v1 | https://arxiv.org/pdf/2109.04988v1.pdf | Panoptic Narrative Grounding | This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics, and we propose a strong baseline method to serve as stepping stone for future work. We exploit the intrinsic semantic richness in an image by including panoptic categories, and we approach visual grounding at a fine-grained level by using segmentations. In terms of ground truth, we propose an algorithm to automatically transfer Localized Narratives annotations to specific regions in the panoptic segmentations of the MS COCO dataset. To guarantee the quality of our annotations, we take advantage of the semantic structure contained in WordNet to exclusively incorporate noun phrases that are grounded to a meaningfully related panoptic segmentation region. The proposed baseline achieves a performance of 55.4 absolute Average Recall points. This result is a suitable foundation to push the envelope further in the development of methods for Panoptic Narrative Grounding. | ['P. Arbeláez', 'J. Pont-Tuset', 'J. Hernández', 'I. Hernández', 'N. Ayobi', 'C. González'] | 2021-09-10 | null | null | null | null | ['natural-language-visual-grounding'] | ['reasoning'] | [ 2.31077433e-01 3.50487471e-01 -4.93911564e-01 -2.63666868e-01
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41da7b4a-bbb7-48e3-b064-77f6f52ddaf4 | deep-generative-modeling-for-protein-design | 2109.13754 | null | https://arxiv.org/abs/2109.13754v1 | https://arxiv.org/pdf/2109.13754v1.pdf | Deep Generative Modeling for Protein Design | Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design. | ['Philip M. Kim', 'Alexey Strokach'] | 2021-08-31 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.19169492e-01 1.49176583e-01 -2.57690281e-01 -5.90291798e-01
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af8932e7-da06-482d-bc0c-d4c218d195b1 | team-taurus-at-semeval-2019-task-9-expert | null | null | https://aclanthology.org/S19-2219 | https://aclanthology.org/S19-2219.pdf | Team Taurus at SemEval-2019 Task 9: Expert-informed pattern recognition for suggestion mining | This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human understanding the task than by a machine learner only able to count features. For Semeval-2019 Task 9, the expert rules clearly outperformed our machine learning model when training and testing on equally balanced testsets. | ['Nelleke Oostdijk', 'Hans van Halteren'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.36377707e-01 5.89119494e-01 -1.80174857e-01 -7.84704745e-01
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8fbada1b-182f-4486-a57b-6452da4eddae | atrial-fibrillation-detection-using-weight | 2206.07649 | null | https://arxiv.org/abs/2206.07649v1 | https://arxiv.org/pdf/2206.07649v1.pdf | Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks | Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In this work, a convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram (ECG) signals. The model demonstrates high performance despite being trained on limited, variable-length input data. Weight pruning and logarithmic quantisation are combined to introduce sparsity and reduce model size, which can be exploited for reduced data movement and lower computational complexity. The final model achieved a 91.1% model compression ratio while maintaining high model accuracy of 91.7% and less than 1% loss. | ['Deepu John', 'Rajesh C. Panicker', 'Li Xiaolin', 'Wang He', 'Rui Han', 'Shuhui Wang', 'Benjamin Chen Ming Choong', 'Ann Feng Chew', 'Xiu Qi Chang'] | 2022-06-14 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 3.64977211e-01 -1.44996375e-01 -4.49736863e-01 -3.25819165e-01
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d1a07499-2eb8-4f57-95d2-dea12f826650 | myope-models-are-face-presentation-attack | 2111.11127 | null | https://arxiv.org/abs/2111.11127v1 | https://arxiv.org/pdf/2111.11127v1.pdf | Myope Models -- Are face presentation attack detection models short-sighted? | Presentation attacks are recurrent threats to biometric systems, where impostors attempt to bypass these systems. Humans often use background information as contextual cues for their visual system. Yet, regarding face-based systems, the background is often discarded, since face presentation attack detection (PAD) models are mostly trained with face crops. This work presents a comparative study of face PAD models (including multi-task learning, adversarial training and dynamic frame selection) in two settings: with and without crops. The results show that the performance is consistently better when the background is present in the images. The proposed multi-task methodology beats the state-of-the-art results on the ROSE-Youtu dataset by a large margin with an equal error rate of 0.2%. Furthermore, we analyze the models' predictions with Grad-CAM++ with the aim to investigate to what extent the models focus on background elements that are known to be useful for human inspection. From this analysis we can conclude that the background cues are not relevant across all the attacks. Thus, showing the capability of the model to leverage the background information only when necessary. | ['Jaime S. Cardoso', 'Ana F. Sequeira', 'Pedro C. Neto'] | 2021-11-22 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.10207903e-01 -2.44679809e-01 1.13246739e-02 1.71175718e-01
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f0cce92c-7c45-4791-8b89-e5e90d84a07e | cramnet-camera-radar-fusion-with-ray | 2210.09267 | null | https://arxiv.org/abs/2210.09267v2 | https://arxiv.org/pdf/2210.09267v2.pdf | CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection | Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different environmental conditions. Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicular axis, that is, depth is unknown to camera and elevation is unknown to radar. We propose the camera-radar matching network CramNet, an efficient approach to fuse the sensor readings from camera and radar in a joint 3D space. To leverage radar range measurements for better camera depth predictions, we propose a novel ray-constrained cross-attention mechanism that resolves the ambiguity in the geometric correspondences between camera features and radar features. Our method supports training with sensor modality dropout, which leads to robust 3D object detection, even when a camera or radar sensor suddenly malfunctions on a vehicle. We demonstrate the effectiveness of our fusion approach through extensive experiments on the RADIATE dataset, one of the few large-scale datasets that provide radar radio frequency imagery. A camera-only variant of our method achieves competitive performance in monocular 3D object detection on the Waymo Open Dataset. | ['Dragomir Anguelov', 'Tiffany Chen', 'Nicholas Armstrong-Crews', 'Sean Rafferty', 'Joshua Manela', 'Henrik Kretzschmar', 'Jyh-Jing Hwang'] | 2022-10-17 | null | null | null | null | ['monocular-3d-object-detection', 'robust-3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.01748121e-01 -2.64471591e-01 -1.24075361e-01 -6.51454926e-01
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8ad96060-fb2c-4225-8eb5-26e980066172 | at-human-speed-deep-reinforcement-learning | 1810.07286 | null | http://arxiv.org/abs/1810.07286v1 | http://arxiv.org/pdf/1810.07286v1.pdf | At Human Speed: Deep Reinforcement Learning with Action Delay | There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of tasks, from video games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning and reinforcement learning, that learn to play from experience
with minimal prior knowledge. However, these machines often do not win through
intelligence alone -- they possess vastly superior speed and precision,
allowing them to act in ways a human never could. To level the playing field,
we restrict the machine's reaction time to a human level, and find that
standard deep reinforcement learning methods quickly drop in performance. We
propose a solution to the action delay problem inspired by human perception --
to endow agents with a neural predictive model of the environment which
"undoes" the delay inherent in their environment -- and demonstrate its
efficacy against professional players in Super Smash Bros. Melee, a popular
console fighting game. | ['Tina Ju', 'Josh Tenenbaum', 'Vlad Firoiu'] | 2018-10-16 | null | null | null | null | ['board-games'] | ['playing-games'] | [-9.49071646e-02 2.15588108e-01 1.99947804e-02 1.45524785e-01
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cfe3c04c-0bb2-4994-9b82-0ac590e07e3b | dcid-deep-canonical-information-decomposition | 2306.15619 | null | https://arxiv.org/abs/2306.15619v1 | https://arxiv.org/pdf/2306.15619v1.pdf | DCID: Deep Canonical Information Decomposition | We consider the problem of identifying the signal shared between two one-dimensional target variables, in the presence of additional multivariate observations. Canonical Correlation Analysis (CCA)-based methods have traditionally been used to identify shared variables, however, they were designed for multivariate targets and only offer trivial solutions for univariate cases. In the context of Multi-Task Learning (MTL), various models were postulated to learn features that are sparse and shared across multiple tasks. However, these methods were typically evaluated by their predictive performance. To the best of our knowledge, no prior studies systematically evaluated models in terms of correctly recovering the shared signal. Here, we formalize the setting of univariate shared information retrieval, and propose ICM, an evaluation metric which can be used in the presence of ground-truth labels, quantifying 3 aspects of the learned shared features. We further propose Deep Canonical Information Decomposition (DCID) - a simple, yet effective approach for learning the shared variables. We benchmark the models on a range of scenarios on synthetic data with known ground-truths and observe DCID outperforming the baselines in a wide range of settings. Finally, we demonstrate a real-life application of DCID on brain Magnetic Resonance Imaging (MRI) data, where we are able to extract more accurate predictors of changes in brain regions and obesity. The code for our experiments as well as the supplementary materials are available at https://github.com/alexrakowski/dcid | ['Christoph Lippert', 'Alexander Rakowski'] | 2023-06-27 | null | null | null | null | ['multi-task-learning', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [ 4.16846067e-01 -2.38131717e-01 -3.04071963e-01 -2.81142354e-01
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0f34c0ff-d7f7-484e-9b34-91c3a1c638b6 | backdoor-attacks-on-crowd-counting | 2207.05641 | null | https://arxiv.org/abs/2207.05641v1 | https://arxiv.org/pdf/2207.05641v1.pdf | Backdoor Attacks on Crowd Counting | Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to backdoor attacks, a major security threat to deep learning. A backdoor attack implants a backdoor trigger into a target model via data poisoning so as to control the model's predictions at test time. Different from image classification models on which most of existing backdoor attacks have been developed and tested, crowd counting models are regression models that output multi-dimensional density maps, thus requiring different techniques to manipulate. In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA$^{-}$ and DMBA$^{+}$) to attack the model to produce arbitrarily large or small density estimations. Experimental results demonstrate the effectiveness of our DMBA attacks on five classic crowd counting models and four types of datasets. We also provide an in-depth analysis of the unique challenges of backdooring crowd counting models and reveal two key elements of effective attacks: 1) full and dense triggers and 2) manipulation of the ground truth counts or density maps. Our work could help evaluate the vulnerability of crowd counting models to potential backdoor attacks. | ['Lichao', 'Yu Cheng', 'Xing Di', 'Zichuan Xu', 'Jian Lou', 'Pan Zhou', 'Xingjun Ma', 'Tailai Zhang', 'Yuhua Sun'] | 2022-07-12 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-1.47954330e-01 -2.62373209e-01 1.62979007e-01 3.84749360e-02
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cb12648b-7e04-41d1-8534-3463f5a5276f | learning-high-level-representations-from | 1802.06604 | null | http://arxiv.org/abs/1802.06604v3 | http://arxiv.org/pdf/1802.06604v3.pdf | Learning High-level Representations from Demonstrations | Hierarchical learning (HL) is key to solving complex sequential decision
problems with long horizons and sparse rewards. It allows learning agents to
break-up large problems into smaller, more manageable subtasks. A common
approach to HL, is to provide the agent with a number of high-level skills that
solve small parts of the overall problem. A major open question, however, is
how to identify a suitable set of reusable skills. We propose a principled
approach that uses human demonstrations to infer a set of subgoals based on
changes in the demonstration dynamics. Using these subgoals, we decompose the
learning problem into an abstract high-level representation and a set of
low-level subtasks. The abstract description captures the overall problem
structure, while subtasks capture desired skills. We demonstrate that we can
jointly optimize over both levels of learning. We show that the resulting
method significantly outperforms previous baselines on two challenging
problems: the Atari 2600 game Montezuma's Revenge, and a simulated robotics
problem moving the ant robot through a maze. | ['Haitham Bou-Ammar', 'Peter Vrancx', 'Garrett Andersen'] | 2018-02-19 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [ 1.21382743e-01 3.67947102e-01 -1.16691880e-01 -1.45458981e-01
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de160972-17d7-40d9-b373-49e0bc28a0c1 | growing-a-brain-fine-tuning-by-increasing-1 | 1907.07844 | null | https://arxiv.org/abs/1907.07844v1 | https://arxiv.org/pdf/1907.07844v1.pdf | Growing a Brain: Fine-Tuning by Increasing Model Capacity | CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. In this work, we analyze what components and parameters change during fine-tuning, and discover that increasing model capacity allows for more natural model adaptation through fine-tuning. By making an analogy to developmental learning, we demonstrate that "growing" a CNN with additional units, either by widening existing layers or deepening the overall network, significantly outperforms classic fine-tuning approaches. But in order to properly grow a network, we show that newly-added units must be appropriately normalized to allow for a pace of learning that is consistent with existing units. We empirically validate our approach on several benchmark datasets, producing state-of-the-art results. | ['Yu-Xiong Wang', 'Deva Ramanan', 'Martial Hebert'] | 2019-07-18 | growing-a-brain-fine-tuning-by-increasing | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Growing_a_Brain_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Growing_a_Brain_CVPR_2017_paper.pdf | cvpr-2017-7 | ['developmental-learning'] | ['robots'] | [ 4.26286101e-01 1.57174230e-01 -8.27311426e-02 -5.72513521e-01
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23f8057e-b4b7-4f6b-bc35-aa83ac407dc7 | tackling-interpretability-in-audio | 2305.07132 | null | https://arxiv.org/abs/2305.07132v1 | https://arxiv.org/pdf/2305.07132v1.pdf | Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization | This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high performance. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, an interpreter is trained to generate a regularized intermediate embedding from hidden layers of a target network, learnt as time-activations of a pre-learnt NMF dictionary. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on a variety of classification tasks, including multi-label data for real-world audio and music. | ["Florence d'Alché-Buc", 'Gaël Richard', 'Pavlo Mozharovskyi', 'Sanjeel Parekh', 'Jayneel Parekh'] | 2023-05-11 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 9.86026347e-01 7.58157313e-01 1.34568617e-01 -7.99488962e-01
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26c9b03a-835c-4fd7-a0df-75f0a0dc088c | msctd-a-multimodal-sentiment-chat-translation | 2202.13645 | null | https://arxiv.org/abs/2202.13645v1 | https://arxiv.org/pdf/2202.13645v1.pdf | MSCTD: A Multimodal Sentiment Chat Translation Dataset | Multimodal machine translation and textual chat translation have received considerable attention in recent years. Although the conversation in its natural form is usually multimodal, there still lacks work on multimodal machine translation in conversations. In this work, we introduce a new task named Multimodal Chat Translation (MCT), aiming to generate more accurate translations with the help of the associated dialogue history and visual context. To this end, we firstly construct a Multimodal Sentiment Chat Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in 3,079 bilingual dialogues. Each utterance pair, corresponding to the visual context that reflects the current conversational scene, is annotated with a sentiment label. Then, we benchmark the task by establishing multiple baseline systems that incorporate multimodal and sentiment features for MCT. Preliminary experiments on four language directions (English-Chinese and English-German) verify the potential of contextual and multimodal information fusion and the positive impact of sentiment on the MCT task. Additionally, as a by-product of the MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment analysis. Our work can facilitate research on both multimodal chat translation and multimodal dialogue sentiment analysis. | ['Jie zhou', 'Yufeng Chen', 'Jinan Xu', 'Fandong Meng', 'Yunlong Liang'] | 2022-02-28 | null | https://aclanthology.org/2022.acl-long.186 | https://aclanthology.org/2022.acl-long.186.pdf | acl-2022-5 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.00600147e-01 -2.09353477e-01 -5.40705621e-02 -4.88055676e-01
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9719c5cc-e11b-4702-aac6-04b8dbc0f9c3 | open-vocabulary-argument-role-prediction-for | 2211.01577 | null | https://arxiv.org/abs/2211.01577v1 | https://arxiv.org/pdf/2211.01577v1.pdf | Open-Vocabulary Argument Role Prediction for Event Extraction | The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction. The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new event extraction dataset from WikiPpedia including 142 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin. Source code and dataset are available on our GitHub repository: https://github.com/yzjiao/RolePred | ['Jiawei Han', 'Heng Ji', 'Ming Zhong', 'Yiqing Xie', 'Sha Li', 'Yizhu Jiao'] | 2022-11-03 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 4.99235123e-01 4.35589194e-01 -4.65801746e-01 -6.07053816e-01
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51ecb5a0-b51f-4ad2-82f6-4bcd5d33a75c | layoutxlm-multimodal-pre-training-for | 2104.08836 | null | https://arxiv.org/abs/2104.08836v3 | https://arxiv.org/pdf/2104.08836v3.pdf | LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding | Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also introduce a multilingual form understanding benchmark dataset named XFUND, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUND dataset. The pre-trained LayoutXLM model and the XFUND dataset are publicly available at https://aka.ms/layoutxlm. | ['Furu Wei', 'Cha Zhang', 'Dinei Florencio', 'Yijuan Lu', 'Guoxin Wang', 'Lei Cui', 'Tengchao Lv', 'Yiheng Xu'] | 2021-04-18 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 1.66389808e-01 -1.32967830e-01 -2.35903725e-01 -2.99314499e-01
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0eeea5e1-0e7e-4862-9153-8e874e5b5685 | hierarchical-gumbel-attention-network-for | null | null | https://www.researchgate.net/publication/346192190_Hierarchical_Gumbel_Attention_Network_for_Text-based_Person_Search | https://www.researchgate.net/publication/346192190_Hierarchical_Gumbel_Attention_Network_for_Text-based_Person_Search | Hierarchical Gumbel Attention Network for Text-based Person Search | Text-based person search aims to retrieve the pedestrian images that best match a given textual description from gallery images. Previous methods utilize the soft-attention mechanism to infer the semantic alignments between the regions of image and the corresponding words in sentence. However, these methods may fuse the irrelevant multi-modality features together which cause matching redundancy problem. In this work, we propose a novel hierarchical Gumbel attention network for text-based person search via Gumbel top-k re-parameterization algorithm. Specifically, it adaptively selects the strong semantically relevant image regions and words/phrases from images and texts for precise alignment and similarity calculation. This hard selection strategy is able to fuse the strong-relevant multi-modality features for alleviating the problem of matching redundancy. Meanwhile, a Gumbel top-k reparameterization algorithm is designed as a low-variance, unbiased gradient estimator to handle the discreteness problem of hard attention mechanism by an end-to-end manner. Moreover, a hierarchical adaptive matching strategy is employed by the model from three different granularities, i.e., word-level, phrase-level, and sentencelevel, towards fine-grained matching. Extensive experimental results demonstrate the state-of-the-art performance. Compared the existed best method, we achieve the 8.24% Rank-1 and 7.6% mAP relative improvements in the text-to-image retrieval task, and 5.58% Rank-1 and 6.3% mAP relative improvements in the image-to-text retrieval task on CUHK-PEDES dataset, respectively | ['Tao Mei', 'Zheng-Jun Zha', 'Jiawei Liu', 'Wu Liu', 'Kecheng Zheng'] | 2020-10-10 | null | null | null | null | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [-1.88520120e-03 -5.64171255e-01 -1.49176881e-01 -4.69431609e-01
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84530f40-16cd-446f-9672-e6ebc02d1645 | end-to-end-neural-ad-hoc-ranking-with-kernel | 1706.06613 | null | http://arxiv.org/abs/1706.06613v1 | http://arxiv.org/pdf/1706.06613v1.pdf | End-to-End Neural Ad-hoc Ranking with Kernel Pooling | This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank layer that combines those features into the final ranking
score. The whole model is trained end-to-end. The ranking layer learns desired
feature patterns from the pairwise ranking loss. The kernels transfer the
feature patterns into soft-match targets at each similarity level and enforce
them on the translation matrix. The word embeddings are tuned accordingly so
that they can produce the desired soft matches. Experiments on a commercial
search engine's query log demonstrate the improvements of K-NRM over prior
feature-based and neural-based states-of-the-art, and explain the source of
K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric
tailored for matching query words to document words, and provides effective
multi-level soft matches. | ['Jamie Callan', 'Zhuyun Dai', 'Chenyan Xiong', 'Zhiyuan Liu', 'Russell Power'] | 2017-06-20 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-8.59806836e-02 -3.42660397e-01 -9.80736613e-01 -6.69543684e-01
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bebec923-a1a3-4bab-9d29-912e14c46ca9 | 3d-magic-mirror-clothing-reconstruction-from | 2204.13096 | null | https://arxiv.org/abs/2204.13096v2 | https://arxiv.org/pdf/2204.13096v2.pdf | 3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective | This research aims to study a self-supervised 3D clothing reconstruction method, which recovers the geometry shape and texture of human clothing from a single image. Compared with existing methods, we observe that three primary challenges remain: (1) 3D ground-truth meshes of clothing are usually inaccessible due to annotation difficulties and time costs; (2) Conventional template-based methods are limited to modeling non-rigid objects, e.g., handbags and dresses, which are common in fashion images; (3) The inherent ambiguity compromises the model training, such as the dilemma between a large shape with a remote camera or a small shape with a close camera. In an attempt to address the above limitations, we propose a causality-aware self-supervised learning method to adaptively reconstruct 3D non-rigid objects from 2D images without 3D annotations. In particular, to solve the inherent ambiguity among four implicit variables, i.e., camera position, shape, texture, and illumination, we introduce an explainable structural causal map (SCM) to build our model. The proposed model structure follows the spirit of the causal map, which explicitly considers the prior template in the camera estimation and shape prediction. When optimization, the causality intervention tool, i.e., two expectation-maximization loops, is deeply embedded in our algorithm to (1) disentangle four encoders and (2) facilitate the prior template. Extensive experiments on two 2D fashion benchmarks (ATR and Market-HQ) show that the proposed method could yield high-fidelity 3D reconstruction. Furthermore, we also verify the scalability of the proposed method on a fine-grained bird dataset, i.e., CUB. The code is available at https://github.com/layumi/ 3D-Magic-Mirror . | ['Tat-Seng Chua', 'Yi Yang', 'Wei Ji', 'Jiayin Zhu', 'Zhedong Zheng'] | 2022-04-27 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 6.60178214e-02 1.02279022e-01 -1.82439566e-01 -2.44038343e-01
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e6f3f1da-2552-46c3-ba7e-2146c1a2dcb5 | towards-robust-monocular-visual-odometry-for | 2109.05509 | null | https://arxiv.org/abs/2109.05509v1 | https://arxiv.org/pdf/2109.05509v1.pdf | Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions | In the future, extraterrestrial expeditions will not only be conducted by rovers but also by flying robots. The technical demonstration drone Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by terrain traversability. Robust self-localization is crucial for that. Cameras that are lightweight, cheap and information-rich sensors are already used to estimate the ego-motion of vehicles. However, methods proven to work in man-made environments cannot simply be deployed on other planets. The highly repetitive textures present in the wastelands of Mars pose a huge challenge to descriptor matching based approaches. In this paper, we present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking to obtain feature correspondences between images and a refined keyframe selection criterion. In contrast to most other approaches, our framework can also handle rotation-only motions that are particularly challenging for monocular odometry systems. Furthermore, we present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix. This way we obtain an implicit measure of uncertainty. We evaluate the validity of our approach on all sequences of a challenging real-world dataset captured in a Mars-like environment and show that it outperforms state-of-the-art approaches. | ['Wolfgang Stürzl', 'Daniel Cremers', 'Rudolph Triebel', 'Armin Wedler', 'Nikolaus Demmel', 'Marcus G. Müller', 'Martin Wudenka'] | 2021-09-12 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-6.50191233e-02 -2.58920938e-01 -1.20906380e-03 -1.96684167e-01
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117a0435-64cd-432d-ae41-b4e319373a0e | mixing-context-granularities-for-improved | 1804.08460 | null | http://arxiv.org/abs/1804.08460v1 | http://arxiv.org/pdf/1804.08460v1.pdf | Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories | The first stage of every knowledge base question answering approach is to
link entities in the input question. We investigate entity linking in the
context of a question answering task and present a jointly optimized neural
architecture for entity mention detection and entity disambiguation that models
the surrounding context on different levels of granularity. We use the Wikidata
knowledge base and available question answering datasets to create benchmarks
for entity linking on question answering data. Our approach outperforms the
previous state-of-the-art system on this data, resulting in an average 8%
improvement of the final score. We further demonstrate that our model delivers
a strong performance across different entity categories. | ['Iryna Gurevych', 'Daniil Sorokin'] | 2018-04-23 | mixing-context-granularities-for-improved-1 | https://aclanthology.org/S18-2007 | https://aclanthology.org/S18-2007.pdf | semeval-2018-6 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.74905556e-01 7.21634448e-01 -1.96035117e-01 -2.95907855e-01
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788919c0-8b6b-4640-9231-2f80aaabe573 | o-gnn-incorporating-ring-priors-into | null | null | https://openreview.net/forum?id=5cFfz6yMVPU | https://openreview.net/pdf?id=5cFfz6yMVPU | O-GNN: Incorporating Ring Priors into Molecular Modeling | Cyclic compounds that contain at least one ring play an important role in drug design. Despite the recent success of molecular modeling with graph neural networks (GNNs), few models explicitly take rings in compounds into consideration, consequently limiting the expressiveness of the models. In this work, we design a new variant of GNN, ring-enhanced GNN (O-GNN), that explicitly models rings in addition to atoms and bonds in compounds. In O-GNN, each ring is represented by a latent vector, which contributes to and is iteratively updated by atom and bond representations. Theoretical analysis shows that O-GNN is able to distinguish two isomorphic subgraphs lying on different rings using only one layer while conventional graph convolutional neural networks require multiple layers to distinguish, demonstrating that O-GNN is more expressive. Through experiments, O-GNN shows good performance on public datasets. In particular, it achieves state-of-the-art validation result on the PCQM4Mv1 benchmark (outperforming the previous KDDCup champion solution) and the drug-drug interaction prediction task on DrugBank. Furthermore, O-GNN outperforms strong baselines (without modeling rings) on the molecular property prediction and retrosynthesis prediction tasks. | ['Tie-Yan Liu', 'Houqiang Li', 'Wengang Zhou', 'Tao Qin', 'Lijun Wu', 'Qi Meng', 'Shufang Xie', 'Yingce Xia', 'Bohan Wang', 'Kehan Wu', 'Jinhua Zhu'] | 2023-05-01 | null | null | null | iclr-2023-5 | ['graph-regression', 'retrosynthesis', 'property-prediction', 'molecular-property-prediction'] | ['graphs', 'medical', 'medical', 'miscellaneous'] | [ 3.84761482e-01 3.07995975e-01 -7.98708200e-01 5.60664684e-02
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de0a735c-4fa4-4ede-a73e-c2d9834747ed | weakly-supervised-rotation-invariant-aerial | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.pdf | Weakly Supervised Rotation-Invariant Aerial Object Detection Network | Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images. Existing predominant WSOD approaches built on regular CNNs which are not inherently designed to tackle object rotations without corresponding constraints, thereby leading to rotation-sensitive object detector. Meanwhile, current solutions have been prone to fall into the issue with unstable detectors, as they ignore lower-scored instances and may regard them as backgrounds. To address these issues, in this paper, we construct a novel end-to-end weakly supervised Rotation-Invariant aerial object detection Network (RINet). It is implemented with a flexible multi-branch online detector refinement, to be naturally more rotation-perceptive against oriented objects. Specifically, RINet first performs label propagating from the predicted instances to their rotated ones in a progressive refinement manner. Meanwhile, we propose to couple the predicted instance labels among different rotation-perceptive branches for generating rotation-consistent supervision and meanwhile pursuing all possible instances. With the rotation-consistent supervisions, RINet enforces and encourages consistent yet complementary feature learning for WSOD without additional annotations and hyper-parameters. On the challenging NWPU VHR-10.v2 and DIOR datasets, extensive experiments clearly demonstrate that we significantly boost existing WSOD methods to a new state-of-the-art performance. The code will be available at: https://github.com/XiaoxFeng/RINet. | ['Junwei Han', 'Gong Cheng', 'Xiwen Yao', 'Xiaoxu Feng'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 4.09212887e-01 1.15032047e-01 -3.56285363e-01 -2.13013560e-01
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dae22820-88e3-4b2b-90a5-c181656889d6 | img2pose-face-alignment-and-detection-via | 2012.07791 | null | https://arxiv.org/abs/2012.07791v2 | https://arxiv.org/pdf/2012.07791v2.pdf | img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation | We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without face detection or landmark localization. We observe that estimating the 6DoF rigid transformation of a face is a simpler problem than facial landmark detection, often used for 3D face alignment. In addition, 6DoF offers more information than face bounding box labels. We leverage these observations to make multiple contributions: (a) We describe an easily trained, efficient, Faster R-CNN--based model which regresses 6DoF pose for all faces in the photo, without preliminary face detection. (b) We explain how pose is converted and kept consistent between the input photo and arbitrary crops created while training and evaluating our model. (c) Finally, we show how face poses can replace detection bounding box training labels. Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators. Remarkably, our method also surpasses SotA models of comparable complexity on the WIDER FACE detection benchmark, despite not been optimized on bounding box labels. | ['Vítor Albiero', 'Tal Hassner', 'Guan Pang', 'Xi Yin', 'Xingyu Chen'] | 2020-12-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['head-pose-estimation', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-1.67730346e-01 2.21759398e-02 -1.79212123e-01 -5.10899723e-01
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1c1d2f41-4e57-4523-ab51-674a83e4fa3c | text-free-non-parallel-many-to-many-voice | 2203.08009 | null | https://arxiv.org/abs/2203.08009v1 | https://arxiv.org/pdf/2203.08009v1.pdf | Text-free non-parallel many-to-many voice conversion using normalising flows | Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all other information from the source speech is retained is a large challenge. This is particularly challenging in the scenario where at inference-time we have no knowledge of the text being read, i.e., text-free VC. To mitigate this, we investigate information-preserving VC approaches. Normalising flows have gained attention for text-to-speech synthesis, however have been under-explored for VC. Flows utilize invertible functions to learn the likelihood of the data, thus provide a lossless encoding of speech. We investigate normalising flows for VC in both text-conditioned and text-free scenarios. Furthermore, for text-free VC we compare pre-trained and jointly-learnt priors. Flow-based VC evaluations show no degradation between text-free and text-conditioned VC, resulting in improvements over the state-of-the-art. Also, joint-training of the prior is found to negatively impact text-free VC quality. | ['Daniel Korzekwa', 'Roberto Barra-Chicote', 'Kamil Pokora', 'Magdalena Proszewska', 'Piotr Biliński', 'Abdelhamid Ezzerg', 'Thomas Merritt'] | 2022-03-15 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 4.50455815e-01 1.83746263e-01 -9.60951764e-03 -7.82458037e-02
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01dd5f49-9e4a-41a3-b75d-90ac4196ee50 | natural-image-matting-via-guided-contextual | 2001.04069 | null | https://arxiv.org/abs/2001.04069v1 | https://arxiv.org/pdf/2001.04069v1.pdf | Natural Image Matting via Guided Contextual Attention | Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha estimations, but typically yield blurry structures or textures in the semitransparent area. This is due to the local ambiguity of transparent objects. One possible solution is to leverage the far-surrounding information to estimate the local opacity. Traditional affinity-based methods often suffer from the high computational complexity, which are not suitable for high resolution alpha estimation. Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting. Guided contextual attention module directly propagates high-level opacity information globally based on the learned low-level affinity. The proposed method can mimic information flow of affinity-based methods and utilize rich features learned by deep neural networks simultaneously. Experiment results on Composition-1k testing set and alphamatting.com benchmark dataset demonstrate that our method outperforms state-of-the-art approaches in natural image matting. Code and models are available at https://github.com/Yaoyi-Li/GCA-Matting. | ['Hongtao Lu', 'Yaoyi Li'] | 2020-01-13 | null | null | null | null | ['transparent-objects', 'semantic-image-matting'] | ['computer-vision', 'computer-vision'] | [ 9.94437113e-02 -8.65649059e-02 1.37776256e-01 -1.20012097e-01
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26885a5b-b66d-466b-8df0-e33fbf7c6e47 | vlpd-context-aware-pedestrian-detection-via | 2304.03135 | null | https://arxiv.org/abs/2304.03135v1 | https://arxiv.org/pdf/2304.03135v1.pdf | VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision | Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via Vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised Vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://github.com/lmy98129/VLPD. | ['Xu-Cheng Yin', 'Chao Zhu', 'Jie Jiang', 'Mengyin Liu'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_VLPD_Context-Aware_Pedestrian_Detection_via_Vision-Language_Semantic_Self-Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pedestrian-detection'] | ['computer-vision'] | [ 6.93963096e-02 -4.16716635e-01 -2.59894371e-01 -7.00781703e-01
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dd5a75f3-cf1d-4dc3-88ab-238c735e024c | towards-annotating-and-creating-sub-sentence | 1910.07659 | null | https://arxiv.org/abs/1910.07659v1 | https://arxiv.org/pdf/1910.07659v1.pdf | Towards Annotating and Creating Sub-Sentence Summary Highlights | Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level. | ['Fei Liu', 'Parminder Bhatia', 'Kristjan Arumae'] | 2019-10-17 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 5.50798237e-01 2.88215131e-01 -3.71774912e-01 -2.95007050e-01
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d4b2db6b-0b88-4d5a-a759-2b2b2ede1d7f | modeling-unknown-semantic-labels-as | null | null | https://openreview.net/forum?id=-BBL3b4Tqfo | https://openreview.net/pdf?id=-BBL3b4Tqfo | Modeling Unknown Semantic Labels as Uncertainty in the Prediction: Evidential Deep Learning for Class-Incremental Semantic Segmentation | Class-Incremental Learning is an essential component for expanding the knowledge of previously trained neural networks.
This is especially useful if the system needs to be able to handle new objects but the original training data is unavailable.
While the semantic segmentation problem has received less attention than classification, it is faced with its own set of challenges in terms of unlabeled classes in the images.
In this paper we address the problem of how to model unlabeled classes to avoid unnecessary feature clustering of uncorrelated classes.
We propose to use Evidential Deep Learning to model the evidence of the classes with a Dirichlet distribution.
Our method factorizes the problem into a separate foreground class probability, calculated by the expected value of the Dirichlet distribution, and an unknown class probability corresponding to the uncertainty of the estimate.
In our novel formulation the background probability is implicitly modelled, avoiding the feature space clustering that comes from forcing the model to output a high background score for these pixels.
Experiments on the incremental Pascal VOC and ADE20k show that our method is superior to state-of-the-art methods, especially when repeatedly learning new classes. | ['Lena Klasen', 'Michael Felsberg', 'Karl Holmquist'] | 2021-09-29 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 5.86715460e-01 3.56240273e-01 -5.95233962e-03 -5.98011851e-01
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9b6bf4a0-f2ba-4406-a64f-19053d1d2b13 | snapture-a-novel-neural-architecture-for | 2205.15862 | null | https://arxiv.org/abs/2205.15862v1 | https://arxiv.org/pdf/2205.15862v1.pdf | Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition | As robots are expected to get more involved in people's everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communication and, thus, are an integral part of seamless Human-Robot Interaction (HRI). Recent years have witnessed an immense evolution of computational models powered by deep learning. However, state-of-the-art models fall short in expanding across different gesture domains, such as emblems and co-speech. In this paper, we propose a novel hybrid hand gesture recognition system. Our architecture enables learning both static and dynamic gestures: by capturing a so-called "snapshot" of the gesture performance at its peak, we integrate the hand pose along with the dynamic movement. Moreover, we present a method for analyzing the motion profile of a gesture to uncover its dynamic characteristics and which allows regulating a static channel based on the amount of motion. Our evaluation demonstrates the superiority of our approach on two gesture benchmarks compared to a CNNLSTM baseline. We also provide an analysis on a gesture class basis that unveils the potential of our Snapture architecture for performance improvements. Thanks to its modular implementation, our framework allows the integration of other multimodal data like facial expressions and head tracking, which are important cues in HRI scenarios, into one architecture. Thus, our work contributes both to gesture recognition research and machine learning applications for non-verbal communication with robots. | ['Stefan Wermter', 'Doreen Jirak', 'Hassan Ali'] | 2022-05-28 | null | null | null | null | ['hand-gesture-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [-1.12236209e-01 -2.58770347e-01 -4.11743492e-01 -3.96697700e-01
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76a67f11-f28d-4cf1-a330-c340989491ae | colonmapper-topological-mapping-and | 2305.05546 | null | https://arxiv.org/abs/2305.05546v1 | https://arxiv.org/pdf/2305.05546v1.pdf | ColonMapper: topological mapping and localization for colonoscopy | Mapping and localization in endoluminal cavities from colonoscopies or gastroscopies has to overcome the challenge of significant shape and illumination changes between reobservations of the same endoluminal location. Instead of geometrical maps that strongly rely on a fixed scene geometry, topological maps are more adequate because they focus on visual place recognition, i.e. the capability to determine if two video shots are imaging the same location. We propose a topological mapping and localization system able to operate on real human colonoscopies. The map is a graph where each node codes a colon location by a set of real images of that location. The edges represent traversability between two nodes. For close-in-time images, where scene changes are minor, place recognition can be successfully managed with the recent transformers-based image-matching algorithms. However, under long-term changes --such as different colonoscopies of the same patient-- feature-based matching fails. To address this, we propose a GeM global descriptor able to achieve high recall with significant changes in the scene. The addition of a Bayesian filter processing the map graph boosts the accuracy of the long-term place recognition, enabling relocalization in a previously built map. In the experiments, we construct a map during the withdrawal phase of a first colonoscopy. Subsequently, we prove the ability to relocalize within this map during a second colonoscopy of the same patient two weeks later. Code and models will be available upon acceptance. | ['J. M. M. Montiel', 'Juan D. Tardós', 'Javier Morlana'] | 2023-05-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.11502074e-01 5.87370172e-02 1.14233270e-01 -2.34909922e-01
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60dc78bf-79e4-49d1-a806-de863291720d | move-unsupervised-movable-object-segmentation | 2210.07920 | null | https://arxiv.org/abs/2210.07920v2 | https://arxiv.org/pdf/2210.07920v2.pdf | MOVE: Unsupervised Movable Object Segmentation and Detection | We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initial position and result in realistic (undistorted) new images. This property allows us to train a segmentation model on a dataset of images without annotation and to achieve state of the art (SotA) performance on several evaluation datasets for unsupervised salient object detection and segmentation. In unsupervised single object discovery, MOVE gives an average CorLoc improvement of 7.2% over the SotA, and in unsupervised class-agnostic object detection it gives a relative AP improvement of 53% on average. Our approach is built on top of self-supervised features (e.g. from DINO or MAE), an inpainting network (based on the Masked AutoEncoder) and adversarial training. | ['Paolo Favaro', 'Adam Bielski'] | 2022-10-14 | null | null | null | null | ['single-object-discovery', 'object-discovery', 'class-agnostic-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.07694352e-01 7.39077032e-01 -6.95903897e-02 -1.77367955e-01
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a325f861-d3cf-4c1c-99db-9f9c2d6d7f8a | abstractive-text-summarization-enhancing | null | null | https://aclanthology.org/2021.cl-4.27 | https://aclanthology.org/2021.cl-4.27.pdf | Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization | Abstract Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence-to-sequence neural-based text summarization along with structure and semantic-based methodologies. The proposed framework is capable of dealing with the problem of out-of-vocabulary or rare words, improving the performance of the deep learning models. The overall methodology is based on a well-defined theoretical model of knowledge-based content generalization and deep learning predictions for generating abstractive summaries. The framework is composed of three key elements: (i) a pre-processing task, (ii) a machine learning methodology, and (iii) a post-processing task. The pre-processing task is a knowledge-based approach, based on ontological knowledge resources, word sense disambiguation, and named entity recognition, along with content generalization, that transforms ordinary text into a generalized form. A deep learning model of attentive encoder-decoder architecture, which is expanded to enable a coping and coverage mechanism, as well as reinforcement learning and transformer-based architectures, is trained on a generalized version of text-summary pairs, learning to predict summaries in a generalized form. The post-processing task utilizes knowledge resources, word embeddings, word sense disambiguation, and heuristic algorithms based on text similarity methods in order to transform the generalized version of a predicted summary to a final, human-readable form. An extensive experimental procedure on three popular data sets evaluates key aspects of the proposed framework, while the obtained results exhibit promising performance, validating the robustness of the proposed approach. | ['Andreas Stafylopatis', 'Georgios Alexandridis', 'Panagiotis Kouris'] | null | null | null | null | cl-acl-2021-12 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 7.17639208e-01 3.64683688e-01 -8.12933370e-02 -1.13235183e-01
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8e629c78-1bc5-4b5f-bf54-7b92ef61dbaf | on-decoding-strategies-for-neural-text | 2203.15721 | null | https://arxiv.org/abs/2203.15721v1 | https://arxiv.org/pdf/2203.15721v1.pdf | On Decoding Strategies for Neural Text Generators | When generating text from probabilistic models, the chosen decoding strategy has a profound effect on the resulting text. Yet the properties elicited by various decoding strategies do not always transfer across natural language generation tasks. For example, while mode-seeking methods like beam search perform remarkably well for machine translation, they have been observed to lead to incoherent and repetitive text in story generation. Despite such observations, the effectiveness of decoding strategies is often assessed with respect to only a single task. This work -- in contrast -- provides a comprehensive analysis of the interaction between language generation tasks and decoding strategies. Specifically, we measure changes in attributes of generated text as a function of both decoding strategy and task using human and automatic evaluation. Our results reveal both previously-observed and surprising findings. For example, the nature of the diversity-quality trade-off in language generation is very task-specific; the length bias often attributed to beam search is not constant across tasks. | ['Ryan Cotterell', 'Clara Meister', 'Gian Wiher'] | 2022-03-29 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 5.66507339e-01 1.05282463e-01 -4.32132855e-02 -1.91256627e-01
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14d00ec5-7cbe-49f9-a9ca-cf2e3f701533 | leveraging-weak-complementary-labels-to | 2302.01813 | null | https://arxiv.org/abs/2302.01813v1 | https://arxiv.org/pdf/2302.01813v1.pdf | Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides | In this paper, we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementary labels that indicate to which class a sample cannot belong to. To integrate these labels, we formulate a complementary loss for segmentation. Motivated by the medical application, we demonstrate for general segmentation tasks that including additional patches with solely weak complementary labels during model training can significantly improve the predictive performance and robustness of a model. On the task of diagnostic differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma, we achieve a balanced accuracy of 0.91 (CI 95%: 0.86 - 0.95) at case level for 165 hold-out patients. Furthermore, we also show that leveraging complementary labels improves the robustness of segmentation and increases performance at case level. | ['Frederick Klauschen', 'Frank Tacke', 'Christoph Roderburg', 'Adrien Guillot', 'Simon Schallenberg', 'Maximilian Alber', 'Lukas Ruff', 'Johannes Eschrich', 'Miriam Hägele'] | 2023-02-03 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [-1.30224451e-02 1.89405173e-01 -4.14310962e-01 -4.79871839e-01
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e2cde3a5-5e65-4ddd-8906-ae9edf7d889e | masked-autoencoders-are-scalable-vision | 2111.06377 | null | https://arxiv.org/abs/2111.06377v2 | https://arxiv.org/pdf/2111.06377v2.pdf | Masked Autoencoders Are Scalable Vision Learners | This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pre-training and shows promising scaling behavior. | ['Ross Girshick', 'Piotr Dollár', 'Yanghao Li', 'Saining Xie', 'Xinlei Chen', 'Kaiming He'] | 2021-11-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.41584116e-01 6.84333980e-01 -3.48616898e-01 -3.46619338e-01
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bca04ff6-183c-4433-afc6-8ec2b6ef3a64 | biomarker-clustering-of-colorectal-cancer | 1307.1601 | null | http://arxiv.org/abs/1307.1601v1 | http://arxiv.org/pdf/1307.1601v1.pdf | Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification | In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. Attempts are made
to cluster this dataset and important subsets of it in an effort to
characterize the data and validate existing standards for tumour
classification. It is apparent from optimal clustering that existing tumour
classification is largely unrelated to immunological factors within a patient
and that there may be scope for re-evaluating treatment options and survival
estimates based on a combination of tumour physiology and patient
histochemistry. | ['Chris Roadknight', 'John Scholefield', 'Daniele Soria', 'Uwe Aickelin', 'Alex Ladas', 'Lindy Durrant'] | 2013-07-05 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 2.28339568e-01 -2.23887384e-01 -4.90780294e-01 -1.00617066e-01
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48711674-cfc2-401c-8785-4dd753a5101b | evaluation-and-generation-of-physical | 2203.04623 | null | https://arxiv.org/abs/2203.04623v2 | https://arxiv.org/pdf/2203.04623v2.pdf | Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition | Recent studies have revealed the vulnerability of face recognition models against physical adversarial patches, which raises security concerns about the deployed face recognition systems. However, it is still challenging to ensure the reproducibility for most attack algorithms under complex physical conditions, which leads to the lack of a systematic evaluation of the existing methods. It is therefore imperative to develop a framework that can enable a comprehensive evaluation of the vulnerability of face recognition in the physical world. To this end, we propose to simulate the complex transformations of faces in the physical world via 3D-face modeling, which serves as a digital counterpart of physical faces. The generic framework allows us to control different face variations and physical conditions to conduct reproducible evaluations comprehensively. With this digital simulator, we further propose a Face3DAdv method considering the 3D face transformations and realistic physical variations. Extensive experiments validate that Face3DAdv can significantly improve the effectiveness of diverse physically realizable adversarial patches in both simulated and physical environments, against various white-box and black-box face recognition models. | ['Jun Zhu', 'Hang Su', 'Zihao Xiao', 'Tianyu Pang', 'Yinpeng Dong', 'Xiao Yang'] | 2022-03-09 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [-5.17537370e-02 -3.24168593e-01 3.94541234e-01 3.28068510e-02
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bb82d35f-36eb-42ab-978a-6ce2264c0435 | deepfgs-fine-grained-scalable-coding-for | 2201.01173 | null | https://arxiv.org/abs/2201.01173v1 | https://arxiv.org/pdf/2201.01173v1.pdf | DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression | Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, the existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. In this paper, we propose the first learned fine-grained scalable image compression model (DeepFGS) to overcome the above two shortcomings. Specifically, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. In this way, we can generate a continuously scalable bitstream via one-pass encoding. In addition, we reuse the decoder to reduce the parameters and computational complexity of DeepFGS. Experiments demonstrate that our DeepFGS outperforms all learning-based scalable image compression models and conventional scalable image codecs in PSNR and MS-SSIM metrics. To the best of our knowledge, our DeepFGS is the first exploration of learned fine-grained scalable coding, which achieves the finest scalability compared with learning-based methods. | ['Ronggang Wang', 'Yongqi Zhai', 'Yi Ma'] | 2022-01-04 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.48882943e-01 -5.26641309e-01 -6.42182052e-01 -3.40511024e-01
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a23fb3d1-b861-49ea-ab09-f1ace34c4375 | wiris-transformer-for-ris-assisted-device | 2304.06475 | null | https://arxiv.org/abs/2304.06475v2 | https://arxiv.org/pdf/2304.06475v2.pdf | WiRiS: Transformer for RIS-Assisted Device-Free Sensing for Joint People Counting and Localization using Wi-Fi CSI | Channel State Information (CSI) is widely adopted as a feature for indoor localization. Taking advantage of the abundant information from the CSI, people can be accurately sensed even without equipped devices. However, the positioning error increases severely in non-line-of-sight (NLoS) regions. Reconfigurable intelligent surface (RIS) has been introduced to improve signal coverage in NLoS areas, which can re-direct and enhance reflective signals with massive meta-material elements. In this paper, we have proposed a Transformer-based RIS-assisted device-free sensing for joint people counting and localization (WiRiS) system to precisely predict the number of people and their corresponding locations through configuring RIS. A series of predefined RIS beams is employed to create inputs of fingerprinting CSI features as sequence-to-sequence learning database for Transformer. We have evaluated the performance of proposed WiRiS system in both ray-tracing simulators and experiments. Both simulation and real-world experiments demonstrate that people counting accuracy exceeds 90%, and the localization error can achieve the centimeter-level, which outperforms the existing benchmarks without employment of RIS. | ['Sheng-Fuh Chang', 'Shih-Cheng Lin', 'Yuan-Chun Lin', 'Kai-Ten Feng', 'Li-Hsiang Shen', 'Wei-Yu Chung'] | 2023-03-25 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 2.82918721e-01 -4.80523020e-01 3.23329329e-01 -1.74641415e-01
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6ee133fb-6b89-435d-8c9f-cf0d33c3d0ba | zero-shot-stance-detection-based-on-cross | 2210.03380 | null | https://arxiv.org/abs/2210.03380v1 | https://arxiv.org/pdf/2210.03380v1.pdf | Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning | Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-shot stance detection. In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge. Specifically, we first augment the data by masking the topic words of sentences, and then feed the augmented data to an unsupervised contrastive learning module to capture transferable features. Then, to fit a specific target, we encode the raw texts as target-specific features. Finally, we adopt an attention mechanism, which combines syntactic expression patterns with target-specific features to obtain enhanced features for predicting previously unseen targets. Experiments demonstrate that our model outperforms competitive baselines on four benchmark datasets. | ['Lei Tian', 'Bin Zhou', 'Feng Xie', 'Zhong Zhang', 'Jiaying Zou', 'Xuechen Zhao'] | 2022-10-07 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 5.38180232e-01 4.76380885e-02 -6.13372564e-01 -6.97022796e-01
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b358951f-0585-48b6-b533-f1de65834115 | generalizable-metric-network-for-cross-domain | 2306.11991 | null | https://arxiv.org/abs/2306.11991v1 | https://arxiv.org/pdf/2306.11991v1.pdf | Generalizable Metric Network for Cross-domain Person Re-identification | Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackle this challenge, most existing methods aim to learn domain-invariant or robust features for all domains. In this paper, we observe that the data-distribution gap between the training and test sets is smaller in the sample-pair space than in the sample-instance space. Based on this observation, we propose a Generalizable Metric Network (GMN) to further explore sample similarity in the sample-pair space. Specifically, we add a Metric Network (M-Net) after the main network and train it on positive and negative sample-pair features, which is then employed during the test stage. Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity. Moreover, we develop a Pair-Identity Center (PIC) loss to enhance the model's discrimination by ensuring that sample-pair features with the same pair-identity are consistent. We validate the effectiveness of our proposed method through a lot of experiments on multiple benchmark datasets and confirm the value of each module in our GMN. | ['Xin Geng', 'Yinghuan Shi', 'Ziang Liu', 'Lei Qi'] | 2023-06-21 | null | null | null | null | ['person-re-identification', 'domain-generalization'] | ['computer-vision', 'methodology'] | [ 1.82651907e-01 -3.63857001e-01 -1.05423607e-01 -5.50395727e-01
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42ff7f88-b333-440d-9101-47fe8844a1a4 | leveraging-skill-to-skill-supervision-for | 2306.06841 | null | https://arxiv.org/abs/2306.06841v1 | https://arxiv.org/pdf/2306.06841v1.pdf | Leveraging Skill-to-Skill Supervision for Knowledge Tracing | Knowledge tracing plays a pivotal role in intelligent tutoring systems. This task aims to predict the probability of students answering correctly to specific questions. To do so, knowledge tracing systems should trace the knowledge state of the students by utilizing their problem-solving history and knowledge about the problems. Recent advances in knowledge tracing models have enabled better exploitation of problem solving history. However, knowledge about problems has not been studied, as well compared to students' answering histories. Knowledge tracing algorithms that incorporate knowledge directly are important to settings with limited data or cold starts. Therefore, we consider the problem of utilizing skill-to-skill relation to knowledge tracing. In this work, we introduce expert labeled skill-to-skill relationships. Moreover, we also provide novel methods to construct a knowledge-tracing model to leverage human experts' insight regarding relationships between skills. The results of an extensive experimental analysis show that our method outperformed a baseline Transformer model. Furthermore, we found that the extent of our model's superiority was greater in situations with limited data, which allows a smooth cold start of our model. | ['Kyungwoo Song', 'Yun Jegal', 'Minjae Lee', 'Jinwoo Nam', 'Hyeondey Kim'] | 2023-06-12 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [ 4.46766764e-02 2.85494268e-01 -4.73812312e-01 -3.65043014e-01
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35e57009-cfc3-4f93-8f0f-e49978f2b6f0 | novel-features-for-time-series-analysis-a | 2110.09888 | null | https://arxiv.org/abs/2110.09888v3 | https://arxiv.org/pdf/2110.09888v3.pdf | Novel Features for Time Series Analysis: A Complex Networks Approach | Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature. | ['Fernando Silva', 'Pedro Ribeiro', 'Maria Eduarda Silva', 'Vanessa Freitas Silva'] | 2021-10-11 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-1.56268626e-02 -3.39796305e-01 -6.33058995e-02 -2.36260921e-01
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a0cf13b5-d92a-4a93-bb7a-8dc6833deced | investigating-correlations-of-inter-coder | 1907.10450 | null | https://arxiv.org/abs/1907.10450v1 | https://arxiv.org/pdf/1907.10450v1.pdf | Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data | Video indexing approaches such as visual concept classification and person recognition are essential to enable fine-grained semantic search in large-scale video archives such as the historical video collection of former German Democratic Republic (GDR) maintained by the German Broadcasting Archive (DRA). Typically, a lexicon of visual concepts has to be defined for semantic search. However, the definition of visual concepts can be more or less subjective due to individually differing judgments of annotators, which may have an impact on annotation quality and subsequently training of supervised machine learning methods. In this paper, we analyze the inter-coder agreement for historical TV data of the former GDR for visual concept classification and person recognition. The inter-coder agreement is evaluated for a group of expert as well as non-expert annotators in order to determine differences in annotation homogeneity. Furthermore, correlations between visual recognition performance and inter-annotator agreement are measured. In this context, information about image quantity and agreement are used to predict average precision for concept classification. Finally, the influence of expert vs. non-expert annotations acquired in the study are used to evaluate person recognition. | ['Angelika Hörth', 'Sabrina Bernhöft', 'Markus Mühling', 'Kader Pustu-Iren', 'Joanna Bars', 'Bernd Freisleben', 'Ralph Ewerth', 'Nikolaus Korfhage'] | 2019-07-24 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.99573506e-02 -2.38335729e-01 4.20739986e-02 -4.77376461e-01
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5c93308f-bd7c-44c2-8845-c61c3ce6d10d | end-to-end-neural-pipeline-for-goal-oriented | null | null | https://aclanthology.org/2020.acl-main.54 | https://aclanthology.org/2020.acl-main.54.pdf | End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 | The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to integrate with external systems or to provide interpretable information about why the system generated a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the human evaluation, our dialogue system achieved the success rate of 68.32{\%}, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8). | ['Kee-Eung Kim', 'Jeong-Gwan Lee', 'Donghoon Ham', 'Youngsoo Jang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-8.89953747e-02 6.56322360e-01 4.54426169e-01 -6.98255599e-01
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9c4ce0c3-abcf-4c5a-9cee-2611faefc1d0 | inter-instance-similarity-modeling-for | 2306.12243 | null | https://arxiv.org/abs/2306.12243v3 | https://arxiv.org/pdf/2306.12243v3.pdf | Inter-Instance Similarity Modeling for Contrastive Learning | The existing contrastive learning methods widely adopt one-hot instance discrimination as pretext task for self-supervised learning, which inevitably neglects rich inter-instance similarities among natural images, then leading to potential representation degeneration. In this paper, we propose a novel image mix method, PatchMix, for contrastive learning in Vision Transformer (ViT), to model inter-instance similarities among images. Following the nature of ViT, we randomly mix multiple images from mini-batch in patch level to construct mixed image patch sequences for ViT. Compared to the existing sample mix methods, our PatchMix can flexibly and efficiently mix more than two images and simulate more complicated similarity relations among natural images. In this manner, our contrastive framework can significantly reduce the gap between contrastive objective and ground truth in reality. Experimental results demonstrate that our proposed method significantly outperforms the previous state-of-the-art on both ImageNet-1K and CIFAR datasets, e.g., 3.0% linear accuracy improvement on ImageNet-1K and 8.7% kNN accuracy improvement on CIFAR100. Moreover, our method achieves the leading transfer performance on downstream tasks, object detection and instance segmentation on COCO dataset. The code is available at https://github.com/visresearch/patchmix | ['Jianxin Wang', 'Zhe Qu', 'Hao Tang', 'Dawei Liu', 'Chengchao Shen'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'instance-segmentation', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 3.02660763e-01 -1.68642789e-01 -2.38268510e-01 -2.77373403e-01
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f76f1f07-701b-46f7-bf09-1cea2a4f9345 | knowledge-transfer-for-dynamic-multi | 2306.10668 | null | https://arxiv.org/abs/2306.10668v1 | https://arxiv.org/pdf/2306.10668v1.pdf | Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives | Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMOPs with a changing number of objectives lacks sufficient diversity when the fitness landscape and Pareto front shape present nonseparability, deceptiveness or other challenging features. Therefore, we propose a knowledge transfer dynamic multi-objective evolutionary algorithm (KTDMOEA) to enhance population diversity after changes by expanding/contracting the Pareto set in response to an increase/decrease in the number of objectives. This enables a solution set with good convergence and diversity to be obtained after optimization. Comprehensive studies using 13 DMOP benchmarks with a changing number of objectives demonstrate that our proposed KTDMOEA is successful in enhancing population diversity compared to state-of-the-art algorithms, improving optimization especially in fast changing environments. | ['Xin Yao', 'Bernhard Sendhoff', 'Stefan Menzel', 'Leandro L. Minku', 'Gan Ruan'] | 2023-06-19 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 2.18932584e-01 -5.85588753e-01 2.31176481e-01 1.98741361e-01
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6a820d00-2049-4342-b1a7-71c389fb6d00 | beyondpixels-a-comprehensive-review-of-the | 2306.03000 | null | https://arxiv.org/abs/2306.03000v1 | https://arxiv.org/pdf/2306.03000v1.pdf | BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields | Neural rendering combines ideas from classical computer graphics and machine learning to synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is a recent innovation that uses AI algorithms to create 3D objects from 2D images. By leveraging an interpolation approach, NeRF can produce new 3D reconstructed views of complicated scenes. Rather than directly restoring the whole 3D scene geometry, NeRF generates a volumetric representation called a ``radiance field,'' which is capable of creating color and density for every point within the relevant 3D space. The broad appeal and notoriety of NeRF make it imperative to examine the existing research on the topic comprehensively. While previous surveys on 3D rendering have primarily focused on traditional computer vision-based or deep learning-based approaches, only a handful of them discuss the potential of NeRF. However, such surveys have predominantly focused on NeRF's early contributions and have not explored its full potential. NeRF is a relatively new technique continuously being investigated for its capabilities and limitations. This survey reviews recent advances in NeRF and categorizes them according to their architectural designs, especially in the field of novel view synthesis. | ['Chengcui Zhang', 'Akm Shahariar Azad Rabby'] | 2023-06-05 | null | null | null | null | ['neural-rendering', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 3.51629555e-01 -9.70442742e-02 1.71442434e-01 -9.04991627e-02
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adea5fbf-224f-4f57-94fe-c325830c545e | mitigating-adversarial-attacks-in-deepfake | 2302.11704 | null | https://arxiv.org/abs/2302.11704v1 | https://arxiv.org/pdf/2302.11704v1.pdf | Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniques | Deep learning is a crucial aspect of machine learning, but it also makes these techniques vulnerable to adversarial examples, which can be seen in a variety of applications. These examples can even be targeted at humans, leading to the creation of false media, such as deepfakes, which are often used to shape public opinion and damage the reputation of public figures. This article will explore the concept of adversarial examples, which are comprised of perturbations added to clean images or videos, and their ability to deceive DL algorithms. The proposed approach achieved a precision value of accuracy of 76.2% on the DFDC dataset. | ['David Ada Adama', 'Farhad Fassihi Tash', 'Isibor Kennedy Ihianle', 'Pedro Machado', 'Laura Fontes', 'Saminder Dhesi'] | 2023-02-22 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-8.64538848e-02 1.63120523e-01 3.17584664e-01 -6.47549704e-02
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01f7453a-68c9-485a-9a03-65d16667ff08 | dynamic-temporal-alignment-of-speech-to-lips | 1808.06250 | null | http://arxiv.org/abs/1808.06250v1 | http://arxiv.org/pdf/1808.06250v1.pdf | Dynamic Temporal Alignment of Speech to Lips | Many speech segments in movies are re-recorded in a studio during
postproduction, to compensate for poor sound quality as recorded on location.
Manual alignment of the newly-recorded speech with the original lip movements
is a tedious task. We present an audio-to-video alignment method for automating
speech to lips alignment, stretching and compressing the audio signal to match
the lip movements. This alignment is based on deep audio-visual features,
mapping the lips video and the speech signal to a shared representation. Using
this shared representation we compute the lip-sync error between every short
speech period and every video frame, followed by the determination of the
optimal corresponding frame for each short sound period over the entire video
clip. We demonstrate successful alignment both quantitatively, using a human
perception-inspired metric, as well as qualitatively. The strongest advantage
of our audio-to-video approach is in cases where the original voice in unclear,
and where a constant shift of the sound can not give a perfect alignment. In
these cases state-of-the-art methods will fail. | ['Shmuel Peleg', 'Ariel Ephrat', 'Tavi Halperin'] | 2018-08-19 | null | null | null | null | ['video-alignment', 'lip-sync-1'] | ['computer-vision', 'computer-vision'] | [ 3.80401224e-01 -1.39005840e-01 -2.56019160e-02 -1.75887540e-01
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d6204400-76e4-4670-962f-59fe78f6c947 | infobert-zero-shot-approach-to-natural | null | null | https://aclanthology.org/2021.ranlp-main.25 | https://aclanthology.org/2021.ranlp-main.25.pdf | InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding | Natural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an approach to zero-shot transfer learning for the tasks of intent classification and slot-filling based on pre-trained language models. We use deep contextualized models feeding them with utterances and natural language descriptions of user intents to get text embeddings. These embeddings then used by a small neural network to produce predictions for intent and slot probabilities. This architecture achieves new state-of-the-art results in two zero-shot scenarios. One is a single language new skill adaptation and another one is a cross-lingual adaptation. | ['Irina Piontkovskaya', 'Valentin Malykh', 'Andrey Bout', 'Pavel Burnyshev'] | null | null | https://aclanthology.org/2021.ranlp-1.25 | https://aclanthology.org/2021.ranlp-1.25.pdf | ranlp-2021-9 | ['slot-filling'] | ['natural-language-processing'] | [ 1.83306932e-01 7.36787379e-01 -2.67865986e-01 -7.13158131e-01
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328e2d10-df80-4c0a-97b4-0c3dab31876a | poda-prompt-driven-zero-shot-domain | 2212.03241 | null | https://arxiv.org/abs/2212.03241v2 | https://arxiv.org/pdf/2212.03241v2.pdf | PØDA: Prompt-driven Zero-shot Domain Adaptation | Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions. In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a single general textual description of the target domain, i.e., a prompt. First, we leverage a pretrained contrastive vision-language model (CLIP) to optimize affine transformations of source features, steering them towards target text embeddings, while preserving their content and semantics. Second, we show that augmented features can be used to perform zero-shot domain adaptation for semantic segmentation. Experiments demonstrate that our method significantly outperforms CLIP-based style transfer baselines on several datasets for the downstream task at hand. Our prompt-driven approach even outperforms one-shot unsupervised domain adaptation on some datasets, and gives comparable results on others. Our code is available at https://github.com/astra-vision/PODA. | ['Raoul de Charette', 'Patrick Pérez', 'Andrei Bursuc', 'Tuan-Hung Vu', 'Mohammad Fahes'] | 2022-12-06 | null | null | null | null | ['prompt-driven-zero-shot-domain-adaptation'] | ['computer-vision'] | [ 4.81665641e-01 1.56184882e-01 -2.93880284e-01 -5.02394021e-01
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6423008e-41f5-4e51-8f98-03b59d82bff7 | how-to-track-your-dragon-a-multi-attentional | 2004.10335 | null | https://arxiv.org/abs/2004.10335v3 | https://arxiv.org/pdf/2004.10335v3.pdf | How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking | We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects. Such a problem poses multiple challenges originating both from the objects' nature and their interaction with their environment, which previous approaches have failed to fully address. The proposed framework encapsulates methods for background clutter and occlusion handling by integrating multiple parallel soft spatial attention modules into a multitask Convolutional Neural Network (CNN) architecture. Moreover, we consider the special geometrical properties of both the object's 3D model and the pose space, and we use a more sophisticated approach for data augmentation during training. The provided experimental results confirm the effectiveness of the proposed multi-attentional architecture, as it improves the State-of-the-Art (SoA) tracking performance by an average score of 34.03% for translation and 40.01% for rotation, when tested on the most complete dataset designed, up to date,for the problem of RGB-D object tracking. | ['Georgios Retsinas', 'Nikos Kardaris', 'Georgia Chalvatzaki', 'Petros Maragos', 'Petros Koutras', 'Isidoros Marougkas'] | 2020-04-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.00765303e-01 -2.02341154e-01 1.25966474e-01 -1.87238809e-02
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f1343e68-7940-4098-886c-6720f4aa5a2b | slimmable-encoders-for-flexible-split-dnns-in | 2306.12691 | null | https://arxiv.org/abs/2306.12691v1 | https://arxiv.org/pdf/2306.12691v1.pdf | Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems | The execution of large deep neural networks (DNN) at mobile edge devices requires considerable consumption of critical resources, such as energy, while imposing demands on hardware capabilities. In approaches based on edge computing the execution of the models is offloaded to a compute-capable device positioned at the edge of 5G infrastructures. The main issue of the latter class of approaches is the need to transport information-rich signals over wireless links with limited and time-varying capacity. The recent split computing paradigm attempts to resolve this impasse by distributing the execution of DNN models across the layers of the systems to reduce the amount of data to be transmitted while imposing minimal computing load on mobile devices. In this context, we propose a novel split computing approach based on slimmable ensemble encoders. The key advantage of our design is the ability to adapt computational load and transmitted data size in real-time with minimal overhead and time. This is in contrast with existing approaches, where the same adaptation requires costly context switching and model loading. Moreover, our model outperforms existing solutions in terms of compression efficacy and execution time, especially in the context of weak mobile devices. We present a comprehensive comparison with the most advanced split computing solutions, as well as an experimental evaluation on GPU-less devices. | ['Marco Levorato', 'Eduardo Valle', 'J. C. S. Santos Filho', 'Juliano S. Assine'] | 2023-06-22 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 1.66420951e-01 -2.08334640e-01 -1.30166829e-01 -2.20452592e-01
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518d15f7-0b4c-4293-925d-a1912750f7eb | time-series-prediction-under-distribution | 2207.11486 | null | https://arxiv.org/abs/2207.11486v1 | https://arxiv.org/pdf/2207.11486v1.pdf | Time Series Prediction under Distribution Shift using Differentiable Forgetting | Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under distribution shift as a weighted empirical risk minimisation problem. The weighting of previous observations in the empirical risk is determined by a forgetting mechanism which controls the trade-off between the relevancy and effective sample size that is used for the estimation of the predictive model. In contrast to previous work, we propose a gradient-based learning method for the parameters of the forgetting mechanism. This speeds up optimisation and therefore allows more expressive forgetting mechanisms. | ['Jase Clarkson', 'Stefanos Bennett'] | 2022-07-23 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.42532632e-01 -5.99287525e-02 -3.29713106e-01 -3.68171901e-01
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2f87757a-ea6b-4207-a667-d5809b9a6acb | disentangling-sources-of-risk-for | null | null | https://openreview.net/forum?id=5qwA7LLbgP0 | https://openreview.net/pdf?id=5qwA7LLbgP0 | Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning | In cooperative multi-agent reinforcement learning, state transitions, rewards, and actions can all induce randomness (or uncertainty) in the observed long-term returns. These randomnesses are reflected from two risk sources: (a) agent-wise risk (i.e., how cooperative our teammates act for a given agent) and (b) environment-wise risk (i.e., transition stochasticity). Although these two sources are both important factors for learning robust policies of agents, prior works do not separate them or deal with only a single risk source, which could lead to suboptimal equilibria. In this paper, we propose Disentangled RIsk-sensitive Multi-Agent reinforcement learning (DRIMA), a novel framework being capable of disentangling risk sources. Our main idea is to separate risk-level leverages (i.e., quantiles) in both centralized training and decentralized execution with a hierarchical quantile structure and quantile regression. Our experiments demonstrate that DRIMA significantly outperforms prior-arts across various scenarios in StarCraft Multi-agent Challenge. Notably, DRIMA shows robust performance regardless of reward shaping, exploration schedule, where prior methods learn only a suboptimal policy. | ['Jinwoo Shin', 'Yung Yi', 'Junsu Kim', 'Kyunghwan Son'] | 2021-09-29 | null | null | null | null | ['smac-1'] | ['playing-games'] | [-4.68429327e-01 4.74790186e-02 -4.95272815e-01 1.75683856e-01
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ce2159ee-adb9-4d44-9d80-5eecdf2c1b79 | weakly-supervised-dense-video-captioning-via | 2105.08252 | null | https://arxiv.org/abs/2105.08252v1 | https://arxiv.org/pdf/2105.08252v1.pdf | Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching | This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation. First, we adopt the knowledge distilled from relevant and well solved tasks to generate high-quality event proposals. Then we incorporate contrastive loss and cycle-consistency loss typically applied to cross-modal retrieval tasks to build semantic matching between the proposals and sentences, which are eventually used to train the caption generation module. In addition, the parameters of matching module are initialized via pre-training based on annotated images to improve the matching performance. Extensive experiments on ActivityNet-Caption dataset reveal the significance of distillation-based event proposal generation and cross-modal retrieval-based semantic matching to weakly supervised DVC, and demonstrate the superiority of our method to existing state-of-the-art methods. | ['Hua Wu', 'Jian Zhang', 'Xinyan Xiao', 'Jun Yu', 'guocheng niu', 'Bofeng Wu'] | 2021-05-18 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 3.13596040e-01 3.82835083e-02 -3.04224759e-01 -5.58273137e-01
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85c37a6b-7303-4570-98cf-6a7a45dff63a | learning-with-noisy-labels-by-targeted | 2110.08355 | null | https://arxiv.org/abs/2110.08355v2 | https://arxiv.org/pdf/2110.08355v2.pdf | Clean or Annotate: How to Spend a Limited Data Collection Budget | Crowdsourcing platforms are often used to collect datasets for training machine learning models, despite higher levels of inaccurate labeling compared to expert labeling. There are two common strategies to manage the impact of such noise. The first involves aggregating redundant annotations, but comes at the expense of labeling substantially fewer examples. Secondly, prior works have also considered using the entire annotation budget to label as many examples as possible and subsequently apply denoising algorithms to implicitly clean the dataset. We find a middle ground and propose an approach which reserves a fraction of annotations to explicitly clean up highly probable error samples to optimize the annotation process. In particular, we allocate a large portion of the labeling budget to form an initial dataset used to train a model. This model is then used to identify specific examples that appear most likely to be incorrect, which we spend the remaining budget to relabel. Experiments across three model variations and four natural language processing tasks show our approach outperforms or matches both label aggregation and advanced denoising methods designed to handle noisy labels when allocated the same finite annotation budget. | ['Samuel R. Bowman', 'Zhou Yu', 'Derek Chen'] | 2021-10-15 | null | https://aclanthology.org/2022.deeplo-1.17 | https://aclanthology.org/2022.deeplo-1.17.pdf | deeplo-2022-7 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.24327111e-01 4.89836305e-01 7.85936564e-02 -8.38203669e-01
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7ade7326-e182-42e1-8dbd-a5de46261f1b | compressive-visual-representations | 2109.12909 | null | https://arxiv.org/abs/2109.12909v3 | https://arxiv.org/pdf/2109.12909v3.pdf | Compressive Visual Representations | Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. Recently, two families of self-supervised methods, contrastive learning and latent bootstrapping, exemplified by SimCLR and BYOL respectively, have made significant progress. In this work, we hypothesize that adding explicit information compression to these algorithms yields better and more robust representations. We verify this by developing SimCLR and BYOL formulations compatible with the Conditional Entropy Bottleneck (CEB) objective, allowing us to both measure and control the amount of compression in the learned representation, and observe their impact on downstream tasks. Furthermore, we explore the relationship between Lipschitz continuity and compression, showing a tractable lower bound on the Lipschitz constant of the encoders we learn. As Lipschitz continuity is closely related to robustness, this provides a new explanation for why compressed models are more robust. Our experiments confirm that adding compression to SimCLR and BYOL significantly improves linear evaluation accuracies and model robustness across a wide range of domain shifts. In particular, the compressed version of BYOL achieves 76.0% Top-1 linear evaluation accuracy on ImageNet with ResNet-50, and 78.8% with ResNet-50 2x. | ['Ian Fischer', 'John Canny', 'Sergio Guadarrama', 'Anurag Arnab', 'Kuang-Huei Lee'] | 2021-09-27 | null | http://proceedings.neurips.cc/paper/2021/hash/a29a5ba2cb7bdeabba22de8c83321b46-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a29a5ba2cb7bdeabba22de8c83321b46-Paper.pdf | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.80932149e-01 2.23970383e-01 -6.13178670e-01 -3.89007777e-01
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a03d3005-c039-4f24-9cdf-4c3c17778d02 | learning-causal-representation-for-training | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Learning_Causal_Representation_for_Training_Cross-Domain_Pose_Estimator_via_Generative_ICCV_2021_paper.pdf | Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions | 3D pose estimation has attracted increasing attention with the availability of high-quality benchmark datasets. However, prior works show that deep learning models tend to learn spurious correlations, which fail to generalize beyond the specific dataset they are trained on. In this work, we take a step towards training robust models for cross-domain pose estimation task, which brings together ideas from causal representation learning and generative adversarial networks. Specifically, this paper introduces a novel framework for causal representation learning which explicitly exploits the causal structure of the task. We consider changing domain as interventions on images under the data-generation process and steer the generative model to produce counterfactual features. This help the model learn transferable and causal relations across different domains. Our framework is able to learn with various types of unlabeled datasets. We demonstrate the efficacy of our proposed method on both human and hand pose estimation task. The experiment results show the proposed approach achieves state-of-the-art performance on most datasets for both domain adaptation and domain generalization settings. | ['Weidong Geng', 'Xiangdong Li', 'Mohan Kankanhalli', 'Juwei Lu', 'Xiaofei Wu', 'Yongkang Wong', 'Xiheng Zhang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.58554876e-01 1.85321078e-01 -4.65254247e-01 -4.30453002e-01
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93976464-24b7-48ee-b6f4-6f1a68921484 | self-training-with-dual-uncertainty-for-semi | 2304.04441 | null | https://arxiv.org/abs/2304.04441v1 | https://arxiv.org/pdf/2304.04441v1.pdf | Self-training with dual uncertainty for semi-supervised medical image segmentation | In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimation | ['Zhi Yang', 'Zhongwei Huang', 'Ming Shi', 'Haitao Gan', 'Zhanhong Qiu'] | 2023-04-10 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.81517434e-01 6.10010087e-01 -7.31287420e-01 -8.37988853e-01
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a0cd91b5-c238-433f-8ddb-015f8d4162b8 | bethe-admm-for-tree-decomposition-based | 1309.6829 | null | http://arxiv.org/abs/1309.6829v1 | http://arxiv.org/pdf/1309.6829v1.pdf | Bethe-ADMM for Tree Decomposition based Parallel MAP Inference | We consider the problem of maximum a posteriori (MAP) inference in discrete
graphical models. We present a parallel MAP inference algorithm called
Bethe-ADMM based on two ideas: tree-decomposition of the graph and the
alternating direction method of multipliers (ADMM). However, unlike the
standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based
proximal function, which makes each subproblem in ADMM easy to solve in
parallel using the sum-product algorithm. We rigorously prove global
convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on
both synthetic and real datasets to illustrate its effectiveness. Further, the
parallel Bethe-ADMM is shown to scale almost linearly with increasing number of
cores. | ['Qiang Fu', 'Huahua Wang', 'Arindam Banerjee'] | 2013-09-26 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 2.32333001e-02 2.80653507e-01 -5.83513686e-03 -5.57654500e-01
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5c320f98-b96c-4585-a718-30ce942527f4 | deep-multimodal-fusion-for-generalizable | 2211.00933 | null | https://arxiv.org/abs/2211.00933v3 | https://arxiv.org/pdf/2211.00933v3.pdf | Deep Multimodal Fusion for Generalizable Person Re-identification | Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, we propose a Deep Multimodal Fusion network to elaborate rich semantic knowledge for assisting in representation learning during the pre-training. Importantly, a multimodal fusion strategy is introduced to translate the features of different modalities into the common space, which can significantly boost generalization capability of Re-ID model. As for the fine-tuning stage, a realistic dataset is adopted to fine-tune the pre-trained model for better distribution alignment with real-world data. Comprehensive experiments on benchmarks demonstrate that our method can significantly outperform previous domain generalization or meta-learning methods with a clear margin. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF. | ['Zefang Yu', 'Wei Ran', 'Yuzhuo Fu', 'Dahong Qian', 'Ting Liu', 'Hao Chen', 'Suncheng Xiang'] | 2022-11-02 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 2.70371526e-01 -4.32361901e-01 -2.69815803e-01 -5.26198089e-01
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5bab7ea6-7fdc-4d6a-9c7f-1376e5bf08bd | choosing-a-sampling-frequency-for-ecg-qrs | 2007.02052 | null | https://arxiv.org/abs/2007.02052v1 | https://arxiv.org/pdf/2007.02052v1.pdf | Choosing a sampling frequency for ECG QRS detection using convolutional networks | Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data sampled at a certain frequency may have an impact on the model's generalisation capacity, and complexity. There exist some studies that investigate the effects of ECG sample frequencies on traditional filter-based methods, however, an extensive study of the effect of ECG sample frequency on deep learning-based models (convolutional networks), exploring their generalisability and complexity is yet to be explored. This experimental research investigates the impact of six different sample frequencies (50, 100, 250, 500, 1000, and 2000Hz) on four different convolutional network-based models' generalisability and complexity in order to form a basis to decide on an appropriate sample frequency for the QRS detection task for a particular performance requirement. Intra-database tests report an accuracy improvement no more than approximately 0.6\% from 100Hz to 250Hz and the shorter interquartile range for those two frequencies for all CNN-based models. The findings reveal that convolutional network-based deep learning models are capable of scoring higher levels of detection accuracies on ECG signals sampled at frequencies as low as 100Hz or 250Hz while maintaining lower model complexity (number of trainable parameters and training time). | ['John Yearwood', 'Chandan Karmakar', 'Ahsan Habib'] | 2020-07-04 | null | null | null | null | ['ecg-qrs-detection'] | ['medical'] | [ 9.95251834e-02 -4.18267161e-01 -1.12327442e-01 -2.07668051e-01
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01c008e8-4e98-4760-9cb9-2fca2148216c | towards-controllable-and-interpretable-face | null | null | https://openreview.net/forum?id=ryxUkTVYvH | https://openreview.net/pdf?id=ryxUkTVYvH | Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs | Face completion is a challenging conditional image synthesis task. This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks (GANs) progressively from low resolution to high resolution. We present structure-aware and frequency-oriented attentive GANs. The proposed structure-aware component leverages off-the-shelf facial landmark detectors and proposes a simple yet effective method of integrating the detected landmarks in generative learning. It facilitates facial expression transfer together with facial attributes control, and helps regularize the structural consistency in progressive training. The proposed frequency-oriented attentive module (FOAM) encourages GANs to attend to only finer details in the coarse-to-fine progressive training, thus enabling progressive attention to face structures. The learned FOAMs show a strong pattern of switching its attention from low-frequency to high-frequency signals. In experiments, the proposed method is tested on the CelebA-HQ benchmark. Experiment results show that our approach outperforms state-of-the-art face completion methods. The proposed method is also fast with mean inference time of 0.54 seconds for images at 1024x1024 resolution (using a Titan Xp GPU). | ['Christopher G. Healey', 'Tianfu Wu', 'Shaoliang Nie', 'Zeyuan Chen'] | 2019-09-25 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.18844861e-01 3.23074937e-01 1.17260784e-01 -5.23329675e-01
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29af88cc-31f2-4203-929f-23b96011fce0 | a-study-of-global-and-episodic-bonuses-for | 2306.03236 | null | https://arxiv.org/abs/2306.03236v1 | https://arxiv.org/pdf/2306.03236v1.pdf | A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs | Exploration in environments which differ across episodes has received increasing attention in recent years. Current methods use some combination of global novelty bonuses, computed using the agent's entire training experience, and \textit{episodic novelty bonuses}, computed using only experience from the current episode. However, the use of these two types of bonuses has been ad-hoc and poorly understood. In this work, we shed light on the behavior of these two types of bonuses through controlled experiments on easily interpretable tasks as well as challenging pixel-based settings. We find that the two types of bonuses succeed in different settings, with episodic bonuses being most effective when there is little shared structure across episodes and global bonuses being effective when more structure is shared. We develop a conceptual framework which makes this notion of shared structure precise by considering the variance of the value function across contexts, and which provides a unifying explanation of our empirical results. We furthermore find that combining the two bonuses can lead to more robust performance across different degrees of shared structure, and investigate different algorithmic choices for defining and combining global and episodic bonuses based on function approximation. This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge. | ['Roberta Raileanu', 'Minqi Jiang', 'Mikael Henaff'] | 2023-06-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-5.70110651e-03 -3.30937415e-01 -6.39877841e-02 -2.17611521e-01
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1d04092c-a65c-42a2-b836-9a7e6553ca5b | insight-1-at-semeval-2016-task-5-deep | 1609.02748 | null | http://arxiv.org/abs/1609.02748v2 | http://arxiv.org/pdf/1609.02748v2.pdf | INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis | This paper describes our deep learning-based approach to multilingual
aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a
convolutional neural network (CNN) for both aspect extraction and aspect-based
sentiment analysis. We cast aspect extraction as a multi-label classification
problem, outputting probabilities over aspects parameterized by a threshold. To
determine the sentiment towards an aspect, we concatenate an aspect vector with
every word embedding and apply a convolution over it. Our constrained system
(unconstrained for English) achieves competitive results across all languages
and domains, placing first or second in 5 and 7 out of 11 language-domain pairs
for aspect category detection (slot 1) and sentiment polarity (slot 3)
respectively, thereby demonstrating the viability of a deep learning-based
approach for multilingual aspect-based sentiment analysis. | ['Parsa Ghaffari', 'John G. Breslin', 'Sebastian Ruder'] | 2016-09-09 | insight-1-at-semeval-2016-task-5-deep-1 | https://aclanthology.org/S16-1053 | https://aclanthology.org/S16-1053.pdf | semeval-2016-6 | ['aspect-extraction', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.09924690e-02 2.03776881e-01 -4.37518448e-01 -6.79993927e-01
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9fc66c65-eee9-43d4-a86e-0dd7ac2d5b05 | handwritten-digit-recognition-using-improved | 2111.05483 | null | https://arxiv.org/abs/2111.05483v1 | https://arxiv.org/pdf/2111.05483v1.pdf | Handwritten Digit Recognition Using Improved Bounding Box Recognition Technique | The project comes with the technique of OCR (Optical Character Recognition) which includes various research sides of computer science. The project is to take a picture of a character and process it up to recognize the image of that character like a human brain recognize the various digits. The project contains the deep idea of the Image Processing techniques and the big research area of machine learning and the building block of the machine learning called Neural Network. There are two different parts of the project. Training part comes with the idea of to train a child by giving various sets of similar characters but not the totally same and to say them the output of this is this. Like this idea one has to train the newly built neural network with so many characters. This part contains some new algorithm which is self-created and upgraded as the project need. The testing part contains the testing of a new dataset .This part always comes after the part of the training .At first one has to teach the child how to recognize the character .Then one has to take the test whether he has given right answer or not. If not, one has to train him harder by giving new dataset and new entries. Just like that one has to test the algorithm also. There are many parts of statistical modeling and optimization techniques which come into the project requiring a lot of modeling concept of statistics like optimizer technique and filtering process, that how the mathematics and prediction behind that filtering or the algorithms comes after or which result one actually needs to and ultimately for the prediction of a predictive model creation. Machine learning algorithm is built by concepts of prediction and programming. | ['M. Sathya', 'Arkaprabha Basu'] | 2021-11-10 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.99989989e-01 -7.37661589e-03 3.31586689e-01 -7.21033454e-01
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ffb23f31-a6a5-4573-a52d-42bd2e9f6dd0 | sotab-the-wdc-schema-org-table-annotation | null | null | https://ceur-ws.org/Vol-3320/paper1.pdf | https://ceur-ws.org/Vol-3320/paper1.pdf | SOTAB: The WDC Schema.org Table Annotation Benchmark | Understanding the semantics of table elements is a prerequisite for many data integration and data discovery tasks. Table annotation is the task of labeling table elements with terms from a given vocabulary. This paper presents the WDC Schema.org Table Annotation Benchmark (SOTAB) for comparing the performance of table annotation systems. SOTAB covers the column type annotation (CTA) and columns property annotation (CPA) tasks. SOTAB provides ∼50,000 annotated tables for each of the tasks containing Schema.org data from different websites. The tables cover 17 different types of entities such as movie, event, local business, recipe, job posting, or product. The tables stem from the WDC Schema.org Table Corpus which was created by extracting Schema.org annotations from the Common Crawl. Consequently, the labels used for annotating columns in SOTAB are part of the Schema.org vocabulary. The benchmark covers 91 types for CTA and 176 properties for CPA distributed across textual, numerical and date/time columns. The tables are split into fixed training, validation and test sets. The test sets are further divided into subsets focusing on specific challenges, such as columns with missing values or different value formats, in order to allow a more fine-grained comparison of annotation systems. The evaluation of SOTAB using Doduo and TURL shows that the benchmark is difficult to solve for current state-of-the-art systems. | ['Christian Bizer', 'Ralph Peeters', 'Keti Korini'] | 2023-01-09 | null | null | null | semtab-iswc-2023-1 | ['table-annotation', 'data-integration', 'table-annotation', 'column-type-annotation', 'columns-property-annotation'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.26360878e-01 2.51519412e-01 -4.80268627e-01 -2.32624203e-01
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1be69a93-2cd8-4bfe-99a6-a7c1f853dc33 | temporal-embeddings-and-transformer-models | 2003.08811 | null | https://arxiv.org/abs/2003.08811v1 | https://arxiv.org/pdf/2003.08811v1.pdf | Temporal Embeddings and Transformer Models for Narrative Text Understanding | We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. An empirical analysis of the corresponding character trajectories shows that such approaches are effective in depicting dynamic evolution. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters. The empirical validation shows that such events (e.g., two characters belonging to the same family) might be spotted with good accuracy, even when using automatically annotated data. This provides a deeper understanding of narrative plots based on the identification of key facts. Standard clustering techniques are finally used for character de-aliasing, a necessary pre-processing step for both approaches. Overall, deep learning models appear to be suitable for narrative text understanding, while also providing a challenging and unexploited benchmark for general natural language understanding. | ['Simone Mellace', 'Vani K', 'Alessandro Antonucci'] | 2020-03-19 | null | null | null | null | ['de-aliasing', 'diachronic-word-embeddings'] | ['computer-vision', 'natural-language-processing'] | [ 4.61293124e-02 1.61752746e-01 -3.12846214e-01 -7.19738379e-02
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8ffb5c65-56fd-4180-8f19-e4ebd8e358a9 | parameter-prediction-for-unseen-deep | 2110.13100 | null | https://arxiv.org/abs/2110.13100v1 | https://arxiv.org/pdf/2110.13100v1.pdf | Parameter Prediction for Unseen Deep Architectures | Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the past knowledge of training other networks. We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet. By leveraging advances in graph neural networks, we propose a hypernetwork that can predict performant parameters in a single forward pass taking a fraction of a second, even on a CPU. The proposed model achieves surprisingly good performance on unseen and diverse networks. For example, it is able to predict all 24 million parameters of a ResNet-50 achieving a 60% accuracy on CIFAR-10. On ImageNet, top-5 accuracy of some of our networks approaches 50%. Our task along with the model and results can potentially lead to a new, more computationally efficient paradigm of training networks. Our model also learns a strong representation of neural architectures enabling their analysis. | ['Adriana Romero-Soriano', 'Graham W. Taylor', 'Michal Drozdzal', 'Boris Knyazev'] | 2021-10-25 | null | http://proceedings.neurips.cc/paper/2021/hash/f6185f0ef02dcaec414a3171cd01c697-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f6185f0ef02dcaec414a3171cd01c697-Paper.pdf | neurips-2021-12 | ['parameter-prediction'] | ['miscellaneous'] | [-2.59717315e-01 2.77965009e-01 -3.91183188e-03 -6.07543707e-01
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34bf9b16-02c7-4b51-9797-c5d162c95806 | how-to-avoid-being-eaten-by-a-grue | 2002.08795 | null | https://arxiv.org/abs/2002.08795v1 | https://arxiv.org/pdf/2002.08795v1.pdf | How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents | Text-based games -- in which an agent interacts with the world through textual natural language -- present us with the problem of combinatorially-sized action-spaces. Most current reinforcement learning algorithms are not capable of effectively handling such a large number of possible actions per turn. Poor sample efficiency, consequently, results in agents that are unable to pass bottleneck states, where they are unable to proceed because they do not see the right action sequence to pass the bottleneck enough times to be sufficiently reinforced. Building on prior work using knowledge graphs in reinforcement learning, we introduce two new game state exploration strategies. We compare our exploration strategies against strong baselines on the classic text-adventure game, Zork1, where prior agent have been unable to get past a bottleneck where the agent is eaten by a Grue. | ['Zhaochen Luo', 'Ethan Tien', 'Prithviraj Ammanabrolu', 'Mark O. Riedl'] | 2020-02-19 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-5.48636029e-03 3.80571514e-01 -1.37511536e-01 2.66766697e-01
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0d9bdfd2-3b3b-4dcd-8c6f-105ac248ae09 | knowledgenet-a-benchmark-dataset-for | null | null | https://aclanthology.org/D19-1069 | https://aclanthology.org/D19-1069.pdf | KnowledgeNet: A Benchmark Dataset for Knowledge Base Population | KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). We discuss five baseline approaches, where the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79{\%} (0.28). However, our best baseline is far from reaching human performance (0.82), indicating our dataset is challenging. The KnowledgeNet dataset and baselines are available at https://github.com/diffbot/knowledge-net | ['Filipe Mesquita', 'Paramita Mirza', 'Jordan Schmidek', 'Denilson Barbosa', 'Matteo Cannaviccio'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.22154307e-01 7.38417208e-01 -5.90521872e-01 1.28382891e-02
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d9ebb936-c47f-4978-9f9a-d3fd78905e1a | speechlmscore-evaluating-speech-generation | 2212.04559 | null | https://arxiv.org/abs/2212.04559v1 | https://arxiv.org/pdf/2212.04559v1.pdf | SpeechLMScore: Evaluating speech generation using speech language model | While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming. Previous studies on automatic speech quality assessment address the problem by predicting human evaluation scores with machine learning models. However, they rely on supervised learning and thus suffer from high annotation costs and domain-shift problems. We propose SpeechLMScore, an unsupervised metric to evaluate generated speech using a speech-language model. SpeechLMScore computes the average log-probability of a speech signal by mapping it into discrete tokens and measures the average probability of generating the sequence of tokens. Therefore, it does not require human annotation and is a highly scalable framework. Evaluation results demonstrate that the proposed metric shows a promising correlation with human evaluation scores on different speech generation tasks including voice conversion, text-to-speech, and speech enhancement. | ['Shinji Watanabe', 'Takaaki Saeki', 'Yifan Peng', 'Soumi Maiti'] | 2022-12-08 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.30844399e-01 6.22675121e-02 -1.35126188e-01 -5.20735323e-01
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db732e35-e53c-4a37-af5c-ce62eacd6e26 | the-effect-of-masking-strategies-on-knowledge | 2306.07185 | null | https://arxiv.org/abs/2306.07185v1 | https://arxiv.org/pdf/2306.07185v1.pdf | The Effect of Masking Strategies on Knowledge Retention by Language Models | Language models retain a significant amount of world knowledge from their pre-training stage. This allows knowledgeable models to be applied to knowledge-intensive tasks prevalent in information retrieval, such as ranking or question answering. Understanding how and which factual information is acquired by our models is necessary to build responsible models. However, limited work has been done to understand the effect of pre-training tasks on the amount of knowledge captured and forgotten by language models during pre-training. Building a better understanding of knowledge acquisition is the goal of this paper. Therefore, we utilize a selection of pre-training tasks to infuse knowledge into our model. In the following steps, we test the model's knowledge retention by measuring its ability to answer factual questions. Our experiments show that masking entities and principled masking of correlated spans based on pointwise mutual information lead to more factual knowledge being retained than masking random tokens. Our findings demonstrate that, like the ability to perform a task, the (factual) knowledge acquired from being trained on that task is forgotten when a model is trained to perform another task (catastrophic forgetting) and how to prevent this phenomenon. To foster reproducibility, the code, as well as the data used in this paper, are openly available. | ['Avishek Anand', 'Tianyi Zhang', 'Jonas Wallat'] | 2023-06-12 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 2.62541950e-01 3.68205875e-01 -9.65221748e-02 -2.59456933e-01
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004e020b-0db2-4d55-8041-00bb22af586d | cluster-guided-asymmetric-contrastive | 2106.07846 | null | https://arxiv.org/abs/2106.07846v2 | https://arxiv.org/pdf/2106.07846v2.pdf | Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification | Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by the colors in images especially in the unsupervised setting. In this paper, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which cluster structure is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively. Extensive experiments conducted on three benchmark datasets demonstrate superior performance of our proposal. | ['Jun Guo', 'Chun-Guang Li', 'Mingkun Li'] | 2021-06-15 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.31713256e-01 -4.99599427e-01 -1.28896546e-03 -5.93875706e-01
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ca71db2b-ce15-428e-8ee3-7a843a08bb30 | spherevlad-attention-based-and-signal | 2207.02958 | null | https://arxiv.org/abs/2207.02958v2 | https://arxiv.org/pdf/2207.02958v2.pdf | SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor | LiDAR-based localization approach is a fundamental module for large-scale navigation tasks, such as last-mile delivery and autonomous driving, and localization robustness highly relies on viewpoints and 3D feature extraction. Our previous work provides a viewpoint-invariant descriptor to deal with viewpoint differences; however, the global descriptor suffers from a low signal-noise ratio in unsupervised clustering, reducing the distinguishable feature extraction ability. We develop SphereVLAD++, an attention-enhanced viewpoint invariant place recognition method in this work. SphereVLAD++ projects the point cloud on the spherical perspective for each unique area and captures the contextual connections between local features and their dependencies with global 3D geometry distribution. In return, clustered elements within the global descriptor are conditioned on local and global geometries and support the original viewpoint-invariant property of SphereVLAD. In the experiments, we evaluated the localization performance of SphereVLAD++ on both public KITTI360 datasets and self-generated datasets from the city of Pittsburgh. The experiment results show that SphereVLAD++ outperforms all relative state-of-the-art 3D place recognition methods under small or even totally reversed viewpoint differences and shows 0.69% and 15.81% successful retrieval rates with better than the second best. Low computation requirements and high time efficiency also help its application for low-cost robots. | ['Sebastian Scherer', 'Ge Yi', 'Peng Yin', 'Shiqi Zhao'] | 2022-07-06 | null | null | null | null | ['3d-place-recognition'] | ['computer-vision'] | [-5.11534989e-01 -6.20074749e-01 -1.26613081e-02 -5.74614942e-01
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e842b6ec-fbd2-4756-86b6-75c7343c4952 | end-to-end-diarization-for-variable-number-of | 2105.02096 | null | https://arxiv.org/abs/2105.02096v1 | https://arxiv.org/pdf/2105.02096v1.pdf | End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings | We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling speaker overlap and enabling straightforward handling of discriminative training, unlike traditional clustering-based diarization methods. The proposed system is designed to handle meetings with unknown numbers of speakers, using variable-number permutation-invariant cross-entropy based loss functions. We introduce several components that appear to help with diarization performance, including a local convolutional network followed by a global self-attention module, multi-task transfer learning using a speaker identification component, and a sequential approach where the model is refined with a second stage. These are trained and validated on simulated meeting data based on LibriSpeech and LibriTTS datasets; final evaluations are done using LibriCSS, which consists of simulated meetings recorded using real acoustics via loudspeaker playback. The proposed model performs better than previously proposed end-to-end diarization models on these data. | ['John R. Hershey', 'Shinji Watanabe', 'Scott Wisdom', 'Kevin Wilson', 'Hakan Erdogan', 'Soumi Maiti'] | 2021-05-05 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.04734875e-01 1.76897243e-01 7.22632885e-01 -7.02954590e-01
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30062c5e-5c32-4e37-ad91-6417c1a17873 | deeplofargram-a-deep-learning-based | 1912.00605 | null | https://arxiv.org/abs/1912.00605v1 | https://arxiv.org/pdf/1912.00605v1.pdf | DeepLofargram: A Deep Learning based Fluctuating Dim Frequency Line Detection and Recovery | This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, time integration long enough can always enhance the detection characteristic. But this does not hold for irregularly fluctuating lines. Deep learning has been shown to perform very well for sophisticated visual inference tasks. With the composition of multiple processing layers, very complex high level representation that amplify the important aspects of input while suppresses irrelevant variations can be learned. Hence we propose a new DeepLofargram, composed of deep convolutional neural network and its visualization counterpart. Plugging into specifically designed multi-task loss, an end-to-end training jointly learns to detect and recover the spatial location of potential lines. Leveraging on this deep architecture, the performance boundary is -24dB on average, and -26dB for some. This is far beyond the perception of human visual and significantly improves the state-of-the-art. | ['Yuyan Li', 'Yuanliang Ma', 'Yina Han', 'Qingyu Liu'] | 2019-12-02 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [-1.21177882e-01 -2.44653765e-02 -2.95467041e-02 -1.45876795e-01
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1.13767147e+00 -5.04880488e-01 -6.07063651e-01 -2.25095734e-01] | [11.159549713134766, -2.081875801086426] |
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