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0742dcf8-9cad-4f59-8a4b-d974370a1096 | real-time-geo-localization-using-satellite | 2108.03344 | null | https://arxiv.org/abs/2108.03344v1 | https://arxiv.org/pdf/2108.03344v1.pdf | Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles | The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localization at the cost of long and varying computation time in various situations, which often necessitates the use of powerful ground station machines. In order to make image-based geo-localization online and pragmatic for lightweight embedded systems on UAVs, we propose a framework that is reliable in changing scenes, flexible about computing resource allocation and adaptable to common camera placements. The framework is comprised of two stages: offline database preparation and online inference. At the first stage, color images and depth maps are rendered as seen from potential vehicle poses quantized over the satellite and topography maps of anticipated flying areas. A database is then populated with the global and local descriptors of the rendered images. At the second stage, for each captured real-world query image, top global matches are retrieved from the database and the vehicle pose is further refined via local descriptor matching. We present field experiments of image-based localization on two different UAV platforms to validate our results. | ['Koushil Sreenath', 'Mark W. Mueller', 'Xiangyu Wu', 'Shuxiao Chen'] | 2021-08-07 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-9.68529563e-03 -6.29637659e-01 1.24093413e-01 -3.69256139e-01
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19e4a3db-b295-4481-b598-e8caff9717f9 | improving-open-set-semi-supervised-learning | 2301.10127 | null | https://arxiv.org/abs/2301.10127v2 | https://arxiv.org/pdf/2301.10127v2.pdf | Improving Open-Set Semi-Supervised Learning with Self-Supervision | Open-set semi-supervised learning (OSSL) is a realistic setting of semi-supervised learning where the unlabeled training set contains classes that are not present in the labeled set. Many existing OSSL methods assume that these out-of-distribution data are harmful and put effort into excluding data from unknown classes from the training objective. In contrast, we propose an OSSL framework that facilitates learning from all unlabeled data through self-supervision. Additionally, we utilize an energy-based score to accurately recognize data belonging to the known classes, making our method well-suited for handling uncurated data in deployment. We show through extensive experimental evaluations on several datasets that our method shows overall unmatched robustness and performance in terms of closed-set accuracy and open-set recognition compared with state-of-the-art for OSSL. Our code will be released upon publication. | ['Lars Hammarstrand', 'Fredrik Kahl', 'Lennart Svensson', 'Erik Wallin'] | 2023-01-24 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.29888529e-01 4.48479533e-01 -6.66806221e-01 -7.15749323e-01
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73f359b2-6d54-4983-b62b-ff0a3111e6d4 | learning-empirical-bregman-divergence-for | 2304.07689 | null | https://arxiv.org/abs/2304.07689v3 | https://arxiv.org/pdf/2304.07689v3.pdf | Learning Empirical Bregman Divergence for Uncertain Distance Representation | Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems. | ['Anca L. Ralescu', 'Anna Zou', 'Ziru Liu', 'Zhiyuan Li'] | 2023-04-16 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-2.29422107e-01 -3.43439549e-01 -4.65630181e-02 -8.90126944e-01
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d719c2dc-d7d4-4ab2-8ae5-f3576a9e7b9c | supervised-and-unsupervised-categorization-of | null | null | https://link.springer.com/chapter/10.1007/978-3-030-98997-2_6 | https://link.springer.com/chapter/10.1007/978-3-030-98997-2_6 | Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset | The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization.
The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization.
The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes.
Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80\% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive.
In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages. | ['Laura Po', 'Giovanni Bonisoli', 'Federica Rollo'] | 2022-03-22 | null | null | null | lecture-notes-in-business-information | ['text-categorization', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-6.85253143e-02 1.24946542e-01 -2.57859856e-01 -2.65460491e-01
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1bc5902d-c7c7-4f4a-830f-4ef36df2831b | one-shot-instance-segmentation | 1811.11507 | null | https://arxiv.org/abs/1811.11507v2 | https://arxiv.org/pdf/1811.11507v2.pdf | One-Shot Instance Segmentation | We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within a complex scene. To address this challenging new task, we propose Siamese Mask R-CNN. It extends Mask R-CNN by a Siamese backbone encoding both reference image and scene, allowing it to target detection and segmentation towards the reference category. We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult. Our work provides a first strong baseline for one-shot instance segmentation and will hopefully inspire further research into more powerful and flexible scene analysis algorithms. Code is available at: https://github.com/bethgelab/siamese-mask-rcnn | ['Ivan Ustyuzhaninov', 'Matthias Bethge', 'Claudio Michaelis', 'Alexander S. Ecker'] | 2018-11-28 | null | null | null | null | ['one-shot-object-detection', 'one-shot-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.26062810e-01 3.35033685e-01 -2.99216270e-01 -4.22968447e-01
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e735786e-8d0b-4186-b47a-f6ee848f11ec | beamformer-guided-target-speaker-extraction | 2303.08702 | null | https://arxiv.org/abs/2303.08702v1 | https://arxiv.org/pdf/2303.08702v1.pdf | Beamformer-Guided Target Speaker Extraction | We propose a Beamformer-guided Target Speaker Extraction (BG-TSE) method to extract a target speaker's voice from a multi-channel recording informed by the direction of arrival of the target. The proposed method employs a front-end beamformer steered towards the target speaker to provide an auxiliary signal to a single-channel TSE system. By allowing for time-varying embeddings in the single-channel TSE block, the proposed method fully exploits the correspondence between the front-end beamformer output and the target speech in the microphone signal. Experimental evaluation on simulated multi-channel 2-speaker mixtures, in both anechoic and reverberant conditions, demonstrates the advantage of the proposed method compared to recent single-channel and multi-channel baselines. | ['Emanuël A. P. Habets', 'Srikanth Raj Chetupalli', 'Mohamed Elminshawi'] | 2023-03-15 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.08935207e-01 -1.82213873e-01 5.67385137e-01 -1.67994142e-01
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a2633e47-d8bf-4984-9d9f-f38a1ef03a12 | mixtext-linguistically-informed-interpolation | 2004.12239 | null | https://arxiv.org/abs/2004.12239v1 | https://arxiv.org/pdf/2004.12239v1.pdf | MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification | This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data.By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at https://github.com/GT-SALT/MixText. | ['Zichao Yang', 'Jiaao Chen', 'Diyi Yang'] | 2020-04-25 | mixtext-linguistically-informed-interpolation-1 | https://aclanthology.org/2020.acl-main.194 | https://aclanthology.org/2020.acl-main.194.pdf | acl-2020-6 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 1.93125784e-01 3.76487821e-01 -8.37200761e-01 -6.79807842e-01
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d3b769f0-8805-44ca-bb34-55409416dc41 | discrete-valued-neural-communication | 2107.02367 | null | https://arxiv.org/abs/2107.02367v3 | https://arxiv.org/pdf/2107.02367v3.pdf | Discrete-Valued Neural Communication | Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a "cat" is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model. | ['Dianbo Liu', 'Yoshua Bengio', 'Michael Curtis Mozer', 'Chen Sun', 'Anirudh Goyal', 'Kenji Kawaguchi', 'Alex Lamb'] | 2021-07-06 | null | http://proceedings.neurips.cc/paper/2021/hash/10907813b97e249163587e6246612e21-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/10907813b97e249163587e6246612e21-Paper.pdf | neurips-2021-12 | ['systematic-generalization'] | ['reasoning'] | [-7.33092651e-02 7.08790183e-01 -5.42329438e-02 -1.51288807e-01
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69d8df5a-1143-4762-b669-b2540cd6dfea | multi-talker-asr-for-an-unknown-number-of | 2006.02786 | null | https://arxiv.org/abs/2006.02786v3 | https://arxiv.org/pdf/2006.02786v3.pdf | Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR | Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and combine it with a single-talker speech recognizer to form the first end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our system generalizes well to a larger number of speakers than it ever saw during training, as shown in experiments with the WSJ0-4mix database. | ['Reinhold Haeb-Umbach', 'Tomohiro Nakatani', 'Marc Delcroix', 'Thilo von Neumann', 'Keisuke Kinoshita', 'Lukas Drude', 'Christoph Boeddeker'] | 2020-06-04 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 1.42897606e-01 -4.66530174e-02 1.16999000e-01 -4.39559519e-01
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7b96b6eb-14ba-4014-bbde-e8fe5a006fbb | complexity-guided-slimmable-decoder-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.pdf | Complexity-Guided Slimmable Decoder for Efficient Deep Video Compression | In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression. Specifically, given the target complexity constraints, in our cgSlimDecoder, we introduce a set of new channel width selection modules to automatically decide the optimal channel width of each slimmable convolution layer. By optimizing the complexity-rate-distortion related objective function to directly learn the parameters of the newly introduced channel width selection modules and other modules in the decoder, our cgSlimDecoder can automatically allocate the optimal numbers of parameters for different types of modules (e.g., motion/residual decoder and the motion compensation network) and simultaneously support multiple complexity levels by using a single learnt decoder instead of multiple decoders. In addition, our proposed SaEC can further accelerate the entropy decoding procedure in both motion and residual decoders by simply skipping the entropy coding process for the elements in the encoded feature maps that are already well-predicted by the hyperprior network. As demonstrated in our comprehensive experiments, our newly proposed methods cgSlimDecoder and SaEC are general and can be readily incorporated into three widely used deep video codecs (i.e., DVC, FVC and DCVC) to significantly improve their coding efficiency with negligible performance drop. | ['Dong Xu', 'Zhihao Hu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-compensation'] | ['computer-vision'] | [ 1.25367001e-01 2.99267974e-02 -3.32184732e-01 -1.74491465e-01
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e5a3647e-7de6-45d6-81ad-e83276e0eb6a | prioritized-sipp-for-multi-agent-path-finding | 2108.05145 | null | https://arxiv.org/abs/2108.05145v1 | https://arxiv.org/pdf/2108.05145v1.pdf | Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints | Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence in which one needs to find a set of collision-free paths for a group of mobile agents (robots) operating in the shared workspace. Due to its importance, the problem is well-studied and multiple optimal and approximate algorithms are known. However, many of them abstract away from the kinematic constraints and assume that the agents can accelerate/decelerate instantaneously. This complicates the application of the algorithms on the real robots. In this paper, we present a method that mitigates this issue to a certain extent. The suggested solver is essentially, a prioritized planner based on the well-known Safe Interval Path Planning (SIPP) algorithm. Within SIPP we explicitly reason about the speed and the acceleration thus the constructed plans directly take kinematic constraints of agents into account. We suggest a range of heuristic functions for that setting and conduct a thorough empirical evaluation of the suggested algorithm. | ['Konstantin Yakovlev', 'Zain Alabedeen Ali'] | 2021-08-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.62476671e-02 3.13096166e-01 -1.61027625e-01 -1.02388114e-02
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0be64347-cac6-4c32-81fe-e17f48b917e9 | vocalist-an-audio-visual-synchronisation | 2204.0209 | null | https://arxiv.org/abs/2204.02090v2 | https://arxiv.org/pdf/2204.02090v2.pdf | VocaLiST: An Audio-Visual Synchronisation Model for Lips and Voices | In this paper, we address the problem of lip-voice synchronisation in videos containing human face and voice. Our approach is based on determining if the lips motion and the voice in a video are synchronised or not, depending on their audio-visual correspondence score. We propose an audio-visual cross-modal transformer-based model that outperforms several baseline models in the audio-visual synchronisation task on the standard lip-reading speech benchmark dataset LRS2. While the existing methods focus mainly on lip synchronisation in speech videos, we also consider the special case of the singing voice. The singing voice is a more challenging use case for synchronisation due to sustained vowel sounds. We also investigate the relevance of lip synchronisation models trained on speech datasets in the context of singing voice. Finally, we use the frozen visual features learned by our lip synchronisation model in the singing voice separation task to outperform a baseline audio-visual model which was trained end-to-end. The demos, source code, and the pre-trained models are available on https://ipcv.github.io/VocaLiST/ | ['Gloria Haro', 'Juan F. Montesinos', 'Venkatesh S. Kadandale'] | 2022-04-05 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization', 'music-source-separation'] | ['audio', 'computer-vision', 'music'] | [-2.85706744e-02 -8.25119987e-02 -2.12113619e-01 1.45719066e-01
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e3f2e677-8ad9-4bbe-9d8a-3dfa15659784 | rlss-a-deep-reinforcement-learning-algorithm | 2206.02544 | null | https://arxiv.org/abs/2206.02544v1 | https://arxiv.org/pdf/2206.02544v1.pdf | RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation | We present RLSS: a reinforcement learning algorithm for sequential scene generation. This is based on employing the proximal policy optimization (PPO) algorithm for generative problems. In particular, we consider how to effectively reduce the action space by including a greedy search algorithm in the learning process. Our experiments demonstrate that our method converges for a relatively large number of actions and learns to generate scenes with predefined design objectives. This approach is placing objects iteratively in the virtual scene. In each step, the network chooses which objects to place and selects positions which result in maximal reward. A high reward is assigned if the last action resulted in desired properties whereas the violation of constraints is penalized. We demonstrate the capability of our method to generate plausible and diverse scenes efficiently by solving indoor planning problems and generating Angry Birds levels. | ['Dominik L. Michels', 'Peter Wonka', 'Azimkhon Ostonov'] | 2022-06-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 0.5997109 0.4916908 -0.0358826 -0.046065 -0.64904994 -0.596285
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2c0fdd4f-c075-414f-97c7-56b8b6ec1ea4 | smooth-ap-smoothing-the-path-towards-large | 2007.12163 | null | https://arxiv.org/abs/2007.12163v2 | https://arxiv.org/pdf/2007.12163v2.pdf | Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval | Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods. To this end, we introduce an objective that optimises instead a smoothed approximation of AP, coined Smooth-AP. Smooth-AP is a plug-and-play objective function that allows for end-to-end training of deep networks with a simple and elegant implementation. We also present an analysis for why directly optimising the ranking based metric of AP offers benefits over other deep metric learning losses. We apply Smooth-AP to standard retrieval benchmarks: Stanford Online products and VehicleID, and also evaluate on larger-scale datasets: INaturalist for fine-grained category retrieval, and VGGFace2 and IJB-C for face retrieval. In all cases, we improve the performance over the state-of-the-art, especially for larger-scale datasets, thus demonstrating the effectiveness and scalability of Smooth-AP to real-world scenarios. | ['Weidi Xie', 'Andrew Zisserman', 'Andrew Brown', 'Vicky Kalogeiton'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/929_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540647.pdf | eccv-2020-8 | ['image-instance-retrieval'] | ['computer-vision'] | [-2.42253974e-01 -1.33714899e-01 -4.49321382e-02 -7.76710927e-01
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6d224a40-a388-47a4-97bd-da766d3a084e | learning-feature-recovery-transformer-for | 2301.01879 | null | https://arxiv.org/abs/2301.01879v1 | https://arxiv.org/pdf/2301.01879v1.pdf | Learning Feature Recovery Transformer for Occluded Person Re-identification | One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e., the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its $k$-nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2\% Rank-1 accuracy and 7.2\% mAP scores on the challenging Occluded-Duke dataset. The code is available at https://github.com/xbq1994/Feature-Recovery-Transformer. | ['Zhenan Sun', 'Jian Liang', 'Lingxiao He', 'Boqiang Xu'] | 2023-01-05 | null | null | null | null | ['person-re-identification', 'graph-similarity', 'graph-matching'] | ['computer-vision', 'graphs', 'graphs'] | [-1.60160720e-01 -4.67570305e-01 2.25389495e-01 -1.58649743e-01
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af537d18-24a7-4d81-b1a3-8e390cc36ed2 | a-theoretical-understanding-of-neural-network | 2206.05604 | null | https://arxiv.org/abs/2206.05604v2 | https://arxiv.org/pdf/2206.05604v2.pdf | A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation | The goal of model compression is to reduce the size of a large neural network while retaining a comparable performance. As a result, computation and memory costs in resource-limited applications may be significantly reduced by dropping redundant weights, neurons, or layers. There have been many model compression algorithms proposed that provide impressive empirical success. However, a theoretical understanding of model compression is still limited. One problem is understanding if a network is more compressible than another of the same structure. Another problem is quantifying how much one can prune a network with theoretically guaranteed accuracy degradation. In this work, we propose to use the sparsity-sensitive $\ell_q$-norm ($0<q<1$) to characterize compressibility and provide a relationship between soft sparsity of the weights in the network and the degree of compression with a controlled accuracy degradation bound. We also develop adaptive algorithms for pruning each neuron in the network informed by our theory. Numerical studies demonstrate the promising performance of the proposed methods compared with standard pruning algorithms. | ['Yuhong Yang', 'Jie Ding', 'Ganghua Wang', 'Wenjing Yang'] | 2022-06-11 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 6.45323634e-01 7.26755559e-02 -2.29700357e-01 -3.65739524e-01
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f90148a2-d01a-4f14-9cad-2b6a0e9ffdbe | modular-storygan-with-background-and-theme | null | null | https://link.springer.com/chapter/10.1007/978-3-031-09037-0_23 | https://link.springer.com/chapter/10.1007/978-3-031-09037-0_23 | Modular StoryGAN with Background and Theme Awareness for Story Visualization | Story visualization is a novel topic that intersects computer vision and natural language processing. In this task, given a series of natural language sentences that compose a story, a sequence of images should be generated that correspond to the sentences. Prior works have introduced recurrent generative models which outperform text-to-image models on this task; however, local and global consistency is a challenging attribute of these solutions. For the improvement, we proposed a new modular model architecture named Modular StoryGAN containing the best promising components of prior works. To measure the local and global consistency we introduced background and theme awareness, which are expected attributes of the solutions. Based on the human evaluation, the generated images demonstrate that Modular StoryGAN possesses background and theme awareness. Besides the subjective evaluation, the objective one also shows that our model outperforms the state-of-the-art CP-CSV and DuCo models. | ['Modafar Al-Shouha', 'Gábor Szűcs'] | 2022-06-02 | null | null | null | icprai-2022-3rd-international-conference-on | ['story-visualization'] | ['computer-vision'] | [-4.29717824e-02 1.32379040e-01 7.04550445e-02 -1.29590884e-01
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76d4b20e-d03f-4eb6-9e26-558e6d2ba554 | graph-augmented-learning-to-rank-for-querying | 2111.10541 | null | https://arxiv.org/abs/2111.10541v4 | https://arxiv.org/pdf/2111.10541v4.pdf | Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph | Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs, and an Enhanced Bilateral Multi-Perspective Matching model is proposed to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach. | ['Bo Long', 'Fangli Xu', 'Zhihua Wei', 'Po Hu', 'Lingfei Wu', 'Hanning Gao'] | 2021-11-20 | null | null | null | null | ['graph-question-answering', 'answer-selection'] | ['graphs', 'natural-language-processing'] | [-1.39901653e-01 3.52836162e-01 -2.66212970e-01 -6.91271275e-02
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30783302-94b1-4f9f-bb07-5ff27453a50a | transforming-ecg-diagnosis-an-in-depth-review | 2306.01249 | null | https://arxiv.org/abs/2306.01249v1 | https://arxiv.org/pdf/2306.01249v1.pdf | Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection | The emergence of deep learning has significantly enhanced the analysis of electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart health. Despite the complexity of ECG interpretation, advanced deep learning models outperform traditional methods. However, the increasing complexity of ECG data and the need for real-time and accurate diagnosis necessitate exploring more robust architectures, such as transformers. Here, we present an in-depth review of transformer architectures that are applied to ECG classification. Originally developed for natural language processing, these models capture complex temporal relationships in ECG signals that other models might overlook. We conducted an extensive search of the latest transformer-based models and summarize them to discuss the advances and challenges in their application and suggest potential future improvements. This review serves as a valuable resource for researchers and practitioners and aims to shed light on this innovative application in ECG interpretation. | ['Zibin Zhao'] | 2023-06-02 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.77341682e-01 -2.82088429e-01 2.47430131e-02 -4.26085293e-01
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cc63f21d-e96e-4868-b34b-89a1a5de5561 | nested-graph-neural-networks | 2110.13197 | null | https://arxiv.org/abs/2110.13197v1 | https://arxiv.org/pdf/2110.13197v1.pdf | Nested Graph Neural Networks | Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a center node, both 1-WL and GNN obtain a node representation that encodes a rooted subtree around the center node. These rooted subtree representations are then pooled into a single representation to represent the whole graph. However, rooted subtrees are of limited expressiveness to represent a non-tree graph. To address it, we propose Nested Graph Neural Networks (NGNNs). NGNN represents a graph with rooted subgraphs instead of rooted subtrees, so that two graphs sharing many identical subgraphs (rather than subtrees) tend to have similar representations. The key is to make each node representation encode a subgraph around it more than a subtree. To achieve this, NGNN extracts a local subgraph around each node and applies a base GNN to each subgraph to learn a subgraph representation. The whole-graph representation is then obtained by pooling these subgraph representations. We provide a rigorous theoretical analysis showing that NGNN is strictly more powerful than 1-WL. In particular, we proved that NGNN can discriminate almost all r-regular graphs, where 1-WL always fails. Moreover, unlike other more powerful GNNs, NGNN only introduces a constant-factor higher time complexity than standard GNNs. NGNN is a plug-and-play framework that can be combined with various base GNNs. We test NGNN with different base GNNs on several benchmark datasets. NGNN uniformly improves their performance and shows highly competitive performance on all datasets. | ['Pan Li', 'Muhan Zhang'] | 2021-10-25 | null | http://proceedings.neurips.cc/paper/2021/hash/8462a7c229aea03dde69da754c3bbcc4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8462a7c229aea03dde69da754c3bbcc4-Paper.pdf | neurips-2021-12 | ['graph-property-prediction'] | ['graphs'] | [ 2.43066102e-01 6.47923350e-01 -4.24940288e-01 -2.26844087e-01
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c36d2465-6f76-4e1a-a400-499cb8f62396 | 2305-15001 | 2305.15001 | null | https://arxiv.org/abs/2305.15001v2 | https://arxiv.org/pdf/2305.15001v2.pdf | Contrastive Training of Complex-Valued Autoencoders for Object Discovery | Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; all slots have equal capacity; training has high computational cost; there are no object-level relational factors within slots. Synchrony-based models in principle can address these limitations by using complex-valued activations which store binding information in their phase components. However, working examples of such synchrony-based models have been developed only very recently, and are still limited to toy grayscale datasets and simultaneous storage of less than three objects in practice. Here we introduce architectural modifications and a novel contrastive learning method that greatly improve the state-of-the-art synchrony-based model. For the first time, we obtain a class of synchrony-based models capable of discovering objects in an unsupervised manner in multi-object color datasets and simultaneously representing more than three objects | ['Jürgen Schmidhuber', 'Kazuki Irie', 'Anand Gopalakrishnan', 'Aleksandar Stanić'] | 2023-05-24 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 1.90759841e-02 1.15829697e-02 -2.85019606e-01 -7.55443946e-02
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db1869b2-acf9-4fe0-9275-e9694a753845 | learned-video-compression-via-heterogeneous | 2207.04589 | null | https://arxiv.org/abs/2207.04589v3 | https://arxiv.org/pdf/2207.04589v3.pdf | Learned Video Compression via Heterogeneous Deformable Compensation Network | Learned video compression has recently emerged as an essential research topic in developing advanced video compression technologies, where motion compensation is considered one of the most challenging issues. In this paper, we propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance caused by single-size deformable kernels in downsampled feature domain. More specifically, instead of utilizing optical flow warping or single-size-kernel deformable alignment, the proposed algorithm extracts features from the two adjacent frames to estimate content-adaptive heterogeneous deformable (HetDeform) kernel offsets. Then we transform the reference features with the HetDeform convolution to accomplish motion compensation. Moreover, we design a Spatial-Neighborhood-Conditioned Divisive Normalization (SNCDN) to achieve more effective data Gaussianization combined with the Generalized Divisive Normalization. Furthermore, we propose a multi-frame enhanced reconstruction module for exploiting context and temporal information for final quality enhancement. Experimental results indicate that HDCVC achieves superior performance than the recent state-of-the-art learned video compression approaches. | ['Chang Wen Chen', 'Zhenzhong Chen', 'Huairui Wang'] | 2022-07-11 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.71562898e-01 -5.80763996e-01 -5.80007657e-02 -3.93237889e-01
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63bc377d-f400-42a0-9ea1-4a274112f341 | impact-of-deep-learning-libraries-on-online | 2305.00595 | null | https://arxiv.org/abs/2305.00595v2 | https://arxiv.org/pdf/2305.00595v2.pdf | Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection | Providing online adaptive lightweight time series anomaly detection without human intervention and domain knowledge is highly valuable. Several such anomaly detection approaches have been introduced in the past years, but all of them were only implemented in one deep learning library. With the development of deep learning libraries, it is unclear how different deep learning libraries impact these anomaly detection approaches since there is no such evaluation available. Randomly choosing a deep learning library to implement an anomaly detection approach might not be able to show the true performance of the approach. It might also mislead users in believing one approach is better than another. Therefore, in this paper, we investigate the impact of deep learning libraries on online adaptive lightweight time series anomaly detection by implementing two state-of-the-art anomaly detection approaches in three well-known deep learning libraries and evaluating how these two approaches are individually affected by the three deep learning libraries. A series of experiments based on four real-world open-source time series datasets were conducted. The results provide a good reference to select an appropriate deep learning library for online adaptive lightweight anomaly detection. | ['Jia-Chun Lin', 'Ming-Chang Lee'] | 2023-04-30 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-5.64936876e-01 -5.84376812e-01 4.48923618e-01 -3.20504755e-01
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64e448f8-b96a-414d-9fed-91a6fddec5fc | abstractive-document-summarization-with-a | null | null | https://aclanthology.org/P17-1108 | https://aclanthology.org/P17-1108.pdf | Abstractive Document Summarization with a Graph-Based Attentional Neural Model | Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods. | ['Jiwei Tan', 'Xiaojun Wan', 'Jianguo Xiao'] | 2017-07-01 | null | null | null | acl-2017-7 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 7.31830657e-01 4.99544859e-01 -2.13196769e-01 -7.45312795e-02
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0139d90c-5af3-4ff4-8e5e-52102499b8af | commonaccent-exploring-large-acoustic | 2305.18283 | null | https://arxiv.org/abs/2305.18283v1 | https://arxiv.org/pdf/2305.18283v1.pdf | CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice | Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingual accent classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which have been proven to perform well on a variety of speech-related downstream tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7.0 (English) and Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new state-of-the-art for English accent classification with as high as 95% accuracy. We also study the internal categorization of the Wav2Vev 2.0 embeddings through t-SNE, noting that there is a level of clustering based on phonological similarity. (Our recipe is open-source in the SpeechBrain toolkit, see: https://github.com/speechbrain/speechbrain/tree/develop/recipes) | ['Cem Subakan', 'Danielius Visockas', 'Sara Ahmed', 'Juan Zuluaga-Gomez'] | 2023-05-29 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-2.44920701e-01 -6.87630698e-02 7.73389786e-02 -6.18165016e-01
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ba5d4edf-fe75-4c08-ad70-75cd7d6a0a5c | systematic-categorization-of-influencing | 2105.00279 | null | https://arxiv.org/abs/2105.00279v1 | https://arxiv.org/pdf/2105.00279v1.pdf | Systematic Categorization of Influencing Factors on Radar-Based Perception to Facilitate Complex Real-World Data Evaluation | For the assessment of machine perception for automated driving it is important to understand the influence of certain environment factors on the sensors used. Especially when investigating large amounts of real-world data to find and understand perception uncertainties, a smart concept is needed to structure and categorize such complex data depending on the level of detail desired for the investigation. Information on performance limitation causes can support realistic sensor modeling, help determining scenarios containing shortcomings of sensors and above all is essential to reach perception safety. The paper at hand looks into influencing factors on radar sensors. It utilizes the fact that radar sensors have been used in vehicles for several decades already. Therefore, previous findings on influencing factors can be used as a starting point when assessing radar-based perception for driver assistance systems and automated driving functions. On top of the literature review on environment factors influencing radar sensors, the paper introduces a modular structuring concept for such that can facilitate real-world data analysis by categorizing the factors possibly leading to performance limitations into different independent clusters in order to reduce the level of detail in complex real-world environments. | ['Lutz Eckstein', 'Maike Scholtes'] | 2021-05-01 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [ 4.16646034e-01 1.56141028e-01 2.08952054e-02 -8.01920712e-01
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21ccf4af-3401-41e2-a205-76eff732fba6 | rice-leaf-disease-classification-and | 2209.01579 | null | https://arxiv.org/abs/2209.01579v1 | https://arxiv.org/pdf/2209.01579v1.pdf | Rice Leaf Disease Classification and Detection Using YOLOv5 | A staple food in more than a hundred nations worldwide is rice (Oryza sativa). The cultivation of rice is vital to global economic growth. However, the main issue facing the agricultural industry is rice leaf disease. The quality and quantity of the crops have declined, and this is the main cause. As farmers in any country do not have much knowledge about rice leaf disease, they cannot diagnose rice leaf disease properly. That's why they cannot take proper care of rice leaves. As a result, the production is decreasing. From literature survey, it has seen that YOLOv5 exhibit the better result compare to others deep learning method. As a result of the continual advancement of object detection technology, YOLO family algorithms, which have extraordinarily high precision and better speed have been used in various scene recognition tasks to build rice leaf disease monitoring systems. We have annotate 1500 collected data sets and propose a rice leaf disease classification and detection method based on YOLOv5 deep learning. We then trained and evaluated the YOLOv5 model. The simulation outcomes show improved object detection result for the augmented YOLOv5 network proposed in this article. The required levels of recognition precision, recall, mAP value, and F1 score are 90\%, 67\%, 76\%, and 81\% respectively are considered as performance metrics. | ['Manoranjan Paul', 'Samiul Ul Hoque', 'Iftekhar Junaeid', 'Ashikur Rahman', 'Md Ershadul Haque'] | 2022-09-04 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-2.88005948e-01 -4.95865494e-01 -1.49461254e-01 -2.39710733e-02
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0bd62158-b971-4ed7-985e-f4a61c405898 | state-of-the-art-economic-load-dispatch-of | 1812.1161 | null | http://arxiv.org/abs/1812.11610v1 | http://arxiv.org/pdf/1812.11610v1.pdf | State-of-the-Art Economic Load Dispatch of Power Systems Using Particle Swarm Optimization | Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one
of the most promising optimization techniques in solving highly constrained
non-linear and non-convex optimization problems in different areas of
electrical engineering. Economic operation of the power system is one of the
most important areas of electrical engineering where PSO has been used
efficiently in solving various issues of practical systems. In this paper, a
comprehensive survey of research works in solving various aspects of economic
load dispatch (ELD) problems of power system engineering using different types
of PSO algorithms is presented. Five important areas of ELD problems have been
identified, and the papers published in the general area of ELD using PSO have
been classified into these five sections. These five areas are (i) single
objective economic load dispatch, (ii) dynamic economic load dispatch, (iii)
economic load dispatch with non-conventional sources, (iv) multi-objective
environmental/economic dispatch, and (v) economic load dispatch of microgrids.
At the end of each category, a table is provided which describes the main
features of the papers in brief. The promising future works are given at the
conclusion of the review. | ['Mahamad Nabab Alam'] | 2018-12-30 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-2.41587952e-01 -4.97591317e-01 -2.19865113e-01 1.48646221e-01
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28cc559e-ded4-40ea-bbf4-290f2d0a3b03 | learning-to-resolve-conflicts-for-multi-agent | 2012.06005 | null | https://arxiv.org/abs/2012.06005v1 | https://arxiv.org/pdf/2012.06005v1.pdf | Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search | Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. At the high level, CBS repeatedly detects conflicts and resolves one of them by splitting the current problem into two subproblems. Previous work chooses the conflict to resolve by categorizing the conflict into three classes and always picking a conflict from the highest-priority class. In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work. However, the computation of the oracle is slow. Thus, we propose a machine-learning framework for conflict selection that observes the decisions made by the oracle and learns a conflict-selection strategy represented by a linear ranking function that imitates the oracle's decisions accurately and quickly. Experiments on benchmark maps indicate that our method significantly improves the success rates, the search tree sizes and runtimes over the current state-of-the-art CBS solver. | ['Sven Koenig', 'Bistra Dilkina', 'Taoan Huang'] | 2020-12-10 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.77316030e-02 4.95846793e-02 -5.27766228e-01 -4.85191643e-02
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8e478bbf-03b1-447a-852c-5d77588a2587 | semantic-context-encoding-for-accurate-3d | null | null | https://ieeexplore.ieee.org/document/9136884 | https://ieeexplore.ieee.org/document/9136884 | Semantic Context Encoding for Accurate 3D Point Cloud Segmentation | Semantic context plays a significant role in image segmentation. However, few prior works have explored semantic contexts for 3D point cloud segmentation. In this paper, we propose a simple yet effective Point Context Encoding (PointCE) module to capture semantic contexts of a point cloud and adaptively highlight intermediate feature maps. We also introduce a Semantic Context Encoding loss (SCE-loss) to supervise the network to learn rich semantic context features. To avoid hyperparameter tuning and achieve better convergence performance, we further propose a geometric mean loss to integrate both SCE-loss and segmentation loss. Our PointCE module is general and lightweight, and can be integrated into any point cloud segmentation architecture to improve its segmentation performance with only marginal extra overheads. Experimental results on the ScanNet, S3DIS and Semantic3D datasets show that consistent and significant improvement can be achieved for several different networks by integrating our PointCE module. | ['and Gongjian Wen', 'Yinjie Lei', 'Y anni Ma', 'Y ulan Guo', 'Hao liu'] | 2020-08-01 | null | null | null | ieee-2020-8 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.80869251e-01 -6.56180084e-02 -9.07730088e-02 -7.26612985e-01
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c2903a67-2324-4191-aa65-403fc2fd2495 | using-synthetic-data-for-conversational | 2204.02653 | null | https://arxiv.org/abs/2204.02653v1 | https://arxiv.org/pdf/2204.02653v1.pdf | Using Synthetic Data for Conversational Response Generation in Low-resource Settings | Response generation is a task in natural language processing (NLP) where a model is trained to respond to human statements. Conversational response generators take this one step further with the ability to respond within the context of previous responses. While there are existing techniques for training such models, they all require an abundance of conversational data which are not always available for low-resource languages. In this research, we make three contributions. First, we released the first Filipino conversational dataset collected from a popular Philippine online forum, which we named the PEx Conversations Dataset. Second, we introduce a data augmentation (DA) methodology for Filipino data by employing a Tagalog RoBERTa model to increase the size of the existing corpora. Lastly, we published the first Filipino conversational response generator capable of generating responses related to the previous 3 responses. With the supplementary synthetic data, we were able to improve the performance of the response generator by up to 12.2% in BERTScore, 10.7% in perplexity, and 11.7% in content word usage as compared to training with zero synthetic data. | ['Charibeth Cheng', 'Jan Christian Blaise Cruz', 'Denzel Adrian Co', 'Schuyler Ng', 'Adrian Paule Ty', 'Gabriel Louis Tan'] | 2022-04-06 | null | null | null | null | ['conversational-response-generation'] | ['natural-language-processing'] | [ 3.99538934e-01 5.06525397e-01 3.38571578e-01 -4.85389471e-01
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93809d26-cd00-4578-9434-cad0cb4a6451 | mastering-pair-trading-with-risk-aware | 2304.00364 | null | https://arxiv.org/abs/2304.00364v1 | https://arxiv.org/pdf/2304.00364v1.pdf | Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning | Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requires RL methods to make thousands of correct actions that nevertheless have no obvious relations to the overall trading profit, and to reason over infinite states of the time-varying market most of which have never appeared in history. However, existing RL methods ignore the temporal connections between asset price movements and the risk of the performed trading. These lead to frequent tradings with high transaction costs and potential losses, which barely reach the human expertise level of trading. Therefore, we introduce CREDIT, a risk-aware agent capable of learning to exploit long-term trading opportunities in pair trading similar to a human expert. CREDIT is the first to apply bidirectional GRU along with the temporal attention mechanism to fully consider the temporal correlations embedded in the states, which allows CREDIT to capture long-term patterns of the price movements of two assets to earn higher profit. We also design the risk-aware reward inspired by the economic theory, that models both the profit and risk of the tradings during the trading period. It helps our agent to master pair trading with a robust trading preference that avoids risky trading with possible high returns and losses. Experiments show that it outperforms existing reinforcement learning methods in pair trading and achieves a significant profit over five years of U.S. stock data. | ['Min Peng', 'Yanzhao Lai', 'Boyi Zhang', 'Qianqian Xie', 'Jimin Huang', 'Weiguang Han'] | 2023-04-01 | null | null | null | null | ['pair-trading'] | ['time-series'] | [-6.91967964e-01 2.20412597e-01 -1.31530628e-01 6.93849400e-02
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7cb8a2ea-92a4-4045-98f2-291ad64583de | sparseformer-sparse-visual-recognition-via | 2304.03768 | null | https://arxiv.org/abs/2304.03768v1 | https://arxiv.org/pdf/2304.03768v1.pdf | SparseFormer: Sparse Visual Recognition via Limited Latent Tokens | Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly. However, most current vision networks follow a dense paradigm, processing every single visual unit (e.g,, pixel or patch) in a uniform manner. In this paper, we challenge this dense paradigm and present a new method, coined SparseFormer, to imitate human's sparse visual recognition in an end-to-end manner. SparseFormer learns to represent images using a highly limited number of tokens (down to 49) in the latent space with sparse feature sampling procedure instead of processing dense units in the original pixel space. Therefore, SparseFormer circumvents most of dense operations on the image space and has much lower computational costs. Experiments on the ImageNet classification benchmark dataset show that SparseFormer achieves performance on par with canonical or well-established models while offering better accuracy-throughput tradeoff. Moreover, the design of our network can be easily extended to the video classification with promising performance at lower computational costs. We hope that our work can provide an alternative way for visual modeling and inspire further research on sparse neural architectures. The code will be publicly available at https://github.com/showlab/sparseformer | ['Mike Zheng Shou', 'LiMin Wang', 'Zhan Tong', 'Ziteng Gao'] | 2023-04-07 | null | null | null | null | ['video-classification', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 2.63061017e-01 -1.72434136e-01 -2.24175841e-01 -5.36510885e-01
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178f2bd6-6dc8-4d21-a02d-29b76bf00754 | class-incremental-learning-based-on-label | 2306.12619 | null | https://arxiv.org/abs/2306.12619v1 | https://arxiv.org/pdf/2306.12619v1.pdf | Class-Incremental Learning based on Label Generation | Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin. | ['Bing Liu', 'Dongyan Zhao', 'Yiduo Guo', 'Yijia Shao'] | 2023-06-22 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.19073343e-01 2.48340920e-01 -5.93477428e-01 -3.05925369e-01
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b361ce77-9f32-411a-af6b-31be76dbff29 | unsupervised-keyphrase-extraction-via | null | null | https://openreview.net/forum?id=u2Pt8ULp7Kd | https://openreview.net/pdf?id=u2Pt8ULp7Kd | Unsupervised Keyphrase Extraction via Interpretable Neural Networks | Keyphrase extraction aims at automatically extracting a list of "important'' phrases which represent the key concepts in a document. Traditionally, it has been approached from an information-theoretic angle using phrase co-occurrence statistics. This work proposes a novel unsupervised approach to keyphrase extraction that uses a more intuitive notion of phrase importance, inspired by interpretability research. In particular, we use a self-explaining neural model to measure the predictive impact of input phrases on downstream task performance, and consider the resulting interpretations as document keyphrases for the target task. We show the efficacy of our approach on four datasets in two domains---scientific publications and news articles---attaining state-of-the-art results in unsupervised keyphrase extraction. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 4.38629925e-01 5.02027750e-01 -7.30789304e-01 -1.66168902e-02
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63b66629-d30a-488d-bca3-4bcf2ea32bfa | event-driven-headline-generation | null | null | https://aclanthology.org/P15-1045 | https://aclanthology.org/P15-1045.pdf | Event-Driven Headline Generation | null | ['Meishan Zhang', 'Yue Zhang', 'Donghong Ji', 'Rui Sun'] | 2015-07-01 | event-driven-headline-generation-1 | https://aclanthology.org/P15-1045 | https://aclanthology.org/P15-1045.pdf | ijcnlp-2015-7 | ['headline-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f593f07b-f795-47cc-9dc5-64f80af4cb01 | an-interleaving-semantics-of-the-timed | 2306.07675 | null | https://arxiv.org/abs/2306.07675v2 | https://arxiv.org/pdf/2306.07675v2.pdf | An Interleaving Semantics of the Timed Concurrent Language for Argumentation to Model Debates and Dialogue Games | Time is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents' behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents to communicate and reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps, with maximum parallelism for time elapsing. Following this approach, only one of the enabled agents is executed at each moment. To demonstrate the capabilities of language, we also show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents. Lastly, we present an implementation of the language that can be accessed via a web interface. Under consideration in Theory and Practice of Logic Programming (TPLP). | ['Carlo Taticchi', 'Maria Chiara Meo', 'Stefano Bistarelli'] | 2023-06-13 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [ 2.33453244e-01 5.80594063e-01 -2.31802533e-03 -3.47256869e-01
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11261b4d-b1d8-422a-bbf4-29d92ec991a6 | meta-rppg-remote-heart-rate-estimation-using | 2007.06786 | null | https://arxiv.org/abs/2007.06786v1 | https://arxiv.org/pdf/2007.06786v1.pdf | Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner | Remote heart rate estimation is the measurement of heart rate without any physical contact with the subject and is accomplished using remote photoplethysmography (rPPG) in this work. rPPG signals are usually collected using a video camera with a limitation of being sensitive to multiple contributing factors, e.g. variation in skin tone, lighting condition and facial structure. End-to-end supervised learning approach performs well when training data is abundant, covering a distribution that doesn't deviate too much from the distribution of testing data or during deployment. To cope with the unforeseeable distributional changes during deployment, we propose a transductive meta-learner that takes unlabeled samples during testing (deployment) for a self-supervised weight adjustment (also known as transductive inference), providing fast adaptation to the distributional changes. Using this approach, we achieve state-of-the-art performance on MAHNOB-HCI and UBFC-rPPG. | ['Chen-Yi Lee', 'Eugene Lee', 'Evan Chen'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5772_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720392.pdf | eccv-2020-8 | ['heart-rate-estimation'] | ['medical'] | [ 3.48127663e-01 1.93787403e-02 -2.87882566e-01 -7.16161609e-01
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1c264cf8-ef70-43d7-bab1-063f7d34e11c | bs3d-building-scale-3d-reconstruction-from | 2301.01057 | null | https://arxiv.org/abs/2301.01057v1 | https://arxiv.org/pdf/2301.01057v1.pdf | BS3D: Building-scale 3D Reconstruction from RGB-D Images | Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and infrared images. We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera. Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms. Compared to similar systems, we utilize raw depth maps for odometry computation and loop closure refinement which results in better reconstructions. We acquire a building-scale 3D dataset (BS3D) and demonstrate its value by training an improved monocular depth estimation model. As a unique experiment, we benchmark visual-inertial odometry methods using both color and active infrared images. | ['Janne Heikkilä', 'Li Liu', 'Esa Rahtu', 'Juho Kannala', 'Janne Mustaniemi'] | 2023-01-03 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-8.19075182e-02 -2.50903100e-01 -3.47610116e-02 -5.02472043e-01
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a2d4461d-8b64-40d5-bf5b-8d9850e60e39 | progressively-complementarity-aware-fusion | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Progressively_Complementarity-Aware_Fusion_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Progressively_Complementarity-Aware_Fusion_CVPR_2018_paper.pdf | Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection | How to incorporate cross-modal complementarity sufficiently is the cornerstone question for RGB-D salient object detection. Previous works mainly address this issue by simply concatenating multi-modal features or combining unimodal predictions. In this paper, we answer this question from two perspectives: (1) We argue that if the complementary part can be modelled more explicitly, the cross-modal complement is likely to be better captured. To this end, we design a novel complementarity-aware fusion (CA-Fuse) module when adopting the Convolutional Neural Network (CNN). By introducing cross-modal residual functions and complementarity-aware supervisions in each CA-Fuse module, the problem of learning complementary information from the paired modality is explicitly posed as asymptotically approximating the residual function. (2) Exploring the complement across all the levels. By cascading the CA-Fuse module and adding level-wise supervision from deep to shallow densely, the cross-level complement can be selected and combined progressively. The proposed RGB-D fusion network disambiguates both cross-modal and cross-level fusion processes and enables more sufficient fusion results. The experiments on public datasets show the effectiveness of the proposed CA-Fuse module and the RGB-D salient object detection network. | ['Youfu Li', 'Hao Chen'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.08185068e-01 2.94430494e-01 -1.44099712e-01 -2.20459864e-01
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0db6cf31-fac1-4e22-8ce9-07de65e445ee | semi-supervised-domain-adaptation-for-cross | 2211.00677 | null | https://arxiv.org/abs/2211.00677v3 | https://arxiv.org/pdf/2211.00677v3.pdf | Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection | In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class). | ['Stefan M. Wild', 'Gabriel N. Perdue', 'Brian Nord', 'Sandeep Madireddy', 'Kevin Pedro', 'Ashia Lewis', 'Aleksandra Ćiprijanović'] | 2022-11-01 | null | null | null | null | ['morphology-classification', 'universal-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 1.00819856e-01 -8.65346864e-02 2.63343215e-01 -4.42740947e-01
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3cccf8b5-f449-4e22-9561-44f177cd7394 | variational-quantum-algorithms-for-chemical | 2211.07854 | null | https://arxiv.org/abs/2211.07854v1 | https://arxiv.org/pdf/2211.07854v1.pdf | Variational Quantum Algorithms for Chemical Simulation and Drug Discovery | Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest problems of biochemistry. Each protein folds distinctively, and the difficulty of finding its stable shape rapidly increases with an increase in the number of amino acids in the chain. A moderate protein has about 100 amino acids, and the number of combinations one needs to verify to find the stable structure is enormous. At some point, the number of these combinations will be so vast that classical computers cannot even attempt to solve them. In this paper, we examine how this problem can be solved with the help of quantum computing using two different algorithms, Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), using Qiskit Nature. We compare the results of different quantum hardware and simulators and check how error mitigation affects the performance. Further, we make comparisons with SoTA algorithms and evaluate the reliability of the method. | ['Srinjoy Ganguly', 'Prateek Jain', 'Sai Nandan Morapakula', 'Hasan Mustafa'] | 2022-11-15 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 2.90906820e-02 -3.10623765e-01 2.04679608e-01 -1.32161751e-01
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3a951797-cd45-4ae3-b74a-7095d13fabd9 | quality-signals-in-generated-stories | null | null | https://aclanthology.org/S18-2024 | https://aclanthology.org/S18-2024.pdf | Quality Signals in Generated Stories | We study the problem of measuring the quality of automatically-generated stories. We focus on the setting in which a few sentences of a story are provided and the task is to generate the next sentence ({``}continuation{''}) in the story. We seek to identify what makes a story continuation interesting, relevant, and have high overall quality. We crowdsource annotations along these three criteria for the outputs of story continuation systems, design features, and train models to predict the annotations. Our trained scorer can be used as a rich feature function for story generation, a reward function for systems that use reinforcement learning to learn to generate stories, and as a partial evaluation metric for story generation. | ['Lifu Tu', 'Kevin Gimpel', 'Manasvi Sagarkar', 'John Wieting'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['story-continuation'] | ['computer-vision'] | [ 3.39615077e-01 6.43212974e-01 -2.38412783e-01 -4.71360862e-01
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821f5779-fd9f-420d-8fb9-bd92b3044045 | wavenilm-a-causal-neural-network-for-power | 1902.08736 | null | https://arxiv.org/abs/1902.08736v2 | https://arxiv.org/pdf/1902.08736v2.pdf | Wavenilm: A causal neural network for power disaggregation from the complex power signal | Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems; however, many of them are not causal which is important for real-time application. We present a causal 1-D convolutional neural network inspired by WaveNet for NILM on low-frequency data. We also study using various components of the complex power signal for NILM, and demonstrate that using all four components available in a popular NILM dataset (current, active power, reactive power, and apparent power) we achieve faster convergence and higher performance than state-of-the-art results for the same dataset. | ['Ivan V. Bajić', 'Alon Harell', 'Stephen Makonin'] | 2019-02-23 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.45606725e-02 -7.56383091e-02 -4.86351311e-01 -1.72000855e-01
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cc940561-fcf3-4047-9acb-b05ed6883337 | multi-task-learning-improves-synthetic-speech | null | null | https://ieeexplore.ieee.org/abstract/document/9746059 | https://ieeexplore.ieee.org/abstract/document/9746059 | Multi-task learning improves synthetic speech detection | With the development of deep learning, synthetic speech has become more and more realistic and easier to spoof Automatic Speaker Verification (ASV) devices. Based on mining more effective hand-crafted features and proposing more powerful networks, many algorithms have been proposed to detect this malicious attack. In this paper, by observing that deepening the network impairs the performance of the network in detecting unknown attacks, we propose that the synthetic speech detection problem is an out-of-distribution (OOD) generalization problem and we enhance the robustness of networks by using multi-task learning. In our system, three auxiliary tasks are used to assist synthetic speech detection: bonafide speech reconstruction, spoofing voice conversion and speaker classification. Experimental results show that our approach can be applied to multiple architectures and can significantly improve the performance on both known attacks (development set) and unknown attacks (evaluation set). In addition, our best-performing network is quite competitive to recent state-of-the-art (SOTA) systems. It demonstrates the potential application of multi-task learning in synthetic speech detection. | ['Shilin Wang', 'Yichuan Mo'] | 2022-04-27 | null | null | null | 2022-ieee-international-conference-on | ['voice-conversion', 'synthetic-speech-detection', 'voice-conversion', 'speaker-verification'] | ['audio', 'audio', 'speech', 'speech'] | [ 3.20338249e-01 -1.65815011e-01 -1.72350705e-01 -2.46148095e-01
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c7706bd5-7c2f-4892-87ce-e74927251f29 | deepedit-deep-editable-learning-for | 2305.10655 | null | https://arxiv.org/abs/2305.10655v1 | https://arxiv.org/pdf/2305.10655v1.pdf | DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images | Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators. In this paper, we introduce DeepEdit, a deep learning-based method for volumetric medical image annotation, that allows automatic and semi-automatic segmentation, and click-based refinement. DeepEdit combines the power of two methods: a non-interactive (i.e. automatic segmentation using nnU-Net, UNET or UNETR) and an interactive segmentation method (i.e. DeepGrow), into a single deep learning model. It allows easy integration of uncertainty-based ranking strategies (i.e. aleatoric and epistemic uncertainty computation) and active learning. We propose and implement a method for training DeepEdit by using standard training combined with user interaction simulation. Once trained, DeepEdit allows clinicians to quickly segment their datasets by using the algorithm in auto segmentation mode or by providing clicks via a user interface (i.e. 3D Slicer, OHIF). We show the value of DeepEdit through evaluation on the PROSTATEx dataset for prostate/prostatic lesions and the Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset for abdominal CT segmentation, using state-of-the-art network architectures as baseline for comparison. DeepEdit could reduce the time and effort annotating 3D medical images compared to DeepGrow alone. Source code is available at https://github.com/Project-MONAI/MONAILabel | ['M. Jorge Cardoso', 'Sebastien Ourselin', 'Abood Quraini', 'Andrew Feng', 'Tom Vercauteren', 'Prerna Dogra', 'Daguang Xu', 'Holger R. Roth', 'Steve Pieper', 'Ron Alkalay', 'Csaba Pinter', 'Daniel Palkovics', 'Michela Antonelli', 'Alvin Ihsani', 'Vishwesh Nath', 'Richard Brown', 'Muhammad Asad', 'Sachidanand Alle', 'Pritesh Mehta', 'Andres Diaz-Pinto'] | 2023-05-18 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [-2.71833986e-02 7.95411646e-01 -2.76625603e-01 -6.53003514e-01
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35972599-3a44-4968-a809-87bbe6b9bafa | adversarial-multi-task-learning-for-end-to | 2305.16638 | null | https://arxiv.org/abs/2305.16638v1 | https://arxiv.org/pdf/2305.16638v1.pdf | Adversarial Multi-task Learning for End-to-end Metaphor Detection | Metaphor detection (MD) suffers from limited training data. In this paper, we started with a linguistic rule called Metaphor Identification Procedure and then proposed a novel multi-task learning framework to transfer knowledge in basic sense discrimination (BSD) to MD. BSD is constructed from word sense disambiguation (WSD), which has copious amounts of data. We leverage adversarial training to align the data distributions of MD and BSD in the same feature space, so task-invariant representations can be learned. To capture fine-grained alignment patterns, we utilize the multi-mode structures of MD and BSD. Our method is totally end-to-end and can mitigate the data scarcity problem in MD. Competitive results are reported on four public datasets. Our code and datasets are available. | ['Ying Liu', 'Shenglong Zhang'] | 2023-05-26 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.78231716e-01 -6.26057461e-02 -2.57861614e-01 -2.88082659e-01
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0918046f-6d35-49db-ab20-ac46c852b25e | unsupervised-space-time-network-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.pdf | Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions | Motion segmentation is one of the main tasks in computer vision and is relevant for many applications. The optical flow (OF) is the input generally used to segment every frame of a video sequence into regions of coherent motion. Temporal consistency is a key feature of motion segmentation, but it is often neglected. In this paper, we propose an original unsupervised spatio-temporal framework for motion segmentation from optical flow that fully investigates the temporal dimension of the problem. More specifically, we have defined a 3D network for multiple motion segmentation that takes as input a sub-volume of successive optical flows and delivers accordingly a sub-volume of coherent segmentation maps. Our network is trained in a fully unsupervised way, and the loss function combines a flow reconstruction term involving spatio-temporal parametric motion models, and a regularization term enforcing temporal consistency on the masks. We have specified an easy temporal linkage of the predicted segments. Besides, we have proposed a flexible and efficient way of coding U-nets. We report experiments on several VOS benchmarks with convincing quantitative results, while not using appearance and not training with any ground-truth data. We also highlight through visual results the distinctive contribution of the short- and long-term temporal consistency brought by our OF segmentation method. | ['Patrick Bouthemy', 'Etienne Meunier'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-segmentation'] | ['computer-vision'] | [ 5.40121645e-02 -3.10465023e-02 -2.12634832e-01 -2.19975904e-01
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a3a3f8c9-1b86-429d-a0e0-fd39dd5114cb | a-relaxed-optimization-approach-for-1 | 2306.08492 | null | https://arxiv.org/abs/2306.08492v1 | https://arxiv.org/pdf/2306.08492v1.pdf | A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models | In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversarial examples that are semantically similar to the original sentences but destroy the translation generated by the target NMT model. This optimization problem is discrete, and we propose a continuous relaxation to solve it. With this relaxation, we find a probability distribution for each token in the adversarial example, and then we can generate multiple adversarial examples by sampling from these distributions. Experimental results show that our attack significantly degrades the translation quality of multiple NMT models while maintaining the semantic similarity between the original and adversarial sentences. Furthermore, our attack outperforms the baselines in terms of success rate, similarity preservation, effect on translation quality, and token error rate. Finally, we propose a black-box extension of our attack by sampling from an optimized probability distribution for a reference model whose gradients are accessible. | ['Pascal Frossard', 'Ljiljana Dolamic', 'Clément Barbier', 'Sahar Sadrizadeh'] | 2023-06-14 | null | null | null | null | ['adversarial-attack', 'nmt', 'machine-translation', 'semantic-textual-similarity', 'semantic-similarity'] | ['adversarial', 'computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.92254460e-01 3.16214681e-01 7.10222591e-03 -2.42076471e-01
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be6f331b-f6c8-4e2a-b48f-031a776ecd34 | complicated-background-suppression-of-visar | 2209.10431 | null | https://arxiv.org/abs/2209.10431v1 | https://arxiv.org/pdf/2209.10431v1.pdf | Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection | The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR. The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR. | ['Xu Zhan', 'Xiaoling Zhang', 'Zhenyu Yang'] | 2022-09-21 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.89478111e-01 -8.16981792e-01 4.80490237e-01 3.38440128e-02
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bca03064-e6d2-4068-bdf2-60c1c63c1c09 | an-end-to-end-framework-for-marketing | 2302.04477 | null | https://arxiv.org/abs/2302.04477v1 | https://arxiv.org/pdf/2302.04477v1.pdf | An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint | Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level budget allocation is an essential task in marketing campaigns. Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution. Since the objectives of these two stages might not be perfectly aligned, such a two-stage paradigm could hurt the overall marketing effectiveness. In this paper, we propose a novel end-to-end framework to directly optimize the business goal under budget constraints. Our core idea is to construct a regularizer to represent the marketing goal and optimize it efficiently using gradient estimation techniques. As such, the obtained models can learn to maximize the marketing goal directly and precisely. We extensively evaluate our proposed method in both offline and online experiments, and experimental results demonstrate that our method outperforms current state-of-the-art methods. Our proposed method is currently deployed to allocate marketing budgets for hundreds of millions of users on a short video platform and achieves significant business goal improvements. Our code will be publicly available. | ['Peng Jiang', 'Jingjian Lin', 'Guorui Zhou', 'Shusen Wang', 'Ziang Yan'] | 2023-02-09 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.25186870e-01 3.17575522e-02 -1.25174844e+00 -5.79459488e-01
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ddcf95d2-d1c7-46ad-99a9-52ee992aec9c | active-learning-with-pseudo-labels-for-multi | 2112.13709 | null | https://arxiv.org/abs/2112.13709v2 | https://arxiv.org/pdf/2112.13709v2.pdf | Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training | Pose estimation of the human body and hands is a fundamental problem in computer vision, and learning-based solutions require a large amount of annotated data. In this work, we improve the efficiency of the data annotation process for 3D pose estimation problems with Active Learning (AL) in a multi-view setting. AL selects examples with the highest value to annotate under limited annotation budgets (time and cost), but choosing the selection strategy is often nontrivial. We present a framework to efficiently extend existing single-view AL strategies. We then propose two novel AL strategies that make full use of multi-view geometry. Moreover, we demonstrate additional performance gains by incorporating pseudo-labels computed during the AL process, which is a form of self-training. Our system significantly outperforms simulated annotation baselines in 3D body and hand pose estimation on two large-scale benchmarks: CMU Panoptic Studio and InterHand2.6M. Notably, on CMU Panoptic Studio, we are able to reduce the turn-around time by 60% and annotation cost by 80% when compared to the conventional annotation process. | ['Yuting Ye', 'Cem Keskin', 'He Wen', 'Kun He', 'Qi Feng'] | 2021-12-27 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 6.47753999e-02 1.55739352e-01 -3.35502476e-01 -3.26651901e-01
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7af1ea1e-ae42-40f2-b4fa-3d5fddac7abe | automated-feature-extraction-on-asmap-for | 2201.12055 | null | https://arxiv.org/abs/2201.12055v2 | https://arxiv.org/pdf/2201.12055v2.pdf | Automated Feature Extraction on AsMap for Emotion Classification using EEG | Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automatic feature extraction methods has been proposed. The asymmetry in different brain regions is captured in a 2D vector, termed the AsMap, from the differential entropy features of EEG signals. These AsMaps are then used to extract features automatically using a convolutional neural network model. The proposed feature extraction method has been compared with differential entropy and other feature extraction methods such as relative asymmetry, differential asymmetry and differential caudality. Experiments are conducted using the SJTU emotion EEG dataset and the DEAP dataset on different classification problems based on the number of classes. Results obtained indicate that the proposed method of feature extraction results in higher classification accuracy, outperforming the other feature extraction methods. The highest classification accuracy of 97.10% is achieved on a three-class classification problem using the SJTU emotion EEG dataset. Further, this work has also assessed the impact of window size on classification accuracy. | ['Ebrahim Ghaderpour', 'Souvik Phadikar', 'Nidul Sinha', 'Md. Zaved Iqubal Ahmed'] | 2022-01-28 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.40622770e-02 -2.55633175e-01 4.55262780e-01 -4.92296755e-01
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d6a82889-7e9b-49d3-9478-c1932a923863 | towards-locally-consistent-object-counting | 1904.03373 | null | http://arxiv.org/abs/1904.03373v1 | http://arxiv.org/pdf/1904.03373v1.pdf | Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks | High-density object counting in surveillance scenes is challenging mainly due
to the drastic variation of object scales. The prevalence of deep learning has
largely boosted the object counting accuracy on several benchmark datasets.
However, does the global counts really count? Armed with this question we dive
into the predicted density map whose summation over the whole regions reports
the global counts for more in-depth analysis. We observe that the object
density map generated by most existing methods usually lacks of local
consistency, i.e., counting errors in local regions exist unexpectedly even
though the global count seems to well match with the ground-truth. Towards this
problem, in this paper we propose a constrained multi-stage Convolutional
Neural Networks (CNNs) to jointly pursue locally consistent density map from
two aspects. Different from most existing methods that mainly rely on the
multi-column architectures of plain CNNs, we exploit a stacking formulation of
plain CNNs. Benefited from the internal multi-stage learning process, the
feature map could be repeatedly refined, allowing the density map to approach
the ground-truth density distribution. For further refinement of the density
map, we also propose a grid loss function. With finer local-region-based
supervisions, the underlying model is constrained to generate locally
consistent density values to minimize the training errors considering both the
global and local counts accuracy. Experiments on two widely-tested object
counting benchmarks with overall significant results compared with
state-of-the-art methods demonstrate the effectiveness of our approach. | ['Jian Zhang', 'Wenjun Zhang', 'Muming Zhao', 'Chongyang Zhang'] | 2019-04-06 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-6.75123334e-02 -2.30813622e-01 -1.20001033e-01 -3.91563326e-01
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b1ee191a-bebb-45f6-bcfb-f27a43965ac6 | the-npu-system-for-the-2020-personalized | 2102.13552 | null | https://arxiv.org/abs/2102.13552v1 | https://arxiv.org/pdf/2102.13552v1.pdf | The NPU System for the 2020 Personalized Voice Trigger Challenge | This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge. Our submitted system consists of two independently trained subsystems: a small footprint keyword spotting (KWS) system and a speaker verification (SV) system. For the KWS system, a multi-scale dilated temporal convolutional (MDTC) network is proposed to detect wake-up word (WuW). For SV system, Write something here. The KWS predicts posterior probabilities of whether an audio utterance contains WuW and estimates the location of WuW at the same time. When the posterior probability ofWuW reaches a predefined threshold, the identity information of triggered segment is determined by the SV system. On evaluation dataset, our submitted system obtains detection costs of 0.081and 0.091 in close talking and far-field tasks, respectively. | ['Lei Xie', 'Qijie Shao', 'Zhanheng Yang', 'Qing Wang', 'Yihui Fu', 'Li Zhang', 'Jingyong Hou'] | 2021-02-26 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 9.36984345e-02 2.87972480e-01 -7.68594369e-02 -4.48360741e-01
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26fd59ce-77ee-4a4a-a98c-91e008bc47b4 | a-reinforcement-learning-framework-for-online | 2302.10924 | null | https://arxiv.org/abs/2302.10924v1 | https://arxiv.org/pdf/2302.10924v1.pdf | A Reinforcement Learning Framework for Online Speaker Diarization | Speaker diarization is a task to label an audio or video recording with the identity of the speaker at each given time stamp. In this work, we propose a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online and reinforcement learning setting. Our framework combines embedding extraction, clustering, and resegmentation into the same problem as an online decision-making problem. We discuss practical considerations and advanced techniques such as the offline reinforcement learning, semi-supervision, and domain adaptation to address the challenges of limited training data and out-of-distribution environments. Our approach considers speaker diarization as a fully online learning problem of the speaker recognition task, where the agent receives no pretraining from any training set before deployment, and learns to detect speaker identity on the fly through reward feedbacks. The paradigm of the reinforcement learning approach to speaker diarization presents an adaptive, lightweight, and generalizable system that is useful for multi-user teleconferences, where many people might come and go without extensive pre-registration ahead of time. Lastly, we provide a desktop application that uses our proposed approach as a proof of concept. To the best of our knowledge, this is the first approach to apply a reinforcement learning approach to the speaker diarization task. | ['Xinxin Zhang', 'Baihan Lin'] | 2023-02-21 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 2.10366696e-01 8.09455812e-02 4.93138423e-03 -5.85343897e-01
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f5296e93-f32b-4c0b-a92b-2eed06fc67cb | overview-of-the-icassp-2023-general-meeting | 2303.13932 | null | https://arxiv.org/abs/2303.13932v1 | https://arxiv.org/pdf/2303.13932v1.pdf | Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG) | ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings. MUG includes five tracks, including topic segmentation, topic-level and session-level extractive summarization, topic title generation, keyphrase extraction, and action item detection. To facilitate MUG, we construct and release a large-scale meeting dataset, the AliMeeting4MUG Corpus. | ['Zhou Zhao', 'Yi Ren', 'Jinglin Liu', 'Zhijie Yan', 'Wen Wang', 'Qian Chen', 'Hai Yu', 'Jiaqing Liu', 'Chong Deng', 'Qinglin Zhang'] | 2023-03-24 | null | null | null | null | ['extractive-summarization', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.02490795e-01 6.12458527e-01 -2.13903755e-01 -2.35412776e-01
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16da0ee6-f73b-4d5e-936f-97aded8fdafc | co-embedding-of-nodes-and-edges-with-graph | 2010.13242 | null | https://arxiv.org/abs/2010.13242v1 | https://arxiv.org/pdf/2010.13242v1.pdf | Co-embedding of Nodes and Edges with Graph Neural Networks | Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of machine learning tasks. Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space to a low dimensional and structural space, which is easily exploited by other machine learning algorithms. We have witnessed a huge surge of such embedding methods, from statistical approaches to recent deep learning methods such as the graph convolutional networks (GCN). Deep learning approaches usually outperform the traditional methods in most graph learning benchmarks by building an end-to-end learning framework to optimize the loss function directly. However, most of the existing GCN methods can only perform convolution operations with node features, while ignoring the handy information in edge features, such as relations in knowledge graphs. To address this problem, we present CensNet, Convolution with Edge-Node Switching graph neural network, for learning tasks in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach achieves or matches the state-of-the-art performance in four graph learning tasks, including semi-supervised node classification, multi-task graph classification, graph regression, and link prediction. | ['Sheng Li', 'Pengsheng Ji', 'Ronghang Zhu', 'Xiaodong Jiang'] | 2020-10-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 1.02391653e-01 2.38737628e-01 -2.37596795e-01 -2.70554662e-01
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7db0192f-25b6-4356-a8e4-115a8aebd731 | lp-dif-learning-local-pattern-specific-deep | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.pdf | LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes | Deep Implicit Function (DIF) has gained much popularity as an efficient 3D shape representation. To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder, where the decoder shares the geometric similarities among different local regions. Although such local methods can capture more local details, a large diversity of different local regions increases the difficulty of learning an implicit function when treating all regions equally using only a single decoder. In addition, these local regions often exhibit imbalanced distributions, where certain regions have significantly fewer observations. This leads that fine geometry details could not be preserved well. To solve this problem, we propose a novel Local Pattern-specific Implicit Function, named LP-DIF, for representing a shape with some clusters of local regions and multiple decoders, where each decoder only focuses on one cluster of local regions which share a certain pattern. Specifically, we first extract local codes for all regions, and then cluster them into multiple groups in the latent space, where similar regions sharing a common pattern fall into one group. After that, we train multiple decoders for mining local patterns of different groups, which simplifies learning of fine geometric details by reducing the diversity of local regions seen by each decoder. To further alleviate the data-imbalance problem, we introduce a region re-weighting module to each pattern-specific decoder by kernel density estimator, which dynamically re-weights the regions during learning. Our LP-DIF can restore more geometry details, and thus improve the quality of 3D reconstruction. Experiments demonstrate that our method can achieve the state-of-the-art performance over previous methods. Code is available at https://github.com/gtyxyz/lpdif. | ['Zhizhong Han', 'Yi Fang', 'Kanle Shi', 'Yue Gao', 'Yu-Shen Liu', 'Meng Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-representation', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [-3.50118250e-01 -2.96560898e-02 -3.23124290e-01 -2.79073209e-01
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2036a32a-587c-4234-99e0-4f3c4712f0f7 | document-level-event-extraction-via | 2105.14924 | null | https://arxiv.org/abs/2105.14924v1 | https://arxiv.org/pdf/2105.14924v1.pdf | Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker | Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al., 2019) show GIT outperforms the previous methods by 2.8 F1. Further analysis reveals GIT is effective in extracting multiple correlated events and event arguments that scatter across the document. Our code is available at https://github.com/RunxinXu/GIT. | ['Baobao Chang', 'Lei LI', 'Tianyu Liu', 'Runxin Xu'] | 2021-05-31 | null | https://aclanthology.org/2021.acl-long.274 | https://aclanthology.org/2021.acl-long.274.pdf | acl-2021-5 | ['document-level-event-extraction'] | ['natural-language-processing'] | [-1.23999305e-01 1.81634933e-01 -3.42124492e-01 -2.96910852e-02
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8a0e8e22-502f-4bf8-84df-2c0c92ac9333 | deep-polarimetric-hdr-reconstruction | 2203.1419 | null | https://arxiv.org/abs/2203.14190v1 | https://arxiv.org/pdf/2203.14190v1.pdf | Deep Polarimetric HDR Reconstruction | This paper proposes a novel learning based high-dynamic-range (HDR) reconstruction method using a polarization camera. We utilize a previous observation that polarization filters with different orientations can attenuate natural light differently, and we treat the multiple images acquired by the polarization camera as a set acquired under different exposure times, to introduce the development of solutions for the HDR reconstruction problem. We propose a deep HDR reconstruction framework with a feature masking mechanism that uses polarimetric cues available from the polarization camera, called Deep Polarimetric HDR Reconstruction (DPHR). The proposed DPHR obtains polarimetric information to propagate valid features through the network more effectively to regress the missing pixels. We demonstrate through both qualitative and quantitative evaluations that the proposed DPHR performs favorably than state-of-the-art HDR reconstruction algorithms. | ['Hong Zhang', 'Moein Shakeri', 'Juiwen Ting'] | 2022-03-27 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 1.35084704e-01 -1.56659886e-01 2.72208035e-01 -4.32123244e-01
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85ba3d72-81c7-418e-a1c0-eac766dadbda | bidirectional-cross-modal-knowledge | 2301.00182 | null | https://arxiv.org/abs/2301.00182v2 | https://arxiv.org/pdf/2301.00182v2.pdf | Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models | Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://github.com/whwu95/BIKE . | ['Wanli Ouyang', 'Yi Yang', 'Jingdong Wang', 'Haipeng Luo', 'Xiaohan Wang', 'Wenhao Wu'] | 2022-12-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-action-recognition', 'video-recognition', 'action-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.01706696e-02 -5.62128067e-01 -6.51930451e-01 -2.42800593e-01
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3518df0e-2654-44f5-8f73-2914a2f527d5 | sequential-changepoint-approach-for-online | 1407.5978 | null | http://arxiv.org/abs/1407.5978v3 | http://arxiv.org/pdf/1407.5978v3.pdf | Sequential Changepoint Approach for Online Community Detection | We present new algorithms for detecting the emergence of a community in large
networks from sequential observations. The networks are modeled using
Erdos-Renyi random graphs with edges forming between nodes in the community
with higher probability. Based on statistical changepoint detection
methodology, we develop three algorithms: the Exhaustive Search (ES), the
mixture, and the Hierarchical Mixture (H-Mix) methods. Performance of these
methods is evaluated by the average run length (ARL), which captures the
frequency of false alarms, and the detection delay. Numerical comparisons show
that the ES method performs the best; however, it is exponentially complex. The
mixture method is polynomially complex by exploiting the fact that the size of
the community is typically small in a large network. However, it may react to a
group of active edges that do not form a community. This issue is resolved by
the H-Mix method, which is based on a dendrogram decomposition of the network.
We present an asymptotic analytical expression for ARL of the mixture method
when the threshold is large. Numerical simulation verifies that our
approximation is accurate even in the non-asymptotic regime. Hence, it can be
used to determine a desired threshold efficiently. Finally, numerical examples
show that the mixture and the H-Mix methods can both detect a community quickly
with a lower complexity than the ES method. | ['David Marangoni-Simonsen', 'Yao Xie'] | 2014-07-22 | null | null | null | null | ['online-community-detection'] | ['graphs'] | [-1.77349113e-02 -5.01494221e-02 -8.77640694e-02 4.56055611e-01
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65d17c69-8083-489e-b358-b918b128f1e8 | sparse-graph-learning-for-spatiotemporal-time | 2205.13492 | null | https://arxiv.org/abs/2205.13492v2 | https://arxiv.org/pdf/2205.13492v2.pdf | Sparse Graph Learning from Spatiotemporal Time Series | Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data-generating process is unavailable and the practitioner is left with the problem of inferring from data which relational graph to use in the subsequent processing stages. We propose novel, principled - yet practical - probabilistic score-based methods that learn the relational dependencies as distributions over graphs while maximizing end-to-end the performance at task. The proposed graph learning framework is based on consolidated variance reduction techniques for Monte Carlo score-based gradient estimation, is theoretically grounded, and, as we show, effective in practice. In this paper, we focus on the time series forecasting problem and show that, by tailoring the gradient estimators to the graph learning problem, we are able to achieve state-of-the-art performance while controlling the sparsity of the learned graph and the computational scalability. We empirically assess the effectiveness of the proposed method on synthetic and real-world benchmarks, showing that the proposed solution can be used as a stand-alone graph identification procedure as well as a graph learning component of an end-to-end forecasting architecture. | ['Cesare Alippi', 'Daniele Zambon', 'Andrea Cini'] | 2022-05-26 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.51701719e-01 1.78836480e-01 1.83436479e-02 -3.83714765e-01
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e6135a3d-8c1f-4652-94db-368dd92aa404 | sign-language-translation-with-hierarchical | 2111.07258 | null | https://arxiv.org/abs/2111.07258v1 | https://arxiv.org/pdf/2111.07258v1.pdf | Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network | Sign language translation (SLT), which generates text in a spoken language from visual content in a sign language, is important to assist the hard-of-hearing community for their communications. Inspired by neural machine translation (NMT), most existing SLT studies adopted a general sequence to sequence learning strategy. However, SLT is significantly different from general NMT tasks since sign languages convey messages through multiple visual-manual aspects. Therefore, in this paper, these unique characteristics of sign languages are formulated as hierarchical spatio-temporal graph representations, including high-level and fine-level graphs of which a vertex characterizes a specified body part and an edge represents their interactions. Particularly, high-level graphs represent the patterns in the regions such as hands and face, and fine-level graphs consider the joints of hands and landmarks of facial regions. To learn these graph patterns, a novel deep learning architecture, namely hierarchical spatio-temporal graph neural network (HST-GNN), is proposed. Graph convolutions and graph self-attentions with neighborhood context are proposed to characterize both the local and the global graph properties. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed method. | ['Zhiyong Wang', 'Mohammed Bennamounm', 'Ah Chung Tsoi', 'Markus Hagenbuchner', 'Kun Hu', 'Jichao Kan'] | 2021-11-14 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.78085878e-01 8.79864767e-02 -1.44765556e-01 -2.17461780e-01
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63521fc7-ba43-4680-96e4-12992a85ab40 | creating-and-evaluating-resources-for | null | null | https://aclanthology.org/2021.wassa-1.20 | https://aclanthology.org/2021.wassa-1.20.pdf | Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi | In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sentiment score is assigned to each Sindhi opinion word. Afterwards, we determine the coverage of the proposed lexicon with subjectivity analysis. Moreover, we crawl multi-domain tweet corpus of news, sports, and finance. The crawled corpus is annotated by experienced annotators using the Doccano text annotation tool. The sentiment annotated corpus is evaluated by employing support vector machine (SVM), recurrent neural network (RNN) variants, and convolutional neural network (CNN). | ['Zenglin Xu', 'Saifullah Tumrani', 'Jay Kumar', 'Yong Dai', 'Naveed Ali', 'Wazir Ali'] | null | null | null | null | eacl-wassa-2021-4 | ['subjectivity-analysis', 'text-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.73434066e-03 -2.24794708e-02 -5.18489599e-01 -2.89549500e-01
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2a72043a-a29e-4bd7-989e-8565844bfcb9 | practical-fixed-parameter-algorithms-for | 2112.13175 | null | https://arxiv.org/abs/2112.13175v1 | https://arxiv.org/pdf/2112.13175v1.pdf | Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs | Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated as a Stackelberg game between one defender and one attacker. The attack graph contains one destination node and multiple entry nodes. The attacker's entry node is chosen by nature. The defender chooses to block a set of edges limited by his budget. The attacker then picks the shortest unblocked attack path. The defender aims to maximize the expected shortest path length for the attacker, where the expectation is taken over entry nodes. We observe that practical Active Directory attack graphs have small maximum attack path lengths and are structurally close to trees. We first show that even if the maximum attack path length is a constant, the problem is still $W[1]$-hard with respect to the defender's budget. Having a small maximum attack path length and a small budget is not enough to design fixed-parameter algorithms. If we further assume that the number of entry nodes is small, then we derive a fixed-parameter tractable algorithm. We then propose two other fixed-parameter algorithms by exploiting the tree-like features. One is based on tree decomposition and requires a small tree width. The other assumes a small number of splitting nodes (nodes with multiple out-going edges). Finally, the last algorithm is converted into a graph convolutional neural network based heuristic, which scales to larger graphs with more splitting nodes. | ['Hung Nguyen', 'Frank Neumann', 'Aneta Neumann', 'Jialiang Li', 'Mingyu Guo'] | 2021-12-25 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-1.12541273e-01 4.11160469e-01 -5.32208622e-01 3.13820451e-01
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63a1cc35-aee5-4887-be16-2d3935ff86b2 | improved-mutual-information-estimation | null | null | https://openreview.net/forum?id=S1lslCEYPB | https://openreview.net/pdf?id=S1lslCEYPB | Improved Mutual Information Estimation | We propose a new variational lower bound on the KL divergence and show that the Mutual Information (MI) can be estimated by maximizing this bound using a witness function on a hypothesis function class and an auxiliary scalar variable. If the function class is in a Reproducing Kernel Hilbert Space (RKHS), this leads to a jointly convex problem. We analyze the bound by deriving its dual formulation and show its connection to a likelihood ratio estimation problem. We show that the auxiliary variable introduced in our variational form plays the role of a Lagrange multiplier that enforces a normalization constraint on the likelihood ratio. By extending the function space to neural networks, we propose an efficient neural MI estimator, and validate its performance on synthetic examples, showing advantage over the existing baselines. We then demonstrate the strength of our estimator in large-scale self-supervised representation learning through MI maximization. | ['Tom Sercu*', 'Jerret Ross*', 'Pierre Dognin*', 'Igor Melnyk*', 'Youssef Mroueh*'] | 2019-09-25 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.22770500e-01 4.28133249e-01 -2.92436719e-01 -4.20717061e-01
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2e72337b-8183-49a2-a0e7-793d864b4a21 | local-region-learning-modules-for-point-cloud | 2303.17338 | null | https://arxiv.org/abs/2303.17338v1 | https://arxiv.org/pdf/2303.17338v1.pdf | Local region-learning modules for point cloud classification | Data organization via forming local regions is an integral part of deep learning networks that process 3D point clouds in a hierarchical manner. At each level, the point cloud is sampled to extract representative points and these points are used to be centers of local regions. The organization of local regions is of considerable importance since it determines the location and size of the receptive field at a particular layer of feature aggregation. In this paper, we present two local region-learning modules: Center Shift Module to infer the appropriate shift for each center point, and Radius Update Module to alter the radius of each local region. The parameters of the modules are learned through optimizing the loss associated with the particular task within an end-to-end network. We present alternatives for these modules through various ways of modeling the interactions of the features and locations of 3D points in the point cloud. We integrated both modules independently and together to the PointNet++ object classification architecture, and demonstrated that the modules contributed to a significant increase in classification accuracy for the ScanObjectNN data set. | ['Helin Dutagaci', 'Kaya Turgut'] | 2023-03-30 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.27167413e-01 7.02913776e-02 1.46532714e-01 -7.50157416e-01
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f06fcc19-343b-4d8f-be24-92e364cb41b6 | compositional-3d-scene-generation-using | 2303.12218 | null | https://arxiv.org/abs/2303.12218v2 | https://arxiv.org/pdf/2303.12218v2.pdf | Compositional 3D Scene Generation using Locally Conditioned Diffusion | Designing complex 3D scenes has been a tedious, manual process requiring domain expertise. Emerging text-to-3D generative models show great promise for making this task more intuitive, but existing approaches are limited to object-level generation. We introduce \textbf{locally conditioned diffusion} as an approach to compositional scene diffusion, providing control over semantic parts using text prompts and bounding boxes while ensuring seamless transitions between these parts. We demonstrate a score distillation sampling--based text-to-3D synthesis pipeline that enables compositional 3D scene generation at a higher fidelity than relevant baselines. | ['Gordon Wetzstein', 'Ryan Po'] | 2023-03-21 | null | null | null | null | ['scene-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 4.84608293e-01 2.78154165e-01 1.80723146e-01 -6.19861424e-01
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0fc161a5-c7fb-4400-967d-0b64b8036796 | multi-agent-path-finding-with-delay | 1612.05309 | null | http://arxiv.org/abs/1612.05309v1 | http://arxiv.org/pdf/1612.05309v1.pdf | Multi-Agent Path Finding with Delay Probabilities | Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to
large MAPF instances by searching for MAPF plans on 2 levels: The high-level
search resolves collisions between agents, and the low-level search plans paths
for single agents under the constraints imposed by the high-level search. We
make the following contributions to solve the MAPF problem with imperfect plan
execution with small average makespans: First, we formalize the MAPF Problem
with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the
use of robust plan-execution policies for valid MAPF-DP plans to control how
each agent proceeds along its path. Second, we discuss 2 classes of
decentralized robust plan-execution policies (called Fully Synchronized
Policies and Minimal Communication Policies) that prevent collisions during
plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP
solver (called Approximate Minimization in Expectation) that generates valid
MAPF-DP plans. | ['Sven Koenig', 'T. K. Satish Kumar', 'Hang Ma'] | 2016-12-15 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 6.73272386e-02 8.71072531e-01 -3.13678622e-01 -1.82431743e-01
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93d631d8-a896-47d1-9207-cc8fb51115d7 | efficient-deep-embedded-subspace-clustering | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.pdf | Efficient Deep Embedded Subspace Clustering | Recently deep learning methods have shown significant progress in data clustering tasks. Deep clustering methods (including distance-based methods and subspace-based methods) integrate clustering and feature learning into a unified framework, where there is a mutual promotion between clustering and representation. However, deep subspace clustering methods are usually in the framework of self-expressive model and hence have quadratic time and space complexities, which prevents their applications in large-scale clustering and real-time clustering. In this paper, we propose a new mechanism for deep clustering. We aim to learn the subspace bases from deep representation in an iterative refining manner while the refined subspace bases help learning the representation of the deep neural networks in return. The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios. More importantly, the clustering accuracy of the proposed method is much higher than its competitors. Extensive comparison studies with state-of-the-art clustering approaches on benchmark datasets demonstrate the superiority of the proposed method. | ['Zhao Zhang', 'Yunhe Zhang', 'Shiping Wang', 'Wenzhong Guo', 'Jicong Fan', 'Jinyu Cai'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['online-clustering'] | ['computer-vision'] | [-5.26309788e-01 -5.97638249e-01 4.69357185e-02 -3.49383205e-01
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f3f83344-05e8-4e80-adba-ab78d4aab5e9 | x-clip-end-to-end-multi-grained-contrastive | 2207.07285 | null | https://arxiv.org/abs/2207.07285v2 | https://arxiv.org/pdf/2207.07285v2.pdf | X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine-grained representations, has rarely been explored in prior research. Compared with fine-grained or coarse-grained contrasts, cross-grained contrast calculate the correlation between coarse-grained features and each fine-grained feature, and is able to filter out the unnecessary fine-grained features guided by the coarse-grained feature during similarity calculation, thus improving the accuracy of retrieval. To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP, for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity. To address this challenge, we propose the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and words on retrieval results. With multi-grained contrast and the proposed AOSM module, X-CLIP achieves outstanding performance on five widely-used video-text retrieval datasets, including MSR-VTT (49.3 R@1), MSVD (50.4 R@1), LSMDC (26.1 R@1), DiDeMo (47.8 R@1) and ActivityNet (46.2 R@1). It outperforms the previous state-of-theart by +6.3%, +6.6%, +11.1%, +6.7%, +3.8% relative improvements on these benchmarks, demonstrating the superiority of multi-grained contrast and AOSM. | ['Rongrong Ji', 'Ji Zhang', 'Ming Yan', 'Xiaoshuai Sun', 'Guohai Xu', 'Yiwei Ma'] | 2022-07-15 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 3.22825946e-02 -1.04251754e+00 -2.49530803e-02 -9.73152220e-02
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064b5900-dcfa-46f9-8a3f-4cc6b6d0e012 | transfer-learning-improves-french-cross | null | null | https://aclanthology.org/2022.vardial-1.12 | https://aclanthology.org/2022.vardial-1.12.pdf | Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022 | We describe the systems developed by the National Research Council Canada for the French Cross-Domain Dialect Identification shared task at the 2022 VarDial evaluation campaign. We evaluated two different approaches to this task: SVM and probabilistic classifiers exploiting n-grams as features, and trained from scratch on the data provided; and a pre-trained French language model, CamemBERT, that we fine-tuned on the dialect identification task. The latter method turned out to improve the macro-F1 score on the test set from 0.344 to 0.430 (25% increase), which indicates that transfer learning can be helpful for dialect identification. | ['Cyril Goutte', 'Serge Leger', 'Gabriel Bernier-Colborne'] | null | null | null | null | vardial-coling-2022-10 | ['dialect-identification'] | ['natural-language-processing'] | [-2.68704385e-01 -2.40198765e-02 -1.08441815e-01 -9.52406287e-01
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bdf2308c-c7bd-4f90-808f-1a94e8945b77 | learning-from-self-sampled-correct-and | 2205.14318 | null | https://arxiv.org/abs/2205.14318v2 | https://arxiv.org/pdf/2205.14318v2.pdf | Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions | Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one reference solution for each problem, despite the fact that there are often alternative solutions resembling different reasoning paths to the final answer. This way, the finetuned models are biased towards the limited reference solutions, which limits their generalization to unseen examples. To mitigate this issue, we propose to let the model perform sampling during training and learn from both self-sampled fully-correct solutions, which yield the correct answer upon execution, and partially-correct solutions, whose intermediate state matches an intermediate state of a known correct solution. We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space. Additionally, we explore various training objectives to support learning from multiple solutions per example and find they greatly affect the performance. Experiments on two math reasoning datasets show the effectiveness of our method compared to learning from a single reference solution with MLE, where we improve PASS@100 from 35.5% to 44.5% for GSM8K, and 27.6% to 36.2% PASS@80 for MathQA. Such improvements are also consistent across different model sizes. Our code is available at https://github.com/microsoft/TraceCodegen. | ['Jianfeng Gao', 'Dragomir Radev', 'Christopher Meek', 'Oleksandr Polozov', 'Chenglong Wang', 'Jeevana Priya Inala', 'Ansong Ni'] | 2022-05-28 | null | null | null | null | ['program-synthesis', 'gsm8k', 'arithmetic-reasoning'] | ['computer-code', 'natural-language-processing', 'reasoning'] | [ 7.29148602e-03 7.85457641e-02 -4.75057870e-01 -5.08471608e-01
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09cd4a7f-4d5f-451d-8cd4-a3f1cc7cbd37 | segmentation-free-direct-iris-localization | 2210.10403 | null | https://arxiv.org/abs/2210.10403v1 | https://arxiv.org/pdf/2210.10403v1.pdf | Segmentation-free Direct Iris Localization Networks | This paper proposes an efficient iris localization method without using iris segmentation and circle fitting. Conventional iris localization methods first extract iris regions by using semantic segmentation methods such as U-Net. Afterward, the inner and outer iris circles are localized using the traditional circle fitting algorithm. However, this approach requires high-resolution encoder-decoder networks for iris segmentation, so it causes computational costs to be high. In addition, traditional circle fitting tends to be sensitive to noise in input images and fitting parameters, causing the iris recognition performance to be poor. To solve these problems, we propose an iris localization network (ILN), that can directly localize pupil and iris circles with eyelid points from a low-resolution iris image. We also introduce a pupil refinement network (PRN) to improve the accuracy of pupil localization. Experimental results show that the combination of ILN and PRN works in 34.5 ms for one iris image on a CPU, and its localization performance outperforms conventional iris segmentation methods. In addition, generalized evaluation results show that the proposed method has higher robustness for datasets in different domain than other segmentation methods. Furthermore, we also confirm that the proposed ILN and PRN improve the iris recognition accuracy. | ['Masato Tsukada', 'Koichi Takahashi', 'Takahiro Toizumi'] | 2022-10-19 | null | null | null | null | ['iris-recognition', 'iris-segmentation'] | ['computer-vision', 'medical'] | [ 1.44115254e-01 -3.08104515e-01 -3.99413228e-01 -2.09274113e-01
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d518948b-5b9d-4803-9623-2dbb558c18fd | meta-auxiliary-learning-for-facial-action | 2105.0662 | null | https://arxiv.org/abs/2105.06620v1 | https://arxiv.org/pdf/2105.06620v1.pdf | Meta Auxiliary Learning for Facial Action Unit Detection | Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two highly correlated tasks, and facial expression (FE) is relatively easy to annotate, we consider learning AU detection and FER in a multi-task manner. However, the performance of the AU detection task cannot be always enhanced due to the negative transfer in the multi-task scenario. To alleviate this issue, we propose a Meta Auxiliary Learning method (MAL) that automatically selects highly related FE samples by learning adaptative weights for the training FE samples in a meta learning manner. The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties. Experimental results on several popular AU datasets demonstrate MAL consistently improves the AU detection performance compared with the state-of-the-art multi-task and auxiliary learning methods. MAL automatically estimates adaptive weights for the auxiliary FE samples according to their semantic relevance with the primary AU detection task. | ['Shiguang Shan', 'Yong Li'] | 2021-05-14 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection', 'auxiliary-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.41501349e-01 -1.12684583e-02 -1.39339328e-01 -5.32985449e-01
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66ff9e86-a329-4a4f-b565-f828c1c8bf19 | unsupervised-person-re-identification-with | 2103.0458 | null | https://arxiv.org/abs/2103.04580v1 | https://arxiv.org/pdf/2103.04580v1.pdf | Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering | Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations across disjoint camera views. In this paper, we address the unsupervised person Re-ID with a conceptually novel yet simple framework, termed as Multi-label Learning guided self-paced Clustering (MLC). MLC mainly learns discriminative features with three crucial modules, namely a multi-scale network, a multi-label learning module, and a self-paced clustering module. Specifically, the multi-scale network generates multi-granularity person features in both global and local views. The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank. After multi-label training for several epochs, the self-paced clustering joins in training and assigns a pseudo label for each image. The benefits of our MLC come from three aspects: i) the multi-scale person features for better similarity measurement, ii) the multi-label assignment based on the whole dataset ensures that every image can be trained, and iii) the self-paced clustering removes some noisy samples for better feature learning. Extensive experiments on three popular large-scale Re-ID benchmarks demonstrate that our MLC outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID. | ['Qi Hao', 'Yu Qiao', 'Xiaojiang Peng', 'Qing Li'] | 2021-03-08 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.06811598e-01 -4.69538122e-01 -1.71438962e-01 -5.90437770e-01
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c8b9c2b8-3605-4e17-9f65-70424336362c | grammatical-analysis-of-pretrained-sentence | 1901.03438 | null | https://arxiv.org/abs/1901.03438v4 | https://arxiv.org/pdf/1901.03438v4.pdf | Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments | Recent work on evaluating grammatical knowledge in pretrained sentence encoders gives a fine-grained view of a small number of phenomena. We introduce a new analysis dataset that also has broad coverage of linguistic phenomena. We annotate the development set of the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018) for the presence of 13 classes of syntactic phenomena including various forms of argument alternations, movement, and modification. We use this analysis set to investigate the grammatical knowledge of three pretrained encoders: BERT (Devlin et al., 2018), GPT (Radford et al., 2018), and the BiLSTM baseline from Warstadt et al. We find that these models have a strong command of complex or non-canonical argument structures like ditransitives (Sue gave Dan a book) and passives (The book was read). Sentences with long distance dependencies like questions (What do you think I ate?) challenge all models, but for these, BERT and GPT have a distinct advantage over the baseline. We conclude that recent sentence encoders, despite showing near-human performance on acceptability classification overall, still fail to make fine-grained grammaticality distinctions for many complex syntactic structures. | ['Alex Warstadt', 'Samuel R. Bowman'] | 2019-01-11 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 5.83602861e-02 5.00039399e-01 9.82967839e-02 -9.00247693e-01
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e9f8a4c3-73da-4e71-a7af-620cf0ac95e3 | maximal-cliques-on-multi-frame-proposal-graph | 2301.12352 | null | https://arxiv.org/abs/2301.12352v1 | https://arxiv.org/pdf/2301.12352v1.pdf | Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation | Unsupervised Video Object Segmentation (UVOS) aims at discovering objects and tracking them through videos. For accurate UVOS, we observe if one can locate precise segment proposals on key frames, subsequent processes are much simpler. Hence, we propose to reason about key frame proposals using a graph built with the object probability masks initially generated from multiple frames around the key frame and then propagated to the key frame. On this graph, we compute maximal cliques, with each clique representing one candidate object. By making multiple proposals in the clique to vote for the key frame proposal, we obtain refined key frame proposals that could be better than any of the single-frame proposals. A semi-supervised VOS algorithm subsequently tracks these key frame proposals to the entire video. Our algorithm is modular and hence can be used with any instance segmentation and semi-supervised VOS algorithm. We achieve state-of-the-art performance on the DAVIS-2017 validation and test-dev dataset. On the related problem of video instance segmentation, our method shows competitive performance with the previous best algorithm that requires joint training with the VOS algorithm. | ['Li Fuxin', 'Chanho Kim', 'Hung Nguyen', 'Jay Patravali', 'Jialin Yuan'] | 2023-01-29 | null | null | null | null | ['video-instance-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.87393421e-01 4.31837946e-01 -6.01385713e-01 -9.84347314e-02
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1c299687-ab4f-4562-b6bd-7c21ea23593a | lakebench-benchmarks-for-data-discovery-over | 2307.04217 | null | https://arxiv.org/abs/2307.04217v1 | https://arxiv.org/pdf/2307.04217v1.pdf | LakeBench: Benchmarks for Data Discovery over Data Lakes | Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to find related tables in data repositories. These tables can be unionable, joinable, or subsets of each other. There is a dearth of benchmarks for these tasks in the public domain, with related work targeting private datasets. In LakeBench, we develop multiple benchmarks for these tasks by using the tables that are drawn from a diverse set of data sources such as government data from CKAN, Socrata, and the European Central Bank. We compare the performance of 4 publicly available tabular foundational models on these tasks. None of the existing models had been trained on the data discovery tasks that we developed for this benchmark; not surprisingly, their performance shows significant room for improvement. The results suggest that the establishment of such benchmarks may be useful to the community to build tabular models usable for data discovery in data lakes. | ['Horst Samulowitz', 'Subhajit Chaudhury', 'Tejaswini Pedapati', 'Aamod Khatiwada', 'Harsha Kokel', 'Oktie Hassanzadeh', 'Ibrahim Abdelaziz', 'Julian Dolby', 'Kavitha Srinivas'] | 2023-07-09 | null | null | null | null | ['navigate'] | ['reasoning'] | [-5.33577204e-01 3.61640632e-01 -3.40942740e-01 -4.09227759e-01
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01682951-7fdb-4856-8c79-4d183c886bb9 | aero-audio-super-resolution-in-the-spectral | 2211.12232 | null | https://arxiv.org/abs/2211.12232v2 | https://arxiv.org/pdf/2211.12232v2.pdf | AERO: Audio Super Resolution in the Spectral Domain | We present AERO, a audio super-resolution model that processes speech and music signals in the spectral domain. AERO is based on an encoder-decoder architecture with U-Net like skip connections. We optimize the model using both time and frequency domain loss functions. Specifically, we consider a set of reconstruction losses together with perceptual ones in the form of adversarial and feature discriminator loss functions. To better handle phase information the proposed method operates over the complex-valued spectrogram using two separate channels. Unlike prior work which mainly considers low and high frequency concatenation for audio super-resolution, the proposed method directly predicts the full frequency range. We demonstrate high performance across a wide range of sample rates considering both speech and music. AERO outperforms the evaluated baselines considering Log-Spectral Distance, ViSQOL, and the subjective MUSHRA test. Audio samples and code are available at https://pages.cs.huji.ac.il/adiyoss-lab/aero | ['Yossi Adi', 'Or Tal', 'Moshe Mandel'] | 2022-11-22 | null | null | null | null | ['bandwidth-extension', 'audio-super-resolution', 'audio-super-resolution', 'bandwidth-extension'] | ['audio', 'audio', 'music', 'speech'] | [ 2.59396970e-01 -1.92464948e-01 -8.03909525e-02 -2.86671855e-02
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36cd9217-673c-4e9f-8c3e-567411c65760 | sparsestep-approximating-the-counting-norm | 1701.06967 | null | https://arxiv.org/abs/1701.06967v1 | https://arxiv.org/pdf/1701.06967v1.pdf | SparseStep: Approximating the Counting Norm for Sparse Regularization | The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem. The algorithm works by adding an approximation of the exact counting norm as a constraint on the model parameters and iteratively strengthening this approximation to arrive at a sparse solution. Theoretical analysis of the penalty function shows that the estimator yields unbiased estimates of the parameter vector. An iterative majorization algorithm is derived which has a straightforward implementation reminiscent of ridge regression. In addition, the SparseStep algorithm is compared with similar methods through a rigorous simulation study which shows it often outperforms existing methods in both model fit and prediction accuracy. | ['Andreas Alfons', 'Patrick J. F. Groenen', 'Gerrit J. J. van den Burg'] | 2017-01-24 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.04869494e-01 7.61276260e-02 -6.34042084e-01 -4.09820050e-01
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580059ad-6d0f-45b3-84fa-66fac51641d6 | efficient-modeling-of-morphing-wing-flight | 2110.01057 | null | https://arxiv.org/abs/2110.01057v1 | https://arxiv.org/pdf/2110.01057v1.pdf | Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules | Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very complex, particularly when the rules from insect flight dynamics (fast flapping dynamics and light wings) are not applicable. In these situations, widely used averaging techniques can fail quickly. The primary motivation is to find efficient models for complex forms of aerial locomotion where wings constitute a large part of body mass (i.e., dominant inertial effects) and deform in multiple directions (i.e., morphing wing). In these systems, high degrees of freedom yields complex inertial, Coriolis, and gravity terms. We use Algorithmic Differentiation (AD) and Bayesian filters computed with cubature rules conjointly to quickly estimate complex fluid-structure interactions. In general, Bayesian filters involve finding complex numerical integration (e.g., find posterior integrals). Using cubature rules to compute Gaussian-weighted integrals and AD, we show that the complex multi-degrees-of-freedom dynamics of morphing MAVs can be computed very efficiently and accurately. Therefore, our work facilitates closed-loop feedback control of these morphing MAVs. | ['Alireza Ramezani', 'Deniz Erdogmus', 'Yunus Bicer', 'Paul Ghanem'] | 2021-10-03 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.62125903e-01 -5.84607542e-01 3.05829883e-01 4.22486454e-01
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545370b8-95d0-4ca9-adda-6153a60a56c0 | monte-carlo-for-protein-structures | 2307.02177 | null | https://arxiv.org/abs/2307.02177v2 | https://arxiv.org/pdf/2307.02177v2.pdf | Monte Carlo for Protein Structures | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter "Molecular Dynamics" we have considered protein simulations from a dynamical point of view, using Newton's laws. In the current Chapter, we first take a step back and return to the bare minimum needed to simulate proteins, and show that proteins may be simulated in a more simple fashion, using the partition function directly. This means we do not have to calculate explicit forces, velocities, moments and do not even consider time explicitly. Instead, we will rely on the fact that for most systems we will want to simulate, the system is in a dynamic equilibrium; and that we want to find the most stable states in such systems by determining the relative stabilities between those states. | ['Sanne Abeln', 'K. Anton Feenstra', 'Jocelyne Vreede', 'Arriën Symon Rauh', 'Halima Mouhib', 'Maurits Dijkstra', 'Juami H. M. van Gils'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 2.02359796e-01 2.51474939e-02 -2.54723337e-02 -6.42176121e-02
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b6b07d2a-2770-49f7-b185-f3acb94bbd83 | flow-based-perturbation-for-cause-effect | null | null | https://scholar.google.com/citations?view_op=view_citation&hl=en&user=I1HeYbAAAAAJ&sortby=pubdate&citation_for_view=I1HeYbAAAAAJ:KlAtU1dfN6UC | https://dl.acm.org/doi/pdf/10.1145/3511808.3557326 | Flow-based Perturbation for Cause-effect Inference | A new causal discovery method is introduced to solve the bivariate causal discovery problem. The proposed algorithm leverages the expressive power of flow-based models and tries to learn the complex relationship between two variables. Algorithms have been developed to infer the causal direction according to empirical perturbation errors obtained from an invertible flow-based function. Theoretical results as well as experimental studies are presented to verify the proposed approach. Empirical evaluations demonstrate that our proposed method could outperform baseline methods on both synthetic and real-world datasets. | ['Ping Li', 'Shaogang Ren'] | 2022-10-17 | null | null | null | proceedings-of-the-31st-acm-international | ['causal-discovery', 'causal-identification'] | ['knowledge-base', 'reasoning'] | [ 2.22942859e-01 1.69552624e-01 -8.36793065e-01 -3.78790647e-01
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8ede2d4c-0ee1-4129-9bfa-092751b5a539 | bb_twtr-at-semeval-2017-task-4-twitter | 1704.06125 | null | http://arxiv.org/abs/1704.06125v1 | http://arxiv.org/pdf/1704.06125v1.pdf | BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs | In this paper we describe our attempt at producing a state-of-the-art Twitter
sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short
Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled
data to pre-train word embeddings. We then use a subset of the unlabeled data
to fine tune the embeddings using distant supervision. The final CNNs and LSTMs
are trained on the SemEval-2017 Twitter dataset where the embeddings are fined
tuned again. To boost performances we ensemble several CNNs and LSTMs together.
Our approach achieved first rank on all of the five English subtasks amongst 40
teams. | ['Mathieu Cliche'] | 2017-04-20 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.88061208e-01 4.44326140e-02 -3.37529600e-01 -8.34268153e-01
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f43bb5b4-9de0-45ab-8043-61eb34492b63 | posegan-a-pose-to-image-translation-framework | 2006.12712 | null | https://arxiv.org/abs/2006.12712v1 | https://arxiv.org/pdf/2006.12712v1.pdf | PoseGAN: A Pose-to-Image Translation Framework for Camera Localization | Camera localization is a fundamental requirement in robotics and computer vision. This paper introduces a pose-to-image translation framework to tackle the camera localization problem. We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image translation. PoseGANs feature a number of innovations including a distance metric based conditional discriminator to conduct camera localization and a pose estimation technique for generated camera images as a stronger constraint to improve camera localization performance. Compared with learning-based regression methods such as PoseNet, PoseGANs can achieve better performance with model sizes that are 70% smaller. In addition, PoseGANs introduce the view synthesis technique to establish the correspondence between the 2D images and the scene, \textit{i.e.}, given a pose, PoseGANs are able to synthesize its corresponding camera images. Furthermore, we demonstrate that PoseGANs differ in principle from structure-based localization and learning-based regressions for camera localization, and show that PoseGANs exploit the geometric structures to accomplish the camera localization task, and is therefore more stable than and superior to learning-based regressions which rely on local texture features instead. In addition to camera localization and view synthesis, we also demonstrate that PoseGANs can be successfully used for other interesting applications such as moving object elimination and frame interpolation in video sequences. | ['Qing Li', 'Guoping Qiu', 'Kanglin Liu'] | 2020-06-23 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 2.40815207e-01 9.74214897e-02 -6.06227629e-02 5.86434044e-02
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aa122251-217a-4f83-81f1-e7cb12d055d8 | time-series-prediction-about-air-quality | 2111.11848 | null | https://arxiv.org/abs/2111.11848v1 | https://arxiv.org/pdf/2111.11848v1.pdf | Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping | This systematic mapping study investigates the use of Long short-term memory networks to predict time series data about air quality, trying to understand the reasons, characteristics and methods available in the scientific literature, identify gaps in the researched area and potential approaches that can be exploited on later studies. | ['Vinicius F. S. Mota', 'Lucas L. S. Sachetti'] | 2021-11-22 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.14003226e-01 -1.50750458e-01 -6.22377634e-01 -2.96240479e-01
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4dad19ba-3943-4d37-8a23-e3ef9cbd4552 | introducing-explicit-gaze-constraints-to-face | 2305.16138 | null | https://arxiv.org/abs/2305.16138v1 | https://arxiv.org/pdf/2305.16138v1.pdf | Introducing Explicit Gaze Constraints to Face Swapping | Face swapping combines one face's identity with another face's non-appearance attributes (expression, head pose, lighting) to generate a synthetic face. This technology is rapidly improving, but falls flat when reconstructing some attributes, particularly gaze. Image-based loss metrics that consider the full face do not effectively capture the perceptually important, yet spatially small, eye regions. Improving gaze in face swaps can improve naturalness and realism, benefiting applications in entertainment, human computer interaction, and more. Improved gaze will also directly improve Deepfake detection efforts, serving as ideal training data for classifiers that rely on gaze for classification. We propose a novel loss function that leverages gaze prediction to inform the face swap model during training and compare against existing methods. We find all methods to significantly benefit gaze in resulting face swaps. | ['Eakta Jain', 'Frederick Shic', 'Ethan Wilson'] | 2023-05-25 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'eye-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.96637857e-01 4.08116996e-01 -1.48860306e-01 -7.42510617e-01
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6dc4bba8-bab7-4b52-b83d-af534970ba22 | causal-neural-graph-collaborative-filtering | 2307.04384 | null | https://arxiv.org/abs/2307.04384v1 | https://arxiv.org/pdf/2307.04384v1.pdf | Causal Neural Graph Collaborative Filtering | Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF) models. One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings for CF models. However, the quality of the embeddings significantly impacts the recommendation performance of GCF models. In this paper, we argue that existing graph learning methods are insufficient in generating satisfactory embeddings for CF models. This is because they aggregate neighboring node messages directly, which can result in incorrect estimations of user-item correlations. To overcome this limitation, we propose a novel approach that incorporates causal modeling to explicitly encode the causal effects of neighboring nodes on the target node. This approach enables us to identify spurious correlations and uncover the root causes of user preferences. We introduce Causal Neural Graph Collaborative Filtering (CNGCF), the first causality-aware graph learning framework for CF. CNGCF integrates causal modeling into the graph representation learning process, explicitly coupling causal effects between node pairs into the core message-passing process of graph learning. As a result, CNGCF yields causality-aware embeddings that promote robust recommendations. Our extensive experiments demonstrate that CNGCF provides precise recommendations that align with user preferences. Therefore, our proposed framework can address the limitations of existing GCF models and offer a more effective solution for recommendation systems. | ['Guandong Xu', 'Wei Huang', 'Dianer Yu', 'Qian Li', 'Xiangmeng Wang'] | 2023-07-10 | null | null | null | null | ['graph-learning', 'representation-learning', 'graph-representation-learning', 'recommendation-systems', 'collaborative-filtering'] | ['graphs', 'methodology', 'methodology', 'miscellaneous', 'miscellaneous'] | [-3.61494541e-01 3.75541821e-02 -5.47395468e-01 -2.72542268e-01
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1b4ccf4f-b3da-4683-b02b-6f88d0b2504d | improving-paraphrase-generation-models-with | null | null | https://openreview.net/forum?id=IYnDDdoHCyO | https://openreview.net/pdf?id=IYnDDdoHCyO | Improving Paraphrase Generation models with machine translation generated pre-training | Paraphrase generation is a fundamental and longstanding problem in the Natural Language Processing field. With the huge success of pre-trained transformers, the pre-train–fine-tune approach has become a standard choice. At the same time, popular task-agnostic pre-trainings usually require terabyte datasets and hundreds of GPUs, while available pre-trained models are limited to architecture and size. We propose a simple and efficient pre-training approach specifically for paraphrase generation, which noticeably boosts model quality and doesn't require significant computing power. We also investigate how this procedure influences the scores across different architectures and show that it helps them all. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.78851727e-02 -4.64191556e-01 -1.45616114e-01 -3.63420665e-01
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b57ba8a5-081f-4ff7-a43b-301c6cb36904 | habitat-matterport-3d-dataset-hm3d-1000-large | 2109.08238 | null | https://arxiv.org/abs/2109.08238v1 | https://arxiv.org/pdf/2109.08238v1.pdf | Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI | We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-floor residences, stores, and other private indoor spaces. HM3D surpasses existing datasets available for academic research in terms of physical scale, completeness of the reconstruction, and visual fidelity. HM3D contains 112.5k m^2 of navigable space, which is 1.4 - 3.7x larger than other building-scale datasets such as MP3D and Gibson. When compared to existing photorealistic 3D datasets such as Replica, MP3D, Gibson, and ScanNet, images rendered from HM3D have 20 - 85% higher visual fidelity w.r.t. counterpart images captured with real cameras, and HM3D meshes have 34 - 91% fewer artifacts due to incomplete surface reconstruction. The increased scale, fidelity, and diversity of HM3D directly impacts the performance of embodied AI agents trained using it. In fact, we find that HM3D is `pareto optimal' in the following sense -- agents trained to perform PointGoal navigation on HM3D achieve the highest performance regardless of whether they are evaluated on HM3D, Gibson, or MP3D. No similar claim can be made about training on other datasets. HM3D-trained PointNav agents achieve 100% performance on Gibson-test dataset, suggesting that it might be time to retire that episode dataset. | ['Dhruv Batra', 'Yili Zhao', 'Manolis Savva', 'Angel X. Chang', 'Andrew Westbury', 'Wojciech Galuba', 'Eric Undersander', 'John Turner', 'Alex Clegg', 'Oleksandr Maksymets', 'Erik Wijmans', 'Aaron Gokaslan', 'Santhosh K. Ramakrishnan'] | 2021-09-16 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-2.60377049e-01 1.64384797e-01 3.03771704e-01 1.62964277e-02
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c05afc99-ea9d-4c1f-929b-95ce59fac17a | prototypical-pseudo-label-denoising-and | 2101.10979 | null | https://arxiv.org/abs/2101.10979v2 | https://arxiv.org/pdf/2101.10979v2.pdf | Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation | Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods. We will make the code publicly available. | ['Fang Wen', 'Yong Wang', 'Dong Chen', 'Ting Zhang', 'Bo Zhang', 'Pan Zhang'] | 2021-01-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.81569153e-01 1.78562969e-01 -5.84345222e-01 -8.13349545e-01
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ddc1f08b-8073-4ad0-a1b5-ee48f9a6a83c | mrf-zoom-a-fast-dictionary-searching | 1506.05393 | null | http://arxiv.org/abs/1506.05393v1 | http://arxiv.org/pdf/1506.05393v1.pdf | MRF-ZOOM: A Fast Dictionary Searching Algorithm for Magnetic Resonance Fingerprinting | Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously
quantifying multiple MR parameters using one temporally resolved MR scan. But
its brute-force dictionary generating and searching (DGS) process causes a huge
disk space demand and computational burden, prohibiting it from a practical
multiple slice high-definition imaging. The purpose of this paper was to
provide a fast and space efficient DGS algorithm for MRF. Based on an empirical
analysis of properties of the distance function of the acquired MRF signal and
the pre-defined MRF dictionary entries, we proposed a parameter separable MRF
DGS method, which breaks the multiplicative computation complexity into an
additive one and enabling a resolution scalable multi-resolution DGS process,
which was dubbed as MRF ZOOM. The evaluation results showed that MRF ZOOM was
hundreds or thousands of times faster than the original brute-force DGS method.
The acceleration was even higher when considering the time difference for
generating the dictionary. Using a high precision quantification, MRF can find
the right parameter values for a 64x64 imaging slice in 117 secs. Our data also
showed that spatial constraints can be used to further speed up MRF ZOOM. | ['Ze Wang'] | 2015-06-17 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 1.97118744e-01 -4.33206946e-01 1.82315275e-01 -2.78170615e-01
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4109893b-0960-496e-b7f8-955bd3c356e2 | interactive-video-object-segmentation-in-the | 1801.00269 | null | http://arxiv.org/abs/1801.00269v1 | http://arxiv.org/pdf/1801.00269v1.pdf | Interactive Video Object Segmentation in the Wild | In this paper we present our system for human-in-the-loop video object
segmentation. The backbone of our system is a method for one-shot video object
segmentation. While fast, this method requires an accurate pixel-level
segmentation of one (or several) frames as input. As manually annotating such a
segmentation is impractical, we propose a deep interactive image segmentation
method, that can accurately segment objects with only a handful of clicks. On
the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on
average, setting the new state of the art. Furthermore, as our method
iteratively refines an initial segmentation, it can effectively correct frames
where the video object segmentation fails, thus allowing users to quickly
obtain high quality results even on challenging sequences. Finally, we
investigate usage patterns and give insights in how many steps users take to
annotate frames, what kind of corrections they provide, etc., thus giving
important insights for further improving interactive video segmentation. | ['Arnaud Benard', 'Michael Gygli'] | 2017-12-31 | null | null | null | null | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 2.36342892e-01 -2.08860333e-03 -1.22139297e-01 -4.16039884e-01
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6218c370-84a6-43c3-a5e4-eca3f4245eb0 | displacement-filed-calculation-of-large-scale | 2303.15868 | null | https://arxiv.org/abs/2303.15868v2 | https://arxiv.org/pdf/2303.15868v2.pdf | Displacement field calculation of large-scale structures using computer vision with physical constraints | Because of the advantages of easy deployment, low cost and non-contact, computer vision-based structural displacement acquisition technique has received wide attention and research in recent years. However, the displacement field acquisition of large-scale structures is a challenging topic due to the contradiction of camera field of view and resolution. This paper presents a large-scale structural displacement field calculation framework with integrated computer vision and physical constraints using only one camera. Firstly, the full-field image of the large-scale structure is obtained by processing the multi-view image using image stitching technique; secondly, the full-field image is meshed and the node displacements are calculated using an improved template matching method; and finally, the non-node displacements are described using shape functions considering physical constraints. The developed framework was validated using a scaled bridge model and evaluated by the proposed evaluation index for displacement field calculation accuracy. This paper can provide an effective way to obtain displacement fields of large-scale structures efficiently and cost-effectively. | ['Shunlong Li', 'Hao Di', 'Fanzeng Meng', 'Yi Zhuo', 'Peng Zhong', 'Yapeng Guo'] | 2023-03-28 | null | null | null | null | ['template-matching', 'image-stitching'] | ['computer-vision', 'computer-vision'] | [ 2.61494875e-01 -3.10348481e-01 4.61508155e-01 1.13350123e-01
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aa3c33c6-512b-418a-9cfc-f92be284bf17 | a-study-on-cross-corpus-speech-emotion | 2201.03511 | null | https://arxiv.org/abs/2201.03511v1 | https://arxiv.org/pdf/2201.03511v1.pdf | A study on cross-corpus speech emotion recognition and data augmentation | Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when presented with speakers or acoustic conditions that were not visible during training. This paper investigates the impact of cross-corpus data complementation and data augmentation on the performance of SER models in matched (test-set from same corpus) and mismatched (test-set from different corpus) conditions. Investigations using six emotional speech corpora that include single and multiple speakers as well as variations in emotion style (acted, elicited, natural) and recording conditions are presented. Observations show that, as expected, models trained on single corpora perform best in matched conditions while performance decreases between 10-40% in mismatched conditions, depending on corpus specific features. Models trained on mixed corpora can be more stable in mismatched contexts, and the performance reductions range from 1 to 8% when compared with single corpus models in matched conditions. Data augmentation yields additional gains up to 4% and seem to benefit mismatched conditions more than matched ones. | ['Svetlana Stoyanchev', 'Simon Keizer', 'Rama Doddipatla', 'Norbert Braunschweiler'] | 2022-01-10 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 1.93925560e-01 1.09343499e-01 4.60792392e-01 -5.69253147e-01
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f90bf90d-51b7-4145-b83d-985f53c16120 | contrastive-predictive-autoencoders-for | 2305.12959 | null | https://arxiv.org/abs/2305.12959v1 | https://arxiv.org/pdf/2305.12959v1.pdf | Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning | We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal representations. Specifically, dense point cloud segments are first input into an encoder to extract embeddings. All but the last ones are then aggregated by a context-aware autoregressor to make predictions for the last target segment. Towards the goal of modeling multi-granularity structures, local and global contrastive learning are performed between predictions and targets. To further improve the generalization of representations, the predictions are also utilized to reconstruct raw point cloud sequences by a decoder, where point cloud colorization is employed to discriminate against different frames. By combining classic contrast and reconstruction paradigms, it makes the learned representations with both global discrimination and local perception. We conduct experiments on four point cloud sequence benchmarks, and report the results on action recognition and gesture recognition under multiple experimental settings. The performances are comparable with supervised methods and show powerful transferability. | ['Gang Xiao', 'Zhiqiang Shen', 'Xiaoxiao Sheng'] | 2023-05-22 | null | null | null | null | ['colorization', 'gesture-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.19524729e-01 -4.18161780e-01 -3.23441088e-01 -5.30192435e-01
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2aba6bed-5941-4db6-8a45-5fb5b49e4179 | reconstruction-driven-dynamic-refinement | 2304.04581 | null | https://arxiv.org/abs/2304.04581v1 | https://arxiv.org/pdf/2304.04581v1.pdf | Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation | Glaucoma is one of the leading causes of irreversible blindness. Segmentation of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in glaucoma screening. Although many deep learning models have been constructed for this task, it remains challenging to train an OD/OC segmentation model that could be deployed successfully to different healthcare centers. The difficulties mainly comes from the domain shift issue, i.e., the fundus images collected at these centers usually vary greatly in the tone, contrast, and brightness. To address this issue, in this paper, we propose a novel unsupervised domain adaptation (UDA) method called Reconstruction-driven Dynamic Refinement Network (RDR-Net), where we employ a due-path segmentation backbone for simultaneous edge detection and region prediction and design three modules to alleviate the domain gap. The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way. It also uses a style-consistency constraint to force the network to retain more domain-invariant information. The low-level feature refinement (LFR) module employs input-specific dynamic convolutions to suppress the domain-variant information in the obtained low-level features. The prediction-map alignment (PMA) module elaborates the entropy-driven adversarial learning to encourage the network to generate source-like boundaries and regions. We evaluated our RDR-Net against state-of-the-art solutions on four public fundus image datasets. Our results indicate that RDR-Net is superior to competing models in both segmentation performance and generalization ability | ['Yong Xia', 'Yongsheng Pan', 'Ziyang Chen'] | 2023-04-10 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 3.55046839e-01 2.40668282e-01 -2.59587616e-02 -2.41476223e-01
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