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5d6a2638-c858-469c-967a-96e894a8ef2e | deepgd-a-multi-objective-black-box-test | 2303.04878 | null | https://arxiv.org/abs/2303.04878v1 | https://arxiv.org/pdf/2303.04878v1.pdf | DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks | Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN models may be challenging due to the complexity and size of their input domain. Particularly, testing DNN models often requires generating or exploring large unlabeled datasets. In practice, DNN test oracles, which identify the correct outputs for inputs, often require expensive manual effort to label test data, possibly involving multiple experts to ensure labeling correctness. In this paper, we propose DeepGD, a black-box multi-objective test selection approach for DNN models. It reduces the cost of labeling by prioritizing the selection of test inputs with high fault revealing power from large unlabeled datasets. DeepGD not only selects test inputs with high uncertainty scores to trigger as many mispredicted inputs as possible but also maximizes the probability of revealing distinct faults in the DNN model by selecting diverse mispredicted inputs. The experimental results conducted on four widely used datasets and five DNN models show that in terms of fault-revealing ability: (1) White-box, coverage-based approaches fare poorly, (2) DeepGD outperforms existing black-box test selection approaches in terms of fault detection, and (3) DeepGD also leads to better guidance for DNN model retraining when using selected inputs to augment the training set. | ['Lionel Briand', 'Mahboubeh Dadkhah', 'Manel Abdellatif', 'Zohreh Aghababaeyan'] | 2023-03-08 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.50122362e-01 -4.70874757e-02 -2.73656845e-01 -5.27002096e-01
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e5a16158-93f8-4e2a-be6c-2f4e5b177700 | semi-supervised-acoustic-model-training-for-1 | 1906.08647 | null | https://arxiv.org/abs/1906.08647v2 | https://arxiv.org/pdf/1906.08647v2.pdf | Semi-supervised acoustic model training for five-lingual code-switched ASR | This paper presents recent progress in the acoustic modelling of under-resourced code-switched (CS) speech in multiple South African languages. We consider two approaches. The first constructs separate bilingual acoustic models corresponding to language pairs (English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho). The second constructs a single unified five-lingual acoustic model representing all the languages (English, isiZulu, isiXhosa, Setswana and Sesotho). For these two approaches we consider the effectiveness of semi-supervised training to increase the size of the very sparse acoustic training sets. Using approximately 11 hours of untranscribed speech, we show that both approaches benefit from semi-supervised training. The bilingual TDNN-F acoustic models also benefit from the addition of CNN layers (CNN-TDNN-F), while the five-lingual system does not show any significant improvement. Furthermore, because English is common to all language pairs in our data, it dominates when training a unified language model, leading to improved English ASR performance at the expense of the other languages. Nevertheless, the five-lingual model offers flexibility because it can process more than two languages simultaneously, and is therefore an attractive option as an automatic transcription system in a semi-supervised training pipeline. | ['Febe De Wet', 'Emre Yilmaz', 'Thomas Niesler', 'Astik Biswas', 'Ewald van der Westhuizen'] | 2019-06-20 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [-1.03787452e-01 -1.65375546e-02 2.19863057e-01 -3.37828696e-01
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8327fbf2-4d26-440a-806f-6cf00fe2e3e1 | autocolor-learned-light-power-control-for | 2305.01611 | null | https://arxiv.org/abs/2305.01611v1 | https://arxiv.org/pdf/2305.01611v1.pdf | AutoColor: Learned Light Power Control for Multi-Color Holograms | Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce \projectname, the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that \projectname significantly decreases the number of steps required to optimize multi-color holograms from $>1000$ to $70$ iteration steps without compromising image quality. | ['Kaan Akşit', 'Qi Sun', 'Hakan Urey', 'Koray Kavaklı', 'Yicheng Zhan'] | 2023-05-02 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 2.35876501e-01 -1.37482002e-01 5.71769059e-01 -3.52047265e-01
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0c7adbea-dd02-4e2f-8b4f-38a51317a572 | mots-multi-object-tracking-and-segmentation | 1902.03604 | null | http://arxiv.org/abs/1902.03604v2 | http://arxiv.org/pdf/1902.03604v2.pdf | MOTS: Multi-Object Tracking and Segmentation | This paper extends the popular task of multi-object tracking to multi-object
tracking and segmentation (MOTS). Towards this goal, we create dense
pixel-level annotations for two existing tracking datasets using a
semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel
masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames.
For evaluation, we extend existing multi-object tracking metrics to this new
task. Moreover, we propose a new baseline method which jointly addresses
detection, tracking, and segmentation with a single convolutional network. We
demonstrate the value of our datasets by achieving improvements in performance
when training on MOTS annotations. We believe that our datasets, metrics and
baseline will become a valuable resource towards developing multi-object
tracking approaches that go beyond 2D bounding boxes. We make our annotations,
code, and models available at https://www.vision.rwth-aachen.de/page/mots. | ['Berin Balachandar Gnana Sekar', 'Jonathon Luiten', 'Bastian Leibe', 'Paul Voigtlaender', 'Michael Krause', 'Andreas Geiger', 'Aljosa Osep'] | 2019-02-10 | mots-multi-object-tracking-and-segmentation-2 | http://openaccess.thecvf.com/content_CVPR_2019/html/Voigtlaender_MOTS_Multi-Object_Tracking_and_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Voigtlaender_MOTS_Multi-Object_Tracking_and_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 6.90651536e-02 -3.58273923e-01 -2.18015015e-01 -2.71652073e-01
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f4c5752d-48b7-4cb8-9237-11dc9b3fa565 | a-copy-mechanism-for-handling-knowledge-base | 2211.10271 | null | https://arxiv.org/abs/2211.10271v1 | https://arxiv.org/pdf/2211.10271v1.pdf | A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation | Neural Machine Translation (NMT) models from English to SPARQL are a promising development for SPARQL query generation. However, current architectures are unable to integrate the knowledge base (KB) schema and handle questions on knowledge resources, classes, and properties unseen during training, rendering them unusable outside the scope of topics covered in the training set. Inspired by the performance gains in natural language processing tasks, we propose to integrate a copy mechanism for neural SPARQL query generation as a way to tackle this issue. We illustrate our proposal by adding a copy layer and a dynamic knowledge base vocabulary to two Seq2Seq architectures (CNNs and Transformers). This layer makes the models copy KB elements directly from the questions, instead of generating them. We evaluate our approach on state-of-the-art datasets, including datasets referencing unknown KB elements and measure the accuracy of the copy-augmented architectures. Our results show a considerable increase in performance on all datasets compared to non-copy architectures. | ['Samuel Reyd', 'Amal Zouaq', 'Rose Hirigoyen'] | 2022-11-18 | null | null | null | null | ['nmt'] | ['computer-code'] | [-3.59523520e-02 6.83519304e-01 -1.21633828e-01 -5.11433601e-01
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360482b1-b033-4656-a98b-54caf8f6a508 | explainability-aware-one-point-attack-for | 2110.04158 | null | https://arxiv.org/abs/2110.04158v3 | https://arxiv.org/pdf/2110.04158v3.pdf | Explainability-Aware One Point Attack for Point Cloud Neural Networks | With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interest to investigate the reliability of point cloud networks by adversarial attacks. However, most of the existing studies aim to deceive humans or defense algorithms, while the few that address the operation principles of the models themselves remain flawed in terms of critical point selection. In this work, we propose two adversarial methods: One Point Attack (OPA) and Critical Traversal Attack (CTA), which incorporate the explainability technologies and aim to explore the intrinsic operating principle of point cloud networks and their sensitivity against critical points perturbations. Our results show that popular point cloud networks can be deceived with almost $100\%$ success rate by shifting only one point from the input instance. In addition, we show the interesting impact of different point attribution distributions on the adversarial robustness of point cloud networks. Finally, we discuss how our approaches facilitate the explainability study for point cloud networks. To the best of our knowledge, this is the first point-cloud-based adversarial approach concerning explainability. Our code is available at https://github.com/Explain3D/Exp-One-Point-Atk-PC. | ['Helena Kotthaus', 'Hanxiao Tan'] | 2021-10-08 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-2.64164865e-01 1.84029117e-01 1.11101851e-01 -8.55904371e-02
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552dc2f6-e104-41e6-95e9-3adbb25f9167 | topic-guided-variational-auto-encoder-for | null | null | https://aclanthology.org/N19-1015 | https://aclanthology.org/N19-1015.pdf | Topic-Guided Variational Auto-Encoder for Text Generation | We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each mixture component corresponds to a latent topic, which provides a guidance to generate sentences under the topic. The neural topic module and the VAE-based neural sequence module in our model are learned jointly. In particular, a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during the model inference. Experimental results show that our TGVAE outperforms its competitors on both unconditional and conditional text generation, which can also generate semantically-meaningful sentences with various topics. | ['Lawrence Carin', 'Dinghan Shen', 'Guoyin Wang', 'Wenlin Wang', 'Changyou Chen', 'Zhe Gan', 'Hongteng Xu', 'Ruiyi Zhang'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 8.40214267e-02 5.80609620e-01 -1.20432273e-01 -3.38060230e-01
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1dc5120b-07ee-4ca2-b703-7273d206af08 | spectroscopic-and-photometric-redshift | 2101.02532 | null | https://arxiv.org/abs/2101.02532v1 | https://arxiv.org/pdf/2101.02532v1.pdf | Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS) | The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the Universe, e.g. weak lensing and galaxy clustering. In this work, we explore the accuracies of spec-z and photo-z that can be obtained in the China Space Station Optical Surveys (CSS-OS), which is a next-generation space survey, using neural networks. The 1-dimensional Convolutional Neural Networks (1-d CNN) and Multi-Layer Perceptron (MLP, one of the simplest forms of Artificial Neural Network) are employed to derive the spec-z and photo-z, respectively. The mock spectral and photometric data used for training and testing the networks are generated based on the COSMOS catalog. The networks have been trained with noisy data by creating Gaussian random realizations to reduce the statistical effects, resulting in similar redshift accuracy for both high-SNR (signal to noise ratio) and low-SNR data. The probability distribution functions (PDFs) of the predicted redshifts are also derived via Gaussian random realizations of the testing data, and then the best-fit redshifts and 1-sigma errors also can be obtained. We find that our networks can provide excellent redshift estimates with accuracies ~0.001 and 0.01 on spec-z and photo-z, respectively. Compared to existing photo-z codes, our MLP has similar accuracy but is more efficient in the training process. The fractions of catastrophic redshifts or outliers can be dramatically suppressed comparing to the ordinary template-fitting method. This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys. | ['Liping Fu', 'Zuhui Fan', 'Valeria Amaro', 'Xuelei Chen', 'Ye Cao', 'Xin Zhang', 'Xian-Min Meng', 'Yan Gong', 'Xingchen Zhou'] | 2021-01-07 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [-3.67133021e-01 -1.20515600e-01 3.47264737e-01 -6.74131691e-01
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09fd9fac-2332-47f5-ac8e-d1bd700c9b2b | communication-efficient-federated-learning-6 | 2104.12416 | null | https://arxiv.org/abs/2104.12416v1 | https://arxiv.org/pdf/2104.12416v1.pdf | Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression | Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients. During the training process of FL, the central server and distributed clients need to exchange a vast amount of model information periodically. To address the challenge of communication-intensive training, we propose a new training method, referred to as federated learning with dual-side low-rank compression (FedDLR), where the deep learning model is compressed via low-rank approximations at both the server and client sides. The proposed FedDLR not only reduces the communication overhead during the training stage but also directly generates a compact model to speed up the inference process. We shall provide convergence analysis, investigate the influence of the key parameters, and empirically show that FedDLR outperforms the state-of-the-art solutions in terms of both the communication and computation efficiency. | ['Khaled B. Letaief', 'Jun Zhang', 'Xianghao Yu', 'Zhefeng Qiao'] | 2021-04-26 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [-2.28653893e-01 -3.01593006e-01 -2.95821577e-01 -5.95162392e-01
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4a061838-d8da-4069-9ab0-10513e737a8d | cubifae-3d-monocular-camera-space | 2006.04080 | null | https://arxiv.org/abs/2006.04080v2 | https://arxiv.org/pdf/2006.04080v2.pdf | CubifAE-3D: Monocular Camera Space Cubification for Auto-Encoder based 3D Object Detection | We introduce a method for 3D object detection using a single monocular image. Starting from a synthetic dataset, we pre-train an RGB-to-Depth Auto-Encoder (AE). The embedding learnt from this AE is then used to train a 3D Object Detector (3DOD) CNN which is used to regress the parameters of 3D object poses after the encoder from the AE generates a latent embedding from the RGB image. We show that we can pre-train the AE using paired RGB and depth images from simulation data once and subsequently only train the 3DOD network using real data, comprising of RGB images and 3D object pose labels (without the requirement of dense depth). Our 3DOD network utilizes a particular `cubification' of 3D space around the camera, where each cuboid is tasked with predicting N object poses, along with their class and confidence values. The AE pre-training and this method of dividing the 3D space around the camera into cuboids give our method its name - CubifAE-3D. We demonstrate results for monocular 3D object detection in the Autonomous Vehicle (AV) use-case with the Virtual KITTI 2 and the KITTI datasets. | ['Punarjay Chakravarty', 'Shubham Shrivastava'] | 2020-06-07 | null | null | null | null | ['3d-object-detection-from-monocular-images'] | ['computer-vision'] | [-2.97448546e-01 3.78065437e-01 9.80558526e-03 -3.78365219e-01
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9af5d2db-86fd-4bb7-a8e3-782525d17b29 | glancenets-interpretabile-leak-proof-concept | 2205.15612 | null | https://arxiv.org/abs/2205.15612v2 | https://arxiv.org/pdf/2205.15612v2.pdf | GlanceNets: Interpretabile, Leak-proof Concept-based Models | There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear notions of interpretability, and fail to acquire concepts with the intended semantics. We address this by providing a clear definition of interpretability in terms of alignment between the model's representation and an underlying data generation process, and introduce GlanceNets, a new CBM that exploits techniques from disentangled representation learning and open-set recognition to achieve alignment, thus improving the interpretability of the learned concepts. We show that GlanceNets, paired with concept-level supervision, achieve better alignment than state-of-the-art approaches while preventing spurious information from unintendedly leaking into the learned concepts. | ['Stefano Teso', 'Andrea Passerini', 'Emanuele Marconato'] | 2022-05-31 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.10410309e-01 8.09358418e-01 -4.06516284e-01 -5.37493229e-01
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239396c5-e7ab-4a35-8387-e838aaa4d629 | revisiting-gaussian-neurons-for-online | 2205.00920 | null | https://arxiv.org/abs/2205.00920v2 | https://arxiv.org/pdf/2205.00920v2.pdf | Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters | Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks. Although these weaknesses are not specifically addressed, a novel local learning rule is presented that performs online clustering with an upper limit on the number of clusters to be found rather than a fixed cluster count. Instead of using orthogonal weight or output activation constraints, activation sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy the same location in the input domain. An update method is also presented for adjusting the widths of the Gaussian neurons in cases where the data samples can be represented by means and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets to create filters capturing the input patterns of pixel patches of various sizes. The experimental results demonstrate stability in the learned parameters across a large number of training samples. | ['Ole Christian Eidheim'] | 2022-05-02 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 2.19640657e-01 2.16234773e-01 7.50582889e-02 -2.16935664e-01
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b32254d0-a33b-4c6a-b8a6-d25687696c2d | automated-multi-process-ctc-detection-using | 2109.12709 | null | https://arxiv.org/abs/2109.12709v1 | https://arxiv.org/pdf/2109.12709v1.pdf | Automated Multi-Process CTC Detection using Deep Learning | Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor prognosis. However, the process of identification and later enumeration of CTCs require manual labor, which is error-prone and time-consuming. The recent developments in object detection via Deep Learning using Mask-RCNNs and wider availability of pre-trained models have enabled sensitive tasks with limited data of such to be tackled with unprecedented accuracy. In this report, we present a novel 3-stage detection model for automated identification of Circulating Tumor Cells in multi-channel darkfield microscopic images comprised of: RetinaNet based identification of Cytokeratin (CK) stains, Mask-RCNN based cell detection of DAPI cell nuclei and Otsu thresholding to detect CD-45s. The training dataset is composed of 46 high variance data points, with 10 Negative and 36 Positive data points. The test set is composed of 420 negative data points. The final accuracy of the pipeline is 98.81%. | ['Andrew F. Laine', 'Kam W. Leong', 'Elena Ivanova'] | 2021-09-26 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.04828465e-01 -4.84098941e-01 1.40921578e-01 -2.74328794e-02
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88064a41-2590-4c35-9072-16e3ebec8fd3 | siamese-instance-search-for-tracking | 1605.05863 | null | http://arxiv.org/abs/1605.05863v1 | http://arxiv.org/pdf/1605.05863v1.pdf | Siamese Instance Search for Tracking | In this paper we present a tracker, which is radically different from
state-of-the-art trackers: we apply no model updating, no occlusion detection,
no combination of trackers, no geometric matching, and still deliver
state-of-the-art tracking performance, as demonstrated on the popular online
tracking benchmark (OTB) and six very challenging YouTube videos. The presented
tracker simply matches the initial patch of the target in the first frame with
candidates in a new frame and returns the most similar patch by a learned
matching function. The strength of the matching function comes from being
extensively trained generically, i.e., without any data of the target, using a
Siamese deep neural network, which we design for tracking. Once learned, the
matching function is used as is, without any adapting, to track previously
unseen targets. It turns out that the learned matching function is so powerful
that a simple tracker built upon it, coined Siamese INstance search Tracker,
SINT, which only uses the original observation of the target from the first
frame, suffices to reach state-of-the-art performance. Further, we show the
proposed tracker even allows for target re-identification after the target was
absent for a complete video shot. | ['Arnold W. M. Smeulders', 'Efstratios Gavves', 'Ran Tao'] | 2016-05-19 | siamese-instance-search-for-tracking-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Tao_Siamese_Instance_Search_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Tao_Siamese_Instance_Search_CVPR_2016_paper.pdf | cvpr-2016-6 | ['geometric-matching', 'instance-search'] | ['computer-vision', 'computer-vision'] | [-2.85009474e-01 -1.47909582e-01 -2.19687372e-01 2.87656456e-01
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601a86d9-c295-4330-b1d9-d7cba3ae0e3b | sensing-and-recognizing-surface-textures | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Li_Sensing_and_Recognizing_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Sensing_and_Recognizing_2013_CVPR_paper.pdf | Sensing and Recognizing Surface Textures Using a GelSight Sensor | Sensing surface textures by tou ch is a valuable was difficult to build capability for robots. Until recently it a a compliant sensor with high sennnsitivity and high resolution. The GelSight sensor is cooompliant and offers sensitivity and resolution exceeding that of the human fingertips. This opens the possibility of measuring and recognizing highly detailed surface texxxtures. The GelSight sensor, when pressed against a surfaccce, delivers a height map. This can be treated as an image, aaand processed using the tools of visual texture analysis. W WW have devised a simple yet effective texture recognitiooon system based on local binary patterns, and enhanced it by the use of a multi-scale pyramid and a Hellinger dddistance metric. We built a database with 40 classes of taaactile textures using materials such as fabric, wood, and sannndpaper. Our system can correctly categorize materials from m this database with high accuracy. This suggests that the G GelSight sensor can be useful for material recognition by rooobots. | ['Rui Li', 'Edward H. Adelson'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['material-recognition'] | ['computer-vision'] | [ 3.51547003e-01 -1.43419495e-02 2.13570714e-01 -2.35396042e-01
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25670457-bc87-4d15-ab00-70eca23b667f | accelerated-convergence-of-nesterov-s | 2306.08109 | null | https://arxiv.org/abs/2306.08109v1 | https://arxiv.org/pdf/2306.08109v1.pdf | Accelerated Convergence of Nesterov's Momentum for Deep Neural Networks under Partial Strong Convexity | Current state-of-the-art analyses on the convergence of gradient descent for training neural networks focus on characterizing properties of the loss landscape, such as the Polyak-Lojaciewicz (PL) condition and the restricted strong convexity. While gradient descent converges linearly under such conditions, it remains an open question whether Nesterov's momentum enjoys accelerated convergence under similar settings and assumptions. In this work, we consider a new class of objective functions, where only a subset of the parameters satisfies strong convexity, and show Nesterov's momentum achieves acceleration in theory for this objective class. We provide two realizations of the problem class, one of which is deep ReLU networks, which --to the best of our knowledge--constitutes this work the first that proves accelerated convergence rate for non-trivial neural network architectures. | ['Anastasios Kyrillidis', 'Fangshuo Liao'] | 2023-06-13 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-2.33728856e-01 2.91006356e-01 -2.95594543e-01 -3.43101680e-01
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94918315-3798-48a8-9e7f-cc9379845dd8 | a-transformer-based-user-satisfaction | 2212.03817 | null | https://arxiv.org/abs/2212.03817v1 | https://arxiv.org/pdf/2212.03817v1.pdf | A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS | Recently, spoken dialogue systems have been widely deployed in a variety of applications, serving a huge number of end-users. A common issue is that the errors resulting from noisy utterances, semantic misunderstandings, or lack of knowledge make it hard for a real system to respond properly, possibly leading to an unsatisfactory user experience. To avoid such a case, we consider a proactive interaction mechanism where the system predicts the user satisfaction with the candidate response before giving it to the user. If the user is not likely to be satisfied according to the prediction, the system will ask the user a suitable question to determine the real intent of the user instead of providing the response directly. With such an interaction with the user, the system can give a better response to the user. Previous models that predict the user satisfaction are not applicable to DuerOS which is a large-scale commercial dialogue system. They are based on hand-crafted features and thus can hardly learn the complex patterns lying behind millions of conversations and temporal dependency in multiple turns of the conversation. Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system. To face these challenges, we propose a pipeline to predict the user satisfaction to help DuerOS decide whether to ask for clarification in each turn. Specifically, we propose to first generate a large number of weak labels and then train a transformer-based model to predict the user satisfaction with these weak labels. Empirically, we deploy and evaluate our model on DuerOS, and observe a 19% relative improvement on the accuracy of user satisfaction prediction and 2.3% relative improvement on user experience. | ['Jian Xie', 'Xuyun Zhang', 'Chuheng Zhang', 'Xiaonan He', 'Wei Shen'] | 2022-12-05 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [ 1.54749781e-01 2.57079720e-01 9.39492807e-02 -7.89055169e-01
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49114284-9b22-4f9a-ae81-48cb3eaf2c59 | a-distant-supervision-corpus-for-extracting | 2204.06584 | null | https://arxiv.org/abs/2204.06584v1 | https://arxiv.org/pdf/2204.06584v1.pdf | A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes | We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models. Our dataset contains 80k biomedical research abstracts labeled with mentions of chemicals, diseases, and genes, portions of which human experts labeled with 18 types of biomedical relationships between these entities (intended for evaluation), and the remainder of which (intended for training) has been distantly labeled via the CTD database with approximately 78\% accuracy. In comparison to similar preexisting datasets, ours is both substantially larger and cleaner; it also includes annotations linking mentions to their entities. We also provide three baseline deep neural network relation extraction models trained and evaluated on our new dataset. | ['Andrew McCallum', 'Michaela Torkar', 'Sunil Mohan', 'Dongxu Zhang'] | 2022-04-13 | null | https://aclanthology.org/2022.lrec-1.116 | https://aclanthology.org/2022.lrec-1.116.pdf | lrec-2022-6 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.66612878e-01 8.70459676e-01 -7.80104756e-01 -3.29313964e-01
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ff9e6925-1e60-4b0e-85eb-79e19f0f6484 | dual-feature-warping-based-motion-model | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Li_Dual-Feature_Warping-Based_Motion_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Li_Dual-Feature_Warping-Based_Motion_ICCV_2015_paper.pdf | Dual-Feature Warping-Based Motion Model Estimation | To break down the geometry assumptions of traditional motion models (e.g., homography, affine), warping-based motion model recently becomes very popular and is adopted in many latest applications (e.g., image stitching, video stabilization). With high degrees of freedom, the accuracy of model heavily relies on data-terms (keypoint correspondences). In some low-texture environments (e.g., indoor) where keypoint feature is insufficient or unreliable, the warping model is often erroneously estimated. In this paper we propose a simple and effective approach by considering both keypoint and line segment correspondences as data-term. Line segment is a prominent feature in artificial environments and it can supply sufficient geometrical and structural information of scenes, which not only helps guild to a correct warp in low-texture condition, but also prevents the undesired distortion induced by warping. The combination aims to complement each other and benefit for a wider range of scenes. Our method is general and can be ported to many existing applications. Experiments demonstrate that using dual-feature yields more robust and accurate result especially for those low-texture images. | ['Long Quan', 'Jian Sun', 'Shiwei Li', 'Lu Yuan'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['image-stitching', 'video-stabilization'] | ['computer-vision', 'computer-vision'] | [ 1.98262647e-01 -5.52697837e-01 -2.73760617e-01 9.87657756e-02
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d6db20f4-3d77-420f-a030-87d285212056 | scalable-and-explainable-1-bit-matrix | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/16863 | https://ojs.aaai.org/index.php/AAAI/article/view/16863/16670 | Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning | One-bit matrix completion is an important class of positiveunlabeled (PU) learning problems where the observations consist of only positive examples, eg, in top-N recommender systems. For the first time, we show that 1-bit matrix completion can be formulated as the problem of recovering clean graph signals from noise-corrupted signals in hypergraphs. This makes it possible to enjoy recent advances in graph signal learning. Then, we propose the spectral graph matrix completion (SGMC) method, which can recover the underlying matrix in distributed systems by filtering the noisy data in the graph frequency domain. Meanwhile, it can provide microand macro-level explanations by following vertex-frequency analysis. To tackle the computational and memory issue of performing graph signal operations on large graphs, we construct a scalable Nyström algorithm which can efficiently compute orthonormal eigenvectors. Furthermore, we also develop polynomial and sparse frequency filters to remedy the accuracy loss caused by the approximations. We demonstrate the effectiveness of our algorithms on top-N recommendation tasks, and the results on three large-scale real-world datasets show that SGMC can outperform state-of-the-art top-N recommendation algorithms in accuracy while only requiring a small fraction of training time compared to the baselines. | ['Xiaokang Yang', 'Hanchi Huang', 'Junchi Yan', 'Dongsheng Li', 'Chao Chen'] | 2021-05-18 | null | null | null | aaai-2021-5 | ['collaborative-ranking'] | ['graphs'] | [ 3.47620636e-01 6.36737868e-02 -3.30669284e-01 3.53801288e-02
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20f5b554-0a31-4e43-9664-ab9d1bdf204f | improving-pedestrian-attribute-recognition | 1910.04562 | null | https://arxiv.org/abs/1910.04562v1 | https://arxiv.org/pdf/1910.04562v1.pdf | Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization | Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible Attribute Localization Module (ALM) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels. Moreover, a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance. The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision. Extensive experiments show that the proposed method achieves state-of-the-art results on three pedestrian attribute datasets, including PETA, RAP, and PA-100K. | ['Zhao-Xiang Zhang', 'Lu Sheng', 'Chufeng Tang', 'Xiaolin Hu'] | 2019-10-10 | improving-pedestrian-attribute-recognition-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Tang_Improving_Pedestrian_Attribute_Recognition_With_Weakly-Supervised_Multi-Scale_Attribute-Specific_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Tang_Improving_Pedestrian_Attribute_Recognition_With_Weakly-Supervised_Multi-Scale_Attribute-Specific_Localization_ICCV_2019_paper.pdf | iccv-2019-10 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 8.58480949e-03 5.02758026e-02 -3.48222733e-01 -8.48192155e-01
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33505626-bd87-4aa3-ae0d-2182aa408552 | evaluating-various-tokenizers-for-arabic-text | 2106.07540 | null | https://arxiv.org/abs/2106.07540v2 | https://arxiv.org/pdf/2106.07540v2.pdf | Evaluating Various Tokenizers for Arabic Text Classification | The first step in any NLP pipeline is to split the text into individual tokens. The most obvious and straightforward approach is to use words as tokens. However, given a large text corpus, representing all the words is not efficient in terms of vocabulary size. In the literature, many tokenization algorithms have emerged to tackle this problem by creating subwords which in turn limits the vocabulary size in a given text corpus. Most tokenization techniques are language-agnostic i.e they don't incorporate the linguistic features of a given language. Not to mention the difficulty of evaluating such techniques in practice. In this paper, we introduce three new tokenization algorithms for Arabic and compare them to three other baselines using unsupervised evaluations. In addition to that, we compare all the six algorithms by evaluating them on three supervised classification tasks which are sentiment analysis, news classification and poetry classification using six publicly available datasets. Our experiments show that none of the tokenization technique is the best choice overall and that the performance of a given tokenization algorithm depends on the size of the dataset, type of the task, and the amount of morphology that exists in the dataset. However, some tokenization techniques are better overall as compared to others on various text classification tasks. | ['Irfan Ahmad', 'Mustafa Ghaleb', 'Maged S. Al-shaibani', 'Zaid Alyafeai'] | 2021-06-14 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-1.06253698e-01 -1.46335304e-01 -3.09804827e-01 -2.72322863e-01
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a49c94c4-e92f-4bd4-a93d-bc811acf9d03 | mgadn-a-multi-task-graph-anomaly-detection | 2211.12141 | null | https://arxiv.org/abs/2211.12141v2 | https://arxiv.org/pdf/2211.12141v2.pdf | MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series | Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp. To be exact, they only want to know how does the data in the past influence which in the future. However, one sensor sometimes intervenes in other sensor such as the speed of wind may cause decrease of temperature. Secondly, there exist two categories of model for time series anomaly detection: prediction model and reconstruction model. Prediction model is adept at learning timely representation while short of capability when faced with sparse anomaly. Conversely, reconstruction model is opposite. Therefore, how can we efficiently get the relationship both in terms of both timestamp and sensors becomes our main topic. Our approach uses GAT, which is originated from graph neural network, to obtain connection between sensors. And LSTM is used to obtain relationships timely. Our approach is also designed to be double headed to calculate both prediction loss and reconstruction loss via VAE(Variational Auto-Encoder). In order to take advantage of two sorts of model, multi-task optimization algorithm is used in this model. | ['Xiaochen Sun', 'Weixuan Xiong'] | 2022-11-22 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.10559061e-01 -2.45560363e-01 1.25624716e-01 -2.50789583e-01
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e653e34f-16bb-4ec2-94f9-9904b4af18cf | a-deep-visual-correspondence-embedding-model | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Chen_A_Deep_Visual_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Chen_A_Deep_Visual_ICCV_2015_paper.pdf | A Deep Visual Correspondence Embedding Model for Stereo Matching Costs | This paper presents a data-driven matching cost for stereo matching. A novel deep visual correspondence embedding model is trained via Convolutional Neural Network on a large set of stereo images with ground truth disparities. This deep embedding model leverages appearance data to learn visual similarity relationships between corresponding image patches, and explicitly maps intensity values into an embedding feature space to measure pixel dissimilarities. Experimental results on KITTI and Middlebury data sets demonstrate the effectiveness of our model. First, we prove that the new measure of pixel dissimilarity outperforms traditional matching costs. Furthermore, when integrated with a global stereo framework, our method ranks top 3 among all two-frame algorithms on the KITTI benchmark. Finally, cross-validation results show that our model is able to make correct predictions for unseen data which are outside of its labeled training set. | ['Xun Sun', 'Liang Wang', 'Chang Huang', 'Yinan Yu', 'Zhuoyuan Chen'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 1.02155723e-01 -2.13061959e-01 -3.13043505e-01 -6.15356863e-01
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ed3bf35c-4cd9-4886-a222-9ffd6e9daa0f | deep-content-user-embedding-model-for-music | 1807.06786 | null | http://arxiv.org/abs/1807.06786v1 | http://arxiv.org/pdf/1807.06786v1.pdf | Deep Content-User Embedding Model for Music Recommendation | Recently deep learning based recommendation systems have been actively
explored to solve the cold-start problem using a hybrid approach. However, the
majority of previous studies proposed a hybrid model where collaborative
filtering and content-based filtering modules are independently trained. The
end-to-end approach that takes different modality data as input and jointly
trains the model can provide better optimization but it has not been fully
explored yet. In this work, we propose deep content-user embedding model, a
simple and intuitive architecture that combines the user-item interaction and
music audio content. We evaluate the model on music recommendation and music
auto-tagging tasks. The results show that the proposed model significantly
outperforms the previous work. We also discuss various directions to improve
the proposed model further. | ['Jang-Yeon Park', 'Jiyoung Park', 'Juhan Nam', 'Jongpil Lee', 'Kyungyun Lee'] | 2018-07-18 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [-2.31299326e-01 -3.97902846e-01 -9.82793272e-02 -5.08938372e-01
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ab1168e6-8034-4255-94ec-0e76b7d7a061 | provably-efficient-iterated-cvar | 2307.02842 | null | https://arxiv.org/abs/2307.02842v1 | https://arxiv.org/pdf/2307.02842v1.pdf | Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation | Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we investigate a novel risk-sensitive RL formulation with an Iterated Conditional Value-at-Risk (CVaR) objective under linear and general function approximations. This new formulation, named ICVaR-RL with function approximation, provides a principled way to guarantee safety at each decision step. For ICVaR-RL with linear function approximation, we propose a computationally efficient algorithm ICVaR-L, which achieves an $\widetilde{O}(\sqrt{\alpha^{-(H+1)}(d^2H^4+dH^6)K})$ regret, where $\alpha$ is the risk level, $d$ is the dimension of state-action features, $H$ is the length of each episode, and $K$ is the number of episodes. We also establish a matching lower bound $\Omega(\sqrt{\alpha^{-(H-1)}d^2K})$ to validate the optimality of ICVaR-L with respect to $d$ and $K$. For ICVaR-RL with general function approximation, we propose algorithm ICVaR-G, which achieves an $\widetilde{O}(\sqrt{\alpha^{-(H+1)}DH^4K})$ regret, where $D$ is a dimensional parameter that depends on the eluder dimension and covering number. Furthermore, our analysis provides several novel techniques for risk-sensitive RL, including an efficient approximation of the CVaR operator, a new ridge regression with CVaR-adapted features, and a refined elliptical potential lemma. | ['Longbo Huang', 'Desheng Wu', 'Siwei Wang', 'Pihe Hu', 'Yihan Du', 'Yu Chen'] | 2023-07-06 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-1.02239706e-01 1.64322332e-01 -2.89226890e-01 -1.47863656e-01
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4d459eff-34ff-4d7e-bae3-589c760f78c6 | machine-learning-of-percolation-models-using | 2207.03368 | null | https://arxiv.org/abs/2207.03368v2 | https://arxiv.org/pdf/2207.03368v2.pdf | Machine learning of percolation models using graph convolutional neural networks | Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and so on. Prediction of percolation thresholds with machine learning methods remains challenging. In this paper, we build a powerful graph convolutional neural network to study the percolation in both supervised and unsupervised ways. From a supervised learning perspective, the graph convolutional neural network simultaneously and correctly trains data of different lattice types, such as the square and triangular lattices. For the unsupervised perspective, combining the graph convolutional neural network and the confusion method, the percolation threshold can be obtained by the "W" shaped performance. The finding of this work opens up the possibility of building a more general framework that can probe the percolation-related phenomenon. | ['Wanzhou Zhang', 'Youjin Deng', 'Lirong Zhang', 'Hua Tian'] | 2022-07-07 | null | null | null | null | ['epidemiology'] | ['medical'] | [-1.52471513e-01 -3.77798565e-02 1.34820836e-02 -2.01006029e-02
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84eb362f-3201-4fb6-bdae-6c735aade93d | portfolio-optimization-on-nifty-thematic | 2202.02723 | null | https://arxiv.org/abs/2202.02723v1 | https://arxiv.org/pdf/2202.02723v1.pdf | Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model | Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing optimum risk and eigen portfolios for five thematic sectors of the NSE of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are designed based on ten critical stocks from the sector. An LSTM model is designed for predicting future stock prices. Seven months after the portfolios were formed, on Aug 3, 2021, the actual returns of the portfolios are compared with the LSTM-predicted returns. The predicted and the actual returns indicate a very high-level accuracy of the LSTM model. | ['Sidra Mehtab', 'Saikat Mondal', 'Jaydip Sen'] | 2022-02-06 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.67553943e-01 -1.16652653e-01 -3.19737159e-02 -2.29665771e-01
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233c15e1-dbe1-4476-8a97-73c01223e9dc | visual-recognition-driven-image-restoration | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.pdf | Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery | Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual Recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification. | ['Feng Zhao', 'Jinghao Zhang', 'Hu Yu', 'Man Zhou', 'Jiahao Chang', 'Jie Huang', 'Zizheng Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.09970188e-01 -4.95751768e-01 1.30665861e-02 -5.38842499e-01
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66f1ef2f-9c51-40b1-b20c-c470346c0e85 | neural-unification-for-logic-reasoning-over | 2109.08460 | null | https://arxiv.org/abs/2109.08460v1 | https://arxiv.org/pdf/2109.08460v1.pdf | Neural Unification for Logic Reasoning over Natural Language | Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (facts and rules). There exists several successful ATPs where conjectures and axioms are formally provided (e.g. formalised as First Order Logic formulas). Recent approaches, such as (Clark et al., 2020), have proposed transformer-based architectures for deriving conjectures given axioms expressed in natural language (English). The conjecture is verified through a binary text classifier, where the transformers model is trained to predict the truth value of a conjecture given the axioms. The RuleTaker approach of (Clark et al., 2020) achieves appealing results both in terms of accuracy and in the ability to generalize, showing that when the model is trained with deep enough queries (at least 3 inference steps), the transformers are able to correctly answer the majority of queries (97.6%) that require up to 5 inference steps. In this work we propose a new architecture, namely the Neural Unifier, and a relative training procedure, which achieves state-of-the-art results in term of generalisation, showing that mimicking a well-known inference procedure, the backward chaining, it is possible to answer deep queries even when the model is trained only on shallow ones. The approach is demonstrated in experiments using a diverse set of benchmark data. | ['Vanessa Lopez Garcia', 'Marco Luca Sbodio', 'Hoang Thanh Lam', 'Gabriele Picco'] | 2021-09-17 | null | https://aclanthology.org/2021.findings-emnlp.331 | https://aclanthology.org/2021.findings-emnlp.331.pdf | findings-emnlp-2021-11 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.24489802e-01 7.69591212e-01 -7.42477179e-03 -3.64300191e-01
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753e2e56-30c8-4df0-9b5f-facb36dfb20d | cross-modal-image-fusion-theory-guided-by | 1912.10718 | null | https://arxiv.org/abs/1912.10718v1 | https://arxiv.org/pdf/1912.10718v1.pdf | Cross-Modal Image Fusion Theory Guided by Subjective Visual Attention | The human visual perception system has very strong robustness and contextual awareness in a variety of image processing tasks. This robustness and the perception ability of contextual awareness is closely related to the characteristics of multi-task auxiliary learning and subjective attention of the human visual perception system. In order to improve the robustness and contextual awareness of image fusion tasks, we proposed a multi-task auxiliary learning image fusion theory guided by subjective attention. The image fusion theory effectively unifies the subjective task intention and prior knowledge of human brain. In order to achieve our proposed image fusion theory, we first analyze the mechanism of multi-task auxiliary learning, build a multi-task auxiliary learning network. Secondly, based on the human visual attention perception mechanism, we introduce the human visual attention network guided by subjective tasks on the basis of the multi-task auxiliary learning network. The subjective intention is introduced by the subjective attention task model, so that the network can fuse images according to the subjective intention. Finally, in order to verify the superiority of our image fusion theory, we carried out experiments on the combined vision system image data set, and the infrared and visible image data set for experimental verification. The experimental results demonstrate the superiority of our fusion theory over state-of-arts in contextual awareness and robustness. | ['Yanning Zhang', 'Xinbo Zhao', 'Aiqing Fang'] | 2019-12-23 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.55444995e-01 -4.23606724e-01 2.11325943e-01 -1.17874309e-01
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2b76c896-6632-42c2-a96e-04c838884c19 | prompt-as-triggers-for-backdoor-attack | 2305.01219 | null | https://arxiv.org/abs/2305.01219v4 | https://arxiv.org/pdf/2305.01219v4.pdf | Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models | The prompt-based learning paradigm, which bridges the gap between pre-training and fine-tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot settings. Despite being widely applied, prompt-based learning is vulnerable to backdoor attacks. Textual backdoor attacks are designed to introduce targeted vulnerabilities into models by poisoning a subset of training samples through trigger injection and label modification. However, they suffer from flaws such as abnormal natural language expressions resulting from the trigger and incorrect labeling of poisoned samples. In this study, we propose ProAttack, a novel and efficient method for performing clean-label backdoor attacks based on the prompt, which uses the prompt itself as a trigger. Our method does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack. With extensive experiments on rich-resource and few-shot text classification tasks, we empirically validate ProAttack's competitive performance in textual backdoor attacks. Notably, in the rich-resource setting, ProAttack achieves state-of-the-art attack success rates in the clean-label backdoor attack benchmark without external triggers. | ['Jie Fu', 'Junbo Zhao', 'Luu Anh Tuan', 'Jinming Wen', 'Shuai Zhao'] | 2023-05-02 | null | null | null | null | ['backdoor-attack', 'few-shot-text-classification'] | ['adversarial', 'natural-language-processing'] | [ 8.62303078e-02 -3.63362283e-01 -5.46466708e-01 -1.29922450e-01
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44f51b78-1eba-4dd4-ba94-707f8c7f41a3 | simon-dravidianlangtech-eacl2021-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.41 | https://aclanthology.org/2021.dravidianlangtech-1.41.pdf | Simon @ DravidianLangTech-EACL2021: Meme Classification for Tamil with BERT | In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today’s society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a unique form of emotional expression on social media – meme. The meme is an expression that is often ironic. This also gives the meme a unique sense of humor. But it’s not just positive content on social media. There’s also a lot of offensive content. Meme’s unique expression makes it often used by some users to post offensive content. Therefore, it is very urgent to detect the offensive content of the meme. Our team uses the natural language processing method to classify the offensive content of the meme. Our team combines the BERT model with the CNN to improve the model’s ability to collect statement information. Finally, the F1-score of our team in the official test set is 0.49, and our method ranks 5th. | ['Qinyu Que'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-6.84471190e-01 -1.88188046e-01 -5.49804121e-02 -1.26692787e-01
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75cee697-eb38-4aa8-9103-c562f9377ef5 | guidelines-for-the-regularization-of-gammas | 2205.07260 | null | https://arxiv.org/abs/2205.07260v1 | https://arxiv.org/pdf/2205.07260v1.pdf | Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks | L2 regularization for weights in neural networks is widely used as a standard training trick. However, L2 regularization for gamma, a trainable parameter of batch normalization, remains an undiscussed mystery and is applied in different ways depending on the library and practitioner. In this paper, we study whether L2 regularization for gamma is valid. To explore this issue, we consider two approaches: 1) variance control to make the residual network behave like identity mapping and 2) stable optimization through the improvement of effective learning rate. Through two analyses, we specify the desirable and undesirable gamma to apply L2 regularization and propose four guidelines for managing them. In several experiments, we observed the increase and decrease in performance caused by applying L2 regularization to gamma of four categories, which is consistent with our four guidelines. Our proposed guidelines were validated through various tasks and architectures, including variants of residual networks and transformers. | ['Sang Woo Kim', 'Wonseok Jeong', 'Dong Gu Lee', 'Hyeonah Jang', 'Hyeyeon Choi', 'Bum Jun Kim'] | 2022-05-15 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-7.26423860e-02 1.93066761e-01 -2.13532954e-01 -5.34080565e-01
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c0798fa9-4f5b-46cd-ac35-6db11d658cd4 | zeroth-order-hard-thresholding-gradient-error | 2210.05279 | null | https://arxiv.org/abs/2210.05279v1 | https://arxiv.org/pdf/2210.05279v1.pdf | Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity | $\ell_0$ constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Hard-thresholding gradient descent is a dominant technique to solve this problem. However, first-order gradients of the objective function may be either unavailable or expensive to calculate in a lot of real-world problems, where zeroth-order (ZO) gradients could be a good surrogate. Unfortunately, whether ZO gradients can work with the hard-thresholding operator is still an unsolved problem. To solve this puzzle, in this paper, we focus on the $\ell_0$ constrained black-box stochastic optimization problems, and propose a new stochastic zeroth-order gradient hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator powered by a novel random support sampling. We provide the convergence analysis of SZOHT under standard assumptions. Importantly, we reveal a conflict between the deviation of ZO estimators and the expansivity of the hard-thresholding operator, and provide a theoretical minimal value of the number of random directions in ZO gradients. In addition, we find that the query complexity of SZOHT is independent or weakly dependent on the dimensionality under different settings. Finally, we illustrate the utility of our method on a portfolio optimization problem as well as black-box adversarial attacks. | ['Bin Gu', 'Xiao-Tong Yuan', 'Huimin Wu', 'Hualin Zhang', 'William de Vazelhes'] | 2022-10-11 | null | null | null | null | ['sparse-learning', 'portfolio-optimization'] | ['methodology', 'time-series'] | [-4.00821492e-02 -2.69004613e-01 -1.78991079e-01 -1.76220313e-01
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d9d3c349-3810-4b9e-b89e-0f2a393d7447 | residual-force-control-for-agile-human | 2006.07364 | null | https://arxiv.org/abs/2006.07364v2 | https://arxiv.org/pdf/2006.07364v2.pdf | Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis | Reinforcement learning has shown great promise for synthesizing realistic human behaviors by learning humanoid control policies from motion capture data. However, it is still very challenging to reproduce sophisticated human skills like ballet dance, or to stably imitate long-term human behaviors with complex transitions. The main difficulty lies in the dynamics mismatch between the humanoid model and real humans. That is, motions of real humans may not be physically possible for the humanoid model. To overcome the dynamics mismatch, we propose a novel approach, residual force control (RFC), that augments a humanoid control policy by adding external residual forces into the action space. During training, the RFC-based policy learns to apply residual forces to the humanoid to compensate for the dynamics mismatch and better imitate the reference motion. Experiments on a wide range of dynamic motions demonstrate that our approach outperforms state-of-the-art methods in terms of convergence speed and the quality of learned motions. Notably, we showcase a physics-based virtual character empowered by RFC that can perform highly agile ballet dance moves such as pirouette, arabesque and jet\'e. Furthermore, we propose a dual-policy control framework, where a kinematic policy and an RFC-based policy work in tandem to synthesize multi-modal infinite-horizon human motions without any task guidance or user input. Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3.6M) and generates diverse long-term motions. Code and videos are available at https://www.ye-yuan.com/rfc. | ['Ye Yuan', 'Kris Kitani'] | 2020-06-12 | null | http://proceedings.neurips.cc/paper/2020/hash/f76a89f0cb91bc419542ce9fa43902dc-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/f76a89f0cb91bc419542ce9fa43902dc-Paper.pdf | neurips-2020-12 | ['humanoid-control'] | ['robots'] | [-1.75458163e-01 1.83102086e-01 -2.20205173e-01 4.30697739e-01
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3d9846d3-48b6-41a7-94ba-e6d062919176 | on-the-universality-of-graph-neural-networks | 2105.13099 | null | https://arxiv.org/abs/2105.13099v2 | https://arxiv.org/pdf/2105.13099v2.pdf | On the Universality of Graph Neural Networks on Large Random Graphs | We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain "continuous" models known as c-GNNs, which directly enables a study of their approximation power on random graph models. In the absence of input node features however, just as GNNs are limited by the Weisfeiler-Lehman isomorphism test, c-GNNs will be severely limited on simple random graph models. For instance, they will fail to distinguish the communities of a well-separated Stochastic Block Model (SBM) with constant degree function. Thus, we consider recently proposed architectures that augment GNNs with unique node identifiers, referred to as Structural GNNs here (SGNNs). We study the convergence of SGNNs to their continuous counterpart (c-SGNNs) in the large random graph limit, under new conditions on the node identifiers. We then show that c-SGNNs are strictly more powerful than c-GNNs in the continuous limit, and prove their universality on several random graph models of interest, including most SBMs and a large class of random geometric graphs. Our results cover both permutation-invariant and permutation-equivariant architectures. | ['Samuel Vaiter', 'Alberto Bietti', 'Nicolas Keriven'] | 2021-05-27 | null | http://proceedings.neurips.cc/paper/2021/hash/38181d991caac98be8fb2ecb8bd0f166-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/38181d991caac98be8fb2ecb8bd0f166-Paper.pdf | neurips-2021-12 | ['stochastic-block-model'] | ['graphs'] | [ 1.86365217e-01 6.32364035e-01 -2.06028476e-01 -1.54837640e-02
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700ab3fa-82ac-4bf0-8f5e-67224a0828aa | german-arabic-speech-to-speech-translation | null | null | https://aclanthology.org/2020.wanlp-1.1 | https://aclanthology.org/2020.wanlp-1.1.pdf | German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis | In this paper we present the natural language processing components of our German-Arabic speech-to-speech translation system which is being deployed in the context of interpretation during psychiatric, diagnostic interviews. For this purpose we have built a pipe-lined speech-to-speech translation system consisting of automatic speech recognition, text post-processing/segmentation, machine translation and speech synthesis systems. We have implemented two pipe-lines, from German to Arabic and Arabic to German, in order to be able to conduct interpreted two-way dialogues between psychiatrists and potential patients. All systems in our pipeline have been realized as all-neural end-to-end systems, using different architectures suitable for the different components. The speech recognition systems use an encoder/decoder + attention architecture, the text segmentation component and the machine translation system are based on the Transformer architecture, and for the speech synthesis systems we use Tacotron 2 for generating spectrograms and WaveGlow as vocoder. The speech translation is deployed in a server-based speech translation application that implements a turn based translation between a German speaking psychiatrist administrating the Mini-International Neuropsychiatric Interview (M.I.N.I.) and an Arabic speaking person answering the interview. As this is a very specific domain, in addition to the linguistic challenges posed by translating between Arabic and German, we also focus in this paper on the methods we implemented for adapting our speech translation system to the domain of this psychiatric interview. | ['Alexander Waibel', 'Sebastian Stüker', 'M. Amin Cheragui', 'Moritz Behr', 'Mohammed Mediani', 'Juan Hussain'] | null | null | null | null | coling-wanlp-2020-12 | ['speech-to-speech-translation'] | ['speech'] | [ 3.42177361e-01 6.56423569e-01 6.01828754e-01 -7.22266197e-01
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ef8d8ef3-3290-4fc4-86cf-fa2104ae4536 | training-robust-tree-ensembles-for-security | 1912.01149 | null | https://arxiv.org/abs/1912.01149v5 | https://arxiv.org/pdf/1912.01149v5.pdf | Cost-Aware Robust Tree Ensembles for Security Applications | There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be precisely captured by existing cost models based on $L_p$-norm robustness. In this paper, we utilize such domain knowledge to increase the attack cost of evading classifiers, specifically, tree ensemble models that are widely used by security tasks. We propose a new cost modeling method to capture the feature manipulation cost as constraint, and then we integrate the cost-driven constraint into the node construction process to train robust tree ensembles. During the training process, we use the constraint to find data points that are likely to be perturbed given the feature manipulation cost, and we use a new robust training algorithm to optimize the quality of the trees. Our cost-aware training method can be applied to different types of tree ensembles, including gradient boosted decision trees and random forest models. Using Twitter spam detection as the case study, our evaluation results show that we can increase the attack cost by 10.6X compared to the baseline. Moreover, our robust training method using cost-driven constraint can achieve higher accuracy, lower false positive rate, and stronger cost-aware robustness than the state-of-the-art training method using $L_\infty$-norm cost model. Our code is available at https://github.com/surrealyz/growtrees. | ['Yizheng Chen', 'Shiqi Wang', 'Suman Jana', 'Asaf Cidon', 'Weifan Jiang'] | 2019-12-03 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-8.03801641e-02 -3.80779535e-01 -2.28105068e-01 -3.24056119e-01
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4cda4ea5-a699-4ccf-bfc0-22d7ede94cc7 | amicron-a-framework-for-generating | 2306.13149 | null | https://arxiv.org/abs/2306.13149v1 | https://arxiv.org/pdf/2306.13149v1.pdf | AmicroN: A Framework for Generating Annotations for Human Activity Recognition with Granular Micro-Activities | Efficient human activity recognition (HAR) using sensor data needs a significant volume of annotated data. The growing volume of unlabelled sensor data has challenged conventional practices for gathering HAR annotations with human-in-the-loop approaches, often leading to the collection of shallower annotations. These shallower annotations ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, first analyze this lack of granular annotations from available pre-annotated datasets to understand the practical inconsistencies and also perform a detailed survey to look into the human perception surrounding annotations. Drawing motivations from these, we next develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures and the available coarse-grain macro-activity labels. In the backend, AmicroN applies change-point detection followed by zero-shot learning with activity embeddings to identify the unseen micro-activities in an unsupervised manner. Rigorous evaluation on publicly available datasets shows that AmicroN can accurately generate micro-activity annotations with a median F1-score of >0.75. Additionally, we also show that AmicroN can be used in a plug-and-play manner with Large Language Models (LLMs) to obtain the micro-activity labels, thus making it more practical for realistic applications. | ['Sandip Chakraborty', 'Bivas Mitra', 'Soumyajit Chatterjee'] | 2023-06-22 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'change-point-detection', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series', 'time-series'] | [ 4.43520427e-01 3.08051080e-01 -1.38322636e-01 -3.26676697e-01
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ab1025c9-02d7-44f4-9353-29fd3e947776 | designing-strong-baselines-for-ternary-neural | 2306.17442 | null | https://arxiv.org/abs/2306.17442v1 | https://arxiv.org/pdf/2306.17442v1.pdf | Designing strong baselines for ternary neural network quantization through support and mass equalization | Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensive to run. This computational burden can be dramatically reduced by quantizing (in either data-free (DFQ), post-training (PTQ) or quantization-aware training (QAT) scenarios) floating point values to ternary values (2 bits, with each weight taking value in {-1,0,1}). In this context, we observe that rounding to nearest minimizes the expected error given a uniform distribution and thus does not account for the skewness and kurtosis of the weight distribution, which strongly affects ternary quantization performance. This raises the following question: shall one minimize the highest or average quantization error? To answer this, we design two operators: TQuant and MQuant that correspond to these respective minimization tasks. We show experimentally that our approach allows to significantly improve the performance of ternary quantization through a variety of scenarios in DFQ, PTQ and QAT and give strong insights to pave the way for future research in deep neural network quantization. | ['Kevin Bailly', 'Arnaud Dapogny', 'Edouard Yvinec'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 3.92699659e-01 3.57078388e-02 -8.94952342e-02 -4.53726798e-01
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0ba6b611-a971-4871-9456-30abf632986c | inharmonious-region-localization-by | 2209.15368 | null | https://arxiv.org/abs/2209.15368v1 | https://arxiv.org/pdf/2209.15368v1.pdf | Inharmonious Region Localization by Magnifying Domain Discrepancy | Inharmonious region localization aims to localize the region in a synthetic image which is incompatible with surrounding background. The inharmony issue is mainly attributed to the color and illumination inconsistency produced by image editing techniques. In this work, we tend to transform the input image to another color space to magnify the domain discrepancy between inharmonious region and background, so that the model can identify the inharmonious region more easily. To this end, we present a novel framework consisting of a color mapping module and an inharmonious region localization network, in which the former is equipped with a novel domain discrepancy magnification loss and the latter could be an arbitrary localization network. Extensive experiments on image harmonization dataset show the superiority of our designed framework. Our code is available at https://github.com/bcmi/MadisNet-Inharmonious-Region-Localization. | ['Teng Long', 'Fengjun Guo', 'Penghao Wu', 'Li Niu', 'Jing Liang'] | 2022-09-30 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 9.28057134e-02 2.40921900e-02 2.37662680e-02 6.25731796e-02
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2e959b5d-82b5-4e95-8440-eafd88d744eb | solving-statistical-mechanics-using | 1809.10606 | null | http://arxiv.org/abs/1809.10606v2 | http://arxiv.org/pdf/1809.10606v2.pdf | Solving Statistical Mechanics Using Variational Autoregressive Networks | We propose a general framework for solving statistical mechanics of systems
with finite size. The approach extends the celebrated variational mean-field
approaches using autoregressive neural networks, which support direct sampling
and exact calculation of normalized probability of configurations. It computes
variational free energy, estimates physical quantities such as entropy,
magnetizations and correlations, and generates uncorrelated samples all at
once. Training of the network employs the policy gradient approach in
reinforcement learning, which unbiasedly estimates the gradient of variational
parameters. We apply our approach to several classic systems, including 2D
Ising models, the Hopfield model, the Sherrington-Kirkpatrick model, and the
inverse Ising model, for demonstrating its advantages over existing variational
mean-field methods. Our approach sheds light on solving statistical physics
problems using modern deep generative neural networks. | ['Dian Wu', 'Pan Zhang', 'Lei Wang'] | 2018-09-27 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-7.24269915e-03 2.09606029e-02 1.46758050e-01 -7.26191029e-02
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4df51ef3-05b5-49ce-945b-49e8a5ded9a2 | using-local-knowledge-graph-construction-to | 1910.08435 | null | https://arxiv.org/abs/1910.08435v1 | https://arxiv.org/pdf/1910.08435v1.pdf | Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs | Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations within a standard Sequence-to-Sequence setting. For two generative tasks with very long text input, long-form question answering and multi-document summarization, feeding graph representations as input can achieve better performance than using retrieved text portions. | ['Claire Gardent', 'Chloe Braud', 'Angela Fan', 'Antoine Bordes'] | 2019-10-18 | using-local-knowledge-graph-construction-to-1 | https://aclanthology.org/D19-1428 | https://aclanthology.org/D19-1428.pdf | ijcnlp-2019-11 | ['long-form-question-answering'] | ['natural-language-processing'] | [ 7.77943313e-01 5.98975122e-01 -7.78238058e-01 -2.46152461e-01
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540d2d36-8b48-440a-a7e1-334fa98eeefd | continual-multimodal-knowledge-graph | 2305.08698 | null | https://arxiv.org/abs/2305.08698v1 | https://arxiv.org/pdf/2305.08698v1.pdf | Continual Multimodal Knowledge Graph Construction | Multimodal Knowledge Graph Construction (MMKC) refers to the process of creating a structured representation of entities and relationships through multiple modalities such as text, images, videos, etc. However, existing MMKC models have limitations in handling the introduction of new entities and relations due to the dynamic nature of the real world. Moreover, most state-of-the-art studies in MMKC only consider entity and relation extraction from text data while neglecting other multi-modal sources. Meanwhile, the current continual setting for knowledge graph construction only consider entity and relation extraction from text data while neglecting other multi-modal sources. Therefore, there arises the need to explore the challenge of continuous multimodal knowledge graph construction to address the phenomenon of catastrophic forgetting and ensure the retention of past knowledge extracted from different forms of data. This research focuses on investigating this complex topic by developing lifelong multimodal benchmark datasets. Based on the empirical findings that several state-of-the-art MMKC models, when trained on multimedia data, might unexpectedly underperform compared to those solely utilizing textual resources in a continual setting, we propose a Lifelong MultiModal Consistent Transformer Framework (LMC) for continuous multimodal knowledge graph construction. By combining the advantages of consistent KGC strategies within the context of continual learning, we achieve greater balance between stability and plasticity. Our experiments demonstrate the superior performance of our method over prevailing continual learning techniques or multimodal approaches in dynamic scenarios. Code and datasets can be found at https://github.com/zjunlp/ContinueMKGC. | ['Ningyu Zhang', 'Huajun Chen', 'Luo Si', 'Yongheng Wang', 'Shumin Deng', 'Tongtong Wu', 'Xiaohan Wang', 'Jintian Zhang', 'Xiang Chen'] | 2023-05-15 | null | null | null | null | ['graph-construction', 'relation-extraction'] | ['graphs', 'natural-language-processing'] | [-7.35532399e-03 6.04971610e-02 -4.85723734e-01 2.19340220e-01
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f8ce0ccd-8ece-4864-a24e-7a8a0dd66f8e | learning-character-agnostic-motion-for-motion | 1905.01680 | null | https://arxiv.org/abs/1905.01680v1 | https://arxiv.org/pdf/1905.01680v1.pdf | Learning Character-Agnostic Motion for Motion Retargeting in 2D | Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer animation, is the retargeting of motion from one performer to another. While humans move in three dimensions, the vast majority of human motions are captured using video, requiring 2D-to-3D pose and camera recovery, before existing retargeting approaches may be applied. In this paper, we present a new method for retargeting video-captured motion between different human performers, without the need to explicitly reconstruct 3D poses and/or camera parameters. In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view. Our key idea is to train a deep neural network to decompose temporal sequences of 2D poses into three components: motion, skeleton, and camera view-angle. Having extracted such a representation, we are able to re-combine motion with novel skeletons and camera views, and decode a retargeted temporal sequence, which we compare to a ground truth from a synthetic dataset. We demonstrate that our framework can be used to robustly extract human motion from videos, bypassing 3D reconstruction, and outperforming existing retargeting methods, when applied to videos in-the-wild. It also enables additional applications, such as performance cloning, video-driven cartoons, and motion retrieval. | ['Daniel Cohen-Or', 'Rundi Wu', 'Kfir Aberman', 'Dani Lischinski', 'Baoquan Chen'] | 2019-05-05 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 3.24443281e-01 -2.30648011e-01 -1.66860551e-01 1.55265123e-01
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333286e1-d0ca-444d-8912-7c997cef431d | macro-average-rare-types-are-important-too | 2104.05700 | null | https://arxiv.org/abs/2104.05700v1 | https://arxiv.org/pdf/2104.05700v1.pdf | Macro-Average: Rare Types Are Important Too | While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods' outputs. | ['Jonathan May', 'Constantine Lignos', 'Weiqiu You', 'Thamme Gowda'] | 2021-04-12 | null | https://aclanthology.org/2021.naacl-main.90 | https://aclanthology.org/2021.naacl-main.90.pdf | naacl-2021-4 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 2.90424168e-01 -4.21280526e-02 -8.73248816e-01 -6.41383648e-01
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309fb648-9704-4d8b-99a5-2697f939e27d | image-clustering-with-optimization-algorithms | null | null | https://doi.org/10.3390/e20040296 | https://www.mdpi.com/1099-4300/20/4/296/htm | Image Clustering with Optimization Algorithms and Color Space | In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the number of thresholds. However, the determination of multiple thresholds is a typical issue. Moreover, the conventional thresholding algorithms could not be used in color image segmentation. In this study, a new color image clustering algorithm with multilevel thresholding has been presented and, it has been shown how to use the multilevel thresholding techniques for color image clustering. Thus, initially, threshold selection techniques such as the Otsu and Kapur methods were employed for each color channel separately. The objective functions of both approaches have been integrated with the forest optimization algorithm (FOA) and particle swarm optimization (PSO) algorithm. In the next stage, thresholds determined by optimization algorithms were used to divide color space into small cubes or prisms. Each sub-cube or prism created in the color space was evaluated as a cluster. As the volume of prisms affects the homogeneity of the clusters created, multiple thresholds were employed to reduce the sizes of the sub-cubes. The performance of the proposed method was tested with different images. It was observed that the results obtained were more efficient than conventional methods. | ['Mohammad-Reza Feizi-Derakhshi', 'Recep Demirci', 'Taymaz Rahkar Farshi'] | 2018-04-18 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.17284286e-01 -3.97725761e-01 2.09670842e-01 -1.16442584e-01
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4e636306-fa0d-4293-9077-f11157e23df0 | detection-of-clouds-in-multiple-wind-velocity | 2105.03535 | null | https://arxiv.org/abs/2105.03535v3 | https://arxiv.org/pdf/2105.03535v3.pdf | Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images | Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. The optimal decision criterion to find the number of clusters in the mixture models is analyzed and compared between different Bayesian metrics and a sequential hidden Markov model. The motion vectors are computed using a weighted implementation of the Lucas-Kanade algorithm. The correlations between the cloud velocity vectors and temperatures are analyzed to find the method that leads to the most accurate results. We have found that the sequential hidden Markov model outperformed the detection accuracy of the Bayesian metrics. | ['Manel Martínez-Ramón', 'Guillermo Terrén-Serrano'] | 2021-05-07 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-1.89811990e-01 -1.00970459e+00 -1.17235206e-01 -1.70277819e-01
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20f6fa0e-fa73-4a94-8e37-e9593fb6098c | active-object-localization-in-visual | 1607.00548 | null | http://arxiv.org/abs/1607.00548v1 | http://arxiv.org/pdf/1607.00548v1.pdf | Active Object Localization in Visual Situations | We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images. | ['Anthony D. Rhodes', 'Melanie Mitchell', 'Max H. Quinn'] | 2016-07-02 | null | null | null | null | ['active-object-localization'] | ['computer-vision'] | [ 2.00273022e-01 -1.81909531e-01 -2.17484713e-01 -7.43158817e-01
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769d2f12-a6d7-4da8-832a-c1018ab2242a | noise2music-text-conditioned-music-generation | 2302.03917 | null | https://arxiv.org/abs/2302.03917v2 | https://arxiv.org/pdf/2302.03917v2.pdf | Noise2Music: Text-conditioned Music Generation with Diffusion Models | We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground fine-grained semantics of the prompt. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. Generated examples: https://google-research.github.io/noise2music | ['Zhifeng Chen', 'Wei Han', 'William Chan', 'Quoc V. Le', 'Jesse Engel', 'Christian Frank', 'Jiahui Yu', 'Zhishuai Zhang', 'Zhengdong Zhang', 'Nanxin Chen', 'Andy Ly', 'Timo I. Denk', 'Tao Wang', 'Daniel S. Park', 'Qingqing Huang'] | 2023-02-08 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [ 1.12305604e-01 8.23625252e-02 1.70386329e-01 5.89826237e-03
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95784d10-ef23-413e-b062-4ca934db6c77 | effectiveness-of-artificial-intelligence-in | 2107.01031 | null | https://arxiv.org/abs/2107.01031v1 | https://arxiv.org/pdf/2107.01031v1.pdf | Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning | This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. In the technical analysis, the historical price data is exploited from Yahoo Finance, and in fundamental analysis, public tweets on Twitter associated with the stock market are investigated to assess the impact of sentiments on the stock market's forecast. The results show a median performance, implying that with the current technology of AI, it is too soon to claim AI can beat the stock markets. | ['Jin Liu', 'Kang K. Yen', 'Sohrab Mokhtari'] | 2021-06-30 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-4.74224299e-01 1.88112497e-01 -5.52071035e-01 1.86623245e-01
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aea4e81c-4a0c-4194-9bf1-7ee37baa8219 | fec-fast-euclidean-clustering-for-point-cloud | 2208.07678 | null | https://arxiv.org/abs/2208.07678v2 | https://arxiv.org/pdf/2208.07678v2.pdf | FEC: Fast Euclidean Clustering for Point Cloud Segmentation | Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. In this paper, we present a fast solution to point cloud instance segmentation with small computational demands. To this end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies a pointwise scheme over the clusterwise scheme used in existing works. Our approach is conceptually simple, easy to implement (40 lines in C++), and achieves two orders of magnitudes faster against the classical segmentation methods while producing high-quality results. | ['Yizhen Lao', 'Huiqing Zhang', 'Yifei Xue', 'Yancheng Wang', 'Yu Cao'] | 2022-08-16 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.38001740e-01 -1.74614936e-01 1.03251435e-01 -2.60771155e-01
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65863011-3ad5-454a-8ca7-0a40c7143cc2 | multi-branch-learning-for-weakly-labeled | 2002.09661 | null | http://arxiv.org/abs/2002.09661v1 | http://arxiv.org/pdf/2002.09661v1.pdf | Multi-Branch Learning for Weakly-Labeled Sound Event Detection | There are two sub-tasks implied in the weakly-supervised SED: audio tagging
and event boundary detection. Current methods which combine multi-task learning
with SED requires annotations both for these two sub-tasks. Since there are
only annotations for audio tagging available in weakly-supervised SED, we
design multiple branches with different learning purposes instead of pursuing
multiple tasks. Similar to multiple tasks, multiple different learning purposes
can also prevent the common feature which the multiple branches share from
overfitting to any one of the learning purposes. We design these multiple
different learning purposes based on combinations of different MIL strategies
and different pooling methods. Experiments on the DCASE 2018 Task 4 dataset and
the URBAN-SED dataset both show that our method achieves competitive
performance. | [] | 2020-02-22 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 5.86803108e-02 1.38477907e-01 -3.95567119e-01 -2.31748551e-01
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5afe0773-c767-486f-9d1b-df5cb065bc9a | federated-generalized-face-presentation | 2104.06595 | null | https://arxiv.org/abs/2104.06595v2 | https://arxiv.org/pdf/2104.06595v2.pdf | Federated Generalized Face Presentation Attack Detection | Face presentation attack detection plays a critical role in the modern face recognition pipeline. A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (both real face images and spoof images) are not directly shared between data owners due to legal and privacy issues. In this paper, with the motivation of circumventing this challenge, we propose a Federated Face Presentation Attack Detection (FedPAD) framework that simultaneously takes advantage of rich fPAD information available at different data owners while preserving data privacy. In the proposed framework, each data center locally trains its own fPAD model. A server learns a global fPAD model by iteratively aggregating model updates from all data centers without accessing private data in each of them. To equip the aggregated fPAD model in the server with better generalization ability to unseen attacks from users, following the basic idea of FedPAD, we further propose a Federated Generalized Face Presentation Attack Detection (FedGPAD) framework. A federated domain disentanglement strategy is introduced in FedGPAD, which treats each data center as one domain and decomposes the fPAD model into domain-invariant and domain-specific parts in each data center. Two parts disentangle the domain-invariant and domain-specific features from images in each local data center, respectively. A server learns a global fPAD model by only aggregating domain-invariant parts of the fPAD models from data centers and thus a more generalized fPAD model can be aggregated in server. We introduce the experimental setting to evaluate the proposed FedPAD and FedGPAD frameworks and carry out extensive experiments to provide various insights about federated learning for fPAD. | ['Vishal M. Patel', 'Pong C. Yuen', 'Pramuditha Perera', 'Rui Shao'] | 2021-04-14 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [-9.81394108e-03 -3.41708153e-01 -1.51139259e-01 -2.86302269e-01
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89a448dc-c835-4a87-9a98-a2224b7e3059 | visual-semantic-re-ranker-for-text-spotting | 1810.09776 | null | http://arxiv.org/abs/1810.09776v2 | http://arxiv.org/pdf/1810.09776v2.pdf | Visual Semantic Re-ranker for Text Spotting | Many current state-of-the-art methods for text recognition are based on
purely local information and ignore the semantic correlation between text and
its surrounding visual context. In this paper, we propose a post-processing
approach to improve the accuracy of text spotting by using the semantic
relation between the text and the scene. We initially rely on an off-the-shelf
deep neural network that provides a series of text hypotheses for each input
image. These text hypotheses are then re-ranked using the semantic relatedness
with the object in the image. As a result of this combination, the performance
of the original network is boosted with a very low computational cost. The
proposed framework can be used as a drop-in complement for any text-spotting
algorithm that outputs a ranking of word hypotheses. We validate our approach
on ICDAR'17 shared task dataset. | ['Lluís Padró', 'Francesc Moreno-Noguer', 'Ahmed Sabir'] | 2018-10-23 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.31324089e-01 -2.94490248e-01 2.17858136e-01 -5.53533137e-01
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20459f42-4bd8-40ec-ac79-a3d08545f4f3 | attention-is-all-you-need | 1706.03762 | null | http://arxiv.org/abs/1706.03762v5 | http://arxiv.org/pdf/1706.03762v5.pdf | Attention Is All You Need | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on attention mechanisms, dispensing with recurrence and convolutions
entirely. Experiments on two machine translation tasks show these models to be
superior in quality while being more parallelizable and requiring significantly
less time to train. Our model achieves 28.4 BLEU on the WMT 2014
English-to-German translation task, improving over the existing best results,
including ensembles by over 2 BLEU. On the WMT 2014 English-to-French
translation task, our model establishes a new single-model state-of-the-art
BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction
of the training costs of the best models from the literature. We show that the
Transformer generalizes well to other tasks by applying it successfully to
English constituency parsing both with large and limited training data. | ['Niki Parmar', 'Lukasz Kaiser', 'Llion Jones', 'Illia Polosukhin', 'Noam Shazeer', 'Jakob Uszkoreit', 'Ashish Vaswani', 'Aidan N. Gomez'] | 2017-06-12 | attention-is-all-you-need-1 | http://papers.nips.cc/paper/7181-attention-is-all-you-need | http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf | neurips-2017-12 | ['few-shot-3d-point-cloud-classification', 'multimodal-machine-translation'] | ['computer-vision', 'natural-language-processing'] | [ 5.18665493e-01 6.75925910e-02 -1.89653426e-01 -2.36462414e-01
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09091eac-d737-495a-9cc9-af0e6bf8d279 | learning-a-deep-generative-model-like-a | 2011.11063 | null | https://arxiv.org/abs/2011.11063v1 | https://arxiv.org/pdf/2011.11063v1.pdf | Learning a Deep Generative Model like a Program: the Free Category Prior | Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts. In this process, we make "infinite use of finite means", enabling us to learn new concepts quickly and nest concepts within each-other. While program induction and synthesis remain at the heart of foundational theories of artificial intelligence, only recently has the community moved forward in attempting to use program learning as a benchmark task itself. The cognitive science community has thus often assumed that if the brain has simulation and reasoning capabilities equivalent to a universal computer, then it must employ a serialized, symbolic representation. Here we confront that assumption, and provide a counterexample in which compositionality is expressed via network structure: the free category prior over programs. We show how our formalism allows neural networks to serve as primitives in probabilistic programs. We learn both program structure and model parameters end-to-end. | ['Eli Sennesh'] | 2020-11-22 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.77398795e-01 2.87398309e-01 -6.43625632e-02 -5.66070318e-01
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8f662d38-17d1-42ca-b008-715892f7181a | hierarchical-graph-matching-networks-for-deep | null | null | https://openreview.net/forum?id=rkeqn1rtDH | https://openreview.net/pdf?id=rkeqn1rtDH | Hierarchical Graph Matching Networks for Deep Graph Similarity Learning | While the celebrated graph neural networks yields effective representations for individual nodes of a graph, there has been relatively less success in extending to deep graph similarity learning.
Recent work has considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich cross-level interactions between parts of a graph and a whole graph.
In this paper, we propose a Hierarchical Graph Matching Network (HGMN) for computing the graph similarity between any pair of graph-structured objects. Our model jointly learns graph representations and a graph matching metric function for computing graph similarity in an end-to-end fashion. The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs. Our comprehensive experiments demonstrate that our proposed HGMN consistently outperforms state-of-the-art graph matching networks baselines for both classification and regression tasks. | ['Shouling Ji', 'Chunming Wu', 'Fangli Xu', 'Tengfei Ma', 'Saizhuo Wang', 'Lingfei Wu', 'Xiang Ling'] | 2019-09-25 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-8.10265318e-02 4.14731324e-01 -8.04835930e-02 -4.10437465e-01
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d9442f67-61f5-4a67-870c-5c32d1b6587b | indicbart-a-pre-trained-model-for-natural | 2109.02903 | null | https://arxiv.org/abs/2109.02903v2 | https://arxiv.org/pdf/2109.02903v2.pdf | IndicBART: A Pre-trained Model for Indic Natural Language Generation | In this paper, we study pre-trained sequence-to-sequence models for a group of related languages, with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT and extreme summarization show that a model specific to related languages like IndicBART is competitive with large pre-trained models like mBART50 despite being significantly smaller. It also performs well on very low-resource translation scenarios where languages are not included in pre-training or fine-tuning. Script sharing, multilingual training, and better utilization of limited model capacity contribute to the good performance of the compact IndicBART model. | ['Pratyush Kumar', 'Mitesh M. Khapra', 'Ratish Puduppully', 'Anoop Kunchukuttan', 'Himani Shrotriya', 'Raj Dabre'] | 2021-09-07 | indicbart-a-pre-trained-model-for-indic-1 | https://aclanthology.org/2022.findings-acl.145 | https://aclanthology.org/2022.findings-acl.145.pdf | findings-acl-2022-5 | ['extreme-summarization'] | ['natural-language-processing'] | [ 3.72942537e-01 3.71450745e-02 -3.96039754e-01 -4.51089621e-01
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399844e5-cffd-4f3a-b641-4313352ad385 | factored-neus-reconstructing-surfaces | 2305.17929 | null | https://arxiv.org/abs/2305.17929v1 | https://arxiv.org/pdf/2305.17929v1.pdf | Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects | We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists of three stages. First, we reconstruct the scene radiance and signed distance function (SDF) with our novel regularization strategy for specular reflections. Our approach considers both the diffuse and specular colors, which allows for handling complex view-dependent lighting effects for surface reconstruction. Second, we distill light visibility and indirect illumination from the learned SDF and radiance field using learnable mapping functions. Third, we design a method for estimating the ratio of incoming direct light represented via Spherical Gaussians reflected in a specular manner and then reconstruct the materials and direct illumination of the scene. Experimental results demonstrate that the proposed method outperforms the current state-of-the-art in recovering surfaces, materials, and lighting without relying on any additional data. | ['Yiqun Wang', 'Peter Wonka', 'Evgeny Burnaev', 'Savva Ignatyev', 'Oleg Voynov', 'Ivan Skorokhodov', 'Yue Fan'] | 2023-05-29 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 7.75332451e-01 -4.37540978e-01 6.30254567e-01 -4.94568288e-01
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ba1b993c-5f58-4cf4-b940-530884a6e90e | unsupervised-discovery-of-object-landmarks-as | 1804.04412 | null | http://arxiv.org/abs/1804.04412v1 | http://arxiv.org/pdf/1804.04412v1.pdf | Unsupervised Discovery of Object Landmarks as Structural Representations | Deep neural networks can model images with rich latent representations, but
they cannot naturally conceptualize structures of object categories in a
human-perceptible way. This paper addresses the problem of learning object
structures in an image modeling process without supervision. We propose an
autoencoding formulation to discover landmarks as explicit structural
representations. The encoding module outputs landmark coordinates, whose
validity is ensured by constraints that reflect the necessary properties for
landmarks. The decoding module takes the landmarks as a part of the learnable
input representations in an end-to-end differentiable framework. Our discovered
landmarks are semantically meaningful and more predictive of manually annotated
landmarks than those discovered by previous methods. The coordinates of our
landmarks are also complementary features to pretrained deep-neural-network
representations in recognizing visual attributes. In addition, the proposed
method naturally creates an unsupervised, perceptible interface to manipulate
object shapes and decode images with controllable structures. The project
webpage is at http://ytzhang.net/projects/lmdis-rep | ['Yijun Luo', 'Yuting Zhang', 'Zhiyuan He', 'Yijie Guo', 'Honglak Lee', 'Yixin Jin'] | 2018-04-12 | unsupervised-discovery-of-object-landmarks-as-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Unsupervised_Discovery_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Unsupervised_Discovery_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 2.23768115e-01 5.99577069e-01 -3.27110171e-01 -8.10442924e-01
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2a7f058a-11a4-4fb9-8cbc-bb5a17d718c3 | sumbt-slot-utterance-matching-for-universal | 1907.07421 | null | https://arxiv.org/abs/1907.07421v1 | https://arxiv.org/pdf/1907.07421v1.pdf | SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking | In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy. | ['Tae-Yoon Kim', 'Jinsik Lee', 'Hwaran Lee'] | 2019-07-17 | sumbt-slot-utterance-matching-for-universal-1 | https://aclanthology.org/P19-1546 | https://aclanthology.org/P19-1546.pdf | acl-2019-7 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-4.62664872e-01 7.94568896e-01 -4.58716959e-01 -7.70731390e-01
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ded4a645-84dc-41ab-9056-f56a8fb7538c | understanding-cross-domain-few-shot-learning | 2202.01339 | null | https://arxiv.org/abs/2202.01339v3 | https://arxiv.org/pdf/2202.01339v3.pdf | Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty | Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for handling large differences between the source and target domains--an important concern in real-world scenarios. To overcome these large differences, recent works have considered exploiting small-scale unlabeled data from the target domain during the pre-training stage. This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain. In this paper, we empirically investigate which pre-training is preferred based on domain similarity and few-shot difficulty of the target domain. We discover that the performance gain of self-supervised pre-training over supervised pre-training becomes large when the target domain is dissimilar to the source domain, or the target domain itself has low few-shot difficulty. We further design two pre-training schemes, mixed-supervised and two-stage learning, that improve performance. In this light, we present six findings for CD-FSL, which are supported by extensive experiments and analyses on three source and eight target benchmark datasets with varying levels of domain similarity and few-shot difficulty. Our code is available at https://github.com/sungnyun/understanding-cdfsl. | ['Se-Young Yun', 'Hwanjun Song', 'Jin-Hwa Kim', 'Namgyu Ho', 'Sungnyun Kim', 'Jaehoon Oh'] | 2022-02-01 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.35776931e-01 -9.04668048e-02 -6.43058062e-01 -4.93342906e-01
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101624ab-196f-4754-aaf5-3474e72c233f | fine-grained-sentence-functions-for-short | 1907.10302 | null | https://arxiv.org/abs/1907.10302v3 | https://arxiv.org/pdf/1907.10302v3.pdf | Fine-Grained Sentence Functions for Short-Text Conversation | Sentence function is an important linguistic feature referring to a user's purpose in uttering a specific sentence. The use of sentence function has shown promising results to improve the performance of conversation models. However, there is no large conversation dataset annotated with sentence functions. In this work, we collect a new Short-Text Conversation dataset with manually annotated SEntence FUNctions (STC-Sefun). Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query. We later train conversation models conditioned on the sentence functions, including information retrieval-based and neural generative models. Experimental results demonstrate that the use of sentence functions can help improve the quality of the returned responses. | ['Xiaojiang Liu', 'Jun Gao', 'Shuming Shi', 'Wei Bi'] | 2019-07-24 | fine-grained-sentence-functions-for-short-1 | https://aclanthology.org/P19-1389 | https://aclanthology.org/P19-1389.pdf | acl-2019-7 | ['short-text-conversation'] | ['natural-language-processing'] | [ 2.93350607e-01 1.08466774e-01 1.98047891e-01 -9.54645634e-01
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cab0db00-7f83-4548-9cf2-c34d2f9fb222 | learnt-deep-hyperparameter-selection-in | 2302.14516 | null | https://arxiv.org/abs/2302.14516v1 | https://arxiv.org/pdf/2302.14516v1.pdf | Learnt Deep Hyperparameter selection in Adversarial Training for compressed video enhancement with perceptual critic | Image based Deep Feature Quality Metrics (DFQMs) have been shown to better correlate with subjective perceptual scores over traditional metrics. The fundamental focus of these DFQMs is to exploit internal representations from a large scale classification network as the metric feature space. Previously, no attention has been given to the problem of identifying which layers are most perceptually relevant. In this paper we present a new method for selecting perceptually relevant layers from such a network, based on a neuroscience interpretation of layer behaviour. The selected layers are treated as a hyperparameter to the critic network in a W-GAN. The critic uses the output from these layers in the preliminary stages to extract perceptual information. A video enhancement network is trained adversarially with this critic. Our results show that the introduction of these selected features into the critic yields up to 10% (FID) and 15% (KID) performance increase against other critic networks that do not exploit the idea of optimised feature selection. | ['Anil Kokaram', 'Darren Ramsook'] | 2023-02-28 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 4.94089305e-01 2.78210461e-01 2.45122656e-01 -3.06886613e-01
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55237073-bcad-4b3e-9e68-2ab76935612e | active-ensemble-deep-learning-for | 2006.15771 | null | https://arxiv.org/abs/2006.15771v1 | https://arxiv.org/pdf/2006.15771v1.pdf | Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification | Although deep learning has achieved great success in image classification tasks, its performance is subject to the quantity and quality of training samples. For classification of polarimetric synthetic aperture radar (PolSAR) images, it is nearly impossible to annotate the images from visual interpretation. Therefore, it is urgent for remote sensing scientists to develop new techniques for PolSAR image classification under the condition of very few training samples. In this letter, we take the advantage of active learning and propose active ensemble deep learning (AEDL) for PolSAR image classification. We first show that only 35\% of the predicted labels of a deep learning model's snapshots near its convergence were exactly the same. The disagreement between snapshots is non-negligible. From the perspective of multiview learning, the snapshots together serve as a good committee to evaluate the importance of unlabeled instances. Using the snapshots committee to give out the informativeness of unlabeled data, the proposed AEDL achieved better performance on two real PolSAR images compared with standard active learning strategies. It achieved the same classification accuracy with only 86% and 55% of the training samples compared with breaking ties active learning and random selection for the Flevoland dataset. | ['Sheng-Jie Liu', 'Qian Shi', 'Haowen Luo'] | 2020-06-29 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 4.38451357e-02 2.39328831e-01 -2.38079876e-01 -5.95813155e-01
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90ce70bd-5ff4-436c-8b01-5b329746c684 | neural-network-for-low-memory-iot-devices-and | 2006.02824 | null | https://arxiv.org/abs/2006.02824v2 | https://arxiv.org/pdf/2006.02824v2.pdf | Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map | This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3-96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks. | ['Andrei Velichko'] | 2020-06-04 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-5.73495589e-03 -4.14072722e-01 8.12055841e-02 -2.78182570e-02
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1c7043be-e209-4557-8817-6f9b9952171d | direct-molecular-conformation-generation-1 | 2202.01356 | null | https://arxiv.org/abs/2202.01356v2 | https://arxiv.org/pdf/2202.01356v2.pdf | Direct Molecular Conformation Generation | Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its 3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates the bond and atom information and iteratively refines the coordinates of the generated conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs datasets. Further analysis shows that our generated conformations have closer properties (e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The code is released at https://github.com/DirectMolecularConfGen/DMCG | ['Haiguang Liu', 'Yusong Wang', 'Tie-Yan Liu', 'Houqiang Li', 'Tao Qin', 'Wengang Zhou', 'Tong Wang', 'Shufang Xie', 'Lijun Wu', 'Chang Liu', 'Yingce Xia', 'Jinhua Zhu'] | 2022-02-03 | direct-molecular-conformation-generation | https://openreview.net/forum?id=kcrIligNnl | https://openreview.net/pdf?id=kcrIligNnl | null | ['molecular-docking'] | ['medical'] | [-1.22043388e-02 -1.03033647e-01 -3.91439170e-01 -3.30130011e-01
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2aaaca51-9722-472d-b81a-c6e498b3ca9c | uncertainty-aware-ab3dmot-by-variational-3d | 2302.05923 | null | https://arxiv.org/abs/2302.05923v1 | https://arxiv.org/pdf/2302.05923v1.pdf | Uncertainty-Aware AB3DMOT by Variational 3D Object Detection | Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world. Uncertainty estimation is an effective tool to provide statistically accurate predictions, while the associated detection uncertainty can be used to implement a more safe navigation protocol or include the user in the loop. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty and introduce these detections to an uncertainty-aware AB3DMOT tracker. This is done by applying a linear transformation to the estimated uncertainty matrix, which is subsequently used as a measurement noise for the adopted Kalman filter. We implement two ways to estimate output uncertainty, i.e., internally, by computing the variance of the CNNs outputs and then propagating the uncertainty through the post-processing, and externally, by associating the final predictions of different samples and computing the covariance of each predicted box. In experiments, we show that the external uncertainty estimation leads to better results, outperforming both internal uncertainty estimation and classical tracking approaches. Furthermore, we propose a method to initialize the Variational 3D object detector with a pretrained TANet model, which leads to the best performing models. | ['Alexandros Iosifidis', 'Illia Oleksiienko'] | 2023-02-12 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-3.11832875e-01 2.16008097e-01 2.60811061e-01 -2.96477646e-01
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29ba31f7-922d-41c8-90c6-bf636ae52ac4 | iterative-prompt-learning-for-unsupervised | 2303.17569 | null | https://arxiv.org/abs/2303.17569v1 | https://arxiv.org/pdf/2303.17569v1.pdf | Iterative Prompt Learning for Unsupervised Backlit Image Enhancement | We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the open-world CLIP prior not only aids in distinguishing between backlit and well-lit images, but also in perceiving heterogeneous regions with different luminance, facilitating the optimization of the enhancement network. Unlike high-level and image manipulation tasks, directly applying CLIP to enhancement tasks is non-trivial, owing to the difficulty in finding accurate prompts. To solve this issue, we devise a prompt learning framework that first learns an initial prompt pair by constraining the text-image similarity between the prompt (negative/positive sample) and the corresponding image (backlit image/well-lit image) in the CLIP latent space. Then, we train the enhancement network based on the text-image similarity between the enhanced result and the initial prompt pair. To further improve the accuracy of the initial prompt pair, we iteratively fine-tune the prompt learning framework to reduce the distribution gaps between the backlit images, enhanced results, and well-lit images via rank learning, boosting the enhancement performance. Our method alternates between updating the prompt learning framework and enhancement network until visually pleasing results are achieved. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of visual quality and generalization ability, without requiring any paired data. | ['Chen Change Loy', 'Ruicheng Feng', 'Shangchen Zhou', 'Chongyi Li', 'Zhexin Liang'] | 2023-03-30 | null | null | null | null | ['image-enhancement', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 7.22230494e-01 -3.53582591e-01 -5.29666990e-02 -4.09276038e-01
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37eeb63b-7c2f-40b5-8581-fa7f54245ef1 | fast-unsupervised-dependency-parsing-with-arc | null | null | https://aclanthology.org/W12-0701 | https://aclanthology.org/W12-0701.pdf | Fast Unsupervised Dependency Parsing with Arc-Standard Transitions | null | ['Mohammad Sadegh Rasooli', 'Heshaam Faili'] | 2012-04-01 | null | null | null | ws-2012-4 | ['dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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307ce0b7-7667-4ef9-926c-5cd646a627c9 | heterogeneous-graph-contrastive-learning-for | 2303.00995 | null | https://arxiv.org/abs/2303.00995v1 | https://arxiv.org/pdf/2303.00995v1.pdf | Heterogeneous Graph Contrastive Learning for Recommendation | Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL. | ['Ronghua Luo', 'Yong Xu', 'Wei Wei', 'Lianghao Xia', 'Chao Huang', 'Mengru Chen'] | 2023-03-02 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-1.46546111e-01 -2.33192965e-02 -7.34558225e-01 -3.10725659e-01
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1fe2557e-c3e2-42b3-9018-90a335c51771 | editing-language-model-based-knowledge-graph | 2301.10405 | null | https://arxiv.org/abs/2301.10405v4 | https://arxiv.org/pdf/2301.10405v4.pdf | Editing Language Model-based Knowledge Graph Embeddings | Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, which are challenging to modify without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. The proposed task aims to enable data-efficient and fast updates to KG embeddings without damaging the performance of the rest. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hyper network to edit/add facts. Comprehensive experimental results demonstrate that KGEditor can perform better when updating specific facts while not affecting the rest with low training resources. Code and datasets will be available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG. | ['Huajun Chen', 'Wei Guo', 'Feiyu Xiong', 'Zelin Dai', 'Bozhong Tian', 'Ningyu Zhang', 'Siyuan Cheng'] | 2023-01-25 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.07123417e-01 4.49297428e-01 -2.14116544e-01 -1.53561696e-01
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eb920e09-d6e3-4e39-b5fe-6f7f20f7f696 | sample-prior-guided-robust-model-learning-to | 2112.01197 | null | https://arxiv.org/abs/2112.01197v3 | https://arxiv.org/pdf/2112.01197v3.pdf | Sample Prior Guided Robust Model Learning to Suppress Noisy Labels | Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance. Several recent effective methods for handling noisy labels have two key steps: 1) dividing samples into cleanly labeled and wrongly labeled sets by training loss, 2) using semi-supervised methods to generate pseudo-labels for samples in the wrongly labeled set. However, current methods always hurt the informative hard samples due to the similar loss distribution between the hard samples and the noisy ones. In this paper, we proposed PGDF (Prior Guided Denoising Framework), a novel framework to learn a deep model to suppress noise by generating the samples' prior knowledge, which is integrated into both dividing samples step and semi-supervised step. Our framework can save more informative hard clean samples into the cleanly labeled set. Besides, our framework also promotes the quality of pseudo-labels during the semi-supervised step by suppressing the noise in the current pseudo-labels generating scheme. To further enhance the hard samples, we reweight the samples in the cleanly labeled set during training. We evaluated our method using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world datasets WebVision and Clothing1M. The results demonstrate substantial improvements over state-of-the-art methods. | ['Tiejun Huang', 'Mengting Li', 'Yi Chen', 'Chuang Zhu', 'Wenkai Chen'] | 2021-12-02 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.21799278e-01 2.84764320e-02 1.85837433e-01 -8.90847504e-01
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cc759f17-99d9-48e6-ba16-8326d88acb6d | the-effect-of-dependency-representation | null | null | https://aclanthology.org/U14-1002 | https://aclanthology.org/U14-1002.pdf | The Effect of Dependency Representation Scheme on Syntactic Language Modelling | null | ['Mark Johnson', 'John Pate', 'Sunghwan Kim'] | 2014-11-01 | the-effect-of-dependency-representation-1 | https://aclanthology.org/U14-1002 | https://aclanthology.org/U14-1002.pdf | alta-2014-11 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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980a5dec-1a3c-4e49-a846-4d7fe3bdebb2 | dialogue-act-classification-for-augmentative | null | null | https://aclanthology.org/2021.nlp4posimpact-1.12 | https://aclanthology.org/2021.nlp4posimpact-1.12.pdf | Dialogue Act Classification for Augmentative and Alternative Communication | Augmentative and Alternative Communication (AAC) devices and applications are intended to make it easier for individuals with complex communication needs to participate in conversations. However, these devices have low adoption and retention rates. We review prior work with text recommendation systems that have not been successful in mitigating these problems. To address these gaps, we propose applying Dialogue Act classification to AAC conversations. We evaluated the performance of a state of the art model on a limited AAC dataset that was trained on both AAC and non-AAC datasets. The one trained on AAC (accuracy = 38.6%) achieved better performance than that trained on a non-AAC corpus (accuracy = 34.1%). These results reflect the need to incorporate representative datasets in later experiments. We discuss the need to collect more labeled AAC datasets and propose areas of future work. | ['E. Margaret Perkoff'] | null | null | null | null | acl-nlp4posimpact-2021-8 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.34900486e-01 5.67931652e-01 -2.76932865e-01 -5.83586693e-01
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b5e5bf0e-0c02-4f69-8988-349fd0919d16 | learning-generalized-zero-shot-learners-for | 2302.00275 | null | https://arxiv.org/abs/2302.00275v1 | https://arxiv.org/pdf/2302.00275v1.pdf | Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization | Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo. It is an unsolved problem relying on the ability to combine visual clues with general knowledge about the world to make accurate predictions across geographies. We present $\href{https://huggingface.co/geolocal/StreetCLIP}{\text{StreetCLIP}}$, a robust, publicly available foundation model not only achieving state-of-the-art performance on multiple open-domain image geolocalization benchmarks but also doing so in a zero-shot setting, outperforming supervised models trained on more than 4 million images. Our method introduces a meta-learning approach for generalized zero-shot learning by pretraining CLIP from synthetic captions, grounding CLIP in a domain of choice. We show that our method effectively transfers CLIP's generalized zero-shot capabilities to the domain of image geolocalization, improving in-domain generalized zero-shot performance without finetuning StreetCLIP on a fixed set of classes. | ['Michal Skreta', 'Silas Alberti', 'Lukas Haas'] | 2023-02-01 | null | null | null | null | ['photo-geolocation-estimation', 'generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.11953706e-01 1.05624102e-01 -5.24023175e-01 -4.08875734e-01
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94242f2b-7345-4b7e-9dce-d896572c4a33 | inferring-super-resolution-depth-from-a | 1912.06501 | null | https://arxiv.org/abs/1912.06501v1 | https://arxiv.org/pdf/1912.06501v1.pdf | Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach | A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects from multiple viewpoints with unknown motion. This non-static camera-to-object setup is described with a nonconvex variational approach such that no calibration on lighting or camera motion is required due to the formulation of an end-to-end joint optimization problem. Solving the proposed variational model results in high resolution depth, reflectance and camera pose estimates, as we show on challenging synthetic and real-world datasets. | ['Lu Sang', 'Daniel Cremers', 'Bjoern Haefner'] | 2019-12-13 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 4.82665628e-01 -3.65456529e-02 3.36173862e-01 -3.85612249e-01
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f9aed454-25fb-4bcc-bfd6-daa24519e84a | an-optical-physics-inspired-cnn-approach-for | 2105.10076 | null | https://arxiv.org/abs/2105.10076v1 | https://arxiv.org/pdf/2105.10076v1.pdf | An Optical physics inspired CNN approach for intrinsic image decomposition | Intrinsic Image Decomposition is an open problem of generating the constituents of an image. Generating reflectance and shading from a single image is a challenging task specifically when there is no ground truth. There is a lack of unsupervised learning approaches for decomposing an image into reflectance and shading using a single image. We propose a neural network architecture capable of this decomposition using physics-based parameters derived from the image. Through experimental results, we show that (a) the proposed methodology outperforms the existing deep learning-based IID techniques and (b) the derived parameters improve the efficacy significantly. We conclude with a closer analysis of the results (numerical and example images) showing several avenues for improvement. | ['Roshan Godaliyadda', 'Vijitha Herath', 'Roshan Ragel', 'Parakrama Ekanayake', 'Suren Sritharan', 'Gihan Jayatilaka', 'Harshana Weligampola'] | 2021-05-21 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 8.86232734e-01 1.90085575e-01 5.54245234e-01 -4.35793370e-01
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93c42b11-219f-4515-a383-cb9d66c325c6 | targeted-attack-on-gpt-neo-for-the-satml | 2302.07735 | null | https://arxiv.org/abs/2302.07735v1 | https://arxiv.org/pdf/2302.07735v1.pdf | Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge | Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the training data, which has massive privacy implications. The construction of data extraction attacks is challenging, current attacks are quite inefficient, and there exists a significant gap in the extraction capabilities of untargeted attacks and memorization. Thus, targeted attacks are proposed, which identify if a given sample from the training data, is extractable from a model. In this work, we apply a targeted data extraction attack to the SATML2023 Language Model Training Data Extraction Challenge. We apply a two-step approach. In the first step, we maximise the recall of the model and are able to extract the suffix for 69% of the samples. In the second step, we use a classifier-based Membership Inference Attack on the generations. Our AutoSklearn classifier achieves a precision of 0.841. The full approach reaches a score of 0.405 recall at a 10% false positive rate, which is an improvement of 34% over the baseline of 0.301. | ['Arie van Deursen', 'Maliheh Izadi', 'Ali Al-Kaswan'] | 2023-02-13 | null | null | null | null | ['inference-attack', 'membership-inference-attack', 'memorization'] | ['adversarial', 'computer-vision', 'natural-language-processing'] | [ 4.66588348e-01 5.05459607e-01 -2.79255390e-01 -1.84829101e-01
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fb03b681-c856-4c6c-b5ec-f1420cd96ce5 | a-sequential-model-for-classifying-temporal | 1707.07343 | null | http://arxiv.org/abs/1707.07343v1 | http://arxiv.org/pdf/1707.07343v1.pdf | A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events | We present a sequential model for temporal relation classification between
intra-sentence events. The key observation is that the overall syntactic
structure and compositional meanings of the multi-word context between events
are important for distinguishing among fine-grained temporal relations.
Specifically, our approach first extracts a sequence of context words that
indicates the temporal relation between two events, which well align with the
dependency path between two event mentions. The context word sequence, together
with a parts-of-speech tag sequence and a dependency relation sequence that are
generated corresponding to the word sequence, are then provided as input to
bidirectional recurrent neural network (LSTM) models. The neural nets learn
compositional syntactic and semantic representations of contexts surrounding
the two events and predict the temporal relation between them. Evaluation of
the proposed approach on TimeBank corpus shows that sequential modeling is
capable of accurately recognizing temporal relations between events, which
outperforms a neural net model using various discrete features as input that
imitates previous feature based models. | ['Prafulla Kumar Choubey', 'Ruihong Huang'] | 2017-07-23 | a-sequential-model-for-classifying-temporal-1 | https://aclanthology.org/D17-1190 | https://aclanthology.org/D17-1190.pdf | emnlp-2017-9 | ['temporal-relation-classification'] | ['natural-language-processing'] | [ 5.54897785e-01 -1.06096603e-01 -4.52680200e-01 -7.31497467e-01
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ed8c7169-8501-4755-bcbe-1dd56e170f0c | bi-apc-bidirectional-autoregressive | 2102.06816 | null | https://arxiv.org/abs/2102.06816v1 | https://arxiv.org/pdf/2102.06816v1.pdf | Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR | We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. Typically, SPT performs better. In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. Conventional bidirectional UPT methods, however, predict only a small portion of frames. To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid systems are investigated. For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT. | ['Abeer Alwan', 'Amber Afshan', 'Ruchao Fan'] | 2021-02-12 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 6.67559803e-01 6.01534545e-01 -3.40686649e-01 -5.36977351e-01
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96f68435-b9cb-4d5b-baef-0225fcc36d11 | quantum-motion-segmentation | 2203.13185 | null | https://arxiv.org/abs/2203.13185v1 | https://arxiv.org/pdf/2203.13185v1.pdf | Quantum Motion Segmentation | Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images. This paper introduces the first algorithm for motion segmentation that relies on adiabatic quantum optimization of the objective function. The proposed method achieves on-par performance with the state of the art on problem instances which can be mapped to modern quantum annealers. | ['Vladislav Golyanik', 'Elisa Ricci', 'Marcel Seelbach Benkner', 'Willi Menapace', 'Federica Arrigoni'] | 2022-03-24 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 6.64807677e-01 7.41295740e-02 -3.32489848e-01 3.54398564e-02
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62f5e246-4af5-44cf-a79b-1b54f82d7798 | text-anchor-based-metric-learning-for-small | 2108.05516 | null | https://arxiv.org/abs/2108.05516v1 | https://arxiv.org/pdf/2108.05516v1.pdf | Text Anchor Based Metric Learning for Small-footprint Keyword Spotting | Keyword Spotting (KWS) remains challenging to achieve the trade-off between small footprint and high accuracy. Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size. However, for metric learning, due to data limitations, the speech anchor is highly susceptible to the acoustic environment and speakers. Also, we note that the 1D-CNN models have limited capability to capture long-term temporal acoustic features. To address the above problems, we propose to utilize text anchors to improve the stability of anchors. Furthermore, a new type of model (LG-Net) is exquisitely designed to promote long-short term acoustic feature modeling based on 1D-CNN and self-attention. Experiments are conducted on Google Speech Commands Dataset version 1 (GSCDv1) and 2 (GSCDv2). The results demonstrate that the proposed text anchor based metric learning method shows consistent improvements over speech anchor on representative CNN-based models. Moreover, our LG-Net model achieves SOTA accuracy of 97.67% and 96.79% on two datasets, respectively. It is encouraged to see that our lighter LG-Net with only 74k parameters obtains 96.82% KWS accuracy on the GSCDv1 and 95.77% KWS accuracy on the GSCDv2. | ['Yuexian Zou', 'Nuo Chen', 'Rongzhi Gu', 'Li Wang'] | 2021-08-12 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [-3.62947285e-01 -2.10734934e-01 -1.73994198e-01 -6.35154724e-01
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c09d2c9b-533e-4c30-b6a9-7989c8c40173 | explainable-and-position-aware-learning-in | 2306.08198 | null | https://arxiv.org/abs/2306.08198v1 | https://arxiv.org/pdf/2306.08198v1.pdf | Explainable and Position-Aware Learning in Digital Pathology | Encoding whole slide images (WSI) as graphs is well motivated since it makes it possible for the gigapixel resolution WSI to be represented in its entirety for the purpose of graph learning. To this end, WSIs can be broken into smaller patches that represent the nodes of the graph. Then, graph-based learning methods can be utilized for the grading and classification of cancer. Message passing among neighboring nodes is the foundation of graph-based learning methods. However, they do not take into consideration any positional information for any of the patches, and if two patches are found in topologically isomorphic neighborhoods, their embeddings are nearly similar to one another. In this work, classification of cancer from WSIs is performed with positional embedding and graph attention. In order to represent the positional embedding of the nodes in graph classification, the proposed method makes use of spline convolutional neural networks (CNN). The algorithm is then tested with the WSI dataset for grading prostate cancer and kidney cancer. A comparison of the proposed method with leading approaches in cancer diagnosis and grading verify improved performance. The identification of cancerous regions in WSIs is another critical task in cancer diagnosis. In this work, the explainability of the proposed model is also addressed. A gradient-based explainbility approach is used to generate the saliency mapping for the WSIs. This can be used to look into regions of WSI that are responsible for cancer diagnosis thus rendering the proposed model explainable. | ['Nasim Yahyasoltani', 'Milan Aryal'] | 2023-06-14 | null | null | null | null | ['whole-slide-images', 'graph-attention', 'graph-learning', 'graph-classification'] | ['computer-vision', 'graphs', 'graphs', 'graphs'] | [ 2.72373348e-01 8.42964888e-01 -3.22998852e-01 -1.06440581e-01
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50d6d4f6-01a3-4e94-84fb-286f5144b1bd | is-depth-really-necessary-for-salient-object | 2006.00269 | null | https://arxiv.org/abs/2006.00269v2 | https://arxiv.org/pdf/2006.00269v2.pdf | Is Depth Really Necessary for Salient Object Detection? | Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which faces difficulties in some complex scenarios. To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information. Taking the advantages of RGB and RGBD methods, we propose a novel depth-aware salient object detection framework, which has following superior designs: 1) It only takes the depth information as training data while only relies on RGB information in the testing phase. 2) It comprehensively optimizes SOD features with multi-level depth-aware regularizations. 3) The depth information also serves as error-weighted map to correct the segmentation process. With these insightful designs combined, we make the first attempt in realizing an unified depth-aware framework with only RGB information as input for inference, which not only surpasses the state-of-the-art performances on five public RGB SOD benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a large margin, while adopting less information and implementation light-weighted. The code and model will be publicly available. | ['Jia-Wei Zhao', 'Yifan Zhao', 'Xiaowu Chen', 'Jia Li'] | 2020-05-30 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.79878220e-01 -2.02033333e-02 -1.30667701e-01 -3.89066666e-01
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82bbf272-1e19-4f8b-a446-fe063d8d636c | on-the-efficacy-of-sampling-adapters | 2307.03749 | null | https://arxiv.org/abs/2307.03749v1 | https://arxiv.org/pdf/2307.03749v1.pdf | On the Efficacy of Sampling Adapters | Sampling is a common strategy for generating text from probabilistic models, yet standard ancestral sampling often results in text that is incoherent or ungrammatical. To alleviate this issue, various modifications to a model's sampling distribution, such as nucleus or top-k sampling, have been introduced and are now ubiquitously used in language generation systems. We propose a unified framework for understanding these techniques, which we term sampling adapters. Sampling adapters often lead to qualitatively better text, which raises the question: From a formal perspective, how are they changing the (sub)word-level distributions of language generation models? And why do these local changes lead to higher-quality text? We argue that the shift they enforce can be viewed as a trade-off between precision and recall: while the model loses its ability to produce certain strings, its precision rate on desirable text increases. While this trade-off is not reflected in standard metrics of distribution quality (such as perplexity), we find that several precision-emphasizing measures indeed indicate that sampling adapters can lead to probability distributions more aligned with the true distribution. Further, these measures correlate with higher sequence-level quality scores, specifically, Mauve. | ['Ryan Cotterell', 'Ethan G. Wilcox', 'Luca Malagutti', 'Tiago Pimentel', 'Clara Meister'] | 2023-07-07 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 3.79129529e-01 4.13491637e-01 -2.48954892e-01 -2.90405273e-01
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360333ba-a2dc-491b-83b4-e7519ee90d54 | point-to-voxel-knowledge-distillation-for-1 | 2206.02099 | null | https://arxiv.org/abs/2206.02099v1 | https://arxiv.org/pdf/2206.02099v1.pdf | Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation | This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation. Directly employing previous distillation approaches yields inferior results due to the intrinsic challenges of point cloud, i.e., sparsity, randomness and varying density. To tackle the aforementioned problems, we propose the Point-to-Voxel Knowledge Distillation (PVD), which transfers the hidden knowledge from both point level and voxel level. Specifically, we first leverage both the pointwise and voxelwise output distillation to complement the sparse supervision signals. Then, to better exploit the structural information, we divide the whole point cloud into several supervoxels and design a difficulty-aware sampling strategy to more frequently sample supervoxels containing less-frequent classes and faraway objects. On these supervoxels, we propose inter-point and inter-voxel affinity distillation, where the similarity information between points and voxels can help the student model better capture the structural information of the surrounding environment. We conduct extensive experiments on two popular LiDAR segmentation benchmarks, i.e., nuScenes and SemanticKITTI. On both benchmarks, our PVD consistently outperforms previous distillation approaches by a large margin on three representative backbones, i.e., Cylinder3D, SPVNAS and MinkowskiNet. Notably, on the challenging nuScenes and SemanticKITTI datasets, our method can achieve roughly 75% MACs reduction and 2x speedup on the competitive Cylinder3D model and rank 1st on the SemanticKITTI leaderboard among all published algorithms. Our code is available at https://github.com/cardwing/Codes-for-PVKD. | ['Yikang Li', 'Chen Change Loy', 'Yuexin Ma', 'Xinge Zhu', 'Yuenan Hou'] | 2022-06-05 | point-to-voxel-knowledge-distillation-for | http://openaccess.thecvf.com//content/CVPR2022/html/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-3.11149247e-02 1.04857266e-01 -4.28980052e-01 -3.55990678e-01
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4358f131-d0a5-4554-9f57-5977dbfbd3d0 | flow-packet-hybrid-traffic-classification-for | 2105.00074 | null | https://arxiv.org/abs/2105.00074v1 | https://arxiv.org/pdf/2105.00074v1.pdf | Flow-Packet Hybrid Traffic Classification for Class-Aware Network Routing | Network traffic classification using machine learning techniques has been widely studied. Most existing schemes classify entire traffic flows, but there are major limitations to their practicality. At a network router, the packets need to be processed with minimum delay, so the classifier cannot wait until the end of the flow to make a decision. Furthermore, a complicated machine learning algorithm can be too computationally expensive to implement inside the router. In this paper, we introduce flow-packet hybrid traffic classification (FPHTC), where the router makes a decision per packet based on a routing policy that is designed through transferring the learned knowledge from a flow-based classifier residing outside the router. We analyze the generalization bound of FPHTC and show its advantage over regular packet-based traffic classification. We present experimental results using a real-world traffic dataset to illustrate the classification performance of FPHTC. We show that it is robust toward traffic pattern changes and can be deployed with limited computational resource. | ['Ilijc Albanese', 'Ali Tizghadam', 'Ben Liang', 'Sayantan Chowdhury'] | 2021-04-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.28040656e-01 -3.74101162e-01 -6.61818326e-01 -5.00800788e-01
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33ed2055-4c82-4b49-bff4-9b3906bb9ffb | polito-iit-cini-submission-to-the-epic | 2209.04525 | null | https://arxiv.org/abs/2209.04525v1 | https://arxiv.org/pdf/2209.04525v1.pdf | PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition | In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA setting, we first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignment (RNA). Secondly, we extended this approach to work on unlabelled target data, enabling a simpler adaptation of the model to the target distribution in an unsupervised fashion. To this purpose, we included in our framework UDA algorithms, such as multi-level adversarial alignment and attentive entropy. By analyzing the challenge setting, we notice the presence of a secondary concurrence shift in the data, which is usually called environmental bias. It is caused by the existence of different environments, i.e., kitchens. To deal with these two shifts (environmental and temporal), we extended our system to perform Multi-Source Multi-Target Domain Adaptation. Finally, we employed distinct models in our final proposal to leverage the potential of popular video architectures, and we introduced two more losses for the ensemble adaptation. Our submission (entry 'plnet') is visible on the leaderboard and ranked in 2nd position for 'verb', and in 3rd position for both 'noun' and 'action'. | ['Barbara Caputo', 'Giuseppe Averta', 'Gabriele Trivigno', 'Gabriele Goletto', 'Mirco Planamente'] | 2022-09-09 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.93435889e-01 5.41589409e-02 5.42900749e-02 -2.66914636e-01
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c3594bff-af56-4ca5-ad64-54a6e5fc525d | discovering-dialog-structure-graph-for-open | 2012.15543 | null | https://arxiv.org/abs/2012.15543v1 | https://arxiv.org/pdf/2012.15543v1.pdf | Discovering Dialog Structure Graph for Open-Domain Dialog Generation | Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. In this paper, we conduct unsupervised discovery of dialog structure from chitchat corpora, and then leverage it to facilitate dialog generation in downstream systems. To this end, we present a Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover a unified human-readable dialog structure. The structure is a two-layer directed graph that contains session-level semantics in the upper-layer vertices, utterance-level semantics in the lower-layer vertices, and edges among these semantic vertices. In particular, we integrate GNN into DVAE to fine-tune utterance-level semantics for more effective recognition of session-level semantic vertex. Furthermore, to alleviate the difficulty of discovering a large number of utterance-level semantics, we design a coupling mechanism that binds each utterance-level semantic vertex with a distinct phrase to provide prior semantics. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure, and the use of dialog structure graph as background knowledge can facilitate a graph grounded conversational system to conduct coherent multi-turn dialog generation. | ['Ting Liu', 'Wanxiang Che', 'Hua Wu', 'Zheng-Yu Niu', 'Haifeng Wang', 'Zeyang Lei', 'Jun Xu'] | 2020-12-31 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.10480502e-01 9.08525348e-01 -2.84171663e-02 -7.43468285e-01
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2cb63d07-a8c8-4d60-a5e9-b39374d1c48f | cross-modal-attribute-insertions-for | 2306.11065 | null | https://arxiv.org/abs/2306.11065v1 | https://arxiv.org/pdf/2306.11065v1.pdf | Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning | The robustness of multimodal deep learning models to realistic changes in the input text is critical for their applicability to important tasks such as text-to-image retrieval and cross-modal entailment. To measure robustness, several existing approaches edit the text data, but do so without leveraging the cross-modal information present in multimodal data. Information from the visual modality, such as color, size, and shape, provide additional attributes that users can include in their inputs. Thus, we propose cross-modal attribute insertions as a realistic perturbation strategy for vision-and-language data that inserts visual attributes of the objects in the image into the corresponding text (e.g., "girl on a chair" to "little girl on a wooden chair"). Our proposed approach for cross-modal attribute insertions is modular, controllable, and task-agnostic. We find that augmenting input text using cross-modal insertions causes state-of-the-art approaches for text-to-image retrieval and cross-modal entailment to perform poorly, resulting in relative drops of 15% in MRR and 20% in $F_1$ score, respectively. Crowd-sourced annotations demonstrate that cross-modal insertions lead to higher quality augmentations for multimodal data than augmentations using text-only data, and are equivalent in quality to original examples. We release the code to encourage robustness evaluations of deep vision-and-language models: https://github.com/claws-lab/multimodal-robustness-xmai. | ['Srijan Kumar', 'Gaurav Verma', 'Shivaen Ramshetty'] | 2023-06-19 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 1.61087975e-01 -2.97143310e-01 1.82442948e-01 -4.89806056e-01
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2ba0c0f7-ad41-4ae2-9eb8-86425ba93342 | queryinst-parallelly-supervised-mask-query | 2105.01928 | null | https://arxiv.org/abs/2105.01928v3 | https://arxiv.org/pdf/2105.01928v3.pdf | Instances as Queries | Recently, query based object detection frameworks achieve comparable performance with previous state-of-the-art object detectors. However, how to fully leverage such frameworks to perform instance segmentation remains an open problem. In this paper, we present QueryInst (Instances as Queries), a query based instance segmentation method driven by parallel supervision on dynamic mask heads. The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage. This approach eliminates the explicit multi-stage mask head connection and the proposal distribution inconsistency issues inherent in non-query based multi-stage instance segmentation methods. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in instance segmentation and video instance segmentation (VIS) task. Specifically, using ResNet-101-FPN backbone, QueryInst obtains 48.1 box AP and 42.8 mask AP on COCO test-dev, which is 2 points higher than HTC in terms of both box AP and mask AP, while runs 2.4 times faster. For video instance segmentation, QueryInst achieves the best performance among all online VIS approaches and strikes a decent speed-accuracy trade-off. Code is available at \url{https://github.com/hustvl/QueryInst}. | ['Wenyu Liu', 'Bin Feng', 'Ying Shan', 'Chen Fang', 'Yu Li', 'Xinggang Wang', 'Shusheng Yang', 'Yuxin Fang'] | 2021-05-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Fang_Instances_As_Queries_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-1.89533368e-01 -6.22428656e-02 -4.79596317e-01 -2.94032127e-01
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d5aa5863-243e-46a4-9e22-eeb40543fe00 | human-judgement-as-a-compass-to-navigate | 2204.07549 | null | https://arxiv.org/abs/2204.07549v1 | https://arxiv.org/pdf/2204.07549v1.pdf | Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer | Although text style transfer has witnessed rapid development in recent years, there is as yet no established standard for evaluation, which is performed using several automatic metrics, lacking the possibility of always resorting to human judgement. We focus on the task of formality transfer, and on the three aspects that are usually evaluated: style strength, content preservation, and fluency. To cast light on how such aspects are assessed by common and new metrics, we run a human-based evaluation and perform a rich correlation analysis. We are then able to offer some recommendations on the use of such metrics in formality transfer, also with an eye to their generalisability (or not) to related tasks. | ['Malvina Nissim', 'Antonio Toral', 'Jiali Mao', 'Huiyuan Lai'] | 2022-04-15 | null | https://aclanthology.org/2022.humeval-1.9 | https://aclanthology.org/2022.humeval-1.9.pdf | humeval-acl-2022-5 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.21667522e-01 8.06955621e-02 2.48477887e-02 -3.55892032e-01
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a828228a-117c-4aa2-bc9a-6b9d11ebd149 | active-speaker-detection-as-a-multi-objective | 2106.03821 | null | https://arxiv.org/abs/2106.03821v2 | https://arxiv.org/pdf/2106.03821v2.pdf | Active Speaker Detection as a Multi-Objective Optimization with Uncertainty-based Multimodal Fusion | It is now well established from a variety of studies that there is a significant benefit from combining video and audio data in detecting active speakers. However, either of the modalities can potentially mislead audiovisual fusion by inducing unreliable or deceptive information. This paper outlines active speaker detection as a multi-objective learning problem to leverage best of each modalities using a novel self-attention, uncertainty-based multimodal fusion scheme. Results obtained show that the proposed multi-objective learning architecture outperforms traditional approaches in improving both mAP and AUC scores. We further demonstrate that our fusion strategy surpasses, in active speaker detection, other modality fusion methods reported in various disciplines. We finally show that the proposed method significantly improves the state-of-the-art on the AVA-ActiveSpeaker dataset. | ['Frederic Precioso', 'Charles Bouveyron', 'Leela K. Gudupudi', 'Laurent Pilati', 'Baptiste Pouthier'] | 2021-06-07 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 2.52433568e-01 1.12558194e-01 4.42722924e-02 -3.98669988e-01
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ac613c18-f3dd-40c7-9fcf-609eeb1edcf1 | a-survey-on-few-shot-class-incremental | 2304.08130 | null | https://arxiv.org/abs/2304.08130v1 | https://arxiv.org/pdf/2304.08130v1.pdf | A Survey on Few-Shot Class-Incremental Learning | Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta-learning based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. | ['Prayag Tiwari', 'Xin Ning', 'Hang Ran', 'Weijun Li', 'Lusi Li', 'Songsong Tian'] | 2023-04-17 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.35371464e-01 -1.09427884e-01 -4.53618497e-01 -1.62573442e-01
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95f3313f-f360-4d34-9097-f9f2cb2a0f75 | screening-covid-19-cases-using-deep-neural | null | null | https://www.ijariit.com/manuscript/screening-covid-19-cases-using-deep-neural-networks-with-x-ray-images/ | https://www.ijariit.com/manuscripts/v6i3/V6I3-1626.pdf | Screening COVID-19 cases using Deep Neural Networks with X-ray images | descriptionThe novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. The application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multiclass classification (COVID vs. No-Findings vs. Pneumonia). My model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in my study as a classifier for you only look once (YOLO) real-time object detection system. I implemented 17 convolutional layers and introduced different filtering on each layer. My model can be employed to assist radiologists in invalidating their initial screening, and can also be employed via the cloud to immediately screen patients. With the ever-increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. | ['Tarit Sengupta'] | 2020-11-24 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 4.47717980e-02 -3.03040862e-01 -9.35440045e-03 -4.81201597e-02
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31a8d993-85dc-417a-8446-26b72b898b6f | on-learning-the-structure-of-clusters-in | 2212.14345 | null | https://arxiv.org/abs/2212.14345v1 | https://arxiv.org/pdf/2212.14345v1.pdf | On Learning the Structure of Clusters in Graphs | Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering algorithms which make strong simplifying assumptions about the structure of the graph. This thesis addresses the natural question of whether the structure of clusters can be learned efficiently and describes four new algorithmic results for learning such structure in graphs and hypergraphs. All of the presented theoretical results are extensively evaluated on both synthetic and real-word datasets of different domains, including image classification and segmentation, migration networks, co-authorship networks, and natural language processing. These experimental results demonstrate that the newly developed algorithms are practical, effective, and immediately applicable for learning the structure of clusters in real-world data. | ['Peter Macgregor'] | 2022-12-29 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 2.50584900e-01 1.41437531e-01 -3.33648384e-01 -2.90161669e-01
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14be7a27-8e4b-4ee8-9620-f15cf3764137 | legal-prompting-teaching-a-language-model-to | 2212.01326 | null | https://arxiv.org/abs/2212.01326v2 | https://arxiv.org/pdf/2212.01326v2.pdf | Legal Prompting: Teaching a Language Model to Think Like a Lawyer | Large language models that are capable of zero or few-shot prompting approaches have given rise to the new research area of prompt engineering. Recent advances showed that for example Chain-of-Thought (CoT) prompts can improve arithmetic or common sense tasks significantly. We explore how such approaches fare with legal reasoning tasks and take the COLIEE entailment task based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning approaches. Our findings show that while CoT prompting and fine-tuning with explanations approaches show improvements, the best results are produced by prompts that are derived from specific legal reasoning techniques such as IRAC (Issue, Rule, Application, Conclusion). Based on our experiments we improve the 2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best system of 0.6789 accuracy with an accuracy of 0.7431. | ['Frank Schilder', 'Lee Quartey', 'FangYi Yu'] | 2022-12-02 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.30165026e-01 4.21243429e-01 -2.29891688e-01 -5.03393769e-01
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