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6daa7768-c442-442f-bec6-0f7751428ac2 | exploration-by-random-network-distillation-1 | null | null | https://openreview.net/forum?id=Qg6O7xH7boe | https://openreview.net/pdf?id=Qg6O7xH7boe | Exploration by Random Network Distillation | We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access the underlying state of the game, and occasionally completes the first level. This suggests that relatively simple methods that scale well can be sufficient to tackle challenging exploration problems. | ['Anonymous'] | 2022-01-17 | null | null | null | iclr-track-blog-2022-5 | ['montezumas-revenge'] | ['playing-games'] | [-2.19321668e-01 5.71273208e-01 -1.37304068e-01 4.76697832e-03
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0806f4cc-24fc-4d5f-ad14-a7cecb530728 | semi-supervised-few-shot-learning-for-medical | 2003.08462 | null | https://arxiv.org/abs/2003.08462v2 | https://arxiv.org/pdf/2003.08462v2.pdf | Semi-supervised few-shot learning for medical image segmentation | Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain. Furthermore, training such models in a low-data regime highly increases the risk of overfitting. Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm, which addresses this shortcoming by learning a novel class from only a few labeled examples. In this context, a segmentation model is trained on episodes, which represent different segmentation problems, each of them trained with a very small labeled dataset. In this work, we propose a novel few-shot learning framework for semantic segmentation, where unlabeled images are also made available at each episode. To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning. We show that including unlabeled surrogate tasks in the episodic training leads to more powerful feature representations, which ultimately results in better generability to unseen tasks. We demonstrate the efficiency of our method in the task of skin lesion segmentation in two publicly available datasets. Furthermore, our approach is general and model-agnostic, which can be combined with different deep architectures. | ['Reza Azad', 'Abdur R Feyjie', 'Marco Pedersoli', 'Claude Kauffman', 'Jose Dolz', 'Ismail Ben Ayed'] | 2020-03-18 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.48781240e-01 4.09239560e-01 -2.92287469e-01 -4.74655867e-01
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3862ed33-74e8-4c3f-8dd0-41ee9268f535 | ltg-st-at-nadi-shared-task-1-arabic-dialect | null | null | https://aclanthology.org/2020.wanlp-1.34 | https://aclanthology.org/2020.wanlp-1.34.pdf | LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier | This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a stacking classifier using Multinomial Naive Bayes, Linear SVC, and Logistic Regression classifiers as estimators; followed by a Logistic Regression as final estimator. Despite the fact that the results on the test set were low, with a macro F1 of 17.71, we were able to show that a simple approach can achieve comparable results to more sophisticated solutions. Moreover, the insights of our error analysis, and of the corpus content in general, can be used to develop and improve future systems. | ['Samia Touileb'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-3.12667787e-01 -1.80984028e-02 2.66764879e-01 -6.52767181e-01
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fdc2f669-80a0-49c2-a388-56d328e26ef4 | semi-supervised-cell-recognition-under-point | 2306.08240 | null | https://arxiv.org/abs/2306.08240v1 | https://arxiv.org/pdf/2306.08240v1.pdf | Semi-supervised Cell Recognition under Point Supervision | Cell recognition is a fundamental task in digital histopathology image analysis. Point-based cell recognition (PCR) methods normally require a vast number of annotations, which is extremely costly, time-consuming and labor-intensive. Semi-supervised learning (SSL) can provide a shortcut to make full use of cell information in gigapixel whole slide images without exhaustive labeling. However, research into semi-supervised point-based cell recognition (SSPCR) remains largely overlooked. Previous SSPCR works are all built on density map-based PCR models, which suffer from unsatisfactory accuracy, slow inference speed and high sensitivity to hyper-parameters. To address these issues, end-to-end PCR models are proposed recently. In this paper, we develop a SSPCR framework suitable for the end-to-end PCR models for the first time. Overall, we use the current models to generate pseudo labels for unlabeled images, which are in turn utilized to supervise the models training. Besides, we introduce a co-teaching strategy to overcome the confirmation bias problem that generally exists in self-training. A distribution alignment technique is also incorporated to produce high-quality, unbiased pseudo labels for unlabeled data. Experimental results on four histopathology datasets concerning different types of staining styles show the effectiveness and versatility of the proposed framework. Code is available at \textcolor{magenta}{\url{https://github.com/windygooo/SSPCR} | ['Lin Yang', 'Chenglu Zhu', 'Xiaoxuan Yu', 'Shichuan Zhang', 'Honglin Li', 'Yunlong Zhang', 'Sunyi Zheng', 'Yizhi Zhao', 'Zhongyi Shui'] | 2023-06-14 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.54247308e-01 -2.06690222e-01 -3.31317753e-01 -4.53415751e-01
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3d618837-d444-4f9a-82cb-17e1134bd023 | learning-to-rank-in-generative-retrieval | 2306.15222 | null | https://arxiv.org/abs/2306.15222v1 | https://arxiv.org/pdf/2306.15222v1.pdf | Learning to Rank in Generative Retrieval | Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generation models and represents a new paradigm distinct from traditional learning-to-rank methods. However, despite its rapid development, current generative retrieval methods are still limited. They typically rely on a heuristic function to transform predicted identifiers into a passage rank list, which creates a gap between the learning objective of generative retrieval and the desired passage ranking target. Moreover, the inherent exposure bias problem of text generation also persists in generative retrieval. To address these issues, we propose a novel framework, called LTRGR, that combines generative retrieval with the classical learning-to-rank paradigm. Our approach involves training an autoregressive model using a passage rank loss, which directly optimizes the autoregressive model toward the optimal passage ranking. This framework only requires an additional training step to enhance current generative retrieval systems and does not add any burden to the inference stage. We conducted experiments on three public datasets, and our results demonstrate that LTRGR achieves state-of-the-art performance among generative retrieval methods, indicating its effectiveness and robustness. | ['Wenjie Li', 'Furu Wei', 'Liang Wang', 'Nan Yang', 'Yongqi Li'] | 2023-06-27 | null | null | null | null | ['learning-to-rank', 'retrieval', 'learning-to-rank', 'text-generation', 'passage-ranking'] | ['graphs', 'methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.98062748e-01 -3.37434560e-01 -3.39761168e-01 -2.29703575e-01
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8e600ad8-0aa7-4334-b3fa-71c05c40b55c | exploiting-transliterated-words-for-finding | 2206.11860 | null | https://arxiv.org/abs/2206.11860v1 | https://arxiv.org/pdf/2206.11860v1.pdf | Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning | Finding similarities between two inter-language news articles is a challenging problem of Natural Language Processing (NLP). It is difficult to find similar news articles in a different language other than the native language of user, there is a need for a Machine Learning based automatic system to find the similarity between two inter-language news articles. In this article, we propose a Machine Learning model with the combination of English Urdu word transliteration which will show whether the English news article is similar to the Urdu news article or not. The existing approaches to find similarities has a major drawback when the archives contain articles of low-resourced languages like Urdu along with English news article. The existing approaches to find similarities has drawback when the archives contain low-resourced languages like Urdu along with English news articles. We used lexicon to link Urdu and English news articles. As Urdu language processing applications like machine translation, text to speech, etc are unable to handle English text at the same time so this research proposed technique to find similarities in English and Urdu news articles based on transliteration. | ['Abdul Basit Mughal', 'Syed Mujtaba Haider', 'Dr. Arif ur Rahman', 'Sameea Naeem'] | 2022-05-29 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-4.12815660e-01 -5.23386240e-01 -3.12153697e-01 2.57482752e-02
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47aec201-9b45-402a-9ca7-813c5fd8d624 | zsd-yolo-zero-shot-yolo-detection-using | 2109.12066 | null | https://arxiv.org/abs/2109.12066v2 | https://arxiv.org/pdf/2109.12066v2.pdf | Zero-shot Object Detection Through Vision-Language Embedding Alignment | Recent approaches have shown that training deep neural networks directly on large-scale image-text pair collections enables zero-shot transfer on various recognition tasks. One central issue is how this can be generalized to object detection, which involves the non-semantic task of localization as well as semantic task of classification. To solve this problem, we introduce a vision-language embedding alignment method that transfers the generalization capabilities of a pretrained model such as CLIP to an object detector like YOLOv5. We formulate a loss function that allows us to align the image and text embeddings from the pretrained model CLIP with the modified semantic prediction head from the detector. With this method, we are able to train an object detector that achieves state-of-the-art performance on the COCO, ILSVRC, and Visual Genome zero-shot detection benchmarks. During inference, our model can be adapted to detect any number of object classes without additional training. We also find that standard object detection scaling can transfer well to our method and find consistent improvements across various scales of YOLOv5 models and the YOLOv3 model. Lastly, we develop a self-labeling method that provides a significant score improvement without needing extra images nor labels. | ['Shuai Zheng', 'Johnathan Xie'] | 2021-09-24 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 1.90810949e-01 5.02581485e-02 -1.34274945e-01 -3.24990124e-01
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6dd9145b-09f4-447b-b1ae-42f7f68b96cc | ppgnet-deep-network-for-device-independent | 1903.08912 | null | http://arxiv.org/abs/1903.08912v1 | http://arxiv.org/pdf/1903.08912v1.pdf | PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram | Photoplethysmogram (PPG) is increasingly used to provide monitoring of the
cardiovascular system under ambulatory conditions. Wearable devices like
smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in
free living conditions. PPG based heart rate measurement is unfortunately
highly susceptible to motion artifacts, particularly when measured from the
wrist. Traditional machine learning and deep learning approaches rely on
tri-axial accelerometer data along with PPG to perform heart rate estimation.
The conventional learning based approaches have not addressed the need for
device-specific modeling due to differences in hardware design among PPG
devices. In this paper, we propose a novel end to end deep learning model to
perform heart rate estimation using 8 second length input PPG signal. We
evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean
absolute error of 3.36+-4.1BPM for HR estimation on 12 subjects without
requiring patient specific training. We also studied the feasibility of
applying transfer learning along with sparse retraining from a comprehensive in
house PPG dataset for heart rate estimation across PPG devices with different
hardware design. | ['Vignesh Ravichandran', 'Mohanasankar Sivaprakasam', 'Jayaraj Joseph', 'Preejith S. P', 'Shyam A'] | 2019-03-21 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.41340137e-01 9.11360681e-02 -1.22871123e-01 -3.52876067e-01
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f2fb9417-4645-40fe-b108-bf00bde56127 | source-free-open-set-domain-adaptation-for | 2307.04596 | null | https://arxiv.org/abs/2307.04596v1 | https://arxiv.org/pdf/2307.04596v1.pdf | Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer | There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the distributionally shifted unlabeled target domain, and simultaneously iii) detect Open Set samples, i.e., unseen novel categories not present in the training source domain. This paper proposes a highly practical setting by addressing the abovementioned challenges in one fell swoop, i.e., source-free Open Set domain adaptation (SF-OSDA), which addresses the situation where a model pre-trained on the inaccessible source dataset can be adapted on the unlabeled target dataset containing Open Set samples. The central tenet of our proposed method is distilling knowledge from a self-supervised vision transformer trained in the target domain. We propose a novel style-based data augmentation used as hard positives for self-training a vision transformer in the target domain, yielding strongly contextualized embedding. Subsequently, semantically similar target images are clustered while the source model provides their corresponding weak pseudo-labels with unreliable confidence. Furthermore, we propose cluster relative maximum logit score (CRMLS) to rectify the confidence of the weak pseudo-labels and compute weighted class prototypes in the contextualized embedding space that are utilized for adapting the source model on the target domain. Our method significantly outperforms the previous methods, including open set detection, test-time adaptation, and SF-OSDA methods, setting the new state-of-the-art on three public histopathological datasets of colorectal cancer (CRC) assessment- Kather-16, Kather-19, and CRCTP. Our code is available at https://github.com/LTS5/Proto-SF-OSDA. | ['Jean-Philippe Thiran', 'Behzad Bozorgtabar', 'Devavrat Tomar', 'Guillaume Vray'] | 2023-07-10 | null | null | null | null | ['data-augmentation', 'domain-adaptation'] | ['methodology', 'methodology'] | [ 3.94193828e-01 4.50608492e-01 -3.35737139e-01 -1.45892128e-01
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5d6712ab-83e0-450e-9890-3ccff79c78f1 | mvloc-multimodal-variational-geometry-aware | 2003.07289 | null | https://arxiv.org/abs/2003.07289v5 | https://arxiv.org/pdf/2003.07289v5.pdf | VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization | Recent learning-based approaches have achieved impressive results in the field of single-shot camera localization. However, how best to fuse multiple modalities (e.g., image and depth) and to deal with degraded or missing input are less well studied. In particular, we note that previous approaches towards deep fusion do not perform significantly better than models employing a single modality. We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality. To address this, we propose an end-to-end framework, termed VMLoc, to fuse different sensor inputs into a common latent space through a variational Product-of-Experts (PoE) followed by attention-based fusion. Unlike previous multimodal variational works directly adapting the objective function of vanilla variational auto-encoder, we show how camera localization can be accurately estimated through an unbiased objective function based on importance weighting. Our model is extensively evaluated on RGB-D datasets and the results prove the efficacy of our model. The source code is available at https://github.com/kaichen-z/VMLoc. | ['Muhamad Risqi U. Saputra', 'Kaichen Zhou', 'Bing Wang', 'Andrew Markham', 'Niki Trigoni', 'Changhao Chen'] | 2020-03-12 | null | null | null | null | ['camera-localization', 'camera-relocalization'] | ['computer-vision', 'computer-vision'] | [ 3.09499539e-02 -6.90212026e-02 -5.40733077e-02 -3.45927298e-01
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3829396e-2e71-4efb-8c5f-8ddd15e31e0f | generalized-many-way-few-shot-video | 2007.04755 | null | https://arxiv.org/abs/2007.04755v2 | https://arxiv.org/pdf/2007.04755v2.pdf | Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation | Few-shot learning aims to recognize novel classes from a few examples. Although significant progress has been made in the image domain, few-shot video classification is relatively unexplored. We argue that previous methods underestimate the importance of video feature learning and propose to learn spatiotemporal features using a 3D CNN. Proposing a two-stage approach that learns video features on base classes followed by fine-tuning the classifiers on novel classes, we show that this simple baseline approach outperforms prior few-shot video classification methods by over 20 points on existing benchmarks. To circumvent the need of labeled examples, we present two novel approaches that yield further improvement. First, we leverage tag-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities. Second, we learn generative adversarial networks that generate video features of novel classes from their semantic embeddings. Moreover, we find existing benchmarks are limited because they only focus on 5 novel classes in each testing episode and introduce more realistic benchmarks by involving more novel classes, i.e. few-shot learning, as well as a mixture of novel and base classes, i.e. generalized few-shot learning. The experimental results show that our retrieval and feature generation approach significantly outperform the baseline approach on the new benchmarks. | ['Zeynep Akata', 'Lorenzo Torresani', 'Bruno Korbar', 'Yongqin Xian', 'Matthijs Douze', 'Bernt Schiele'] | 2020-07-09 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 2.63437212e-01 -3.45385790e-01 -3.95448893e-01 -4.89155829e-01
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6738bb14-5e0a-4e44-9a5d-45a61a644033 | objects2action-classifying-and-localizing | 1510.06939 | null | http://arxiv.org/abs/1510.06939v1 | http://arxiv.org/pdf/1510.06939v1.pdf | Objects2action: Classifying and localizing actions without any video example | The goal of this paper is to recognize actions in video without the need for
examples. Different from traditional zero-shot approaches we do not demand the
design and specification of attribute classifiers and class-to-attribute
mappings to allow for transfer from seen classes to unseen classes. Our key
contribution is objects2action, a semantic word embedding that is spanned by a
skip-gram model of thousands of object categories. Action labels are assigned
to an object encoding of unseen video based on a convex combination of action
and object affinities. Our semantic embedding has three main characteristics to
accommodate for the specifics of actions. First, we propose a mechanism to
exploit multiple-word descriptions of actions and objects. Second, we
incorporate the automated selection of the most responsive objects per action.
And finally, we demonstrate how to extend our zero-shot approach to the
spatio-temporal localization of actions in video. Experiments on four action
datasets demonstrate the potential of our approach. | ['Thomas Mensink', 'Jan C. van Gemert', 'Mihir Jain', 'Cees G. M. Snoek'] | 2015-10-23 | objects2action-classifying-and-localizing-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Jain_Objects2action_Classifying_and_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Jain_Objects2action_Classifying_and_ICCV_2015_paper.pdf | iccv-2015-12 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.39992255e-01 -1.21841334e-01 -4.34138596e-01 -7.06677735e-01
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aae10f4c-abfd-4316-bb3d-22159d5e029c | machine-learned-adversarial-attacks-against | 2303.18136 | null | https://arxiv.org/abs/2303.18136v1 | https://arxiv.org/pdf/2303.18136v1.pdf | Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids | In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks | ['Giovanni Servedio', 'Fatemeh Nazary', 'Eugenio Di Sciascio', 'Tommaso Di Noia', 'Yashar Deldjoo', 'Carmelo Ardito'] | 2023-03-28 | null | null | null | null | ['fault-localization', 'defect-detection', 'load-forecasting', 'fault-detection'] | ['computer-code', 'computer-vision', 'miscellaneous', 'miscellaneous'] | [-4.34419885e-02 -1.49515435e-01 4.26181018e-01 -9.30019692e-02
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6942b19e-a69a-436a-b8c4-5f3f76d269d2 | shadows-can-be-dangerous-stealthy-and | 2203.03818 | null | https://arxiv.org/abs/2203.03818v3 | https://arxiv.org/pdf/2203.03818v3.pdf | Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon | Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid colors. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack. | ['Xiangyang Ji', 'Junjun Jiang', 'Deming Zhai', 'Xianming Liu', 'Yiqi Zhong'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.pdf | cvpr-2022-1 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.13757509e-01 -1.31383777e-01 3.67328197e-01 1.76999301e-01
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8b6f15c3-8e7f-409d-a0e6-6565eca807ee | searching-for-pneumothorax-in-half-a-million | 2007.15429 | null | https://arxiv.org/abs/2007.15429v1 | https://arxiv.org/pdf/2007.15429v1.pdf | Searching for Pneumothorax in Half a Million Chest X-Ray Images | Pneumothorax, a collapsed or dropped lung, is a fatal condition typically detected on a chest X-ray by an experienced radiologist. Due to shortage of such experts, automated detection systems based on deep neural networks have been developed. Nevertheless, applying such systems in practice remains a challenge. These systems, mostly compute a single probability as output, may not be enough for diagnosis. On the contrary, content-based medical image retrieval (CBIR) systems, such as image search, can assist clinicians for diagnostic purposes by enabling them to compare the case they are examining with previous (already diagnosed) cases. However, there is a lack of study on such attempt. In this study, we explored the use of image search to classify pneumothorax among chest X-ray images. All chest X-ray images were first tagged with deep pretrained features, which were obtained from existing deep learning models. Given a query chest X-ray image, the majority voting of the top K retrieved images was then used as a classifier, in which similar cases in the archive of past cases are provided besides the probability output. In our experiments, 551,383 chest X-ray images were obtained from three large recently released public datasets. Using 10-fold cross-validation, it is shown that image search on deep pretrained features achieved promising results compared to those obtained by traditional classifiers trained on the same features. To the best of knowledge, it is the first study to demonstrate that deep pretrained features can be used for CBIR of pneumothorax in half a million chest X-ray images. | ['Antonio Sze-To', 'Hamid Tizhoosh'] | 2020-07-30 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 3.44664991e-01 -2.64497578e-01 -2.50939101e-01 -2.75969923e-01
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cc2d9368-e198-485a-9181-e893b677e60a | picture-it-in-your-mind-generating-high-level | 1606.07287 | null | http://arxiv.org/abs/1606.07287v1 | http://arxiv.org/pdf/1606.07287v1.pdf | Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions | In this paper we tackle the problem of image search when the query is a short
textual description of the image the user is looking for. We choose to
implement the actual search process as a similarity search in a visual feature
space, by learning to translate a textual query into a visual representation.
Searching in the visual feature space has the advantage that any update to the
translation model does not require to reprocess the, typically huge, image
collection on which the search is performed. We propose Text2Vis, a neural
network that generates a visual representation, in the visual feature space of
the fc6-fc7 layers of ImageNet, from a short descriptive text. Text2Vis
optimizes two loss functions, using a stochastic loss-selection method. A
visual-focused loss is aimed at learning the actual text-to-visual feature
mapping, while a text-focused loss is aimed at modeling the higher-level
semantic concepts expressed in language and countering the overfit on
non-relevant visual components of the visual loss. We report preliminary
results on the MS-COCO dataset. | ['Alejandro Moreo Fernández', 'Andrea Esuli', 'Tiziano Fagni', 'Fabrizio Falchi', 'Fabio Carrara'] | 2016-06-23 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 4.24352974e-01 -3.17455642e-02 -1.30319400e-02 -4.95820582e-01
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d93e19ff-3451-4c72-8595-b105b9d24d5a | towards-robust-speech-to-text-adversarial | 2103.08095 | null | https://arxiv.org/abs/2103.08095v1 | https://arxiv.org/pdf/2103.08095v1.pdf | Towards Robust Speech-to-Text Adversarial Attack | This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo. Our approach is based on developing an extension for the conventional distortion condition of the adversarial optimization formulation using the Cram\`er integral probability metric. Minimizing over this metric, which measures the discrepancies between original and adversarial samples' distributions, contributes to crafting signals very close to the subspace of legitimate speech recordings. This helps to yield more robust adversarial signals against playback over-the-air without employing neither costly expectation over transformation operations nor static room impulse response simulations. Our approach outperforms other targeted and non-targeted algorithms in terms of word error rate and sentence-level-accuracy with competitive performance on the crafted adversarial signals' quality. Compared to seven other strong white and black-box adversarial attacks, our proposed approach is considerably more resilient against multiple consecutive playbacks over-the-air, corroborating its higher robustness in noisy environments. | ['Alessandro Lameiras Koerich', 'Patrick Cardinal', 'Mohammad Esmaeilpour'] | 2021-03-15 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 3.41185242e-01 3.84125113e-01 7.18666434e-01 -1.40157074e-01
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77ca6202-7cc6-4628-bc72-e3e2c38431eb | tempsal-uncovering-temporal-information-for-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.pdf | TempSAL - Uncovering Temporal Information for Deep Saliency Prediction | Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our experiments show that our method outperforms the state-of-the-art models, including a multi-duration saliency model, on the SALICON benchmark and CodeCharts1k dataset. Our code is publicly available on GitHub. | ['Sabine Süsstrunk', 'Mathieu Salzmann', 'Tong Zhang', 'Ludo Hoffstetter', 'Bahar Aydemir'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['saliency-prediction', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.81653965e-01 -3.21621113e-02 -6.77183807e-01 -5.03506720e-01
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20124511-bf6e-4663-9cf0-6f93bc0a4903 | memd-a-diversity-promoting-learning-framework | null | null | https://aclanthology.org/C18-1109 | https://aclanthology.org/C18-1109.pdf | MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation | Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like {``}I don{'}t know{''} regardless of what the input is. In this paper, we analyze this problem from a new perspective: latent vectors. Based on it, we propose an easy-to-extend learning framework named MEMD (Multi-Encoder to Multi-Decoder), in which an auxiliary encoder and an auxiliary decoder are introduced to provide necessary training guidance without resorting to extra data or complicating network{'}s inner structure. Experimental results demonstrate that our method effectively improve the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies. | ['Zhi-Hong Deng', 'Haokun Liu', 'Meng Zou', 'Xihan Li'] | 2018-08-01 | memd-a-diversity-promoting-learning-framework-1 | https://aclanthology.org/C18-1109 | https://aclanthology.org/C18-1109.pdf | coling-2018-8 | ['short-text-conversation', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.13341832e-01 1.34543538e-01 3.58282682e-03 -5.20248175e-01
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db08a914-951e-4648-a88f-e2cedf5996e9 | aggregating-layers-for-deepfake-detection | 2210.05478 | null | https://arxiv.org/abs/2210.05478v1 | https://arxiv.org/pdf/2210.05478v1.pdf | Aggregating Layers for Deepfake Detection | The increasing popularity of facial manipulation (Deepfakes) and synthetic face creation raises the need to develop robust forgery detection solutions. Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network. This is not how things work in practice. Instead, we consider the case where the network is trained on one Deepfake algorithm, and tested on Deepfakes generated by another algorithm. Typically, supervised techniques follow a pipeline of visual feature extraction from a deep backbone, followed by a binary classification head. Instead, our algorithm aggregates features extracted across all layers of one backbone network to detect a fake. We evaluate our approach on two domains of interest - Deepfake detection and Synthetic image detection, and find that we achieve SOTA results. | ['Shai Avidan', 'Amir Jevnisek'] | 2022-10-11 | null | null | null | null | ['synthetic-image-detection'] | ['computer-vision'] | [ 3.00879717e-01 2.98861712e-01 7.12189674e-02 -1.75840840e-01
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4642c52c-401e-4c8b-8dc6-8db01c5fc067 | differentially-private-synthetic-data-using | 2306.13211 | null | https://arxiv.org/abs/2306.13211v1 | https://arxiv.org/pdf/2306.13211v1.pdf | Differentially Private Synthetic Data Using KD-Trees | Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based approaches have emerged in recent years for answering statistical queries in a differentially private manner. However, for synthetic data generation problem, recent research has been mainly focused on deep generative models. In contrast, we exploit space partitioning techniques together with noise perturbation and thus achieve intuitive and transparent algorithms. We propose both data independent and data dependent algorithms for $\epsilon$-differentially private synthetic data generation whose kernel density resembles that of the real dataset. Additionally, we provide theoretical results on the utility-privacy trade-offs and show how our data dependent approach overcomes the curse of dimensionality and leads to a scalable algorithm. We show empirical utility improvements over the prior work, and discuss performance of our algorithm on a downstream classification task on a real dataset. | ['Manuela Veloso', 'Tucker Balch', 'Vamsi K. Potluru', 'Navid Nouri', 'Eleonora Kreačić'] | 2023-06-19 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.21924147e-01 1.95970207e-01 7.32278004e-02 -5.48276722e-01
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d3b3fb9a-bcbd-4f0a-bdfa-b53bfd0ccf49 | domain-agnostic-learning-with-anatomy | 1908.10489 | null | https://arxiv.org/abs/1908.10489v1 | https://arxiv.org/pdf/1908.10489v1.pdf | Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation | Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from the source domain to a specific known target domain. Domain Agnostic Learning (DAL) proposes a new task of transferring knowledge from the source domain to data from multiple heterogeneous target domains. In this work, we propose the Domain-Agnostic Learning framework with Anatomy-Consistent Embedding (DALACE) that works on both domain-transfer and task-transfer to learn a disentangled representation, aiming to not only be invariant to different modalities but also preserve anatomical structures for the DA and DAL tasks in cross-modality liver segmentation. We validated and compared our model with state-of-the-art methods, including CycleGAN, Task Driven Generative Adversarial Network (TD-GAN), and Domain Adaptation via Disentangled Representations (DADR). For the DA task, our DALACE model outperformed CycleGAN, TD-GAN ,and DADR with DSC of 0.847 compared to 0.721, 0.793 and 0.806. For the DAL task, our model improved the performance with DSC of 0.794 from 0.522, 0.719 and 0.742 by CycleGAN, TD-GAN, and DADR. Further, we visualized the success of disentanglement, which added human interpretability of the learned meaningful representations. Through ablation analysis, we specifically showed the concrete benefits of disentanglement for downstream tasks and the role of supervision for better disentangled representation with segmentation consistency to be invariant to domains with the proposed Domain-Agnostic Module (DAM) and to preserve anatomical information with the proposed Anatomy-Preserving Module (APM). | ['Juntang Zhuang', 'Junlin Yang', 'James S. Duncan', 'MingDe Lin', 'Julius Chapiro', 'Nicha C. Dvornek', 'Fan Zhang'] | 2019-08-27 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.46516338e-01 4.46925849e-01 -6.74316809e-02 -1.71177343e-01
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70aacf5d-1ccc-43f4-b3b7-caf0fa023825 | algorithms-for-audio-inpainting-based-on | 2206.13768 | null | https://arxiv.org/abs/2206.13768v2 | https://arxiv.org/pdf/2206.13768v2.pdf | Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization | Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization (NMF) in a probabilistic framework. First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain. Then, we treat the missing samples as parameters, and we address this novel problem by deriving an alternating minimization scheme. We assess the potential of these algorithms for the task of restoring short- to middle-length gaps in music signals. Experiments reveal great convergence properties of the proposed methods, as well as competitive performance when compared to state-of-the-art audio inpainting techniques. | ['Cédric Févotte', 'Thomas Oberlin', 'Paul Magron', 'Ondřej Mokrý'] | 2022-06-28 | null | null | null | null | ['audio-inpainting'] | ['audio'] | [ 5.02539396e-01 -2.18820125e-01 2.10585184e-02 5.44735976e-02
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1450f0b6-bd32-49db-b1c1-aafa85f53946 | epos-estimating-6d-pose-of-objects-with | 2004.00605 | null | https://arxiv.org/abs/2004.00605v1 | https://arxiv.org/pdf/2004.00605v1.pdf | EPOS: Estimating 6D Pose of Objects with Symmetries | We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk.cvut.cz/epos. | ['Jiri Matas', 'Daniel Barath', 'Tomas Hodan'] | 2020-04-01 | epos-estimating-6d-pose-of-objects-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.pdf | cvpr-2020-6 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 1.94432005e-01 2.07926780e-02 -2.70719767e-01 -3.05787355e-01
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39639a8d-4600-4cf6-92c5-cac5349c9d4d | first-person-hand-action-benchmark-with-rgb-d | 1704.02463 | null | http://arxiv.org/abs/1704.02463v2 | http://arxiv.org/pdf/1704.02463v2.pdf | First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations | In this work we study the use of 3D hand poses to recognize first-person
dynamic hand actions interacting with 3D objects. Towards this goal, we
collected RGB-D video sequences comprised of more than 100K frames of 45 daily
hand action categories, involving 26 different objects in several hand
configurations. To obtain hand pose annotations, we used our own mo-cap system
that automatically infers the 3D location of each of the 21 joints of a hand
model via 6 magnetic sensors and inverse kinematics. Additionally, we recorded
the 6D object poses and provide 3D object models for a subset of hand-object
interaction sequences. To the best of our knowledge, this is the first
benchmark that enables the study of first-person hand actions with the use of
3D hand poses. We present an extensive experimental evaluation of RGB-D and
pose-based action recognition by 18 baselines/state-of-the-art approaches. The
impact of using appearance features, poses, and their combinations are
measured, and the different training/testing protocols are evaluated. Finally,
we assess how ready the 3D hand pose estimation field is when hands are
severely occluded by objects in egocentric views and its influence on action
recognition. From the results, we see clear benefits of using hand pose as a
cue for action recognition compared to other data modalities. Our dataset and
experiments can be of interest to communities of 3D hand pose estimation, 6D
object pose, and robotics as well as action recognition. | ['Tae-Kyun Kim', 'Guillermo Garcia-Hernando', 'Seungryul Baek', 'Shanxin Yuan'] | 2017-04-08 | first-person-hand-action-benchmark-with-rgb-d-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Garcia-Hernando_First-Person_Hand_Action_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Garcia-Hernando_First-Person_Hand_Action_CVPR_2018_paper.pdf | cvpr-2018-6 | ['egocentric-activity-recognition'] | ['computer-vision'] | [-4.48606946e-02 -3.10845137e-01 -1.66767150e-01 1.85013320e-02
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39a5084a-00d2-4b53-8c36-2531add22146 | boosting-semantic-segmentation-with-semantic | 2304.09427 | null | https://arxiv.org/abs/2304.09427v1 | https://arxiv.org/pdf/2304.09427v1.pdf | Boosting Semantic Segmentation with Semantic Boundaries | In this paper, we present the Semantic Boundary Conditioned Backbone (SBCB) framework, a simple yet effective training framework that is model-agnostic and boosts segmentation performance, especially around the boundaries. Motivated by the recent development in improving semantic segmentation by incorporating boundaries as auxiliary tasks, we propose a multi-task framework that uses semantic boundary detection (SBD) as an auxiliary task. The SBCB framework utilizes the nature of the SBD task, which is complementary to semantic segmentation, to improve the backbone of the segmentation head. We apply an SBD head that exploits the multi-scale features from the backbone, where the model learns low-level features in the earlier stages, and high-level semantic understanding in the later stages. This head perfectly complements the common semantic segmentation architectures where the features from the later stages are used for classification. We can improve semantic segmentation models without additional parameters during inference by only conditioning the backbone. Through extensive evaluations, we show the effectiveness of the SBCB framework by improving various popular segmentation heads and backbones by 0.5% ~ 3.0% IoU on the Cityscapes dataset and gains 1.6% ~ 4.1% in boundary Fscores. We also apply this framework on customized backbones and the emerging vision transformer models and show the effectiveness of the SBCB framework. | ['Yoshimitsu Aoki', 'Haruya Ishikawa'] | 2023-04-19 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 5.42890370e-01 5.11618257e-01 -2.20184609e-01 -4.73460138e-01
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3d3eeffa-ba3f-4008-a485-08dc4ca7186d | a-fusion-based-gender-recognition-method | 1711.06451 | null | http://arxiv.org/abs/1711.06451v1 | http://arxiv.org/pdf/1711.06451v1.pdf | A Fusion-based Gender Recognition Method Using Facial Images | This paper proposes a fusion-based gender recognition method which uses
facial images as input. Firstly, this paper utilizes pre-processing and a
landmark detection method in order to find the important landmarks of faces.
Thereafter, four different frameworks are proposed which are inspired by
state-of-the-art gender recognition systems. The first framework extracts
features using Local Binary Pattern (LBP) and Principal Component Analysis
(PCA) and uses back propagation neural network. The second framework uses Gabor
filters, PCA, and kernel Support Vector Machine (SVM). The third framework uses
lower part of faces as input and classifies them using kernel SVM. The fourth
framework uses Linear Discriminant Analysis (LDA) in order to classify the side
outline landmarks of faces. Finally, the four decisions of frameworks are fused
using weighted voting. This paper takes advantage of both texture and
geometrical information, the two dominant types of information in facial gender
recognition. Experimental results show the power and effectiveness of the
proposed method. This method obtains recognition rate of 94% for neutral faces
of FEI face dataset, which is equal to state-of-the-art rate for this dataset. | ['Hoda Mohammadzade', 'Saeed Bagheri Shouraki', 'Benyamin Ghojogh', 'Ensieh Iranmehr'] | 2017-11-17 | null | null | null | null | ['age-and-gender-classification', 'gender-prediction'] | ['computer-vision', 'computer-vision'] | [-2.91739181e-02 -1.62101492e-01 -2.17507482e-01 -4.50849175e-01
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0cdd5d97-19fb-452f-a0e8-d54b29d7cb5d | efficient-universal-shuffle-attack-for-visual | 2203.06898 | null | https://arxiv.org/abs/2203.06898v1 | https://arxiv.org/pdf/2203.06898v1.pdf | Efficient universal shuffle attack for visual object tracking | Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames. However, previous work only generates the video-specific perturbations, which restricts its application scenarios. In addition, existing attacks are difficult to implement in reality due to the real-time of tracking and the re-initialization mechanism. To address these issues, we propose an offline universal adversarial attack called Efficient Universal Shuffle Attack. It takes only one perturbation to cause the tracker malfunction on all videos. To improve the computational efficiency and attack performance, we propose a greedy gradient strategy and a triple loss to efficiently capture and attack model-specific feature representations through the gradients. Experimental results show that EUSA can significantly reduce the performance of state-of-the-art trackers on OTB2015 and VOT2018. | ['Zhongxue Gan', 'Wenqiang Zhang', 'Jiafeng Wang', 'Jiwei Zhu', 'Wei Li', 'Zhaoyu Chen', 'Siao Liu'] | 2022-03-14 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-2.58539945e-01 -3.87435108e-01 -1.55434668e-01 1.57637849e-01
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d97365a4-0b2a-40e8-90ac-f51766d24782 | random-forest-model-identifies-serve-strength | 1910.03203 | null | https://arxiv.org/abs/1910.03203v1 | https://arxiv.org/pdf/1910.03203v1.pdf | Random forest model identifies serve strength as a key predictor of tennis match outcome | Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80% accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes. | ['Zijian Gao', 'Amanda Kowalczyk'] | 2019-10-08 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-2.33330116e-01 -1.15655214e-01 -7.31480837e-01 -2.20946223e-01
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f1196285-ae47-4cf8-97d0-250a9d52df60 | unav-an-infrastructure-independent-vision | 2209.11336 | null | https://arxiv.org/abs/2209.11336v1 | https://arxiv.org/pdf/2209.11336v1.pdf | UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision | Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases from robotics to assistive technologies. Compared to sensor-based solutions, vision-based localization does not require pre-installed sensor infrastructure, which is costly, time-consuming, and/or often infeasible at scale. Herein, we propose a novel vision-based localization pipeline for a specific use case: navigation support for end-users with blindness and low vision. Given a query image taken by an end-user on a mobile application, the pipeline leverages a visual place recognition (VPR) algorithm to find similar images in a reference image database of the target space. The geolocations of these similar images are utilized in downstream tasks that employ a weighted-average method to estimate the end-user's location and a perspective-n-point (PnP) algorithm to estimate the end-user's direction. Additionally, this system implements Dijkstra's algorithm to calculate a shortest path based on a navigable map that includes trip origin and destination. The topometric map used for localization and navigation is built using a customized graphical user interface that projects a 3D reconstructed sparse map, built from a sequence of images, to the corresponding a priori 2D floor plan. Sequential images used for map construction can be collected in a pre-mapping step or scavenged through public databases/citizen science. The end-to-end system can be installed on any internet-accessible device with a camera that hosts a custom mobile application. For evaluation purposes, mapping and localization were tested in a complex hospital environment. The evaluation results demonstrate that our system can achieve localization with an average error of less than 1 meter without knowledge of the camera's intrinsic parameters, such as focal length. | ['John-Ross Rizzo', 'Chen Feng', 'Pattanasak Mongkolwat', 'Wachara Riewpaiboon', 'Rajesh Vedanthan', 'Todd E Hudson', 'Mahya Beheshti', 'Anbang Yang'] | 2022-09-22 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 3.94392163e-02 -1.21504508e-01 1.61546677e-01 -3.39875728e-01
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05f5d74b-fe48-4322-9e99-803c109b2bc2 | relative-instance-credibility-inference-for | null | null | https://openreview.net/forum?id=tvKdi-Nodsx | https://openreview.net/pdf?id=tvKdi-Nodsx | Relative Instance Credibility Inference for Learning with Noisy Labels | The existence of noisy labels usually leads to the degradation of generalization and robustness of neural networks in supervised learning. In this paper, we propose to use a simple theoretically guaranteed sample selection framework as a plug-in module to handle noisy labels. Specifically, we re-purpose a sparse linear model with incidental parameters as a unified Relative Instance Credibility Inference (RICI) framework, which will detect and remove outliers in the forward pass of each mini-batch and use the remaining instances to train the network. The credibility of instances is measured by the sparsity of incidental parameters, which can be ranked among other instances within each mini-batch to get a relatively consistent training mini-batch. The proposed RICI framework yields two variants that enjoy superior performance on the symmetric and asymmetric noise settings, respectively. We prove that our RICI can theoretically recover the clean data. Experimental results on several benchmark datasets and a real-world noisy dataset show the effectiveness of our framework. | ['Yanwei Fu', 'Xinwei Sun', 'Yikai Wang'] | 2021-09-29 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.33791372e-01 9.51297358e-02 -6.82958886e-02 -6.13555491e-01
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5a969baa-0ad8-4ac6-8da2-a096a8ca63d4 | landmark-detection-and-3d-face-reconstruction | 2004.09190 | null | https://arxiv.org/abs/2004.09190v2 | https://arxiv.org/pdf/2004.09190v2.pdf | Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model | Caricature is an artistic abstraction of the human face by distorting or exaggerating certain facial features, while still retains a likeness with the given face. Due to the large diversity of geometric and texture variations, automatic landmark detection and 3D face reconstruction for caricature is a challenging problem and has rarely been studied before. In this paper, we propose the first automatic method for this task by a novel 3D approach. To this end, we first build a dataset with various styles of 2D caricatures and their corresponding 3D shapes, and then build a parametric model on vertex based deformation space for 3D caricature face. Based on the constructed dataset and the nonlinear parametric model, we propose a neural network based method to regress the 3D face shape and orientation from the input 2D caricature image. Ablation studies and comparison with state-of-the-art methods demonstrate the effectiveness of our algorithm design. Extensive experimental results demonstrate that our method works well for various caricatures. Our constructed dataset, source code and trained model are available at https://github.com/Juyong/CaricatureFace. | ['Yudong Guo', 'Hongrui Cai', 'Juyong Zhang', 'Zhuang Peng'] | 2020-04-20 | null | null | null | null | ['caricature'] | ['computer-vision'] | [-1.53759152e-01 9.73888040e-02 -7.08578601e-02 -3.11998844e-01
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e57691b0-f28f-49ca-90d3-ae5f660c894e | vitpose-vision-transformer-foundation-model | 2212.04246 | null | https://arxiv.org/abs/2212.04246v1 | https://arxiv.org/pdf/2212.04246v1.pdf | ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation | In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose. Specifically, ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints in either a top-down or a bottom-up manner. It can be scaled up from about 20M to 1B parameters by taking advantage of the scalable model capacity and high parallelism of the vision transformer, setting a new Pareto front for throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, and pre-training and fine-tuning strategy. Based on the flexibility, a novel ViTPose+ model is proposed to deal with heterogeneous body keypoint categories in different types of body pose estimation tasks via knowledge factorization, i.e., adopting task-agnostic and task-specific feed-forward networks in the transformer. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our ViTPose model outperforms representative methods on the challenging MS COCO Human Keypoint Detection benchmark at both top-down and bottom-up settings. Furthermore, our ViTPose+ model achieves state-of-the-art performance simultaneously on a series of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII for human keypoint detection, COCO-Wholebody for whole-body keypoint detection, as well as AP-10K and APT-36K for animal keypoint detection, without sacrificing inference speed. | ['DaCheng Tao', 'Qiming Zhang', 'Jing Zhang', 'Yufei Xu'] | 2022-12-07 | null | null | null | null | ['keypoint-detection', 'animal-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.95352748e-01 5.10995388e-02 -1.66022867e-01 -1.17267303e-01
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485ca345-e82f-4a41-a810-c3a0944d7528 | federated-cycling-fedcy-semi-supervised | 2203.07345 | null | https://arxiv.org/abs/2203.07345v2 | https://arxiv.org/pdf/2203.07345v2.pdf | Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases | Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning task-specific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain. | ['Nicolas Padoy', 'Alexandros Karargyris', 'AI4SafeChole Consortium', 'Pietro Mascagni', 'Deepak Alapatt', 'Hasan Kassem'] | 2022-03-14 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 1.46802574e-01 8.23871419e-03 -7.90153921e-01 -5.01611948e-01
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5f6443f6-8f1f-406c-a91a-5ee8fab1ab71 | logically-sound-arguments-for-the | 2111.02649 | null | https://arxiv.org/abs/2111.02649v2 | https://arxiv.org/pdf/2111.02649v2.pdf | Logically Sound Arguments for the Effectiveness of ML Safety Measures | We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening leads to introducing a conservative post-processor after the standard non-max-suppression as a counter-measure. We then propose a semi-formal assurance case for arguing the effectiveness of the post-processor, which is further translated into formal proof obligations for demonstrating the soundness of the arguments. Applying theorem proving not only discovers the need to introduce missing claims and mathematical concepts but also reveals the limitation of Dempster-Shafer's rules used in semi-formal argumentation. | ['Simon Burton', 'Tobias Schuster', 'Chih-Hong Cheng'] | 2021-11-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.38376200e-01 9.35899019e-01 -1.09219179e-01 -2.52409101e-01
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f59bdb25-18c9-4e2f-a7c2-655605fe5563 | detection-segmentation-convolutional-neural | 2306.17485 | null | https://arxiv.org/abs/2306.17485v1 | https://arxiv.org/pdf/2306.17485v1.pdf | Detection-segmentation convolutional neural network for autonomous vehicle perception | Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-performance computing platforms. In the case of autonomous vehicles, i.e. cars, but also drones, it is necessary to use embedded platforms with limited computing power, which makes it difficult to meet the requirements described above. A reduction in the complexity of the network can be achieved by using an appropriate: architecture, representation (reduced numerical precision, quantisation, pruning), and computing platform. In this paper, we focus on the first factor - the use of so-called detection-segmentation networks as a component of a perception system. We considered the task of segmenting the drivable area and road markings in combination with the detection of selected objects (pedestrians, traffic lights, and obstacles). We compared the performance of three different architectures described in the literature: MultiTask V3, HybridNets, and YOLOP. We conducted the experiments on a custom dataset consisting of approximately 500 images of the drivable area and lane markings, and 250 images of detected objects. Of the three methods analysed, MultiTask V3 proved to be the best, achieving 99% mAP_50 for detection, 97% MIoU for drivable area segmentation, and 91% MIoU for lane segmentation, as well as 124 fps on the RTX 3060 graphics card. This architecture is a good solution for embedded perception systems for autonomous vehicles. The code is available at: https://github.com/vision-agh/MMAR_2023. | ['Tomasz Kryjak', 'Mateusz Wasala', 'Robert Synoczek', 'Maciej Baczmanski'] | 2023-06-30 | null | null | null | null | ['object-detection', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [-8.43078643e-02 -1.13825826e-02 1.45630285e-01 -2.58958012e-01
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230f4f6d-034b-497a-a151-2902833324eb | unsupervised-object-discovery-and-co | 1707.06397 | null | http://arxiv.org/abs/1707.06397v1 | http://arxiv.org/pdf/1707.06397v1.pdf | Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming | Reusable model design becomes desirable with the rapid expansion of computer
vision and machine learning applications. In this paper, we focus on the
reusability of pre-trained deep convolutional models. Specifically, different
from treating pre-trained models as feature extractors, we reveal more
treasures beneath convolutional layers, i.e., the convolutional activations
could act as a detector for the common object in the image co-localization
problem. We propose a simple yet effective method, termed Deep Descriptor
Transforming (DDT), for evaluating the correlations of descriptors and then
obtaining the category-consistent regions, which can accurately locate the
common object in a set of unlabeled images, i.e., unsupervised object
discovery. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data. Beyond those, DDT can be also employed
for harvesting web images into valid external data sources for improving
performance of both image recognition and object detection. | ['Zhi-Hua Zhou', 'Chen-Lin Zhang', 'Xiu-Shen Wei', 'Chunhua Shen', 'Jianxin Wu'] | 2017-07-20 | null | null | null | null | ['single-object-discovery'] | ['computer-vision'] | [-1.12727717e-01 -3.64436775e-01 -1.79764792e-01 -3.18436533e-01
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36c08087-510d-4434-9f62-74aaabf6b71e | cost-effective-selection-of-pretraining-data | 2010.01150 | null | https://arxiv.org/abs/2010.01150v1 | https://arxiv.org/pdf/2010.01150v1.pdf | Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media | Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject matter, e.g., biology or computer science. Given the range of applications using social media text, and its unique language variety, we pretrain two models on tweets and forum text respectively, and empirically demonstrate the effectiveness of these two resources. In addition, we investigate how similarity measures can be used to nominate in-domain pretraining data. We publicly release our pretrained models at https://bit.ly/35RpTf0. | ['Cecile Paris', 'Ben Hachey', 'Sarvnaz Karimi', 'Xiang Dai'] | 2020-10-02 | null | https://aclanthology.org/2020.findings-emnlp.151 | https://aclanthology.org/2020.findings-emnlp.151.pdf | findings-of-the-association-for-computational | ['clinical-concept-extraction'] | ['medical'] | [-1.16303399e-01 -5.12430556e-02 -5.95013797e-01 -7.21079409e-01
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7ca5f5d2-da51-46f3-9757-d17d1f2d3329 | feature-selection-simultaneously-preserving | 2307.03902 | null | https://arxiv.org/abs/2307.03902v1 | https://arxiv.org/pdf/2307.03902v1.pdf | Feature selection simultaneously preserving both class and cluster structures | When a data set has significant differences in its class and cluster structure, selecting features aiming only at the discrimination of classes would lead to poor clustering performance, and similarly, feature selection aiming only at preserving cluster structures would lead to poor classification performance. To the best of our knowledge, a feature selection method that simultaneously considers class discrimination and cluster structure preservation is not available in the literature. In this paper, we have tried to bridge this gap by proposing a neural network-based feature selection method that focuses both on class discrimination and structure preservation in an integrated manner. In addition to assessing typical classification problems, we have investigated its effectiveness on band selection in hyperspectral images. Based on the results of the experiments, we may claim that the proposed feature/band selection can select a subset of features that is good for both classification and clustering. | ['Nikhil R. Pal', 'Suchismita Das'] | 2023-07-08 | null | null | null | null | ['clustering', 'classification-1'] | ['methodology', 'methodology'] | [ 4.79813367e-01 -4.03596818e-01 -1.66475937e-01 -3.68823349e-01
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6e89bca5-85c5-4754-a2a6-6dd33b573ad2 | reconstruction-distortion-of-learned-image | 2306.01125 | null | https://arxiv.org/abs/2306.01125v1 | https://arxiv.org/pdf/2306.01125v1.pdf | Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations | Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored. In this paper, we introduce an imperceptible attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where any object in the reconstructed images is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed adversarial examples. Further, leveraging the insensitivity of high-frequency components to human vision, we introduce Imperceptibility Constraint (IC) to ensure that the perturbations remain inconspicuous. Experiments conducted on the Kodak dataset using various LIC models demonstrate effectiveness. In addition, we provide several findings and suggestions for designing future defenses. | ['Zhenzhong Chen', 'Shan Liu', 'Xiaozhong Xu', 'Xiang Pan', 'Ding Ding', 'Zhuohang Li', 'Yang Sui'] | 2023-06-01 | null | null | null | null | ['image-reconstruction', 'image-compression'] | ['computer-vision', 'computer-vision'] | [ 7.71007955e-01 -6.58785552e-02 1.04883812e-01 1.64439492e-02
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ee9584c9-119f-486f-9a3a-766455ff3267 | pelphix-surgical-phase-recognition-from-x-ray | 2304.09285 | null | https://arxiv.org/abs/2304.09285v1 | https://arxiv.org/pdf/2304.09285v1.pdf | Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation | Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity -- corridor, activity, view, and frame value -- simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data. Using added supervision from detection of bony corridors, tools, and anatomy, we learn image representations that are fed into a transformer model to regress surgical phases at the four granularity levels. Our approach demonstrates the feasibility of X-ray-based SPR, achieving an average accuracy of 93.8% on simulated sequences and 67.57% in cadaver across all granularity levels, with up to 88% accuracy for the target corridor in real data. This work constitutes the first step toward SPR for the X-ray domain, establishing an approach to categorizing phases in X-ray-guided surgery, simulating realistic image sequences to enable machine learning model development, and demonstrating that this approach is feasible for the analysis of real procedures. As X-ray-based SPR continues to mature, it will benefit procedures in orthopedic surgery, angiography, and interventional radiology by equipping intelligent surgical systems with situational awareness in the operating room. | ['Mathias Unberath', 'Greg Osgood', 'Russel H. Taylor', 'Mehran Armand', 'Jan Mangulabnan', 'Han Zhang', 'Benjamin D. Killeen'] | 2023-04-18 | null | null | null | null | ['surgical-phase-recognition', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 4.09530133e-01 4.75871205e-01 -4.74056721e-01 4.03682478e-02
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f6e18fae-3422-4f1e-9288-35aa7abc372c | diffusion-based-mel-spectrogram-enhancement | 2305.10891 | null | https://arxiv.org/abs/2305.10891v2 | https://arxiv.org/pdf/2305.10891v2.pdf | Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data | Creating synthetic voices with found data is challenging, as real-world recordings often contain various types of audio degradation. One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for text-to-speech (TTS) model training. This paper investigates the use of conditional diffusion models for generalized speech enhancement, which aims at addressing multiple types of audio degradation simultaneously. The enhancement is performed on the log Mel-spectrogram domain to align with the TTS training objective. Text information is introduced as an additional condition to improve the model robustness. Experiments on real-world recordings demonstrate that the synthetic voice built on data enhanced by the proposed model produces higher-quality synthetic speech, compared to those trained on data enhanced by strong baselines. Code and pre-trained parameters of the proposed enhancement model are available at \url{https://github.com/dmse4tts/DMSE4TTS} | ['Tan Lee', 'Wei Liu', 'Yusheng Tian'] | 2023-05-18 | null | null | null | null | ['speech-enhancement', 'speech-synthesis'] | ['speech', 'speech'] | [ 3.88712078e-01 1.64291020e-02 3.83690417e-01 -1.16762973e-01
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6d901c68-e19b-4786-aa43-2f51603b7b3d | depth-supervised-nerf-fewer-views-and-faster | 2107.02791 | null | https://arxiv.org/abs/2107.02791v2 | https://arxiv.org/pdf/2107.02791v2.pdf | Depth-supervised NeRF: Fewer Views and Faster Training for Free | A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We formalize the above assumption through DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision. We leverage the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can render better images given fewer training views while training 2-3x faster. Further, we show that our loss is compatible with other recently proposed NeRF methods, demonstrating that depth is a cheap and easily digestible supervisory signal. And finally, we find that DS-NeRF can support other types of depth supervision such as scanned depth sensors and RGB-D reconstruction outputs. | ['Deva Ramanan', 'Jun-Yan Zhu', 'Andrew Liu', 'Kangle Deng'] | 2021-07-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.pdf | cvpr-2022-1 | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 3.92793000e-01 4.41753209e-01 -1.69407219e-01 -6.82002902e-01
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4f7f688d-2041-4571-b38b-6b6f0daf9b14 | a-novel-perspective-to-look-at-attention-bi | 2203.07216 | null | https://arxiv.org/abs/2203.07216v2 | https://arxiv.org/pdf/2203.07216v2.pdf | A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification | Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline. However, the inherent characteristics of deep learning models and the flexibility of the attention mechanism increase the models' complexity, thus leading to challenges in model explainability. In this paper, to address this challenge, we propose a novel practical framework by utilizing a two-tier attention architecture to decouple the complexity of explanation and the decision-making process. We apply it in the context of a news article classification task. The experiments on two large-scaled news corpora demonstrate that the proposed model can achieve competitive performance with many state-of-the-art alternatives and illustrate its appropriateness from an explainability perspective. | ['Ruihai Dong', 'Derek Greene', 'Dairui Liu'] | 2022-03-14 | null | https://aclanthology.org/2022.findings-acl.178 | https://aclanthology.org/2022.findings-acl.178.pdf | findings-acl-2022-5 | ['news-classification'] | ['natural-language-processing'] | [-1.24114677e-01 4.51515615e-01 -3.92883420e-01 -4.76232797e-01
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046ac6cb-6c49-419a-8a63-dcc975a8a788 | how-useful-is-active-learning-for-image-based | 2006.04255 | null | https://arxiv.org/abs/2006.04255v3 | https://arxiv.org/pdf/2006.04255v3.pdf | How useful is Active Learning for Image-based Plant Phenotyping? | Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required. | ['Fateme Fotouhi Ardakani', 'Baskar Ganapathysubramanian', 'Asheesh K. Singh', 'Arti Singh', 'Talukder Z. Jubery', 'Koushik Nagasubramanian', 'Soumik Sarkar', 'Seyed Vahid Mirnezami'] | 2020-06-07 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 5.94999909e-01 2.32380286e-01 -7.15510845e-01 -2.89368629e-01
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dcfeaf3d-c45c-425f-a402-5d95d796e737 | spatio-temporal-cnn-baseline-method-for-the | 2112.12074 | null | https://arxiv.org/abs/2112.12074v1 | https://arxiv.org/pdf/2112.12074v1.pdf | Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark | This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task. | ['Pierre-Etienne Martin'] | 2021-12-16 | null | null | null | null | ['stroke-classification'] | ['methodology'] | [ 8.66350755e-02 1.27961963e-01 -2.95163840e-01 -1.13111034e-01
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c112e91f-2747-481a-a346-d374926d3b04 | can-chatgpt-s-responses-boost-traditional | 2307.04648 | null | https://arxiv.org/abs/2307.04648v1 | https://arxiv.org/pdf/2307.04648v1.pdf | Can ChatGPT's Responses Boost Traditional Natural Language Processing? | The employment of foundation models is steadily expanding, especially with the launch of ChatGPT and the release of other foundation models. These models have shown the potential of emerging capabilities to solve problems, without being particularly trained to solve. A previous work demonstrated these emerging capabilities in affective computing tasks; the performance quality was similar to traditional Natural Language Processing (NLP) techniques, but falling short of specialised trained models, like fine-tuning of the RoBERTa language model. In this work, we extend this by exploring if ChatGPT has novel knowledge that would enhance existing specialised models when they are fused together. We achieve this by investigating the utility of verbose responses from ChatGPT about solving a downstream task, in addition to studying the utility of fusing that with existing NLP methods. The study is conducted on three affective computing problems, namely sentiment analysis, suicide tendency detection, and big-five personality assessment. The results conclude that ChatGPT has indeed novel knowledge that can improve existing NLP techniques by way of fusion, be it early or late fusion. | ['Björn W. Schuller', 'Erik Cambria', 'Mostafa M. Amin'] | 2023-07-06 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 1.46119431e-01 5.80986798e-01 2.19081238e-01 -6.32478297e-01
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5459552b-6d68-426c-b3d6-8713a4e47752 | masked-multi-step-multivariate-time-series | 2209.14413 | null | https://arxiv.org/abs/2209.14413v1 | https://arxiv.org/pdf/2209.14413v1.pdf | Masked Multi-Step Multivariate Time Series Forecasting with Future Information | In this paper, we introduce Masked Multi-Step Multivariate Forecasting (MMMF), a novel and general self-supervised learning framework for time series forecasting with known future information. In many real-world forecasting scenarios, some future information is known, e.g., the weather information when making a short-to-mid-term electricity demand forecast, or the oil price forecasts when making an airplane departure forecast. Existing machine learning forecasting frameworks can be categorized into (1) sample-based approaches where each forecast is made independently, and (2) time series regression approaches where the future information is not fully incorporated. To overcome the limitations of existing approaches, we propose MMMF, a framework to train any neural network model capable of generating a sequence of outputs, that combines both the temporal information from the past and the known information about the future to make better predictions. Experiments are performed on two real-world datasets for (1) mid-term electricity demand forecasting, and (2) two-month ahead flight departures forecasting. They show that the proposed MMMF framework outperforms not only sample-based methods but also existing time series forecasting models with the exact same base models. Furthermore, once a neural network model is trained with MMMF, its inference speed is similar to that of the same model trained with traditional regression formulations, thus making MMMF a better alternative to existing regression-trained time series forecasting models if there is some available future information. | ['Nurali Virani', 'Honggang Wang', 'Yiwei Fu'] | 2022-09-28 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.93901077e-01 -4.46032703e-01 -3.74541551e-01 -7.11528718e-01
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5ef171ca-5b0e-4b7a-9c30-ffde946a4cd5 | adversarial-genetic-programming-for-cyber | 2004.04647 | null | https://arxiv.org/abs/2004.04647v1 | https://arxiv.org/pdf/2004.04647v1.pdf | Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters | Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security encompasses extant and immediate research efforts in a vital problem domain, arguably occupying a position at the frontier where GP matters. Additionally, it prompts research questions around evolving complex behavior by expressing different abstractions with GP and opportunities to reconnect to the Machine Learning, Artificial Life, Agent-Based Modeling and Cyber Security communities. We present a framework called RIVALS which supports the study of network security arms races. Its goal is to elucidate the dynamics of cyber networks under attack by computationally modeling and simulating them. | ['Dennis Garcia', "Una-May O'Reilly", 'Jamal Toutouh', 'Erik Hemberg', 'Daniel Prado Sanchez', 'Anthony Erb Luogo', 'Marcos Pertierra', 'Jonathan Kelly'] | 2020-04-07 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.74950594e-01 5.18529594e-01 1.33832067e-01 6.65368974e-01
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ab701653-73eb-4050-b8b7-2c3b51766fe6 | dreamcoder-growing-generalizable | 2006.08381 | null | https://arxiv.org/abs/2006.08381v1 | https://arxiv.org/pdf/2006.08381v1.pdf | DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning | Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A ``wake-sleep'' learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton's and Coulomb's laws. Concepts are built compositionally from those learned earlier, yielding multi-layered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience. | ['Joshua B. Tenenbaum', 'Armando Solar-Lezama', 'Mathias Sable-Meyer', 'Maxwell Nye', 'Lucas Morales', 'Kevin Ellis', 'Luc Cary', 'Catherine Wong', 'Luke Hewitt'] | 2020-06-15 | null | null | null | null | ['program-induction', 'drawing-pictures'] | ['computer-code', 'computer-vision'] | [-1.36398718e-01 2.35921875e-01 1.08410753e-01 -5.42513788e-01
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8089d054-213d-4704-844f-a8f6b5be3724 | hybrid-sd-text-h-text-sd-a-new-hybrid | 2211.01722 | null | https://arxiv.org/abs/2211.01722v2 | https://arxiv.org/pdf/2211.01722v2.pdf | Hybrid-SD (H_SD): A new hybrid evaluation metric for automatic speech recognition tasks | Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems, particularly when used for spoken language understanding tasks such as intent recognition and dialogue systems. In this paper, we propose Hybrid-SD (H_SD), a new hybrid evaluation metric for ASR systems that takes into account both semantic correctness and error rate. To generate sentence dissimilarity scores (SD), we built a fast and lightweight SNanoBERT model using distillation techniques. Our experiments show that the SNanoBERT model is 25.9x smaller and 38.8x faster than SRoBERTa while achieving comparable results on well-known benchmarks. Hence, making it suitable for deploying with ASR models on edge devices. We also show that H_SD correlates more strongly with downstream tasks such as intent recognition and named-entity recognition (NER). | ['T. V. Prabhakar', 'Supreeth Rao', 'Harsha Yelchuri', 'Zitha Sasindran'] | 2022-11-03 | null | null | null | null | ['intent-recognition', 'spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.00555964e-01 3.54138874e-02 2.31982291e-01 -6.87335014e-01
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c9c7d7e7-e6da-4fde-b15d-e0a8c7b50c3f | a-multifactor-analysis-model-for-stock-market | null | null | https://www.ijcst.org/Volume14/Issue1.html | https://www.ijcst.org/Volume14/Issue1/p1_14_1.pdf | A Multifactor Analysis Model for Stock Market Prediction | Stock Market predictions have historically been a problem tackled by different singular approaches even though markets are influenced by many different factors. This paper presents a novel multi-factor analysis model for stock price prediction that combines Technical analysis, Fundamental analysis, Machine learning, and, Sentiment Analysis (TFMS Analysis). The proposed model leverages Random Forest Regressor (RFR) to predict a stock price and long short-term memory(LSTM) approach to predict a multiplier. Sentiment analysis is then used to capture the impact of various factors on stock prices, including market trends, economic indicators, and public opinion. The results of the model are compared to traditional prediction models using historical stock data, and it is shown that the proposed model provides improved accuracy in predicting future stock prices. The proposed model represents a significant step forward in stock price prediction, providing a more comprehensive and effective approach to predicting stock prices based on multiple factors. | ['Akash Deep'] | 2023-03-05 | null | null | null | international-journal-of-computer-science-and | ['stock-market-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-6.20264888e-01 -6.37627900e-01 -6.43573940e-01 -9.89721864e-02
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19c915f7-097d-4321-a3c9-fca2fe88d1ae | testing-the-robustness-of-learned-index | 2207.11575 | null | https://arxiv.org/abs/2207.11575v1 | https://arxiv.org/pdf/2207.11575v1.pdf | Testing the Robustness of Learned Index Structures | While early empirical evidence has supported the case for learned index structures as having favourable average-case performance, little is known about their worst-case performance. By contrast, classical structures are known to achieve optimal worst-case behaviour. This work evaluates the robustness of learned index structures in the presence of adversarial workloads. To simulate adversarial workloads, we carry out a data poisoning attack on linear regression models that manipulates the cumulative distribution function (CDF) on which the learned index model is trained. The attack deteriorates the fit of the underlying ML model by injecting a set of poisoning keys into the training dataset, which leads to an increase in the prediction error of the model and thus deteriorates the overall performance of the learned index structure. We assess the performance of various regression methods and the learned index implementations ALEX and PGM-Index. We show that learned index structures can suffer from a significant performance deterioration of up to 20% when evaluated on poisoned vs. non-poisoned datasets. | ['Benjamin I. P. Rubinstein', 'Renata Borovica-Gajic', 'Matthias Bachfischer'] | 2022-07-23 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.48218781e-01 -1.34229183e-01 -1.04089342e-01 2.90867239e-01
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c5ce7343-1207-44e2-9888-86531b1237dd | small-footprint-slimmable-networks-for | 2304.12183 | null | https://arxiv.org/abs/2304.12183v1 | https://arxiv.org/pdf/2304.12183v1.pdf | Small-footprint slimmable networks for keyword spotting | In this work, we present Slimmable Neural Networks applied to the problem of small-footprint keyword spotting. We show that slimmable neural networks allow us to create super-nets from Convolutioanl Neural Networks and Transformers, from which sub-networks of different sizes can be extracted. We demonstrate the usefulness of these models on in-house Alexa data and Google Speech Commands, and focus our efforts on models for the on-device use case, limiting ourselves to less than 250k parameters. We show that slimmable models can match (and in some cases, outperform) models trained from scratch. Slimmable neural networks are therefore a class of models particularly useful when the same functionality is to be replicated at different memory and compute budgets, with different accuracy requirements. | ['Yuzong Liu', 'Dongsu Du', 'Mohammad Omar Khursheed', 'Zuhaib Akhtar'] | 2023-04-21 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting'] | ['speech', 'speech'] | [-6.11753240e-02 1.06036127e-01 -1.58536404e-01 -1.91239446e-01
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8034f100-b686-4355-baf5-c20be5d1f75a | a-bayesian-algorithm-for-retrosynthesis | 2003.03190 | null | https://arxiv.org/abs/2003.03190v1 | https://arxiv.org/pdf/2003.03190v1.pdf | A Bayesian algorithm for retrosynthesis | The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space. At present, emerging machine-learning technologies are overturning the process of retrosynthetic planning. The objective of this study is to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of two steps: a deep neural network is trained that forwardly predicts a product of the given reactants with a high level of accuracy, following which this forward model is inverted into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. The Bayesian retrosynthesis algorithm could successfully rediscover 80.3% and 50.0% of known synthetic routes of single-step and two-step reactions within top-10 accuracy, respectively, thereby outperforming state-of-the-art algorithms in terms of the overall accuracy. Remarkably, the Monte Carlo method, which was specifically designed for the presence of diverse multiple routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We investigated the potential applicability of such diverse candidates based on expert knowledge from synthetic organic chemistry. | ['Ryo Yoshida', 'Stephen Wu', 'Mitsuru Ohno', 'Zhongliang Guo'] | 2020-03-06 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.23820007e-01 -9.93295237e-02 -1.62808180e-01 -1.30072251e-01
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eb830c35-daf7-4e42-8b4f-edd4ca920738 | tfcnet-temporal-fully-connected-networks-for | 2203.05928 | null | https://arxiv.org/abs/2203.05928v1 | https://arxiv.org/pdf/2203.05928v1.pdf | TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal Reasoning | Temporal Reasoning is one important functionality for vision intelligence. In computer vision research community, temporal reasoning is usually studied in the form of video classification, for which many state-of-the-art Neural Network structures and dataset benchmarks are proposed in recent years, especially 3D CNNs and Kinetics. However, some recent works found that current video classification benchmarks contain strong biases towards static features, thus cannot accurately reflect the temporal modeling ability. New video classification benchmarks aiming to eliminate static biases are proposed, with experiments on these new benchmarks showing that the current clip-based 3D CNNs are outperformed by RNN structures and recent video transformers. In this paper, we find that 3D CNNs and their efficient depthwise variants, when video-level sampling strategy is used, are actually able to beat RNNs and recent vision transformers by significant margins on static-unbiased temporal reasoning benchmarks. Further, we propose Temporal Fully Connected Block (TFC Block), an efficient and effective component, which approximates fully connected layers along temporal dimension to obtain video-level receptive field, enhancing the spatiotemporal reasoning ability. With TFC blocks inserted into Video-level 3D CNNs (V3D), our proposed TFCNets establish new state-of-the-art results on synthetic temporal reasoning benchmark, CATER, and real world static-unbiased dataset, Diving48, surpassing all previous methods. | ['Shiwen Zhang'] | 2022-03-11 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.72153085e-01 -2.88326740e-01 -6.63087308e-01 -1.37211144e-01
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3c258235-14e0-422c-a0b9-a846f399b61e | machine-learning-assisted-bayesian-inference | 2304.13660 | null | https://arxiv.org/abs/2304.13660v1 | https://arxiv.org/pdf/2304.13660v1.pdf | Machine Learning-assisted Bayesian Inference for Jamming Detection in 5G NR | The increased flexibility and density of spectrum access in 5G NR have made jamming detection a critical research area. To detect coexisting jamming and subtle interference that can affect legitimate communications performance, we introduce machine learning (ML)-assisted Bayesian Inference for jamming detection methodologies. Our methodology leverages cross-layer critical signaling data collected on a 5G NR Non-Standalone (NSA) testbed via supervised learning models, and are further assessed, calibrated, and revealed using Bayesian Network Model (BNM)-based inference. The models can operate on both instantaneous and sequential time-series data samples, achieving an Area under Curve (AUC) in the range of 0.947 to 1 for instantaneous models and between 0.933 to 1 for sequential models including the echo state network (ESN) from the reservoir computing (RC) family, for jamming scenarios spanning multiple frequency bands and power levels. Our approach not only serves as a validation method and a resilience enhancement tool for ML-based jamming detection, but also enables root cause identification for any observed performance degradation. Our proof-of-concept is successful in addressing 72.2\% of the erroneous predictions in sequential models caused by insufficient data samples collected in the observation period, demonstrating its applicability in 5G NR and Beyond-5G (B5G) network infrastructure and user devices. | ['Lingjia Liu', 'Shehadi Dayekh', 'Ishan Aryendu', 'Ying Wang', 'Shashank Jere'] | 2023-04-26 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.44300899e-02 -2.07307279e-01 -1.11102894e-01 2.51210600e-01
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af46b785-2a1d-4ac8-8973-5887cd682c42 | unsupervised-part-representation-by-flow | 2011.13920 | null | https://arxiv.org/abs/2011.13920v2 | https://arxiv.org/pdf/2011.13920v2.pdf | Unsupervised part representation by Flow Capsules | Capsule networks aim to parse images into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descriptions. To address this issue we propose a way to learn primary capsule encoders that detect atomic parts from a single image. During training we exploit motion as a powerful perceptual cue for part definition, with an expressive decoder for part generation within a layered image model with occlusion. Experiments demonstrate robust part discovery in the presence of multiple objects, cluttered backgrounds, and occlusion. The part decoder infers the underlying shape masks, effectively filling in occluded regions of the detected shapes. We evaluate FlowCapsules on unsupervised part segmentation and unsupervised image classification. | ['David J. Fleet', 'Geoffrey E. Hinton', 'Soroosh Yazdani', 'Andrea Tagliasacchi', 'Sara Sabour'] | 2020-11-27 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 4.88180459e-01 5.37597835e-01 -5.06740987e-01 -3.11399400e-01
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6566ac32-b19d-4e5c-81e0-4b6681ff9585 | hate-speech-and-offensive-language-detection-2 | 2302.08777 | null | https://arxiv.org/abs/2302.08777v1 | https://arxiv.org/pdf/2302.08777v1.pdf | Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder | The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusive content. Therefore, automatically detecting this content is essential for banning inappropriate information, and reducing toxicity and violence on social media platforms. The existing works on hate speech and offensive language detection produce promising results based on pre-trained transformer models, however, they considered only the analysis of abusive content features generated through annotated datasets. This paper addresses a multi-task joint learning approach which combines external emotional features extracted from another corpora in dealing with the imbalanced and scarcity of labeled datasets. Our analysis are using two well-known Transformer-based models, BERT and mBERT, where the later is used to address abusive content detection in multi-lingual scenarios. Our model jointly learns abusive content detection with emotional features by sharing representations through transformers' shared encoder. This approach increases data efficiency, reduce overfitting via shared representations, and ensure fast learning by leveraging auxiliary information. Our findings demonstrate that emotional knowledge helps to more reliably identify hate speech and offensive language across datasets. Our hate speech detection Multi-task model exhibited 3% performance improvement over baseline models, but the performance of multi-task models were not significant for offensive language detection task. More interestingly, in both tasks, multi-task models exhibits less false positive errors compared to single task scenario. | ['Noel Crespi', 'Reza Farahbakhsh', 'Praboda Rajapaksha', 'Khouloud Mnassri'] | 2023-02-17 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-8.67372826e-02 -1.60723001e-01 -2.78864861e-01 -1.78558052e-01
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74f70295-c8e1-4ae1-81b2-a1f40002b758 | efficient-multi-view-performance-capture-of | 1602.02023 | null | http://arxiv.org/abs/1602.02023v1 | http://arxiv.org/pdf/1602.02023v1.pdf | Efficient Multi-view Performance Capture of Fine-Scale Surface Detail | We present a new effective way for performance capture of deforming meshes
with fine-scale time-varying surface detail from multi-view video. Our method
builds up on coarse 4D surface reconstructions, as obtained with commonly used
template-based methods. As they only capture models of coarse-to-medium scale
detail, fine scale deformation detail is often done in a second pass by using
stereo constraints, features, or shading-based refinement. In this paper, we
propose a new effective and stable solution to this second step. Our framework
creates an implicit representation of the deformable mesh using a dense
collection of 3D Gaussian functions on the surface, and a set of 2D Gaussians
for the images. The fine scale deformation of all mesh vertices that maximizes
photo-consistency can be efficiently found by densely optimizing a new
model-to-image consistency energy on all vertex positions. A principal
advantage is that our problem formulation yields a smooth closed form energy
with implicit occlusion handling and analytic derivatives. Error-prone
correspondence finding, or discrete sampling of surface displacement values are
also not needed. We show several reconstructions of human subjects wearing
loose clothing, and we qualitatively and quantitatively show that we robustly
capture more detail than related methods. | ['Edilson de Aguiar', 'Thomas Helten', 'Nadia Robertini', 'Christian Theobalt'] | 2016-02-05 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 2.26868272e-01 -4.03495207e-02 4.08058614e-01 -1.87711507e-01
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91a3e70c-d781-473c-9786-4186e8a46afc | the-joint-role-of-geometry-and-illumination | 2101.02496 | null | https://arxiv.org/abs/2101.02496v2 | https://arxiv.org/pdf/2101.02496v2.pdf | The joint role of geometry and illumination on material recognition | Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly non-linear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly non-linear models and humans may use similar high-level factors for material recognition tasks. | ['Belen Masia', 'Diego Gutierrez', 'Ana Serrano', 'Manuel Lagunas'] | 2021-01-07 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 2.61437088e-01 -8.07913482e-01 7.07428232e-02 -3.87623310e-01
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f5bf0e12-2680-4a8d-b2e5-9ad8b8d58b5a | streaming-small-footprint-keyword-spotting | 1710.09617 | null | http://arxiv.org/abs/1710.09617v1 | http://arxiv.org/pdf/1710.09617v1.pdf | Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models | We develop streaming keyword spotting systems using a recurrent neural
network transducer (RNN-T) model: an all-neural, end-to-end trained,
sequence-to-sequence model which jointly learns acoustic and language model
components. Our models are trained to predict either phonemes or graphemes as
subword units, thus allowing us to detect arbitrary keyword phrases, without
any out-of-vocabulary words. In order to adapt the models to the requirements
of keyword spotting, we propose a novel technique which biases the RNN-T system
towards a specific keyword of interest.
Our systems are compared against a strong sequence-trained, connectionist
temporal classification (CTC) based "keyword-filler" baseline, which is
augmented with a separate phoneme language model. Overall, our RNN-T system
with the proposed biasing technique significantly improves performance over the
baseline system. | ['Kanishka Rao', 'Anton Bakhtin', 'Wei Li', 'Yanzhang He', 'Rohit Prabhavalkar', 'Ian McGraw'] | 2017-10-26 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 7.00326443e-01 4.29218225e-02 -3.45592409e-01 -2.96743333e-01
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9cf3a274-781c-448d-b5ca-9977521eabde | inferring-sensitive-attributes-from-model | 2208.09967 | null | https://arxiv.org/abs/2208.09967v2 | https://arxiv.org/pdf/2208.09967v2.pdf | Inferring Sensitive Attributes from Model Explanations | Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to its corresponding model prediction. The dependency of explanations on input raises privacy concerns for sensitive user data. However, current literature has limited discussion on privacy risks of model explanations. We focus on the specific privacy risk of attribute inference attack wherein an adversary infers sensitive attributes of an input (e.g., race and sex) given its model explanations. We design the first attribute inference attack against model explanations in two threat models where model builder either (a) includes the sensitive attributes in training data and input or (b) censors the sensitive attributes by not including them in the training data and input. We evaluate our proposed attack on four benchmark datasets and four state-of-the-art algorithms. We show that an adversary can successfully infer the value of sensitive attributes from explanations in both the threat models accurately. Moreover, the attack is successful even by exploiting only the explanations corresponding to sensitive attributes. These suggest that our attack is effective against explanations and poses a practical threat to data privacy. On combining the model predictions (an attack surface exploited by prior attacks) with explanations, we note that the attack success does not improve. Additionally, the attack success on exploiting model explanations is better compared to exploiting only model predictions. These suggest that model explanations are a strong attack surface to exploit for an adversary. | ['Antoine Boutet', 'Vasisht Duddu'] | 2022-08-21 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 5.80466568e-01 5.61023891e-01 -5.66414714e-01 -7.17890859e-01
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4a134913-b4a2-4e65-8e72-48086682723c | towards-code-generation-from-bdd-test-case | 2305.11619 | null | https://arxiv.org/abs/2305.11619v1 | https://arxiv.org/pdf/2305.11619v1.pdf | Towards Code Generation from BDD Test Case Specifications: A Vision | Automatic code generation has recently attracted large attention and is becoming more significant to the software development process. Solutions based on Machine Learning and Artificial Intelligence are being used to increase human and software efficiency in potent and innovative ways. In this paper, we aim to leverage these developments and introduce a novel approach to generating frontend component code for the popular Angular framework. We propose to do this using behavior-driven development test specifications as input to a transformer-based machine learning model. Our approach aims to drastically reduce the development time needed for web applications while potentially increasing software quality and introducing new research ideas toward automatic code generation. | ['Mira Mezini', 'Krishna Narasimhan', 'Mariam Naveed', 'Hani Aldebes', 'David Reichenbach', 'Leon Chemnitz'] | 2023-05-19 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.27117097e-01 1.53435752e-01 -1.45436645e-01 -3.83257687e-01
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af4d2b69-f9b6-4028-a5c7-9e749c5558fc | towards-comparability-in-non-intrusive-load | 2001.07708 | null | https://arxiv.org/abs/2001.07708v1 | https://arxiv.org/pdf/2001.07708v1.pdf | Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation | Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techniques, performance evaluation and comparability remains an open research question. The lack of standardisation and consensus on evaluation procedures makes reproducibility and comparability extremely difficult. In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms. We divide discussion on comparability into data aspects, performance metrics, and give a close view on evaluation processes. Detailed information on pre-processing as well as data cleaning methods, the importance of unified performance reporting, and the need for complexity measures in load disaggregation are found to be the most urgent issues in NILM-related research. In addition, our evaluation suggests that datasets should be chosen carefully. We conclude by formulating suggestions for future work to enhance comparability. | ['Stephen Makonin', 'Wilfried Elmenreich', 'Christoph Klemenjak'] | 2020-01-20 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.80131093e-01 -2.17018366e-01 -2.03341350e-01 -5.53886533e-01
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05a4b96c-9aeb-4a25-affe-f3d1210cabb4 | on-the-performance-of-data-compression-in | 2207.00223 | null | https://arxiv.org/abs/2207.00223v1 | https://arxiv.org/pdf/2207.00223v1.pdf | On the Performance of Data Compression in Clustered Fog Radio Access Networks | The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been considered as one of the promising techniques to reduce the transmission latency. By compressing the computation tasks before transmitting, the transmission delay is reduced due to the shrink transmitted data size, and the original computing task can be retrieved by employing data decompressing (DD) at the edge nodes or the centre cloud. Nevertheless, the DC and DD incorporate extra processing latency, and the latency performance has not been investigated in the large-scale DC-enabled F-RAN. Therefore, in this work, the successful data compression probability (SDCP) is defined to analyse the latency performance of the F-RAN. Moreover, to analyse the effect of compression offloading ratio (COR), a novel hybrid compression mode is proposed based on the queueing theory. Based on this, the closed-form result of SDCP in the large-scale DC-enabled F-RAN is derived by combining the Matern cluster process and M/G/1 queueing model, and validated by Monte Carlo simulations. Based on the derived SDCP results, the effects of COR on the SDCP is analysed numerically. The results show that the SDCP with the optimal COR can be enhanced with a maximum value of 0.3 and 0.55 as compared with the cases of compressing all computing tasks at the edge and at the UE, respectively. Moreover, for the system requiring the minimal latency, the proposed hybrid compression mode can alleviate the requirement on the backhaul capacity. | ['Jie Zhang', 'Qianbin Chen', 'Yanan Zheng', 'Jiliang Zhang', 'Yan Jiang', 'Haonan Hu'] | 2022-07-01 | null | null | null | null | ['data-compression'] | ['time-series'] | [-1.50432780e-01 -2.97708809e-02 1.22312173e-01 3.56127024e-01
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6164391f-587b-4b97-b890-e79767c21e79 | modelling-sequential-music-track-skips-using | 1903.08408 | null | http://arxiv.org/abs/1903.08408v1 | http://arxiv.org/pdf/1903.08408v1.pdf | Modelling Sequential Music Track Skips using a Multi-RNN Approach | Modelling sequential music skips provides streaming companies the ability to
better understand the needs of the user base, resulting in a better user
experience by reducing the need to manually skip certain music tracks. This
paper describes the solution of the University of Copenhagen DIKU-IR team in
the 'Spotify Sequential Skip Prediction Challenge', where the task was to
predict the skip behaviour of the second half in a music listening session
conditioned on the first half. We model this task using a Multi-RNN approach
consisting of two distinct stacked recurrent neural networks, where one network
focuses on encoding the first half of the session and the other network focuses
on utilizing the encoding to make sequential skip predictions. The encoder
network is initialized by a learned session-wide music encoding, and both of
them utilize a learned track embedding. Our final model consists of a majority
voted ensemble of individually trained models, and ranked 2nd out of 45
participating teams in the competition with a mean average accuracy of 0.641
and an accuracy on the first skip prediction of 0.807. Our code is released at
https://github.com/Varyn/WSDM-challenge-2019-spotify. | ['Christina Lioma', 'Christian Hansen', 'Stephen Alstrup', 'Casper Hansen', 'Jakob Grue Simonsen'] | 2019-03-20 | null | null | null | null | ['sequential-skip-prediction'] | ['time-series'] | [ 2.28533283e-01 4.26076651e-02 -3.94552737e-01 -3.24235559e-01
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744751e3-3ffe-4f31-a135-ea4fc712774d | low-complexity-acoustic-echo-cancellation | 2207.11388 | null | https://arxiv.org/abs/2207.11388v2 | https://arxiv.org/pdf/2207.11388v2.pdf | Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering | The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance. The performance of Kalman filter is closely related to the estimation accuracy of the state noise covariance and the observation noise covariance. The estimation error may lead to unacceptable results, especially when the echo path suffers abrupt changes, the tracking performance of the Kalman filter could be degraded significantly. In this paper, we propose the neural Kalman filtering (NKF), which uses neural networks to implicitly model the covariance of the state noise and observation noise and to output the Kalman gain in real-time. Experimental results on both synthetic test sets and real-recorded test sets show that, the proposed NKF has superior convergence and re-convergence performance while ensuring low near-end speech degradation comparing with the state-of-the-art model-based methods. Moreover, the model size of the proposed NKF is merely 5.3 K and the RTF is as low as 0.09, which indicates that it can be deployed in low-resource platforms. | ['Muyong Cao', 'Xuefei Fang', 'Wei Wu', 'Fei Jiang', 'Dong Yang'] | 2022-07-23 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-1.96875691e-01 -4.40347493e-01 4.24705088e-01 -3.74910980e-02
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c174fb00-fe5b-4e5f-bae1-8d179a1a495c | efficient-sparse-spherical-k-means-for | 2108.00895 | null | https://arxiv.org/abs/2108.00895v1 | https://arxiv.org/pdf/2108.00895v1.pdf | Efficient Sparse Spherical k-Means for Document Clustering | Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the size of the collection. Optimizations targeted at the Euclidean k-Means algorithm largely do not apply because the cosine distance is not a metric. We therefore propose an efficient indexing structure to improve the scalability of Spherical k-Means with respect to k. Our approach exploits the sparsity of the input vectors and the convergence behavior of k-Means to reduce the number of comparisons on each iteration significantly. | ['Thomas Ertl', 'Steffen Koch', 'Johannes Knittel'] | 2021-07-30 | null | null | null | null | ['text-clustering', 'short-text-clustering', 'unsupervised-spatial-clustering'] | ['natural-language-processing', 'natural-language-processing', 'time-series'] | [-3.67079765e-01 -5.52097023e-01 -1.42844304e-01 -3.25397074e-01
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bad163b9-1cdd-4fd6-b6db-3ba8566c0a56 | visual-semantic-matching-by-exploring-high | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.pdf | Visual-Semantic Matching by Exploring High-Order Attention and Distraction | Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In this work, we address this task from two previously-ignored aspects: high-order semantic information (e.g., object-predicate-subject triplet, object-attribute pair) and visual distraction (i.e., despite the high relevance to textual query, images may also contain many prominent distracting objects or visual relations). Specifically, we build scene graphs for both visual and textual modalities. Our technical contributions are two-folds: firstly, we formulate the visual-semantic matching task as an attention-driven cross-modality scene graph matching problem. Graph convolutional networks (GCNs) are used to extract high-order information from two scene graphs. A novel cross-graph attention mechanism is proposed to contextually reweigh graph elements and calculate the inter-graph similarity; Secondly, some top-ranked samples are indeed false matching due to the co-occurrence of both highly-relevant and distracting information. We devise an information-theoretic measure for estimating semantic distraction and re-ranking the initial retrieval results. Comprehensive experiments and ablation studies on two large public datasets (MS-COCO and Flickr30K) demonstrate the superiority of the proposed method and the effectiveness of both high-order attention and distraction.
| [' Yadong Mu', ' Duo Zhang', 'Yongzhi Li'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['graph-similarity'] | ['graphs'] | [ 2.75176674e-01 -6.71839714e-02 -6.53802454e-02 -1.99956834e-01
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d3929ec1-24a5-4dad-90f3-866d550acb72 | keynet-keypoint-detection-by-handcrafted-and | 1904.00889 | null | https://arxiv.org/abs/1904.00889v3 | https://arxiv.org/pdf/1904.00889v3.pdf | Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters | We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity. | ['Axel Barroso-Laguna', 'Krystian Mikolajczyk', 'Edgar Riba', 'Daniel Ponsa'] | 2019-04-01 | key-net-keypoint-detection-by-handcrafted-and | http://openaccess.thecvf.com/content_ICCV_2019/html/Barroso-Laguna_Key.Net_Keypoint_Detection_by_Handcrafted_and_Learned_CNN_Filters_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Barroso-Laguna_Key.Net_Keypoint_Detection_by_Handcrafted_and_Learned_CNN_Filters_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-matching'] | ['computer-vision'] | [-3.35937023e-01 -4.23908979e-01 -3.47412467e-01 -3.59482914e-01
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6d7043f0-738c-4f38-8b14-ef27af7380b5 | counterfactual-explanations-of-neural-network | 2304.04063 | null | https://arxiv.org/abs/2304.04063v2 | https://arxiv.org/pdf/2304.04063v2.pdf | Counterfactual Explanations of Neural Network-Generated Response Curves | Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (referred to as "active feature") might depend on the values of the other input features (referred to as "passive features"). In this work, we consider the case of systems whose response is approximated using regression neural networks. We propose to use counterfactual explanations (CFEs) for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes. CFEs are generated by a genetic algorithm-based approach that solves a multi-objective optimization problem. In particular, given a response curve generated for an active feature, a CFE finds the minimum combination of passive features that need to be modified to alter the shape of the response curve. We tested our method on a synthetic dataset with 1-D inputs and two crop yield prediction datasets with 2-D inputs. The relevance ranking of features and feature combinations obtained on the synthetic dataset coincided with the analysis of the equation that was used to generate the problem. Results obtained on the yield prediction datasets revealed that the impact on fertilizer responsivity of passive features depends on the terrain characteristics of each field. | ['John Sheppard', 'Giorgio Morales'] | 2023-04-08 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 7.67961860e-01 5.33441566e-02 -6.43073693e-02 -3.88131499e-01
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538a9f7c-a496-4e21-b71f-c473c0b7a326 | dwnet-deep-wide-network-for-3d-action | 1908.11036 | null | https://arxiv.org/abs/1908.11036v1 | https://arxiv.org/pdf/1908.11036v1.pdf | DWnet: Deep-Wide Network for 3D Action Recognition | We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions. Compared with the deep structure, the novel model saves lots of testing time and almost achieves real-time testing. Furthermore, the DWnet can capture better features than broad learning system can. In terms of methodology, we use pruned hierarchical co-occurrence network (PruHCN) to learn local and global spatial-temporal features. To obtain sufficient global information, BLS is used to expand features extracted by PruHCN. Experiments on two common skeletal datasets demonstrate the advantage of the proposed model on testing time and the effectiveness of the novel model to recognize the action. | ['Jianqin Yin', 'Fuxing Yang', 'Yonghao Dang'] | 2019-08-29 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [-2.04000458e-01 -1.27814829e-01 -4.56817120e-01 -1.17637686e-01
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137afd30-8ccb-42ee-bcb9-c15e72b44744 | smart-non-intrusive-appliance-identification | 2102.04808 | null | https://arxiv.org/abs/2102.04808v1 | https://arxiv.org/pdf/2102.04808v1.pdf | Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier | Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power histogramming (LPH) descriptor, in which appliance power signals are transformed into 2D space and short histograms are extracted to represent each device. Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. This results in highly improving the discrimination ability between appliances belonging to distinct categories. A deep evaluation of the proposed LPH-IKNN based solution is investigated under different data sets, in which the proposed scheme leads to promising performance. An accuracy of up to 99.65% and 98.51% has been achieved on GREEND and UK-DALE data sets, respectively. While an accuracy of more than 96% has been attained on both WHITED and PLAID data sets. This proves the validity of using 2D descriptors to accurately identify appliances and create new perspectives for the NILM problem. | ['Abbes Amira', 'Faycal Bensaali', 'Abdullah Alsalemi', 'Yassine Himeur'] | 2021-02-09 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.64911941e-01 -1.55992568e-01 -3.26064348e-01 -2.86756217e-01
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88793fdf-9df3-41e9-bac7-a3462418921b | weakly-supervised-learning-of-instance | 1904.05044 | null | https://arxiv.org/abs/1904.05044v3 | https://arxiv.org/pdf/1904.05044v3.pdf | Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations | This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries. To this end, we propose IRNet, which estimates rough areas of individual instances and detects boundaries between different object classes. It thus enables to assign instance labels to the seeds and to propagate them within the boundaries so that the entire areas of instances can be estimated accurately. Furthermore, IRNet is trained with inter-pixel relations on the attention maps, thus no extra supervision is required. Our method with IRNet achieves an outstanding performance on the PASCAL VOC 2012 dataset, surpassing not only previous state-of-the-art trained with the same level of supervision, but also some of previous models relying on stronger supervision. | ['Jiwoon Ahn', 'Sunghyun Cho', 'Suha Kwak'] | 2019-04-10 | weakly-supervised-learning-of-instance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ahn_Weakly_Supervised_Learning_of_Instance_Segmentation_With_Inter-Pixel_Relations_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ahn_Weakly_Supervised_Learning_of_Instance_Segmentation_With_Inter-Pixel_Relations_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-level-supervised-instance-segmentation'] | ['computer-vision'] | [ 6.01409376e-01 8.24395120e-01 -4.34921652e-01 -6.06537342e-01
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1a69c4af-d99e-4199-8201-a7d869dab206 | wave-u-net-a-multi-scale-neural-network-for | 1806.03185 | null | http://arxiv.org/abs/1806.03185v1 | http://arxiv.org/pdf/1806.03185v1.pdf | Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation | Models for audio source separation usually operate on the magnitude spectrum,
which ignores phase information and makes separation performance dependant on
hyper-parameters for the spectral front-end. Therefore, we investigate
end-to-end source separation in the time-domain, which allows modelling phase
information and avoids fixed spectral transformations. Due to high sampling
rates for audio, employing a long temporal input context on the sample level is
difficult, but required for high quality separation results because of
long-range temporal correlations. In this context, we propose the Wave-U-Net,
an adaptation of the U-Net to the one-dimensional time domain, which repeatedly
resamples feature maps to compute and combine features at different time
scales. We introduce further architectural improvements, including an output
layer that enforces source additivity, an upsampling technique and a
context-aware prediction framework to reduce output artifacts. Experiments for
singing voice separation indicate that our architecture yields a performance
comparable to a state-of-the-art spectrogram-based U-Net architecture, given
the same data. Finally, we reveal a problem with outliers in the currently used
SDR evaluation metrics and suggest reporting rank-based statistics to alleviate
this problem. | ['Simon Dixon', 'Sebastian Ewert', 'Daniel Stoller'] | 2018-06-08 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 3.83245498e-01 -4.04320657e-01 -7.22447701e-04 -1.52200937e-01
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62de4b9d-8972-4f7b-8ae4-a23b694bcc92 | weakly-supervised-temporal-action-6 | 2303.12332 | null | https://arxiv.org/abs/2303.12332v1 | https://arxiv.org/pdf/2303.12332v1.pdf | Weakly-Supervised Temporal Action Localization by Inferring Snippet-Feature Affinity | Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos, only taking video-level labels as the supervised information. Pseudo label generation is a promising strategy to solve the challenging problem, but most existing methods are limited to employing snippet-wise classification results to guide the generation, and they ignore that the natural temporal structure of the video can also provide rich information to assist such a generation process. In this paper, we propose a novel weakly-supervised temporal action localization method by inferring snippet-feature affinity. First, we design an affinity inference module that exploits the affinity relationship between temporal neighbor snippets to generate initial coarse pseudo labels. Then, we introduce an information interaction module that refines the coarse labels by enhancing the discriminative nature of snippet-features through exploring intra- and inter-video relationships. Finally, the high-fidelity pseudo labels generated from the information interaction module are used to supervise the training of the action localization network. Extensive experiments on two publicly available datasets, i.e., THUMOS14 and ActivityNet v1.3, demonstrate our proposed method achieves significant improvements compared to the state-of-the-art methods. | ['Huadong Ma', 'Chuanming Wang', 'Mengshi Qi', 'Wulian Yun'] | 2023-03-22 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.07320344e-01 -1.92889675e-01 -7.93130159e-01 -4.74817961e-01
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2f051e3f-2148-4f54-a72e-bbcf8043effc | carrnn-a-continuous-autoregressive-recurrent | 2104.03739 | null | https://arxiv.org/abs/2104.03739v1 | https://arxiv.org/pdf/2104.03739v1.pdf | CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data | Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths. In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data using an integrated deep learning architecture based on a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags to describe the changes caused by the irregularity and asynchronicity. It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction, where the proposed model based on a gated recurrent unit (GRU) achieves the lowest prediction errors among the proposed RNN-based models and state-of-the-art methods using GRUs and long short-term memory (LSTM) networks in their architecture. | ['Mads Nielsen', 'Sébastien Ourselin', 'Lauge Sørensen', 'Mostafa Mehdipour Ghazi'] | 2021-04-08 | null | null | null | null | ['icu-mortality', 'time-series-regression'] | ['medical', 'time-series'] | [ 1.69307724e-01 -3.00530583e-01 -1.86152253e-02 -3.67266655e-01
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aed55ed8-4eeb-4a9e-b8f4-62a16f457601 | object-contour-detection-with-a-fully | 1603.04530 | null | http://arxiv.org/abs/1603.04530v1 | http://arxiv.org/pdf/1603.04530v1.pdf | Object Contour Detection with a Fully Convolutional Encoder-Decoder Network | We develop a deep learning algorithm for contour detection with a fully
convolutional encoder-decoder network. Different from previous low-level edge
detection, our algorithm focuses on detecting higher-level object contours. Our
network is trained end-to-end on PASCAL VOC with refined ground truth from
inaccurate polygon annotations, yielding much higher precision in object
contour detection than previous methods. We find that the learned model
generalizes well to unseen object classes from the same super-categories on MS
COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning.
By combining with the multiscale combinatorial grouping algorithm, our method
can generate high-quality segmented object proposals, which significantly
advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62
to 0.67) with a relatively small amount of candidates ($\sim$1660 per image). | ['Ming-Hsuan Yang', 'Brian Price', 'Scott Cohen', 'Honglak Lee', 'Jimei Yang'] | 2016-03-15 | object-contour-detection-with-a-fully-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Object_Contour_Detection_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Object_Contour_Detection_CVPR_2016_paper.pdf | cvpr-2016-6 | ['contour-detection'] | ['computer-vision'] | [ 1.04583964e-01 1.59994915e-01 -9.22092572e-02 -2.15259999e-01
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2a286283-b62f-422a-90b3-8e5ef3c52873 | interpretable-unified-language-checking | 2304.03728 | null | https://arxiv.org/abs/2304.03728v1 | https://arxiv.org/pdf/2304.03728v1.pdf | Interpretable Unified Language Checking | Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-task language checkers based on their latent representations of natural and social knowledge. We present an interpretable, unified, language checking (UniLC) method for both human and machine-generated language that aims to check if language input is factual and fair. While fairness and fact-checking tasks have been handled separately with dedicated models, we find that LLMs can achieve high performance on a combination of fact-checking, stereotype detection, and hate speech detection tasks with a simple, few-shot, unified set of prompts. With the ``1/2-shot'' multi-task language checking method proposed in this work, the GPT3.5-turbo model outperforms fully supervised baselines on several language tasks. The simple approach and results suggest that based on strong latent knowledge representations, an LLM can be an adaptive and explainable tool for detecting misinformation, stereotypes, and hate speech. | ['James Glass', 'Helen Meng', 'Danny Fox', 'Xixin Wu', 'Thomas Hartvigsen', 'Luc Gaitskell', 'Wei Fang', 'Yung-Sung Chuang', 'Hongyin Luo', 'Tianhua Zhang'] | 2023-04-07 | null | null | null | null | ['misinformation', 'hate-speech-detection'] | ['miscellaneous', 'natural-language-processing'] | [-6.74602017e-02 4.84004319e-01 -4.67631221e-01 -3.55107874e-01
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73163e6a-0871-40f1-86c7-03a88dd2c9ba | a-semi-supervised-algorithm-for-improving-the | 2209.04360 | null | https://arxiv.org/abs/2209.04360v1 | https://arxiv.org/pdf/2209.04360v1.pdf | A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification | Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data, as it was done to generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians to diagnose the underlying diseases present in a limited number of uploaded recordings. However, this approach suffers from potential mislabeling of the coughs, as well as notable disagreement between experts. In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification. First, we leverage existing SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classification models. The consistency of the re-labeled data is demonstrated in that it exhibits a high degree of class separability, 3x higher than that of the user-labeled data, despite the expert label inconsistency present in the original dataset. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs, which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a cough classifier. This SSL approach can be used to combine the medical knowledge of several experts to improve the database consistency for any diagnostic classification task. | ['David Atienza', 'Tomas Teijeiro', 'Lara Orlandic'] | 2022-09-09 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.43346378e-01 8.00303444e-02 -1.33009717e-01 -1.69176236e-01
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8ce94333-a3f8-48f5-a4db-6c5ff0d20a9c | boundary-regularized-building-footprint-1 | null | null | https://www.researchgate.net/publication/343400679_BOUNDARY_REGULARIZED_BUILDING_FOOTPRINT_EXTRACTION_FROM_SATELLITE_IMAGES_USING_DEEP_NEURAL_NETWORKS | https://arxiv.org/ftp/arxiv/papers/2006/2006.13176.pdf | BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS | In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to
support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce
and update spatial maps of built environment due to its wide coverage with high resolution data. However, reconstructing spatial maps
from satellite imagery is not a trivial vision task as it requires reconstructing a scene or object with high-level representation such as
primitives. For the last decade, significant advancement in object detection and representation using visual data has been achieved, but
the primitive-based object representation still remains as a challenging vision task. Thus, a high-quality spatial map is mainly produced
through complex labour-intensive processes. In this paper, we propose a novel deep neural network, which enables to jointly detect
building instance and regularize noisy building boundary shapes from a single satellite imagery. The proposed deep learning method
consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network
using graph models to learn geometric information of the polygon shapes. Extensive experiments show that our model can accomplish
multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously. In terms of building
extraction accuracy, computation efficiency and boundary regularization performance, our model outperforms the state-of-the-art
baseline models. | ['Gunho Sohn', 'Muhammad Kamran', 'Kang Zhao'] | 2020-06-23 | null | null | null | arxiv-2020-6 | ['object-localization'] | ['computer-vision'] | [ 4.08560544e-01 -2.73087204e-01 9.59968194e-02 -4.00039226e-01
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d23c4e84-b1f7-490a-bd6f-050149f16d5e | adaptively-scheduled-multitask-learning-the | null | null | https://aclanthology.org/D19-5618 | https://aclanthology.org/D19-5618.pdf | Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation | Neural Machine Translation (NMT), a data-hungry technology, suffers from the lack of bilingual data in low-resource scenarios. Multitask learning (MTL) can alleviate this issue by injecting inductive biases into NMT, using auxiliary syntactic and semantic tasks. However, an effective \textit{training schedule} is required to balance the importance of tasks to get the best use of the training signal. The role of training schedule becomes even more crucial in \textit{biased-MTL} where the goal is to improve one (or a subset) of tasks the most, e.g. translation quality. Current approaches for biased-MTL are based on brittle \textit{hand-engineered} heuristics that require trial and error, and should be (re-)designed for each learning scenario. To the best of our knowledge, ours is the first work on \textit{adaptively} and \textit{dynamically} changing the training schedule in biased-MTL. We propose a rigorous approach for automatically reweighing the training data of the main and auxiliary tasks throughout the training process based on their contributions to the generalisability of the main NMT task. Our experiments on translating from English to Vietnamese/Turkish/Spanish show improvements of up to +1.2 BLEU points, compared to strong baselines. Additionally, our analyses shed light on the dynamic of needs throughout the training of NMT: from syntax to semantic. | ['Gholamreza Haffari', 'Poorya Zaremoodi'] | 2019-11-01 | null | null | null | ws-2019-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.60599250e-01 2.21704226e-03 -4.44425046e-01 -5.16504824e-01
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0756f5c0-c9ce-4ca8-85eb-eb817cb809e8 | direction-of-arrival-estimation-and-phase | 2202.07781 | null | https://arxiv.org/abs/2202.07781v1 | https://arxiv.org/pdf/2202.07781v1.pdf | Direction of Arrival Estimation and Phase-Correction for Non-Coherent Sub-Arrays: A Convex Optimization Approach | Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization and radar. In this paper, we consider a challenging multi-source DOA estimation task, where the receiving antenna array is composed of non-coherent sub-arrays, i.e., sub-arrays that observe different unknown phase shifts at every snapshot (e.g., due to waiving the demanding synchronization of local oscillators across the entire array). We formulate this problem as the reconstruction of joint sparse and low-rank matrices, and solve the problem's convex relaxation. To scale the optimization complexity with the number of snapshots better than general-purpose solvers, we design an optimization scheme, based on integrating the alternating direction method of multipliers and the accelerated proximal gradient techniques, that exploits the structure of the problem. While the DOAs can be estimated from the solution of the aforementioned convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the sub-arrays' phase shifts. This is done using another, computationally-light, convex relaxation that is practically tight. Using the estimated phase shifts, "phase-corrected" observations are created and a final plain ("coherent") DOA estimation method can be applied. Numerical experiments show the performance advantages of the proposed strategies over existing methods. | ['Oded Bialer', 'Tom Tirer'] | 2022-02-15 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.22097838e-02 -1.30614176e-01 2.10061625e-01 1.55637071e-01
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ca834361-04e3-4cd8-a699-56764e3856c3 | temporal-context-matters-enhancing-single | 2203.01933 | null | https://arxiv.org/abs/2203.01933v2 | https://arxiv.org/pdf/2203.01933v2.pdf | Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations | Clinical outcome or severity prediction from medical images has largely focused on learning representations from single-timepoint or snapshot scans. It has been shown that disease progression can be better characterized by temporal imaging. We therefore hypothesized that outcome predictions can be improved by utilizing the disease progression information from sequential images. We present a deep learning approach that leverages temporal progression information to improve clinical outcome predictions from single-timepoint images. In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory. Meanwhile, a Vision Transformer is pretrained in a self-supervised fashion to extract features from single-timepoint images. The key contribution is to design a recalibration module that employs maximum mean discrepancy loss (MMD) to align distributions of the above two contextual representations. We train our system to predict clinical outcomes and severity grades from single-timepoint images. Experiments on chest and osteoarthritis radiography datasets demonstrate that our approach outperforms other state-of-the-art techniques. | ['Prateek Prasanna', 'Chao Chen', 'Joseph Bae', 'Xuan Xu', 'Aishik Konwer'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.pdf | cvpr-2022-1 | ['severity-prediction'] | ['computer-vision'] | [ 4.59327012e-01 -9.16069653e-03 -5.95720410e-01 -8.05826068e-01
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53f5459e-b928-47e2-939a-81f25aa512c4 | forecasting-human-object-interaction-joint | 1911.10967 | null | https://arxiv.org/abs/1911.10967v2 | https://arxiv.org/pdf/1911.10967v2.pdf | Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video | We address the challenging task of anticipating human-object interaction in first person videos. Most existing methods ignore how the camera wearer interacts with the objects, or simply consider body motion as a separate modality. In contrast, we observe that the international hand movement reveals critical information about the future activity. Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action. Specifically, we consider the future hand motion as the motor attention, and model this attention using latent variables in our deep model. The predicted motor attention is further used to characterise the discriminative spatial-temporal visual features for predicting actions and interaction hotspots. We present extensive experiments demonstrating the benefit of the proposed joint model. Importantly, our model produces new state-of-the-art results for action anticipation on both EGTEA Gaze+ and the EPIC-Kitchens datasets. Our project page is available at https://aptx4869lm.github.io/ForecastingHOI/ | ['James Rehg', 'Yin Li', 'Miao Liu', 'Siyu Tang'] | 2019-11-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2641_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460681.pdf | eccv-2020-8 | ['action-anticipation'] | ['computer-vision'] | [ 1.37820885e-01 -5.30604720e-02 -5.79196572e-01 -3.34484190e-01
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6b07f941-39ca-49f9-98b4-012e3c4c72c5 | t5lephone-bridging-speech-and-text-self | 2211.00586 | null | https://arxiv.org/abs/2211.00586v1 | https://arxiv.org/pdf/2211.00586v1.pdf | T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5 | In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e.g. HuBERT) and pretrained language models (PLM, e.g. T5). Most previous works use pretrained language models with subword-based tokenization. However, the granularity of input units affects the alignment of speech model outputs and language model inputs, and PLM with character-based tokenization is underexplored. In this work, we conduct extensive studies on how PLMs with different tokenization strategies affect spoken language understanding task including spoken question answering (SQA) and speech translation (ST). We further extend the idea to create T5lephone(pronounced as telephone), a variant of T5 that is pretrained using phonemicized text. We initialize T5lephone with existing PLMs to pretrain it using relatively lightweight computational resources. We reached state-of-the-art on NMSQA, and the T5lephone model exceeds T5 with other types of units on end-to-end SQA and ST. | ['Yu Tsao', 'Hung-Yi Lee', 'Ho-Lam Chung', 'Chan-Jan Hsu'] | 2022-11-01 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.06829095e-01 4.10507262e-01 -1.66418895e-01 -8.57450545e-01
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b26f7bcf-1573-4439-9246-eb3a02625a71 | a-dataset-for-sentence-retrieval-for-open | 2205.11685 | null | https://arxiv.org/abs/2205.11685v1 | https://arxiv.org/pdf/2205.11685v1.pdf | A Dataset for Sentence Retrieval for Open-Ended Dialogues | We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conversational search. To address a broader scope of this task where any type of dialogue can be used, we constructed a dataset that includes open-ended dialogues from Reddit, candidate sentences from Wikipedia for each dialogue and human annotations for the sentences. We report the performance of several retrieval baselines, including neural retrieval models, over the dataset. To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. Using this training set significantly improved the performance over training on the MS MARCO dataset. | ['Oren Kurland', 'Idan Szpektor', 'Hagai Taitelbaum', 'Itay Harel'] | 2022-05-24 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.01104605e-01 7.19227374e-01 -2.70567741e-03 -5.59745610e-01
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8e62fe10-5cdf-4cf5-ac11-95e691055952 | neuroadaptive-electroencephalography-a-proof | 2106.06029 | null | https://arxiv.org/abs/2106.06029v1 | https://arxiv.org/pdf/2106.06029v1.pdf | Neuroadaptive electroencephalography: a proof-of-principle study in infants | A core goal of functional neuroimaging is to study how the environment is processed in the brain. The mainstream paradigm involves concurrently measuring a broad spectrum of brain responses to a small set of environmental features preselected with reference to previous studies or a theoretical framework. As a complement, we invert this approach by allowing the investigator to record the modulation of a preselected brain response by a broad spectrum of environmental features. Our approach is optimal when theoretical frameworks or previous empirical data are impoverished. By using a prespecified closed-loop design, the approach addresses fundamental challenges of reproducibility and generalisability in brain research. These conditions are particularly acute when studying the developing brain, where our theories based on adult brain function may fundamentally misrepresent the topography of infant cognition and where there are substantial practical challenges to data acquisition. Our methodology employs machine learning to map modulation of a neural feature across a space of experimental stimuli. Our method collects, processes and analyses EEG brain data in real-time; and uses a neuro-adaptive Bayesian optimisation algorithm to adjust the stimulus presented depending on the prior samples of a given participant. Unsampled stimuli can be interpolated by fitting a Gaussian process regression along the dataset. We show that our method can automatically identify the face of the infant's mother through online recording of their Nc brain response to a face continuum. We can retrieve model statistics of individualised responses for each participant, opening the door for early identification of atypical development. This approach has substantial potential in infancy research and beyond for improving power and generalisability of mapping the individual cognitive topography of brain function. | ['Emily J. H. Jones', 'Robert Leech', 'Anna Gui', 'Luke Mason', 'Elena Throm', 'Rianne Haartsen', 'Pedro F. da Costa'] | 2021-06-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 8.18005860e-01 -2.60310024e-01 1.59904420e-01 -5.27947664e-01
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89fa43d1-3491-4cc6-bc67-933dadd9e751 | spin-nerf-multiview-segmentation-and | 2211.12254 | null | https://arxiv.org/abs/2211.12254v2 | https://arxiv.org/pdf/2211.12254v2.pdf | SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields | Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3D inpainting. In 3D, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3D inpainting method that addresses these challenges. Given a small set of posed images and sparse annotations in a single input image, our framework first rapidly obtains a 3D segmentation mask for a target object. Using the mask, a perceptual optimizationbased approach is then introduced that leverages learned 2D image inpainters, distilling their information into 3D space, while ensuring view consistency. We also address the lack of a diverse benchmark for evaluating 3D scene inpainting methods by introducing a dataset comprised of challenging real-world scenes. In particular, our dataset contains views of the same scene with and without a target object, enabling more principled benchmarking of the 3D inpainting task. We first demonstrate the superiority of our approach on multiview segmentation, comparing to NeRFbased methods and 2D segmentation approaches. We then evaluate on the task of 3D inpainting, establishing state-ofthe-art performance against other NeRF manipulation algorithms, as well as a strong 2D image inpainter baseline. Project Page: https://spinnerf3d.github.io | ['Alex Levinshtein', 'Igor Gilitschenski', 'Marcus A. Brubaker', 'Jonathan Kelly', 'Konstantinos G. Derpanis', 'Tristan Aumentado-Armstrong', 'Ashkan Mirzaei'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-instance-segmentation-1', '3d-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.76229668e-01 1.59098804e-02 -1.82322562e-02 -2.93332219e-01
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95e8a31a-9207-443d-b424-496f949069f6 | relation-modeling-in-spatio-temporal-action | 2106.08061 | null | https://arxiv.org/abs/2106.08061v2 | https://arxiv.org/pdf/2106.08061v2.pdf | Relation Modeling in Spatio-Temporal Action Localization | This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of relation modeling methods for spatio-temporal action detection and adopts a training strategy to integrate multiple relation modeling in end-to-end training over the two large-scale video datasets. Learning with memory bank and finetuning for long-tailed distribution are also investigated to further improve the performance. In this paper, we detail the implementations of our solution and provide experiments results and corresponding discussions. We finally achieve 40.67 mAP on the test set of AVA-Kinetics. | ['Yue Gao', 'Mingqian Tang', 'Shiwei Zhang', 'Xiang Wang', 'Zhiwu Qing', 'Ziyuan Huang', 'Jianwen Jiang', 'Yutong Feng'] | 2021-06-15 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 9.38558206e-02 -3.64620119e-01 -6.34822071e-01 -2.61597455e-01
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7d8d38e2-db60-4462-b50b-be9a6783587b | neural-label-search-for-zero-shot-multi | 2204.13512 | null | https://arxiv.org/abs/2204.13512v2 | https://arxiv.org/pdf/2204.13512v2.pdf | Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization | In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages. Given English gold summaries and documents, sentence-level labels for extractive summarization are usually generated using heuristics. However, these monolingual labels created on English datasets may not be optimal on datasets of other languages, for that there is the syntactic or semantic discrepancy between different languages. In this way, it is possible to translate the English dataset to other languages and obtain different sets of labels again using heuristics. To fully leverage the information of these different sets of labels, we propose NLSSum (Neural Label Search for Summarization), which jointly learns hierarchical weights for these different sets of labels together with our summarization model. We conduct multilingual zero-shot summarization experiments on MLSUM and WikiLingua datasets, and we achieve state-of-the-art results using both human and automatic evaluations across these two datasets. | ['Furu Wei', 'Zheng Lin', 'Shi Wang', 'Yanan Cao', 'Xingxing Zhang', 'Ruipeng Jia'] | 2022-04-28 | null | https://aclanthology.org/2022.acl-long.42 | https://aclanthology.org/2022.acl-long.42.pdf | acl-2022-5 | ['extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.17711890e-01 3.83699447e-01 -4.97234702e-01 -2.87391841e-01
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7481d0de-a3bb-4362-820e-4bd48ee38c37 | improving-named-entity-recognition-with | 2010.15466 | null | https://arxiv.org/abs/2010.15466v1 | https://arxiv.org/pdf/2010.15466v1.pdf | Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information | Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets. | ['Xiang Wan', 'Xiang Ao', 'Yan Song', 'Yuanhe Tian', 'Yuyang Nie'] | 2020-10-29 | null | https://aclanthology.org/2020.findings-emnlp.378 | https://aclanthology.org/2020.findings-emnlp.378.pdf | findings-of-the-association-for-computational | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.14031501e-01 -1.37986228e-01 -1.51902318e-01 -5.26798785e-01
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c3aae061-5404-41da-ad19-6153a9788b5a | defocus-blur-detection-via-multi-stream | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Defocus_Blur_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Defocus_Blur_Detection_CVPR_2018_paper.pdf | Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network | Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an image. This process has been paid considerable attention because of its remarkable potential applications. Accurate differentiation of homogeneous regions and detection of low-contrast focal regions, as well as suppression of background clutter, are challenges associated with DBD. To address these issues, we propose a multi-stream bottom-top-bottom fully convolutional network (BTBNet), which is the first attempt to develop an end-to-end deep network for DBD. First, we develop a fully convolutional BTBNet to integrate low-level cues and high-level semantic information. Then, considering that the degree of defocus blur is sensitive to scales, we propose multi-stream BTBNets that handle input images with different scales to improve the performance of DBD. Finally, we design a fusion and recursive reconstruction network to recursively refine the preceding blur detection maps. To promote further study and evaluation of the DBD models, we construct a new database of 500 challenging images and their pixel-wise defocus blur annotations. Experimental results on the existing and our new datasets demonstrate that the proposed method achieves significantly better performance than other state-of-the-art algorithms. | ['Wenda Zhao', 'Fan Zhao', 'Huchuan Lu', 'Dong Wang'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['defocus-blur-detection', 'defocus-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.79794565e-01 -7.63757885e-01 3.62077743e-01 -5.98756552e-01
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5d1099aa-a167-4441-a609-1453825bb5dc | dual-embodied-symbolic-concept | 2203.00600 | null | https://arxiv.org/abs/2203.00600v1 | https://arxiv.org/pdf/2203.00600v1.pdf | Dual Embodied-Symbolic Concept Representations for Deep Learning | Motivated by recent findings from cognitive neural science, we advocate the use of a dual-level model for concept representations: the embodied level consists of concept-oriented feature representations, and the symbolic level consists of concept graphs. Embodied concept representations are modality specific and exist in the form of feature vectors in a feature space. Symbolic concept representations, on the other hand, are amodal and language specific, and exist in the form of word / knowledge-graph embeddings in a concept / knowledge space. The human conceptual system comprises both embodied representations and symbolic representations, which typically interact to drive conceptual processing. As such, we further advocate the use of dual embodied-symbolic concept representations for deep learning. To demonstrate their usage and value, we discuss two important use cases: embodied-symbolic knowledge distillation for few-shot class incremental learning, and embodied-symbolic fused representation for image-text matching. Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss two important examples of such integration: scene graph generation with knowledge graph bridging, and multimodal knowledge graphs. | ['Daniel T. Chang'] | 2022-03-01 | null | null | null | null | ['scene-graph-generation', 'knowledge-graph-embeddings', 'few-shot-class-incremental-learning', 'knowledge-graph-embeddings'] | ['computer-vision', 'graphs', 'methodology', 'methodology'] | [ 3.19486171e-01 4.35817540e-01 -2.51996666e-01 -3.68983835e-01
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a9da8fe7-46f6-4c0f-bd6b-935f00dde8e9 | learning-to-forecast-and-refine-residual | 1807.09951 | null | http://arxiv.org/abs/1807.09951v1 | http://arxiv.org/pdf/1807.09951v1.pdf | Learning to Forecast and Refine Residual Motion for Image-to-Video Generation | We consider the problem of image-to-video translation, where an input image
is translated into an output video containing motions of a single object.
Recent methods for such problems typically train transformation networks to
generate future frames conditioned on the structure sequence. Parallel work has
shown that short high-quality motions can be generated by spatiotemporal
generative networks that leverage temporal knowledge from the training data. We
combine the benefits of both approaches and propose a two-stage generation
framework where videos are generated from structures and then refined by
temporal signals. To model motions more efficiently, we train networks to learn
residual motion between the current and future frames, which avoids learning
motion-irrelevant details. We conduct extensive experiments on two
image-to-video translation tasks: facial expression retargeting and human pose
forecasting. Superior results over the state-of-the-art methods on both tasks
demonstrate the effectiveness of our approach. | ['Dimitris Metaxas', 'Xi Peng', 'Yu Tian', 'Mubbasir Kapadia', 'Long Zhao'] | 2018-07-26 | learning-to-forecast-and-refine-residual-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.pdf | eccv-2018-9 | ['human-pose-forecasting', 'image-to-video'] | ['computer-vision', 'computer-vision'] | [ 6.73270047e-01 2.28940219e-01 -1.42105877e-01 -4.74241287e-01
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f772fdb7-943d-4737-9e1a-6257315cf6b2 | denmune-density-peak-based-clustering-using | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0031320320303927 | https://www.sciencedirect.com/science/article/abs/pii/S0031320320303927 | DenMune: Density peak based clustering using mutual nearest neighbors | Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm “DenMune” is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms. | ['Amin Shoukry', 'Adel El-Zoghabi', 'Mohamed Abbas'] | 2021-01-01 | null | null | null | pattern-recognition-2021-1 | ['unsupervised-mnist'] | ['methodology'] | [-4.55973953e-01 -4.28355336e-01 -9.69137475e-02 -9.79722068e-02
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7160c74e-27c5-48c1-a5a9-d177796e23e9 | the-use-of-ai-for-thermal-emotion-recognition | 2009.10589 | null | https://arxiv.org/abs/2009.10589v1 | https://arxiv.org/pdf/2009.10589v1.pdf | The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data | With the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has been ongoing since the late nineties. This research lies at the intersections of medicine, psychology, machine learning, optics, and affective computing. We will review the known factors of thermal vs. RGB imaging for facial emotion recognition. But we also propose that thermal imagery may provide a semi-anonymous modality for computer vision, over RGB, which has been plagued by misuse in facial recognition. However, the transition to adopting thermal imagery as a source for any human-centered AI task is not easy and relies on the availability of high fidelity data sources across multiple demographics and thorough validation. This paper takes the reader on a short review of machine learning in thermal FER and the limitations of collecting and developing thermal FER data for AI training. Our motivation is to provide an introductory overview into recent advances for thermal FER and stimulate conversation about the limitations in current datasets. | ['Sanjay Purushotham', 'Edward Raff', 'Catherine Ordun'] | 2020-09-22 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 6.00876153e-01 -2.12576360e-01 -2.92701591e-02 -6.15819633e-01
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b2e3aba6-fa80-4b20-9fc4-371a7a8a6317 | efficient-constituency-parsing-by-pointing-1 | 2006.13557 | null | https://arxiv.org/abs/2006.13557v1 | https://arxiv.org/pdf/2006.13557v1.pdf | Efficient Constituency Parsing by Pointing | We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-the-art while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing. | ['Xiao-Li Li', 'Xuan-Phi Nguyen', 'Thanh-Tung Nguyen', 'Shafiq Joty'] | 2020-06-24 | efficient-constituency-parsing-by-pointing | https://aclanthology.org/2020.acl-main.301 | https://aclanthology.org/2020.acl-main.301.pdf | acl-2020-6 | ['constituency-parsing'] | ['natural-language-processing'] | [-6.81200251e-03 6.90306723e-01 -3.92213941e-01 -6.76737249e-01
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8bfe260a-6ac4-47b0-927a-b4895d1d532f | scene-text-recognition-with-single-point | 2209.01914 | null | https://arxiv.org/abs/2209.01914v1 | https://arxiv.org/pdf/2209.01914v1.pdf | Scene Text Recognition with Single-Point Decoding Network | In recent years, attention-based scene text recognition methods have been very popular and attracted the interest of many researchers. Attention-based methods can adaptively focus attention on a small area or even single point during decoding, in which the attention matrix is nearly one-hot distribution. Furthermore, the whole feature maps will be weighted and summed by all attention matrices during inference, causing huge redundant computations. In this paper, we propose an efficient attention-free Single-Point Decoding Network (dubbed SPDN) for scene text recognition, which can replace the traditional attention-based decoding network. Specifically, we propose Single-Point Sampling Module (SPSM) to efficiently sample one key point on the feature map for decoding one character. In this way, our method can not only precisely locate the key point of each character but also remove redundant computations. Based on SPSM, we design an efficient and novel single-point decoding network to replace the attention-based decoding network. Extensive experiments on publicly available benchmarks verify that our SPDN can greatly improve decoding efficiency without sacrificing performance. | ['XuCheng Yin', 'Chun Yang', 'Shi-Xue Zhang', 'Haibo Qin', 'Lei Chen'] | 2022-09-05 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.02394828e-01 -5.36879182e-01 -9.48240682e-02 -2.97811180e-01
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6.54887334e-02 -4.49040979e-01 -1.51167154e-01 -3.52681160e-01] | [11.914837837219238, 2.1841330528259277] |
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