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b84d3ca2-23e1-4547-a5ff-bed54621aef9 | keyword-spotting-system-and-evaluation-of | 2208.02765 | null | https://arxiv.org/abs/2208.02765v1 | https://arxiv.org/pdf/2208.02765v1.pdf | Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers | Keyword spotting (KWS) is beneficial for voice-based user interactions with low-power devices at the edge. The edge devices are usually always-on, so edge computing brings bandwidth savings and privacy protection. The devices typically have limited memory spaces, computational performances, power and costs, for example, Cortex-M based microcontrollers. The challenge is to meet the high computation and low-latency requirements of deep learning on these devices. This paper firstly shows our small-footprint KWS system running on STM32F7 microcontroller with Cortex-M7 core @216MHz and 512KB static RAM. Our selected convolutional neural network (CNN) architecture has simplified number of operations for KWS to meet the constraint of edge devices. Our baseline system generates classification results for each 37ms including real-time audio feature extraction part. This paper further evaluates the actual performance for different pruning and quantization methods on microcontroller, including different granularity of sparsity, skipping zero weights, weight-prioritized loop order, and SIMD instruction. The result shows that for microcontrollers, there are considerable challenges for accelerate unstructured pruned models, and the structured pruning is more friendly than unstructured pruning. The result also verified that the performance improvement for quantization and SIMD instruction. | ['Shengchen Li', 'Jingyi Wang'] | 2022-08-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-7.45569393e-02 1.10165209e-01 -3.96399081e-01 -3.89290363e-01
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787bb7d6-50fe-4f16-a497-aa16f542af51 | modeling-motion-with-multi-modal-features-for | 2204.02547 | null | https://arxiv.org/abs/2204.02547v1 | https://arxiv.org/pdf/2204.02547v1.pdf | Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation | Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance and linguistic modalities is crucial yet has been largely ignored by previous work. In this paper, we design a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation. Specifically, we propose a multi-modal video transformer, which can fuse and aggregate multi-modal and temporal features between frames. Furthermore, we design a language-guided feature fusion module to progressively fuse appearance and motion features in each feature level with guidance from linguistic features. Finally, a multi-modal alignment loss is proposed to alleviate the semantic gap between features from different modalities. Extensive experiments on A2D Sentences and J-HMDB Sentences verify the performance and the generalization ability of our method compared to the state-of-the-art methods. | ['Yang You', 'Xinchao Wang', 'Fuzhao Xue', 'Xiangxiang Chu', 'Kai Wang', 'Wangbo Zhao'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['referring-expression-segmentation'] | ['computer-vision'] | [-6.01384789e-02 -4.34386522e-01 -2.23116934e-01 -5.90464532e-01
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21ea277d-44d5-4798-8192-f75ff7256e63 | hive-harnessing-human-feedback-for | 2303.09618 | null | https://arxiv.org/abs/2303.09618v1 | https://arxiv.org/pdf/2303.09618v1.pdf | HIVE: Harnessing Human Feedback for Instructional Visual Editing | Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere to the correct instructions and preferences of users. In this paper, we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically, we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides, to mitigate the bias brought by the limitation of data, we contribute a new 1M training dataset, a 3.6K reward dataset for rewards learning, and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively, showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin. | ['ran Xu', 'Caiming Xiong', 'Stefano Ermon', 'Silvio Savarese', 'Huan Wang', 'Zeyuan Chen', 'Ning Yu', 'Chia-Chih Chen', 'Can Qin', 'Yihao Feng', 'Xinyi Yang', 'Shu Zhang'] | 2023-03-16 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 3.17972422e-01 1.28622681e-01 -3.67159963e-01 -5.78750491e-01
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3f15a5b2-52b7-40ce-bbca-5d90a4573813 | learning-single-view-3d-reconstruction-with | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.pdf | Learning Single-View 3D Reconstruction with Limited Pose Supervision | It is expensive to label images with 3D structure or precise camera pose. Yet, this is precisely the kind of annotation required to train single-view 3D reconstruction models. In contrast, unlabeled images or images with just category labels are easy to acquire, but few current models can use this weak supervision. We present a unified framework that can combine both types of supervision: a small amount of camera pose annotations are used to enforce pose-invariance and view-point consistency, and unlabeled images combined with an adversarial loss are used to enforce the realism of rendered, generated models. We use this unified framework to measure the impact of each form of supervision in three paradigms: semi-supervised, multi-task, and transfer learning. We show that with a combination of these ideas, we can train single-view reconstruction models that improve up to 7 points in performance (AP) when using only 1% pose annotated training data. | ['Bharath Hariharan', 'Guandao Yang', 'Serge Belongie', 'Yin Cui'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 2.17437580e-01 2.09809259e-01 -1.49969041e-01 -4.79117155e-01
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5bdc1b7c-2608-4ad9-b1f5-88d416d0a74b | diagnosis-and-prognosis-of-head-and-neck | 2306.00034 | null | https://arxiv.org/abs/2306.00034v1 | https://arxiv.org/pdf/2306.00034v1.pdf | Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence | Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and they combine that information with clinical data for patient prognosis. The process is mostly challenging and time-consuming. Machine learning and deep learning can automate these tasks to help clinicians with highly promising results. This work studies two approaches for H&N tumor segmentation: (i) exploration and comparison of vision transformer (ViT)-based and convolutional neural network-based models; and (ii) proposal of a novel 2D perspective to working with 3D data. Furthermore, this work proposes two new architectures for the prognosis task. An ensemble of several models predicts patient outcomes (which won the HECKTOR 2021 challenge prognosis task), and a ViT-based framework concurrently performs patient outcome prediction and tumor segmentation, which outperforms the ensemble model. | ['Ikboljon Sobirov'] | 2023-05-31 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-6.20462447e-02 3.44261587e-01 -6.98902488e-01 -3.07242155e-01
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ecabaa1b-e746-45a5-823d-800f04a17244 | mixtape-breaking-the-softmax-bottleneck | null | null | http://papers.nips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently | http://papers.nips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf | Mixtape: Breaking the Softmax Bottleneck Efficiently | The softmax bottleneck has been shown to limit the expressiveness of neural language models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques---logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar or better performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax by a large margin in perplexity and translation quality. Notably, Mixtape achieves state-of-the-art results of 29.8 BLEU on WMT'14 English-German and 43.9 BLEU on WMT'14 English-French. | ['Russ R. Salakhutdinov', 'Quoc V. Le', 'Thang Luong', 'Zhilin Yang'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['tree-decomposition'] | ['graphs'] | [ 6.10901862e-02 1.14654541e-01 -7.30675697e-01 -4.06701028e-01
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a71626a6-91de-4ec9-bee9-3b0335ffbf1e | counterfactual-prediction-under-outcome | 2302.11121 | null | https://arxiv.org/abs/2302.11121v2 | https://arxiv.org/pdf/2302.11121v2.pdf | Counterfactual Prediction Under Outcome Measurement Error | Across domains such as medicine, employment, and criminal justice, predictive models often target labels that imperfectly reflect the outcomes of interest to experts and policymakers. For example, clinical risk assessments deployed to inform physician decision-making often predict measures of healthcare utilization (e.g., costs, hospitalization) as a proxy for patient medical need. These proxies can be subject to outcome measurement error when they systematically differ from the target outcome they are intended to measure. However, prior modeling efforts to characterize and mitigate outcome measurement error overlook the fact that the decision being informed by a model often serves as a risk-mitigating intervention that impacts the target outcome of interest and its recorded proxy. Thus, in these settings, addressing measurement error requires counterfactual modeling of treatment effects on outcomes. In this work, we study intersectional threats to model reliability introduced by outcome measurement error, treatment effects, and selection bias from historical decision-making policies. We develop an unbiased risk minimization method which, given knowledge of proxy measurement error properties, corrects for the combined effects of these challenges. We also develop a method for estimating treatment-dependent measurement error parameters when these are unknown in advance. We demonstrate the utility of our approach theoretically and via experiments on real-world data from randomized controlled trials conducted in healthcare and employment domains. As importantly, we demonstrate that models correcting for outcome measurement error or treatment effects alone suffer from considerable reliability limitations. Our work underscores the importance of considering intersectional threats to model validity during the design and evaluation of predictive models for decision support. | ['Zhiwei Steven Wu', 'Kenneth Holstein', 'Amanda Coston', 'Luke Guerdan'] | 2023-02-22 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 6.28390312e-01 2.03042939e-01 -1.10363364e+00 -4.64686185e-01
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73b0da22-830a-41a9-afb8-71c45c2f237a | faceformer-scale-aware-blind-face-restoration | 2207.0979 | null | https://arxiv.org/abs/2207.09790v1 | https://arxiv.org/pdf/2207.09790v1.pdf | FaceFormer: Scale-aware Blind Face Restoration with Transformers | Blind face restoration usually encounters with diverse scale face inputs, especially in the real world. However, most of the current works support specific scale faces, which limits its application ability in real-world scenarios. In this work, we propose a novel scale-aware blind face restoration framework, named FaceFormer, which formulates facial feature restoration as scale-aware transformation. The proposed Facial Feature Up-sampling (FFUP) module dynamically generates upsampling filters based on the original scale-factor priors, which facilitate our network to adapt to arbitrary face scales. Moreover, we further propose the facial feature embedding (FFE) module which leverages transformer to hierarchically extract diversity and robustness of facial latent. Thus, our FaceFormer achieves fidelity and robustness restored faces, which possess realistic and symmetrical details of facial components. Extensive experiments demonstrate that our proposed method trained with synthetic dataset generalizes better to a natural low quality images than current state-of-the-arts. | ['Xintao Wang', 'Lei Sun', 'Gen Li', 'Aijin Li'] | 2022-07-20 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 1.20526813e-01 -2.81655341e-01 9.32801142e-02 -4.48718876e-01
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71bb1312-e084-47c1-bd49-f560d3b0f0d7 | large-scale-lexical-analysis | null | null | https://aclanthology.org/L12-1268 | https://aclanthology.org/L12-1268.pdf | Large Scale Lexical Analysis | The following paper presents a lexical analysis component as implemented in the PANACEA project. The goal is to automatically extract lexicon entries from crawled corpora, in an attempt to use corpus-based methods for high-quality linguistic text processing, and to focus on the quality of data without neglecting quantitative aspects. Lexical analysis has the task to assign linguistic information (like: part of speech, inflectional class, gender, subcategorisation frame, semantic properties etc.) to all parts of the input text. If tokens are ambiguous, lexical analysis must provide all possible sets of annotation for later (syntactic) disambiguation, be it tagging, or full parsing. The paper presents an approach for assigning part-of-speech tags for German and English to large input corpora ({\textgreater} 50 mio tokens), providing a workflow which takes as input crawled corpora and provides POS-tagged lemmata ready for lexicon integration. Tools include sentence splitting, lexicon lookup, decomposition, and POS defaulting. Evaluation shows that the overall error rate can be brought down to about 2{\%} if language resources are properly designed. The complete workflow is implemented as a sequence of web services integrated into the PANACEA platform. | ["Vera Aleksi{\\'c}", 'Christoph Schwarz', 'Gregor Thurmair'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-4.81907576e-02 3.13657075e-01 2.82364674e-02 -4.14441019e-01
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e047729a-a44c-4e68-aacd-610d6a9ed436 | attention-based-transformers-for-instance | 2011.09763 | null | https://arxiv.org/abs/2011.09763v2 | https://arxiv.org/pdf/2011.09763v2.pdf | Attention-Based Transformers for Instance Segmentation of Cells in Microstructures | Detecting and segmenting object instances is a common task in biomedical applications. Examples range from detecting lesions on functional magnetic resonance images, to the detection of tumours in histopathological images and extracting quantitative single-cell information from microscopy imagery, where cell segmentation is a major bottleneck. Attention-based transformers are state-of-the-art in a range of deep learning fields. They have recently been proposed for segmentation tasks where they are beginning to outperforming other methods. We present a novel attention-based cell detection transformer (Cell-DETR) for direct end-to-end instance segmentation. While the segmentation performance is on par with a state-of-the-art instance segmentation method, Cell-DETR is simpler and faster. We showcase the method's contribution in a the typical use case of segmenting yeast in microstructured environments, commonly employed in systems or synthetic biology. For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances. The fast and accurate instance segmentation performance increases the experimental information yield for a posteriori data processing and makes online monitoring of experiments and closed-loop optimal experimental design feasible. | ['Heinz Koeppl', 'Christoph Reich', 'Tim Prangemeier'] | 2020-11-19 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 5.39805055e-01 3.22136283e-01 1.11266887e-02 -9.91652086e-02
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db5eadf8-e354-4a54-99e7-a72aade8540c | green-steganalyzer-a-green-learning-approach | 2306.04008 | null | https://arxiv.org/abs/2306.04008v1 | https://arxiv.org/pdf/2306.04008v1.pdf | Green Steganalyzer: A Green Learning Approach to Image Steganalysis | A novel learning solution to image steganalysis based on the green learning paradigm, called Green Steganalyzer (GS), is proposed in this work. GS consists of three modules: 1) pixel-based anomaly prediction, 2) embedding location detection, and 3) decision fusion for image-level detection. In the first module, GS decomposes an image into patches, adopts Saab transforms for feature extraction, and conducts self-supervised learning to predict an anomaly score of their center pixel. In the second module, GS analyzes the anomaly scores of a pixel and its neighborhood to find pixels of higher embedding probabilities. In the third module, GS focuses on pixels of higher embedding probabilities and fuses their anomaly scores to make final image-level classification. Compared with state-of-the-art deep-learning models, GS achieves comparable detection performance against S-UNIWARD, WOW and HILL steganography schemes with significantly lower computational complexity and a smaller model size, making it attractive for mobile/edge applications. Furthermore, GS is mathematically transparent because of its modular design. | ['C. -C. Jay Kuo', 'Ronald Salloum', 'Hong-Shuo Chen', 'Xinyu Wang', 'Yao Zhu'] | 2023-06-06 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 6.85133398e-01 -4.97404533e-03 -2.26056516e-01 2.78787225e-01
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704b7f41-cdb3-4e6f-b8b9-238f01796883 | self-supervised-video-representation-learning-8 | 2108.08426 | null | https://arxiv.org/abs/2108.08426v2 | https://arxiv.org/pdf/2108.08426v2.pdf | Self-Supervised Video Representation Learning with Meta-Contrastive Network | Self-supervised learning has been successfully applied to pre-train video representations, which aims at efficient adaptation from pre-training domain to downstream tasks. Existing approaches merely leverage contrastive loss to learn instance-level discrimination. However, lack of category information will lead to hard-positive problem that constrains the generalization ability of this kind of methods. We find that the multi-task process of meta learning can provide a solution to this problem. In this paper, we propose a Meta-Contrastive Network (MCN), which combines the contrastive learning and meta learning, to enhance the learning ability of existing self-supervised approaches. Our method contains two training stages based on model-agnostic meta learning (MAML), each of which consists of a contrastive branch and a meta branch. Extensive evaluations demonstrate the effectiveness of our method. For two downstream tasks, i.e., video action recognition and video retrieval, MCN outperforms state-of-the-art approaches on UCF101 and HMDB51 datasets. To be more specific, with R(2+1)D backbone, MCN achieves Top-1 accuracies of 84.8% and 54.5% for video action recognition, as well as 52.5% and 23.7% for video retrieval. | ['Yan Lu', 'Xun Guo', 'Yuanze Lin'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lin_Self-Supervised_Video_Representation_Learning_With_Meta-Contrastive_Network_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lin_Self-Supervised_Video_Representation_Learning_With_Meta-Contrastive_Network_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.58945721e-01 -3.89669925e-01 -7.23575890e-01 -4.04041260e-01
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9fc1a4ca-bd86-4d21-aefd-967e88bdf9c6 | inferring-class-label-distribution-of | 2211.04157 | null | https://arxiv.org/abs/2211.04157v1 | https://arxiv.org/pdf/2211.04157v1.pdf | Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack | Property inference attacks against machine learning (ML) models aim to infer properties of the training data that are unrelated to the primary task of the model, and have so far been formulated as binary decision problems, i.e., whether or not the training data have a certain property. However, in industrial and healthcare applications, the proportion of labels in the training data is quite often also considered sensitive information. In this paper we introduce a new type of property inference attack that unlike binary decision problems in literature, aim at inferring the class label distribution of the training data from parameters of ML classifier models. We propose a method based on \emph{shadow training} and a \emph{meta-classifier} trained on the parameters of the shadow classifiers augmented with the accuracy of the classifiers on auxiliary data. We evaluate the proposed approach for ML classifiers with fully connected neural network architectures. We find that the proposed \emph{meta-classifier} attack provides a maximum relative improvement of $52\%$ over state of the art. | ['György Dán', 'Raksha Ramakrishna'] | 2022-11-08 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 9.08369839e-01 5.68449736e-01 -4.32051122e-01 -3.88916224e-01
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875dc21f-839e-464e-b08f-a3b3850a245a | weakly-supervised-scientific-document | 2306.07193 | null | https://arxiv.org/abs/2306.07193v1 | https://arxiv.org/pdf/2306.07193v1.pdf | Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training | Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only. In scientific domains, label names often include domain-specific concepts that may not appear in the document corpus, making it difficult to match labels and documents precisely. To tackle this issue, we propose WANDER, which leverages dense retrieval to perform matching in the embedding space to capture the semantics of label names. We further design the label name expansion module to enrich the label name representations. Lastly, a self-training step is used to refine the predictions. The experiments on three datasets show that WANDER outperforms the best baseline by 11.9% on average. Our code will be published at https://github.com/ritaranx/wander. | ['Carl Yang', 'Joyce C. Ho', 'Yue Yu', 'ran Xu'] | 2023-06-12 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-6.25330349e-03 -3.17911923e-01 -4.76478577e-01 -6.29366457e-01
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0ca3c6dd-c622-4fd4-96f1-fa485a86b29b | multilingual-named-entity-recognition-and | null | null | https://aclanthology.org/2021.bsnlp-1.9 | https://aclanthology.org/2021.bsnlp-1.9.pdf | Multilingual Named Entity Recognition and Matching Using BERT and Dedupe for Slavic Languages | This paper describes the University of Ljubljana (UL FRI) Group’s submissions to the shared task at the Balto-Slavic Natural Language Processing (BSNLP) 2021 Workshop. We experiment with multiple BERT-based models, pre-trained in multi-lingual, Croatian-Slovene-English and Slovene-only data. We perform training iteratively and on the concatenated data of previously available NER datasets. For the normalization task we use Stanza lemmatizer, while for entity matching we implemented a baseline using the Dedupe library. The performance of evaluations suggests that multi-source settings outperform less-resourced approaches. The best NER models achieve 0.91 F-score on Slovene training data splits while the best official submission achieved F-scores of 0.84 and 0.78 for relaxed partial matching and strict settings, respectively. In multi-lingual NER setting we achieve F-scores of 0.82 and 0.74. | ['Slavko Zitnik', 'Marko Prelevikj'] | null | null | null | null | eacl-bsnlp-2021-4 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-5.22152483e-01 4.06093188e-02 -4.92079966e-02 -3.77059489e-01
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c0bc88e5-a366-4202-a491-b70cd47e6f7c | pneumonia-detection-in-chest-x-rays-using | 2204.03618 | null | https://arxiv.org/abs/2204.03618v1 | https://arxiv.org/pdf/2204.03618v1.pdf | Pneumonia Detection in Chest X-Rays using Neural Networks | With the advancement in AI, deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The proposed method is based on a non-complex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The RSNA benchmark MAP score is 0.25, but using the Mask RCNN model on a stratified sample of 3017 along with image augmentation gave a MAP score of 0.15. Meanwhile, the YoloV3 without any hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps decreasing. Running the model for a greater number of iterations can give better results. | ['Anwesh Reddy Paduri', 'Vivek Kumar', 'Ketul Kumar', 'Devendra Trivedi', 'Dany Bright', 'Ashish Ranjan', 'Narayana Darapaneni'] | 2022-04-07 | null | null | null | null | ['image-augmentation', 'pneumonia-detection'] | ['computer-vision', 'medical'] | [-7.22930161e-03 -7.98680335e-02 -1.35218233e-01 -1.18180424e-01
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3cb57c75-54c9-4de8-b2dd-e1fd88b0f245 | what-can-we-learn-by-predicting-accuracy | 2208.01358 | null | https://arxiv.org/abs/2208.01358v2 | https://arxiv.org/pdf/2208.01358v2.pdf | What can we Learn by Predicting Accuracy? | This paper seeks to answer the following question: \textit{"What can we learn by predicting accuracy?"}. Indeed, classification is one of the most popular tasks in machine learning, and many loss functions have been developed to maximize this non-differentiable objective function. Unlike past work on loss function design, which was guided mainly by intuition and theory before being validated by experimentation, here we propose to approach this problem in the opposite way: we seek to extract knowledge by experimentation. This data-driven approach is similar to that used in physics to discover general laws from data. We used a symbolic regression method to automatically find a mathematical expression highly correlated with a linear classifier's accuracy. The formula discovered on more than 260 datasets of embeddings has a Pearson's correlation of 0.96 and a $r^2$ of 0.93. More interestingly, this formula is highly explainable and confirms insights from various previous papers on loss design. We hope this work will open new perspectives in the search for new heuristics leading to a deeper understanding of machine learning theory. | ['Olivier Risser-Maroix', 'Benjamin Chamand'] | 2022-08-02 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.92146523e-02 3.21749121e-01 -3.93097728e-01 -6.74647033e-01
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df16e5ff-d7fd-49b4-bff4-993d813f92c3 | memsum-extractive-summarization-of-long | 2107.08929 | null | https://arxiv.org/abs/2107.08929v2 | https://arxiv.org/pdf/2107.08929v2.pdf | MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes | We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When MemSum iteratively selects sentences into the summary, it considers a broad information set that would intuitively also be used by humans in this task: 1) the text content of the sentence, 2) the global text context of the rest of the document, and 3) the extraction history consisting of the set of sentences that have already been extracted. With a lightweight architecture, MemSum obtains state-of-the-art test-set performance (ROUGE) in summarizing long documents taken from PubMed, arXiv, and GovReport. Ablation studies demonstrate the importance of local, global, and history information. A human evaluation confirms the high quality and low redundancy of the generated summaries, stemming from MemSum's awareness of extraction history. | ['Richard H. R. Hahnloser', 'Elliott Ash', 'Nianlong Gu'] | 2021-07-19 | null | https://aclanthology.org/2022.acl-long.450 | https://aclanthology.org/2022.acl-long.450.pdf | acl-2022-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 1.40868947e-01 3.22214812e-01 -4.79461014e-01 -3.29970047e-02
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d5760d0e-9b37-4131-8276-7213bddcecc4 | v2meow-meowing-to-the-visual-beat-via-music | 2305.06594 | null | https://arxiv.org/abs/2305.06594v1 | https://arxiv.org/pdf/2305.06594v1.pdf | V2Meow: Meowing to the Visual Beat via Music Generation | Generating high quality music that complements the visual content of a video is a challenging task. Most existing visual conditioned music generation systems generate symbolic music data, such as MIDI files, instead of raw audio waveform. Given the limited availability of symbolic music data, such methods can only generate music for a few instruments or for specific types of visual input. In this paper, we propose a novel approach called V2Meow that can generate high-quality music audio that aligns well with the visual semantics of a diverse range of video input types. Specifically, the proposed music generation system is a multi-stage autoregressive model which is trained with a number of O(100K) music audio clips paired with video frames, which are mined from in-the-wild music videos, and no parallel symbolic music data is involved. V2Meow is able to synthesize high-fidelity music audio waveform solely conditioned on pre-trained visual features extracted from an arbitrary silent video clip, and it also allows high-level control over the music style of generation examples via supporting text prompts in addition to the video frames conditioning. Through both qualitative and quantitative evaluations, we demonstrate that our model outperforms several existing music generation systems in terms of both visual-audio correspondence and audio quality. | ['Timo I. Denk', 'Mauro Verzetti', 'Yu Wang', 'Aren Jansen', 'Fei Sha', 'Chris Donahue', 'Joonseok Lee', 'Dima Kuzmin', 'Qingqing Huang', 'Judith Yue Li', 'Kun Su'] | 2023-05-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.93889666e-01 -3.88422579e-01 -3.55254300e-02 1.10248245e-01
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3c9916bc-2c68-4a37-b767-2b8e709654ff | self-dictionary-sparse-regression-for | 1409.432 | null | http://arxiv.org/abs/1409.4320v2 | http://arxiv.org/pdf/1409.4320v2.pdf | Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related | This paper considers a recently emerged hyperspectral unmixing formulation
based on sparse regression of a self-dictionary multiple measurement vector
(SD-MMV) model, wherein the measured hyperspectral pixels are used as the
dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is
special in that it allows simultaneous identification of the endmember spectral
signatures and the number of endmembers. Previous SD-MMV studies mainly focus
on convex relaxations. In this study, we explore the alternative of greedy
pursuit, which generally provides efficient and simple algorithms. In
particular, we design a greedy SD-MMV algorithm using simultaneous orthogonal
matching pursuit. Intriguingly, the proposed greedy algorithm is shown to be
closely related to some existing pure pixel search algorithms, especially, the
successive projection algorithm (SPA). Thus, a link between SD-MMV and pure
pixel search is revealed. We then perform exact recovery analyses, and prove
that the proposed greedy algorithm is robust to noise---including its
identification of the (unknown) number of endmembers---under a sufficiently low
noise level. The identification performance of the proposed greedy algorithm is
demonstrated through both synthetic and real-data experiments. | ['José M. Bioucas-Dias', 'Tsung-Han Chan', 'Wing-Kin Ma', 'Xiao Fu'] | 2014-09-15 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.77461052e-01 -4.44694668e-01 -1.02645040e-01 1.43867865e-01
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6cc241ea-1b7a-4a40-b4e0-09c3bd735b71 | tackling-face-verification-edge-cases-in | 2304.08134 | null | https://arxiv.org/abs/2304.08134v2 | https://arxiv.org/pdf/2304.08134v2.pdf | Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach | Nowadays, face recognition systems surpass human performance on several datasets. However, there are still edge cases that the machine can't correctly classify. This paper investigates the effect of a combination of machine and human operators in the face verification task. First, we look closer at the edge cases for several state-of-the-art models to discover common datasets' challenging settings. Then, we conduct a study with 60 participants on these selected tasks with humans and provide an extensive analysis. Finally, we demonstrate that combining machine and human decisions can further improve the performance of state-of-the-art face verification systems on various benchmark datasets. Code and data are publicly available on GitHub. | ['Gerhard Rigoll', 'Martin Knoche'] | 2023-04-17 | null | null | null | null | ['face-recognition', 'face-verification'] | ['computer-vision', 'computer-vision'] | [-9.77939293e-02 -2.31998146e-01 -2.62879461e-01 -8.52100611e-01
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7a0c2f2b-76fc-4e2f-bbca-4a3a0c39a9e8 | votehmr-occlusion-aware-voting-network-for | 2110.08729 | null | https://arxiv.org/abs/2110.08729v1 | https://arxiv.org/pdf/2110.08729v1.pdf | VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds | 3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding. Previous works in this field either require high-quality 3D human scans or sequential point clouds, which cannot be easily applied to low-quality 3D scans captured by consumer-level depth sensors. In this paper, we make the first attempt to reconstruct reliable 3D human shapes from single-frame partial point clouds.To achieve this, we propose an end-to-end learnable method, named VoteHMR. The core of VoteHMR is a novel occlusion-aware voting network that can first reliably produce visible joint-level features from the input partial point clouds, and then complete the joint-level features through the kinematic tree of the human skeleton. Compared with holistic features used by previous works, the joint-level features can not only effectively encode the human geometry information but also be robust to noisy inputs with self-occlusions and missing areas. By exploiting the rich complementary clues from the joint-level features and global features from the input point clouds, the proposed method encourages reliable and disentangled parameter predictions for statistical 3D human models, such as SMPL. The proposed method achieves state-of-the-art performances on two large-scale datasets, namely SURREAL and DFAUST. Furthermore, VoteHMR also demonstrates superior generalization ability on real-world datasets, such as Berkeley MHAD. | ['Lu Sheng', 'Yu Rong', 'Guanze Liu'] | 2021-10-17 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-1.33665189e-01 1.39777690e-01 -1.68445393e-01 -4.61083114e-01
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70a11e00-12e4-4f7f-8b77-ca494b64b1d2 | a-novel-privacy-preserving-deep-learning | 1908.07701 | null | https://arxiv.org/abs/1908.07701v2 | https://arxiv.org/pdf/1908.07701v2.pdf | A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component | Recently, deep learning, which uses Deep Neural Networks (DNN), plays an important role in many fields. A secure neural network model with a secure training/inference scheme is indispensable to many applications. To accomplish such a task usually needs one of the entities (the customer or the service provider) to provide private information (customer's data or the model) to the other. Without a secure scheme and the mutual trust between the service providers and their customers, it will be an impossible mission. In this paper, we propose a novel privacy-preserving deep learning model and a secure training/inference scheme to protect the input, the output, and the model in the application of the neural network. We utilize the innate properties of a deep neural network to design a secure mechanism without using any complicated cryptography component. The security analysis shows our proposed scheme is secure and the experimental results also demonstrate that our method is very efficient and suitable for real applications. | ['Allen C. -H. Wu', 'Chin-Yu Sun', 'TingTing Hwang'] | 2019-08-21 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.47054568e-01 -1.41479731e-01 8.82165283e-02 -5.72632611e-01
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bea675dc-398e-4324-9989-c66b9c09362e | implicit-temporal-modeling-with-learnable | 2304.10465 | null | https://arxiv.org/abs/2304.10465v1 | https://arxiv.org/pdf/2304.10465v1.pdf | Implicit Temporal Modeling with Learnable Alignment for Video Recognition | Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While modeling temporal information within straight through tube is widely adopted in literature, we find that simple frame alignment already provides enough essence without temporal attention. To this end, in this paper, we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes the temporal modeling effort while achieving incredibly high performance. Specifically, for a frame pair, an interactive point is predicted in each frame, serving as a mutual information rich region. By enhancing the features around the interactive point, two frames are implicitly aligned. The aligned features are then pooled into a single token, which is leveraged in the subsequent spatial self-attention. Our method allows eliminating the costly or insufficient temporal self-attention in video. Extensive experiments on benchmarks demonstrate the superiority and generality of our module. Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is released at https://github.com/Francis-Rings/ILA . | ['Yu-Gang Jiang', 'Han Hu', 'Zhi-Qi Cheng', 'Zuxuan Wu', 'Qi Dai', 'Shuyuan Tu'] | 2023-04-20 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-8.14791992e-02 -2.41135269e-01 -4.23036188e-01 -4.24282789e-01
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239b91dc-e8c8-4fe3-962b-ac3b023d793b | robust-online-multi-target-visual-tracking | 1908.03945 | null | https://arxiv.org/abs/1908.03945v6 | https://arxiv.org/pdf/1908.03945v6.pdf | Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning | We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. We also adopt deep CNN appearance representation by training a verification-identification network (VerIdNet) on large-scale person re-identification data sets. We construct an augmented likelihood in a principled manner using this deep CNN appearance features and spatio-temporal information. Furthermore, we solve the problem of two or more targets having identical label considering the weight propagated with each confirmed hypothesis. Extensive experiments on MOT16 and MOT17 benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy. | ['Nathanael L. Baisa'] | 2019-08-11 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-1.47521511e-01 -6.02144420e-01 7.26977140e-02 -1.51806951e-01
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ef2343bd-ec70-40bd-b4fb-255e25d55f87 | image-classification-of-stroke-blood-clot | 2305.16492 | null | https://arxiv.org/abs/2305.16492v1 | https://arxiv.org/pdf/2305.16492v1.pdf | Image Classification of Stroke Blood Clot Origin using Deep Convolutional Neural Networks and Visual Transformers | Stroke is one of two main causes of death worldwide. Many individuals suffer from ischemic stroke every year. Only in US more over 700,000 individuals meet ischemic stroke due to blood clot blocking an artery to the brain every year. The paper describes particular approach how to apply Artificial Intelligence for purposes of separating two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis. Four deep neural network architectures and simple ensemble method are used in the approach. | ['David Azatyan'] | 2023-05-25 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.75471231e-01 4.94769476e-02 -2.39446133e-01 -2.71087259e-01
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05d46fe1-5372-405f-8450-945f64544c30 | style-based-variational-autoencoder-for-real | 1912.10227 | null | https://arxiv.org/abs/1912.10227v2 | https://arxiv.org/pdf/1912.10227v2.pdf | Exploiting Style and Attention in Real-World Super-Resolution | Real-world image super-resolution (SR) is a challenging image translation problem. Low-resolution (LR) images are often generated by various unknown transformations rather than by applying simple bilinear down-sampling on high-resolution (HR) images. To address this issue, this paper proposes a novel pipeline which exploits style and attention mechanism in real-world SR. Our pipeline consists of a style Variational Autoencoder (styleVAE) and a SR network incorporated with attention mechanism. To get real-world-like low-quality images paired with the HR images, we design the styleVAE to transfer the complex nuisance factors in real-world LR images to the generated LR images. We also use mutual information estimation (MI) to get better style information. For our SR network, we firstly propose a global attention residual block to learn long-range dependencies in images. Then another local attention residual block is proposed to enforce the attention of SR network moving to local areas of images in which texture detail will be filled. It is worth noticing that styleVAE can be presented in a plug-and-play manner and thus can help to improve the generalization and robustness of our SR method as well as other SR methods. Extensive experiments demonstrate that our method surpasses the state-of-the-art work, both quantitatively and qualitatively. | ['Yi Li', 'Mandi Luo', 'Huaibo Huang', 'Xin Ma', 'Ran He'] | 2019-12-21 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.67961675e-01 1.19554978e-02 2.88745016e-01 -4.38618362e-01
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dc5d372c-39db-41a9-8a61-a356878b7969 | ditch-the-gold-standard-re-evaluating | null | null | https://openreview.net/forum?id=L-j8a3P22cy | https://openreview.net/pdf?id=L-j8a3P22cy | Ditch the Gold Standard: Re-evaluating Conversational Question Answering | Conversational question answering (CQA) systems aim to provide natural-language answers to users in information-seeking conversations. Existing benchmarks compare CQA models on pre-collected human-human conversations, with ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development, or current systems can well generalize to real-world human-machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art CQA systems, where human evaluators converse with models and judge the correctness of their answers. We find that the distribution of human-machine conversations drastically differs from that of human-human conversations, and evaluations using gold answers are inconsistent with human evaluations. We further investigate how to improve automatic evaluations and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments. Finally, we analyze the impact of various modeling strategies. We hope that our findings can shed light on how to develop better CQA systems in the future. | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['question-rewriting'] | ['natural-language-processing'] | [-1.25182435e-01 4.68524307e-01 2.00380668e-01 -5.47990918e-01
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8bd329a8-0772-4581-93e3-d3519dd19f1f | low-resource-accent-classification-in | 2206.12759 | null | https://arxiv.org/abs/2206.12759v2 | https://arxiv.org/pdf/2206.12759v2.pdf | Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective | Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based pretrained models have achieved superb performances in both areas. However, most accent classification tasks focused on classifying different kinds of English accents and little attention was paid to geographically-proximate accent classification, especially under a low-resource setting where forensic speech science tasks usually encounter. In this paper, we explored three main accent modelling methods combined with two different classifiers based on 105 speaker recordings retrieved from five urban varieties in Northern England. Although speech representations generated from pretrained models generally have better performances in downstream classification, traditional methods like Mel Frequency Cepstral Coefficients (MFCCs) and formant measurements are equipped with specific strengths. These results suggest that in forensic phonetics scenario where data are relatively scarce, a simple modelling method and classifier could be competitive with state-of-the-art pretrained speech models as feature extractors, which could enhance a sooner estimation for the accent information in practices. Besides, our findings also cross-validated a new methodology in quantifying sociophonetic changes. | ['Jie Yang', 'Peilin Zhou', 'Dading Chong', 'Qingcheng Zeng'] | 2022-06-26 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [-7.31210634e-02 -5.36489598e-02 5.52637428e-02 -5.75947165e-01
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24620454-3cbd-485f-a48b-6967bdf34e2c | annotating-character-relationships-in | 1512.00728 | null | http://arxiv.org/abs/1512.00728v1 | http://arxiv.org/pdf/1512.00728v1.pdf | Annotating Character Relationships in Literary Texts | We present a dataset of manually annotated relationships between characters
in literary texts, in order to support the training and evaluation of automatic
methods for relation type prediction in this domain (Makazhanov et al., 2014;
Kokkinakis, 2013) and the broader computational analysis of literary character
(Elson et al., 2010; Bamman et al., 2014; Vala et al., 2015; Flekova and
Gurevych, 2015). In this work, we solicit annotations from workers on Amazon
Mechanical Turk for 109 texts ranging from Homer's _Iliad_ to Joyce's _Ulysses_
on four dimensions of interest: for a given pair of characters, we collect
judgments as to the coarse-grained category (professional, social, familial),
fine-grained category (friend, lover, parent, rival, employer), and affinity
(positive, negative, neutral) that describes their primary relationship in a
text. We do not assume that this relationship is static; we also collect
judgments as to whether it changes at any point in the course of the text. | ['Philip Massey', 'Noah A. Smith', 'David Bamman', 'Patrick Xia'] | 2015-12-02 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-2.83666968e-01 3.21251214e-01 -2.60524005e-01 -4.51227218e-01
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b5c6be84-32e2-48b8-9d29-66bdd15ec6a9 | online-arbitrary-shaped-clustering-through | 2302.06335 | null | https://arxiv.org/abs/2302.06335v1 | https://arxiv.org/pdf/2302.06335v1.pdf | Online Arbitrary Shaped Clustering through Correlated Gaussian Functions | There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed. A novel online clustering algorithm is presented that can produce arbitrary shaped clusters from inputs in an unsupervised manner, and requires no prior knowledge of the number of clusters in the input data. This is achieved by finding correlated outputs from functions that capture commonly occurring input patterns. The algorithm can be deemed more biologically plausible than model optimization through backpropagation, although practical applicability may require additional research. However, the method yields satisfactory results on several toy datasets on a noteworthy range of hyperparameters. | ['Ole Christian Eidheim'] | 2023-02-13 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 2.22290173e-01 7.18200281e-02 -1.65445194e-01 -4.07389611e-01
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5783a566-f085-4bd8-b5a8-700615085d49 | approximate-sampling-and-estimation-of | 2209.10423 | null | https://arxiv.org/abs/2209.10423v1 | https://arxiv.org/pdf/2209.10423v1.pdf | Approximate sampling and estimation of partition functions using neural networks | We consider the closely related problems of sampling from a distribution known up to a normalizing constant, and estimating said normalizing constant. We show how variational autoencoders (VAEs) can be applied to this task. In their standard applications, VAEs are trained to fit data drawn from an intractable distribution. We invert the logic and train the VAE to fit a simple and tractable distribution, on the assumption of a complex and intractable latent distribution, specified up to normalization. This procedure constructs approximations without the use of training data or Markov chain Monte Carlo sampling. We illustrate our method on three examples: the Ising model, graph clustering, and ranking. | ['George T. Cantwell'] | 2022-09-21 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.28054425e-01 1.57825217e-01 -2.24845618e-01 -3.66202146e-01
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7cea2c0d-9f7c-4df1-a211-5718578b8730 | turku-semantic-dependency-parsing-as-a | null | null | https://aclanthology.org/S15-2161 | https://aclanthology.org/S15-2161.pdf | Turku: Semantic Dependency Parsing as a Sequence Classification | null | ['Juhani Luotolahti', 'Filip Ginter', 'Jenna Kanerva'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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57ea427d-2eb3-4856-a8d0-93c0669e44dc | applications-and-challenges-of-sentiment | 2301.09912 | null | https://arxiv.org/abs/2301.09912v1 | https://arxiv.org/pdf/2301.09912v1.pdf | Applications and Challenges of Sentiment Analysis in Real-life Scenarios | Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a driving force for research in SA. This chapter describes some of these applications and related challenges in real-life scenarios. In this chapter, we focus on five applications of SA: health, social policy, e-commerce, digital humanities and other areas of NLP. This chapter is intended to equip an NLP researcher with the `what', `why' and `how' of applications of SA: what is the application about, why it is important and challenging and how current research in SA deals with the application. We note that, while the use of deep learning techniques is a popular paradigm that spans these applications, challenges around privacy and selection bias of datasets is a recurring theme across several applications. | ['Aditya Joshi', 'Diptesh Kanojia'] | 2023-01-24 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 1.72841296e-01 -1.04867863e-02 -3.57726425e-01 -5.71191430e-01
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fc0a9fb1-3c8b-4ec0-8088-7fa1fec1ed1b | evaluating-large-language-models-with | 2306.12567 | null | https://arxiv.org/abs/2306.12567v1 | https://arxiv.org/pdf/2306.12567v1.pdf | Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases | This paper investigates whether current large language models exhibit biases in logical reasoning, similar to humans. Specifically, we focus on syllogistic reasoning, a well-studied form of inference in the cognitive science of human deduction. To facilitate our analysis, we introduce a dataset called NeuBAROCO, originally designed for psychological experiments that assess human logical abilities in syllogistic reasoning. The dataset consists of syllogistic inferences in both English and Japanese. We examine three types of biases observed in human syllogistic reasoning: belief biases, conversion errors, and atmosphere effects. Our findings demonstrate that current large language models struggle more with problems involving these three types of biases. | ['Mitsuhiro Okada', 'Koji Mineshima', 'Hirohiko Abe', 'Takanobu Morishita', 'Risako Ando'] | 2023-06-21 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-4.52098757e-01 4.56219971e-01 -1.36361405e-01 -3.57744902e-01
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0e3b0ccb-2fdd-46bf-ae50-0f6aa7481d7e | open-the-box-of-digital-neuromorphic | 2303.15224 | null | https://arxiv.org/abs/2303.15224v1 | https://arxiv.org/pdf/2303.15224v1.pdf | Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design | Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algorithms, it isn't easy to properly benchmark and optimize their computational cost without hardware in the loop. Although digital neuromorphic processors have been widely adopted to benchmark SNN algorithms, their black-box nature is problematic for algorithm-hardware co-optimization. In this work, we open the black box of the digital neuromorphic processor for algorithm designers by presenting the neuron processing instruction set and detailed energy consumption of the SENeCA neuromorphic architecture. For convenient benchmarking and optimization, we provide the energy cost of the essential neuromorphic components in SENeCA, including neuron models and learning rules. Moreover, we exploit the SENeCA's hierarchical memory and exhibit an advantage over existing neuromorphic processors. We show the energy efficiency of SNN algorithms for video processing and online learning, and demonstrate the potential of our work for optimizing algorithm designs. Overall, we present a practical approach to enable algorithm designers to accurately benchmark SNN algorithms and pave the way towards effective algorithm-hardware co-design. | ['Amirreza Yousefzadeh', 'Gert-Jan van Schaik', 'Manolis Sifalakis', 'Mario Konijnenburg', 'Stefano Traferro', 'Paul Detterer', 'Kevin Shidqi', 'Ali Safa', 'Guangzhi Tang'] | 2023-03-27 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 4.01633084e-01 -4.38206017e-01 1.82651579e-01 -3.88373621e-02
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e0afbe4d-787e-4d4d-9a04-b96b410b2fd1 | putting-figures-on-influences-on-moroccan | null | null | https://aclanthology.org/2018.gwc-1.46 | https://aclanthology.org/2018.gwc-1.46.pdf | Putting Figures on Influences on Moroccan Darija from Arabic, French and Spanish using the WordNet | Moroccan Darija is a variant of Arabic with many influences. Using the Open Multilingual WordNet (OMW), we compare the lemmas in the Moroccan Darija Wordnet (MDW) with the standard Arabic, French and Spanish ones. We then compared the lemmas in each synset with their translation equivalents. Transliteration is used to bridge alphabet differences and match lemmas in the closest phonological way. The results put figures on the similarity Moroccan Darija has with Arabic, French and Spanish: respectively 42.0%, 2.8% and 2.2%. | ['Francis Bond', 'Khalil Mrini'] | null | null | null | null | gwc-2018-1 | ['transliteration'] | ['natural-language-processing'] | [-5.64796805e-01 1.13465935e-01 5.32879643e-02 2.33056217e-01
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dd18c5c1-875a-4e9a-bb97-bc5ce497e5b0 | reconstruction-of-perceived-images-from-fmri | 2202.12692 | null | https://arxiv.org/abs/2202.12692v1 | https://arxiv.org/pdf/2202.12692v1.pdf | Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs | Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research. Prior studies had success in reconstructing either the low-level image features or the semantic/high-level aspects, but rarely both. In this study, we utilized an Instance-Conditioned GAN (IC-GAN) model to reconstruct images from fMRI patterns with both accurate semantic attributes and preserved low-level details. The IC-GAN model takes as input a 119-dim noise vector and a 2048-dim instance feature vector extracted from a target image via a self-supervised learning model (SwAV ResNet-50); these instance features act as a conditioning for IC-GAN image generation, while the noise vector introduces variability between samples. We trained ridge regression models to predict instance features, noise vectors, and dense vectors (the output of the first dense layer of the IC-GAN generator) of stimuli from corresponding fMRI patterns. Then, we used the IC-GAN generator to reconstruct novel test images based on these fMRI-predicted variables. The generated images presented state-of-the-art results in terms of capturing the semantic attributes of the original test images while remaining relatively faithful to low-level image details. Finally, we use the learned regression model and the IC-GAN generator to systematically explore and visualize the semantic features that maximally drive each of several regions-of-interest in the human brain. | ['Rufin VanRullen', 'Leila Reddy', 'Milad Mozafari', 'Bhavin Choksi', 'Furkan Ozcelik'] | 2022-02-25 | null | null | null | null | ['2048'] | ['playing-games'] | [ 7.50656188e-01 1.57289758e-01 2.00947657e-01 -6.09312177e-01
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a067ad19-e479-4e11-8969-0864acb003a6 | an-investigation-of-preprocessing-filters-and | null | null | https://ieeexplore.ieee.org/abstract/document/9940921 | https://ieeexplore.ieee.org/iel7/6287639/6514899/09940921.pdf | An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data | The illegal exploitation of protected marine environments has consistently threatened the
biodiversity and economic development of coastal regions. Extensive monitoring in these – often remote
– areas is challenging. Machine learning methods are useful in object detection and classification tasks and
have the potential to underpin techniques for the development of robust monitoring systems to overcome
this problem. However, development is hindered due to the limited number of publicly available labelled
and curated datasets. Furthermore, there are relatively few open-source state-of-the-art methods to be used
for evaluation. This paper presents an investigation of automated classification methods using underwater
acoustic signals to infer the presence and type of vessels navigating in coastal regions. Various combinations
of deep convolutional neural network architectures, and preprocessing filter layers, were evaluated using a
new dataset based on a subset of the extensive open-source Ocean Networks Canada hydrophone data. Tests
were conducted in which VGGNet and ResNet networks were applied to classify the input data. The data was
preprocessed using either Constant Q Transform (CQT), Gammatone, Mel spectrogram, or a combination of
these filters. With over 97% accuracy, using all three preprocessing representations simultaneously yielded
the most reliable result. However, high accuracies of 94.95% were achieved using CQT as the preprocessing
filter for a ResNet-based convolutional neural network, providing a trade-off between model complexity
and accuracy; a result that is more than 10% higher than previously reported approaches. This more accurate
classifier for underwater acoustics could be used as a reliable autonomous monitoring system in maritime
environments. | ['Karl Sammut', 'Russell S. A. Brinkworth', 'Phillip S. M. Skelton', 'Paulo E. Santos', 'Lucas Cesar Ferreira Domingos'] | 2022-11-07 | null | null | null | ieee-access-2022-11 | ['underwater-acoustic-classification'] | ['audio'] | [ 2.58907825e-01 -9.82364118e-02 9.52129006e-01 -3.48008364e-01
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973bb8ca-94e1-4b93-9cfb-89fa1471f5c4 | developing-asr-for-indonesian-english | null | null | https://aclanthology.org/2021.calcs-1.17 | https://aclanthology.org/2021.calcs-1.17.pdf | Developing ASR for Indonesian-English Bilingual Language Teaching | Usage-based analyses of teacher corpora and code-switching (Boztepe, 2003) are an important next stage in understanding language acquisition. Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. Using quantitative methods to understand language learning and teaching is difficult work as the ‘transcription bottleneck’ constrains the size of datasets. We found that using an automatic speech recognition (ASR) toolkit with a small set of training data is likely to speed data collection in this context (Maxwelll-Smith et al., 2020). | ['Ben Foley', 'Zara Maxwelll-Smith'] | null | null | null | null | naacl-calcs-2021-6 | ['language-acquisition'] | ['natural-language-processing'] | [ 3.45050581e-02 -1.19464882e-01 -4.40131962e-01 -4.47980613e-01
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07d233e3-ff13-4a81-861e-a8dbf221fcd2 | nadi-2022-the-third-nuanced-arabic-dialect | 2210.09582 | null | https://arxiv.org/abs/2210.09582v2 | https://arxiv.org/pdf/2210.09582v2.pdf | NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task | We describe findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI 2022). NADI aims at advancing state of the art Arabic NLP, including on Arabic dialects. It does so by affording diverse datasets and modeling opportunities in a standardized context where meaningful comparisons between models and approaches are possible. NADI 2022 targeted both dialect identification (Subtask 1) and dialectal sentiment analysis (Subtask 2) at the country level. A total of 41 unique teams registered for the shared task, of whom 21 teams have actually participated (with 105 valid submissions). Among these, 19 teams participated in Subtask 1 and 10 participated in Subtask 2. The winning team achieved 27.06 F1 on Subtask 1 and F1=75.16 on Subtask 2, reflecting that the two subtasks remain challenging and motivating future work in this area. We describe methods employed by participating teams and offer an outlook for NADI. | ['Nizar Habash', 'Houda Bouamor', 'AbdelRahim Elmadany', 'Chiyu Zhang', 'Muhammad Abdul-Mageed'] | 2022-10-18 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-2.44930521e-01 -1.23196974e-01 -9.15348902e-02 -7.00168312e-01
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588835c5-e631-441f-8d27-9d5cec23819a | generalizing-graph-ode-for-learning-complex | 2307.04287 | null | https://arxiv.org/abs/2307.04287v1 | https://arxiv.org/pdf/2307.04287v1.pdf | Generalizing Graph ODE for Learning Complex System Dynamics across Environments | Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built to learn single system dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity. One simple solution is to learn multiple environment-specific models, but it fails to exploit the potential commonalities among the dynamics across environments and offers poor prediction results where per-environment data is sparse or limited. Here, we present GG-ODE (Generalized Graph Ordinary Differential Equations), a machine learning framework for learning continuous multi-agent system dynamics across environments. Our model learns system dynamics using neural ordinary differential equations (ODE) parameterized by Graph Neural Networks (GNNs) to capture the continuous interaction among agents. We achieve the model generalization by assuming the dynamics across different environments are governed by common physics laws that can be captured via learning a shared ODE function. The distinct latent exogenous factors learned for each environment are incorporated into the ODE function to account for their differences. To improve model performance, we additionally design two regularization losses to (1) enforce the orthogonality between the learned initial states and exogenous factors via mutual information minimization; and (2) reduce the temporal variance of learned exogenous factors within the same system via contrastive learning. Experiments over various physical simulations show that our model can accurately predict system dynamics, especially in the long range, and can generalize well to new systems with few observations. | ['Wei Wang', 'Yizhou Sun', 'Zijie Huang'] | 2023-07-10 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'physical-simulations'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-8.82914364e-02 -2.67386049e-01 8.04290622e-02 2.02360496e-01
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552727c8-9ded-49c5-9468-141e9f008662 | deep-monocular-visual-odometry-for-ground | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9201526 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9201526 | Deep Monocular Visual Odometry for Ground Vehicle | Monocular visual odometry, with the ability to help robots to locate themselves in unexplored
environments, has been a crucial research problem in robotics. Though the existed learning-based endto-end methods can reduce engineering efforts such as accurate camera calibration and tedious case-by-case
parameter tuning, the accuracy is still limited. One of the main reasons is that previous works aim to learn sixdegrees-of-freedom motions despite the constrained motion of a ground vehicle by its mechanical structure
and dynamics. To push the limit, we analyze the motion pattern of a ground vehicle and focus on learning
two-degrees-of-freedom motions by proposed motion focusing and decoupling. The experiments on KITTI
dataset show that the proposed motion focusing and decoupling approach can improve the visual odometry
performance by reducing the relative pose error. Moreover, with the dimension reduction of the learning
objective, our network is much lighter with only four convolution layers, which can quickly converge during
the training stage and run in real-time at over 200 frames per second during the testing stage. | ['HUI ZHANG', 'Xiangwei Wang'] | 2020-09-21 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.43287039e-01 8.25354010e-02 -1.24537848e-01 -1.73740044e-01
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934af0b1-2573-4b08-9350-0e8b3597148a | kam-a-kernel-attention-module-for-emotion | 2208.08161 | null | https://arxiv.org/abs/2208.08161v2 | https://arxiv.org/pdf/2208.08161v2.pdf | KAM -- a Kernel Attention Module for Emotion Classification with EEG Data | In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks. The proposed module utilizes a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The design also provides a scalar for quantitatively examining the amount of attention assigned during deep feature refinement, hence help better interpret a trained model. Using EEGNet as the backbone model, extensive experiments are conducted on the SEED dataset to assess the module's performance on within-subject classification tasks compared to other SOTA attention modules. Requiring only one extra parameter, the inserted module is shown to boost the base model's mean prediction accuracy up to more than 1\% across 15 subjects. A key component of the method is the interpretability of solutions, which is addressed using several different techniques, and is included throughout as part of the dependency analysis. | ['Craig Michoski', 'Dongyang Kuang'] | 2022-08-17 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 1.23240814e-01 3.98563623e-01 2.05851555e-01 -5.87225199e-01
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1ea29977-8447-47be-822c-7014c1195e4c | geometric-anchor-correspondence-mining-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.pdf | Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation | Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared "known" samples and private "unknown" samples. Previous works rarely explore the intrinsic geometrical relationship between the two domains, and they manually set a threshold for the overconfident closed-world classifier to reject "unknown" samples. Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues. Specifically, we first develop a random walk-based anchor mining strategy together with a high-order attention mechanism to build correspondence across domains. Then a global joint local domain alignment paradigm is designed, i.e., geometric adversarial learning for global distribution calibration and subgraph-level contrastive learning for local region aggregation. Toward accurate target private samples detection, GATE introduces a universal incremental classifier by modeling the energy uncertainty. We further efficiently generate novel categories by manifold mixup, and minimize the open-set entropy to learn the "unknown" threshold adaptively. Extensive experiments on three benchmarks demonstrate that GATE significantly outperforms previous state-of-the-art UniDA methods. | ['Minghua Deng', 'Tao Bai', 'Jianzhong He', 'Yihang Lou', 'Liang Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['universal-domain-adaptation'] | ['computer-vision'] | [ 1.90311953e-01 1.39354318e-01 -4.43192631e-01 -4.09103096e-01
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eb29a7d2-48c8-4422-9e61-ea4ee8916145 | integration-of-regularized-l1-tracking-and | 1912.12883 | null | https://arxiv.org/abs/1912.12883v1 | https://arxiv.org/pdf/1912.12883v1.pdf | Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking | We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker under the regularization framework where the tracked object is represented by a sparse dictionary. A novel observation model which establishes consensus between the detector and tracker is formulated that enables us to update the dictionary with the guidance of the deep detector. This yields an efficient representation of the object appearance through the video sequence hence improves robustness to occlusion and pose changes. Moreover we propose a new state vector consisting of translation, rotation, scaling and shearing parameters that allows tracking the deformed object bounding boxes hence significantly increases robustness to scale changes. Numerical results reported on challenging VOT2016 and VOT2018 benchmarking data sets demonstrate that the introduced tracker, L1DPF-M, achieves comparable robustness on both data sets while it outperforms state-of-the-art trackers on both data sets where the improvement achieved in success rate at IoU-th=0.5 is 11% and 9%, respectively. | ['Bilge Gunsel', 'Filiz Gurkan'] | 2019-12-30 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-4.66946214e-01 -3.03828925e-01 -2.21233554e-02 2.95223087e-01
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9741a251-b169-4c34-9a4f-0e8f5dacd756 | self-supervised-video-representation-learning-3 | 2008.02531 | null | https://arxiv.org/abs/2008.02531v2 | https://arxiv.org/pdf/2008.02531v2.pdf | Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework | We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy. In such a case, different modalities of the same video are treated as positives and video clips from a different video are treated as negatives. Because the spatio-temporal information is important for video representation, we extend the negative samples by introducing intra-negative samples, which are transformed from the same anchor video by breaking temporal relations in video clips. With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations. There are many flexible options in our IIC framework and we conduct experiments by using several different configurations. Evaluations are conducted on video retrieval and video recognition tasks using the learned video representation. Our proposed IIC outperforms current state-of-the-art results by a large margin, such as 16.7% and 9.5% points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively. For video recognition, improvements can also be obtained on these two benchmark datasets. Code is available at https://github.com/BestJuly/Inter-intra-video-contrastive-learning. | ['Toshihiko Yamasaki', 'Xueting Wang', 'Li Tao'] | 2020-08-06 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 9.99490395e-02 -5.58070421e-01 -6.96438670e-01 -5.30823529e-01
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8e9f9781-a41b-4a3b-967a-e2bce634f7ba | a-two-steps-approach-to-improve-the | 2205.08265 | null | https://arxiv.org/abs/2205.08265v1 | https://arxiv.org/pdf/2205.08265v1.pdf | A two-steps approach to improve the performance of Android malware detectors | The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps. In this paper, we propose GUIDED RETRAINING, a supervised representation learning-based method that boosts the performance of a malware detector. First, the dataset is split into "easy" and "difficult" samples, where difficulty is associated to the prediction probabilities yielded by a malware detector: for difficult samples, the probabilities are such that the classifier is not confident on the predictions, which have high error rates. Then, we apply our GUIDED RETRAINING method on the difficult samples to improve their classification. For the subset of "easy" samples, the base malware detector is used to make the final predictions since the error rate on that subset is low by construction. For the subset of "difficult" samples, we rely on GUIDED RETRAINING, which leverages the correct predictions and the errors made by the base malware detector to guide the retraining process. GUIDED RETRAINING focuses on the difficult samples: it learns new embeddings of these samples using Supervised Contrastive Learning and trains an auxiliary classifier for the final predictions. We validate our method on four state-of-the-art Android malware detection approaches using over 265k malware and benign apps, and we demonstrate that GUIDED RETRAINING can reduce up to 40.41% prediction errors made by the malware detectors. Our method is generic and designed to enhance the classification performance on a binary classification task. Consequently, it can be applied to other classification problems beyond Android malware detection. | ['Jacques Klein', 'Tegawendé F. Bissyandé', 'Kevin Allix', 'Nadia Daoudi'] | 2022-05-17 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.90934497e-01 2.14752909e-02 -6.73617303e-01 -7.37301707e-02
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3c056325-9a25-4fb7-99be-2cb7cdb0b518 | towards-making-the-most-of-dialogue | 2109.00668 | null | https://arxiv.org/abs/2109.00668v1 | https://arxiv.org/pdf/2109.00668v1.pdf | Towards Making the Most of Dialogue Characteristics for Neural Chat Translation | Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English-German and English-Chinese) verify the effectiveness and superiority of the proposed approach. | ['Jie zhou', 'Jinsong Su', 'Yufeng Chen', 'Jinan Xu', 'Fandong Meng', 'Chulun Zhou', 'Yunlong Liang'] | 2021-09-02 | null | https://aclanthology.org/2021.emnlp-main.6 | https://aclanthology.org/2021.emnlp-main.6.pdf | emnlp-2021-11 | ['speaker-identification'] | ['speech'] | [ 1.85581282e-01 -1.56745762e-02 -1.99139297e-01 -5.41038871e-01
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3064f027-d1b8-4dcd-9c6d-e19f7ab70daa | mask-focal-loss-for-dense-crowd-counting-with | 2212.11542 | null | https://arxiv.org/abs/2212.11542v2 | https://arxiv.org/pdf/2212.11542v2.pdf | Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks | As a fundamental computer vision task, crowd counting predicts the number of pedestrians in a scene, which plays an important role in risk perception and early warning, traffic control and scene statistical analysis. Currently, deep learning based head detection is a promising method for crowd counting. However, the highly concerned object detection networks cannot be well applied to this field for three reasons: (1) The sample imbalance has not been overcome yet in highly dense and complex scenes because the existing loss functions calculate the positive loss at a single key point or in the entire target area with the same weight for all pixels; (2) The canonical object detectors' loss calculation is a hard assignment without taking into account the space coherence from the object location to the background region; and (3) Most of the existing head detection datasets are only annotated with the center points instead of bounding boxes which is mandatory for the canonical detectors. To address these problems, we propose a novel loss function, called Mask Focal Loss (MFL), to redefine the loss contributions according to the situ value of the heatmap with a Gaussian kernel. MFL provides a unifying framework for the loss functions based on both heatmap and binary feature map ground truths. Meanwhile, for better evaluation and comparison, a new synthetic dataset GTA\_Head is built, including 35 sequences, 5096 images and 1732043 head labels with bounding boxes. Experimental results show the overwhelming performance and demonstrate that our proposed MFL framework is applicable to all of the canonical detectors and to various datasets with different annotation patterns. This work provides a strong baseline for surpassing the crowd counting methods based on density estimation. | ['Zongze Wu', 'Weixiang Liu', 'Yuanlong Deng', 'Guankun Wang', 'Xiaopin Zhong'] | 2022-12-22 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-2.67293125e-01 -3.23636979e-01 5.74812554e-02 -3.21991563e-01
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1a2b6512-0cba-4625-97ac-51cd198593ff | towards-plug-n-play-task-level-autonomy-for | 2207.09713 | null | https://arxiv.org/abs/2207.09713v1 | https://arxiv.org/pdf/2207.09713v1.pdf | Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models | To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scripts is challenging since they must intelligently balance the inherent stochasticity of a physical robot's actions and sensors, and the limited information it has. In principle, AI planning can be used to address this challenge and generate good behavior policies automatically. But this requires passing three hurdles. First, the AI must understand each skill's impact on the world. Second, we must bridge the gap between the more abstract level at which we understand what a skill does and the low-level state variables used within its code. Third, much integration effort is required to tie together all components. We describe an approach for integrating robot skills into a working autonomous robot controller that schedules its skills to achieve a specified task and carries four key advantages. 1) Our Generative Skill Documentation Language (GSDL) makes code documentation simpler, compact, and more expressive using ideas from probabilistic programming languages. 2) An expressive abstraction mapping (AM) bridges the gap between low-level robot code and the abstract AI planning model. 3) Any properly documented skill can be used by the controller without any additional programming effort, providing a Plug'n Play experience. 4) A POMDP solver schedules skill execution while properly balancing partial observability, stochastic behavior, and noisy sensing. | ['Ronen I. Brafman', 'Dan R. Suissa', 'Or Wertheim'] | 2022-07-20 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 2.56752282e-01 5.16755283e-01 -1.43628970e-01 -6.10136464e-02
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c1ecf257-0a81-4da2-9fbd-0e9690c0d5db | grounding-plural-phrases-countering | null | null | https://aclanthology.org/2021.alvr-1.4 | https://aclanthology.org/2021.alvr-1.4.pdf | Grounding Plural Phrases: Countering Evaluation Biases by Individuation | Phrase grounding (PG) is a multimodal task that grounds language in images. PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric. This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects as a union box covering all component bounding boxes, in conjunction with the IoU metric. We detect, analyze and quantify an evaluation bias in the grounding of plural phrases and define a novel metric, c-IoU, based on a union box’s component boxes. We experimentally show that our new metric greatly alleviates this bias and recommend using it for fairer evaluation of plural phrases in PG tasks. | ['Anette Frank', 'Letitia Parcalabescu', 'Julia Suter'] | null | null | null | null | naacl-alvr-2021-6 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.62776935e-01 3.63354594e-01 -1.33756578e-01 -2.32695073e-01
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031d99b3-9061-47b5-83f7-be8f16b29ca0 | snowformer-scale-aware-transformer-via | 2208.09703 | null | https://arxiv.org/abs/2208.09703v3 | https://arxiv.org/pdf/2208.09703v3.pdf | SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing | Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task. As prior arts can not handle it ideally, we propose a novel transformer, SnowFormer, which explores efficient cross-attentions to build local-global context interaction across patches and surpasses existing works that employ local operators or vanilla transformers. Compared to prior desnowing methods and universal image restoration methods, SnowFormer has several benefits. Firstly, unlike the multi-head self-attention in recent image restoration Vision Transformers, SnowFormer incorporates the multi-head cross-attention mechanism to perform local-global context interaction between scale-aware snow queries and local-patch embeddings. Second, the snow queries in SnowFormer are generated by the query generator from aggregated scale-aware features, which are rich in potential clean cues, leading to superior restoration results. Third, SnowFormer outshines advanced state-of-the-art desnowing networks and the prevalent universal image restoration transformers on six synthetic and real-world datasets. The code is released in \url{https://github.com/Ephemeral182/SnowFormer}. | ['ErKang Chen', 'Yun Liu', 'Tian Ye', 'Sixiang Chen'] | 2022-08-20 | null | null | null | null | ['single-image-desnowing'] | ['computer-vision'] | [ 1.35519952e-01 -3.74239981e-01 -2.85041593e-02 -2.23997608e-01
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dc086e58-79ab-4a45-91f9-55463cd44de5 | hierarchical-matching-and-reasoning-for-multi | 2306.1446 | null | https://arxiv.org/abs/2306.14460v1 | https://arxiv.org/pdf/2306.14460v1.pdf | Hierarchical Matching and Reasoning for Multi-Query Image Retrieval | As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly focus on a single-level similarity between image regions and text queries, which neglects the hierarchical guidance of multi-level similarities and results in incomplete alignments. Besides, the high-level semantic correlations that intrinsically connect different region-query pairs are rarely considered. To address above limitations, we propose a novel Hierarchical Matching and Reasoning Network (HMRN) for MQIR. It disentangles MQIR into three hierarchical semantic representations, which is responsible to capture fine-grained local details, contextual global scopes, and high-level inherent correlations. HMRN comprises two modules: Scalar-based Matching (SM) module and Vector-based Reasoning (VR) module. Specifically, the SM module characterizes the multi-level alignment similarity, which consists of a fine-grained local-level similarity and a context-aware global-level similarity. Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity. Finally, these three-level similarities are aggregated into a joint similarity space to form the ultimate similarity. Extensive experiments on the benchmark dataset demonstrate that our HMRN substantially surpasses the current state-of-the-art methods. For instance, compared with the existing best method Drill-down, the metric R@1 in the last round is improved by 23.4%. Our source codes will be released at https://github.com/LZH-053/HMRN. | ['Xuelong Li', 'Yanwei Pang', 'Haoran Wang', 'Yan Zhang', 'Zhihao LI', 'Zhong Ji'] | 2023-06-26 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 0.16811563 -0.36565572 -0.43042713 -0.2590881 -1.1950161 -0.40017614
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f7c7efe8-75e0-47c4-8724-eb3267c824af | taca-upgrading-your-visual-foundation-model | 2306.12642 | null | https://arxiv.org/abs/2306.12642v1 | https://arxiv.org/pdf/2306.12642v1.pdf | TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter | Visual foundation models like CLIP excel in learning feature representations from extensive datasets through self-supervised methods, demonstrating remarkable transfer learning and generalization capabilities. A growing number of applications based on visual foundation models are emerging, including innovative solutions such as BLIP-2. These applications employ pre-trained CLIP models as upstream feature extractors and train various downstream modules to accomplish diverse tasks. In situations involving system upgrades that require updating the upstream foundation model, it becomes essential to re-train all downstream modules to adapt to the new foundation model, which is inflexible and inefficient. In this paper, we introduce a parameter-efficient and task-agnostic adapter, dubbed TaCA, that facilitates compatibility across distinct foundation models while ensuring enhanced performance for the new models. TaCA allows downstream applications to seamlessly integrate better-performing foundation models without necessitating retraining. We conduct extensive experimental validation of TaCA using different scales of models with up to one billion parameters on various tasks such as video-text retrieval, video recognition, and visual question answering. The results consistently demonstrate the emergent ability of TaCA on hot-plugging upgrades for visual foundation models. Codes and models will be available at https://github.com/TencentARC/TaCA. | ['Mike Zheng Shou', 'Ying Shan', 'Xuyuan Xu', 'Yixiao Ge', 'Binjie Zhang'] | 2023-06-22 | null | null | null | null | ['video-recognition', 'visual-question-answering-1', 'video-text-retrieval', 'retrieval', 'transfer-learning', 'question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [-1.62592143e-01 -4.93095636e-01 -2.19707131e-01 -3.99421334e-01
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d8c3cc29-45d6-41fd-88a4-f06ba5f08bed | deep-learning-models-delineates-multiple | 1802.04427 | null | http://arxiv.org/abs/1802.04427v2 | http://arxiv.org/pdf/1802.04427v2.pdf | Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections | Nuclear segmentation is an important step for profiling aberrant regions of
histology sections. However, segmentation is a complex problem as a result of
variations in nuclear geometry (e.g., size, shape), nuclear type (e.g.,
epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy).
The problem is further complicated as a result of variations in sample
preparation. It is shown and validated that fusion of very deep convolutional
networks overcomes (i) complexities associated with multiple nuclear
phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on
integrating of networks that learn region- and boundary-based representations.
The system has been validated on a diverse set of nuclear phenotypes that
correspond to the breast and brain histology sections. | ['Bahram Parvin', 'Mina Khoshdeli'] | 2018-02-13 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 2.64900178e-01 1.33223431e-02 2.72747539e-02 -1.57475412e-01
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982c02bd-b757-4f2d-8ee9-30f08c3caf3f | autonomous-manipulation-learning-for-similar | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.pdf | Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration | In comparison with most methods focusing on 3D rigid object recognition and manipulation, deformable objects are more common in our real life but attract less attention. Generally, most existing methods for deformable object manipulation suffer two issues, 1) Massive demonstration: repeating thousands of robot-object demonstrations for model training of one specific instance; 2) Poor generalization: inevitably re-training for transferring the learned skill to a similar/new instance from the same category. Therefore, we propose a category-level deformable 3D object manipulation framework, which could manipulate deformable 3D objects with only one demonstration and generalize the learned skills to new similar instances without re-training. Specifically, our proposed framework consists of two modules. The Nocs State Transform (NST) module transfers the observed point clouds of the target to a pre-defined unified pose state (i.e., Nocs state), which is the foundation for the category-level manipulation learning; the Neural Spatial Encoding (NSE) module generalizes the learned skill to novel instances by encoding the category-level spatial information to pursue the expected grasping point without re-training. The relative motion path is then planned to achieve autonomous manipulation. Both the simulated results via our Cap40 dataset and real robotic experiments justify the effectiveness of our framework. | ['Yang Cong', 'Ronghan Chen', 'Yu Ren'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-recognition', 'deformable-object-manipulation'] | ['computer-vision', 'robots'] | [ 5.50713129e-02 -7.41852373e-02 2.12299991e-02 -3.45866531e-01
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322166d3-713b-4d7d-a0b2-368e1dd61c52 | single-stage-visual-relationship-learning | 2306.05689 | null | https://arxiv.org/abs/2306.05689v1 | https://arxiv.org/pdf/2306.05689v1.pdf | Single-Stage Visual Relationship Learning using Conditional Queries | Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labeling all possible relationships. While showing promising results, the pipeline structure induces large parameter and computation overhead, and typically hinders end-to-end optimizations. To address this, recent research attempts to train single-stage models that are computationally efficient. With the advent of DETR, a set based detection model, one-stage models attempt to predict a set of subject-predicate-object triplets directly in a single shot. However, SGG is inherently a multi-task learning problem that requires modeling entity and predicate distributions simultaneously. In this paper, we propose Transformers with conditional queries for SGG, namely, TraCQ with a new formulation for SGG that avoids the multi-task learning problem and the combinatorial entity pair distribution. We employ a DETR-based encoder-decoder design and leverage conditional queries to significantly reduce the entity label space as well, which leads to 20% fewer parameters compared to state-of-the-art single-stage models. Experimental results show that TraCQ not only outperforms existing single-stage scene graph generation methods, it also beats many state-of-the-art two-stage methods on the Visual Genome dataset, yet is capable of end-to-end training and faster inference. | ['Nuno Vasconcelos', 'Subarna Tripathi', 'Tz-Ying Wu', 'Alakh Desai'] | 2023-06-09 | null | null | null | null | ['scene-graph-generation', 'multi-task-learning'] | ['computer-vision', 'methodology'] | [ 4.90142494e-01 2.33556792e-01 -2.71863133e-01 -3.14363599e-01
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34fa740d-b26a-49e7-8a9e-4d51e3944bfd | tallformer-temporal-action-localization-with | 2204.0168 | null | https://arxiv.org/abs/2204.01680v2 | https://arxiv.org/pdf/2204.01680v2.pdf | TALLFormer: Temporal Action Localization with a Long-memory Transformer | Most modern approaches in temporal action localization divide this problem into two parts: (i) short-term feature extraction and (ii) long-range temporal boundary localization. Due to the high GPU memory cost caused by processing long untrimmed videos, many methods sacrifice the representational power of the short-term feature extractor by either freezing the backbone or using a small spatial video resolution. This issue becomes even worse with the recent video transformer models, many of which have quadratic memory complexity. To address these issues, we propose TALLFormer, a memory-efficient and end-to-end trainable Temporal Action Localization Transformer with Long-term memory. Our long-term memory mechanism eliminates the need for processing hundreds of redundant video frames during each training iteration, thus, significantly reducing the GPU memory consumption and training time. These efficiency savings allow us (i) to use a powerful video transformer feature extractor without freezing the backbone or reducing the spatial video resolution, while (ii) also maintaining long-range temporal boundary localization capability. With only RGB frames as input and no external action recognition classifier, TALLFormer outperforms previous state-of-the-arts by a large margin, achieving an average mAP of 59.1% on THUMOS14 and 35.6% on ActivityNet-1.3. The code is public available: https://github.com/klauscc/TALLFormer. | ['Gedas Bertasius', 'Feng Cheng'] | 2022-04-04 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.25599736e-01 -4.98217702e-01 -4.08810109e-01 -3.50820236e-02
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738b77e4-a4e0-480a-9db1-e1d83d6231be | sequential-clique-optimization-for-video | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yeong_Jun_Koh_Sequential_Clique_Optimization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yeong_Jun_Koh_Sequential_Clique_Optimization_ECCV_2018_paper.pdf | Sequential Clique Optimization for Video Object Segmentation | A novel algorithm to segment out objects in a video sequence is proposed in this work. First, we extract object instances in each frame. Then, we select a visually important object instance in each frame to construct the salient object track through the sequence. This can be formulated as finding the maximal weight clique in a complete k-partite graph, which is NP hard. Therefore, we develop the sequential clique optimization (SCO) technique to efficiently determine the cliques corresponding to salient object tracks. We convert these tracks into video object segmentation results. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art video object segmentation and video salient object detection algorithms on recent benchmark datasets. | ['Chang-Su Kim', 'Young-Yoon Lee', 'Yeong Jun Koh'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.28948581e-01 1.41685888e-01 -5.23060024e-01 -1.91859156e-02
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b75806db-39e2-402d-95e3-47c3f09c1cfa | understanding-the-quality-of-container | 2101.03844 | null | https://arxiv.org/abs/2101.03844v1 | https://arxiv.org/pdf/2101.03844v1.pdf | Understanding the Quality of Container Security Vulnerability Detection Tools | Virtualization enables information and communications technology industry to better manage computing resources. In this regard, improvements in virtualization approaches together with the need for consistent runtime environment, lower overhead and smaller package size has led to the growing adoption of containers. This is a technology, which packages an application, its dependencies and Operating System (OS) to run as an isolated unit. However, the pressing concern with the use of containers is its susceptibility to security attacks. Consequently, a number of container scanning tools are available for detecting container security vulnerabilities. Therefore, in this study, we investigate the quality of existing container scanning tools by proposing two metrics that reflects coverage and accuracy. We analyze 59 popular public container images for Java applications hosted on DockerHub using different container scanning tools (such as Clair, Anchore, and Microscanner). Our findings show that existing container scanning approach does not detect application package vulnerabilities. Furthermore, existing tools do not have high accuracy, since 34% vulnerabilities are being missed by the best performing tool. Finally, we also demonstrate quality of Docker images for Java applications hosted on DockerHub by assessing complete vulnerability landscape i.e., number of vulnerabilities detected in images. | ['Salman Toor', 'Omar Javed'] | 2021-01-11 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-6.29853785e-01 -6.16848648e-01 1.03770174e-01 1.27507403e-01
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7fae61c5-d5d4-4585-a5f0-482ca0e2f5ae | optimization-and-interpretability-of-graph | 2305.16196 | null | https://arxiv.org/abs/2305.16196v1 | https://arxiv.org/pdf/2305.16196v1.pdf | Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications | For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and sparse graph structures a proper optimization is problematic. To surpass limitations, this work proposes architectural modifications of GATv2. In controlled experiments, it is shown that the proposed model adaptions improve prediction performance in a node-level regression task and make it more robust to parameter initialization. This work aims for a better understanding of the attention mechanism and analyzes its interpretability of identifying causal importance. | ['Wolfgang Utschick', 'Michael Botsch', 'Sebastian Dorn', 'Andreas Tollkühn', 'Marion Neumeier'] | 2023-05-25 | null | null | null | null | ['graph-attention'] | ['graphs'] | [ 4.95785624e-02 5.60766041e-01 -4.90220875e-01 -1.98576376e-01
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3355fbaa-2df8-4c45-b4c2-44aec7113c65 | diffute-universal-text-editing-diffusion | 2305.10825 | null | https://arxiv.org/abs/2305.10825v2 | https://arxiv.org/pdf/2305.10825v2.pdf | DiffUTE: Universal Text Editing Diffusion Model | Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https://github.com/chenhaoxing/DiffUTE}. | ['Weiqiang Wang', 'Huijia Zhu', 'Changhua Meng', 'Yaohui Li', 'Xing Zheng', 'Jun Lan', 'Zhangxuan Gu', 'Zhuoer Xu', 'Haoxing Chen'] | 2023-05-18 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 2.91892290e-01 1.01900727e-01 -2.67723221e-02 -2.29375631e-01
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ca688a70-51af-41c9-8cfd-597988fbadc4 | cover-a-heuristic-greedy-adversarial-attack | 2306.05659 | null | https://arxiv.org/abs/2306.05659v2 | https://arxiv.org/pdf/2306.05659v2.pdf | COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models | Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential vulnerabilities have been shown in prompt-based templates that could mislead the predictions of language models, causing serious security concerns. In this paper, we will shed light on some vulnerabilities of PLMs, by proposing a prompt-based adversarial attack on manual templates in black box scenarios. First of all, we design character-level and word-level heuristic approaches to break manual templates separately. Then we present a greedy algorithm for the attack based on the above heuristic destructive approaches. Finally, we evaluate our approach with the classification tasks on three variants of BERT series models and eight datasets. And comprehensive experimental results justify the effectiveness of our approach in terms of attack success rate and attack speed. Further experimental studies indicate that our proposed method also displays good capabilities in scenarios with varying shot counts, template lengths and query counts, exhibiting good generalizability. | ['Yongjian Huang', 'Wenbin Zhu', 'Qingliang Chen', 'Zihao Tan'] | 2023-06-09 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 6.05145060e-02 -8.25666711e-02 1.25787318e-01 2.30561029e-02
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76475404-7539-414d-96e2-c3aad3053dc6 | phrase-retrieval-for-open-domain | 2306.04293 | null | https://arxiv.org/abs/2306.04293v1 | https://arxiv.org/pdf/2306.04293v1.pdf | Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning | Open-Domain Conversational Question Answering (ODConvQA) aims at answering questions through a multi-turn conversation based on a retriever-reader pipeline, which retrieves passages and then predicts answers with them. However, such a pipeline approach not only makes the reader vulnerable to the errors propagated from the retriever, but also demands additional effort to develop both the retriever and the reader, which further makes it slower since they are not runnable in parallel. In this work, we propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words, reducing the conventional two distinct subtasks into a single one. Also, for the first time, we study its capability for ODConvQA tasks. However, simply adopting it is largely problematic, due to the dependencies between previous and current turns in a conversation. To address this problem, we further introduce a novel contrastive learning strategy, making sure to reflect previous turns when retrieving the phrase for the current context, by maximizing representational similarities of consecutive turns in a conversation while minimizing irrelevant conversational contexts. We validate our model on two ODConvQA datasets, whose experimental results show that it substantially outperforms the relevant baselines with the retriever-reader. Code is available at: https://github.com/starsuzi/PRO-ConvQA. | ['Jong C. Park', 'Sung Ju Hwang', 'Jinheon Baek', 'Soyeong Jeong'] | 2023-06-07 | null | null | null | null | ['conversational-question-answering'] | ['natural-language-processing'] | [-7.97748193e-03 1.20267354e-01 4.15563107e-01 -3.79031301e-01
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10019f74-e756-4b62-be99-198deed7b4d1 | knowledge-graph-question-answering-via-sparql | 2109.09475 | null | https://arxiv.org/abs/2109.09475v1 | https://arxiv.org/pdf/2109.09475v1.pdf | Knowledge Graph Question Answering via SPARQL Silhouette Generation | Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large-scale Knowledge Graphs (KGs). Recently Neural Machine Translation based approaches are gaining momentum that translates natural language queries to structured query languages thereby solving the KGQA task. However, most of these methods struggle with out-of-vocabulary words where test entities and relations are not seen during training time. In this work, we propose a modular two-stage neural architecture to solve the KGQA task. The first stage generates a sketch of the target SPARQL called SPARQL silhouette for the input question. This comprises of (1) Noise simulator to facilitate out-of-vocabulary words and to reduce vocabulary size (2) seq2seq model for text to SPARQL silhouette generation. The second stage is a Neural Graph Search Module. SPARQL silhouette generated in the first stage is distilled in the second stage by substituting precise relation in the predicted structure. We simulate ideal and realistic scenarios by designing a noise simulator. Experimental results show that the quality of generated SPARQL silhouette in the first stage is outstanding for the ideal scenarios but for realistic scenarios (i.e. noisy linker), the quality of the resulting SPARQL silhouette drops drastically. However, our neural graph search module recovers it considerably. We show that our method can achieve reasonable performance improving the state-of-art by a margin of 3.72% F1 for the LC-QuAD-1 dataset. We believe, our proposed approach is novel and will lead to dynamic KGQA solutions that are suited for practical applications. | ['G P Shrivatsa Bhargav', 'Dinesh Khandelwal', 'Dinesh Garg', 'Saswati Dana', 'Sukannya Purkayastha'] | 2021-09-06 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [ 1.19329587e-01 6.01297915e-01 -1.07136779e-02 -2.21382692e-01
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54e3ba56-445a-48f2-8a65-2036cbde3b8b | cde-iiith-at-semeval-2016-task-12-extraction | null | null | https://aclanthology.org/S16-1192 | https://aclanthology.org/S16-1192.pdf | CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques | null | ['Veera Raghavendra Chikka'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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fffa1325-7fd6-4c2e-8338-9d9f27faa451 | anomal-e-a-self-supervised-network-intrusion | 2207.06819 | null | https://arxiv.org/abs/2207.06819v5 | https://arxiv.org/pdf/2207.06819v5.pdf | Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks | This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning to generalise graph representations and output embeddings. As network flows are naturally graph-based, GNNs are a suitable fit for analysing and learning network behaviour. The majority of current implementations of GNN-based Network Intrusion Detection Systems (NIDSs) rely heavily on labelled network traffic which can not only restrict the amount and structure of input traffic, but also the NIDSs potential to adapt to unseen attacks. To overcome these restrictions, we present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process. This approach is, to the best our knowledge, the first successful and practical approach to network intrusion detection that utilises network flows in a self-supervised, edge leveraging GNN. Experimental results on two modern benchmark NIDS datasets not only clearly display the improvement of using Anomal-E embeddings rather than raw features, but also the potential Anomal-E has for detection on wild network traffic. | ['Marius Portmann', 'Siamak Layeghy', 'Wai Weng Lo', 'Evan Caville'] | 2022-07-14 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-7.75017515e-02 2.12146100e-02 1.47326402e-02 -2.83742309e-01
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9be78d35-d482-4d00-aeed-158c82a7af27 | anomaly-classification-in-distribution | 1805.04979 | null | http://arxiv.org/abs/1805.04979v1 | http://arxiv.org/pdf/1805.04979v1.pdf | Anomaly Classification in Distribution Networks Using a Quotient Gradient System | The classification of anomalies or sudden changes in power networks versus
normal abrupt changes or switching actions is essential to take appropriate
maintenance actions that guarantee the quality of power delivery. This issue
has increased in importance and has become more complicated with the
proliferation of volatile resources that introduce variability, uncertainty,
and intermittency in circuit behavior that can be observed as variations in
voltage and current phasors. This makes diagnostics applications more
challenging. This paper proposes using quotient gradient system (QGS) to train
two-stage partially recurrent neural network to improve anomaly classification
rate in power distribution networks using high-fidelity data from micro-phasor
measurement units (PMUs). QGS is a systematic approach to finding solutions of
constraint satisfaction problems. We transform the PMUs data from the power
network into a constraint satisfaction problem and use QGS to train a neural
network by solving the resulting optimization problem. Simulation results show
that the proposed supervised classification method can reliably distinguish
between different anomalies in power distribution networks. Comparison with
other neural network classifiers shows that QGS trained networks provide
significantly better classification. Sensitivity analysis is performed
concerning the number of PMUs, reporting rates, noise level and early versus
late data stream fusion frameworks. | [] | 2018-05-14 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 1.00572318e-01 -5.61476588e-01 -1.41556626e-02 -2.33502835e-01
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4355d890-6c26-4779-86e2-9483469dc277 | multi-domain-incremental-learning-for | 2110.12205 | null | https://arxiv.org/abs/2110.12205v1 | https://arxiv.org/pdf/2110.12205v1.pdf | Multi-Domain Incremental Learning for Semantic Segmentation | Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment performed sequentially on three popular road scene segmentation datasets demonstrates that existing segmentation frameworks fail at incrementally learning on a series of visually disparate geographical domains. When learning a new domain, the model catastrophically forgets previously learned knowledge. In this work, we pose the problem of multi-domain incremental learning for semantic segmentation. Given a model trained on a particular geographical domain, the goal is to (i) incrementally learn a new geographical domain, (ii) while retaining performance on the old domain, (iii) given that the previous domain's dataset is not accessible. We propose a dynamic architecture that assigns universally shared, domain-invariant parameters to capture homogeneous semantic features present in all domains, while dedicated domain-specific parameters learn the statistics of each domain. Our novel optimization strategy helps achieve a good balance between retention of old knowledge (stability) and acquiring new knowledge (plasticity). We demonstrate the effectiveness of our proposed solution on domain incremental settings pertaining to real-world driving scenes from roads of Germany (Cityscapes), the United States (BDD100k), and India (IDD). | ['C. V. Jawahar', 'Anbumani Subramanian', 'Chetan Arora', 'Vineeth N Balasubramanian', 'Rohit Saluja', 'Prachi Garg'] | 2021-10-23 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.57545704e-01 4.87503149e-02 -3.39055985e-01 -4.42192286e-01
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b928a92d-773d-4a6a-9792-90a43abf8356 | entity-cloze-by-date-understanding-what-lms | null | null | https://openreview.net/forum?id=InPbQdUhmUH | https://openreview.net/pdf?id=InPbQdUhmUH | Entity Cloze By Date: Understanding what LMs know about unseen entities | Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated. Our world, however, is dynamic, and new entities constantly arise. We propose a framework to analyze what LMs can infer about new entities that did not exist when the LMs were pretrained. We derive a dataset of entities indexed by their origination date and paired with their English Wikipedia articles, from which we can find sentences about each entity. We evaluate LMs' perplexity on masked spans within these sentences. We show that models more informed about the entities, such as those with access to a textual definition of them, achieve lower perplexity on this benchmark. Our experimental results demonstrate that making inferences about new entities remains difficult for LMs. Given its wide coverage on entity knowledge and temporal indexing, our dataset can be used to evaluate LMs and techniques designed to modify or extend their knowledge. Our automatic data collection pipeline can be easily used to continually update our benchmark. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['date-understanding'] | ['reasoning'] | [-3.12235683e-01 5.08575797e-01 -3.92563045e-01 -3.40754360e-01
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94860e21-6c18-43b1-8f23-b1180dd40a21 | single-image-intrinsic-decomposition-without | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Wei-Chiu_Single_Image_Intrinsic_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Wei-Chiu_Single_Image_Intrinsic_ECCV_2018_paper.pdf | Single Image Intrinsic Decomposition without a Single Intrinsic Image | Intrinsic image decomposition---decomposing a natural image into a set of images corresponding to different physical causes---is one of the key and fundamental problems of computer vision. Previous intrinsic decomposition approaches either address the problem in a fully supervised manner, or require multiple images of the same scene as input. These approaches are less desirable in practice, as ground truth intrinsic images are extremely difficult to acquire, and requirement of multiple images pose severe limitation on applicable scenarios. In this paper, we propose to bring the best of both worlds. We present a two stream convolutional neural network framework that is capable of learning the decomposition effectively in the absence of any ground truth intrinsic images, and can be easily extended to a (semi-)supervised setup. At inference time, our model can be easily reduced to a single stream module that performs intrinsic decomposition on a single input image. We demonstrate the effectiveness of our framework through extensive experimental study on both synthetic and real-world datasets, showing superior performance over previous approaches in both single-image and multi-image settings. Notably, our approach outperforms previous state-of-the-art single image methods while using only 50% of ground truth supervision. | ['Wei-Chiu Ma', 'Raquel Urtasun', 'Bolei Zhou', 'Antonio Torralba', 'Hang Chu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 6.09882832e-01 1.25906855e-01 -2.77027972e-02 -8.36142898e-02
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0c251e7b-092d-483b-8442-dafc68e2fef7 | single-image-deraining-via-rain-steaks-aware | 2209.07808 | null | https://arxiv.org/abs/2209.07808v2 | https://arxiv.org/pdf/2209.07808v2.pdf | Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network | It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions. | ['Shiqian Wu', 'Yuwen Li', 'Chaobing Zheng'] | 2022-09-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.90477911e-02 -5.21547735e-01 4.16021466e-01 -6.01175368e-01
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b0b2c8be-3972-4eb4-9a19-1eefcc0b5048 | machine-learning-computer-vision-applications | 2303.0756 | null | https://arxiv.org/abs/2303.07560v1 | https://arxiv.org/pdf/2303.07560v1.pdf | Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California | We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360{\deg} equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems. | ['Kostas Alexandridis'] | 2023-03-14 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 4.12065893e-01 -4.42097872e-01 3.91165167e-01 -5.54989994e-01
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5c3419d8-ee39-4d78-bd3c-18b1b63e602c | deletion-and-insertion-tests-in-regression | 2205.12423 | null | https://arxiv.org/abs/2205.12423v2 | https://arxiv.org/pdf/2205.12423v2.pdf | Deletion and Insertion Tests in Regression Models | A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function $f$. The insertion and deletion tests of Petsiuk et al. (2018) are used to judge the quality of algorithms that rank pixels from most to least important for a classification. Motivated by regression problems we establish a formula for their area under the curve (AUC) criteria in terms of certain main effects and interactions in an anchored decomposition of $f$. We find an expression for the expected value of the AUC under a random ordering of inputs to $f$ and propose an alternative area above a straight line for the regression setting. We use this criterion to compare feature importances computed by integrated gradients (IG) to those computed by Kernel SHAP (KS) as well as LIME, DeepLIFT, vanilla gradient and input$\times$gradient methods. KS has the best overall performance in two datasets we consider but it is very expensive to compute. We find that IG is nearly as good as KS while being much faster. Our comparison problems include some binary inputs that pose a challenge to IG because it must use values between the possible variable levels and so we consider ways to handle binary variables in IG. We show that sorting variables by their Shapley value does not necessarily give the optimal ordering for an insertion-deletion test. It will however do that for monotone functions of additive models, such as logistic regression. | ['Art B. Owen', 'Masayoshi Mase', 'Naofumi Hama'] | 2022-05-25 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.53814250e-01 1.33400112e-01 -2.40577564e-01 -5.65828741e-01
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14776783-02d2-4126-bf3a-29c19ba7f07c | boosting-the-convergence-of-reinforcement | 2107.08815 | null | https://arxiv.org/abs/2107.08815v1 | https://arxiv.org/pdf/2107.08815v1.pdf | Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data | Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. These impediments have greatly restricted the real-world application of RL-based auto-pruning. Thus, in this paper, we propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments have shown that our framework can accelerate the auto-pruning process by 1.5-2.5 times for ResNet20, and 1.81-2.375 times for other neural networks like ResNet56, ResNet18, and MobileNet v1. | ['Wei zhang', 'Wei Lin', 'Jun Yang', 'Feiwen Zhu', 'Mengdi Wang', 'Jiandong Mu'] | 2021-07-16 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 9.10794288e-02 -1.36316657e-01 -2.10600942e-01 -3.39045733e-01
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39e69067-ad9c-42b2-8b56-f1240c644fa0 | uncovering-hidden-challenges-in-query-based | 2009.00325 | null | https://arxiv.org/abs/2009.00325v2 | https://arxiv.org/pdf/2009.00325v2.pdf | Uncovering Hidden Challenges in Query-Based Video Moment Retrieval | The query-based moment retrieval is a problem of localising a specific clip from an untrimmed video according a query sentence. This is a challenging task that requires interpretation of both the natural language query and the video content. Like in many other areas in computer vision and machine learning, the progress in query-based moment retrieval is heavily driven by the benchmark datasets and, therefore, their quality has significant impact on the field. In this paper, we present a series of experiments assessing how well the benchmark results reflect the true progress in solving the moment retrieval task. Our results indicate substantial biases in the popular datasets and unexpected behaviour of the state-of-the-art models. Moreover, we present new sanity check experiments and approaches for visualising the results. Finally, we suggest possible directions to improve the temporal sentence grounding in the future. Our code for this paper is available at https://mayu-ot.github.io/hidden-challenges-MR . | ['Janne Heikkilä', 'Esa Rahtu', 'Yuta Nakashima', 'Mayu Otani'] | 2020-09-01 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.77990937e-01 -2.08601058e-01 -2.03901082e-01 -3.58488530e-01
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169a652a-106d-4513-82e9-4554c15656ed | diverse-demonstrations-improve-in-context | 2212.068 | null | https://arxiv.org/abs/2212.06800v3 | https://arxiv.org/pdf/2212.06800v3.pdf | Diverse Demonstrations Improve In-context Compositional Generalization | In-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution. In this setup, models are typically prompted with demonstrations that are similar to the input utterance. However, in the setup of compositional generalization, where models are tested on outputs with structures that are absent from the training set, selecting similar demonstrations is insufficient, as often no example will be similar enough to the input. In this work, we propose a method to select diverse demonstrations that aims to collectively cover all of the structures required in the output program, in order to encourage the model to generalize to new structures from these demonstrations. We empirically show that combining diverse demonstrations with in-context learning substantially improves performance across three compositional generalization semantic parsing datasets in the pure in-context learning setup and when combined with finetuning. | ['Jonathan Berant', 'Ben Bogin', 'Itay Levy'] | 2022-12-13 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 6.03911221e-01 2.20920846e-01 -5.62809184e-02 -7.22041130e-01
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15b60a9a-a3b3-4362-8a1d-a540ca108f27 | polygen-an-autoregressive-generative-model-of | 2002.1088 | null | https://arxiv.org/abs/2002.10880v1 | https://arxiv.org/pdf/2002.10880v1.pdf | PolyGen: An Autoregressive Generative Model of 3D Meshes | Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approaches have avoided the challenges of working with 3D meshes, instead using alternative object representations that are more compatible with neural architectures and training approaches. We present an approach which models the mesh directly, predicting mesh vertices and faces sequentially using a Transformer-based architecture. Our model can condition on a range of inputs, including object classes, voxels, and images, and because the model is probabilistic it can produce samples that capture uncertainty in ambiguous scenarios. We show that the model is capable of producing high-quality, usable meshes, and establish log-likelihood benchmarks for the mesh-modelling task. We also evaluate the conditional models on surface reconstruction metrics against alternative methods, and demonstrate competitive performance despite not training directly on this task. | ['Peter W. Battaglia', 'S. M. Ali Eslami', 'Charlie Nash', 'Yaroslav Ganin'] | 2020-02-23 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6917-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6917-Paper.pdf | icml-2020-1 | ['3d-shape-generation'] | ['computer-vision'] | [ 2.73395330e-01 5.29292643e-01 1.32548422e-01 -4.50036824e-01
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2293501f-8f63-4a77-bc7b-9dc26cdec9a0 | wepamadm-outlier-detection-weighted-outlier | 2306.06139 | null | https://arxiv.org/abs/2306.06139v1 | https://arxiv.org/pdf/2306.06139v1.pdf | WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining | Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However,creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semisupervised task where the algorithm learns to recognize abnormality while being taught the normal class. | ['Madhuri Bhavsar', 'Rachna Jain', 'Jai Prakash Verma', 'Ravindrakumar Purohit'] | 2023-06-09 | null | null | null | null | ['outlier-detection', 'fault-detection'] | ['methodology', 'miscellaneous'] | [ 3.50031853e-01 -8.38644952e-02 -1.61949694e-01 -3.59338343e-01
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bb495b46-ba88-4971-b75a-ff0648678329 | diagnosis-of-covid-19-based-on-chest | 2212.13032 | null | https://arxiv.org/abs/2212.13032v1 | https://arxiv.org/pdf/2212.13032v1.pdf | Diagnosis of COVID-19 based on Chest Radiography | The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolutional neural network (CNN) model was proposed to aid radiologists in diagnosing COVID-19 patients. First, this work conducted a comparative study on the performance of modified VGG-16, ResNet-50 and DenseNet-121 to classify CXR images into normal, COVID-19 and viral pneumonia. Then, the impact of image augmentation on the classification results was evaluated. The publicly available COVID-19 Radiography Database was used throughout this study. After comparison, ResNet-50 achieved the highest accuracy with 95.88%. Next, after training ResNet-50 with rotation, translation, horizontal flip, intensity shift and zoom augmented dataset, the accuracy dropped to 80.95%. Furthermore, an ablation study on the effect of image augmentation on the classification results found that the combinations of rotation and intensity shift augmentation methods obtained an accuracy higher than baseline, which is 96.14%. Finally, ResNet-50 with rotation and intensity shift augmentations performed the best and was proposed as the final classification model in this work. These findings demonstrated that the proposed classification model can provide a promising result for COVID-19 diagnosis. | ['Hoi Leong Lee', 'Mei Gah Lim'] | 2022-12-26 | null | null | null | null | ['image-augmentation', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 1.22152783e-01 -2.79251367e-01 -5.08002788e-02 -1.05318971e-01
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121b8637-25f3-4f0c-b06c-5d03d09e4e3a | counterfactual-edits-for-generative | 2303.01555 | null | https://arxiv.org/abs/2303.01555v1 | https://arxiv.org/pdf/2303.01555v1.pdf | Counterfactual Edits for Generative Evaluation | Evaluation of generative models has been an underrepresented field despite the surge of generative architectures. Most recent models are evaluated upon rather obsolete metrics which suffer from robustness issues, while being unable to assess more aspects of visual quality, such as compositionality and logic of synthesis. At the same time, the explainability of generative models remains a limited, though important, research direction with several current attempts requiring access to the inner functionalities of generative models. Contrary to prior literature, we view generative models as a black box, and we propose a framework for the evaluation and explanation of synthesized results based on concepts instead of pixels. Our framework exploits knowledge-based counterfactual edits that underline which objects or attributes should be inserted, removed, or replaced from generated images to approach their ground truth conditioning. Moreover, global explanations produced by accumulating local edits can also reveal what concepts a model cannot generate in total. The application of our framework on various models designed for the challenging tasks of Story Visualization and Scene Synthesis verifies the power of our approach in the model-agnostic setting. | ['Giorgos Stamou', 'Konstantinos Thomas', 'Giorgos Filandrianos', 'Maria Lymperaiou'] | 2023-03-02 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 5.01199186e-01 5.23395598e-01 9.11884308e-02 -2.81976968e-01
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d5e4c16b-46c8-49da-9cba-070af4558a05 | 191209654 | 1912.09654 | null | https://arxiv.org/abs/1912.09654v1 | https://arxiv.org/pdf/1912.09654v1.pdf | JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds | In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. Firstly, we build an effective backbone network to extract robust features from the raw point clouds. Secondly, to obtain more discriminative features, a point cloud feature fusion module is proposed to fuse the different layer features of the backbone network. Furthermore, a joint instance semantic segmentation module is developed to transform semantic features into instance embedding space, and then the transformed features are further fused with instance features to facilitate instance segmentation. Meanwhile, this module also aggregates instance features into semantic feature space to promote semantic segmentation. Finally, the instance predictions are generated by applying a simple mean-shift clustering on instance embeddings. As a result, we evaluate the proposed JSNet on a large-scale 3D indoor point cloud dataset S3DIS and a part dataset ShapeNet, and compare it with existing approaches. Experimental results demonstrate our approach outperforms the state-of-the-art method in 3D instance segmentation with a significant improvement in 3D semantic prediction and our method is also beneficial for part segmentation. The source code for this work is available at https://github.com/dlinzhao/JSNet. | ['Wenbing Tao', 'Lin Zhao'] | 2019-12-20 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-1.50472508e-04 5.23445383e-02 -2.36895587e-02 -5.64032972e-01
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aa68b9e3-f9fe-4eae-bcb7-070780ca5cfd | identifying-the-origin-of-finger-vein-samples | 2102.03992 | null | https://arxiv.org/abs/2102.03992v1 | https://arxiv.org/pdf/2102.03992v1.pdf | Identifying the Origin of Finger Vein Samples Using Texture Descriptors | Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and for initiating sensor-specific processing pipelines in sensor-heterogeneous environments. Motivated by shortcomings of the photo response non-uniformity (PRNU) based method in the biometric context, we use a texture classification approach to detect the origin of finger vein sample images. Based on eight publicly available finger vein datasets and applying eight classical yet simple texture descriptors and SVM classification, we demonstrate excellent sensor model identification results for raw finger vein samples as well as for the more challenging region of interest data. The observed results establish texture descriptors as effective competitors to PRNU in finger vein sensor model identification. | ['Andreas Uhl', 'Babak Maser'] | 2021-02-08 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 8.41353357e-01 -3.30489993e-01 -2.92140186e-01 -2.49999180e-01
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150ec280-420a-4cdd-a181-980f08fcf7e7 | a-hybrid-transmission-model-for-plasmodium | 2208.10403 | null | https://arxiv.org/abs/2208.10403v1 | https://arxiv.org/pdf/2208.10403v1.pdf | A hybrid transmission model for Plasmodium vivax accounting for superinfection, immunity and the hypnozoite reservoir | Malaria is a vector-borne disease that exacts a grave toll in the Global South. The epidemiology of Plasmodium vivax, the most geographically expansive agent of human malaria, is characterised by the accrual of a reservoir of dormant parasites known as hypnozoites. Relapses, arising from hypnozoite activation events, comprise the majority of the blood-stage infection burden, with implications for the acquisition of immunity and the distribution of superinfection. Here, we construct a hybrid transmission model for P. vivax that concurrently accounts for the accrual of the hypnozoite reservoir, (blood-stage) superinfection and the acquisition of immunity. We begin by analytically characterising within-host dynamics as a function of mosquito-to-human transmission intensity, extending our previous model (comprising an open network of infinite server queues) to capture a discretised immunity level. To model transmission-blocking and antidisease immunity, we allow for geometric decay in the respective probabilities of successful human-to-mosquito transmission and symptomatic blood-stage infection as a function of this immunity level. Under a hybrid approximation -- whereby probabilistic within-host distributions are cast as expected population-level proportions -- we couple host and vector dynamics to recover a deterministic compartmental model in line with Ross-Macdonald theory. We then perform a steady-state analysis for this compartmental model, informed by the (analytic) distributions derived at the within-host level. To characterise transient dynamics, we derive a reduced system of integrodifferential equations (IDEs), likewise informed by our within-host queueing network, allowing us to recover population-level distributions for various quantities of epidemiological interest. Our model provides insights into important, but poorly understood, epidemiological features of P. vivax. | ['Jennifer A. Flegg', 'James M. McCaw', 'Peter G. Taylor', 'Somya Mehra'] | 2022-08-22 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 9.67606977e-02 -3.14909399e-01 2.71480203e-01 1.61248803e-01
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78d968c0-a5cb-4931-946a-37e190c3b4a8 | hhh-an-online-medical-chatbot-system-based-on-1 | 2002.0314 | null | https://arxiv.org/abs/2002.03140v1 | https://arxiv.org/pdf/2002.03140v1.pdf | HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention | This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. Based on this chatbot framework, we build HHH, an online question-and-answer (QA) Healthcare Helper system for answering complex medical questions. HHH maintains a knowledge graph constructed from medical data collected from the Internet. HHH also implements a novel text representation and similarity deep learning model, Hierarchical BiLSTM Attention Model (HBAM), to find the most similar question from a large QA dataset. We compare HBAM with other state-of-the-art language models such as bidirectional encoder representation from transformers (BERT) and Manhattan LSTM Model (MaLSTM). We train and test the models with a subset of the Quora duplicate questions dataset in the medical area. The experimental results show that our model is able to achieve a superior performance than these existing methods. | ['Jiamou Liu', 'Lin Ni', 'Qiming Bao'] | 2020-02-08 | hhh-an-online-medical-chatbot-system-based-on | https://www.researchgate.net/publication/338926786_HHH_An_Online_Medical_Chatbot_System_based_on_Knowledge_Graph_and_Hierarchical_Bi-Directional_Attention | https://www.researchgate.net/publication/338926786_HHH_An_Online_Medical_Chatbot_System_based_on_Knowledge_Graph_and_Hierarchical_Bi-Directional_Attention | proceedings-of-the-australasian-computer | ['medical-question-pair-similarity-computation'] | ['natural-language-processing'] | [-1.27467394e-01 8.07575762e-01 2.23131493e-01 -3.90014648e-01
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4dff9bdb-2e38-441e-ac39-c0a66f179bb5 | diagnosing-and-preventing-instabilities-in | 2010.05099 | null | https://arxiv.org/abs/2010.05099v3 | https://arxiv.org/pdf/2010.05099v3.pdf | Diagnosing and Preventing Instabilities in Recurrent Video Processing | Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and show that they tend to fail catastrophically at inference time on long video sequences. To address this issue, we (1) introduce a diagnostic tool which produces input sequences optimized to trigger instabilities and that can be interpreted as visualizations of temporal receptive fields, and (2) propose two approaches to enforce the stability of a model during training: constraining the spectral norm or constraining the stable rank of its convolutional layers. We then introduce Stable Rank Normalization for Convolutional layers (SRN-C), a new algorithm that enforces these constraints. Our experimental results suggest that SRN-C successfully enforces stability in recurrent video processing models without a significant performance loss. | ['Gregory Slabaugh', 'Ales Leonardis', 'Philip Torr', 'Puneet K. Dokania', 'Matteo Maggioni', 'Aivar Sootla', 'Thomas Tanay'] | 2020-10-10 | null | null | null | null | ['video-denoising', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.78869265e-01 -1.14492431e-01 1.93923429e-01 8.19332376e-02
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15d80f7d-10b5-4db7-82c9-a988446d2062 | patch-based-discriminative-feature-learning | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Patch-Based_Discriminative_Feature_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Patch-Based_Discriminative_Feature_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2019_paper.pdf | Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification | While discriminative local features have been shown effective in solving the person re-identification problem, they are limited to be trained on fully pairwise labelled data which is expensive to obtain. In this work, we overcome this problem by proposing a patch-based unsupervised learning framework in order to learn discriminative feature from patches instead of the whole images. The patch-based learning leverages similarity between patches to learn a discriminative model. Specifically, we develop a PatchNet to select patches from the feature map and learn discriminative features for these patches. To provide effective guidance for the PatchNet to learn discriminative patch feature on unlabeled datasets, we propose an unsupervised patch-based discriminative feature learning loss. In addition, we design an image-level feature learning loss to leverage all the patch features of the same image to serve as an image-level guidance for the PatchNet. Extensive experiments validate the superiority of our method for unsupervised person re-id. Our code is available at https://github.com/QizeYang/PAUL.
| [' Wei-Shi Zheng', ' Ancong Wu', ' Hong-Xing Yu', 'Qize Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.65751104e-02 -1.68722406e-01 -4.06609088e-01 -7.75881886e-01
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5d44bfc9-dfc4-446a-b3a9-d9bdf73aab64 | good-view-hunting-learning-photo-composition | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wei_Good_View_Hunting_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Good_View_Hunting_CVPR_2018_paper.pdf | Good View Hunting: Learning Photo Composition From Dense View Pairs | Finding views with good photo composition is a challenging task for machine learning methods. A key difficulty is the lack of well annotated large scale datasets. Most existing datasets only provide a limited number of annotations for good views, while ignoring the comparative nature of view selection. In this work, we present the first large scale Comparative Photo Composition dataset, which contains over one million comparative view pairs annotated using a cost-effective crowdsourcing workflow. We show that these comparative view annotations are essential for training a robust neural network model for composition. In addition, we propose a novel knowledge transfer framework to train a fast view proposal network, which runs at 75+ FPS and achieves state-of-the-art performance in image cropping and thumbnail generation tasks on three benchmark datasets. The superiority of our method is also demonstrated in a user study on a challenging experiment, where our method significantly outperforms the baseline methods in producing diversified well-composed views. | ['Dimitris Samaras', 'Minh Hoai', 'RadomÃ\xadr Mech', 'Xiaohui Shen', 'Zijun Wei', 'Zhe Lin', 'Jianming Zhang'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['image-cropping'] | ['computer-vision'] | [ 4.66148823e-01 -1.97273210e-01 -1.13730900e-01 -3.44355106e-01
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9a5b1d5c-6589-4c09-afad-32a3b4a63cba | backpropagation-clipping-for-deep-learning | 2202.05089 | null | https://arxiv.org/abs/2202.05089v2 | https://arxiv.org/pdf/2202.05089v2.pdf | Backpropagation Clipping for Deep Learning with Differential Privacy | We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each trainable layer's inputs (during the forward pass) and its upstream gradients (during the backward pass) to ensure bounded global sensitivity for the layer's gradient; this combination replaces the gradient clipping step in existing DP-SGD variants. Our approach is simple to implement in existing deep learning frameworks. The results of our empirical evaluation demonstrate that backpropagation clipping provides higher accuracy at lower values for the privacy parameter $\epsilon$ compared to previous work. We achieve 98.7% accuracy for MNIST with $\epsilon = 0.07$ and 74% accuracy for CIFAR-10 with $\epsilon = 3.64$. | ['Joseph P. Near', 'David Slater', 'Calvin Hirsch', 'David Darais', 'Ivoline C. Ngong', 'Timothy Stevens'] | 2022-02-10 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.78205505e-01 1.18654341e-01 -3.86844389e-02 -6.70247078e-01
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f71d6d46-039e-4dc0-994f-12fc13064572 | domain-adaptive-self-supervised-pre-training | 2211.10641 | null | https://arxiv.org/abs/2211.10641v2 | https://arxiv.org/pdf/2211.10641v2.pdf | Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings | Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github.com/barisbatuhan/DASS_Detector. | ['Tevfik Metin Sezgin', 'Deniz Yuret', 'Barış Batuhan Topal'] | 2022-11-19 | null | null | null | null | ['body-detection', 'face-detection', 'weakly-supervised-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.40513673e-02 3.28090265e-02 -2.74191141e-01 -4.09297705e-01
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e1d5c1cd-39da-4d99-b3d8-1622c32c8829 | compact-global-descriptor-for-neural-networks | 1907.09665 | null | https://arxiv.org/abs/1907.09665v10 | https://arxiv.org/pdf/1907.09665v10.pdf | Compact Global Descriptor for Neural Networks | Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks. Yet neither stacks of convolutional operations to enlarge receptive fields nor recent nonlocal modules is computationally efficient. In this paper, we present a generic family of lightweight global descriptors for modeling the interactions between positions across different dimensions (e.g., channels, frames). This descriptor enables subsequent convolutions to access the informative global features with negligible computational complexity and parameters. Benchmark experiments show that the proposed method can complete state-of-the-art long-range mechanisms with a significant reduction in extra computing cost. Code available at https://github.com/HolmesShuan/Compact-Global-Descriptor. | ['Qinghao Hu', 'Qiang Chen', 'Peisong Wang', 'Jian Cheng', 'Xiangyu He', 'Ke Cheng'] | 2019-07-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-4.22784597e-01 -8.16836834e-01 -8.50050747e-02 -3.91946137e-01
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918c7313-ee7a-47ea-82ce-c35949f54fc9 | limsi-cot-at-semeval-2017-task-12-neural | null | null | https://aclanthology.org/S17-2098 | https://aclanthology.org/S17-2098.pdf | LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives | In this paper we present our participation to SemEval 2017 Task 12. We used a neural network based approach for entity and temporal relation extraction, and experimented with two domain adaptation strategies. We achieved competitive performance for both tasks. | ["Aur{\\'e}lie N{\\'e}v{\\'e}ol", 'Olivier Ferret', 'Xavier Tannier', 'Julien Tourille'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-4.39916365e-02 6.33012354e-01 -2.44057730e-01 -6.39116585e-01
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7b641199-6616-48ac-94c2-2cf84230540f | streamlining-cross-document-coreference | 2009.11032 | null | https://arxiv.org/abs/2009.11032v3 | https://arxiv.org/pdf/2009.11032v3.pdf | Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling | Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation of performance. To facilitate proper future research on this task, our primary contribution is proposing a pragmatic evaluation methodology which assumes access to only raw text -- rather than assuming gold mentions, disregards singleton prediction, and addresses typical targeted settings in CD coreference resolution. Aiming to set baseline results for future research that would follow our evaluation methodology, we build the first end-to-end model for this task. Our model adapts and extends recent neural models for within-document coreference resolution to address the CD coreference setting, which outperforms state-of-the-art results by a significant margin. | ['Gabriel Stanovsky', 'Ido Dagan', 'Mandar Joshi', 'Arie Cattan', 'Alon Eirew'] | 2020-09-23 | null | null | null | null | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 4.36863959e-01 3.49566638e-01 -7.57498860e-01 -3.98741663e-01
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7f308bea-f8f6-450d-af9b-a789cb00ede7 | hatemonitors-language-agnostic-abuse | 1909.12642 | null | https://arxiv.org/abs/1909.12642v1 | https://arxiv.org/pdf/1909.12642v1.pdf | HateMonitors: Language Agnostic Abuse Detection in Social Media | Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful content in social media. In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019. We have used a Gradient Boosting model, along with BERT and LASER embeddings, to make the system language agnostic. Our model came at First position for the German sub-task A. We have also made our model public at https://github.com/punyajoy/HateMonitors-HASOC . | ['Binny Mathew', 'Punyajoy Saha', 'Animesh Mukherjee', 'Pawan Goyal'] | 2019-09-27 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-3.44343901e-01 -4.37772945e-02 -2.54298449e-01 -4.27121408e-02
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6092704f-0695-4326-80fc-9dc5f1d1ce7f | single-perspective-warps-in-natural-image | 1802.04645 | null | http://arxiv.org/abs/1802.04645v2 | http://arxiv.org/pdf/1802.04645v2.pdf | Single-Perspective Warps in Natural Image Stitching | Results of image stitching can be perceptually divided into
single-perspective and multiple-perspective. Compared to the
multiple-perspective result, the single-perspective result excels in
perspective consistency but suffers from projective distortion. In this paper,
we propose two single-perspective warps for natural image stitching. The first
one is a parametric warp, which is a combination of the
as-projective-as-possible warp and the quasi-homography warp via dual-feature.
The second one is a mesh-based warp, which is determined by optimizing a total
energy function that simultaneously emphasizes different characteristics of the
single-perspective warp, including alignment, naturalness, distortion and
saliency. A comprehensive evaluation demonstrates that the proposed warp
outperforms some state-of-the-art warps, including homography, APAP,
AutoStitch, SPHP and GSP. | ['Tianli Liao', 'Nan Li'] | 2018-02-13 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.12794417e-01 -4.31148618e-01 8.34260583e-02 1.60971969e-01
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8f8d29d1-7ae4-4fb9-b590-99fb7b92d1cd | multi-reference-image-super-resolution-a | 2212.09988 | null | https://arxiv.org/abs/2212.09988v1 | https://arxiv.org/pdf/2212.09988v1.pdf | Multi-Reference Image Super-Resolution: A Posterior Fusion Approach | Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. Extensive experiments on the CUFED5 dataset demonstrate that the proposed methods can be applied to various state-of-the-art RefSR models to get a consistent improvement in image quality. | ['Tsz Fung Yau', 'Haining Tan', 'Ke Zhao'] | 2022-12-20 | null | null | null | null | ['reference-based-super-resolution'] | ['computer-vision'] | [ 4.57945645e-01 -2.75702298e-01 -5.60545586e-02 -5.38068235e-01
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6fd4bfe7-1c86-42fd-b168-02550298d960 | neural-feature-extraction-for-contextual | null | null | https://aclanthology.org/R19-1091 | https://aclanthology.org/R19-1091.pdf | Neural Feature Extraction for Contextual Emotion Detection | This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes. The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier. This configuration reached 69.93{\%} in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25{\%}. | ['Leila Kosseim', 'Elham Mohammadi', 'Hessam Amini'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.13333739e-01 3.11054081e-01 7.43620545e-02 -4.50443745e-01
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5c07dbec-d7f2-4119-a521-41475f46f413 | model-driven-ct-reconstruction-algorithm-for | 2305.08882 | null | https://arxiv.org/abs/2305.08882v1 | https://arxiv.org/pdf/2305.08882v1.pdf | Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging | The limited imaging performance of low-density objects in a zone plate based nano-resolution hard X-ray computed tomography (CT) system can be significantly improved by accessing the phase information. To do so, a grating-based Lau interferometer needs to be integrated. However, the nano-resolution phase contrast CT, denoted as nPCT, reconstructed from such an interferometer system may suffer resolution loss due to the strong signal diffraction. Aimed at performing accurate nPCT image reconstruction directly from these diffracted projections, a new model-driven nPCT image reconstruction algorithm is developed. First, the diffraction procedure is mathematically modeled into a matrix B, from which the projections without signal splitting can be generated invertedly. Second, a penalized weighed least-square model with total variation (PWLS-TV) is employed to denoise these projections. Finally, nPCT images with high resolution and high accuracy are reconstructed using the filtered-back-projection (FBP) method. Numerical simulations demonstrate that this algorithm is able to deal with diffracted projections having any splitting distances. Interestingly, results reveal that nPCT images with higher signal-to-noise-ratio (SNR) can be reconstructed from projections with larger signal splittings. In conclusion, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nPCT imaging system. | ['Yongshuai Ge', 'Peiping Zhu', 'Jinyou Xu', 'Hairong Zheng', 'Dong Liang', 'Ting Su', 'Yuhang Tan', 'Xuebao Cai'] | 2023-05-14 | null | null | null | null | ['image-reconstruction', 'computed-tomography-ct'] | ['computer-vision', 'methodology'] | [ 6.55100286e-01 -1.23688340e-01 5.25992334e-01 -2.08203271e-01
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c3292121-e775-4841-8407-35a7b38fd04f | ganomaly-semi-supervised-anomaly-detection | 1805.06725 | null | http://arxiv.org/abs/1805.06725v3 | http://arxiv.org/pdf/1805.06725v3.pdf | GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | Anomaly detection is a classical problem in computer vision, namely the
determination of the normal from the abnormal when datasets are highly biased
towards one class (normal) due to the insufficient sample size of the other
class (abnormal). While this can be addressed as a supervised learning problem,
a significantly more challenging problem is that of detecting the
unknown/unseen anomaly case that takes us instead into the space of a
one-class, semi-supervised learning paradigm. We introduce such a novel anomaly
detection model, by using a conditional generative adversarial network that
jointly learns the generation of high-dimensional image space and the inference
of latent space. Employing encoder-decoder-encoder sub-networks in the
generator network enables the model to map the input image to a lower dimension
vector, which is then used to reconstruct the generated output image. The use
of the additional encoder network maps this generated image to its latent
representation. Minimizing the distance between these images and the latent
vectors during training aids in learning the data distribution for the normal
samples. As a result, a larger distance metric from this learned data
distribution at inference time is indicative of an outlier from that
distribution - an anomaly. Experimentation over several benchmark datasets,
from varying domains, shows the model efficacy and superiority over previous
state-of-the-art approaches. | ['Amir Atapour-Abarghouei', 'Toby P. Breckon', 'Samet Akcay'] | 2018-05-17 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 0.6428143 0.3440319 0.15126453 -0.4588701 -0.67756927 -0.28850558
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0.09371965 -0.04290457 0.39103585 0.3168792 -0.26679444 -0.24052326
0.5758749 0.78371793 -0.21520695 -0.848377 -0.56815755 0.80798846
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-0.6760045 1.031427 0.768932 -0.50152016 -0.6909053 0.26873907] | [7.633440971374512, 2.269700050354004] |
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