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4d641808-c9b7-4afe-a403-ae299faab190 | t2v-ddpm-thermal-to-visible-face-translation | 2209.08814 | null | https://arxiv.org/abs/2209.08814v1 | https://arxiv.org/pdf/2209.08814v1.pdf | T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models | Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, and often images are captured in an alternate domain such as the thermal infrared domain. Facial verification in thermal images is often performed after retrieving the corresponding visible domain images. This is a well-established problem often known as the Thermal-to-Visible (T2V) image translation. In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images. During training, the model learns the conditional distribution of visible facial images given their corresponding thermal image through the diffusion process. During inference, the visible domain image is obtained by starting from Gaussian noise and performing denoising repeatedly. The existing inference process for DDPMs is stochastic and time-consuming. Hence, we propose a novel inference strategy for speeding up the inference time of DDPMs, specifically for the problem of T2V image translation. We achieve the state-of-the-art results on multiple datasets. The code and pretrained models are publically available at http://github.com/Nithin-GK/T2V-DDPM | ['Vishal M. Patel', 'Nithin Gopalakrishnan Nair'] | 2022-09-19 | null | null | null | null | ['person-recognition', 'thermal-image-denoising'] | ['computer-vision', 'computer-vision'] | [ 4.68384236e-01 -4.61224884e-01 8.80284160e-02 -4.52557027e-01
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ddf17061-4251-4b23-a430-b35c587f448f | an-empirical-comparison-of-unsupervised | null | null | https://aclanthology.org/2020.acl-main.300 | https://aclanthology.org/2020.acl-main.300.pdf | An Empirical Comparison of Unsupervised Constituency Parsing Methods | Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations. While many methods have been proposed to tackle the problem, including statistical and neural methods, their experimental results are often not directly comparable due to discrepancies in datasets, data preprocessing, lexicalization, and evaluation metrics. In this paper, we first examine experimental settings used in previous work and propose to standardize the settings for better comparability between methods. We then empirically compare several existing methods, including decade-old and newly proposed ones, under the standardized settings on English and Japanese, two languages with different branching tendencies. We find that recent models do not show a clear advantage over decade-old models in our experiments. We hope our work can provide new insights into existing methods and facilitate future empirical evaluation of unsupervised constituency parsing. | ['Kewei Tu', 'Jun Li', 'Jiong Cai', 'Yong Jiang', 'Yifan Cao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.61877215e-01 1.96923271e-01 -5.55241883e-01 -7.63832092e-01
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b37f8e48-507e-4c7a-9777-324ceeaa5e79 | application-of-the-ring-theory-in-the | 1402.4069 | null | http://arxiv.org/abs/1402.4069v2 | http://arxiv.org/pdf/1402.4069v2.pdf | Application of the Ring Theory in the Segmentation of Digital Images | Ring theory is one of the branches of the abstract algebra that has been
broadly used in images. However, ring theory has not been very related with
image segmentation. In this paper, we propose a new index of similarity among
images using Zn rings and the entropy function. This new index was applied as a
new stopping criterion to the Mean Shift Iterative Algorithm with the goal to
reach a better segmentation. An analysis on the performance of the algorithm
with this new stopping criterion is carried out. The obtained results proved
that the new index is a suitable tool to compare images. | ['Roberto Rodríguez', 'Esley Torres', 'Yasel Garcés', 'Osvaldo Pereira'] | 2014-02-17 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [ 3.56922418e-01 3.06843966e-01 -9.92464274e-02 -1.21492138e-02
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b81f8333-0358-473a-a3e9-1a3661c75e46 | learn-from-all-erasing-attention-consistency | 2207.10299 | null | https://arxiv.org/abs/2207.10299v2 | https://arxiv.org/pdf/2207.10299v2.pdf | Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition | Noisy label Facial Expression Recognition (FER) is more challenging than traditional noisy label classification tasks due to the inter-class similarity and the annotation ambiguity. Recent works mainly tackle this problem by filtering out large-loss samples. In this paper, we explore dealing with noisy labels from a new feature-learning perspective. We find that FER models remember noisy samples by focusing on a part of the features that can be considered related to the noisy labels instead of learning from the whole features that lead to the latent truth. Inspired by that, we propose a novel Erasing Attention Consistency (EAC) method to suppress the noisy samples during the training process automatically. Specifically, we first utilize the flip semantic consistency of facial images to design an imbalanced framework. We then randomly erase input images and use flip attention consistency to prevent the model from focusing on a part of the features. EAC significantly outperforms state-of-the-art noisy label FER methods and generalizes well to other tasks with a large number of classes like CIFAR100 and Tiny-ImageNet. The code is available at https://github.com/zyh-uaiaaaa/Erasing-Attention-Consistency. | ['Weihong Deng', 'Xu Ling', 'Chengrui Wang', 'Yuhang Zhang'] | 2022-07-21 | null | null | null | null | ['facial-expression-recognition', 'learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.22599977e-01 1.54472426e-01 -9.23988745e-02 -9.03527677e-01
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60962144-19f2-4d9f-8505-38392c955204 | relation-classification-via-relation | null | null | https://aclanthology.org/2021.semdeep-1.4 | https://aclanthology.org/2021.semdeep-1.4.pdf | Relation Classification via Relation Validation | null | ['Brigitte Grau', 'Antoine Doucet', 'José G. Moreno'] | null | null | null | null | semdeep-2021-1 | ['relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7d0f60b6-e1a4-491a-b112-fd68f1adae5e | comparing-deep-learning-strategies-for-paired | 2101.06979 | null | https://arxiv.org/abs/2101.06979v1 | https://arxiv.org/pdf/2101.06979v1.pdf | Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI | We address the problem of multimodal liver segmentation in paired but unregistered T1 and T2-weighted MR images. We compare several strategies described in the literature, with or without multi-task training, with or without pre-registration. We also compare different loss functions (cross-entropy, Dice loss, and three adversarial losses). All methods achieved comparable performances with the exception of a multi-task setting that performs both segmentations at once, which performed poorly. | ['Isabelle Bloch', 'Laurent Milot', 'Pierre-Jean Valette', 'Anna Sesilia Vlachomitrou', 'Guillaume Pizaine', 'Olivier Nempont', 'Mathilde Trintignac', 'Vincent Couteaux'] | 2021-01-18 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.99103251e-01 4.96226326e-02 1.59114093e-01 -3.21426600e-01
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c9d7a239-d43e-4c34-b3be-b2746245f88b | semantic-embedding-space-for-zero-shot-action | 1502.01540 | null | http://arxiv.org/abs/1502.01540v1 | http://arxiv.org/pdf/1502.01540v1.pdf | Semantic Embedding Space for Zero-Shot Action Recognition | The number of categories for action recognition is growing rapidly. It is
thus becoming increasingly hard to collect sufficient training data to learn
conventional models for each category. This issue may be ameliorated by the
increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a
mapping is constructed between visual features and a human interpretable
semantic description of each category, allowing categories to be recognised in
the absence of any training data. Existing ZSL studies focus primarily on image
data, and attribute-based semantic representations. In this paper, we address
zero-shot recognition in contemporary video action recognition tasks, using
semantic word vector space as the common space to embed videos and category
labels. This is more challenging because the mapping between the semantic space
and space-time features of videos containing complex actions is more complex
and harder to learn. We demonstrate that a simple self-training and data
augmentation strategy can significantly improve the efficacy of this mapping.
Experiments on human action datasets including HMDB51 and UCF101 demonstrate
that our approach achieves the state-of-the-art zero-shot action recognition
performance. | ['Timothy Hospedales', 'Xun Xu', 'Shaogang Gong'] | 2015-02-05 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 6.76437736e-01 -8.06236416e-02 -4.36641932e-01 -4.85697240e-01
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ce56014b-e9d1-49a6-92b6-ce40173d560e | hierarchical-deep-learning-classification-of | 2009.00542 | null | https://arxiv.org/abs/2009.00542v1 | https://arxiv.org/pdf/2009.00542v1.pdf | Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading | Timely cancer reporting data are required in order to understand the impact of cancer, inform public health resource planning and implement cancer policy especially in Sub Saharan Africa where the reporting lag is behind world averages. Unstructured pathology reports, which contain tumor specific data, are the main source of information collected by cancer registries. Due to manual processing and labelling of pathology reports using the International Classification of Disease for oncology (ICD-O) codes, by human coders employed by cancer registries, has led to a considerable lag in cancer reporting. We present a hierarchical deep learning classification method that employs convolutional neural network models to automate the classification of 1813 anonymized breast cancer pathology reports with applicable ICD-O morphology codes across 9 classes. We demonstrate that the hierarchical deep learning classification method improves on performance in comparison to a flat multiclass CNN model for ICD-O morphology classification of the same reports. | ['Tapiwa Chiwewe', 'Waheeda Saib', 'Elvira Singh'] | 2020-08-28 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 2.07190942e-02 3.20624799e-01 -6.78168416e-01 -3.79175395e-01
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f985eb9b-4bd9-4b47-9544-e71f3c4e2453 | repeated-random-sampling-for-minimizing-the | 2305.18424 | null | https://arxiv.org/abs/2305.18424v1 | https://arxiv.org/pdf/2305.18424v1.pdf | Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning | Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks. Behind this success are rigorously designed strategies for identifying informative training examples out of large datasets. However, these strategies come with additional computational costs associated with subset selection or data distillation before training begins, and furthermore, many are shown to even under-perform random sampling in high data compression regimes. As such, many data pruning, coreset selection, or distillation methods may not reduce 'time-to-accuracy', which has become a critical efficiency measure of training deep neural networks over large datasets. In this work, we revisit a powerful yet overlooked random sampling strategy to address these challenges and introduce an approach called Repeated Sampling of Random Subsets (RSRS or RS2), where we randomly sample the subset of training data for each epoch of model training. We test RS2 against thirty state-of-the-art data pruning and data distillation methods across four datasets including ImageNet. Our results demonstrate that RS2 significantly reduces time-to-accuracy compared to existing techniques. For example, when training on ImageNet in the high-compression regime (using less than 10% of the dataset each epoch), RS2 yields accuracy improvements up to 29% compared to competing pruning methods while offering a runtime reduction of 7x. Beyond the above meta-study, we provide a convergence analysis for RS2 and discuss its generalization capability. The primary goal of our work is to establish RS2 as a competitive baseline for future data selection or distillation techniques aimed at efficient training. | ['Theodoros Rekatsinas', 'Nezihe Merve Gürel', 'Dionysis Kalogerias', 'Amin Karbasi', 'Konstantinos E. Nikolakakis', 'Vasilis Mageirakos', 'Roger Waleffe', 'Patrik Okanovic'] | 2023-05-28 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.12485325e-01 3.56937237e-02 -4.54581469e-01 -5.76169789e-01
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61c10703-85e4-4e3a-97cb-610a882fcda8 | simmim-a-simple-framework-for-masked-image | 2111.09886 | null | https://arxiv.org/abs/2111.09886v2 | https://arxiv.org/pdf/2111.09886v2.pdf | SimMIM: A Simple Framework for Masked Image Modeling | This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and tokenization via discrete VAE or clustering. To study what let the masked image modeling task learn good representations, we systematically study the major components in our framework, and find that simple designs of each component have revealed very strong representation learning performance: 1) random masking of the input image with a moderately large masked patch size (e.g., 32) makes a strong pre-text task; 2) predicting raw pixels of RGB values by direct regression performs no worse than the patch classification approaches with complex designs; 3) the prediction head can be as light as a linear layer, with no worse performance than heavier ones. Using ViT-B, our approach achieves 83.8% top-1 fine-tuning accuracy on ImageNet-1K by pre-training also on this dataset, surpassing previous best approach by +0.6%. When applied on a larger model of about 650 million parameters, SwinV2-H, it achieves 87.1% top-1 accuracy on ImageNet-1K using only ImageNet-1K data. We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by $40\times$ less data than that in previous practice, we achieve the state-of-the-art on four representative vision benchmarks. The code and models will be publicly available at https://github.com/microsoft/SimMIM. | ['Han Hu', 'Qi Dai', 'Zhuliang Yao', 'Jianmin Bao', 'Yutong Lin', 'Yue Cao', 'Zheng Zhang', 'Zhenda Xie'] | 2021-11-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.80856663e-01 4.15933013e-01 -3.45038056e-01 -2.62678385e-01
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57602f43-3bdd-4226-b3f9-71f40b189a71 | fdnerf-few-shot-dynamic-neural-radiance | 2208.05751 | null | https://arxiv.org/abs/2208.05751v2 | https://arxiv.org/pdf/2208.05751v2.pdf | FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing | We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first NeRF-based method capable of reconstruction and expression editing of 3D faces based on a small number of dynamic images. Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs. Compared to state-of-the-art few-shot NeRFs designed for modeling static scenes, the proposed FDNeRF accepts view-inconsistent dynamic inputs and supports arbitrary facial expression editing, i.e., producing faces with novel expressions beyond the input ones. To handle the inconsistencies between dynamic inputs, we introduce a well-designed conditional feature warping (CFW) module to perform expression conditioned warping in 2D feature space, which is also identity adaptive and 3D constrained. As a result, features of different expressions are transformed into the target ones. We then construct a radiance field based on these view-consistent features and use volumetric rendering to synthesize novel views of the modeled faces. Extensive experiments with quantitative and qualitative evaluation demonstrate that our method outperforms existing dynamic and few-shot NeRFs on both 3D face reconstruction and expression editing tasks. Code is available at https://github.com/FDNeRF/FDNeRF. | ['Jing Liao', 'Can Wang', 'Ziyu Wan', 'Xiaoyu Li', 'Jingbo Zhang'] | 2022-08-11 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.02325338e-01 -3.09098139e-02 2.96139896e-01 -6.75779462e-01
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426449ec-f754-4cf0-aded-139ea86f2bc3 | streaming-speech-to-confusion-network-speech | 2306.03778 | null | https://arxiv.org/abs/2306.03778v1 | https://arxiv.org/pdf/2306.03778v1.pdf | Streaming Speech-to-Confusion Network Speech Recognition | In interactive automatic speech recognition (ASR) systems, low-latency requirements limit the amount of search space that can be explored during decoding, particularly in end-to-end neural ASR. In this paper, we present a novel streaming ASR architecture that outputs a confusion network while maintaining limited latency, as needed for interactive applications. We show that 1-best results of our model are on par with a comparable RNN-T system, while the richer hypothesis set allows second-pass rescoring to achieve 10-20\% lower word error rate on the LibriSpeech task. We also show that our model outperforms a strong RNN-T baseline on a far-field voice assistant task. | ['Andreas Stolcke', 'Ankur Gandhe', 'Ariya Rastrow', 'Prabhat Pandey', 'Denis Filimonov'] | 2023-06-02 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 5.22220373e-01 4.15795952e-01 1.39140561e-01 -4.48722720e-01
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e52ef8da-f1a8-4617-9b3a-390161844677 | query-reduction-networks-for-question | 1606.04582 | null | http://arxiv.org/abs/1606.04582v6 | http://arxiv.org/pdf/1606.04582v6.pdf | Query-Reduction Networks for Question Answering | In this paper, we study the problem of question answering when reasoning over
multiple facts is required. We propose Query-Reduction Network (QRN), a variant
of Recurrent Neural Network (RNN) that effectively handles both short-term
(local) and long-term (global) sequential dependencies to reason over multiple
facts. QRN considers the context sentences as a sequence of state-changing
triggers, and reduces the original query to a more informed query as it
observes each trigger (context sentence) through time. Our experiments show
that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and
in a real goal-oriented dialog dataset. In addition, QRN formulation allows
parallelization on RNN's time axis, saving an order of magnitude in time
complexity for training and inference. | ['Ali Farhadi', 'Sewon Min', 'Minjoon Seo', 'Hannaneh Hajishirzi'] | 2016-06-14 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 2.92668611e-01 3.58011603e-01 -6.13097399e-02 -7.00165093e-01
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fe2b6175-d886-4417-bc3e-0b428eda3201 | towards-robust-object-detection-bayesian | 2108.00784 | null | https://arxiv.org/abs/2108.00784v2 | https://arxiv.org/pdf/2108.00784v2.pdf | Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling | According to recent studies, commonly used computer vision datasets contain about 4% of label errors. For example, the COCO dataset is known for its high level of noise in data labels, which limits its use for training robust neural deep architectures in a real-world scenario. To model such a noise, in this paper we have proposed the homoscedastic aleatoric uncertainty estimation, and present a series of novel loss functions to address the problem of image object detection at scale. Specifically, the proposed functions are based on Bayesian inference and we have incorporated them into the common community-adopted object detection deep learning architecture RetinaNet. We have also shown that modeling of homoscedastic aleatoric uncertainty using our novel functions allows to increase the model interpretability and to improve the object detection performance being evaluated on the COCO dataset. | ['Andrey Filchenkov', 'Alexey Lapenok', 'Natalia Khanzhina'] | 2021-08-02 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 4.72230986e-02 1.01600252e-01 3.41820955e-01 -5.20276368e-01
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38aa9475-01f5-4b91-93d2-66f4f524253f | spatial-correlation-and-value-prediction-in | 1807.10598 | null | http://arxiv.org/abs/1807.10598v2 | http://arxiv.org/pdf/1807.10598v2.pdf | Spatial Correlation and Value Prediction in Convolutional Neural Networks | Convolutional neural networks (CNNs) are a widely used form of deep neural
networks, introducing state-of-the-art results for different problems such as
image classification, computer vision tasks, and speech recognition. However,
CNNs are compute intensive, requiring billions of multiply-accumulate (MAC)
operations per input. To reduce the number of MACs in CNNs, we propose a value
prediction method that exploits the spatial correlation of zero-valued
activations within the CNN output feature maps, thereby saving convolution
operations. Our method reduces the number of MAC operations by 30.4%, averaged
on three modern CNNs for ImageNet, with top-1 accuracy degradation of 1.7%, and
top-5 accuracy degradation of 1.1%. | ['Uri Weiser', 'Gil Shomron'] | 2018-07-21 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-6.60303682e-02 -2.67652512e-01 -2.54483491e-01 -6.87160730e-01
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53da635f-2d4b-4a53-aa20-76131c14b99e | what-do-the-us-west-coast-public-libraries | 1808.06021 | null | http://arxiv.org/abs/1808.06021v2 | http://arxiv.org/pdf/1808.06021v2.pdf | What do the US West Coast Public Libraries Post on Twitter? | Twitter has provided a great opportunity for public libraries to disseminate
information for a variety of purposes. Twitter data have been applied in
different domains such as health, politics, and history. There are thousands of
public libraries in the US, but no study has yet investigated the content of
their social media posts like tweets to find their interests. Moreover,
traditional content analysis of Twitter content is not an efficient task for
exploring thousands of tweets. Therefore, there is a need for automatic methods
to overcome the limitations of manual methods. This paper proposes a
computational approach to collecting and analyzing using Twitter Application
Programming Interfaces (API) and investigates more than 138,000 tweets from 48
US west coast libraries using topic modeling. We found 20 topics and assigned
them to five categories including public relations, book, event, training, and
social good. Our results show that the US west coast libraries are more
interested in using Twitter for public relations and book-related events. This
research has both practical and theoretical applications for libraries as well
as other organizations to explore social media actives of their customer and
themselves. | ['Matthew Collins', 'Amir Karami'] | 2018-08-17 | null | null | null | null | ['public-relations'] | ['miscellaneous'] | [-5.69672108e-01 -9.54945164e-04 -3.45574379e-01 -2.21703127e-01
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9d366f40-7f22-4d94-a7ef-41586dd01ebf | one-shot-segmentation-of-novel-white-matter | 2303.06852 | null | https://arxiv.org/abs/2303.06852v1 | https://arxiv.org/pdf/2303.06852v1.pdf | One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation | Deep learning based methods have achieved state-of-the-art performance for automated white matter (WM) tract segmentation. In these methods, the segmentation model needs to be trained with a large number of manually annotated scans, which can be accumulated throughout time. When novel WM tracts, i.e., tracts not included in the existing annotated WM tracts, are to be segmented, additional annotations of these novel WM tracts need to be collected. Since tract annotation is time-consuming and costly, it is desirable to make only a few annotations of novel WM tracts for training the segmentation model, and previous work has addressed this problem by transferring the knowledge learned for segmenting existing WM tracts to the segmentation of novel WM tracts. However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts. In this work, we explore the problem of one-shot segmentation of novel WM tracts. Since in the one-shot setting the annotated training data is extremely scarce, based on the existing knowledge transfer framework, we propose to further perform extensive data augmentation for the single annotated scan, where synthetic annotated training data is produced. We have designed several different strategies that mask out regions in the single annotated scan for data augmentation. Our method was evaluated on public and in-house datasets. The experimental results show that our method improves the accuracy of one-shot segmentation of novel WM tracts. | ['Chuyang Ye', 'Yaou Liu', 'Zhizheng Zhuo', 'Qi Lu', 'Wan Liu'] | 2023-03-13 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 3.63337040e-01 2.13266790e-01 -1.03474297e-01 -3.64055812e-01
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3c4633ae-9e9f-4a8a-b4c3-bcfd09339b38 | relation-aware-collaborative-learning-for | null | null | https://aclanthology.org/2020.acl-main.340 | https://aclanthology.org/2020.acl-main.340.pdf | Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is \textit{{``}delicious{''}}, the aspect term must be \textit{{``}food{''}} rather than \textit{{``}place{''}}. In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task. | ['Tieyun Qian', 'Zhuang Chen'] | 2020-07-01 | null | null | null | acl-2020-6 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [ 2.19457522e-01 -2.46718749e-02 -3.16192895e-01 -6.66098475e-01
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fcd2bcbf-44b5-465a-a41e-48aa05e27e19 | combining-contrastive-learning-and-knowledge | 2211.05035 | null | https://arxiv.org/abs/2211.05035v1 | https://arxiv.org/pdf/2211.05035v1.pdf | Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language | Word embeddings play a significant role in today's Natural Language Processing tasks and applications. While pre-trained models may be directly employed and integrated into existing pipelines, they are often fine-tuned to better fit with specific languages or domains. In this paper, we attempt to improve available embeddings in the uncovered niche of the Italian medical domain through the combination of Contrastive Learning (CL) and Knowledge Graph Embedding (KGE). The main objective is to improve the accuracy of semantic similarity between medical terms, which is also used as an evaluation task. Since the Italian language lacks medical texts and controlled vocabularies, we have developed a specific solution by combining preexisting CL methods (multi-similarity loss, contextualization, dynamic sampling) and the integration of KGEs, creating a new variant of the loss. Although without having outperformed the state-of-the-art, represented by multilingual models, the obtained results are encouraging, providing a significant leap in performance compared to the starting model, while using a significantly lower amount of data. | ['Luigi di Caro', 'Roger Ferrod', 'Denys Amore Bondarenko'] | 2022-11-09 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [ 1.39012679e-01 3.49212706e-01 -1.39391392e-01 -1.68577582e-01
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d4e2ca60-5de5-47a6-8268-ab44efcefe9c | rclane-relay-chain-prediction-for-lane | 2207.09399 | null | https://arxiv.org/abs/2207.09399v1 | https://arxiv.org/pdf/2207.09399v1.pdf | RCLane: Relay Chain Prediction for Lane Detection | Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution. In this paper, we present a new method for lane detection based on relay chain prediction. Specifically, our model predicts a segmentation map to classify the foreground and background region. For each pixel point in the foreground region, we go through the forward branch and backward branch to recover the whole lane. Each branch decodes a transfer map and a distance map to produce the direction moving to the next point, and how many steps to progressively predict a relay station (next point). As such, our model is able to capture the keypoints along the lanes. Despite its simplicity, our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS. | ['xiangyang xue', 'Yanwei Fu', 'Hang Xu', 'Li Zhang', 'Bin Zhao', 'Xinyue Cai', 'Shenghua Xu'] | 2022-07-19 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 1.84449583e-01 -5.09426109e-02 -3.92229140e-01 -3.41575474e-01
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8d1a7d3b-04b1-4866-9988-9441ce0b932a | a-multi-domain-vne-algorithm-based-on-load | 2202.05667 | null | https://arxiv.org/abs/2202.05667v1 | https://arxiv.org/pdf/2202.05667v1.pdf | A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks | Virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a lot of research has focused on the evolutionary algorithm's masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time. | ['Joan Serrat-Fernacute', 'Juan-Luis Gorricho', 'Abderrahim Benslimane', 'Chunxiao Jiang', 'Fanglin Liu', 'Peiying Zhang'] | 2022-02-07 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 4.02891897e-02 -3.59747708e-01 -2.82680631e-01 4.44541276e-02
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593c4207-eccf-472b-a9c2-1938dc38a30d | few-shot-action-recognition-via-improved | 2001.03905 | null | https://arxiv.org/abs/2001.03905v3 | https://arxiv.org/pdf/2001.03905v3.pdf | Few-shot Action Recognition with Permutation-invariant Attention | Many few-shot learning models focus on recognising images. In contrast, we tackle a challenging task of few-shot action recognition from videos. We build on a C3D encoder for spatio-temporal video blocks to capture short-range action patterns. Such encoded blocks are aggregated by permutation-invariant pooling to make our approach robust to varying action lengths and long-range temporal dependencies whose patterns are unlikely to repeat even in clips of the same class. Subsequently, the pooled representations are combined into simple relation descriptors which encode so-called query and support clips. Finally, relation descriptors are fed to the comparator with the goal of similarity learning between query and support clips. Importantly, to re-weight block contributions during pooling, we exploit spatial and temporal attention modules and self-supervision. In naturalistic clips (of the same class) there exists a temporal distribution shift--the locations of discriminative temporal action hotspots vary. Thus, we permute blocks of a clip and align the resulting attention regions with similarly permuted attention regions of non-permuted clip to train the attention mechanism invariant to block (and thus long-term hotspot) permutations. Our method outperforms the state of the art on the HMDB51, UCF101, miniMIT datasets. | ['Philip H. S. Torr', 'Xiaojuan Qi', 'Li Zhang', 'Hongguang Zhang', 'Piotr Koniusz', 'Hongdong Li'] | 2020-01-12 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3831_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500511.pdf | eccv-2020-8 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.93970776e-01 -2.54820436e-01 -4.43443805e-01 -3.64460260e-01
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97255f14-968b-4be2-bfce-6f292f9a1491 | hybrid-machine-learning-model-of-extreme | 1910.13574 | null | https://arxiv.org/abs/1910.13574v1 | https://arxiv.org/pdf/1910.13574v1.pdf | Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System | Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model. | ['Laszlo Nadai', 'Narjes Nabipour', 'Javad Hassannataj Joloudari', 'Gergo Pinter', 'Amir Mosavi', 'Sanaz Mojrian', 'Imre Felde'] | 2019-10-29 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-7.52292126e-02 -7.15370625e-02 -1.46938741e-01 -3.66804034e-01
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f71e5568-7e2c-4166-9bdf-48af818eae0c | deep-transformation-invariant-clustering | 2006.11132 | null | https://arxiv.org/abs/2006.11132v2 | https://arxiv.org/pdf/2006.11132v2.pdf | Deep Transformation-Invariant Clustering | Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space. This learning process naturally fits in the gradient-based training of K-means and Gaussian mixture model, without requiring any additional loss or hyper-parameters. It leads us to two new deep transformation-invariant clustering frameworks, which jointly learn prototypes and transformations. More specifically, we use deep learning modules that enable us to resolve invariance to spatial, color and morphological transformations. Our approach is conceptually simple and comes with several advantages, including the possibility to easily adapt the desired invariance to the task and a strong interpretability of both cluster centers and assignments to clusters. We demonstrate that our novel approach yields competitive and highly promising results on standard image clustering benchmarks. Finally, we showcase its robustness and the advantages of its improved interpretability by visualizing clustering results over real photograph collections. | ['Thibault Groueix', 'Mathieu Aubry', 'Tom Monnier'] | 2020-06-19 | null | http://proceedings.neurips.cc/paper/2020/hash/5a5eab21ca2a8fef4af5e35709ecca15-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/5a5eab21ca2a8fef4af5e35709ecca15-Paper.pdf | neurips-2020-12 | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 7.25253746e-02 -1.87559947e-01 2.08594248e-01 -5.52676737e-01
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c47b3568-0d6f-4e37-9dc5-bc88c365b040 | c-3-compositional-counterfactual-constrastive | 2106.08914 | null | https://arxiv.org/abs/2106.08914v1 | https://arxiv.org/pdf/2106.08914v1.pdf | $C^3$: Compositional Counterfactual Constrastive Learning for Video-grounded Dialogues | Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. However, the results are partly accomplished by exploiting biases in the datasets rather than developing multimodal reasoning, resulting in limited generalization. In this paper, we propose a novel approach of Compositional Counterfactual Contrastive Learning ($C^3$) to develop contrastive training between factual and counterfactual samples in video-grounded dialogues. Specifically, we design factual/counterfactual sampling based on the temporal steps in videos and tokens in dialogues and propose contrastive loss functions that exploit object-level or action-level variance. Different from prior approaches, we focus on contrastive hidden state representations among compositional output tokens to optimize the representation space in a generation setting. We achieved promising performance gains on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark and showed the benefits of our approach in grounding video and dialogue context. | ['Steven C. H. Hoi', 'Nancy F. Chen', 'Hung Le'] | 2021-06-16 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 4.28122699e-01 3.43923062e-01 -7.67454132e-02 -5.06485701e-01
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923ebd25-a002-4dec-87c9-bea68337069b | sadm-sequence-aware-diffusion-model-for | 2212.08228 | null | https://arxiv.org/abs/2212.08228v2 | https://arxiv.org/pdf/2212.08228v2.pdf | SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation | Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via image generation. However, most of the medical image generation tasks only rely on the input from a single image, thus ignoring the sequential dependency even when longitudinal data is available. Sequence-aware deep generative models, where model input is a sequence of ordered and timestamped images, are still underexplored in the medical imaging domain that is featured by several unique challenges: 1) Sequences with various lengths; 2) Missing data or frame, and 3) High dimensionality. To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images. Recently, diffusion models have shown promising results in high-fidelity image generation. Our method extends this new technique by introducing a sequence-aware transformer as the conditional module in a diffusion model. The novel design enables learning longitudinal dependency even with missing data during training and allows autoregressive generation of a sequence of images during inference. Our extensive experiments on 3D longitudinal medical images demonstrate the effectiveness of SADM compared with baselines and alternative methods. The code is available at https://github.com/ubc-tea/SADM-Longitudinal-Medical-Image-Generation. | ['Xiaoxiao Li', 'Jia Guo', 'Heung-Il Suk', 'Chenghao Zhang', 'Jee Seok Yoon'] | 2022-12-16 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 2.59590715e-01 -8.05433020e-02 -1.83416590e-01 -3.84596854e-01
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9828df56-d876-490c-b494-5977da631fcc | beta-r-cnn-looking-into-pedestrian-detection-1 | 2210.12758 | null | https://arxiv.org/abs/2210.12758v1 | https://arxiv.org/pdf/2210.12758v1.pdf | Beta R-CNN: Looking into Pedestrian Detection from Another Perspective | Recently significant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be attributed mostly to the widely used representation of pedestrians, i.e., 2D axis-aligned bounding box, which just describes the approximate location and size of the object. Bounding box models the object as a uniform distribution within the boundary, making pedestrians indistinguishable in occluded and crowded scenes due to much noise. To eliminate the problem, we propose a novel representation based on 2D beta distribution, named Beta Representation. It pictures a pedestrian by explicitly constructing the relationship between full-body and visible boxes, and emphasizes the center of visual mass by assigning different probability values to pixels. As a result, Beta Representation is much better for distinguishing highly-overlapped instances in crowded scenes with a new NMS strategy named BetaNMS. What's more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes. | ['Anhong Dang', 'Ye Yuan', 'Banghuai Li', 'Zixuan Xu'] | 2022-10-23 | beta-r-cnn-looking-into-pedestrian-detection | http://proceedings.neurips.cc/paper/2020/hash/e6b4b2a746ed40e1af829d1fa82daa10-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e6b4b2a746ed40e1af829d1fa82daa10-Paper.pdf | neurips-2020-12 | ['pedestrian-detection'] | ['computer-vision'] | [-2.53090113e-01 -2.16572523e-01 1.13803931e-01 -3.73753786e-01
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c04fd09e-d109-4d9c-904a-54caf1ff9e91 | pwr-align-leveraging-part-whole-relationships | 2306.06717 | null | https://arxiv.org/abs/2306.06717v1 | https://arxiv.org/pdf/2306.06717v1.pdf | PWR-Align: Leveraging Part-Whole Relationships for Part-wise Rigid Point Cloud Registration in Mixed Reality Applications | We present an efficient and robust point cloud registration (PCR) workflow for part-wise rigid point cloud alignment using the Microsoft HoloLens 2. Point Cloud Registration (PCR) is an important problem in Augmented and Mixed Reality use cases, and we present a study for a special class of non-rigid transformations. Many commonly encountered objects are composed of rigid parts that move relative to one another about joints resulting in non-rigid deformation of the whole object such as robots with manipulators, and machines with hinges. The workflow presented allows us to register the point cloud with various configurations of the point cloud. | ['Bhaskar Banerjee', 'Manorama Jha'] | 2023-06-11 | null | null | null | null | ['point-cloud-registration', 'mixed-reality'] | ['computer-vision', 'computer-vision'] | [-8.95100646e-03 -3.09682600e-02 4.14931864e-01 -1.50109425e-01
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3a549efc-7991-4f7a-b3e7-c9b56f8128c5 | quantized-dialog-language-model-for-goal | 1812.10356 | null | http://arxiv.org/abs/1812.10356v1 | http://arxiv.org/pdf/1812.10356v1.pdf | Quantized-Dialog Language Model for Goal-Oriented Conversational Systems | We propose a novel methodology to address dialog learning in the context of
goal-oriented conversational systems. The key idea is to quantize the dialog
space into clusters and create a language model across the clusters, thus
allowing for an accurate choice of the next utterance in the conversation. The
language model relies on n-grams associated with clusters of utterances. This
quantized-dialog language model methodology has been applied to the end-to-end
goal-oriented track of the latest Dialog System Technology Challenges (DSTC6).
The objective is to find the correct system utterance from a pool of candidates
in order to complete a dialog between a user and an automated
restaurant-reservation system. Our results show that the technique proposed in
this paper achieves high accuracy regarding selection of the correct candidate
utterance, and outperforms other state-of-the-art approaches based on neural
networks. | ['Jatin Ganhotra', 'R. Chulaka Gunasekara', 'Kshitij P. Fadnis', 'Lazaros C. Polymenakos', 'David Nahamoo'] | 2018-12-26 | null | null | null | null | ['goal-oriented-dialog', 'dialog-learning'] | ['natural-language-processing', 'natural-language-processing'] | [-2.90591061e-01 1.48006633e-01 6.74533173e-02 -7.97769308e-01
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46862929-c901-4a11-b69e-704831f0983f | lightweight-and-scalable-particle-tracking | 1908.03775 | null | https://arxiv.org/abs/1908.03775v3 | https://arxiv.org/pdf/1908.03775v3.pdf | Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories | Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells. Proper characterization of the cell's shape, evolution, and their movement over time is crucial to understanding and modeling the mechanobiology of cell migration in many diseases. One in particular, toxoplasmosis is the disease caused by the parasite Toxoplasma gondii. Roughly, one-third of the world's population tests positive for T. gondii. Its virulence is linked to its lytic cycle, predicated on its motility and ability to enter and exit nucleated cells; therefore, studies elucidating its motility patterns are critical to the eventual development of therapeutic strategies. Here, we present a computational framework for fast and scalable detection, tracking, and identification of T. gondii motion phenotypes in 3D videos, in a completely unsupervised fashion. Our pipeline consists of several different modules including preprocessing, sparsification, cell detection, cell tracking, trajectories extraction, parametrization of the trajectories; and finally, a clustering step. Additionally, we identified the computational bottlenecks, and developed a lightweight and highly scalable pipeline through a combination of task distribution and parallelism. Our results prove both the accuracy and performance of our method. | ['Shannon Quinn', 'Mojtaba S. Fazli', 'BahaaEddin Alaila', 'Gary E. Ward', 'Stephen A. Vella', 'Silvia N. J. Moreno', 'Rachel V. Stadler'] | 2019-08-10 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.45006999e-01 -8.14202726e-01 4.95857857e-02 3.27619046e-01
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623e0caa-a61b-4d50-9347-a5a2321b78cb | ranking-based-autoencoder-for-extreme-multi | 1904.05937 | null | http://arxiv.org/abs/1904.05937v1 | http://arxiv.org/pdf/1904.05937v1.pdf | Ranking-Based Autoencoder for Extreme Multi-label Classification | Extreme Multi-label classification (XML) is an important yet challenging
machine learning task, that assigns to each instance its most relevant
candidate labels from an extremely large label collection, where the numbers of
labels, features and instances could be thousands or millions. XML is more and
more on demand in the Internet industries, accompanied with the increasing
business scale / scope and data accumulation. The extremely large label
collections yield challenges such as computational complexity, inter-label
dependency and noisy labeling. Many methods have been proposed to tackle these
challenges, based on different mathematical formulations. In this paper, we
propose a deep learning XML method, with a word-vector-based self-attention,
followed by a ranking-based AutoEncoder architecture. The proposed method has
three major advantages: 1) the autoencoder simultaneously considers the
inter-label dependencies and the feature-label dependencies, by projecting
labels and features onto a common embedding space; 2) the ranking loss not only
improves the training efficiency and accuracy but also can be extended to
handle noisy labeled data; 3) the efficient attention mechanism improves
feature representation by highlighting feature importance. Experimental results
on benchmark datasets show the proposed method is competitive to
state-of-the-art methods. | ['Kefeng Li', 'Wei Sun', 'Li Chen', 'Hui Zhou', 'Bingyu Wang', 'Kechen Qin'] | 2019-04-11 | ranking-based-autoencoder-for-extreme-multi-1 | https://aclanthology.org/N19-1289 | https://aclanthology.org/N19-1289.pdf | naacl-2019-6 | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.29044592e-01 -2.50166327e-01 -6.07413836e-02 -6.58335567e-01
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f18d2a73-c6b5-4be4-a43a-15294b2df976 | a-case-based-reasoning-approach-for-answer | 1503.02917 | null | http://arxiv.org/abs/1503.02917v1 | http://arxiv.org/pdf/1503.02917v1.pdf | A Case Based Reasoning Approach for Answer Reranking in Question Answering | In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved answers of the QA system, due to the growing case base. In the paper,
I present the adequate structuring of the case base and the appropriate
selection of relevant similarity measures, in order to solve the answer
validation problem. The structural case base is built from annotated MultiNet
graphs, which provide representations for natural language expressions, and
corresponding graph similarity measures. I cover a priori relations to
experienced answer candidates for former questions. I compare the CBR System
results to current approaches in an experiment integrating CBR into an existing
framework for answer validation and reranking. This integration is achieved by
adding CBR-related features to the input of a learned ranking model that
determines the final answer ranking. In the experiments based on QA@CLEF
questions, the best learned models make heavy use of CBR features. Observing
the results with a continually growing case base, I present a positive effect
of the size of the case base on the accuracy of the CBR subsystem. | ['Karl-Heinz Weis'] | 2015-03-10 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-4.13693860e-02 8.29889417e-01 1.19897470e-01 -6.36135399e-01
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bb8e211b-3917-4650-b7c1-687a0f5db6cd | deep-learning-on-home-drone-searching-for-the | 2209.11064 | null | https://arxiv.org/abs/2209.11064v1 | https://arxiv.org/pdf/2209.11064v1.pdf | Deep Learning on Home Drone: Searching for the Optimal Architecture | We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was \$15) attached to a toy-drone. In particular, since the Raspberry Pi weighs less than $16$ grams, and its size is half of a credit card, we could easily attach it to the common commercial DJI Tello toy-drone (<\$100, <90 grams, 98 $\times$ 92.5 $\times$ 41 mm). The result is an autonomous drone (no laptop nor human in the loop) that can detect and classify objects in real-time from a video stream of an on-board monocular RGB camera (no GPS or LIDAR sensors). The companion videos demonstrate how this Tello drone scans the lab for people (e.g. for the use of firefighters or security forces) and for an empty parking slot outside the lab. Existing deep learning solutions are either much too slow for real-time computation on such IoT devices, or provide results of impractical quality. Our main challenge was to design a system that takes the best of all worlds among numerous combinations of networks, deep learning platforms/frameworks, compression techniques, and compression ratios. To this end, we provide an efficient searching algorithm that aims to find the optimal combination which results in the best tradeoff between the network running time and its accuracy/performance. | ['Dan Feldman', 'Daniela Rus', 'Oren Gal', 'Barak Diker', 'Yotam Gurfinkel', 'Alaa Maalouf'] | 2022-09-21 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 2.16981247e-02 1.07004359e-01 -1.16164833e-01 -5.60967736e-02
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7c755b05-698b-485f-aa44-cf672b667583 | sc-transformer-structured-context-transformer | 2206.12634 | null | https://arxiv.org/abs/2206.12634v1 | https://arxiv.org/pdf/2206.12634v1.pdf | SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection | This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is used to further boost performance. The proposed method achieves 86.49% F1 score on Kinetics-GEBD test set. which improves 2.86% F1 score compared to the previous SOTA method. | ['Longyin Wen', 'YuFei Wang', 'CongCong Li', 'Xinyao Wang', 'Xiaoqi Ma', 'Dexiang Hong'] | 2022-06-25 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.56827265e-01 2.84207053e-02 -2.27010772e-01 -1.82003126e-01
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70314449-ec28-4a32-9495-0974b8e32b8c | nemo-3d-neural-motion-fields-from-multiple | 2212.13660 | null | https://arxiv.org/abs/2212.13660v1 | https://arxiv.org/pdf/2212.13660v1.pdf | NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action | The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines. | ['Serena Yeung', 'C. Karen Liu', 'Jeffrey Gu', 'Joao Pedro Araujo', 'Maria Xenochristou', 'Zhenzhen Weng', 'Kuan-Chieh Wang'] | 2022-12-28 | null | null | null | null | ['keypoint-detection', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [-2.00663418e-01 -4.09084827e-01 -5.53668261e-01 2.62859076e-01
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929fd8b1-8200-4d3f-9e82-307244664cb4 | hyperparameter-optimization-through-neural | 2304.14766 | null | https://arxiv.org/abs/2304.14766v1 | https://arxiv.org/pdf/2304.14766v1.pdf | Hyperparameter Optimization through Neural Network Partitioning | Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. They can enforce appropriate inductive biases, regularize the model and improve performance -- especially in the presence of limited data. In this work, we propose a simple and efficient way for optimizing hyperparameters inspired by the marginal likelihood, an optimization objective that requires no validation data. Our method partitions the training data and a neural network model into $K$ data shards and parameter partitions, respectively. Each partition is associated with and optimized only on specific data shards. Combining these partitions into subnetworks allows us to define the ``out-of-training-sample" loss of a subnetwork, i.e., the loss on data shards unseen by the subnetwork, as the objective for hyperparameter optimization. We demonstrate that we can apply this objective to optimize a variety of different hyperparameters in a single training run while being significantly computationally cheaper than alternative methods aiming to optimize the marginal likelihood for neural networks. Lastly, we also focus on optimizing hyperparameters in federated learning, where retraining and cross-validation are particularly challenging. | ['Christos Louizos', 'Matthias Reisser', 'Bruno Mlodozeniec'] | 2023-04-28 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 5.74583001e-02 2.34678209e-01 -2.20796853e-01 -7.35994577e-01
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13bb233f-e96c-449c-9b7b-156a1ebfe371 | beyond-parallel-data-joint-word-alignment-and | null | null | https://aclanthology.org/D14-1061 | https://aclanthology.org/D14-1061.pdf | Beyond Parallel Data: Joint Word Alignment and Decipherment Improves Machine Translation | null | ['Qing Dou', 'Ashish Vaswani', 'Kevin Knight'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['decipherment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1d09b0fc-5ffc-4414-b6cd-e7dbc3360060 | multilingual-sequence-labeling-approach-to | null | null | https://aclanthology.org/2021.wnut-1.51 | https://aclanthology.org/2021.wnut-1.51.pdf | Multilingual Sequence Labeling Approach to solve Lexical Normalization | The task of converting a nonstandard text to a standard and readable text is known as lexical normalization. Almost all the Natural Language Processing (NLP) applications require the text data in normalized form to build quality task-specific models. Hence, lexical normalization has been proven to improve the performance of numerous natural language processing tasks on social media. This study aims to solve the problem of Lexical Normalization by formulating the Lexical Normalization task as a Sequence Labeling problem. This paper proposes a sequence labeling approach to solve the problem of Lexical Normalization in combination with the word-alignment technique. The goal is to use a single model to normalize text in various languages namely Croatian, Danish, Dutch, English, Indonesian-English, German, Italian, Serbian, Slovenian, Spanish, Turkish, and Turkish-German. This is a shared task in “2021 The 7th Workshop on Noisy User-generated Text (W-NUT)” in which the participants are expected to create a system/model that performs lexical normalization, which is the translation of non-canonical texts into their canonical equivalents, comprising data from over 12 languages. The proposed single multilingual model achieves an overall ERR score of 43.75 on intrinsic evaluation and an overall Labeled Attachment Score (LAS) score of 63.12 on extrinsic evaluation. Further, the proposed method achieves the highest Error Reduction Rate (ERR) score of 61.33 among the participants in the shared task. This study highlights the effects of using additional training data to get better results as well as using a pre-trained Language model trained on multiple languages rather than only on one language. | ['Apurva Nagvenkar', 'Divesh Kubal'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.26221883e-01 2.16393024e-02 -5.99384420e-02 -5.18163800e-01
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fae737bc-eb8a-41d7-a77b-18b5790b4256 | visual-concept-reasoning-networks | 2008.11783 | null | https://arxiv.org/abs/2008.11783v1 | https://arxiv.org/pdf/2008.11783v1.pdf | Visual Concept Reasoning Networks | A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties. Dependencies or interactions between these representations are typically defined by dense and local operations, however, without any adaptiveness or high-level reasoning. In this work, we propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. We associate each branch with a visual concept and derive a compact concept state by selecting a few local descriptors through an attention module. These concept states are then updated by graph-based interaction and used to adaptively modulate the local descriptors. We describe our proposed model by split-transform-attend-interact-modulate-merge stages, which are implemented by opting for a highly modularized architecture. Extensive experiments on visual recognition tasks such as image classification, semantic segmentation, object detection, scene recognition, and action recognition show that our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%. | ['Sungwoong Kim', 'Yoshua Bengio', 'Taesup Kim'] | 2020-08-26 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 4.00127798e-01 1.45142451e-01 -3.48847300e-01 -7.47254550e-01
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38bcad6f-f672-449a-9a09-9a41be6a0f59 | heart-rate-variability-code-does-it-exist-and | 2001.08264 | null | https://arxiv.org/abs/2001.08264v4 | https://arxiv.org/pdf/2001.08264v4.pdf | Heart rate variability code: Does it exist and can we hack it? | Heart rate variability (HRV) has been studied for over 50 years, yet an integrative concept is missing on what HRV's mathematical properties represent physiologically. Here I introduce the notion of HRV code as an attempt to address this challenge systematically. I review the existing evidence from physiological studies in various species to support this concept and propose experiments to help validate and expand this notion further. | ['Martin G. Frasch'] | 2020-01-22 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.35154545e-01 -2.97238350e-01 -3.93203437e-01 -4.80631620e-01
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8dba3b03-4eaf-47b9-a99d-4922e1a3b8d8 | cross-modal-3d-shape-generation-and | 2207.11795 | null | https://arxiv.org/abs/2207.11795v1 | https://arxiv.org/pdf/2207.11795v1.pdf | Cross-Modal 3D Shape Generation and Manipulation | Creating and editing the shape and color of 3D objects require tremendous human effort and expertise. Compared to direct manipulation in 3D interfaces, 2D interactions such as sketches and scribbles are usually much more natural and intuitive for the users. In this paper, we propose a generic multi-modal generative model that couples the 2D modalities and implicit 3D representations through shared latent spaces. With the proposed model, versatile 3D generation and manipulation are enabled by simply propagating the editing from a specific 2D controlling modality through the latent spaces. For example, editing the 3D shape by drawing a sketch, re-colorizing the 3D surface via painting color scribbles on the 2D rendering, or generating 3D shapes of a certain category given one or a few reference images. Unlike prior works, our model does not require re-training or fine-tuning per editing task and is also conceptually simple, easy to implement, robust to input domain shifts, and flexible to diverse reconstruction on partial 2D inputs. We evaluate our framework on two representative 2D modalities of grayscale line sketches and rendered color images, and demonstrate that our method enables various shape manipulation and generation tasks with these 2D modalities. | ['Sergey Tulyakov', 'Subhransu Maji', 'Zeng Huang', 'Kyle Olszewski', 'Hsin-Ying Lee', 'Jian Ren', 'Menglei Chai', 'Zezhou Cheng'] | 2022-07-24 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 4.92695987e-01 -4.26016450e-02 2.13563651e-01 -3.03548902e-01
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b7ee04fe-9be4-4dc4-b140-e953c0b9bcd1 | tabular-data-deep-learning-is-not-all-you | 2106.03253 | null | https://arxiv.org/abs/2106.03253v2 | https://arxiv.org/pdf/2106.03253v2.pdf | Tabular Data: Deep Learning is Not All You Need | A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone. | ['Amitai Armon', 'Ravid Shwartz-Ziv'] | 2021-06-06 | null | https://openreview.net/forum?id=vdgtepS1pV | https://openreview.net/pdf?id=vdgtepS1pV | icml-workshop-automl-2021-7 | ['classification'] | ['methodology'] | [-7.15398610e-01 -6.56470433e-02 -7.06374049e-01 -6.77360117e-01
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faa5de2f-9d6e-45ee-9540-3dbbb68935c7 | rpvnet-a-deep-and-efficient-range-point-voxel | 2103.12978 | null | https://arxiv.org/abs/2103.12978v1 | https://arxiv.org/pdf/2103.12978v1.pdf | RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation | Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometrically accurate, but it is disordered, which makes it difficult to find local neighbors efficiently. The voxel-based view is regular, but sparse, and computation grows cubically when voxel resolution increases. The range-based view is regular and generally dense, however spherical projection makes physical dimensions distorted. Both voxel- and range-based views suffer from quantization loss, especially for voxels when facing large-scale scenes. In order to utilize different view's advantages and alleviate their own shortcomings in fine-grained segmentation task, we propose a novel range-point-voxel fusion network, namely RPVNet. In this network, we devise a deep fusion framework with multiple and mutual information interactions among these three views and propose a gated fusion module (termed as GFM), which can adaptively merge the three features based on concurrent inputs. Moreover, the proposed RPV interaction mechanism is highly efficient, and we summarize it into a more general formulation. By leveraging this efficient interaction and relatively lower voxel resolution, our method is also proved to be more efficient. Finally, we evaluated the proposed model on two large-scale datasets, i.e., SemanticKITTI and nuScenes, and it shows state-of-the-art performance on both of them. Note that, our method currently ranks 1st on SemanticKITTI leaderboard without any extra tricks. | ['ShiLiang Pu', 'Jie Sun', 'Yushi Zhu', 'Jian Dou', 'Ruixiang Zhang', 'Jianyun Xu'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_RPVNet_A_Deep_and_Efficient_Range-Point-Voxel_Fusion_Network_for_LiDAR_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_RPVNet_A_Deep_and_Efficient_Range-Point-Voxel_Fusion_Network_for_LiDAR_ICCV_2021_paper.pdf | iccv-2021-1 | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [-2.07826551e-02 -3.56171608e-01 -3.62983160e-02 -3.18058312e-01
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03f88d94-86e2-42fb-b07d-bb2b831afd3a | revisiting-implicit-neural-representations-in | 2304.10250 | null | https://arxiv.org/abs/2304.10250v1 | https://arxiv.org/pdf/2304.10250v1.pdf | Revisiting Implicit Neural Representations in Low-Level Vision | Implicit Neural Representation (INR) has been emerging in computer vision in recent years. It has been shown to be effective in parameterising continuous signals such as dense 3D models from discrete image data, e.g. the neural radius field (NeRF). However, INR is under-explored in 2D image processing tasks. Considering the basic definition and the structure of INR, we are interested in its effectiveness in low-level vision problems such as image restoration. In this work, we revisit INR and investigate its application in low-level image restoration tasks including image denoising, super-resolution, inpainting, and deblurring. Extensive experimental evaluations suggest the superior performance of INR in several low-level vision tasks with limited resources, outperforming its counterparts by over 2dB. Code and models are available at https://github.com/WenTXuL/LINR | ['Jianbo Jiao', 'Wentian Xu'] | 2023-04-20 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 5.97739637e-01 -4.23524380e-02 1.84990928e-01 -2.15803847e-01
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4cd4ee88-0032-4fd0-88f0-33bb84fc5cb0 | self-supervised-video-object-segmentation | 2006.12480 | null | https://arxiv.org/abs/2006.12480v1 | https://arxiv.org/pdf/2006.12480v1.pdf | Self-supervised Video Object Segmentation | The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking). We make the following contributions: (i) we propose to improve the existing self-supervised approach, with a simple, yet more effective memory mechanism for long-term correspondence matching, which resolves the challenge caused by the dis-appearance and reappearance of objects; (ii) by augmenting the self-supervised approach with an online adaptation module, our method successfully alleviates tracker drifts caused by spatial-temporal discontinuity, e.g. occlusions or dis-occlusions, fast motions; (iii) we explore the efficiency of self-supervised representation learning for dense tracking, surprisingly, we show that a powerful tracking model can be trained with as few as 100 raw video clips (equivalent to a duration of 11mins), indicating that low-level statistics have already been effective for tracking tasks; (iv) we demonstrate state-of-the-art results among the self-supervised approaches on DAVIS-2017 and YouTube-VOS, as well as surpassing most of methods trained with millions of manual segmentation annotations, further bridging the gap between self-supervised and supervised learning. Codes are released to foster any further research (https://github.com/fangruizhu/self_sup_semiVOS). | ['Yanwei Fu', 'Li Zhang', 'Fangrui Zhu', 'Weidi Xie', 'Guodong Guo'] | 2020-06-22 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.24938570e-01 -1.45265386e-01 -5.20355284e-01 -1.71845302e-01
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4ce1cf47-c4bf-4cd6-bc85-d5568c939c6f | medical-phrase-grounding-with-region-phrase | 2303.07618 | null | https://arxiv.org/abs/2303.07618v1 | https://arxiv.org/pdf/2303.07618v1.pdf | Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment | Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not capable of capturing the subtle and specialized features of medical findings, leading to sub-optimal performance in MPG. In this paper, we propose MedRPG, an end-to-end approach for MPG. MedRPG is built on a lightweight vision-language transformer encoder and directly predicts the box coordinates of mentioned medical findings, which can be trained with limited medical data, making it a valuable tool in medical image analysis. To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo). TaCo seeks context alignment to pull both the features and attention outputs of relevant region-phrase pairs close together while pushing those of irrelevant regions far away. This ensures that the final box prediction depends more on its finding-specific regions and phrases. Experimental results on three MPG datasets demonstrate that our MedRPG outperforms state-of-the-art visual grounding approaches by a large margin. Additionally, the proposed TaCo strategy is effective in enhancing finding localization ability and reducing spurious region-phrase correlations. | ['Huazhu Fu', 'Yong liu', 'Xinxing Xu', 'Choon Hua Thng', 'Lionel Cheng', 'Gideon Ooi', 'Liang Wan', 'Junting Zhao', 'Anh Tran', 'Yang Zhou', 'Zhihao Chen'] | 2023-03-14 | null | null | null | null | ['visual-grounding', 'phrase-grounding'] | ['computer-vision', 'natural-language-processing'] | [ 2.41921276e-01 1.86928838e-01 -4.03582454e-01 -1.62533909e-01
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3b6141f5-921d-401b-8cce-899dde70eacd | prototype-memory-and-attention-mechanisms-for | null | null | https://openreview.net/forum?id=lY0-7bj0Vfz | https://openreview.net/pdf?id=lY0-7bj0Vfz | Prototype memory and attention mechanisms for few shot image generation | Recent discoveries indicate that the neural codes in the primary visual cortex (V1) of macaque monkeys are complex, diverse and sparse. This leads us to ponder the computational advantages and functional role of these “grandmother cells." Here, we propose that such cells can serve as prototype memory priors that bias and shape the distributed feature processing within the image generation process in the brain. These memory prototypes are learned by momentum online clustering and are utilized via a memory-based attention operation, which we define as Memory Concept Attention (MoCA). To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model. Interestingly, we also find that our attentional memory mechanism can implicitly modify the horizontal connections by updating the transformation into the prototype embedding space for self-attention. Insofar as GANs can be seen as plausible models for reasoning about the top-down synthesis in the analysis-by-synthesis loop of the hierarchical visual cortex, our findings demonstrate a plausible computational role for these “prototype concept" neurons in visual processing in the brain. | ['Tai Sing Lee', 'Amir Barati Farimani', 'Harold Rockwell', 'Andrew Luo', 'Zijie Li', 'Tianqin Li'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['online-clustering'] | ['computer-vision'] | [ 2.05902264e-01 4.47077990e-01 2.50875652e-01 -3.56006593e-01
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c94e9e95-2fea-4bd3-8510-cbc27f7f2f7e | multi-level-semantic-feature-augmentation-for | 1804.05298 | null | http://arxiv.org/abs/1804.05298v4 | http://arxiv.org/pdf/1804.05298v4.pdf | Multi-level Semantic Feature Augmentation for One-shot Learning | The ability to quickly recognize and learn new visual concepts from limited
samples enables humans to swiftly adapt to new environments. This ability is
enabled by semantic associations of novel concepts with those that have already
been learned and stored in memory. Computers can start to ascertain similar
abilities by utilizing a semantic concept space. A concept space is a
high-dimensional semantic space in which similar abstract concepts appear close
and dissimilar ones far apart. In this paper, we propose a novel approach to
one-shot learning that builds on this idea. Our approach learns to map a novel
sample instance to a concept, relates that concept to the existing ones in the
concept space and generates new instances, by interpolating among the concepts,
to help learning. Instead of synthesizing new image instance, we propose to
directly synthesize instance features by leveraging semantics using a novel
auto-encoder network we call dual TriNet. The encoder part of the TriNet learns
to map multi-layer visual features of deep CNNs, that is, multi-level concepts,
to a semantic vector. In semantic space, we search for related concepts, which
are then projected back into the image feature spaces by the decoder portion of
the TriNet. Two strategies in the semantic space are explored. Notably, this
seemingly simple strategy results in complex augmented feature distributions in
the image feature space, leading to substantially better performance. | ['xiangyang xue', 'Yu-Gang Jiang', 'yinda zhang', 'Yanwei Fu', 'Zitian Chen', 'Leonid Sigal'] | 2018-04-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 5.03690124e-01 -7.83535764e-02 8.91560838e-02 -5.79876244e-01
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d9f2babe-0d9f-426e-abc7-7a60cab7851d | metaset-exploring-shape-and-property-spaces | 2006.02142 | null | https://arxiv.org/abs/2006.02142v3 | https://arxiv.org/pdf/2006.02142v3.pdf | METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design | Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to accelerate the process. However, the dependence on data induces a unique challenge: An imbalanced dataset containing more of certain shapes or physical properties can be detrimental to the efficacy of data-driven approaches. In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning. To select such subsets, we propose METASET, a methodology that 1) uses similarity metrics and positive semi-definite kernels to jointly measure the closeness of unit cells in both shape and property spaces, and 2) incorporates Determinantal Point Processes for efficient subset selection. Moreover, METASET allows the trade-off between shape and property diversity so that subsets can be tuned for various applications. Through the design of 2D metamaterials with target displacement profiles, we demonstrate that smaller, diverse subsets can indeed improve the search process as well as structural performance. By eliminating inherent overlaps in a dataset of 3D unit cells created with symmetry rules, we also illustrate that our flexible method can distill unique subsets regardless of the metric employed. Our diverse subsets are provided publicly for use by any designer. | ['Li-Wei Wang', 'Yu-Chin Chan', 'Wei Chen', 'Faez Ahmed'] | 2020-06-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.81585729e-01 -1.86246693e-01 2.05448992e-03 1.80309057e-01
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7948eb72-dc3e-49ae-908d-9f8a011ed592 | skeleton2mesh-kinematics-prior-injected | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Skeleton2Mesh_Kinematics_Prior_Injected_Unsupervised_Human_Mesh_Recovery_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Skeleton2Mesh_Kinematics_Prior_Injected_Unsupervised_Human_Mesh_Recovery_ICCV_2021_paper.pdf | Skeleton2Mesh: Kinematics Prior Injected Unsupervised Human Mesh Recovery | In this paper, we decouple unsupervised human mesh recovery into the well-studied problems of unsupervised 3D pose estimation, and human mesh recovery from estimated 3D skeletons, focusing on the latter task. The challenges of the latter task are two folds: (1) pose failure (i.e., pose mismatching -- different skeleton definitions in dataset and SMPL , and pose ambiguity -- endpoints have arbitrary joint angle configurations for the same 3D joint coordinates). (2) shape ambiguity (i.e., the lack of shape constraints on body configuration). To address these issues, we propose Skeleton2Mesh, a novel lightweight framework that recovers human mesh from a single image. Our Skeleton2Mesh contains three modules, i.e., Differentiable Inverse Kinematics (DIK), Pose Refinement (PR) and Shape Refinement (SR) modules. DIK is designed to transfer 3D rotation from estimated 3D skeletons, which relies on a minimal set of kinematics prior knowledge. Then PR and SR modules are utilized to tackle the pose ambiguity and shape ambiguity respectively. All three modules can be incorporated into Skeleton2Mesh seamlessly via an end-to-end manner. Furthermore, we utilize an adaptive joint regressor to alleviate the effects of skeletal topology from different datasets. Results on the Human3.6M dataset for human mesh recovery demonstrate that our method improves upon the previous unsupervised methods by 32.6% under the same setting. Qualitative results on in-the-wild datasets exhibit that the recovered 3D meshes are natural, realistic. Our project is available at https://sites.google.com/view/skeleton2mesh. | ['Wenjun Zhang', 'Minsi Wang', 'Chenglong Zhao', 'Bingbing Ni', 'Jingwei Xu', 'Junjie Wang', 'Zhenbo Yu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['3d-pose-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 1.73612863e-01 2.44027138e-01 -3.08098625e-02 -5.18324487e-02
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740c2243-59eb-4ade-a429-819c2b03c426 | deepdpm-deep-clustering-with-an-unknown | 2203.14309 | null | https://arxiv.org/abs/2203.14309v1 | https://arxiv.org/pdf/2203.14309v1.pdf | DeepDPM: Deep Clustering With an Unknown Number of Clusters | Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namely, they require a predefined and fixed number of clusters, denoted by K. When K is unknown, however, using model-selection criteria to choose its optimal value might become computationally expensive, especially in DL as the training process would have to be repeated numerous times. In this work, we bridge this gap by introducing an effective deep-clustering method that does not require knowing the value of K as it infers it during the learning. Using a split/merge framework, a dynamic architecture that adapts to the changing K, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones). While the very few existing deep nonparametric methods lack scalability, we demonstrate ours by being the first to report the performance of such a method on ImageNet. We also demonstrate the importance of inferring K by showing how methods that fix it deteriorate in performance when their assumed K value gets further from the ground-truth one, especially on imbalanced datasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM. | ['Oren Freifeld', 'Shahaf E. Finder', 'Meitar Ronen'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ronen_DeepDPM_Deep_Clustering_With_an_Unknown_Number_of_Clusters_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ronen_DeepDPM_Deep_Clustering_With_an_Unknown_Number_of_Clusters_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-image-classification'] | ['computer-vision'] | [-3.66064608e-01 -1.00405045e-01 -1.00051194e-01 -4.77699816e-01
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a5c7a7d6-8fb6-4429-aa60-3ecf5d6fe430 | towards-table-to-text-generation-with-1 | 2301.02071 | null | https://arxiv.org/abs/2301.02071v1 | https://arxiv.org/pdf/2301.02071v1.pdf | Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach | Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables. Large-scale pretrained language models sound like a promising solution to tackle such issues. However, how to effectively bridge the gap between the structured table and the text input by fully leveraging table information to fuel the pretrained model is still not well explored. Besides, another challenge of integrating the deliberation mechanism into the text-to-text pretrained model for solving the table-to-text task remains seldom studied. In this paper, to implement the table-to-text generation with pretrained language model, we propose a table structure understanding and text deliberating approach, namely TASD. Specifically, we devise a three-layered multi-head attention network to realize the table-structure-aware text generation model with the help of the pretrained language model. Furthermore, a multi-pass decoder framework is adopted to enhance the capability of polishing generated text for table descriptions. The empirical studies, as well as human evaluation, on two public datasets, validate that our approach can generate faithful and fluent descriptive texts for different types of tables. | ['Hui Xiong', 'Dejing Dou', 'Jingbo Zhou', 'Yanyan Li', 'Tong Xu', 'Xinjiang Lu', 'Miao Chen'] | 2023-01-05 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.60580182e-01 4.38415974e-01 -1.00662604e-01 -4.79606450e-01
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5874d463-6651-4d4d-95fe-d5113faef893 | automotive-parts-assessment-applying-real | 2202.00884 | null | https://arxiv.org/abs/2202.00884v1 | https://arxiv.org/pdf/2202.00884v1.pdf | Automotive Parts Assessment: Applying Real-time Instance-Segmentation Models to Identify Vehicle Parts | The problem of automated car damage assessment presents a major challenge in the auto repair and damage assessment industry. The domain has several application areas ranging from car assessment companies such as car rentals and body shops to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take any form including scratches, minor and major dents to missing parts. More often, the assessment area has a significant level of noise such as dirt, grease, oil or rush that makes an accurate identification challenging. Moreover, the identification of a particular part is the first step in the repair industry to have an accurate labour and part assessment where the presence of different car models, shapes and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this research explores and applies various instance segmentation methodologies to evaluate the best performing models. The scope of this work focusses on two genres of real-time instance segmentation models due to their industrial significance, namely SipMask and Yolact. These methodologies are evaluated against a previously reported car parts dataset (DSMLR) and an internally curated dataset extracted from local car repair workshops. The Yolact-based part localization and segmentation method performed well when compared to other real-time instance mechanisms with a mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracies for object detection with a mAP of 57.0 with outcomes for AP_IoU=.50and AP_IoU=.75 reporting 72.0 and 67.0 respectively while Yolact was found to be a better performer for AP_s with 44.0 and 2.6 for object detection and segmentation categories respectively. | ['Riad Souissi', 'Abdulmalik Ali Aldawsari', 'Syed Adnan Yusuf'] | 2022-02-02 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [-2.05335040e-02 4.33109477e-02 8.34439397e-02 1.65031236e-02
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34773616-9572-4f81-94ee-3a9efea40245 | deep-automatic-natural-image-matting | 2107.07235 | null | https://arxiv.org/abs/2107.07235v1 | https://arxiv.org/pdf/2107.07235v1.pdf | Deep Automatic Natural Image Matting | Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation. Simultaneously, the learned semantic features guide the matting network to focus on the transition areas via an attention mechanism. We also construct a test set AIM-500 that contains 500 diverse natural images covering all types along with manually labeled alpha mattes, making it feasible to benchmark the generalization ability of AIM models. Results of the experiments demonstrate that our network trained on available composite matting datasets outperforms existing methods both objectively and subjectively. The source code and dataset are available at https://github.com/JizhiziLi/AIM. | ['DaCheng Tao', 'Jing Zhang', 'Jizhizi Li'] | 2021-07-15 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 5.87653995e-01 2.46487126e-01 -1.13710195e-01 -3.97691220e-01
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99153dfc-b257-4120-9565-80aa8d3b5a07 | navigating-to-objects-specified-by-images | 2304.01192 | null | https://arxiv.org/abs/2304.01192v1 | https://arxiv.org/pdf/2304.01192v1.pdf | Navigating to Objects Specified by Images | Images are a convenient way to specify which particular object instance an embodied agent should navigate to. Solving this task requires semantic visual reasoning and exploration of unknown environments. We present a system that can perform this task in both simulation and the real world. Our modular method solves sub-tasks of exploration, goal instance re-identification, goal localization, and local navigation. We re-identify the goal instance in egocentric vision using feature-matching and localize the goal instance by projecting matched features to a map. Each sub-task is solved using off-the-shelf components requiring zero fine-tuning. On the HM3D InstanceImageNav benchmark, this system outperforms a baseline end-to-end RL policy 7x and a state-of-the-art ImageNav model 2.3x (56% vs 25% success). We deploy this system to a mobile robot platform and demonstrate effective real-world performance, achieving an 88% success rate across a home and an office environment. | ['Devendra Singh Chaplot', 'Stefan Lee', 'Jitendra Malik', 'Dhruv Batra', 'Roozbeh Mottaghi', 'Chris Paxton', 'Austin Wang', 'Karmesh Yadav', 'Theophile Gervet', 'Jacob Krantz'] | 2023-04-03 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-2.24261567e-01 3.21865916e-01 1.29945830e-01 -3.38848561e-01
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b55d3da1-d070-4102-a0b6-f2c16f884991 | dwformer-dynamic-window-transformer-for | 2303.01694 | null | https://arxiv.org/abs/2303.01694v1 | https://arxiv.org/pdf/2303.01694v1.pdf | DWFormer: Dynamic Window transFormer for Speech Emotion Recognition | Speech emotion recognition is crucial to human-computer interaction. The temporal regions that represent different emotions scatter in different parts of the speech locally. Moreover, the temporal scales of important information may vary over a large range within and across speech segments. Although transformer-based models have made progress in this field, the existing models could not precisely locate important regions at different temporal scales. To address the issue, we propose Dynamic Window transFormer (DWFormer), a new architecture that leverages temporal importance by dynamically splitting samples into windows. Self-attention mechanism is applied within windows for capturing temporal important information locally in a fine-grained way. Cross-window information interaction is also taken into account for global communication. DWFormer is evaluated on both the IEMOCAP and the MELD datasets. Experimental results show that the proposed model achieves better performance than the previous state-of-the-art methods. | ['Xiangmin Xu', 'Weidong Chen', 'Weibin Zhang', 'Xiaofen Xing', 'Shuaiqi Chen'] | 2023-03-03 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.63580865e-01 -2.48454049e-01 -2.36840636e-01 -5.86329639e-01
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7e6c3813-86fc-40b4-a70e-70cbfb3b8dba | knowgraph-pm-a-knowledge-graph-based-pricing | 2205.07627 | null | https://arxiv.org/abs/2205.07627v1 | https://arxiv.org/pdf/2205.07627v1.pdf | KnowGraph-PM: a Knowledge Graph based Pricing Model for Semiconductors Supply Chains | Semiconductor supply chains are described by significant demand fluctuation that increases as one moves up the supply chain, the so-called bullwhip effect. To counteract, semiconductor manufacturers aim to optimize capacity utilization, to deliver with shorter lead times and exploit this to generate revenue. Additionally, in a competitive market, firms seek to maintain customer relationships while applying revenue management strategies such as dynamic pricing. Price change potentially generates conflicts with customers. In this paper, we present KnowGraph-PM, a knowledge graph-based dynamic pricing model. The semantic model uses the potential of faster delivery and shorter lead times to define premium prices, thus entail increased profits based on the customer profile. The knowledge graph enables the integration of customer-related information, e.g., customer class and location to customer order data. The pricing algorithm is realized as a SPARQL query that relies on customer profile and order behavior to determine the corresponding price premium. We evaluate the approach by calculating the revenue generated after applying the pricing algorithm. Based on competency questions that translate to SPARQL queries, we validate the created knowledge graph. We demonstrate that semantic data integration enables customer-tailored revenue management. | ['Hans Ehm', 'Javad Chamanara', 'Soren Auer', 'Nour Ramzy'] | 2022-05-13 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-5.39157450e-01 2.57773101e-01 -3.85001868e-01 -4.07582194e-01
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6882b1c6-152f-4dfd-84e9-dd24358e9161 | learning-underrepresented-classes-from | 2206.15353 | null | https://arxiv.org/abs/2206.15353v1 | https://arxiv.org/pdf/2206.15353v1.pdf | Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images | Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in general object recognition tasks, the local medical datasets are more likely to only have images annotated for a subset of classes of interest due to high annotation costs. In this paper, we consider a practical yet under-explored problem, where underrepresented classes only have few labeled instances available and only exist in a few clients of the federated system. We show that standard federated learning approaches fail to learn robust multi-label classifiers with extreme class imbalance and address it by proposing a novel federated learning framework, FedFew. FedFew consists of three stages, where the first stage leverages federated self-supervised learning to learn class-agnostic representations. In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes. Finally, the underrepresented classes are detected based on the energy and a prototype-based nearest-neighbor model is proposed for few-shot matching. We evaluate FedFew on multi-label thoracic disease classification tasks and demonstrate that it outperforms the federated baselines by a large margin. | ['Irina Voiculescu', 'Michael Kampffmeyer', 'Nanqing Dong'] | 2022-06-30 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 3.73366386e-01 3.56121212e-01 -8.49693418e-01 -5.04047096e-01
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c022ffca-3931-4f86-a664-b58a5580e692 | 190412634 | 1904.12634 | null | http://arxiv.org/abs/1904.12634v1 | http://arxiv.org/pdf/1904.12634v1.pdf | DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark | Driver attention prediction is currently becoming the focus in safe driving
research community, such as the DR(eye)VE project and newly emerged Berkeley
DeepDrive Attention (BDD-A) database in critical situations. In safe driving,
an essential task is to predict the incoming accidents as early as possible.
BDD-A was aware of this problem and collected the driver attention in
laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses
the critical situations which do not encounter actual accidents, and just faces
the driver attention prediction task, without a close step for accident
prediction. In contrast to this, we explore the view of drivers' eyes for
capturing multiple kinds of accidents, and construct a more diverse and larger
video benchmark than ever before with the driver attention and the driving
accident annotation simultaneously (named as DADA-2000), which has 2000 video
clips owning about 658,476 frames on 54 kinds of accidents. These clips are
crowd-sourced and captured in various occasions (highway, urban, rural, and
tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and
nighttime). For the driver attention representation, we collect the maps of
fixations, saccade scan path and focusing time. The accidents are annotated by
their categories, the accident window in clips and spatial locations of the
crash-objects. Based on the analysis, we obtain a quantitative and positive
answer for the question in this paper. | ['Dingxin Yan', 'Sen Li', 'Jianru Xue', 'Jiahuan Qiao', 'He Wang', 'Jianwu Fang'] | 2019-04-23 | null | null | null | null | ['driver-attention-monitoring'] | ['computer-vision'] | [-3.27877223e-01 -3.77174288e-01 -5.95651194e-02 -2.77221262e-01
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36ee38a9-f446-4c5d-91ee-859d75582243 | contrastive-instruction-trajectory-learning | 2112.04138 | null | https://arxiv.org/abs/2112.04138v2 | https://arxiv.org/pdf/2112.04138v2.pdf | Contrastive Instruction-Trajectory Learning for Vision-Language Navigation | The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works learn to navigate step-by-step following an instruction. However, these works may fail to discriminate the similarities and discrepancies across instruction-trajectory pairs and ignore the temporal continuity of sub-instructions. These problems hinder agents from learning distinctive vision-and-language representations, harming the robustness and generalizability of the navigation policy. In this paper, we propose a Contrastive Instruction-Trajectory Learning (CITL) framework that explores invariance across similar data samples and variance across different ones to learn distinctive representations for robust navigation. Specifically, we propose: (1) a coarse-grained contrastive learning objective to enhance vision-and-language representations by contrasting semantics of full trajectory observations and instructions, respectively; (2) a fine-grained contrastive learning objective to perceive instructions by leveraging the temporal information of the sub-instructions; (3) a pairwise sample-reweighting mechanism for contrastive learning to mine hard samples and hence mitigate the influence of data sampling bias in contrastive learning. Our CITL can be easily integrated with VLN backbones to form a new learning paradigm and achieve better generalizability in unseen environments. Extensive experiments show that the model with CITL surpasses the previous state-of-the-art methods on R2R, R4R, and RxR. | ['Xiaodan Liang', 'Bing Wang', 'Bingqian Lin', 'Yi Zhu', 'Fengda Zhu', 'Xiwen Liang'] | 2021-12-08 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 5.88542931e-02 -4.38301235e-01 -7.99562410e-02 -4.52821493e-01
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4ed1131e-4ff0-4461-a4d1-59d754d5ab61 | 3d-brainformer-3d-fusion-transformer-for | 2304.14508 | null | https://arxiv.org/abs/2304.14508v1 | https://arxiv.org/pdf/2304.14508v1.pdf | 3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation | Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific research and clinical studies. Precise segmentation of brain tumors facilitates clinical diagnosis, evaluations, and surgical planning. Deep learning has recently emerged to improve brain tumor segmentation and achieved impressive results. Convolutional architectures are widely used to implement those neural networks. By the nature of limited receptive fields, however, those architectures are subject to representing long-range spatial dependencies of the voxel intensities in MRI images. Transformers have been leveraged recently to address the above limitations of convolutional networks. Unfortunately, the majority of current Transformers-based methods in segmentation are performed with 2D MRI slices, instead of 3D volumes. Moreover, it is difficult to incorporate the structures between layers because each head is calculated independently in the Multi-Head Self-Attention mechanism (MHSA). In this work, we proposed a 3D Transformer-based segmentation approach. We developed a Fusion-Head Self-Attention mechanism (FHSA) to combine each attention head through attention logic and weight mapping, for the exploration of the long-range spatial dependencies in 3D MRI images. We implemented a plug-and-play self-attention module, named the Infinite Deformable Fusion Transformer Module (IDFTM), to extract features on any deformable feature maps. We applied our approach to the task of brain tumor segmentation, and assessed it on the public BRATS datasets. The experimental results demonstrated that our proposed approach achieved superior performance, in comparison to several state-of-the-art segmentation methods. | ['Simon K. Warfield', 'Ali Gholipour', 'Jianhui Li', 'Mingzhang Zhao', 'Qiuying Li', 'Yuqi Qian', 'Yao Sui', 'Guoyao Zhang', 'Rui Nian'] | 2023-04-28 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.53810710e-01 2.63682067e-01 8.49247500e-02 -4.67670202e-01
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299d466c-5944-48ec-a655-e674c8075ec4 | learning-chess-blindfolded | null | null | https://openreview.net/forum?id=DGIXvEAJVd | https://openreview.net/pdf?id=DGIXvEAJVd | Learning Chess Blindfolded | Transformer language models have made tremendous strides in natural language
understanding. However, the complexity of natural language makes it challenging
to ascertain how accurately these models are tracking the world state underlying
the text. Motivated by this issue, we consider the task of language modeling for
the game of chess. Unlike natural language, chess notations describe a simple,
constrained, and deterministic domain. Probing for the world (board) state can be
done simply via language modeling prompts. Additionally, we have access to a vast
number of chess games coupled with the exact state at every move, allowing us to
measure the impact of various ways of including grounding during language model
training. Overall, we find that with enough training data, transformers can learn
to track pieces and predict legal moves when trained solely from move sequences.
However, in adverse circumstances (small training sets or prediction following long
move histories), providing access to board state information during training can
yield consistent improvements. | ['Kevin Gimpel', 'Karen Livescu', 'Sam Wiseman', 'Shubham Toshniwal'] | 2021-01-01 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [ 8.60633031e-02 1.96451202e-01 -6.18047357e-01 -9.17766318e-02
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abdd03c7-3294-459f-8a76-8f03dd6151c6 | towards-fair-and-explainable-ai-using-a-human | 2306.07427 | null | https://arxiv.org/abs/2306.07427v1 | https://arxiv.org/pdf/2306.07427v1.pdf | Towards Fair and Explainable AI using a Human-Centered AI Approach | The rise of machine learning (ML) is accompanied by several high-profile cases that have stressed the need for fairness, accountability, explainability and trust in ML systems. The existing literature has largely focused on fully automated ML approaches that try to optimize for some performance metric. However, human-centric measures like fairness, trust, explainability, etc. are subjective in nature, context-dependent, and might not correlate with conventional performance metrics. To deal with these challenges, we explore a human-centered AI approach that empowers people by providing more transparency and human control. In this dissertation, we present 5 research projects that aim to enhance explainability and fairness in classification systems and word embeddings. The first project explores the utility/downsides of introducing local model explanations as interfaces for machine teachers (crowd workers). Our study found that adding explanations supports trust calibration for the resulting ML model and enables rich forms of teaching feedback. The second project presents D-BIAS, a causality-based human-in-the-loop visual tool for identifying and mitigating social biases in tabular datasets. Apart from fairness, we found that our tool also enhances trust and accountability. The third project presents WordBias, a visual interactive tool that helps audit pre-trained static word embeddings for biases against groups, such as females, or subgroups, such as Black Muslim females. The fourth project presents DramatVis Personae, a visual analytics tool that helps identify social biases in creative writing. Finally, the last project presents an empirical study aimed at understanding the cumulative impact of multiple fairness-enhancing interventions at different stages of the ML pipeline on fairness, utility and different population groups. We conclude by discussing some of the future directions. | ['Bhavya Ghai'] | 2023-06-12 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-5.02896070e-01 5.59614897e-01 -3.51525098e-01 -6.78892255e-01
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d530045d-9218-4854-b7da-3add90ababaf | a-systematic-study-and-comprehensive | 2305.18486 | null | https://arxiv.org/abs/2305.18486v4 | https://arxiv.org/pdf/2305.18486v4.pdf | A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets | The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently. However, their evaluation in the benchmark academic datasets remains under-explored due to the difficulty of evaluating the generative outputs produced by this model against the ground truth. In this paper, we aim to present a thorough evaluation of ChatGPT's performance on diverse academic datasets, covering tasks like question-answering, text summarization, code generation, commonsense reasoning, mathematical problem-solving, machine translation, bias detection, and ethical considerations. Specifically, we evaluate ChatGPT across 140 tasks and analyze 255K responses it generates in these datasets. This makes our work the largest evaluation of ChatGPT in NLP benchmarks. In short, our study aims to validate the strengths and weaknesses of ChatGPT in various tasks and provide insights for future research using LLMs. We also report a new emergent ability to follow multi-query instructions that we mostly found in ChatGPT and other instruction-tuned models. Our extensive evaluation shows that even though ChatGPT is capable of performing a wide variety of tasks, and may obtain impressive performance in several benchmark datasets, it is still far from achieving the ability to reliably solve many challenging tasks. By providing a thorough assessment of ChatGPT's performance across diverse NLP tasks, this paper sets the stage for a targeted deployment of ChatGPT-like LLMs in real-world applications. | ['Jimmy Xiangji Huang', 'Shafiq Joty', 'Md Amran Hossen Bhuiyan', 'Mizanur Rahman', 'M Saiful Bari', 'Md Tahmid Rahman Laskar'] | 2023-05-29 | null | null | null | null | ['code-generation', 'bias-detection', 'text-summarization'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 2.84571573e-02 2.61742681e-01 3.98318470e-02 -2.86632746e-01
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1275d75b-3890-453a-aa82-27234c0f3173 | soft-sampling-for-robust-object-detection | 1806.06986 | null | https://arxiv.org/abs/1806.06986v2 | https://arxiv.org/pdf/1806.06986v2.pdf | Soft Sampling for Robust Object Detection | We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will generate an incorrect training signal for the detector. Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset. We provide a detailed explanation for this result. To further bridge the performance gap, we propose a simple yet effective solution, called Soft Sampling. Soft Sampling re-weights the gradients of RoIs as a function of overlap with positive instances. This ensures that the uncertain background regions are given a smaller weight compared to the hardnegatives. Extensive experiments on curated PASCAL VOC datasets demonstrate the effectiveness of the proposed Soft Sampling method at different annotation drop rates. Finally, we show that on OpenImagesV3, which is a real-world dataset with missing annotations, Soft Sampling outperforms standard detection baselines by over 3%. | ['Navaneeth Bodla', 'Zhe Wu', 'Rama Chellappa', 'Mahyar Najibi', 'Larry S. Davis', 'Bharat Singh'] | 2018-06-18 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 3.86780322e-01 3.48546624e-01 -2.20964327e-02 -3.00499856e-01
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02237499-adac-4851-946f-13a82291a207 | islam-imperative-slam | 2306.07894 | null | https://arxiv.org/abs/2306.07894v2 | https://arxiv.org/pdf/2306.07894v2.pdf | iSLAM: Imperative SLAM | Simultaneous localization and mapping (SLAM) stands as one of the critical challenges in robot navigation. Recent advancements suggest that methods based on supervised learning deliver impressive performance in front-end odometry, while traditional optimization-based methods still play a vital role in the back-end for minimizing estimation drift. In this paper, we found that such decoupled paradigm can lead to only sub-optimal performance, consequently curtailing system capabilities and generalization potential. To solve this problem, we proposed a novel self-supervised learning framework, imperative SLAM (iSLAM), which fosters reciprocal correction between the front-end and back-end, thus enhancing performance without necessitating any external supervision. Specifically, we formulate a SLAM system as a bi-level optimization problem so that the two components are bidirectionally connected. As a result, the front-end model is able to learn global geometric knowledge obtained through pose graph optimization by back-propagating the residuals from the back-end. This significantly improves the generalization ability of the entire system and thus achieves the accuracy improvement up to 45%. To the best of our knowledge, iSLAM is the first SLAM system showing that the front-end and back-end can learn jointly and mutually contribute to each other in a self-supervised manner. | ['Chen Wang', 'Shaoshu Su', 'Taimeng Fu'] | 2023-06-13 | null | null | null | null | ['simultaneous-localization-and-mapping', 'robot-navigation'] | ['computer-vision', 'robots'] | [-9.30179656e-02 2.59752065e-01 -4.71847616e-02 -4.27159965e-01
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6b575691-6b3c-4402-9b85-8e89764eced7 | teaching-deep-convolutional-neural-networks | 1412.3409 | null | http://arxiv.org/abs/1412.3409v2 | http://arxiv.org/pdf/1412.3409v2.pdf | Teaching Deep Convolutional Neural Networks to Play Go | Mastering the game of Go has remained a long standing challenge to the field
of AI. Modern computer Go systems rely on processing millions of possible
future positions to play well, but intuitively a stronger and more 'humanlike'
way to play the game would be to rely on pattern recognition abilities rather
then brute force computation. Following this sentiment, we train deep
convolutional neural networks to play Go by training them to predict the moves
made by expert Go players. To solve this problem we introduce a number of novel
techniques, including a method of tying weights in the network to 'hard code'
symmetries that are expect to exist in the target function, and demonstrate in
an ablation study they considerably improve performance. Our final networks are
able to achieve move prediction accuracies of 41.1% and 44.4% on two different
Go datasets, surpassing previous state of the art on this task by significant
margins. Additionally, while previous move prediction programs have not yielded
strong Go playing programs, we show that the networks trained in this work
acquired high levels of skill. Our convolutional neural networks can
consistently defeat the well known Go program GNU Go, indicating it is state of
the art among programs that do not use Monte Carlo Tree Search. It is also able
to win some games against state of the art Go playing program Fuego while using
a fraction of the play time. This success at playing Go indicates high level
principles of the game were learned. | ['Christopher Clark', 'Amos Storkey'] | 2014-12-10 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.50464952e-01 2.69421071e-01 -1.67404786e-01 -7.11863264e-02
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13bca4bb-527c-4f77-bbbe-f81b9ced0aaf | copy-the-old-or-paint-anew-an-adversarial | 1811.09236 | null | http://arxiv.org/abs/1811.09236v1 | http://arxiv.org/pdf/1811.09236v1.pdf | Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization | Parametric generative deep models are state-of-the-art for photo and
non-photo realistic image stylization. However, learning complicated image
representations requires compute-intense models parametrized by a huge number
of weights, which in turn requires large datasets to make learning successful.
Non-parametric exemplar-based generation is a technique that works well to
reproduce style from small datasets, but is also compute-intensive. These
aspects are a drawback for the practice of digital AI artists: typically one
wants to use a small set of stylization images, and needs a fast flexible model
in order to experiment with it. With this motivation, our work has these
contributions: (i) a novel stylization method called Fully Adversarial Mosaics
(FAMOS) that combines the strengths of both parametric and non-parametric
approaches; (ii) multiple ablations and image examples that analyze the method
and show its capabilities; (iii) source code that will empower artists and
machine learning researchers to use and modify FAMOS. | ['Urs Bergmann', 'Nikolay Jetchev', 'Gokhan Yildirim'] | 2018-11-22 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.67506272e-01 7.45725706e-02 1.33859128e-01 2.80544329e-02
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692e4c47-edc1-47be-adae-c05c1a45b947 | keyword-extraction-from-short-texts-with-a | 2209.14008 | null | https://arxiv.org/abs/2209.14008v2 | https://arxiv.org/pdf/2209.14008v2.pdf | Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer | The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short text passages. The evaluation is carried out on the new Polish Open Science Metadata Corpus (POSMAC), which is released with this paper: a collection of 216,214 abstracts of scientific publications compiled in the CURLICAT project. We compare the results obtained by four different methods, i.e. plT5kw, extremeText, TermoPL, KeyBERT and conclude that the plT5kw model yields particularly promising results for both frequent and sparsely represented keywords. Furthermore, a plT5kw keyword generation model trained on the POSMAC also seems to produce highly useful results in cross-domain text labelling scenarios. We discuss the performance of the model on news stories and phone-based dialog transcripts which represent text genres and domains extrinsic to the dataset of scientific abstracts. Finally, we also attempt to characterize the challenges of evaluating a text-to-text model on both intrinsic and extrinsic keyword extraction. | ['Maciej Ogrodniczuk', 'Bartłomiej Nitoń', 'Adam Wawrzyński', 'Agnieszka Mikołajczyk-Bareła', 'Piotr Pęzik'] | 2022-09-28 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 1.02498733e-01 5.93909204e-01 -1.32973492e-01 -5.48211522e-02
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ca9087bf-4a12-4288-a765-38dcddb10563 | return-of-the-rnn-residual-recurrent-networks | 2303.13570 | null | https://arxiv.org/abs/2303.13570v2 | https://arxiv.org/pdf/2303.13570v2.pdf | Return of the RNN: Residual Recurrent Networks for Invertible Sentence Embeddings | This study presents a novel model for invertible sentence embeddings using a residual recurrent network trained on an unsupervised encoding task. Rather than the probabilistic outputs common to neural machine translation models, our approach employs a regression-based output layer to reconstruct the input sequence's word vectors. The model achieves high accuracy and fast training with the ADAM optimizer, a significant finding given that RNNs typically require memory units, such as LSTMs, or second-order optimization methods. We incorporate residual connections and introduce a "match drop" technique, where gradients are calculated only for incorrect words. Our approach demonstrates potential for various natural language processing applications, particularly in neural network-based systems that require high-quality sentence embeddings. | ['Jeremy Wilkerson'] | 2023-03-23 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 4.82981712e-01 3.71728569e-01 -4.18258250e-01 -5.14091372e-01
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e04b9783-0744-4522-b4f8-bd2c0e76618b | regret-bounds-for-information-directed | 2206.04640 | null | https://arxiv.org/abs/2206.04640v2 | https://arxiv.org/pdf/2206.04640v2.pdf | Regret Bounds for Information-Directed Reinforcement Learning | Information-directed sampling (IDS) has revealed its potential as a data-efficient algorithm for reinforcement learning (RL). However, theoretical understanding of IDS for Markov Decision Processes (MDPs) is still limited. We develop novel information-theoretic tools to bound the information ratio and cumulative information gain about the learning target. Our theoretical results shed light on the importance of choosing the learning target such that the practitioners can balance the computation and regret bounds. As a consequence, we derive prior-free Bayesian regret bounds for vanilla-IDS which learns the whole environment under tabular finite-horizon MDPs. In addition, we propose a computationally-efficient regularized-IDS that maximizes an additive form rather than the ratio form and show that it enjoys the same regret bound as vanilla-IDS. With the aid of rate-distortion theory, we improve the regret bound by learning a surrogate, less informative environment. Furthermore, we extend our analysis to linear MDPs and prove similar regret bounds for Thompson sampling as a by-product. | ['Tor Lattimore', 'Botao Hao'] | 2022-06-09 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.70310640e-01 5.58611155e-01 -6.03290915e-01 -2.53118128e-01
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23b70d21-3331-4ce6-a419-14d929c3f96d | yaclc-a-chinese-learner-corpus-with | 2112.15043 | null | https://arxiv.org/abs/2112.15043v1 | https://arxiv.org/pdf/2112.15043v1.pdf | YACLC: A Chinese Learner Corpus with Multidimensional Annotation | Learner corpus collects language data produced by L2 learners, that is second or foreign-language learners. This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction. However, there is little focus on learner corpus for Chinese as Foreign Language (CFL) learners. Therefore, we propose to construct a large-scale, multidimensional annotated Chinese learner corpus. To construct the corpus, we first obtain a large number of topic-rich texts generated by CFL learners. Then we design an annotation scheme including a sentence acceptability score as well as grammatical error and fluency-based corrections. We build a crowdsourcing platform to perform the annotation effectively (https://yaclc.wenmind.net). We name the corpus YACLC (Yet Another Chinese Learner Corpus) and release it as part of the CUGE benchmark (http://cuge.baai.ac.cn). By analyzing the original sentences and annotations in the corpus, we found that YACLC has a considerable size and very high annotation quality. We hope this corpus can further enhance the studies on Chinese International Education and Chinese automatic grammatical error correction. | ['Maosong Sun', 'Erhong Yang', 'Yun Chen', 'Zhenghao Liu', 'Shan He', 'Renfen Hu', 'Xiaorong Lu', 'Yijun Wang', 'Liner Yang', 'Cunliang Kong', 'Yingying Wang'] | 2021-12-30 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-4.41728204e-01 -4.90060002e-02 4.36955225e-03 -2.96711534e-01
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4f1b35b9-a7eb-4463-8a25-cf7d2f3eea64 | robust-and-fine-grained-prosody-control-of | 1811.02122 | null | http://arxiv.org/abs/1811.02122v2 | http://arxiv.org/pdf/1811.02122v2.pdf | Robust and fine-grained prosody control of end-to-end speech synthesis | We propose prosody embeddings for emotional and expressive speech synthesis
networks. The proposed methods introduce temporal structures in the embedding
networks, thus enabling fine-grained control of the speaking style of the
synthesized speech. The temporal structures can be designed either on the
speech side or the text side, leading to different control resolutions in time.
The prosody embedding networks are plugged into end-to-end speech synthesis
networks and trained without any other supervision except for the target speech
for synthesizing. It is demonstrated that the prosody embedding networks
learned to extract prosodic features. By adjusting the learned prosody
features, we could change the pitch and amplitude of the synthesized speech
both at the frame level and the phoneme level. We also introduce the temporal
normalization of prosody embeddings, which shows better robustness against
speaker perturbations during prosody transfer tasks. | ['Young-Gun Lee', 'Taesu Kim'] | 2018-11-06 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-1.19568005e-01 4.44179237e-01 -2.85736978e-01 -4.89845425e-01
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d836833f-32ac-4052-8d22-712af945939f | grm-generative-relevance-modeling-using | 2306.09938 | null | https://arxiv.org/abs/2306.09938v1 | https://arxiv.org/pdf/2306.09938v1.pdf | GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval | Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness. To address this, we propose Generative Relevance Modeling (GRM) that uses Relevance-Aware Sample Estimation (RASE) for more accurate weighting of expansion terms. Specifically, we identify similar real documents for each generated document and use a neural re-ranker to estimate their relevance. Experiments on three standard document ranking benchmarks show that GRM improves MAP by 6-9% and R@1k by 2-4%, surpassing previous methods. | ['Fabio Crestani', 'Jeffrey Dalton', 'Shubham Chatterjee', 'Ivan Sekulic', 'Iain Mackie'] | 2023-06-16 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 2.97816813e-01 9.58880857e-02 -2.32461005e-01 -1.47360906e-01
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c7d05f35-1486-462a-bcba-e2573506a4b0 | weakly-supervised-temporal-action-1 | 2001.07793 | null | https://arxiv.org/abs/2001.07793v1 | https://arxiv.org/pdf/2001.07793v1.pdf | Weakly Supervised Temporal Action Localization Using Deep Metric Learning | Temporal action localization is an important step towards video understanding. Most current action localization methods depend on untrimmed videos with full temporal annotations of action instances. However, it is expensive and time-consuming to annotate both action labels and temporal boundaries of videos. To this end, we propose a weakly supervised temporal action localization method that only requires video-level action instances as supervision during training. We propose a classification module to generate action labels for each segment in the video, and a deep metric learning module to learn the similarity between different action instances. We jointly optimize a balanced binary cross-entropy loss and a metric loss using a standard backpropagation algorithm. Extensive experiments demonstrate the effectiveness of both of these components in temporal localization. We evaluate our algorithm on two challenging untrimmed video datasets: THUMOS14 and ActivityNet1.2. Our approach improves the current state-of-the-art result for THUMOS14 by 6.5% mAP at IoU threshold 0.5, and achieves competitive performance for ActivityNet1.2. | ['Ashraful Islam', 'Richard J. Radke'] | 2020-01-21 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 4.25151020e-01 -1.50703147e-01 -6.67863607e-01 -4.39546734e-01
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3c5119af-7adf-409c-8cfb-60f512f9c364 | kinematic-aware-hierarchical-attention | 2211.15868 | null | https://arxiv.org/abs/2211.15868v1 | https://arxiv.org/pdf/2211.15868v1.pdf | Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos | Previous video-based human pose estimation methods have shown promising results by leveraging aggregated features of consecutive frames. However, most approaches compromise accuracy to mitigate jitter or do not sufficiently comprehend the temporal aspects of human motion. Furthermore, occlusion increases uncertainty between consecutive frames, which results in unsmooth results. To address these issues, we design an architecture that exploits the keypoint kinematic features with the following components. First, we effectively capture the temporal features by leveraging individual keypoint's velocity and acceleration. Second, the proposed hierarchical transformer encoder aggregates spatio-temporal dependencies and refines the 2D or 3D input pose estimated from existing estimators. Finally, we provide an online cross-supervision between the refined input pose generated from the encoder and the final pose from our decoder to enable joint optimization. We demonstrate comprehensive results and validate the effectiveness of our model in various tasks: 2D pose estimation, 3D pose estimation, body mesh recovery, and sparsely annotated multi-human pose estimation. Our code is available at https://github.com/KyungMinJin/HANet. | ['Seong-Whan Lee', 'Tae-Kyung Kang', 'Gun-Hee Lee', 'Byoung-Sung Lim', 'Kyung-Min Jin'] | 2022-11-29 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.85355291e-01 -6.43030852e-02 -3.00893605e-01 -3.08031976e-01
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03cb23b5-6957-44e9-b2eb-6e253c5566fb | a-multi-view-dynamic-fusion-framework-how-to | 2012.11211 | null | https://arxiv.org/abs/2012.11211v1 | https://arxiv.org/pdf/2012.11211v1.pdf | A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views? | When diagnosing the brain tumor, doctors usually make a diagnosis by observing multimodal brain images from the axial view, the coronal view and the sagittal view, respectively. And then they make a comprehensive decision to confirm the brain tumor based on the information obtained from multi-views. Inspired by this diagnosing process and in order to further utilize the 3D information hidden in the dataset, this paper proposes a multi-view dynamic fusion framework to improve the performance of brain tumor segmentation. The proposed framework consists of 1) a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view and 2) the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views as an integrate one and two different fusion methods, the voting method and the weighted averaging method, have been adopted to evaluate the fusing process. Moreover, the multi-view fusion loss, which consists of the segmentation loss, the transition loss and the decision loss, is proposed to facilitate the training process of multi-view learning networks so as to keep the consistency of appearance and space, not only in the process of fusing segmentation results, but also in the process of training the learning network. \par By evaluating the proposed framework on BRATS 2015 and BRATS 2018, it can be found that the fusion results from multi-views achieve a better performance than the segmentation result from the single view and the effectiveness of proposed multi-view fusion loss has also been proved. Moreover, the proposed framework achieves a better segmentation performance and a higher efficiency compared to other counterpart methods. | ['Zhiguang Qin', 'Mingsheng Cao', 'Ji Geng', 'Guozheng Wu', 'Wei Zheng', 'Yi Ding'] | 2020-12-21 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.01711191e-01 -9.51625332e-02 1.13083385e-01 -2.20055148e-01
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6a458585-5d62-46d2-894f-dbde771fb30e | adversarial-language-games-for-advanced | 1911.01622 | null | https://arxiv.org/abs/1911.01622v4 | https://arxiv.org/pdf/1911.01622v4.pdf | Adversarial Language Games for Advanced Natural Language Intelligence | We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker must hide its intention and subtly induce the defender, while a competitive defender must be cautious with its utterances and infer the intention of the attacker. Such language abilities can facilitate many important downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments and empirical studies on several baseline attack and defense strategies show promising and interesting results. Based on the analysis on the game and experiments, we discuss multiple promising directions for future research. | ['Yuan Yao', 'Zhengyan Zhang', 'Haoxi Zhong', 'Xiaozhi Wang', 'Maosong Sun', 'Guoyang Zeng', 'Zhiyuan Liu', 'Xu Han', 'Chaojun Xiao'] | 2019-11-05 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.29531795e-01 5.63374460e-01 3.80657107e-01 7.03027323e-02
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33547fa1-cae4-421c-9ef6-018a834c9964 | adaptive-learning-path-navigation-based-on | 2305.04475 | null | https://arxiv.org/abs/2305.04475v2 | https://arxiv.org/pdf/2305.04475v2.pdf | Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning | This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a novel approach for enhancing E-learning platforms by providing highly adaptive learning paths for students. The ALPN system integrates the Attentive Knowledge Tracing (AKT) model, which assesses students' knowledge states, with the proposed Entropy-enhanced Proximal Policy Optimization (EPPO) algorithm. This new algorithm optimizes the recommendation of learning materials. By harmonizing these models, the ALPN system tailors the learning path to students' needs, significantly increasing learning effectiveness. Experimental results demonstrate that the ALPN system outperforms previous research by 8.2% in maximizing learning outcomes and provides a 10.5% higher diversity in generating learning paths. The proposed system marks a significant advancement in adaptive E-learning, potentially transforming the educational landscape in the digital era. | ['I-Wei Lai', 'Saeed Saeedvand', 'Jyun-Yi Chen'] | 2023-05-08 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-3.53899151e-01 2.46395439e-01 -6.49197996e-01 1.04632959e-01
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d058d536-421a-4f6f-972f-a90bf5c18e9b | learning-a-latent-space-of-style-aware | 2001.05494 | null | https://arxiv.org/abs/2001.05494v2 | https://arxiv.org/pdf/2001.05494v2.pdf | Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders | We address the challenging open problem of learning an effective latent space for symbolic music data in generative music modeling. We focus on leveraging adversarial regularization as a flexible and natural mean to imbue variational autoencoders with context information concerning music genre and style. Through the paper, we show how Gaussian mixtures taking into account music metadata information can be used as an effective prior for the autoencoder latent space, introducing the first Music Adversarial Autoencoder (MusAE). The empirical analysis on a large scale benchmark shows that our model has a higher reconstruction accuracy than state-of-the-art models based on standard variational autoencoders. It is also able to create realistic interpolations between two musical sequences, smoothly changing the dynamics of the different tracks. Experiments show that the model can organise its latent space accordingly to low-level properties of the musical pieces, as well as to embed into the latent variables the high-level genre information injected from the prior distribution to increase its overall performance. This allows us to perform changes to the generated pieces in a principled way. | ['Davide Bacciu', 'Antonio Carta', 'Andrea Valenti'] | 2020-01-15 | null | null | null | null | ['music-modeling'] | ['music'] | [-2.04359423e-02 2.97248214e-01 2.18164206e-01 1.78551316e-01
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b29f6522-d0a2-4045-9992-bd8550562808 | towards-generalizable-surgical-activity | 2001.03728 | null | https://arxiv.org/abs/2001.03728v4 | https://arxiv.org/pdf/2001.03728v4.pdf | Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks | Modeling and recognition of surgical activities poses an interesting research problem. Although a number of recent works studied automatic recognition of surgical activities, generalizability of these works across different tasks and different datasets remains a challenge. We introduce a modality that is robust to scene variation, and that is able to infer part information such as orientational and relative spatial relationships. The proposed modality is based on spatial temporal graph representations of surgical tools in videos, for surgical activity recognition. To explore its effectiveness, we model and recognize surgical gestures with the proposed modality. We construct spatial graphs connecting the joint pose estimations of surgical tools. Then, we connect each joint to the corresponding joint in the consecutive frames forming inter-frame edges representing the trajectory of the joint over time. We then learn hierarchical spatial temporal graph representations using Spatial Temporal Graph Convolutional Networks (ST-GCN). Our experiments show that learned spatial temporal graph representations perform well in surgical gesture recognition even when used individually. We experiment with the Suturing task of the JIGSAWS dataset where the chance baseline for gesture recognition is 10%. Our results demonstrate 68% average accuracy which suggests a significant improvement. Learned hierarchical spatial temporal graph representations can be used either individually, in cascades or as a complementary modality in surgical activity recognition, therefore provide a benchmark for future studies. To our knowledge, our paper is the first to use spatial temporal graph representations of surgical tools, and pose-based skeleton representations in general, for surgical activity recognition. | ['Pierre Jannin', 'Duygu Sarikaya'] | 2020-01-11 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 3.22412878e-01 1.85279220e-01 -6.77003384e-01 6.72531575e-02
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6a6e0667-345c-4ff4-9fe3-6ed78f3672ab | a-regularized-implicit-policy-for-offline | 2202.09673 | null | https://arxiv.org/abs/2202.09673v2 | https://arxiv.org/pdf/2202.09673v2.pdf | A Behavior Regularized Implicit Policy for Offline Reinforcement Learning | Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the training dataset and prone to missing rewarding actions. For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. We further propose a simple modification to the classical policy-matching methods for regularizing with respect to the dual form of the Jensen--Shannon divergence and the integral probability metrics. We theoretically show the correctness of the policy-matching approach, and the correctness and a good finite-sample property of our modification. An effective instantiation of our framework through the GAN structure is provided, together with techniques to explicitly smooth the state-action mapping for robust generalization beyond the static dataset. Extensive experiments and ablation study on the D4RL benchmark validate our framework and the effectiveness of our algorithmic designs. | ['Mingyuan Zhou', 'Yihao Feng', 'Huangjie Zheng', 'Zhendong Wang', 'Shentao Yang'] | 2022-02-19 | null | null | null | null | ['d4rl'] | ['robots'] | [ 1.85373157e-01 2.61417091e-01 -6.23843968e-01 -2.43672177e-01
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7093d30c-ef59-47d2-bd35-efe4dbc19088 | omnidet-surround-view-cameras-based-multi | 2102.07448 | null | https://arxiv.org/abs/2102.07448v3 | https://arxiv.org/pdf/2102.07448v3.pdf | OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving | Surround View fisheye cameras are commonly deployed in automated driving for 360\deg{} near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for an autonomous driving system: depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. We demonstrate that the jointly trained model performs better than the respective single task versions. Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other. We propose a novel camera geometry based adaptation mechanism to encode the fisheye distortion model both at training and inference. This was crucial to enable training on the WoodScape dataset, comprised of data from different parts of the world collected by 12 different cameras mounted on three different cars with different intrinsics and viewpoints. Given that bounding boxes is not a good representation for distorted fisheye images, we also extend object detection to use a polygon with non-uniformly sampled vertices. We additionally evaluate our model on standard automotive datasets, namely KITTI and Cityscapes. We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks. We perform extensive ablation studies on various architecture choices and task weighting methodologies. A short video at https://youtu.be/xbSjZ5OfPes provides qualitative results. | ['Ganesh Sistu', 'Patrick Mäder', 'Stefan Milz', 'Isabelle Leang', 'Christian Witt', 'Hazem Rashed', 'Senthil Yogamani', 'Varun Ravi Kumar'] | 2021-02-15 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-6.79862574e-02 1.26601011e-01 -1.99629106e-02 -7.07922816e-01
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928b7003-abdf-4043-95a2-6d0301f72597 | spatial-pyramid-context-aware-moving-object | 1711.01656 | null | http://arxiv.org/abs/1711.01656v1 | http://arxiv.org/pdf/1711.01656v1.pdf | Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery | A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collaborative Spatial Pyramid Context-aware moving object detection
and Tracking system. The proposed visual tracker is composed of one master
tracker that usually relies on visual object features and two auxiliary
trackers based on object temporal motion information that will be called
dynamically to assist master tracker. SPCT utilizes image spatial context at
different level to make the video tracking system resistant to occlusion,
background noise and improve target localization accuracy and robustness. We
chose a pre-selected seven-channel complementary features including RGB color,
intensity and spatial pyramid of HoG to encode object color, shape and spatial
layout information. We exploit integral histogram as building block to meet the
demands of real-time performance. A novel fast algorithm is presented to
accurately evaluate spatially weighted local histograms in constant time
complexity using an extension of the integral histogram method. Different
techniques are explored to efficiently compute integral histogram on GPU
architecture and applied for fast spatio-temporal median computations and 3D
face reconstruction texturing. We proposed a multi-component framework based on
semantic fusion of motion information with projected building footprint map to
significantly reduce the false alarm rate in urban scenes with many tall
structures. The experiments on extensive VOTC2016 benchmark dataset and aerial
video confirm that combining complementary tracking cues in an intelligent
fusion framework enables persistent tracking for Full Motion Video and Wide
Aerial Motion Imagery. | ['Mahdieh Poostchi'] | 2017-11-05 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.67918339e-01 -9.74955916e-01 -1.13046981e-01 -1.53393283e-01
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c93b08a1-e6f4-4627-ba6a-928436c004ad | seeing-the-forest-and-the-trees-detection-and-1 | null | null | https://aclanthology.org/2020.aespen-1.7 | https://aclanthology.org/2020.aespen-1.7.pdf | Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes | Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references{---}recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines{'} texts and publication dates. | ['Benjamin Radford'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 2.68055767e-01 1.08061209e-01 -3.15592974e-01 -6.30063951e-01
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f9aed005-0a3d-4163-880a-a99e184a24a6 | augmentation-scheme-for-dealing-with | 1901.00204 | null | http://arxiv.org/abs/1901.00204v1 | http://arxiv.org/pdf/1901.00204v1.pdf | Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning | One of the most important tasks in network management is identifying
different types of traffic flows. As a result, a type of management service,
called Network Traffic Classifier (NTC), has been introduced. One type of NTCs
that has gained huge attention in recent years applies deep learning on packets
in order to classify flows. Internet is an imbalanced environment i.e., some
classes of applications are a lot more populated than others e.g., HTTP.
Additionally, one of the challenges in deep learning methods is that they do
not perform well in imbalanced environments in terms of evaluation metrics such
as precision, recall, and $\mathrm{F_1}$ measure. In order to solve this
problem, we recommend the use of augmentation methods to balance the dataset.
In this paper, we propose a novel data augmentation approach based on the use
of Long Short Term Memory (LSTM) networks for generating traffic flow patterns
and Kernel Density Estimation (KDE) for replicating the numerical features of
each class. First, we use the LSTM network in order to learn and generate the
sequence of packets in a flow for classes with less population. Then, we
complete the features of the sequence with generating random values based on
the distribution of a certain feature, which will be estimated using KDE.
Finally, we compare the training of a Convolutional Recurrent Neural Network
(CRNN) in large-scale imbalanced, sampled, and augmented datasets. The
contribution of our augmentation scheme is then evaluated on all of the
datasets through measurements of precision, recall, and F1 measure for every
class of application. The results demonstrate that our scheme is well suited
for network traffic flow datasets and improves the performance of deep learning
algorithms when it comes to above-mentioned metrics. | ['Ramin Hasibi', 'Mehdi Dehghan', 'Matin Shokri'] | 2019-01-01 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-9.80426073e-02 -3.82940859e-01 -2.45466620e-01 -4.31672603e-01
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61bc86cf-9378-40d9-9fb6-c6b0cd2200c5 | gophormer-ego-graph-transformer-for-node | 2110.13094 | null | https://arxiv.org/abs/2110.13094v1 | https://arxiv.org/pdf/2110.13094v1.pdf | Gophormer: Ego-Graph Transformer for Node Classification | Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area, where graph neural network (GNN) has been the dominant paradigm, transformers haven't achieved competitive performance, especially on the node classification task. Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases. To alleviate these issues, we propose a novel Gophormer model which applies transformers on ego-graphs instead of full-graphs. Specifically, Node2Seq module is proposed to sample ego-graphs as the input of transformers, which alleviates the challenge of scalability and serves as an effective data augmentation technique to boost model performance. Moreover, different from the feature-based attention strategy in vanilla transformers, we propose a proximity-enhanced attention mechanism to capture the fine-grained structural bias. In order to handle the uncertainty introduced by the ego-graph sampling, we further propose a consistency regularization and a multi-sample inference strategy for stabilized training and testing, respectively. Extensive experiments on six benchmark datasets are conducted to demonstrate the superiority of Gophormer over existing graph transformers and popular GNNs, revealing the promising future of graph transformers. | ['Yanfang Ye', 'Xing Xie', 'Hao Sun', 'Yuming Liu', 'Yiqi Wang', 'Qianlong Wen', 'Chaozhuo Li', 'Jianan Zhao'] | 2021-10-25 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.58498113e-02 3.00336719e-01 -2.54985571e-01 -2.79977381e-01
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19ff7b32-22de-4ee1-ae49-6b312267bf79 | learn-dynamic-aware-state-embedding-for | 2101.02230 | null | https://arxiv.org/abs/2101.02230v1 | https://arxiv.org/pdf/2101.02230v1.pdf | Learn Dynamic-Aware State Embedding for Transfer Learning | Transfer reinforcement learning aims to improve the sample efficiency of solving unseen new tasks by leveraging experiences obtained from previous tasks. We consider the setting where all tasks (MDPs) share the same environment dynamic except reward function. In this setting, the MDP dynamic is a good knowledge to transfer, which can be inferred by uniformly random policy. However, trajectories generated by uniform random policy are not useful for policy improvement, which impairs the sample efficiency severely. Instead, we observe that the binary MDP dynamic can be inferred from trajectories of any policy which avoids the need of uniform random policy. As the binary MDP dynamic contains the state structure shared over all tasks we believe it is suitable to transfer. Built on this observation, we introduce a method to infer the binary MDP dynamic on-line and at the same time utilize it to guide state embedding learning, which is then transferred to new tasks. We keep state embedding learning and policy learning separately. As a result, the learned state embedding is task and policy agnostic which makes it ideal for transfer learning. In addition, to facilitate the exploration over the state space, we propose a novel intrinsic reward based on the inferred binary MDP dynamic. Our method can be used out-of-box in combination with model-free RL algorithms. We show two instances on the basis of \algo{DQN} and \algo{A2C}. Empirical results of intensive experiments show the advantage of our proposed method in various transfer learning tasks. | ['Kaige Yang'] | 2021-01-06 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-3.82279865e-02 1.44006193e-01 -4.20481384e-01 9.05077159e-03
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86734acb-5d5a-4216-863a-ae78bb4992c3 | a-novel-hybrid-deep-learning-approach-for-non | 2104.07809 | null | https://arxiv.org/abs/2104.07809v1 | https://arxiv.org/pdf/2104.07809v1.pdf | A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks | Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem, aims to interpret the mains user electricity consumption into appliance level measurement. This article presents a new approach for power disaggregation by using a deep recurrent long short term memory (LSTM) network combined with convolutional neural networks (CNN). Deep neural networks have been shown to be a significant way for these types of problems because of their complexity and huge number of trainable paramters. Hybrid method that proposed in the article could significantly increase the overall accuracy of NILM because it benefits from both network advantages. The proposed method used sequence-to-sequence learning, where the input is a window of the mains and the output is a window of the target appliance. The proposed deep neural network approach has been applied to real-world household energy dataset "REFIT". The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses around the UK. The proposed method achieve significant performance, improving accuracy and F1-score measures by 95.93% and 80.93% ,respectively which demonstrates the effectiveness and superiority of the proposed approach for home energy monitoring. Comparison of proposed method and other recently published method has been presented and discussed based on accuracy, number of considered appliances and size of the deep neural network trainable parameters. The proposed method shows remarkable performance compare to other previous methods. | ['Sobhan Naderian'] | 2021-04-15 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.05549090e-01 -2.13038951e-01 1.25025228e-01 -3.75053227e-01
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7345832f-f64a-4b76-bde8-b37cd64db4f5 | adversarial-extreme-multi-label | 1803.01570 | null | http://arxiv.org/abs/1803.01570v1 | http://arxiv.org/pdf/1803.01570v1.pdf | Adversarial Extreme Multi-label Classification | The goal in extreme multi-label classification is to learn a classifier which
can assign a small subset of relevant labels to an instance from an extremely
large set of target labels. Datasets in extreme classification exhibit a long
tail of labels which have small number of positive training instances. In this
work, we pose the learning task in extreme classification with large number of
tail-labels as learning in the presence of adversarial perturbations. This view
motivates a robust optimization framework and equivalence to a corresponding
regularized objective.
Under the proposed robustness framework, we demonstrate efficacy of Hamming
loss for tail-label detection in extreme classification. The equivalent
regularized objective, in combination with proximal gradient based
optimization, performs better than state-of-the-art methods on propensity
scored versions of precision@k and nDCG@k(upto 20% relative improvement over
PFastreXML - a leading tree-based approach and 60% relative improvement over
SLEEC - a leading label-embedding approach). Furthermore, we also highlight the
sub-optimality of a sparse solver in a widely used package for large-scale
linear classification, which is interesting in its own right. We also
investigate the spectral properties of label graphs for providing novel
insights towards understanding the conditions governing the performance of
Hamming loss based one-vs-rest scheme vis-\`a-vis label embedding methods. | ['Bernhard Schölkopf', 'Rohit Babbar'] | 2018-03-05 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 6.45165980e-01 3.18506956e-01 -3.67705852e-01 -4.30887222e-01
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8209e138-5fc5-4d3e-b13a-1acfa6661b4a | sleep-syndromes-onset-detection-based-on | 2107.03387 | null | https://arxiv.org/abs/2107.03387v1 | https://arxiv.org/pdf/2107.03387v1.pdf | Sleep syndromes onset detection based on automatic sleep staging algorithm | In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is proposed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching an accuracy of 86.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss of 0.09. | ['Tinkara Robek', 'Tim Cvetko'] | 2021-07-07 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 3.50148268e-02 -2.17057645e-01 6.39538243e-02 -5.52315414e-01
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64bcad0e-d2e8-4b5a-8c1b-0d81b42722e1 | tclr-temporal-contrastive-learning-for-video | 2101.07974 | null | https://arxiv.org/abs/2101.07974v4 | https://arxiv.org/pdf/2101.07974v4.pdf | TCLR: Temporal Contrastive Learning for Video Representation | Contrastive learning has nearly closed the gap between supervised and self-supervised learning of image representations, and has also been explored for videos. However, prior work on contrastive learning for video data has not explored the effect of explicitly encouraging the features to be distinct across the temporal dimension. We develop a new temporal contrastive learning framework consisting of two novel losses to improve upon existing contrastive self-supervised video representation learning methods. The local-local temporal contrastive loss adds the task of discriminating between non-overlapping clips from the same video, whereas the global-local temporal contrastive aims to discriminate between timesteps of the feature map of an input clip in order to increase the temporal diversity of the learned features. Our proposed temporal contrastive learning framework achieves significant improvement over the state-of-the-art results in various downstream video understanding tasks such as action recognition, limited-label action classification, and nearest-neighbor video retrieval on multiple video datasets and backbones. We also demonstrate significant improvement in fine-grained action classification for visually similar classes. With the commonly used 3D ResNet-18 architecture with UCF101 pretraining, we achieve 82.4\% (+5.1\% increase over the previous best) top-1 accuracy on UCF101 and 52.9\% (+5.4\% increase) on HMDB51 action classification, and 56.2\% (+11.7\% increase) Top-1 Recall on UCF101 nearest neighbor video retrieval. Code released at github.com/DAVEISHAN/TCLR. | ['Mubarak Shah', 'Mamshad Nayeem Rizve', 'Rohit Gupta', 'Ishan Dave'] | 2021-01-20 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.45547312e-01 -3.87974948e-01 -6.94083035e-01 -3.97294641e-01
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5e575b93-6391-4df2-9157-abebb4bc9759 | retroformer-pushing-the-limits-of | 2201.12475 | null | https://arxiv.org/abs/2201.12475v1 | https://arxiv.org/pdf/2201.12475v1.pdf | Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer | Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different levels of dependency on additional chemical knowledge. In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing. Via the proposed local attention head, the model can jointly encode the molecular sequence and graph, and efficiently exchange information between the local reactive region and the global reaction context. Retroformer reaches the new state-of-the-art accuracy for the end-to-end template-free retrosynthesis, and improves over many strong baselines on better molecule and reaction validity. In addition, its generative procedure is highly interpretable and controllable. Overall, Retroformer pushes the limits of the reaction reasoning ability of deep generative models. | ['Shengyu Zhang', 'Chang-Yu Hsieh', 'Benben Liao', 'Yue Wan'] | 2022-01-29 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.59724903e-01 2.53960669e-01 -5.30337334e-01 -5.72414398e-02
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0e91b190-82ba-4459-aada-7e932f5e813a | kga-a-general-machine-unlearning-framework | 2305.06535 | null | https://arxiv.org/abs/2305.06535v1 | https://arxiv.org/pdf/2305.06535v1.pdf | KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment | Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set. Previous work mainly focuses on computer vision scenarios and largely ignores the essentials of unlearning in NLP field, where text data contains more explicit and sensitive personal information than images. In this paper, we propose a general unlearning framework called KGA to induce forgetfulness. Different from previous work that tries to recover gradients or forces models to perform close to one specific distribution, KGA maintains distribution differences (i.e., knowledge gap). This relaxes the distribution assumption. Furthermore, we first apply the unlearning method to various NLP tasks (i.e., classification, translation, response generation) and propose several unlearning evaluation metrics with pertinence. Experiments on large-scale datasets show that KGA yields comprehensive improvements over baselines, where extensive analyses further validate the effectiveness of KGA and provide insight into unlearning for NLP tasks. | ['Hongzhi Yin', 'Kam-Fai Wong', 'Xingshan Zeng', 'Wei Yuan', 'Tong Chen', 'Lingzhi Wang'] | 2023-05-11 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.55538607e-01 2.60672271e-01 -4.15927798e-01 -3.66515577e-01
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4907ae29-d1f2-4bdc-aabe-ae064009b71f | deep-semantic-parsing-of-freehand-sketches | 1910.06023 | null | https://arxiv.org/abs/1910.06023v2 | https://arxiv.org/pdf/1910.06023v2.pdf | Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning | In this paper, we propose a novel deep framework for part-level semantic parsing of freehand sketches, which makes three main contributions that are experimentally shown to have substantial practical merit. First, we propose a homogeneous transformation method to address the problem of domain adaptation. For the task of sketch parsing, there is no available data of labeled freehand sketches that can be directly used for model training. An alternative solution is to learn from datasets of real image parsing, while the domain adaptation is an inevitable problem. Unlike existing methods that utilize the edge maps of real images to approximate freehand sketches, the proposed homogeneous transformation method transforms the data from domains of real images and freehand sketches into a homogeneous space to minimize the semantic gap. Second, we design a soft-weighted loss function as guidance for the training process, which gives attention to both the ambiguous label boundary and class imbalance. Third, we present a staged learning strategy to improve the parsing performance of the trained model, which takes advantage of the shared information and specific characteristic from different sketch categories. Extensive experimental results demonstrate the effectiveness of the above three methods. Specifically, to evaluate the generalization ability of our homogeneous transformation method, additional experiments for the task of sketch-based image retrieval are conducted on the QMUL FG-SBIR dataset. Finally, by integrating the proposed three methods into a unified framework of deep semantic sketch parsing (DeepSSP), we achieve the state-of-the-art on the public SketchParse dataset. | ['Xiaoshuai Sun', 'Ying Zheng', 'Hongxun Yao'] | 2019-10-14 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.40322167e-01 -1.92352995e-01 -4.42576885e-01 -7.38026321e-01
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071836e6-572b-4a03-a866-d15ef46f6f14 | liga-stereo-learning-lidar-geometry-aware | 2108.08258 | null | https://arxiv.org/abs/2108.08258v1 | https://arxiv.org/pdf/2108.08258v1.pdf | LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector | Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images using intermediate depth maps or implicit 3D geometry representations, which provides a low-cost solution for 3D perception. However, its performance is still inferior compared with LiDAR-based detection algorithms. To detect and localize accurate 3D bounding boxes, LiDAR-based models can encode accurate object boundaries and surface normal directions from LiDAR point clouds. However, the detection results of stereo-based detectors are easily affected by the erroneous depth features due to the limitation of stereo matching. To solve the problem, we propose LIGA-Stereo (LiDAR Geometry Aware Stereo Detector) to learn stereo-based 3D detectors under the guidance of high-level geometry-aware representations of LiDAR-based detection models. In addition, we found existing voxel-based stereo detectors failed to learn semantic features effectively from indirect 3D supervisions. We attach an auxiliary 2D detection head to provide direct 2D semantic supervisions. Experiment results show that the above two strategies improved the geometric and semantic representation capabilities. Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively on the official KITTI benchmark. The gap between stereo-based and LiDAR-based 3D detectors is further narrowed. | ['Hongsheng Li', 'Xiaogang Wang', 'Shaoshuai Shi', 'Xiaoyang Guo'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Guo_LIGA-Stereo_Learning_LiDAR_Geometry_Aware_Representations_for_Stereo-Based_3D_Detector_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_LIGA-Stereo_Learning_LiDAR_Geometry_Aware_Representations_for_Stereo-Based_3D_Detector_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [-7.55851790e-02 -4.88408394e-02 8.29264298e-02 -4.93150294e-01
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a6ea4825-9cf0-492e-bda4-2e59ab080e8f | improved-zero-shot-audio-tagging | 2208.11402 | null | https://arxiv.org/abs/2208.11402v1 | https://arxiv.org/pdf/2208.11402v1.pdf | Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers | Standard machine learning models for tagging and classifying acoustic signals cannot handle classes that were not seen during training. Zero-Shot (ZS) learning overcomes this restriction by predicting classes based on adaptable class descriptions. This study sets out to investigate the effectiveness of self-attention-based audio embedding architectures for ZS learning. To this end, we compare the very recent patchout spectrogram transformer with two classic convolutional architectures. We evaluate these three architectures on three tasks and on three different benchmark datasets: general-purpose tagging on AudioSet, environmental sound classification on ESC-50, and instrument tagging on OpenMIC. Our results show that the self-attention-based embedding methods outperform both compared convolutional architectures in all of these settings. By designing training and test data accordingly, we observe that prediction performance suffers significantly when the `semantic distance' between training and new test classes is large, an effect that will deserve more detailed investigations. | ['Gerhard Widmer', 'Paul Primus'] | 2022-08-24 | null | null | null | null | ['audio-tagging', 'environmental-sound-classification', 'sound-classification'] | ['audio', 'audio', 'audio'] | [ 2.74892062e-01 1.99358642e-01 8.39436874e-02 -4.72046673e-01
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5564d573-3375-4914-b7e0-19eec8ac510b | knowledge-graph-self-supervised | 2307.02759 | null | https://arxiv.org/abs/2307.02759v1 | https://arxiv.org/pdf/2307.02759v1.pdf | Knowledge Graph Self-Supervised Rationalization for Recommendation | In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores, KGRec integrates generative and contrastive self-supervised tasks for recommendation through rational masking. To highlight rationales in the knowledge graph, we design a novel generative task in the form of masking-reconstructing. By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales. To further rationalize the effect of collaborative interactions on knowledge graph learning, we introduce a contrastive learning task that aligns signals from knowledge and user-item interaction views. To ensure noise-resistant contrasting, potential noisy edges in both graphs judged by the rational scores are masked. Extensive experiments on three real-world datasets demonstrate that KGRec outperforms state-of-the-art methods. We also provide the implementation codes for our approach at https://github.com/HKUDS/KGRec. | ['Chunzhen Huang', 'Lianghao Xia', 'Chao Huang', 'Yuhao Yang'] | 2023-07-06 | null | null | null | null | ['contrastive-learning', 'graph-learning', 'contrastive-learning'] | ['computer-vision', 'graphs', 'methodology'] | [ 4.74826247e-02 5.55367887e-01 -4.97818738e-01 -2.26900890e-01
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d6d2497b-7186-40d7-b20f-c9cf1fb97847 | mednc-multi-ensemble-deep-neural-network-for | 2304.13135 | null | https://arxiv.org/abs/2304.13135v1 | https://arxiv.org/pdf/2304.13135v1.pdf | MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis | Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 diagnosis to identify high-risk patients and maximize the use of limited medical resources. Motivated by this fact, we proposed the deep learning framework MEDNC for automatic prediction and diagnosis of COVID-19 using computed tomography (CT) images. Our model was trained using two publicly available sets of COVID-19 data. And it was built with the inspiration of transfer learning. Results indicated that the MEDNC greatly enhanced the detection of COVID-19 infections, reaching an accuracy of 98.79% and 99.82% respectively. We tested MEDNC on a brain tumor and a blood cell dataset to show that our model applies to a wide range of problems. The outcomes demonstrated that our proposed models attained an accuracy of 99.39% and 99.28%, respectively. This COVID-19 recognition tool could help optimize healthcare resources and reduce clinicians' workload when screening for the virus. | ['Yudong Zhang', 'Shuihua Wang', 'Lin Yang'] | 2023-04-25 | null | null | null | null | ['covid-19-detection', 'computed-tomography-ct'] | ['medical', 'methodology'] | [-1.42695522e-02 -3.39416236e-01 7.12097958e-02 -1.70462519e-01
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6dd25c45-7953-43f3-aa8a-dafe32ded9aa | dynadog-t-a-parametric-animal-model-for | 2107.07330 | null | https://arxiv.org/abs/2107.07330v2 | https://arxiv.org/pdf/2107.07330v2.pdf | DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation | Synthetic data is becoming increasingly common for training computer vision models for a variety of tasks. Notably, such data has been applied in tasks related to humans such as 3D pose estimation where data is either difficult to create or obtain in realistic settings. Comparatively, there has been less work into synthetic animal data and it's uses for training models. Consequently, we introduce a parametric canine model, DynaDog+T, for generating synthetic canine images and data which we use for a common computer vision task, binary segmentation, which would otherwise be difficult due to the lack of available data. | ['Darren Cosker', 'Kwang In Kim', 'Sinead Kearney', 'Jake Deane'] | 2021-07-15 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.05594912e-01 3.80787283e-01 -1.12266473e-01 -4.48345572e-01
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-7.25181252e-02 -9.66831207e-01 1.32828295e-01 1.84936464e-01] | [7.5688395500183105, -1.0943937301635742] |
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